Compressed sensing for full matrix capture

Abstract

Examples of this topic provide techniques for compressed sampling of acoustic data. The probe can sample in a compressed mode, so that the entire matrix is ​​not sampled at full temporal or spatial resolution. Thus, the amount of initial data captured by the probe is reduced, enabling the use of lower-density hardware (e.g., fewer analog-to-digital conversion channels or related analog front-end hardware) at lower data rates.

Description

[0001] Priority claim

[0002] This patent application claims the benefit of priority to U.S. Provisional Patent Application Serial No. 62 / 993,849, filed on March 24, 2020, and entitled “COMPRESSIVE SENSING FOR FULLMATRIX CAPTURE,” which is hereby incorporated by reference in its entirety. Technical Field

[0003] The present disclosure relates generally to non-destructive sensing techniques, and in particular to compressed sensing. Background Art

[0004] Nondestructive inspection probes (e.g., ultrasonic sensors) typically include a matrix of sensing elements (e.g., acoustic transducers) that generate acoustic data. The matrix of sensing elements can provide electrical signals that, when digitized, generate a large amount of data, especially when the full matrix of sensing elements is used for data capture. Furthermore, the size of the data increases with the number of sensing elements.

[0005] One technique for reducing data size is to apply data compression to the acoustic data. Here, the full matrix of sensing elements is sampled to provide a time-series representation of the signal received at each transducer in the matrix, and the sampled data is then compressed using digital compression techniques. The compressed data can then be sent to post-acquisition applications. However, this approach still has significant drawbacks. For example, it typically involves using high-density hardware and high data rates to sample the full matrix and pre-process the data before data compression. Therefore, as the number of sensing elements increases, the hardware size and complexity are expected to increase as well. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The figures in the accompanying drawings depict only example implementations of the disclosure and should not be considered as limiting the scope of the disclosure.

[0007] Figure 1 An inspection system according to examples of the present subject matter is shown.

[0008] Figure 2 is a flowchart of a process for compressed sampling according to an example of the present subject matter.

[0009] Figure 3 is a graphical representation of a process for compressive sampling and image reconstruction according to an example of the present subject matter.

[0010] Figure 4 is a mathematical representation of compressed sampling and decoding according to examples of the present subject matter.

[0011] Figures 5A to 5CExamples of compression and decoding according to examples of the present subject matter are shown.

[0012] Figure 6 TFM reconstruction using compressed sample data according to an example of the present subject matter is shown.

[0013] Figure 7 Shown are the results of TFM reconstruction using compressive sampling according to an example of the present subject matter.

[0014] Figure 8 A comparison of TFM images according to an example of the present subject matter is shown.

[0015] Figure 9 A block diagram is shown including an example of a machine upon which any one or more of the techniques (eg, methodologies) discussed herein may be performed. DETAILED DESCRIPTION

[0016] Thus, the inventors have recognized that this challenge of processing large amounts of data can be addressed by using compressed sensing as described herein.The techniques for compressed sensing as described herein can be performed without significantly increasing the size or complexity of the hardware.

[0017] A method is described herein that includes obtaining a signal representing one or more acoustic waves received using a matrix of sensing elements, wherein N samples define a full matrix representation of the signal; downsampling the obtained signal by a subset of M samples, wherein N is greater than M; and reconstructing an image based on the M samples.

[0018] Also described herein is a machine storage medium comprising instructions that, when executed by a machine, cause the machine to perform operations comprising: obtaining a signal representing one or more acoustic waves received using a matrix of sensing elements, wherein N samples define a full matrix representation of the signal; downsampling the obtained signal by a subset of M samples, wherein N is greater than M; and reconstructing an image based on the M samples.

[0019] Also described herein is a system having one or more processors of a machine. The system also includes a memory storing instructions that, when executed by the one or more processors, cause the machine to perform operations comprising: obtaining a signal representing one or more acoustic waves received using a matrix of sensing elements, wherein N samples define a full matrix representation of the signal; undersampling the obtained signal by a subset of M samples, wherein N is greater than M; and reconstructing an image based on the M samples.

