(General) digital image - method for performing calculations on a computer architecture at the speed of light or near the speed of light in binary

Abstract

This invention describes a method for performing computations at or near the speed of light on binary computer architectures (CAs), which are "cells" of arbitrary "pixels" such as digital image pixels or exponential proon bases, including numerical representations. This method includes the following steps, referring to digital images and their processing, (typical) digital image transformations (9.1): impression storage (9.2), numerical data / metadata processing (9.3), sorting storage (9.4), sorting collection (9.5), re-impression storage (9.6), and algorithmic programming (9.7). This method, which we call U-mentalism, anchors the camera / image processing universal Turing machine of many nodes / servers on a Turing or computer (M), providing a massively parallel (general) digital CA with many "films" made up of many "frames" or "composites" of (digital) images. This results in exponentially improved logic and computational power.

Description

[Technical Field]

[0001] This invention relates to a method of supercomputation and CA for massively simultaneous processing of (general) digital images, which are binary-processed from the image at each node / server of a communication network on or outside the Internet, using the powerful resources of a camera / image processing universal Turing machine on a Turing machine. This method combines ideas from a very broad field, mainly natural philosophy, which can also be called philosophy of science. [Background technology]

[0002] Considering the structural and functional use of direct processing of image continuums, or imagery configurations in physical and informational sensory impressions, in a general M or computing machine, or in a single Universal Turing Machine, or preferably in as many distributed partial U machines as possible on a network of Turing machines on or off the Internet, the prior art consists of von Neumann's "iconoscope memory" technique.

[0003] (technical issue) The limitations of von Neumann's "iconscope memory" technology are its lack of coverage and technical scope, based on the pre-LCD cathode ray tube.

[0004] There is also great interest and need for the development of methods and CAs that contribute to the structural and functional exploitation of direct processing of continuous images with improved processing performance. [Prior art documents] [Non-patent literature]

[0005] [Non-Patent Document 1] Turing, AM On Computable Numbers, with an Application to the Entscheidungsproblem. (1936) [Non-patent document 2] Backus, John W. Can Programming Be Liberated from the von Neumann Style? A Functional Style and Its Algebra of Programs. (1977) [Non-patent document 3] Cook, Stephen. The complexity of theorem proving procedures. Proceedings of the Third Annual ACM Symposium on Theory of Computing. (1971) pp. 151-158 [Non-patent document 4] von Neumann, John. First Draft of a Report on EDVAC, Moore School of Electrical Engineering, University of Pennsylvania, June 30, 1945, p. 32 [Non-Patent Document 5] The Summit supercomputer specifications and features are: Processor: IBM POWER9TM (2 / node); GPUs: 27,648 NVIDIA Volta V100s (6 / node); Nodes: 4,608; Node Performance: 42TF; Memory / node: 512GB DDR4 + 96GB HBM2; NV Memory / node: 1600GB; Total System Memory: >10PB DDR4 + HBM + Non-volatile; Interconnect Topology: Mellanox EDR 100G InfiniBand, Non-blocking Fat Tree; Peak Power Consumption: 13MW [Non-patent document 6] Homem, Luis. What is U-mentalism? Journal of Advances in Computer Networks. Volume 7, Number 1. pp. (18-24). (2019) Summary of the Invention

[0006] The present invention relates to a method for performing calculations at or near the speed of light on a binary computer architecture (CA), which is a single "cell" of any "pixel" such as a pixel of a digital image containing a numerical representation or an exponential proton basis, and is characterized by comprising the following steps:

[0007] a) Transform a (general) digital image (3) to a given density of pixel equations, i.e., image resolution, resulting in a set of transformed digital images (9.1).

[0008] b) Image / text RGB / binary "pixel"-like (general) digital image impression memory, which in turn refers to a method of performing calculations at or near the speed of light on the assigned numerical data or metadata (see 9.2) c) Image / Text: Process the assigned numeric data or (general) metadata of the digital image (RGB / binary equivalent) to obtain a set of processed digital images (9.3). d) Image / Text RGB / Binary Isomorphically Processed (General) Digital Images: Reorder and recover the numerical representations of the digital images from the least ordered to the most ordered, resulting in a set of well-ordered digital image configurations (9.4), and only the recovered order can be re-assumed whenever the preconditions for the calculation are met (9.6). e) Image / Text RGB / Binary Isomorphic Processing (general) Aligning digital images from lowest to highest numerical / algorithmic representation, resulting in an aligned collection of digital images (9.5).

[0009] f) Image / Text Reprinting: RGB / binary processed and well-prepared (typical) digital images resulting in numerical data or metadata (9.6) g) well-formed algorithmic programming of image / text RGB / binary isomorphic processing based on numerical data or metadata (9.7); and where the numerical representation of a (general) digital image (3) is always an RGB / binary or "pixel"-like photon / binary code, which is isomorphic to the image information, e.g., in the state of the art, RGB refers to a method of performing calculations in an image / text color model at or near the speed of light, and wherein step a) is performed by a Turing machine (M) to a Universal Turing Machine (UTM) protocol or by a Universal Turing Machine (UMT) protocol only, step b) is performed by a Universal Turing Machine (UTM) to a Turing Machine (M) protocol and by a Universal Turing Machine (UTM) protocol only, and steps c), d), e), f), and g) are performed by a Universal Turing Machine (UTM) to a Universal Turing Machine (UTM) protocol; and the loop computations of a) to g) are performed at each node / server in a communication network of Universal Turing Machines (UTMs) on a massively parallel computing Turing machine (Ms).

[0010] The present invention refers to a computer architecture that performs computations at or near the speed of light, characterized by "composing" pixels or any "pixel-like" exponential basis "cells" in a digital image, said digital image having a numerical representation and configured with the following instructions:

[0011] a) Transforming a (general) digital image (3) to a given density of pixels, i.e., image resolution, resulting in a set of transformed digital images (9.1) b) Image / Text RGB / Binary "pixel"-like homomorphic (general) digital image impression memory assigned with numerical data or metadata, resulting in a set of assigned numerical data or metadata (9.2) c) Processing the assigned numeric data or (general) metadata of the image / text RGB / binary isomorphism to obtain a set of processed digital images (9.3) d) Image / Text RGB / Binary Isomorphically Processed (General) Digital Images: Reorder and recover the numerical representations of the digital images from the least ordered to the most ordered, resulting in a set of well-ordered digital image configurations (9.4), and only the recovered order can be re-assumed whenever the preconditions for the calculation are met (9.6). e) Image / Text RGB / Binary Isomorphic Processing (General) Take digital images and order them from smallest to largest numerical / algorithmic representation, resulting in an ordered collection of digital images (9.5).

[0012] f) Reprinting of (general) digital images with RGB / binary homomorphic processing and proper ordering of images / text, resulting in numerical data or metadata (9.6) g) RGB / binary isomorphic and well-aligned algorithmic programming of images / text based on numerical data or metadata (9.7); and where the numerical representation of a (general) digital image is always an RGB / binary or "pixel"-like photon / binary code, isomorphic to image information such as the state-of-the-art RGB image / text color model; and When step a) is performed by a Turing machine (M) to a Universal Turing Machine (UTM) protocol or by the Universal Turing Machine (UTM) protocol alone, and b) is performed by a Universal Turing Machine (UTM) to a Turing Machine (M) protocol or by the Universal Turing Machine (UTM) protocol alone, steps c), f), and g) are performed, and steps c), d), e), f), and g) are performed by a Universal Machine (UTM) → Universal Machine (UTM) protocol, and in the a) to g) loop massively parallel computation, the computation is performed at each node / server of a communication network of Universal Turing Machines (UTMs) on the Turing machine (Ms). [Means for solving the problem]

[0013] The present invention provides a "film" or image-binary "composite" consisting of many "frames" anchored on many node / server camera / image processing UTM(s) on M(s), endowing the (general) digital image with a massively parallel digital image CA consisting of "pixels", or any "pixel"-like exponentially trending basal cells, or electromagnetic wave-like "packets" compatible with quantum computing, where the (general) digital image contains a numerical representation. [Effects of the Invention]

[0014] The advantage of this invention is that it is not just an incremental improvement, but a dramatic, truly exponential improvement in thread and computational power. Because typical digital images are generated by machine learning, and the majority of the feed-in stack must be generated from existing digital images, the overall result is a significant reduction in various memory usages and an exponential increase in processing performance. [Brief explanation of the drawings]

[0015] For the purpose of promoting an understanding of the principles according to embodiments of the present invention, reference will be made to the embodiments shown in the drawings and the language used to describe them. It should be understood that no attempt is made to limit the scope of the present invention to the content of the drawings in any way. Any modifications or subsequent variations of the inventive features illustrated herein, and any additional applications of the principles and embodiments of the present invention as shown, which would normally occur to one skilled in the art upon reading this description, are considered to be within the scope of the present invention as received. [Figure 1] An overview image is shown. DETAILED DESCRIPTION OF THE INVENTION

[0016] The invention described herein is a novel computer architecture (CA) that will have a profound impact on network and Internet communications generally. This means an unprecedented and original computer organization and design from the perspective of hardware and software interfaces. More importantly, by combining symbolic innovations, such as those found in Instruction Set Design (ISD), with implementations, such as those found in Integrated Circuit Design (ICD), implementation, and power consumption, this proposal achieves convergence in the sense that changes from a symbolic perspective inevitably transform the cybernetically applicable and materialized system as a whole. The scope of this inevitable change is as broad as possible: from mainstream computer classes and their inherent system characteristics, network communication systems and Internet protocols, and programming language design to cybernetic scalability, performance, and efficiency. However, the proposed system is designed to interact with the actual state-of-the-art and enable a smooth and ever-evolving overall evolution. In this regard, the cited invention rests on a common symbolic foundation for both general image processing in graphics processing units (GPUs) and more general central processing units (CPUs), and also on a common foundation for advanced massively parallel (MA) computations and more linear centralized control structures, which also explains the technological transfer between CAs and algorithms.

[0017] The present invention consists of a symbolic model (SM) and instruction set architecture (ISA) or CA for general, high-level performance computers, either single or interconnected by a communications network, on or off the Internet. It thus conforms to a set of rules and methods that conform to the model of the Universal Tuning Machine (UTM or U-machine), as well as any computable sequence executed by any computer or M. Details of the U-machine are given in Turing, AM, On Computable Numbers, with Applications to the Entscheidungsproblem (1936). The invention extends the solvability of classic problems in computer science in terms of symbolism, functional composition, logical design, and overall implementation, and indeed conveys solutions that are fully deployable in themselves, beyond the already highly expressive advances in power, speed, and memory.

[0018] Indeed, by connecting or interconnecting one or more nodes / servers (generic) digital image - binary CA - most preferably a model of a U-machine, as opposed to a Turing Machine (TM) - with a communication network on the Internet or beyond, the present invention provides a positive operational change to the so-called "von Neumann bottleneck," as further details of which are described in Backus, John, "Can Programming Be Freed from the Von Neumann Style? The Functional Style and Its Program Algebra." (1977). Furthermore, the present invention applies a new immanent hypercomputation model to exhaustively exercise the boundaries and ranges of computational complexity classes.

[0019] Regarding the "von Neumann bottleneck," the present invention simultaneously introduces a binary code symbology as an alternative CA to von Neumann, defining it as a "composition" of (general) digital images, collectively functioning as U-machines for all possible Turing machines. Henceforth, (general) digital images are processed as isomorphic copies of the binary code of the original (general) digital image, with all its possible variations and repetitions, with the important modification that at every single network node / server corresponding to a camera / computer processor, the "pixels" of the image itself and the "composition" or "frames" of them become new minimal physical patterns (symbols). In the preferred embodiment, each single network node / server functions as a partial U-machine. These symbols are combined into isomorphic binary code structures (formulas), and therefore operations (processing) with the new formulas as factors are possible.

[0020] "Synthesis" is understood here in the abstract sense of Kant's "composition, combination." "Synthesis, in its most general sense, is the act of bringing together different representations and grasping in one act of knowledge the manifold things contained therein" (A77 / B 103, Critique of Pure Reason, 1781), that is, the act of bringing together and combining all the manifold things given in intuition to produce knowledge. While the materialization of knowledge is crucial in this invention, such combinations can be understood relationally, as in Ada Lovelace's Note G in her translation of Luigi Menabrea's treatise on Charles Babbage's Analytical Engine: "By distributing and combining the truths and formulas of analysis in such a way as to render them most easily and rapidly amenable to the mechanical combination of an engine, the relations and nature of the many subjects of this science will necessarily be thrown into a new light and more deeply investigated" (Taylor's Scientific Memoirs, 1842-43). In this way, the fact that these combinations and "compositions" are amenable to the engine is a fundamental point of agreement between the two, depending on the chosen expression.

[0021] As explained above, a "system" has many distinct levels of "composition" according to the concept of "composition." In developing a description of a "system," it's quite realistic to equate the various "compositions" mentioned above with the concept of a classical Turing machine. Here, I'll expand on this topic by pointing out each concept in ascending order. In a typical digital image, a "pixel" typically holds three bytes of RGB color information in RGB / binary format. The nearest neighbor relationship during reading, perhaps like the original CRT and Western writing systems, is to resolve individual showcases of "pixels" in the drawing section of a digital image "frame" from top-left to bottom-right. Regardless of the resolution, the type of pixel (raster or vector), or the quanta information of the "pixel"-like, or even "cellular," packet waveform, I'm thinking of it as the smallest addressable and representable element within a "frame."

[0022] However, this top-left to top-right and bottom-left to bottom-right reading direction of a (general) "digital image" is to be converted into a general simultaneous, orthogonal, and synchronized image impression on a "scanner"-type digital sensor in this invention. As a result, since UTM is read in RGB / binary format, the smallest unit for the time being is the "octet"-"byte" contained within a "pixel." In the following, in abstract terms, a "pixel" is compared to a "cell," or alternatively, to three elements of "octet"-"byte." Subsequently, the "cell," or in this case, a "pixel," is leveraged as a "frame" (FPS) of a (general) "digital image." Thus, in terms of processing, it is important to reiterate that all of the aforementioned are synchronized and orthogonal. At this point, the use of the term "cell" can be equated with the non-literal idea of ​​a "square" that Turing used in abstract terms. In Turing's dictionary, many (FPS) "compositions" of "(general) digital images" as "squares" of "tape" become "films" or "moving images" in this invention, whose symbols change exponentially. Finally, since this invention involves many different "films" and the imminent and possible different "retrievals" (step d, 9.4) from the scanner to the printer or from the eye to the brain, and "assemblies" (step e, 9.5) from the printer to the scanner or from the brain to the eye, the next "composition" to be considered is hereafter all possible "compositions" of ordered "retrievals" and "compositions" (step d, 9.4). 5) The next "composition" to be considered is hereafter all possible "compositions" of ordered "retrievals" and "compositions" (step d; 9.4), as well as all possible "compositions" of disordered "assemblies" and "permutations" (step e, 9.5), with infinite possibilities for application.

