31 results about "Pre processor" patented technology

A system may comprise a data interpolation platform that comprises a data collector, a pre-processor, an artificial intelligence (AI) engine, an interactive graphical user interface (GUI) engine, a data monitor and one or more servers. The data collector may be configured to collect, from among one or more data sources, data and information (“collected data”). The AI engine may be configured to generate, train, validate, test and / or deploy one or more AI models. The interactive GUI engine may be configured to generate and dynamically update an interactive GUI. The one or more servers may comprise one or more processors, a memory and computer-readable instructions that, when executed by the one or more processors, cause the data interpolation platform to determine, display and dynamically update predictive and interpolated pricing data.

An engine identifying segments or portions of one source material or source file common to or found in another source material or file. The engine may receive a first data stream in binary form as well as a second stream in binary form. The engine may include a data stream processor or pre-processor programed to translate the first and second data streams to generate respective first and second processed data streams. The commonality between the first and second processed data streams may be greater than the commonality between the first and second data streams themselves. Also, a comparator may be programmed to compare the first and second process data streams and identify binary segments found in both the first and second processed data streams.

The present invention provides a vision system camera having a multi-core processor, high speed and high resolution imager, field of view extender, auto focus lens, and pre-processor connected to the imager for pre-processing image data, and a coordinated method of operation, which provides highly desirable acquisition and processing speed, as well as image clarity, in a wide range of applications. The mechanism is effective for scanning objects requiring a wide field of view, varying in size, and moving relatively rapidly with respect to the system's field of view. The vision system provides a physical package having a variety of physical interconnect options to support a variety of options and control functions. The package is effective in dissipating heat generated internally by arranging components to optimize heat exchange with the surrounding environment, and including heat dissipation structures to facilitate such heat exchange (e.g. fins). The system also allows for a variety of multi-core process optimization and load balancing of image processing and system operation (e.g. automatic adjustment of tasks).

The application discloses a code scanning payment settlement system and method, which comprises a mobile payment terminal, a seller payment terminal and a server; the seller payment terminal comprises a second positioning module, the mobile payment terminal comprises a first positioning module and a transaction record module, and the server comprises a pre-database and a pre-processor. In the application, whether the real-time position of the mobile payment terminal of a buyer is within the first preset threshold from the transaction position is judged by the pre-processor, and then it is judged whether the alert mode or the security mode is enabled. In the alert mode, the security of the transaction is protected by encrypting the payment code to protect the seller payment terminal of the first transaction with strangers. In the security mode, the transaction is completed as soon as possible in the vicinity of the seller payment terminal of the completed transaction, and the situation that the payment code is stolen and brushed is avoided, so that the payment security of the buyer is improved.

The present invention relates to a management system (100) for controlling a dynamic system configuration. The management system (100) is configured to analyse current and past configurations of a multi-component computing system using a machine learning model. The machine learning model predicts future demands for the multi-component computing system's resources, thereby generating optimised configuration settings to be deployed in the multi-component computing system's environment. The management system (100) comprises a plurality of data collection agents or DCAs (10), a unified metrics collector or UMC (20), a metrics pre-processor or MPP (30), an intelligent analysis module or IAM (40), an execution engine or EE (50), and a plurality of configurator agents or CAs (60).

The invention relates to a system for maintaining reversible dynamic range control information associated with a parametric audio encoder. On the basis of a bitstream (P), an n-channel audio signal (X) is reconstructed by deriving an m-channel core signal (Y) and a multi-channel coding parameter (alpha) from the bitstream, where 1 < = m < n. A pre-processing dynamic range control parameter, i.e., a pre-processing DRC parameter (DRC2), that quantizes the encoder-side dynamic range limitation of the core signal is also derived from the bitstream. The n-channel audio signal is obtained by parameter synthesis according to the multi-channel encoding parameters while eliminating any encoder-side dynamic range limitations based on the pre-processing DRC parameters. In particular embodiments, the reconstruction further includes the use of compensated post-processing DRC parameters that quantify possible decoder-side dynamic range compression. Preferably, encoder-side range limit cancellation and range compression are performed by different decoder-side components. Cancellation and compression may be coordinated by a DRC preprocessor.