[0020] Figure 1An example of an acoustic inspection system 100 is generally shown, including, for example, one or more techniques described and illustrated elsewhere herein. The inspection system 100 may include a test instrument 140, such as a handheld or portable assembly. The test instrument 140 may be electrically coupled to a probe assembly, for example, using a multi-conductor interconnect 130. The probe assembly 150 may include one or more electroacoustic transducers, such as a transducer array 152 including corresponding transducers 154A to 154N. The transducer array may follow a linear or curved profile, or may include an array of elements extending in two axes, for example, to provide a matrix of transducer elements. The element footprint need not be square or arranged along a linear axis. Element size and spacing may vary depending on the inspection application.

[0021] Modular probe assembly 150 configurations can be used, for example, to enable the test instrument 140 to be used with different probe assemblies 150. Typically, the transducer array 152 includes piezoelectric transducers that can be acoustically coupled to a target 158 ​​(e.g., an object under test) via a coupling medium 156, for example. The coupling medium can include a fluid or gel or a solid film (e.g., an elastomer or other polymer material), or a combination of fluids, gels, or solid structures. For example, the acoustic transducer assembly can include a transducer array coupled to a wedge structure comprising a rigid thermoset polymer having known acoustic propagation properties (e.g., a Teflon® polymer available from C-Lec Plastics, Inc.). ), and water can be injected as a coupling medium 156 between the wedge and the structure under test during testing.

[0022] The test instrument 140 may include digital and analog circuitry, such as front-end circuitry 122 including one or more transmit signal chains, receive signal chains, or switching circuitry (e.g., transmit / receive switching circuitry). The transmit signal chain may include amplifier and filter circuitry, for example, to provide transmit pulses that are delivered to the probe assembly 150 via the interconnect 130 for acoustic transmission of the target 158, for example, to image or otherwise detect defects 160 on or within the structure of the target 158 ​​by receiving scattered or reflected acoustic energy induced in response to the acoustic transmission.

[0023] Although Figure 1A single probe assembly 150 and a single transducer array 152 are shown, but other configurations may be used, such as multiple probe assemblies connected to a single test instrument 140, or multiple transducer arrays 152 used with a single or multiple probe assemblies 150 for serial inspection. Similarly, coordination between multiple test instruments 140 may be used to execute a test protocol, such as in response to an overall test plan established from a master test instrument 140 or by another remote system (e.g., computing facility 108 or a general-purpose computing device such as a laptop computer 132, tablet computer, smartphone, desktop computer, etc.). As an illustrative example, the test plan may be established in accordance with published standards or regulatory requirements and may be executed at initial manufacturing or cyclically for ongoing monitoring.

[0024] The receive signal chain of the front-end circuitry 122 may include one or more filter or amplifier circuits and analog-to-digital conversion facilities, for example, to digitize echo signals received using the probe assembly 150. The digitization may be performed coherently, for example, to provide multiple digitized data channels that are aligned or referenced to one another in time or phase. The front-end circuitry 122 may be coupled to and controlled by one or more processor circuits, such as the processor circuitry 102 included as part of the test instrument 140. The processor circuitry may be coupled to memory circuitry, for example, to execute instructions that cause the test instrument 140 to perform one or more of acoustic emission, acoustic acquisition, processing, or storage of data related to an acoustic examination, or otherwise perform the techniques shown and described herein. The test instrument 140 may be communicatively coupled to other portions of the system 100, for example, using a wired or wireless communication interface 120.

[0025] For example, execution of one or more of the techniques shown and described herein can be implemented on the onboard test instrument 140 or using other processing or storage facilities, such as using the computing facility 108 or a general-purpose computing device such as a laptop computer 132, a tablet computer, a smartphone, a desktop computer, etc. For example, a processing task that would be very slow if executed on the onboard test instrument 140 or a processing task that exceeds the capabilities of the test instrument 140 can be performed remotely (e.g., on a separate system), such as in response to a request from the test instrument 140. Similarly, storage of imaging data or intermediate data such as an A-scan matrix of time series data can be implemented using a remote facility communicatively coupled to the test instrument 140. The test instrument can include, for example, a display 110 for presenting configuration information or results and an input device 112 for receiving operator commands, configuration information, or responses to queries, the input device 112 including, for example, one or more of a keyboard, a trackball, function keys or soft keys, a mouse interface, a touch screen, a stylus, etc.