[0023] As shown in Figure 3, a (general) digital image is formed at or near the speed of light from a (general) digital image by means of a binary single- or multi-node CA process, said process comprising: (1) input from a communication network, preferably the Internet, of image / text "pixels" - data in the form of composite images or general image sensing; and (2) input from a communication network, preferably the Internet, of AI & encrypted data, image / text "pixels" - in the form of composite / composite images or general image processing; further comprising a step of streaming film (FL) to a camera processing node / server (5).

[0024] In what follows, it would be best to describe the synthesis of a "(general) digital image" (3) and the "orderly memory" (process d, 9.4) from "scanner to printer" or "eye to brain" on the one hand, and the numerous orderly "permutations and combinations" (process e, 9.5) from "printer to scanner" or "brain to eye" on the other.

[0025] To achieve this, we must introduce the concepts of "composition," "reordering," and "combination," which are mathematically well-defined model theories. Because RGB / binary numbers are isomorphic to any arithmetic system with any number of digits, each "pixel," "frame," and "film" is said to be an RGB / binary number. The role of the "sorting and retrieval" (step d, 9.4) of UTM(s) is to first enumerate and retrieve "(general) digital images" (3) on existing communication networks, preferably the Internet. These are constructed independently of previous encryption methods, as shown in "Internet Data: Images / Text" and "Pixels." Furthermore, like the Sieve of Eratosthenes, they are divided into modules, "frames," and "films," by large numbers of equivalent and isomorphic RGB / binary numbers. From this point on, the simplest search algorithm is set up to move the first of many planes in an "orderly" "permutation / combination" (step e, 9.5) that actively generates and processes new "data:images / text" in UTM(s) as a "collection" of "(general) digital images", keeping in mind that each step is N+1 XOR N-1.

[0026] This discovery in terms of libraries of modules, "frames," and therefore "films," is also, as an important note, precisely due to the distributed nature of networks of communication and encryption methods, especially on the Internet, from the smallest to the most distantly "ordered" (usually modules 8 bits or 1 byte). This is important because it helps us once again to evaluate in more powerful terms the "permutation / combination of collections" (step e, 9.5) itself, i.e., the appropriate "ordering" step (steps d, e; 9.4, 9.5) for the time being of this emphasized and repeated aspect. Consequently, "permutation / combination" transfers authority from the "ordering" isomorphic RGB / binary numbers or arithmetic (step d, 9.4) to the "ordered" isomorphic RGB / binary algorithm or step (step e, 9.5). Thus, here we see the proper connection from the first aspect to its second aspect, which is also repeated again and again, ad infinitum. "Alignment" is common to both (steps d and e; 9.4 and 9.5), and "permutation / combination" (steps e and 9.5) leads to RGB / binary arithmetic "construction" and RGB / binary algorithms, both in the symbolic and physical pathways of knowledge. The creation of such a library of algorithms and steps provides the ideal material and intellectual basis for assembly programming (7 and 8) according to the present invention, which will be further developed in accordance with new patents following the same. Once the material has been recursively and algorithmically illustrated and charted accordingly and fairly, the basis for all possibilities for more advanced programming language design, the present programming language, and all other programming languages ​​and algorithms, should be made available to the community and industry, government and public in the best possible way.

[0027] Although the issues of addressing the correct implementation of technology on the Internet and the clarification and full expression of the meaning of expressions such as "(general) digital image," "pixel," and "exponential basis" may seem essentially inconsistent, they share the same explanatory theoretical basis, since the former evokes Internet protocol data packages (datagrams), while the latter, more radically, evokes quantum information packages. However, at a deeper level, both infiltrate the actual information used as the basis for discrete calculations, such as measurements and statistics. Since this technology, as noted, must be set up on a communications network, preferably the Internet, any other approach that contradicts this setting would hinder the correct implementation and fullest utilization of this technology. In this case, at least initially, it is the CA that must be Internet-compatible, not the other way around.

[0028] Nor is it intended to discourage the use of standard communication protocols. However, in the future, if necessary, a single standardized protocol for communication between the Internet and the technology will be proposed in conjunction with the implementation language of the present invention. For these reasons, at present, only an overview and some interoperability considerations are relevant. The primary goal is flexibility with minimal configuration effort. Thus, it must be said that the protocol should conform to the OSI (Open Systems Interconnection) seven-layer architecture model of communication protocols, common packages and exchange modes, and common link-level hardware protocols. It is expected that common address information, control information, and payload data will be accommodated through the physical, data link, network, communication, session, presentation, and application stages. TCP / IP (IPv6) is the basic protocol for the transport and Internet layers and is essential as the end-to-end data communication protocol for interacting with the Universal Terminal Module (UTM) (5) within the system.

[0029] The UTMS(s)(5) of the present invention thus refers to a hosting facility for cameras / image processing CA servers / data centers that can directly request and respond to various clients, regardless of the type and number of servers. Various types of communication networks, from infrared signals to satellite links, are possible depending on the computerization methods and standards. The reference to the system and CA according to the present invention as a communication network, preferably over the Internet, is intended only to indicate that the system is not intended to be implemented in a closed network linked to a single group of private servers / data centers, although this is feasible, but rather corresponds to all communication networks over the Internet.

[0030] The meaning of terms such as (general) "digital image," "pixel"-like terms, and "exponential basis" will be explained. All of these are embedded in the necessary conditions for the ultimate application of U-mentalism to any electromagnetic modeling calculation, or computational electromagnetism (CEM), for Maxwell's applications under boundary conditions. This is important for the system of the present invention to work, even though there are no "pixels" in this case. We need numerically savvy "pixel" or "cellular" reduction elements that can be arranged / rearranged (composed and permuted / combined) into quantum package "cells" that correspond to RGB / binary waveform elements, creating the final "(general) digital image" in exponent-major order, just like 3 "octets" - 3 "bytes" - "pixels." However, any other suitable quantities, bases, exponents, or suitable quantity packages can be accommodated and reconsidered.

[0031] In a preferred embodiment according to the present invention, each UTM is preferably composed primarily of controlled (energy capacity and photometric beam, focusing and defocusing mechanisms, distortion and aberration lenses, etc.) image sensors (CCCD / CMOS in actual state of the art), i.e. photodetectors, microprocessors, supercomputers connected by powerful integrated circuits interacting with clustered servers / data centers connected to a communications network over the Internet.

[0032] In the preferred embodiment, the UTM(s) (5) are "frame" and "film" moving image computing accelerators, similar to the workings of particle accelerators in physics. Indeed, a "(general) digital image" (3), contained in a well-defined light beam and with equally charged RGB / binary active "pixels," is accelerated through the use of electromagnetically based computation, leading to supercomputation as a feedback effect in cybernetic systems. This comparison is instructive in the sense that particle accelerators in physics generate scientific knowledge, and this invention is dedicated to that purpose, particularly in the context of related programming languages ​​to be developed in the future. The two models are also similar in the sense that the class of electrostatic accelerators has a cathode ray tube screen as its most suitable representation, as von Neumann illustrated in his application of "iconoscope memory" at the time.

[0033] Clearly, no CA was ever built from the beginning, and there still is. There is no such thing as a cybernetic system, not now, that is based on the original high pixel density of "(general) digital images" and the internet-based communication networks proposed here. There are also several other attributes and themes common to particle physics accelerators and UTM(s): linear DC voltage accelerators, the injection / extraction and storage of "(general) digital images," coaxial coordinate systems, 1D to nD case-matching optics, matrices and periodicity conditions.

[0034] This theoretical overview even resembles the so-called wave-particle duality, where the “pixels” are artificial information wave-packet quantum particles of waveform electromagnetic currents, and one of their complementary principles is applied as a container for the exponents corresponding to the basis of a “(general) digital image” (3).

[0035] This invention is a general-purpose CA, and therefore its scope of application extends to all fields of knowledge. Therefore, we will explain each step in block diagram (9) appropriately and then clearly state how it can be applied. (General) Digital Image Conversion (STEP a, 9.1) (CO) is a prerequisite processing method required for all (general) digital images by general image sensing or general image processing. Specifically, several critical aspects must be considered in relation to both (1) "Internet / Data: Image / Text "Pixel" Composition or General Image Sensing" and (2) "AI / Cryptographic Data: Image / Text "Pixel" Composition / Composition or General Image Processing." The deciding factor in this regard depends on the choice of parallel Internet protocols and the synchronous reading of all "pixels" of the (general) digital image (3). Because datagrams are suitable for encryption, both arrival at the UTM and asynchronous reading can contribute to skilled encryption. However, this method is not desirable because it does not achieve synchronous loading, which is the most optimal and fastest way to read (general) digital images (3), and because it requires branching of TCP / IP arrivals to each UTM.

[0036] That is, if each UTM can be encrypted at the moment of the "(general) digital image transformation" (step a, 9.1) upon arrival at the Turing machine "tape" data (4) of the pool (6), or appropriately synchronized with the "(general) digital image storage" (step a, 9.1), in the first case "M to UTM or UTM protocol" (step a, 9.1), in the last case "UTM to M and UTM protocol" (step b, 9.2), operating on the image. This is the same as saying that it is fundamentally important to understand the meaning of the situation in which the same thing happens as with encryption, regardless of whether the "(general) digital image transformation" (step a, 9.1) itself can be performed in advance (M to UTM protocol) rather than forward (UTM protocol). The cipher is either solved synchronously with the "(general) digital image transformation" (step a, 9.1) or with the "(general) digital image storage" (step b, 9.2), i.e., in the first case "M to UTM or UTM protocol" (step a, 9.1), or simultaneously with "UTM to M and UTM protocol" (step a, 9.1). In both of these cases, the "block diagram" (9) shows the formal separation of these two initial steps and the abstract demarcation that proceeds to "(general) digital image numerical data / metadata processing" (step c, 9.3).

[0037] The system's relationship with communication networks such as the Internet or the World Wide Web (WWW) determines the outline of the system, but beyond that, the inventor has no control. To envision a diagonalized encryption scheme prior to "general image / text synthesis or general image data processing" (step c, 9.3), a black-box mechanism is essential, because it simultaneously encrypts and processes "general image sensing" (1), "general image processing" (2), and "(general) digital images" (3) in a way that is unknown to anyone other than the machine, and it is impossible to know which images are being encrypted or processed.

[0038] At this stage, the "processing of (general) digital image numerical data / metadata" (STEP c, 9.3), which is an abstract correlation and complement of the "impression memory" (STEP b, 9.2), is performed only as a "UTM-to-UTM protocol" (STEP c, 9.3). It is assumed that all of the previous operations are processed by integrated circuit operations communicating with the photodetector chips of the camera / image processing device (CCD / CMOS technology) within the UTM (5). Once the homogeneous information is in the form of RGB / binary modules, "frames," and "films," the "UTM-to-UTM protocol" (STEP d, 9.4) performs the "collection and collection of (general) digital images" (STEP d, 9.4) suitable for connecting node / server UTM(s) and data centers. Similarly, the "collection and permutation of (general) digital images" is performed as a "UTM-to-UTM protocol" (STEP e, 9.5). After this, with the identical resulting "alignment" (steps d, e; 9.4, 9.5) and the achieved "permutation / combination" (steps e, 9.5), the CA action diagram and the algorithm processed there are ready to be submitted again to the "reimpression" (UTM) in a loop (LO), overwritten or endlessly repeated manner. 5), and the processed CA action diagram and algorithm are ready to be submitted again to the UTM(s) in a loop (LO), overwritten or endlessly repeated manner, to generate "reimpression memories of (general) digital images" (steps d, e; 9.4, 9.5), that is, more complex "orderings": "synthesis" (steps d, 9.4), "permutation / combination" (steps e, 9.5).

[0039] It is this overall, indeed long-term, final stage and iteration of both "intrinsic" and "extended" processes that can be inferred as "the algorithmic programming process for (generic) digital images" (STEP g, 9.7), clearly an "inter-UTM protocol." It exists here, not as a block diagram (9), but as an exit rather than an end point. Because of this feedback loop mechanism (LO), this stage can be called an "algorithmic mitron," i.e., an accelerator for all classes of algorithms, and thus becomes a procedural mechanism in itself.

[0040] A practical example can be eloquent in explaining CA methods. Here, we choose automobiles (AVs) as such an example. There are two possible technological storylines that address this problem. The "(general) digital image" is either a correct object image of the computable environment, computed by our hypercomputation through general "ordering" (steps d, e; 9.4, 9.5) and "permutation / combination" (steps e, 9.5), or a "(general) digital image" (3) that is different from the object image but automated by our algorithm (step 5), or a "(general) digital image" (3) that is different from the object image, but automated only by our algorithm, while maintaining a closed sensing system, and thus the (sensory information) image is generated by itself.

[0041] In the initial outline, the repertoire of "(general) digital images" captured by the camera is included in the overall system of the present invention, and thus is extremely intractable and less challenging for the purposes of AV, "orderly" "permutations / combinations" (steps d and e; 9.4, 9.5). However, it can still be seen as orderly in the execution program of the U-mentalism (object of the program, program). Consequently, it is natural that in robotics autonomous systems, open interaction with the environment and guidance, the underlying object image represents a direct object of the environment, which is calculated and guided much more rapidly in an accelerating consistency. Because the present invention is open cybernetic, connected to a communication network over the Internet, and has a constant positive feedback mechanism, the evaluation of this initial hypothesis is much more valuable and valuable.

[0042] However, it must be made clear that the exponential improvement in environmental guidance is not due to an intrinsic acceleration of the processing images of AV sensory information, nor to an increase in image resolution, but to an intrinsic "ordering" (steps d, e; 9.4, 9.5), "organization" (steps d, 9.4), "sorting / combination" (steps e, 9.5) into modules, "frames", "films", which are distinct from the target image, and which in fact allow the transfer of digital image processing practices and further applications (classification, feature extraction, multi-scale signal analysis, pattern recognition, projection, etc.).

[0043] The second hypothesis, a self-contained system, i.e., one in which sensory information is separated from the system of the present invention but, conversely, the processing unit is dependent on the algorithm of the present invention (Steps ag, 9.1-9.7), is less recommended (though this is merely an academic hypothesis). The processed image is still calculated at an exponentially higher speed, but without a parallel appropriate feedback mechanism to the appropriate target image of the environment in which the AV is executing its automation. In this case, the sensory information image is not transferred to the U-mentalism system. This could be seen as an "all-or-nothing" approach, but in fact it is not. The AV's digital sensory information image is separate from the "(general) digital image" (3) input to the system of the present invention, which, in the long term, would not benefit the system's effectiveness and the flexibility of the "program processing algorithm" (Step g, 9.7).