A preconditioner processing method includes determining a thread dependency of at least one thread computing task to be executed by a preconditioner, and sequentially executing all of a plurality of threads of the preconditioner processing device, and based on a sequence of memory access, to-be-executed thread computing task whose dependent thread computing tasks are in an executed state, implementing multi-thread parallel execution of the preconditioner.

In a server apparatus of a traffic control system, a pre-processor records a position of a vehicle in a database. A control information generator periodically generates individual control information regarding vehicles by using information in the database, and transmits the individual control information to each vehicle. Each vehicle controls travel of the subject vehicle by using the latest individual control information received from the server apparatus. In the server apparatus, an emergency processor is implemented when received travel information includes information that hinders travel of another vehicle, and generates and transmits information corresponding to the information that hinders the travel of the other vehicle, as the individual control information regarding each of the vehicles.

A state-of-the-art memory controller and methods for using the same are disclosed. The memory controller is intended for use with dynamic random-access memory (DRAM) circuitry. In one example, a memory controller includes a reordering preprocessor circuitry coupled to a reordering scheduler circuitry. The reordering scheduler circuitry is configured to control a reordering scheduler queue, and is coupled to DRAM circuitry. The reordering preprocessor circuitry is configured to control a preprocessor queue and reorder transactions in the preprocessor queue so as to increase the DRAM circuitry performance.

A vehicle storage device according to at least one embodiment includes: a first interface configured to transmit a first signal to an object, and to receive first data generated by analog to digital converting a second signal reflected and returned to the object outside a vehicle; a radar pre-processor configured to generate second data by performing a pre-processing operation on the first data; a second interface configured to output the second data; and a non-volatile memory configured to store the first data.

The embodiment of the invention provides a microcontroller system and a method for waking up a central processing unit. The microcontroller system comprises the central processing unit, a peripheral device and an interrupt preprocessor. The interrupt preprocessor is connected between the central processing unit and the peripheral device. When the central processing unit is powered off, the interrupt preprocessor is configured to: receive an interrupt signal from the peripheral device; when the number of times that the interrupt preprocessor receives the interrupt signal from the peripheral device is larger than or equal to a first preset number of times, a wake-up signal is output to start the central processing unit.

A micro-controller system including a central processing unit, a peripheral device, and an interrupt pre-processor is provided. The interrupt pre-processor is connected between the central processing unit and the peripheral device. When the central processing unit is powered down, the interrupt pre-processor is configured to receive an interrupt signal from the peripheral device and output a wake-up signal to activate the central processing unit when the number of times that the interrupt pre-processor receives the interrupt signal from the peripheral device is higher than or equal to the first predetermined number.

Disclosed is an apparatus including an input interface. The input interface is configured to obtain input sensor data. For example, the input sensor data includes light detection and ranging (LiDAR) data indicative of an environment. The apparatus includes a pre-processor communicatively coupled to the input interface. The pre-processor optionally includes a parser logic. The pre-processor includes a decoder communicatively coupled to the parser logic. The segmentation logic is configured to parse the input sensor data. The decoder is configured to decode the parsed input sensor data. The pre-processor is configured to provide the decoded input sensor data and the parsed input sensor data to a processor for fusion.

A computer system includes a transceiver that receives over a data communications network different types of input data and multiple data transaction objects from multiple source nodes. A pre-processor processes the different types of input data and the data transaction objects to generate an input data structure. Based on the input data structure, one or more predictive machine learning models is trained and used to predict a probability of execution of each of the data transaction objects at a future execution time. Output data messages are then generated for transmission by the transceiver over the data communications network indicating the probability of execution for at least one of the data transaction objects at the future execution time.

Methods, systems, and computer readable media for utilizing an augmented neural network are disclosed. In one embodiment, the method includes utilizing a neural network (NN) pre-processor to convert generic coordinates associated with a dynamical system to canonical coordinates, concatenating a Hamiltonian neural network (HNN) to the NN pre-processor to create a generalized HNN, and training the generalized HNN to learn nonlinear dynamics present in the dynamical system from generic training data. The method also includes utilizing the trained generalized HNN to forecast the nonlinear dynamics, and quantifying chaotic behavior from the forecasted nonlinear dynamics to discover and map one or more transitions between orderly states and chaotic states exhibited by the dynamical system.