[0026] Next, a technique for compressive sensing will be described. Figure 2 2 is a flow chart of a process 200 for compressed sampling according to an example of the present subject matter. At 202, signals from a full matrix of sensing elements (e.g., transducer components) of a probe can be received. The signals can be responsive to acoustic signals transmitted by the probe into an object and can represent echo signals from the object. The received signals can provide N usable samples. At 204, compressed sampling can be performed and a subset M of the N usable samples can be taken, where N is greater than M (N>M); therefore, not all samples will be obtained according to the compressed sampling techniques described herein. This can be referred to as undersampling. The M samples can be randomly selected from the N usable samples.

[0027] The probe can sample in a compressed mode, so that the entire matrix of sensing elements may not be sampled at full temporal or spatial resolution. For example, the probe can acquire samples at a specified temporal resolution up to full temporal resolution, but at a lower average sampling rate. Thus, the amount of initial data captured by the probe is reduced, enabling the use of lower-density hardware (e.g., fewer analog-to-digital conversion channels or related analog front-end hardware) at lower data rates.

[0028] At 206, weights may be assigned to the M samples. The weights may be assigned randomly. The compressed sample data (e.g., the weighted M samples) may be stored and / or transmitted for post-acquisition processing. For example, the compressed sample data may be transmitted to a post-acquisition application. At 208, the compressed sample data may be decoded to reconstruct an image in a manner similar to a full matrix image, but using the compressed sample data, as described in further detail below.

[0029] Figure 3 is a graphical representation of a process 300 for compressed sampling and image reconstruction according to an example of the present subject matter. Process 300 can be organized into two stages: 1) data acquisition and 2) image reconstruction. Data acquisition can be performed, for example, as described above with reference to Figure 1 The acoustic inspection system described herein is performed. At 302, in response to an input signal (e.g., a pulse signal) from a drive circuit, a probe can generate one or more ultrasonic signals and transmit them into an object. The probe can include a matrix of sensing elements that convert the input signal into acoustic waves in a transmit mode. At 304, the acoustic waves can propagate in the object and can be reflected from structures (e.g., defects) in the object. The variable h can represent a theoretical impulse response (IR) of the object. At 306, the reflected or received signals can be captured by the matrix of sensing elements.

[0030] At 308, data can be acquired from the received signal. For example, the acquired data can be provided as an A-scan, which refers to a time series of amplitude measurements with respect to time. Fs can refer to a sampling frequency, and there can be N available samples in the acquired data. At 310, the acquired data can be undersampled so that a subset M of the N available samples is sampled. The M samples can be randomly selected. The count of available samples (N) is greater than the count of retained samples (M). At 312, random weights (e.g., a compression matrix φ) can then be assigned to the compressed samples to generate an observation matrix b. The observation matrix b can be stored for later reconstruction.

[0031] Image reconstruction can begin by obtaining an observation matrix b generated during data acquisition. The full image can then be reconstructed based on the observation matrix b and a model (e.g., a query signal ψ); reconstruction is described in more detail below. At 322, h, which is the reconstructed IR of the object, can be determined. The measurement matrix A can be calculated:

[0032] A=φ·ψ,

[0033] Where ψ represents the model matrix and φ represents the compression matrix φ. Then, h can be determined according to the following expression:

[0034] b=A·h,

[0035] Where I1 is normalized. h can be converted into reconstructed data (A-scan). At 324, the reconstructed data (A-scan) can then be used for post-acquisition applications. In this example, at 326, a total focusing method (TFM) can be employed to generate a final image (e.g., a B-scan) at 328; however, other imaging techniques can also be used.

[0036] Figure 4 is a mathematical representation of compressed sampling and decoding according to an example of the present subject matter.Here, compressed sampling and decoding are shown as three mathematical steps: 1) data acquisition, 2) model (a priori), and 3) signal estimation. Figures 5A to 5C Examples of compression and decoding according to examples of the present subject matter are shown.

[0037] In step 402, data can be acquired at a rate less than the capacity of the full matrix (e.g., compressed sampling). The compressed sampled data is represented by b M×1 matrices, where M is the count of retained samples. φ can represent the randomization weights, shown here as an M×N matrix. The count of available time samples (N) is greater than the count of retained samples (M), i.e., N>M. The purpose of this equation is to solve for the x vector, i.e., the real signal (e.g., A-scan), which will represent the data captured by the full matrix. The x vector can be represented by an N×1 matrix. Figure 5A A graphical representation of the data acquisition steps is shown.