[0044] This last, more negative example has the virtue of describing, inversely, a reliable cybernetic, open, and complex positive feedback mechanism, using target images as direct input to interpret the navigation environment, while at the same time using those very same images as "aligned," "composite," "permutation," and "combination" (steps d, e; 9.4, 9.5) to rapidly construct algorithms within the network. One such preferred method can be appropriately described as "take everything you're given." The same can be said of the input target image "film" of the navigation environment helping the simultaneous supercomputation of many other automated devices connected to the Internet in a communication network over the Internet.

[0045] This makes it clear that it is important to "collect" (step d, 9.4) "general digital images" in advance, and to do so, it is necessary to retain as many sensor cameras, i.e., as many technological perspectives as possible, and therefore different "general digital images" (3), to supply them to the system. We hypothesize that the more "frames" or "films" performed in image / text synthesis or "general image processing" (1), the more complex the algorithms "from printer to scanner" and "from brain to eye" can be, and the more likely it is that "programming processing of algorithms" (step g, 9.7), which transcends combinations and programming languages, will enable general-purpose computing, possibly any AV in any environment, and a higher level of performance for specific tasks.

[0046] Therefore, each step in the block diagram of the computer architecture of the present invention shown in Figure 9 is a proper "composition" of the overall CA process. Furthermore, each step in the block diagram is irreplaceable both in its location and in its order; no step in the order is interchangeable. The only concepts that show a slight flexibility in terms of order are the "(general) digital image transformation" (step a, 9.1) and the encryption (step a, 9.1) of the "(general) digital image transformation" to the "subsequent (general) digital image impression storage." However, due to the necessary technical transformations (CO) inherent in the multiple streams of digital images and for privacy reasons, the first (step a, 9.1) product is necessarily and unavoidably included, and the free memory / processing capacity of the identical "(general) digital image transformation" (step a, 9.1) product is also preserved. Despite this, it is worth noting that the method of "(general) digital image collection, permutation / combination" (STEP e, 9.5) is registered based on (digital) image generation or general image processing and may be exploited by other methods, such as cryptographic techniques, especially machine learning. Overall, the ordered "frames" and "films" are properly encrypted by the algorithm of the assembly programming (7) of the present invention, both in the "ordered collection" (STEP d, 9.4), in the "ordered collection" (STEP e, 9.5) and further in the "permutation / combination" (STEP e, 9.5). The latter is properly encrypted by the algorithm of the assembly programming (7) of the present invention, and furthermore, the algorithm implemented in the programming language of the present invention, in STEP 5), should be properly encrypted at all stages and therefore protected by the communication between the main body and the machine.

[0047] The present invention is said to be an inversion of the classic von Neumann CA in the sense that von Neumann CA is the result of several well-established principles, all of which are anchored in the digital, binary, electronic, and multifunctional underpinnings of real-world technical computation. However, the U-mentalism system (steps a-g, 9) built into the UTM(s) (5) is entirely different. It is essentially an inversion of von Neumann CA because there is no central processing unit; instead, there is a network of synchronized processing units. Furthermore, all elements of the synchronized processing unit are much more closely embedded in the UTM(s) network nodes, in the sense that the control unit, arithmetic / logic unit, and storage unit are indistinguishable. Memory is allocated to the "conversion of (general) digital images" (step a, 9.1) (CO) in the Internet-based communications network, and therefore can freely store only their metadata, rather than precisely the "orderly collection of (general) digital images" (step d, 9.4). Similarly, although they are different from the "combination of (general) digital images" (step e, 9.5), they are "(general) digital images" (3) that do not exist on the Internet, do not currently exist, and need to be generated, and therefore must be fixed in the communication network on the Internet in the form of metadata or data once the "collection" (step e, 9.5) is complete. Similarly, although they are different from the "combination of (general) digital images" (step e, 9.5), they are "(general) digital images" (3) that do not exist on the Internet, do not currently exist, and need to be generated, and therefore must be fixed in the communication network on the Internet in the form of metadata or data once the "collection" (step e, 9.5) is complete. Memory is essentially a space with free potential resources that is assigned to the body of the Internet and can therefore be tasked in close proximity to both the control unit and the arithmetic / logic unit.Crucially, although each UTM (5) constitutes a control unit, its instruction registers and instruction counters are synchronized with those of other UTMs (5) in the network, and more crucially, the symbology implemented by the control and logic units is not solely and exclusively binary, with arithmetic integer operands and results. Instead, it is based on the "composition" of the "(general) digital image" (3) and its smallest unit, the "pixel." A pixel consists of an "octet byte" (or "octet" - 3 "byte"), the RGB / binary equivalent of which is common in graphics and central processing units in hardware. It is important to note in this respect that the U-Mentalism System of the present invention deals with all "pixel-like" or "cellular" forms of the electromagnetic spectrum, such as the photon packages in any waveform or the "cellular" (or package) quanta of information in any electromagnetic wave, which correspond directly to the "(general) digital image" (3).

[0048] The concept of stored programs is also different in the present system compared to von Neumann CA. Once the UTM(s) (5) undergoes certain improvements or machine learning algorithms, not only does it generate the necessary "ordered memory" (step d, 9.4), but more importantly, it also generates sufficient "ordered collection" (step e, 9.5) that is constantly being improved, which is then fed back to the entire network system on the Internet, expanding processing and memory capabilities jointly, thus enabling the cybernetic symbiosis of humans and machines, and machines and machines.

[0049] All the different storage programs processed by Turing Machines or UTM(s), plus the necessary "alignment and collection" (step d, 9.4) and the storage programs that ultimately result from the constantly improving sufficient "alignment and collection" (step e, 9.5) algorithms, are decomposed into the assembly design according to the present invention and are therefore the correct output of the system of the present invention.In terms of input, data images / text (apart from digital signal audio), "pixel" synthesis and "pixel" replacement / combination are exemplified, as well as pooling, either by general image sensing and general image processing.

[0050] In the context of this invention, the pool consists of any frames such as photo images, URL(s) and web pages, emails, instant messages, mobile & tablet frames, digital TV, films, outdoor & consoles, virtual machines & deep web, kernel BIOS & system logs, ATM and GPS, it also consists of any text / image, especially RGB / binary, digital image output.

[0051] For this architecture, it is important to demonstrate that the network of Turing machines in the Internet of Communication networks does not diminish the role of individual computers, but rather represents a very reasonable opportunity for its unique participation in supercomputation. A distinctive feature of von Neumann CA is that instructions and data operations share the same bus, a simple prelude to the so-called von Neumann bottleneck, or cache memory. U-mentalism also readily accommodates all memory and instructions, but its processing power is exponentially greater in proportion to its consumption, data access, and octet-to-byte ratio compared to massively parallel programs. However, because the system is distributed across a shared bus and metadata network, it is essentially both. While sharing the same block backbone structure as von Neumann CA, the general limitations of von Neumann CA, due to state-of-the-art technology and programming language design, are improved by this invention to a higher lower bound, essentially postponing the issues of programming language design and algorithms.

[0052] Then, by stacking functions that are easier to process, we improve it up to the differential limit of a higher symbolic computational complexity class.

[0053] Furthermore, the combination of the present invention, together with the programming language from which it arises, constitutes a fixed program of the system, although it arises from the general self-modifying code of the stored program concept.

[0054] In other words, the design of the combinatorial language of the present invention arises from the premise of both the necessary "aligned collection" (step d, 9.4) and, even more importantly, sufficient "aligned collection" (step e, 9.5) to allow for continuous improvement and repetition. Therefore, it can be considered a kind of reprogrammable design that allows programming itself. The assembly of the present invention is based on a divergent pipeline and cached massively parallel supercomputer (MA), and handles instructions and data undifferentiated in a single cybernetic system. This not only allows for the exploitation of high-level languages ​​in new designs with very low runtime constraints compared to available processing power, but also allows for the merging of the assembler / compiler and linker / loader into a single assembly language. In conclusion, the ability to generate programs from programs of the present invention, due to the premise of the necessary "aligned collection" (step d, 9.4) and, more importantly, sufficient "aligned collection" (step e, 9.5) to allow for continuous improvement, becomes closer to scientific patterns alongside code. Despite its remarkable advantages, von Neumann CA is not realized. In summary, the present invention, like actual state-of-the-art computing, does not incorporate any of the aforementioned digital, binary, electronic, or multi-functional underpinnings. It is analog / digital, not simply digital, because it processes (general) digital images (3), i.e., the "composition" of "frames," continuously without predicting finite intervals. It is RGB / binary, not binary. Similarly, it is electronic / electromagnetic, not electronic, because it processes different (FPS) "(general) digital images" (3) continuously without predicting finite intervals and, in their "pixel" or "cellular" form, has a predictable consistency with quantum information packages. Finally, due to the role of the inventive combination of the necessary "aligned collection" (step d, 9.4), and more importantly, the premise of a constantly improving sufficient "aligned collection" (step e, 9.5), it allows a single program of unified code and libraries consisting of numerous complex algorithms, and is not simply multifunctional, but rather monofunctional / multifunctional.The assembly of the present invention is only concerned with the "alignment" of RGB / binary numbers from both the "(general) digital image collection" (step d, 9.4) and the "(general) digital image collection" (step e, 9.5) in terms of the strict ordering of different (FPS) "frames," like an arithmetic / color system. However, there is a very important realization that the "(general) digital image collection" (step e, 9.5) allows not only "alignment" but also "composition / transformation" (step e, 9.5) of these identical (FPS) "frames." Thus, in the transformation of the "(general) digital image collection," not only bare "alignment" but also "composition / transformation" (step e, 9.5) is possible from it, and the assembly of the present invention starts from itself and steadily builds itself and the programming language. Here again, it is a crucial transition of the system from "collection alignment" to "collection permutation / combination" (step e, 9.5). The combination of U-mentalism with more advanced symbolic and mathematical low-level languages ​​allows for many diverse and powerful programming language designs. Contrary to assembly languages, programming languages ​​according to this invention are intended to be open-source, collaborative, and scientifically driven.

[0055] The present invention consists of a new Physical Symbol System (PSS), also called a formal system, again preferably operating on a partial U-tube, capable of simultaneous digital images and binary numbers (generally).

[0056] In a preferred embodiment, the present invention is particularly suited to systems of UTM(s) computation on any computer, conventional or alternative, but always with an interface that relies on "(general) digital image" information or that outputs in the form of images. A UTM computation system should be partial and distributed, rather than a single entity. Hereafter, the present invention comprises embodiments that compute workloads on a single or multiple Turing machines, or on a single UTM, but is primarily intended for complete implementation on as many interconnected network-communicating U-machine nodes as possible on an Internet of Turing machines. It should not be forgotten at this point that UTM(s) are also Turing machines.

[0057] In a preferred embodiment of the present invention, the Turing machine is a computer having a processor and / or text / image output mechanism or device.

[0058] In a preferred embodiment of the present invention, the Turing machine is selected from the group consisting of personal computers, desktop devices, mobile devices, internet servers, clusters, warehouse scale mainframe computers, embedded computing machines, GPU gaming consoles, cloud computing, digital TV boxes, outdoors, drones, CCTV, ATMs, GPS.

[0059] The present invention imposes a massively parallel "(general) digital image" CA with many modules and many "films" made of (digital) image "frames" or "composites" anchored in many node camera / image processing UTM(s) on a computer. The result is a dramatic increase in threading and computing power that is not merely incremental, but truly exponential.

[0060] While “(general) digital images” (3) can be generated by machine learning, the majority of the feed-in stack should come from extant (digital) images, collectively achieving significant benefits and savings across many types of memory.

[0061] In addition, the exponential improvement of processing performance has made it possible to construct a steady-state approximation for the evaluation of P vs. NP problems in computation, which can be used to evaluate and measure computational complexity classes. The details of the P vs. NP problem are as follows: NP problems are presented in Cook, Stephen. On the complexity of theorem proving procedures: Proceedings of the Third Annual ACM Symposium on Theory of Computing. (1971) pp. 151-158. The relationship between the P vs. NP problem and the so-called von Neumann bottleneck is very similar to that of the present invention. Although for different reasons, the technical features of the present invention affect both problems and the technical materials and techniques. It constitutes the optimal, tractable, and decidable exploitation of Turing machines' capabilities over a communications network, preferably the Internet, insofar as it allows us to postulate and articulate higher lower bounds on the boundaries and relations between computational classes, so as to broaden the route of symbolic machines to the von Neumann bottleneck in Turing machines. Far from being a solution, it nonetheless appears in the eyes of its proponents to be a candidate for the best intermediate technological approach.

[0062] The UTM of each node, camera, and image processor resolves binarized, discontinuous symbols, but by "filming" different modules or "frames" continuously and in parallel at high speed, the CA and instruction set architecture also become image-based and continuous.

[0063] In other embodiments, the present invention precludes the active use of the Internet and World Wide Web (WWW) structure, but as previously mentioned, the practicality of the overall implementation in its bare theoretical definition is that any Turing machine designed in that way suffices. It is meant here that the camera / imaging device nodes referred to as UTMs are understood as appropriate Internet nodes / servers. Furthermore, the use of as many distributed partial U-machines as possible on the Internet allows for the bulk permutation and combination of data across the network, with essentially straightforward processing capabilities.

[0064] In a preferred embodiment according to the present invention, all UTMs are cameras / image processing devices or any "(generic) digital image" interface capable of computing digital image information.

[0065] Thus, what is relevant to this scenario is the direct processing of successive images, or the structural and functional use of imagery structures in physical and informational sensory impressions, in a general M, or possibly in a single or distributed partial U machine on a network of Turing machines. This distinction from von Neumann's "iconoscopic memory" is clearly demonstrated in von Neumann, John, June 30, 1945, Moore School of Electrical Engineering, University of Pennsylvania, p. 32. First Draft of Report on the EDVAC, Moore School of Electrical Engineering, University of Pennsylvania, June 30, 1945, p. 32. There are two main reasons for this. First, our invention represents the smallest symbolic units as both "pixels" and digital images (or "frames"), which directly contrasts with "iconoscopic memory" and is therefore significantly less efficient.

[0066] In this respect, if the term "(general) digital image" (3) is used to refer to any computerized image of signals and data (1-2), including branches in pure photonics, plasmonics, or computer-brain (bidirectional) or neuromodulatory (unidirectional) interfaces, all of which are generated in electromagnetic frequencies, then there is a non-negligible exponential basis factor for the "pixels" in the digital image (3). Indeed, digital images composed of "pixels" (3) are perfectly suited to the exponential growth of computational power, especially when implemented on a large number of Turing machines (3-5) that function as nodes / servers in a network of many Turing machines. Indeed, the "pixel" in the "composition" of the digital image (3) is effectively a physical-symbolic term under any CA, and for the time being, it has proven to be the optimal possible intermediary to a binary code beyond synthesis, especially on such a digital sphere. This means that the balance between the physical and symbolic operation of the "pixel" (3) far exceeds the expansion of bare computation presumed for photonic computing or plasmonics.