In a server apparatus of a traffic control system, a pre-processor records a position of a vehicle in a database. A control information generator periodically generates individual control information regarding vehicles by using information in the database, and transmits the individual control information to each vehicle. Each vehicle controls travel of the subject vehicle by using the individual control information received from the server apparatus. In the server apparatus, an emergency processor is implemented when received travel information includes information that hinders travel of another vehicle, identifies the position of the vehicle related to the travel information, and records passage regulation regions and in the database. The control information generator generates and transmits the individual control information for deceleration or a stop for a vehicle that is likely to travel in the passage regulation regions and in the database.

This application discloses an optoelectronic device, its fabrication method, and a display device. The fabrication method includes the following steps: providing a pre-processing device, the pre-processing device comprising a stacked anode, a hole-functional layer, an interface layer, and an auxiliary layer; subjecting the pre-processing device to an electric current treatment; removing the auxiliary layer; and then disposing a cathode on the side of the interface layer opposite to the hole-functional layer to obtain the optoelectronic device. This application aims to solve the problem that the hole-functional layer and the light-emitting layer are easily damaged during device aging, thus affecting the device's light-emitting performance.

A pre-processor applies a pre-process to an original mass image produced through mass spectrometry of a sample, to produce a model input image. An image quality converter has an image quality conversion model produced through machine learning based on a group of images produced by a scanning electron microscope, and produces a model output image through image quality conversion of the model input image. A post-processor applies a post-process to the model output image, to produce a mass image after image quality conversion.

This application provides a preconditioner processing method and apparatus, a device, and a system, relates to the computer field, and is applied to a computing device for solving a partial differential equation. The method includes: A preconditioner processing device first determines a thread dependency of at least one thread computing task to be executed by a preconditioner, and then all of a plurality of threads of the preconditioner processing device sequentially execute, based on a sequence of memory access, to-be-executed thread computing task whose dependent thread computing tasks are in an executed state, to implement multi-thread parallel execution of the preconditioner. In this way, a dependency between threads in a computing task of the preconditioner is determined, so that full thread synchronization between layers is converted into point-to-point synchronization between threads, thereby reducing time consumed for synchronization between layers and improving parallel execution efficiency of the preconditioner.

Systems and methods are provided to increase the rate at which a system can accept and process data transaction request messages. The system includes a pre-processor that filters incoming data transaction request messages prior to transmitting the messages for further processing, for example, by a matching engine. The pre-processor quickly identifies whether a new arriving data transaction request is transactable. If the new arriving data transaction request is not transactable, then the data transaction request will be transmitted and stored in a main data structure and not transmitted to the matching engine.

A pre-processor (PP) component and related method for a machine learning system (MLS) for processing medical data. The preprocessor comprises an input interface (IN) for receiving a human generated initial finding for a patient and a medical image to which the said finding pertains. An encoder (ENC) DPS of preprocessor encodes the finding and the medical image into encoded data, including encoded image data and encoded finding data. A combiner (COM) component of preprocessor combines the encoded finding and the encoded image data into combined encoded data. An output interface (OUT) provides the combined encoded data to the machine learning system. More robust machine learning performance may be achieved with the proposed pre-processor (PP).

An image sensor includes a sensing unit configured to generate a plurality of images having different exposure times for a same object, a pre-processor configured to generate a merged image by merging the plurality of images, an interface circuit configured to output at least one of the plurality of images and / or the merged image to an external processor, and a controller configured to control the pre-processor to selectively generate the merged image based on information of the plurality of images.