[0038] In step 404, a model (a priori) can be determined. Consider the non-destructive inspection application described above. The real signal x (scan) can be the result of a combination (via convolution) of several defects in the object to be inspected and the IR of the probe. Therefore, the model for the real signal x can be represented by the convolution of a set of non-zero coefficients h with a ψ matrix, which can integrate different possible time positions of the probe IR. For example, ψ can represent an inverse sparse matrix, and h can represent the IR of the object as sparse coefficients. The values ​​of the inverse sparse matrix and the sparse coefficients can be based on the characteristics of the sensing element matrix and / or the object to be measured. ψ can be a model matrix or a convolution matrix; it can act as a dictionary, representing the real signal x from the vector h, consisting of sparse coefficients, such as N non-zero entries. Therefore, the real signal x can be represented by a set of coefficients. The model can have a specific structure and can be optimized for the problem to be solved. The model can be constructed based on experimental estimates of the probe impulse response.

[0039] For example, if the object to be inspected includes two defects, the vector h may include two non-zero coefficients representing the two defects. The true signal x can then be determined by convolution of h with ψ.

[0040] Additionally or alternatively, the model can be based on a mathematical model or equation that describes the behavior of the probe. For example, an inverse DCT matrix can be used. The coefficients h can represent the frequency components of the real signal x. The physical signal is sparse in the frequency domain (i.e., includes several non-zero coefficients to describe the real signal x with sufficient accuracy). The model can be randomized and the desired information can be input. The model can be a deterministic model. Figure 5B A graphical representation of the model calculation steps is shown.

[0041] In step 406, the signal (b=A·h) can be estimated based on the acquired data and the model. Here, the measurement matrix A can be expressed as follows:

[0042] A=φ·ψ

[0043] φ may be a matrix and may contain (much) fewer rows (M) than columns (N), ie, N>M, which enables compression of the signal as described herein.As mentioned above, the ψ matrix may provide information about the model of the signal to be reconstructed.

[0044] The observation matrix b can then be represented as the product of the measurement matrix A and the sparse coefficient h (b = A·h). Therefore, the x vector can be represented as:

[0045] x=ψ·h

[0046] Therefore, the signal estimation of the sparse coefficients h can be performed by inverting the measurement matrix A. Then, the true signal can be recovered based on the estimate of the sparse coefficients h and the ψ matrix (e.g., the model) x . Figure 5C A graphical representation of the signal estimation steps is shown.

[0047] Figure 6 TFM reconstruction using compressed sampled data, according to an example of the present subject matter, is shown. At 602, A-scan data representing a full matrix can be reconstructed as described above. Here, ψ′ can be used instead of ψ. However, ψ′ can be represented by ψ or can be different to adjust the resolution of the TFM image. For example, the bandwidth of the signals comprising the ψ′ matrix can be varied to adjust the resolution of the TFM image. At 604, a TFM image can be generated based on the reconstructed data using known TFM techniques.

[0048] Figure 7 The results of TFM reconstruction using compressed sampling according to an example of the present subject matter are shown. At 702, the acquired data in the form of an A-scan matrix is ​​shown. Here, for illustrative purposes, the sensing elements are provided as an 8×8 matrix, and other configurations (e.g., 32×32) may also be used. At 704, the data is compression sampled to generate a b-measurement matrix, i.e., not all available samples are generated or retained. Undersampling may be performed randomly. At 706, a model of the FMC may be determined. And at 708, the full image may then be reconstructed using the techniques described herein, and a TFM image may be generated.

[0049] Figure 8 A comparison of a TFM image generated using compressed sampling and a TFM image generated without compressed sampling (e.g., full matrix sampling) according to an example of the present subject matter is shown. The settings for both images are identical, e.g., using the same input signal, carrier frequency = 5 MHz, SNR = 20 dB, and sampling frequency = 100 MHz. Data for both images was acquired using the same number of elements (e.g., 32 elements).

[0050] As shown, the TFM images generated using compressed sampling perform comparable to those generated using full-matrix sampling, particularly in terms of defect detection. However, the TFM images generated using compressed sampling in this example used a total of 102,000 samples, compared to 2.3 million samples. Therefore, compressed sampling, as described herein, can reduce the amount of data to be transmitted (e.g., a reduction ratio of 22.5) without sacrificing image quality.