[0067] Given the exponential key factor assigned to the "pixel" of a digital image (3), the computational-physical-semiotic balance of the "pixel" far exceeds the power of the raw computational escalation presumed for photonics and plasmonics. The CA presented here will necessarily evolve into a different balance when (general) digital images (3) consist of directly operable differences and repetitions of quantized, discrete electromagnetic impressions. However, the symbolic necessity of combining numerical values ​​such as color and wavelength into symbols, into the minimal symbolic system of the binary system, is quite exemplified in computable digital images by the "pixel" and the path from the digital image to the binary system presumed here (1-3, 2-3).

[0068] This is proven beyond any doubt by the fixed symbolic communication between the binary code and the homomorphic (RGB) model of the "pixel" (3), the latter calculated as a holistic image detector that detects and transmits information, thereby directly converting the variable attenuation and increment of light waves into a binary code, thus given the readily applicable and newly demonstrated structural and functional engineering process of a CA. Needless to say, such CA is entirely new and of great technological and human utility.

[0069] For the time being, for practical short-term implementations, the "pixels" of a digital image (3) are considered to be the "composition" of the image, and therefore the predictable, short-term feasible, and reasonable implementation of the subject matter, i.e., the process, body, manufacture, and composition of matter.

[0070] In some embodiments according to the invention, the basis for the future use of direct electromagnetic waves as the initial imagery impression of CA must include the inevitable use of one immediately deployable reductionist "cell"-like element as the "pixel" of the "composite" of an image or "frame," provided that such reductionist "cell"-like structure retains the exponential key elements that exemplarily link in a minimal binary numerical system. This is why the immediately and fully deployable solution of the "composite" of CA "pixels" and digital images is particularly compelling and suited to the future wave-like direct impression of CA as a "(general) digital image" (3) as a structural element.

[0071] With regard to the Turing machines configured in the CA of the present invention, most preferably in the preferred model of multiple U-machine image processing internet nodes, all kinds of Turing machines and Ms are included, such as personal computers, desktop and mobile devices, servers, cluster and warehouse-scale mainframe computers, embedded computers and GPU game consoles, TV boxes, cloud computing, or more generally, any computing device with a processor and / or text / image output device. Computing devices consisting of a processor and / or text / image output device include any kind of device that processes audio signals using digital coding, including digital televisions, so long as they are capable of being U-machine-executable on the internet.

[0072] Also of concern in relation to the implementation is the comprehensiveness of the system as a whole to encompass all classes of parallelism in von Neumann or non-von Neumann CAs (generally general-purpose, operand or memory register CAs) as long as they allow for a symbolic binary code scheme, or indeed a numerical or derivative scheme such as HEXA or RGB (6-4; 1-3, 2-3). The same is true for all possible ways that hardware can support data-level or task-level parallelism (5).

[0073] From this, it follows most naturally that all types of ISAs of M(s) (4) or UTM(s) (5) are independent of their type (register-memory or load-store), memory addresses, operand types and sizes, complete operations, control flow instructions, fixed-length or variable-length encoding. Many other issues and practicalities involved in CA design must be technically computable, and most preferably, computable across a distributed network of partial UTM(s) nodes / server cameras / image processors (3-5) across many M(s) on the Internet.

[0074] To simplify the application of the present invention, we will discuss direct image processing on "film" with UTM(s) or M(s). In general, this setup processes direct images of tractable research objects, thus offering an exponential increase in subcomputable functions across all possible areas of research. Its range of scientific and technological applications, e.g., satellite image processing, aerodynamics, fluid dynamics, thermal and fluid objects, etc., will have a strong impact on various areas of cybernetics at a new level.

[0075] With regard to another preferred application of the present invention, it is herein mentioned that the calculations of the appropriate unique binary numbers already disposable as "pixel" colors placed in various "composites" of RGB / binary "film" images are all performed sequentially in massively parallel (MA) instructions, accelerating the calculations. This application can be performed by receiving the digital image to be calculated or by inputting the appropriate equivalent isomorphic binary numbers that populate the digital image. At this level of UTM(s) / computer(s) processing, it may also open the field for the design of new modular programming languages ​​and programming language paradigms. With regard to another preferred application of the present invention, it is herein mentioned that the calculations of the appropriate unique binary numbers already disposable as "pixel" colors placed in various "composites" of RGB / binary "film" images are all performed sequentially in massively parallel (MA) instructions, accelerating the calculations. This application can be performed by receiving the digital image to be calculated or by inputting the appropriate equivalent isomorphic binary numbers that populate the digital image. This level of UTM(s) / computer(s) processing may also open up the field for new modular programming language designs and programming language frameworks.

[0076] One of the notable advantages of the present invention is the excess computing resources it welcomes. In order to free up processing power and accessible memory, and to respect freedom of speech and democratic laws, civil and political rights, and, to the greatest extent possible, compliance with data protection laws in different jurisdictions, the present invention requires the use of cryptographic tools on the entire set of digital images, processed in either the Internet or external communication networks, in any M, or, if possible, in separate parts and distributed UTM nodes, as a "composite" of different images. Where possible, the present invention will employ blockchain technology and all the most secure and modern Internet and communication encryption protocols available for this purpose. In doing so, the present invention contributes to a free and responsible Internet design without compromising technical good practice.

[0077] The invention is fully consistent with standard von Neumann CA and is isomorphic to binary notations, all programming languages, and Internet protocols. Its paradigm-shifting new utility is fully compatible with any standard or new physics and functionality of communications networks fused with massively parallel supercomputers, and with any standard or new design of programming languages ​​and Internet protocols. The "stored program" concept realizes exponential processing performance in units of "pixels" by treating an ordered library of "frames" and "films" as an n-dimensional moduli space.

[0078] The direct use of algebraic and functional programming relations for recalling (7.1-8.1) and collecting (7.2-8.2) (including cryptography, machine learning, AI image processing, and any programming method) (general) digital picture FPS "frames" or "films" (3) should constitute a crucial improvement over supercomputation, according to the U-mentalism combination (7).

[0079] The CA and instruction set architecture of the present invention is applicable to any collection of computerized images of signals and data, including real and near-emerging applications in photonics, plasmonics, computer-brain interfaces, or neuromodulation, as well as Turing-computable alternative systems such as quantum computing.

[0080] As much as possible, CA workloads operate with time synchronization, symbolic uniformity, and physical orthogonality across each camera / image processing UTM node. From this perspective, within the smallest unit, a specific uniformity between bytes and octets must be considered, resulting in 3 octets minus 3 bytes per pixel per frame per second (FPS). Therefore, digital images are read synchronously and orthogonally for each pixel density-converted frame or image composition per FPS.

[0081] This "composite" reading of "frames" will evolve into the "composite" of "film" or "moving images," which will be read and processed at each camera / image processing device node on a communications network, and possibly the Internet.

[0082] The camera / image processor nodes are general-purpose Turing machines, and the communication network (possibly over the Internet) is initially composed of Turing machines operating within conventional computation, but later increasingly of general-purpose Turing machines, and also, possibly over the Internet, with a new combinatorial language, named U-mentalism combinatorial, adapted for supercomputation in the communication network.

[0083] In the preferred embodiment of the present invention, it is the isomorphic "octet"-"byte" relationship that allows Turing machines within the "System" to communicate with UTM(s) over communications networks on the Internet. No matter how the units of reference within the "System" vary, a similarly balanced and orderly bridge must be made between the "(general) digital image" (3) or wave form and numerical symbolism. In the context of "pixels," the "octet"-"byte" correlation is more precisely said to be 3 "octets" - 3 "bytes" per "pixel" at FPS.

[0084] (General) digital images When we speak of "image information" or "digital image interface," we mean something that consists of images / text (1-2) (predominantly RGB, but also black and white). While the current state of technology is dominated by digital images composed of (RGB) "pixels" (3), the breadth of the present invention anticipates the use of any further derivative of Maxwell's equations for the smallest symbolic unit (e.g., photon), as mentioned above. We also anticipate further derivative uses of Maxwell's equations, substituting a fixed set of "pixels" (3) and their optical or plasmonic quantization configurations (3) for the "composition" of an image, i.e., "frames" (3).

[0085] In one embodiment of the present invention, the symbolic unit "composite" is a pixel in a "(general) digital image" (3). If possible, for reasons of state-of-the-art technology and landscape, inseparable from efficient convenience, the "pixel" is considered herein as its smallest symbolic unit in relation to a digital image as a frame "composite." Furthermore, the "pixel" and the image "composite," i.e., their "frame," must be processed simultaneously and synchronously, regardless of the computation speed.

[0086] Any other "composition" of units with reference to a "(general) digital system" is suitable for embodiments according to the invention. The same applies to the omission of all possible units that refer to any other symbolic unit, be it any possible Turing machine working on the technology, or any U-machine on a Turing machine.

[0087] Thus, where possible, digital images include direct programming and processing using one or more codes such as hexadecimal (HEXA) numerical representation, or the looser binary system and the overly image-like red, green, blue (RGB) color symbolic system. Digital Images (3) considers that any digital image present, and therefore soundless, and the numerical information found in any possible digital sound-on-film format, can be fully processed into binary code (or any other symbolic code read by CA) by each UTM node / server (5) and any possible existing Turing machine (4), and even future (general) intermediate applications of digital images, symbolic codes, can be fully processed by each UTM node / server (5) and any possible existing Turing machine (4).

[0088] Also implicated in the economy of CA is the creation of orderly algebraic principles and algorithmic patterns based on the RGB color system, which allow the creation of methods for reading (7.1) or programming (7.2) the functionality of modular linear "pixels" or "frames" of images that approximate existing information flows (isomorphic to binary codes). To this end, in the context of the creation and development of U-mentalism combinations (7), it is also desirable to optimize several different discrete values ​​(7.1-8.1; 7.2-8.2) of the RGB "pixel" model (especially well-aligned modular "frames" and "films"), or other unit models that may be created in the future to address CA.

[0089] Too much emphasis has been placed on the technical subtleties of the expression "(general) digital image" in (3). This means that the technology of U-mentalism CA is overwhelmingly an inverse version of standard CA in the sense that the physical symbol system, and therefore the symbol model (SM) of ISA and CA, has as its primary symbol the direct image before it is quantized into discrete numbers. At present, one such image is certainly what is recognized as a digital image (3) by the current state of the art, which should be processed, if possible, by as many interconnected network communications as possible on Internet U-machine nodes / servers (5).

[0090] More specifically, in U-mentalism CA, the direct image (3) is any electromagnetic physical symbolic impression, to the extent that U-mentalism CA can be implemented with other CA methods (photonics, plasmonics, computer-brain interfaces, or neuromodulation). Thus, beyond the limited meaning of "photograph," or photonics as "graphics of photons" (e.g., plasmons), regardless of the intermediate symbol system used, the "(general) digital image" (3) is more a testament to than a simultaneous interpretation of discrete electrical on / off signals represented as binary digits 0 and 1. Instead, all the discrete differences and repetitions of all possible wave impressions derived by Maxwell are computable as direct symbolic systems in U-mentalism CA, preferably realized by as many interconnected network communications as possible among the nodes / servers of the Internet U-machine on M(s) (3-5). In this case, the physical symbolic system is likely to be the appropriate electromagnetic wave, where the direct (computable) "impression" is. Thus, the technology is primarily pictorial, with the impression of a digital "frame" made up of "pixels," or the direct impression of electromagnetic waves (3). This is much less relevant than the electromagnetic (computable) impression referred to here as the "(general) digital image" (3), but the same can be said for acoustics, for example, mechanical waves in gases, liquids, and solids (from vibrations to sound, from low frequencies to ultrasound), and the computable impression of CA in the (general) digital image (3).

[0091] In other embodiments according to the invention, the "(general) digital image" (3) of the CA or instruction set architecture may be related to quantum computing. Thus, the direct image may be the impression of a physical symbol, such as any electromagnetic wave, or equally any (general) digital image, as a symbol primarily processed within the body. In fact, this argument is valid enough if we remember that the symbol system of a quantum computer mimics binary code (although a qubit is a two-state quantum mechanical symbol system) and the irrevocable fact that a quantum computer can mimic any calculator or Turing machine, and therefore any UTM.

[0092] The computer architecture and instruction set architecture according to the present invention should, if possible, be implemented by as many interconnected networks communicating over Internet U-machine nodes on computing machines, independent of physical symbol systems, and accountable to quantum computing.

[0093] The present invention is consistent with the dominance of von Neumann CAs in the current technological and industrial landscape, going beyond any CA established on symbolic binary systems to physical-symbolic computable "impressions" of suitable electromagnetic waves, as well as high-level abstract programming languages. Indeed, the new CAs as just presented are essentially congruent and isomorphic with classical computation and binary code symbolic systems, truly unlocking the expressive power of all programming languages. Furthermore, programming language features, paradigm shifts, and transitions persist across different applications, and it is hoped that their dimensions and expressive power will increase in interaction and communication with the CAs advocated here.

[0094] Computer architecture and instruction set structure In accordance with the present invention, simultaneous RGB / binary homomorphic parallel to binary code computation of M(s) over a communications network, possibly the Internet, and general purpose high level performance threads computed by CA on each camera / image processing node / server configured with a Universal Turing Machine (UTM) is said to follow a method comprising the following steps, further detailed herein:

[0095] a) Converting (general) digital images to a given density of pixel equations, i.e., image resolution, and obtaining a set of converted digital images.

[0096] b) Image / Text Impression Memory RGB / Binary A (general) digital image of the same type as a "pixel" is assigned with numerical data or metadata, resulting in the formation of a set of assigned numerical data or metadata.

[0097] c) Processing the assigned numerical data or (general) digital image metadata of the image / text RGB / binary isotype to produce a set of processed digital images.

[0098] d) Image / Text RGB / Binary Isomorphically processed (general) digital images are properly sorted and retrieved from the least ordered to the most ordered numerical representation of the digital image, resulting in a set of well-ordered digital image configurations (9.4), and only the retrieved order can be re-assumed whenever the preconditions for the calculation are met (9.6). e) Image / Text: A collection of (general) digital images that have been homomorphically processed using RGB / binary numbers, ordered from the smallest to the most extreme of numerical and algorithmic representation, resulting in a digital image collection.

[0099] f) Representation of (general) digital images with image / text RGB / binary homomorphic processing and alignment based on numerical data or metadata.

[0100] g) RGB / binary isomorphic processing of images / text and programming methodical algorithms based on numerical data or metadata.

[0101] In a possible embodiment of the present invention, the processing of the digital images c) and g) is performed on one or more elements of the group consisting of a graphics processing unit (GPU), a general purpose central processing unit (CPU), and a highly parallel computing system.

[0102] In a possible embodiment of the present invention, in step a) of converting a (general) digital image to a predetermined required image resolution conversion, pixelated densities according to the number of pixels result in a set of converted (general) digital images.