The system may have an energy load data interface to receive energy load data originating from energy use sensors for the building; a weather data interface to receive weather data for a location that includes the building; a weather adjustment pre-processor to process the energy load data and the weather data and to determine a weather-dependent energy use component of the energy load data and a weather-independent energy use component of the energy load data; a baseline adjustment pre-processor to process the weather-independent energy use component of the energy load data and determine a baseline energy use component of the energy load data, wherein the baseline adjustment pre-processor is configured to remove the baseline energy use component from the weather-independent energy use component to determine a variable energy use component of the energy load data; and an energy use disaggregator to process the variable energy use component of the energy load data and determine a plurality of time varying load components of the energy load data.

An audio encoder for encoding audio input data (11) comprises a pre-processor (10) for pre-processing the audio input data (11) to obtain audio data to be encoded, an encoder processor (15) for encoding the audio data to be encoded, and a controller (20) for controlling the encoder processor such that, depending on a first signal characteristic of a first frame of the audio data to be encoded, the number of audio data items to be encoded by the encoder processor (15) for the first frame is reduced compared to a second signal characteristic of a second frame, and a first number of information units used for encoding the reduced number of audio data items for the first frame is more strongly enhanced compared to a second number of information units used for the second frame.

A system may include a prompt pre-processor configured to receive user input and prepare prompts for processing by one or more artificial intelligence models. A system may include a model smart router configured to direct prompts to artificial intelligence models based on prompt characteristics and historical performance data. A system may include a ranking system configured to evaluate responses from multiple artificial intelligence models to determine highest quality outputs. A system may include a configuration system configured to store and manage settings for inline cost management, prompt processing sophistication, and model smart router processing sophistication. A system may include a data and networking system configured to manage data storage and network communications for the platform, wherein the data and networking system includes prompt historical ranked models data storing historical performance information for artificial intelligence models.

Embodiments herein provide a method and an electronic device for compressing a video for AI-based in loop filter (AILF). The method includes obtaining a video comprising a plurality of frames, wherein each frame of the pluralities of frames comprises a plurality of channels. Further, the method includes extracting at least one feature from each frame of the plurality of frames of the video. Further, the method includes selecting, at least one pre-processor from the multi pre-processors for the AILF based on the at least one feature from each frame of the plurality of frames. Further, the method includes generating an encoded video by encoding the image information from each channel of the plurality of channels of each frame of the plurality of frames using the at least one selected pre-processor.

The present invention relates to a management system (100) for controlling a dynamic system configuration. The management system (100) is configured to analyse current and past configurations of a multi-component computing system using a machine learning model. The machine learning model predicts future demands for the multi-component computing system's resources, thereby generating optimised configuration settings to be deployed in the multi-component computing system's environment. The management system (100) comprises a plurality of data collection agents or DCAs (10), a unified metrics collector or UMC (20), a metrics pre-processor or MPP (30), an intelligent analysis module or IAM (40), an execution engine or EE (50), and a plurality of configurator agents or CAs (60).

A processor includes an operation executer configured to execute pipeline processing of an operation instruction, wherein the operation executer includes a first register configured to hold data to be used for an operation; a pre-processor configured to pre-process data held in the first register; a second register configured to hold the data pre-processed by the pre-processor; an operator configured to execute an operation on the data held in the first register or the data held in the second register; and a selector configured to connect an output of the first register or an output of the second register to an input of the operator.

An audio encoder for encoding audio input data (11) comprises a pre-processor (10) for pre-processing the audio input data (11) to obtain audio data to be encoded, an encoder processor (15) for encoding the audio data to be encoded, and a controller (20) for controlling the encoder processor such that, depending on a first signal characteristic of a first frame of the audio data to be encoded, the number of audio data items to be encoded by the encoder processor (15) for the first frame is reduced compared to a second signal characteristic of a second frame, and a first number of information units used for encoding the reduced number of audio data items for the first frame is more strongly enhanced compared to a second number of information units used for the second frame.

In a control system for vehicles, a pre-processor of a server apparatus accumulates and records travel information received from each of the multiple vehicles in a database. A control information generator determines interference with respect to each vehicle traveling toward a merging section, based on the travel information in the database, generates individual control information, and individually transmits the individual control information. The control information generator further generates information regarding waypoints in the merging section, with respect to each vehicle traveling toward the merging section, and individually transmits the information regarding the waypoints.