[0103] Therefore, the Universal Turing Machines (UTMs) included in this invention are the most important entities in a single, virtually new, cybernetic structure that must be integrated into it, independent of the actual reorganization of the cybernetic structure. Therefore, all UTM structures must be arranged in such a way that image processing (converted to RGB in the UTM) and central logic and control processing (converted to binary code in the UTM) are integrated into the same "octet"-"byte" or 3-octet-3-byte system containing "pixels," as mentioned above. Also important are the concepts of orthogonality and simultaneity in each UTM and its memory / processing capabilities, so that the "(general) digital image transformation" (STEP a, 9.1) can be performed without error and within a consistent and precise clock rate across the entire network of communication, ideally the Internet, between FPS (frames per second) digital images. For this reason, it is fair to describe it as massively parallel computing (MA), a network of UTM(s) communicating over the computing machine(s) beneath them, preferably over the Internet, where each UTM is part of a horizontal, non-hierarchical structure, using the Internet as well.

[0104] Nevertheless, it should be noted that many of the Internet's network systems (Vannevar Bush, 1945; Joseph Licklider, 1960; Douglas Engelbart, 1968) rely on hypertext media, such as the horizontally fluent World Wide Web document protocol system (Tim Berners-Lee, 1990), which, while dependent on the hierarchical structure of the network, are essential for the realization of many unrestricted and open applications. Similarly, this invention relates to UTM(s) communication. Workloads, protocols, and applications that rely on the octet-byte or 3 octet-3 byte method can be considered to be built on the premise of some UTM(s), i.e., those designed to register "collected permutations / combinations of (general) digital images" (Step e, 9.5). 5), and therefore, for the processing of the combined image generation algorithm according to the present invention, the loop (LO) and endless repetition of "re-impression storage of (general) digital images" (STEP f, 9.6) and "programming algorithms into (general) digital images" (STEP g, 9.7) must be hierarchical, or better, orderly, and fundamentally primitive beyond the necessarily further implementation of the combined Internet protocol by design. However, this may not be the case due to the distribution of information packages processed within the network (STEP e, 9.5), in addition to the encryption method that conceals which UTM(s) are actually specialized in "collecting, permuting, and combining (general) digital images." Even which UTMs are specialized in "programming algorithms into (general) digital images" (STEP g, 9.7) would not be disclosed, and each UTM would be equally distributed across the network and subject to encryption in addition to computing (general) digital images.Accordingly, as shown in 5, the arrows arranged in a distributed fashion between UTM(s) do not indicate any hierarchical dominance structure, but instead an overly decentralized horizontally parallel network under which all UTM nodes / servers are equally capable of fetching, decoding and executing any instruction order, and the UTM(s) are within a communication network, preferably the Internet.

[0105] There are several aspects to be confirmed regarding parallelism and serialism in the technology, particularly in UTM (5). However, the overall system and its participating services are correctly defined as parallelism, with simultaneous execution of processes and calculus. Key to high-performance computing, both CA and the algorithms it generates have a notable linear combination aspect. The "alignment" of the large number of RGB / binary numbers that make up the "collection" and "collection" of isomorphic modules "frames" and "films" aligned from the shortest to the furthest under "(general) digital image" (steps d, e, 9.4, 9.5) ensures that the "alignment" of the parts of "collection" and "collection" is necessarily linear.

[0106] However, such linear storage is made possible by the massively parallel computation (MA) of the system. For example, much like the Sieve of Eratosthenes algorithm, there exists an interval from the least to the most absent for a series of RGB / binary numbers of a module, a "frame," and therefore a "film." This can be easily solved by parallel computation in a network of communications, preferably quickly over the Internet, since a large number of RGB / binary numbers provide exactly the same interval in an isomorphic, "well-aligned," arithmetic linear fashion (steps d, e, 9.4, 9.5). While the "orderly storage of (general) digital images" (step d, 9.4) is entirely subject-oriented, the action of the "well-organized collection" (step e, 9.5) is similar. 5) concerns the intervals of absence of large numbers in RGB / binary format that underlie the "frames" of the "(general) digital images" (FPS), and thus the "film." This is no longer the case when it triggers the action of a "sorting," "permutation / combination" complex of many different supercomputational algorithms (step e, 9.5), which are definitely directed towards humans and machines (step g, 9.7). This shows that the closest relationship between CA and algorithms is between linear supercomputation and parallel supercomputation.

[0107] This is also why it is said that a symbiosis between humans and machines is being invited, and that a collective of programmers and systems will create non-deterministic oracle-type M(s) and UTM(s) (hopefully approximating their hardness for decidable problems and their ease of verification) from common M(s) and UTM(s) over a communication network, possibly the Internet. Because, in the natural sciences, classical computation is in fact a part of quantum computation, and even in the case of deterministic Turing machines, such systems are always better described as constraints on non-deterministic computation. Here again, the importance of the assembly language according to the present invention is emphasized, as it is clear that the "construction" of "ordered memory" (step d, 9.4) and the "permutation / transformation" of "ordered sets" (step e, 9.5) are followed by the sum of linear and arithmetic large numbers isomorphic to the RGB / binary module, the "frames per second" (FPS). Thus, what follows from the creation of a "film" or "(general) digital image" is the "permutation / combination" of the "(general) digital image" (step e, 9.5), where the programmer and the entity can interact within a frame of reference, i.e., a correspondence between a low-level hypercomputational programming language (step e, 9.5), including the RGB / binary image / text module (FPS) "composition" of the "frame" or "film", its possible "permutations / combinations", and a larger, very high-level, real one, high enough to parallelize linear and arbitrary programming languages, CA machine code instructions, realized over a communications network, possibly the Internet. The reason for this is that assembly code is reasonably recognized as being specific to each CA and system, and as just mentioned, is so in relation to CAs and systems over a network of communication, preferably the Internet, and for that purpose we have chosen to refer to earlier as "assembly programming" (7) which is included in the RGB / binary "composition" of this invention from both "eye to brain" and "brain to eye".

[0108] Additional aspects related to each single illustrated step are presented.

[0109] Transforming (general) digital images to a predefined density of pixels, i.e., image resolution, and generating a collection of transformed digital images. This process consists of inputting and transforming (generic) digital images from many sources on the communication network and of many types, e.g., photographic images of all formats, URLs and web pages, email and instant message images, cell phone and tablet frames, digital television "frame" images of "film", outdoor and game console "frames", virtual machine and deep web images, kernel, biometric and system log images, ATM and GPS, etc. Digital images may be redirected from specialized internet protocols, or even certainly from Turing machines on the communication network, to a generic image / text synthesis or intermediate generic image processing, into a "(representative) digital image impression" (STEP b, 9.2) and forwarded to a generic Turing machine camera / image processing node / server.

[0110] In the preferred embodiment of the present invention, since the "(general) digital image conversion" (step e, 9.1) may be a UTM-only protocol and not an M-to-UTM protocol, i.e., the arriving "(general) digital image" is converted and never processed in the UTM protocol, it is theoretically possible that an encryption method using Internet Protocols could be realized using this technology. However, this is not the method specified in the preferred embodiment of the present invention. What is intended is that the encryption of the input digital information of the modules "frame" and "film", along with the provision and conversion of the ready (general) digital image desired by the M-to-UTM protocol, is exclusively based on the M-to-UTM communication in the UTM protocol and should always be performed before the "(general) digital image numerical data / metadata processing" (step c, 9.3). This means that there should always be an intermediate or parallel application of an encryption method in the "(general) digital image impression storage" (step b, 9.2). If not, and indeed in any case, the "transformation" can always be performed solely by the UTM protocol, and more relevantly, encryption methods can be applied by Internet Protocol, in which case the "compound / permitted" datagrams are preferred. However, what is particularly preferred is that the encryption method be applied instead by a UTM-specific protocol. This protocol provides a purely machine-to-machine black-box mechanism for encrypting / securing "digital images" and ultimately differs significantly from Internet Protocol datagram "permutation / concatenation" in that the encryption is fully secured under the UTM protocol in the preferred method. Therefore, encryption methods must be used to feed or read "digital images" before processing.

[0111] In a preferred embodiment of the present invention, all UTMs are cameras / image processing nodes / servers capable of computing digital image information or any (general) digital image interface.

[0112] In any case, what is done is that each of the general-purpose Turing machine camera / image processor nodes / servers reads in (general) digital images, or "pixelated" exponential "cellular" "synthetic" images from the aforementioned sources. This can be separated into two main sources: the original data, even if encrypted, existing on the communication network, which has the nature of image sensing read in and therefore can be input as "impressions" (step b, 9.2), or the general-purpose image processing images read in as a result of artificial intelligence, machine learning, or general image processing cryptography and therefore ready to be input into each of the general-purpose Turing machine camera / image processor nodes as "memory of impressions" (step b, 9.2).

[0113] In the method just discussed, there is a flow (FL) of different "films" in the general purpose Turing machine camera / image processing node / server, and an address for each "(general) digital image" on a communication network, preferably the Internet, is stored opposite to where the actual image is stored in most cases, hence with a large drain on storage memory.

[0114] Nevertheless, to complement the loading capacity of the camera / image processing generic Turing machine node / server, in the very same process, as detailed below, another block of modules from the “(general) digital image” (3), either “collecting” (step d, 9.4) or “compositing” “frames” or “films”, must be allocated or stored in the very same network of generic Turing machines communicating, preferably on the Internet.

[0115] The stage of transformation of (general) digital images, whereby pixel density according to the total number of pixels indicates image resolution, is implemented as a platform for the transformation of (general) digital images based on numerical methods. One such transformation is best suited to the M to UTM protocol, but can also be performed by the UTM itself, and is therefore referred to herein exclusively as the UTM protocol.

[0116] Image / Text RGB / Binary An impression storage process in which a "pixelated" homogeneous (general) digital image is assigned numerical data / metadata, resulting in a set of assigned numerical data / metadata.

[0117] The process of impression storage of a (general) digital image occurs in a generalized Turing machine camera / image processor node / server within a communication network within or outside the Internet, thus resulting in a (general) digital image RGB / binary image / text isomorphic "pixel" or "pixel"-like sensory input, and UTM to M, UTM protocol is used.

[0118] In some embodiments of the invention, both the stack of RGB / binary "composite" U-mentalism "eye to brain" of modules, "frames" and / or "films" and the stack of RGB / binary "composite" U-mentalism "brain to eye" of modules, "frames" and / or "films" are to be computed in advance and from separate sources as "re-impressions" (step f, 9.6) to the camera / image processing node / server of the Generic Turing Machine, e.g., to be computed in a communication network of Generic Turing Machines, as well as image / text "pixel" composite images in "general image sensing" and image / text "pixel" composite images by "general image processing" methods in artificial intelligence or machine learning.

[0119] In a preferred embodiment of the present invention, the (general) digital image processing impression can be performed from general image sensing data or general image processing in a readout camera / image processing node / server. It is preferable that both the general image / text synthesis and the general image sensing are encrypted. Whether to process based on the general digital image data / metadata or based only on the encrypted image is a matter of appropriate concealment and encryption methods. Due to the black-box machine-to-machine mechanism, the related data / metadata of the original (general) digital image should always be processable in an "orderly" manner. Other than the image / text internet or network communication address, the CA requires no RAM memory, and each frame per second (FPS) has a defined symbolic unit because the "pixels per inch" (PPI) conversion is algorithmically resolved in advance. All related information, including audio and metadata, is recorded in the digital image.

[0120] The (general) digital image impression, consisting of the readout of the "pixels" and "composite" of the (general) digital image, modules or "frames," each frame per second (FPS), is preferably stored on UTM(s) servers / data centers within the communications network, again on the Internet. While doing so, a tandem encryption method is applied to the received impression image. Preferably, each RGB / binary isomorphic (general) digital image, or any direct image electromagnetic / physical symbolic impression applied to the CA infrastructure, as expected, must be assigned and ordered on the server / data center. This ordering consists of uniquely isomorphically ordering each "pixel," "frame," or "film" from its "collection" or "recovery" into a unique ordinal / binary number, as detailed in steps 4 and 5.

[0121] (General) The process of processing the image / text RGB / binary-like assigned numeric data / metadata of a digital image to produce a set of processed digital images. In the process of processing the numerical data / metadata assigned to (general) digital images, the image / text RGB / binary "pixels" or "pixel"-like homogeneous data / metadata is read into a UTM camera / imaging device node / server on the internet or in an external communication network and converted from UTM to UTM protocol.

[0122] Image / Text RGB / Binary isomorphic digital image processing (general) process, from the least ordered to the most ordered numerical representation of the digital image, properly sort and collect, resulting in a set of well-sorted digital image configurations, and only the collected order can be re-assumed whenever the preconditions for calculation are met. The stage of reproducing a (general) digital image in order is where the data / metadata is done by isomorphism like RGB / binary "pixel" or "picture element" from smallest to furthest, usually modules 8 bits or 1 byte, the modules themselves "frames", and therefore the "composite" of the film in order, resulting in a UTM to UTM protocol.

[0123] The process of storing each and every "composite" of modules, "frames," and "films" in the system's global memory and partially distributed storage, marked with ordered, shortest-to-longest n-dimensional modular operations and programming groupings with numerical stacks or abstract data type identification, results in stacks or "films" that are ready-to-use programming instructions, subroutines, or functions for performing massively parallel supercomputations isomorphic to binary code, again to be processed by a Universal Turing Machine over a communications network, preferably the Internet.

[0124] In a preferred embodiment according to the invention, it is possible to use frames per second (FPS) (typical) digital images at each camera / image processing node / server to estimate the input response of the entire linear time-invariant system, and then recover a determined set of RGB / binary text / image data that constitutes a much larger computational "composite" isomorphic to the binary code of traditional CA, in addition to processing at a much greater speed.

[0125] This requires that each "pixel", "module", "frame" and "film" be aligned into two stages that are isomorphic to a unique ordinal / binary order, whether for "collection" or "retrieval".

[0126] The alignment of citations from both the "collection" that designed U-Mentalism "eyes to brain" and the "collection" that designed U-Mentalism "brain to eyes" that constitutes U-Mentalism Assembly is therefore in line with the correct technical and abstract results of the CA and instruction set architecture of this invention. Consequently, the highly specific instructions, macros, and labels obtained from this technology must constitute ready low-level machine instructions and high-level threads that can be loaded and executed for the computer science problem to be solved. It is important to note that while all classes of Turing-complete computer problems are suitable for solving with this invention, its valuable core should be scientific, technical, and knowledge-driven applications.

[0127] Direct use of the algebraic and functional relationships of programming methods applied to "collection" and "collection," including encryption, machine learning, and AI image processing, as well as "(general) digital images" (3) (FPS) "pixels," "modules," "frames," and "films," should also be crucial improvements based on supercomputation.

[0128] The process of arranging image / text RGB / binary homomorphically processed (general) digital images from the smallest to the most distant numerical and algorithmic representations to achieve an ordered series of digital image collections. In the process of collecting and permuting / combining (general) digital images, a new "composition" of modules, "frames", "films" is made by similar least-to-farthest homomorphic alignment of data / metadata RGB / binary "pixels" or "picture elements" by "permutation / combination" general image processing methods, or cryptographic or other, hence UTM to UTM protocols.

[0129] The process of collecting and partially distributing and storing in the system's global memory is a "composition" of all the many modules, "frames," or "films," henceforth marked with unprecedented numerical stacks, subroutines, or function identifications, from shortest to longest, "well-ordered" n-dimensional modular operations and programming groups. Because of their numerical and orderly nature, these stacks can serve as the basis for general image processing techniques, cryptography, and other mathematical methods. Furthermore, more advanced mathematical and programming techniques can be introduced, thereby improving programming techniques. U-mentalism "from scanner to printer" or U-mentalism "from eye to brain" seems more preoccupied with exploring "composition," i.e., the composition of relationships as relative products of factors based on RGB / binary "pixel"-like, exponential, "cellular" foundations. The construction of relationships as relative products of factor relationships based on RGB / binary "pixel"-like exponential "cellular" basis is explored into any "composite" of stacks, or modules, "frames", "films", from which the relative multiplication in the calculation of "pixel" relationships is enriched, and the U-mentalism "printer to scanner" or U-mentalism "brain to eyes" is "reordered / combined", i.e., connected to more things. The prior relative products of factor relationships in terms of the basic "composite" of RGB / binary "pixels", exponential "cells", modules, "frames", "films" are arranged / rearranged / selected into newer isomorphic "ordering" relationships.

[0130] Thus, from the "configuration" of "pixel" or "cell" information relationships resembling "pixels," appropriate "permutation / transformation" programming and algorithmic engineering can be achieved, and the stacks can be freely "orderly" distributed within a topology on a communications network, preferably the Internet.

[0131] In a preferred embodiment of the present invention, it is possible to carry out the "collection" step even by employing AI and machine learning "recovery" beyond their direct computational use (2; step d, 9.4) from general image processing variants to the U-Mentalism system (2; step d, 9.4) (for the reason that the AI ​​and cryptographic data are the originals "recovered" from general image processing to the U-Mentalism system), in order to put all kinds of requirements of CA and instruction set architecture into order to fully program and determine the output signals and codes of any desired dynamic linear system RGB and also "(general) digital image" (3).

[0132] Image / Text Reprinting Process RGB / binary processed and well-ordered (typical) digital image, resulting in numerical data / metadata.

[0133] The process of restoring a (general) digital image to a Universal Turing Machine is that from the previous example, the "recollection" and "collection" of the digital image, including the isomorphic smallest to the furthest of pixels such as RGB / binary numbers of data / metadata (usually modular 8 bits or 1 byte), is freshly assigned as a "recollection" and "collection" sense input to the UTM, and thus protocoled from UTM to UTM.

[0134] The process of programming and processing the RGB / binary homomorphic processing of images / text and sorting algorithms based on numerical data / metadata. The process of algorithmic programming for (general) digital images involves the recursive processing of numerical algorithmic problems into subroutines or functions, stacking both the "collection" and "composition" of modules, "frames," and data / metadata, such as RGB / binary "pixels" or "picture elements" in isomorphic least-to-most alignments (usually 8-bit or 1-byte modules) (these "collection" and "recovery" U-Mentalism names), in networks of communication suitable for the U-Mentalism programming language, on the Internet or elsewhere, preferably driven by scientific knowledge.

[0135] Computer Architecture and Instruction Set Architecture Structural Computational Capabilities To illustrate the computational power of the present invention, first consider the general analogy between a "pixel" and a "byte." Each color (RGB) "pixel" allocates three bytes, representing the number (radix) and position (ordinal) within the "pixel," giving a total of 28, or 256, shades / hues per byte. This effect therefore indicates that a single (RGB) "pixel" holds 16.777.216 bytes and color combinations, or 224 in prime factorization, as is known. This product of prime numbers is important in the context of public-key cryptography. While the idea of ​​allocating 28 for each color (RGB) to each byte of a "pixel" and 224 for a "pixel" is not one of the prior art concepts, it is certainly prior art. Thus, the corresponding value on a resolution-dependent "frame" is also the smallest symbolic unit of CA, from which any U-machine or Turing machine can directly process the image into its binary form, providing a computability theory that replaces all CAs to date. It also reveals that the most important structural and functional feature of this technology is that it realizes CA from a (general) digital image to one or more binary nodes at or near the speed of light.

[0136] It is also intended that all possible variations of the measures relating to "pixels," "compositions" of images, "frames," and consequently all possible subsequent states or configurations are included in this technology and its structural and functional methods. The same applies to the technical standards and device introductions placed herein.

[0137] As an illustration, in the era of photonic computing, it may be advisable to scale up the referenced technology, which involves a large number of discrete packets of energy, or photons, according to two key characteristics: frequency and wavelength. That is, to realize a single "cell" like a "pixel" with exponential fundamental properties, the current 28.3 per "pixel" in a digital image is expanded in this speculative setting to 2568.3 per unit of photonic computation, along with a resolution-dependent corresponding value, in the "composite" of a (general) digital image (3), technically the impression of a wavelength "frame." This explanation is very similar, but in the scenario of photonic quantum computing, the very same value of 28.3 is equivalent to 1 in relation to qubits, even if it is always isomorphic to the classical binary code, but with the appropriately designated value of 2568.3 as the smallest number system unit. This means that all possible bits per unit of photonic quantum computation in this diagram are either in the zero state or the one state in a purely photonic means operated by CA according to the present invention.

[0138] In this case, the formula for "pixel density" as a function of image resolution "total pixels" becomes the norm for the invention, and we can conveniently establish an extension of the computable means from "compositing" synchronous "pixels" and images or "frames" processed at a time. And then we can conveniently establish computable measures, all the way up to synchronous "frames per second" (FPS), and even "films" or moving images, and all the other more complex computable patterns, such as n-dimensional "films" of either M(s) or UTM(s). In this scenario, no matter how orthogonal the "compositing" of digital images may be, the invention is brought into computational programming terms, not in the strict Euclidean notions of points, surfaces, and dimensions, nor in extensions such as Hilbert spaces, but in the most complex version of the holistic sense (informatics and programming) of Riemannian manifolds. This means that, symbolically and physically, the system of M(s) or UTM(s) is invariant due to synchronicity in all positively defined, non-accelerating frames of reference ("frames per second" for digital images) (FPS), and similarly the speed of light in a vacuum is an invariant, non-transcendent limit of technology.

[0139] As an example, the formula for "pixel density" measured in "pixels per inch" (PPI) that can be followed in this specification, as a prelude to complying with a fixed pixel resolution, is 8K Ultra Full HD: 7680x4320 pixels. This is because a (RGB) digital color image is 16:9 (2 in prime factorization). 4 :3 2 ) ratio, each (RGB) digital color image is therefore 796.262.400 bits, or 99.532.800 bytes. This number is the total digital image to binary code that must be processed by the image sensor, but it is 2 bytes per pixel. 8 , or "pixel" per 2 8.3.Only the images need to be identified and processed simultaneously as a "composite" of fully digital images or "frames." In any event, other values ​​of image resolution in "pixels per inch" are suitable for the present invention, as will be understood by those skilled in the art.

[0140] The next value to consider is the "frames per second" (FPS) value of the (RGB) "composite" 99.532.800 bytes of the image or "frame". Following a conservative technical situation, 60 FPS (factorized into 2 2 .3.5) could be chosen, but as the state of the art shows, an astonishing record of about 4,400,000,000 FPS has been achieved, which is a welcome technological feat. The number of bytes per hour of "film" or moving image in computer architecture is then calculated as 60(FPS) x 60(") x 60(') x 99,532,800 bytes, which results in 21,499,085,000,000 = 2,1499,085e+13.

[0141] Also, under these conditions, once we have come across a "film" figure in bytes per hour, the same applies to "film" figures of hours, days, years, and even "compositions" of image distances in light-seconds.

[0142] The value 21.499.085.000.000 = 2.1499085e+13 bytes / hour (2.1499085e+25 terabytes, or 21.499085 × 1015 zettabytes) must be matched to the total number of digital images being processed. This is literally the FPS for all digital images from all media and sources, including the Internet and digital terrestrial television (DTTV: DVB-T, ATSC, ISDB-T, DTMB). Simplified, the total amount of data in 2025 will be 175 zettabytes (175 × 10 21 Therefore, the value of one of them is 2.025463e+19 = 2025463×10 13This corresponds to zettabytes per hour, which can be approximately defined as the "overall problem" of the present invention, most preferably in terms of a distributed partial U-machine on a Turing machine.

[0143] From this example, one hour of "film" using the technology under the conditions cited is 21.499085 × 10 15 We can see that we have zettabytes of processors and computing power, which is 1.2285191e+14 times the amount of data expected in the world in 2025. In reality, to achieve the computing power equivalent to one hour of "film", we would need 175 zettabytes of global data over 120 years, with a 100% annual growth rate.

[0144] In fact, the supercomputing handled by this invention is 48.611.111 terabytes per second (4.8611111 x 10 per second). 19 bytes), 2 per second for 175 zettabytes of world data under CA (2025 estimate) 6539792. The technology itself, in its earliest stages, already exceeds the capabilities of 64-bit architectures. Perhaps 1 Exbibyte (EiB) = 2 60 or 1024 6 It should be noted that this will have no effect on the network of Turing machines on the Internet or on the processing of UTM(s), and that an unlikely development is possible in which only Turing machines without UTM(s) are manufactured under CA technology on the Internet.

[0145] The only "film" of one full day's time in the technology processed by the present invention is 5.1597804e+17 zettabytes (21.499085 x 10) from the illustrated calculations prior to pixel resolution in the "composite" of the image, and therefore in the "film" processed by preferably a network of interconnected UTM(s). 15This has been shown, but the real expectation with this technology is to process many hours, days, or years of "film" in parallel, that is, many "tapes" in the Turing or U-machine sense.

[0146] For now, it seems that combining supercomputing with technology makes sense, as one full day of just one "film" is said to be 1.2285191e+14 times the value of 175 zettabytes (world data estimate for 2025) itself.

[0147] Consistently, in the CA model, the "cells" are now "pixels," the chips are the "composition" of the image or "frame," the cards are the "films" that repeat each FPS, the node cards are the cabinets, computing various parallel "films," and the "system" as a whole is M(s), the communication network over the Internet and most preferably the interconnected U-machine technology.

[0148] In this context, it should be remembered that these figures are conservative estimates of 60 FPS at 8K resolution. In reality, astonishing figures of approximately 10,000,000,000 FPS (Frames Per Second) have been achieved, making it possible to instantly record patterns of light using CUP (Compressed Ultra High Speed ​​Photography). As expected, this technology is best implemented in a single and / or multiple node / server CAs interconnected by a communications network, either on or off the Internet, but preferably in a network of Turing machines, with as many camera / computer processor nodes / servers as possible on the Internet, i.e., UTM(s) on the Internet.

[0149] We can compare the total amount of information (audio, video, and text) in 1999, 12 exabytes, with the corresponding equivalent of 175 zettabytes of world information (audio, video, and text) in 2025. If we assume that 175 zettabytes are transmitted over the internet network in UTM in the same year, 2025, we can see that the total processing per second at the quoted CA in 2025 is equivalent to 1.8446744e+38 times the amount of information (audio, video, and text) in 1999.

[0150] The CA's processing capacity of 21.499085 x 1015 zettabytes per hour of "film" alone is 1.2285191e+14 times the amount of information (audio, video, and text) in the world in 2025. It also processes 5.9719681e+12 zettabytes per second of "film." For comparison, the amount of information (audio, video, and text) in 1999 was 12 exabytes, or 0.0138350580552816 zettabytes. This seemingly impossible feat, manipulated by the CA of the present invention, is essentially possible because the information (audio, video, and text) is matched at a frame rate (FPS) with a high-resolution pixelated digital image, compressing the ergonomic information into the "film."

[0151] When the CA model is concerned with the supercomputing model, it is worth calling Summit or OLCF-4 the fastest supercomputing model on the HPL (High Performance Linpack) benchmark as of November 2019. Regardless of its overall characteristics, the proposed model is based on a parallel discussion of its number of nodes - 4,356 - a speculatively approximately equal number of UTM(s) or computer processing / camera nodes / servers on a communication network, over the Internet, where the invention, along with the discussion, is selected from 5,000 for the implementation of CA. Details of Summit and OLCF-4 are described in the specifications and characteristics of the supercomputing "Summit." Processor: IBM POWER9 TM(2 / node), GPU: 27,648 NVIDIA Volta V100s (6 / node), Nodes: 4,608, Node Performance: 42TF, Memory / Node: 512GB DDR4 + 96GB HBM2; NV Memory / Node: 1600GB, Total System Memory: >10PB DDR4 + HBM + Non-Volatile, Interconnect Topology: Mellanox EDR 100G InfiniBand, Non-blocking Fat Tree, Peak Power Consumption: 13MW.

[0152] The Internet currently has about 50,000,000,000 nodes, far exceeding the 1022 to 1024 FLOPS interval (2015) of all existing computers. Therefore, if we set the number of computer processing / camera UTM nodes / servers to 5000 (1:10,000,000) on the network of CA's Internet of Things computers, it is possible to shorten the interval to a general interval (10-15% in 1.5 to 3 years). Compared to the Super Computation Summit and OLCF-4, which have roughly the same number of nodes in total, 3x10 20 From 1.5x10 21 It is possible to achieve industrial price and energy savings in the new interval up to Therefore, with the CA of the present invention, run by a UTM on a computer on the Internet, it is possible to calculate a probabilistic speculative value of a network communication node / server of (2 x 22 x 5,000) = 220,000.

[0153] To find a plausible indicator of the bytes per FLOP (B / F), i.e., the memory required per unit of performance in our CA, it is appropriate to follow the calculation of FLOPS. First, we consider the total estimated information volume in 2025 (1.75 × 10 23 Bytes), we estimate an arbitrary conservative pattern (70%) of 1.225 × 10 23Let's say it's (1.225e+23) bytes. This value can be expressed as the sum of the product of the color instructions of the image read by the CA - "pixel" and "composite" - "frame". Next, we can assume that the average frequency rate of a CA operating at 8k Ultra Full HD is 60Hz. The following relationship can be considered as the background to this:

[0154]

number

[0155] However, this result suggests that, abstractly, a one-hour "film" of our CA occupies 21.499085x1015 zettabytes / hour, and if we equate this number with the traditional goal of 1 byte / flop, we can say that the CA runs at (21.499085x1015 6060)5.9719681e+12 bytes / second. 15÷60÷60) 5.9719681e+12 bytes / sec, resulting in an abstract value for CA bytes / sec that exceeds the 2.695e+28 ​​flops obtained from the estimated total data volume of 175 zettabytes in 2025.

[0156] Even so, computationally, a CA with an estimated data volume of 175 zettabytes in 2025 is said to operate at 7.4861111e+24 FLOPS / second (2.695e+28 ​​FLOPS ÷ 60 ÷ 60), while a technology with only one abstract "film" is said to operate at 5.9719681e+12 bytes / second. Here, computationally, there is no proliferation of nodes / servers across the communication network, so we can only speculatively match the value (bytes / second) of just one abstract film. 9719681e+12 bytes / second. Here, we nullify the multiplication of nodes / servers across the communication network, and in principle, we can only speculatively match the abstract value (bytes / second) of just one "film" of CA. Considering many more partially distributed UTM(s) nodes / servers across a communication network with Turing machines on the Internet, this can be matched as a linear improvement in floating-point operations per second on the same network. In terms of per-node values ​​at hand, the likely scenario is, by contrast, that there will be many tapes of days' worth of "film" and technologically different "films" over a communication network of massively parallel computations by UTM(s), even if there are reduction factors built into the CA of the invention apart from pure implementation factors (energy, price), such as cryptography and security protocols.

[0157] In contrast, CA according to the present invention can even increase the density of work and the performance of computers in a way that facilitates the use of the technology. Instead of repeating the process over all global data (creation, storage, duplication), the introduction of the present technology can initially be directed, for example, at only the replicated data. This is not desirable, and would be welcome given the large amount of data currently available, but even the estimated expansion over a 10-year period is insufficient in relation to the computational power of CA. In fact, considering that the world data in 2020 is roughly 40 zettabytes, using the abstract and introduced parameters, it is possible to estimate (using very conservative parameters) that one hour of one "film" technically takes up 21.499085 × 10 15 Recall that a zettabyte accounts for 5.3747713e+14 (21.499085 × 10) of the world's information data for a year like 2020, for just one hour of CA's "film." 15 This shows that the required amount of data is 40 times larger than the conventional method. However, to alleviate the very low density of computational performance, the CA of the present invention can be improved by using metadata for each type of information (audio, image / video, text).

[0158] Another advantage of the present invention is the inherent economy of use of CA to display data on the Internet and call URL Internet addresses or metadata codes per function, hence the reduced caching or volatile random access memory requirements and the processing power M(s) at the disposal of each UTM node.

[0159] Therefore, what is relevant is that, within the scope of the appropriate computing power of the CA according to the present invention, not only computer processing / camera image detection (1) but also machine learning image generation (2) are available to increase the amount of data information (audio, image / video, text) at the disposal of the CA. For this reason, in a preferred embodiment of the present invention, cryptography is also relevant, since it constitutes the image generation processing power itself. However, what is needed is to have appropriate methods of artificial intelligence and machine learning, image generation techniques implemented by the UTM(s) in the communication network of the CA according to the present invention, as well as an increase as much as possible of information (audio, image / video, text) from all camera and image interfaces of each Turing machine (4, 6), over-generated by the corresponding cryptographic mathematical key in the UTM(s), in the UTM(s) on the communication network of the CA.

[0160] This is because programming coordinates and algorithms cannot truly specify the connectivity dimensions of a communication network or other "pixels" in a network of the same topology, except through diagonalization methods suitable for computation and programming. The degrees of freedom of "pixels" and "bytes" are so great in programming, constrained only by an ordered "system" of modules, "frames," and "films" (7-1, 7.2 - 8.1, 8.2), that the dimensions of the "system" depend directly on the ever-increasing number of variables and coordinates that the "system" itself creates. This is one of the key roles of U-mental programming languages, which are typically built on U-mental combinatorial languages. It seems well worth exploring the general interrelationship between the rectangular planar "frames" of Euclidean networks and the n-frame quasi-cubic "films" of n-frames in networked physical systems, compared with the n-dimensional manifolds of binary code isomorphism in symbolic systems. The space of Riemannian metrics on a given differentiable manifold is infinite-dimensional, ideally considered to be the information processing and programming space in CA. However, because algebraic manifolds are dense, continuous moduli spaces are isomorphic to binary codes. Furthermore, although finite-dimensional, they also have nontrivial transformations that do not have equivalence within the system, excluding physical reference units such as "pixels" and "frames." Therefore, their symbolic properties may be geometric rather than topological. In other words, given their image-like properties, it is difficult to say whether our CA has a locally continuous, quasi-infinite geometric structure, or whether it has a global discrete modulation topology with quasi-adjacent "pixel" light points between points. For this reason, it may be best suited to the appropriate field of geometric topology. Furthermore, when performing impression storage processing of (general) digital image waveforms in the CA of the present invention, the correct conversion must also be considered.

[0161] Aside from the very conservative parameters introduced, it is also important to note that the CA of the present invention results in a rotation of data to processing power, but if, for example, energy and price requirements are high, the truth is that standards for image detection (1) or image processing (2) can be implemented. Along these lines, virtual machines, deep web, kernels, biological and system logs, and even ATMs, GPS, emails, or instant messages can be truncated so that they do not reach any UTM node / server computer processing / camera on a communication network on the Internet, for example.

[0162] At the same time, it is important to remember that the CA of the present invention intentionally transcends from parallel disparate "films," from a general "system," to an informational (general) digital and programming sense defined by a network of n-dimensional "pixels"-points, "frames"-surfaces, and "bytes"-(processing)times of phased structures, through the network and algorithmic properties of "composition." While the distances and times in the network of UTM(s) on M(s) between "bytes" within a "system" are different for measurements made in different reference "frames," informational space-time intervals are independent of the inertial reference frame in which they are recorded / programmed, through the orderly "frames" and "films" within a "system."

[0163] All of the above is especially true in relation to wave-like physical-symbolic impressions, such as the correct "(general) digital image" (3). Thus, in the case of photonics, plasmonics, computer-brain interfaces or neuromodulation, where well-defined algorithms of programming computational methods, such as photons of electromagnetic waves, are relied upon, these are certainly more responsible for the "synthetic" operation in the global performance of the UTM nodes / servers of the communication network.

[0164] The principle of acceptability of all other possible units of reference in the CA of the present invention cannot be emphasized enough. In this case, "pixel," "byte," "octet," or "bit." CAs using measures such as "HEXA" or "RGB," or "frame" or "film," can be revised and reshaped into any other form. Examples include the fact that a "pixel" like HEXA corresponds to one line, and that a digital image like RGB corresponds to a "frame," as well as the applicability of λ-calculus to the art of calculus, i.e., the application of λ variables, λ abstractions, and λ applications to the corresponding R(ed), G(reen), and B(lue). Again, the objectives of the present invention presented here are achieved and embodied in the inversion of von Neumann CA and the computational key elements for the impression (like a "scanner") / representation (like a "printer") of exponential prominence from (general) digital images to binary, digital, or wave-like physical symbols, or "pixels." Similarly, it is the most abbreviated foundation of both ends, giving as many UTM(s) as possible on the internet, on the computer(s) on the network of communication. These characteristics also foreshadow the implementation of CA's body-machine communication in machine learning and artificial intelligence.

[0165] All of the above is also valid in relation to physical-symbolic "scanner" / "impression" or "printer" / "representation" implementations of (general) digital images - binary electromagnetic waves. All such implementations are based on "pixel", exponentially functional, basic type implementations, with further possible implementation layers of the CA hierarchy. This also means that within the scope of the present invention, the CA also contemplates the use of as many levels of hierarchy as necessary on the UTM(s), in turn on the M(s), depending on the presence of UTM(s) on a computer host, on the Internet, on a communication network, as desired in the technology.

[0166] According to this invention, future assembly languages ​​and future single-function / multi-function programming languages ​​should be crafted to appropriately extend CA (U-mentalism) assembly, i.e., Homem and Luis, as a foundation for handling different possible "compositions" of algebraic and n-dimensional geometric topologies in networks of digital imaging and processing, electromagnetics, and acoustics-based communication, all within a single development programming design. What is U-mentalism? Journal of Advances in Computer Networks. Volume 7, Number 1. pp. (18-24). (2019) "(...) is a philosophical and programming concept that proposes a singular (single, specific, and immanent) and universal (all-encompassing, extensible) programming language that is simultaneously the inverse of all established computer architectures (typically Princeton or von Neumann computer architectures)."

[0167] The present invention addresses the issue of optical data transmission, generally the transmission of information using optical beams and / or wave communications, not for the direct computation of (general) digital images, but rather for the sake of omitting the state-of-the-art practical techniques and state-of-the-art technologies for sensing and performing appropriate digital images. Therefore, within the scope of the present invention, any form of digitally homogeneously formed encoding of a "(general) digital image" (3) that can be represented, processed, compressed, stored, and printed, and that can be used as a direct input to an M, or preferably to any of various UTM(s), within a communication network on or outside the Internet, is considered. Electronic image detection includes CCDs (charge-coupled devices), APSs (active pixel sensors), and CMOSs ​​(complementary metal-oxide semiconductors), which use MOS (metal-oxide semiconductor) technology. These differ in that CCDs use MOS capacitance, while CMOS detection uses MOSFETs (metal-oxide semiconductor field-effect devices). Hybrid CCD / CMOS or sCMOS sensors may also be used in CA. Although each cell of a CCD image detector is considered an analog device, APS (pixel detector) is an image detector that combines active semiconductor devices, so it must be considered to be consistent with the general configuration of a digital image. Therefore, while analog as a component, it is recognized as a digital image, and furthermore, as long as we assume exponential base wave-like cells like "pixels" that can be directly calculated by the camera / image processing M(s) or UTM(s), we can claim the standard of "(general) digital image" (3).

[0168] The same is true for color separation, or the use of any color filter array. Both spectral transmittance and demosaicing algorithms, and the color rendering properties of any color filter array as a whole, are included as processable by CA in accordance with the present invention, as long as the mosaic of color filters on pixel detection of any image sensing technology is an extension of the appropriate digital image. It is also important to include current and future technologies used in CA, such as QIS (Quantum Image Sensing), which takes a step from digital imaging to "(general) digital imaging" (3). Solutions for exponential bases like "pixels" remain binary-isomorphic, whatever the value, even though solutions must also be found in units like "megashots," FPS rates, and "cells."

[0169] It is relevant to characterize the bridge to U-mentalism combinatorial programming, as shown in 7 and 8, with reference to the "system" in CA. To this end, the present invention first distinguishes, in a forward-looking manner, U-mentalism assembly programming and, consequently, the U-mentalism programming language (7, 8), both in the "system" category. The significant difference between U-mentalism combinatorial programming is, in a sense, necessarily the proper CA delimitation. In relation to U-mentalism combinatorial programming, we sequentially settle on two boundaries: "U-mentalism combinatorial scanner" (7.1-8.1), "(general) digital image" detection, photosensitivity, or wave impressions, "U-mentalism combinatorial printer" (7.2-8.2), and "(general) digital image" (3) processing, light emission, or wave emission.

[0170] The latter could also be called the "U-mentalism eye" (7.1-8.1) or the "U-mentalism brain" (7.2-8.2). These terms are derived from the different characteristics they possess from the perspective of the functional anatomy of the human body. Specifically, the "U-mentalism eye" refers to the "scanner" method of sensing a "(general) digital image" (3) as a mere "impression," i.e., CA, before transmitting visible electromagnetic waves to the retina and photoreceptors, which then transmit the signals as electrical signals to the optical nerves and direct them toward the brain. The "U-mentalism brain" then "emits" the "(general) digital image" (3), processes it in a "printer" manner, and "reprints" it, because the appropriate electrical signals are then transmitted toward the brain.

[0171] Furthermore, it is essential to maintain the isomorphism between the "pixel" of a "byte," which is a point-like unit on a plane, and the "octet," which is the photographic and wave-like detection and emission of (general) digital images, that is, the locus of "detection" and "emission." Therefore, in this "octet"-"byte," a perfect symbiosis is established between the "scanner," which is the "eye of U-mentalism" that senses photographs and waveforms, and the "brain of U-mentalism" that emits photographs and waveforms, and it becomes part of the "combination of U-mentalism" of CA, which lies beyond the U-mentalism programming language in the "system."

[0172] One could say that the "U-mentalism eye" to the "U-mentalism brain" is a general wave or photographic combination from the "scanner" to the "printer," specifically a (general) digital image "octet"-"byte"-"combination," and the "U-mentalism brain" to the "U-mentalism eye" is a general wave or photographic combination from the "scanner," specifically a (general) digital image "octet"-"byte" combination. The "set" here is properly a "U-mentalism combination." These assertions are valid according to their implied other units of reference.

[0173] An example of different units in such a pro forma system is the reverse of [U-mentalism eyes to brain]["octet"-"byte"-combination][scanner to printer], where [U-mentalism eyes to brain]["octet"-"byte"-combination][computer to scanner] are both different types of unit measurements for "octet" and "byte." Again, "combination" should be "U-mentalism combination." This pro forma system only illustrates two possible combinations that can be changed to accommodate all possibilities in CA, such as [U-mentalism eyes to brain]["octet"-"hex"-combination][scanner to printer] or [U-mentalism brain to eyes][RGB to binary "frame"-unit][printer to scanner].

[0174] Within the specified parameters of CA, 8K Utra Full HD (7680*4320), 16:9 (2 4 :3 2 This makes it clear that for all units of measurement in this pro forma method, either the "scanner" / "detection" side or the "printer" / "emission" side does not need to be automatically evaluated. This makes it clear that for all units of measurement in this pro forma method, either the "scanner" / "detection" side or the "printer" / "emission" side does not need to be automatically evaluated. 8x3It turns out that the whole value x33.177.600 = 5 does not need to be evaluated by itself. 5662776e+14 specifies the 256 (0-255) possible variations of RGB color in each "octet" of the 3 "bytes" of a "pixel" in the specific terms of the referenced implementation, or in other units of reference and shifting to ultimately other results, but only locally. (General) Digital Image (3) is exactly the same as applying the wave-like "detection" / "scanner" or "emission" / "printer" reference units to "systems" of UTM(s) on M and shifting to ultimately other results in other units of reference of CA, perhaps in photonics, plasmonics, computer-brain interfaces, or even quantum computing.

[0175] While these last points are valid, the referenced pro forma scheme of the combinatorial level of U-mentalism must undergo an information-programming version of the general theory of arithmetic present in RGB, HEXA, and binary codes, similarly suited to machine learning and AI. This encoding is nothing other than the nth order of all the "byte" sequences derived from all the "pixels," primarily "frames" (e.g., 99,532,800 "bytes" for an 8K Ultra Full HD digital image), for all possible collections of "frames" of "film."

[0176] However, in the "U-Mentalism Brain," i.e., in computer architecture, from simple "octet"-"byte"-"combinations" to "combinations" between "printer" and "scanner," the variables that are recommended for use in the "U-Mentalism Combinations" are, of course, the "octet"-"byte"-"combinations" storage in the "U-Mentalism Eye," the orderly "pixel"-derived byte sequence by the "scanner"-"printer," the digital image "frame," "film" storage, and the end of subsequent use in the "U-Mentalism Brain." It is possible and supported to use HEXA and RGB in binary code in conjunction with the U-Mentalism Combinations, either in the "U-Mentalism Brain" collection or in the "U-Mentalism Eye" for interpolation depending on the need for collection.

[0177] The basis for understanding and using this U-mentalism combination in machine learning and AI is, of course, the Fundamental Theorem of Arithmetic (i.e., the use of arithmetic definitions with prime numbers) and the positional number system. It's not just the arbitrary base meaning of the Hindu-Arabic numeral system due to the contingency of the use of non-numeric yet positional symbols in HEXA. It's also not because of the Cartesian nature of RGB, but because of how they are combined in a binary code system, but essentially because the collection of the orderly "U-mentalism combination eye" in the "U-mentalism brain" collective and its programming use broadly applies to CA "frames" and nth constructible "films," preferably executed by UTM(s) over a communications network over M(s) over the Internet.

[0178] In this description, the terms "about" and "approximately" refer to a range of values ​​on the order of about 10% of the specified value.

[0179] As used in this description, "substantially" means that the actual value is within about 10% of the desired value, variable or associated limit, particularly within about 5% of the desired value, variable or associated limit, and particularly within about 1% of the desired value, variable or associated limit.

[0180] The subject matter described above is provided as an example of the present invention and should not be construed as limiting the present invention. Terminology used for the purpose of describing particular embodiments according to the present invention should not be construed as limiting the present invention. As used in this description, the singular definite and indefinite articles include the plural unless the context of the description explicitly indicates otherwise. Additionally, the terms "comprises" and "includes," when used in this description, specify the presence of features, elements, components, steps, and related operations, but do not exclude the possibility of other features, and elements, components, steps, and operations are also contemplated.

[0181] Provided that the essential features of the following claims are not altered, all modifications shall be considered within the protection scope of the present invention. [Industrial Applicability]

[0182] The method and CA of the present invention provide an exponential gain in partially computable functions across all possible research domains. Its scientific and technological applicability can be demonstrated, for example, in satellite image processing, aerodynamics, fluid dynamics, thermal and fluid objects, and will have a strong impact in various fields on a new level of cybernetics. [Explanation of symbols]

[0183] 1. Embodiments involving network communications or internet data: Image / text. Refers to images made up of "pixels" or general image detection.

[0184] 2 Network Communication AI and Encrypted Data: Image / Text "Pixel" Replacement / Synthesis. Shows another embodiment of image or general image processing.

[0185] 3 Denotes (general) digital images in the context of speeds at or near the speed of light. CA of (general) digital images to binary numbers on a single or multiple nodes / servers.

[0186] 4 The tape data of a general Turing machine is shown.

[0187] 5 Denotes a partially distributed and decentralized horizontal massively parallel computing network, preferably over the Internet, including UTM cameras / image processing nodes / servers.

[0188] 6 A pool consisting of (general) digital images including photographs (FPS) images, URLs and web pages (FPS) images, email and instant messaging (FPS) images, and mobile and tablet frames (FPS) images. Digital TV and Film (FPS) images, Outdoor and Console (FPS) images, Virtual Machine and Deep Web (FPS) images, Kernel, BIOS, and System Log (FPS) images, ATM, GPS, CCTV, Drones, Automated AI and Machine Learning (FPS) images.

[0189] 7 U-Mentalism Assembly Both programming instructions and algorithms are different (modules, octets, bytes) Isomorphic to binary code (general) Digital Modules (FPS) Showing images and films (composite or permutation / combination).

[0190] 7.1 U-Mentalism illustrates an embodiment of combinatorial programming, consisting of a combination of modules, (FPS) frames, and / or films, or a "composite" of general blocks, stacked in a series of massively parallel computations.

[0191] 7.2 Demonstrates an embodiment of an orderly collection of modules, (FPS) frames, and / or films, generic blocks or stacks of sequential massively parallel computations, new permutations of combinatorial "compositions" U-mentalism combinations printer-to-scanner or brain-to-eye.

[0192] 8 An embodiment of the previous U-mental combination program is described that consists of an orderly collection of U-mental combination scanner-to-printer or eye-to-brain configurations of modules, (FPS) frames, and / or film "composites" and an orderly collection of U-mental combination computer-to-scanner or brain-to-eyes of new permutations / combined "composites" of modules, (FPS) frames, and / or film.

[0193] 8.1 U-Mentalism Combination Scanner to Printer or Eye to Brain shows an embodiment of the programming of the previous U-Mentalism Combination consisting of the storage of well-organized configurations of modules, (FPS) frames, and / or films, general blocks or stacks of "composites", illustrated by sequential massively parallel computations allocating and recompressing UTM(s) in a looping fashion.

[0194] 8.2 U-Mentalism Combination Printer to Scanner or Brain to Eye shows an embodiment of the programming of the previous U-Mentalism Combination consisting of an orderly collection of new permutations / combined "composites" of modules, (FPS) frames, and / or films, general blocks or stacks, in a loop fashion, consisting of sequential massively parallel processes that are assigned to UTM(s) and / or recompressed.

[0195] 9 A typical block diagram shows the action boxes associated with each block of the process, which is performed using massively parallel computing and looping methods.

[0196] 9.1 shows a box for (general) digital image conversion, whereby the density of pixels or "cells" as "pixels" depends on the number of "cells", image resolution conversion, generally M to UTM or UTM only protocols, processed in a loop with massive parallelism.

[0197] 9.2 shows a (typical) digital image impression storage box consisting of (FPS) image / text RGB / binary isomorphic "pixels" or "picture elements" of a digital image, thus assigned to data / metadata, and generally processed using UTM to M and UTM-only protocols, massively parallel computations, and loop methods.

[0198] 9.3 Explain the box of (general) digital image processing consisting of (FPS) image / text RGB / binary isomorphic "pixels" or "pixel"-like digital images in the form of data / metadata, typically processed in a UTM to UTM protocol, massively parallel computing and looping fashion.

[0199] 9.4 U-Mentalism: The scanner-to-printer or eye-to-brain equivalent of data / metadata. RGB / binary isomorphic "pixel" or "pixel"-like composition module, (FPS) frames, and films (module 8 bit or 1 byte). (Typical) Represents a box of aligned collections of digital images from smallest to furthest, generally processed in a loop-like fashion using UTM to UTM protocol, massively parallel computing.

[0200] 9.5 Data / Metadata RGB / binary isomorphic "pixel" or "picture element" like permutations / combinations. Box represents a smallest to largest ordered collection of (general) digital images (module 8 bit or 1 byte) consisting of modules, (FPS) frames, and film, corresponding to U-mentalism printer-scanner or brain-eye, generally UTM to UTM protocols, processed in a massively parallel and looped manner.

[0201] 9.6 (FPS) Image / Text RGB / Binary Isomorphic "pixel" or "pixel"-like digital images are shown in the box for (general) digital image re-impression storage. Therefore, previously constructed collected or eye-to-brain, and permuted / combined collected or brain-to-eye, data / metadata are assigned, and when only the computational prerequisites are re-impressed, it is usually processed using a UTM-to-UTM only protocol, massively parallel computing, and loop method.

[0202] 9.7 Data / Metadata Shows a box of algorithmic programming processing consisting of both properly ordered permuted / combined collections of (common) digital images (module 8 bit or 1 byte) consisting of RGB / binary numbers, isomorphic "pixels" or "pixel"-like digital images, (FPS) frames, and film, generally a UTM to UTM protocol, processed in a massively parallel and looped manner.

[0203] CO (General) Explain digital image transformations.

[0204] FL denotes the flux of (general) digital images into UTM(s), thereby resulting from either general image sensing or general image processing, into (module 8 bit or 1 byte) image / text (data / metadata RGB / binary etc.) isomorphic "pixels" or "picture elements" like modules (module 8 bit or 1 byte) (FPS) frames, and therefore a film of digital images.

[0205] MA indicates massively parallel computing.

[0206] LO indicates the loop mechanism.

Claims

**Claim 1**: A method for performing calculations at the speed of light or near the speed of light in a binary computer architecture (CA) on a (general) digital image that is a pixel of a digital image containing numerical representations or an exponential-dependent base single "cell" such as any "pixel", the method comprising the following steps. a) Converting a (general) digital image (3) to a predetermined pixel density formula, i.e., image resolution, to obtain a set of converted digital images (9.1) b) Storing in memory that in a (general) digital image assigned numerical data or metadata, the image / text RGB / binary pixels are similar and isomorphic, to obtain a set of assigned numerical data or metadata (9.2). c) Processing the isomorphic image / text RGB / binary of the (general) digital image with the assigned numerical data or metadata to obtain a set of processed digital images (9.3). d) Re-storing the isomorphic image / text RGB / binary of the processed (general) digital image in alignment from the minimum numerical representation to the maximum numerical representation of the digital image, resulting in a composite set of aligned and re-stored digital images (9.4), and if the preconditions for the calculation are met, only the re-stored alignment can be re-represented (9.6). e) Collecting the isomorphic image / text RGB / binary of the processed (general) digital image in a numerical and algorithmic representation aligned from minimum to maximum to form a composite set of aligned digital images (9.5). f) Re-storing the processed and aligned isomorphic image / text RGB / binary in the (general) digital image to generate numerical data or metadata (9.6) g) Programmatically processing the isomorphic image / text RGB / binary of the processed and aligned algorithm based on the numerical data or metadata (9.7) wherein the numerical representation of the (general) digital image (3) is always in RGB / binary or photonic / binary similar to a pixel, and is a code isomorphic to the image information, for example, the RGB image / text color model in the state of the art; Step a) is performed only by the universal Turing machine (UTM) protocol for the Turing machine (M) or only by the universal Turing machine (UTM) protocol, Step b) is executed only by the Turing machine (M) protocol for the universal Turing machine (UTM) and the universal Turing machine (UTM) protocol. Steps c), d), e), f), and g) are executed by the universal Turing machine (UTM) protocol for the universal Turing machine (UTM). Loop large-scale parallel computing is executed at each node / server of the communication network of the universal Turing machine (UTM) on the Turing machine (M) from a) to g). **Claim 2**: The method for performing calculations at the speed of light or near thereto according to Claim 1, characterized in that the entire UTM is a camera / image processing node / server capable of calculating digital image information or any (general) digital image on the interface. **Claim 3**: The method for performing calculations at the speed of light or near thereto according to Claim 1 or Claim 2, characterized in that the Turing machine is any computer having a processor and / or a text / image output device. **Claim 4**: The Turing machine is selected from the group including personal computers, office computers, mobile devices, Internet servers, clusters, large mainframe computers, embedded computing machines, GPU game machines, cloud computing, digital TV boxes, billboards, drones, CCTVs, ATMs, and GPSs. The method for performing calculations at the speed of light or near thereto according to any one of Claims 1 to 3. **Claim 5**: The processing c) and g) of the digital image are executed in one or more elements of the group consisting of a graphics processing unit (GPU), a general-purpose central processing unit (CPU), and a superparallel high-computing system. The method for performing calculations at the speed of light or near the speed of light according to any one of Claims 1 to 4. **Claim 6**: In step a) of converting a (general) digital image, a set of converted (general) digital images is obtained by converting a pixel density formula according to the number of pixels into a predetermined required resolution. The method for performing calculations at the speed of light or near thereto according to any one of Claims 1 to 5. **Claim 7**: A computer architecture that executes calculations at the speed of light or near the speed of light is characterized by the "composition" of pixels in a digital image or exponential-dependence basis "cells" such as any "pixels", where the digital image includes a numerical representation and is composed of the following procedures. a) Convert a (general) digital image (3) into a predetermined pixel density formula, i.e., image resolution, to obtain a set of converted digital images (9.1) b) In a (general) digital image assigned to numerical data or metadata, memorize in memory that the image / text RGB / binary pixels are similar and isomorphic, and obtain a set of assigned numerical data or metadata (9.2). c) Process the isomorphic image / text RGB / binary numbers of the (general) digital image with the assigned numerical data or metadata to obtain a set of processed digital images (9.3). d) Re-store the isomorphic image / text RGB / binary numbers of the processed (general) digital image by aligning them from the minimum numerical representation to the maximum numerical representation of the digital image. As a result, a composite set of aligned and re-stored digital images is obtained (9.4). If the preconditions for calculation are met, only the re-stored alignment can be re-represented (9.6). e) Collect the isomorphic image / text RGB / binary numbers of the processed (general) digital image in a numerical and algorithmic representation aligned from minimum to maximum to form a composite set of aligned digital images (9.5). f) Re-store the processed and aligned isomorphic image / text RGB / binary numbers in the (general) digital image to generate numerical data or metadata (9.6) g) Program and process the isomorphic image / text RGB / binary numbers of the processed and aligned algorithms based on the numerical data or metadata (9.7) Here, the numerical representation of the (general) digital image (3) is always in RGB / binary or photonic / binary similar to pixels, and is a code isomorphic to the image information, for example, the RGB image / text color model in the state of the art; Step a) is executed only by the universal Turing machine (UTM) protocol for the Turing machine (M) or the universal Turing machine (UTM) protocol. Step b) is executed only by the Turing machine (M) protocol for the universal Turing machine (UTM) and the universal Turing machine (UTM) protocol. Steps c), d), e), f), and g) are executed by the universal Turing machine (UTM) protocol for the universal Turing machine (UTM). Loop large-scale parallel calculations are executed at each node / server of the communication network of the universal Turing machine (UTM) on the Turing machine (M) from a) to g). **Claim 8**: The computer architecture for performing calculations at the speed of light or near the speed of light according to claim 7, characterized in that the entire UTM is a camera / image processing node / server capable of calculating digital image information or any (general) digital image on the interface. **Claim 9**: The computer architecture for performing calculations at the speed of light or near the speed of light according to claim 7 or claim 8, characterized in that the Turing machine is any computer having a processor and / or a text / image output device. **Claim 10**: The Turing machine is selected from the group including personal computers, office computers, mobile devices, Internet servers, clusters, large mainframe computers, embedded computing machines, GPU game machines, cloud computing, digital TV boxes, billboards, drones, CCTVs, ATMs, GPSs, and the computer architecture for performing calculations at the speed of light or near it according to any one of claims 7 to 9. **Claim 11**: The processing c) and g) of the digital image are composed of one or more elements of the group consisting of a graphics processing unit (GPU), a general-purpose central processing unit (CPU), and a super-parallel high-computing system, and the computer architecture for performing calculations at the speed of light or near it according to any one of claims 7 to 10. **Claim 12**: In step a) of converting a (general) digital image, a set of converted (general) digital images is obtained by converting a pixel density formula according to the number of pixels to a predetermined required resolution, and the computer architecture for performing calculations at the speed of light or near the speed of light according to any one of claims 7 to 11.

Images