[0051] The techniques shown and described herein can be used as Figure 1A portion or the entirety of the inspection system 100 shown may be used to perform or otherwise utilize the following Figure 9 The machine 900 in question is used for execution. Figure 9 A block diagram is shown of an example of a machine 900 on which any one or more of the techniques (e.g., methods) discussed herein can be performed. In various examples, the machine 900 can operate as a standalone device or can be connected (e.g., networked) to other machines. In a networked deployment, the machine 900 can operate as a server machine, a client machine, or both in a server-client network environment. In an example, the machine 900 can act as a peer machine in a peer-to-peer (P2P) (or other distributed) network environment. The machine 900 can be a personal computer (PC), a tablet device, a set-top box (STB), a personal digital assistant (PDA), a mobile phone, a web application, a network router, a switch or a bridge, or any machine capable of executing (sequentially or otherwise) instructions specifying actions to be taken by the machine. Furthermore, although only a single machine is shown, the term "machine" should also be construed to include any collection of machines that individually or collectively execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein, such as cloud computing, software as a service (SaaS), or other computer cluster configurations.

[0052] As described herein, examples may include, or may be operated by, logic or multiple components or mechanisms. A circuit system is a collection of circuits implemented in a tangible entity including hardware (e.g., simple circuits, gates, logic, etc.). The relationships between components of the circuit system can be flexible over time and with potential hardware variability. The circuit system includes components that can perform specified operations individually or in combination when in operation. In an example, the hardware of the circuit system can be immutably designed to perform specified operations (e.g., hardwired). In an example, the hardware comprising the circuit system may include variably connected physical components (e.g., execution units, transistors, simple circuits, etc.), the variably connected physical components including a computer-readable medium that is physically modified (e.g., magnetically, electrically, such as via a change in physical state or a transformation of another physical property, etc.) to encode instructions for the specified operation. When the physical components are connected, the potential electrical properties of the hardware components can, for example, change from insulating properties to conductive properties or vice versa. The instructions enable embedded hardware (e.g., execution units or loading mechanisms) to create components of the circuit system in hardware via variably connected components to perform a portion of the specified operation when in operation. Thus, when the device is in operation, the computer-readable medium is communicatively coupled to other components of the circuit system. In examples, any physical component can be used in more than one component of more than one circuit system. For example, in operation, an execution unit can be used in a first circuit in a first circuit system at one point in time and can be reused by a second circuit in the first circuit system or a third circuit in the second circuit system at a different point in time.

[0053] The machine (e.g., a computer system) 900 may include a hardware processor 902 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 904, and a static memory 906, some or all of which may communicate with each other via an interconnect (e.g., a bus) 908. The machine 900 may also include a display unit 920, an alphanumeric input device 922 (e.g., a keyboard), and a user interface (UI) navigation device 924 (e.g., a mouse). In an example, the display unit 920, the input device 922, and the UI navigation device 924 may be a touch screen display. The machine 900 may additionally include a storage device (e.g., a drive unit) 926, a signal generating device 928 (e.g., a speaker), a network interface device 920, and one or more sensors 922, such as a global positioning system (GPS) sensor, a compass, an accelerometer, or other sensors. The machine 900 may include an output controller 928, such as a serial (e.g., universal serial bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connection to communicate with or control one or more peripheral devices (e.g., a printer, a card reader, etc.).

[0054] The storage device 926 may include a machine-readable medium 922 having stored thereon one or more sets of data structures or instructions 924 (e.g., software) that implement or are used by any one or more of the techniques or functions described herein. The instructions 924 may also reside, in whole or in part, within the main memory 904, within the static memory 906, or within the hardware processor 902 during execution thereof by the machine 900. In an example, one or any combination of the hardware processor 902, the main memory 904, the static memory 909, or the storage device 926 may constitute a machine-readable medium.

[0055] Although machine-readable medium 922 is illustrated as a single medium, the term “machine-readable medium” may include a single medium or multiple media (eg, a centralized or distributed database and / or associated caches and servers) configured to store one or more instructions 924 .

[0056] The term "machine-readable medium" may include any medium that can store, encode, or carry instructions for execution by the machine 900 and that causes the machine 900 to perform any one or more of the techniques of the present disclosure, or that can store, encode, or carry data structures used by or associated with such instructions. Non-limiting examples of machine-readable media may include solid-state memory and optical and magnetic media. Thus, machine-readable media are not transient propagating signals. Specific examples of high-capacity machine-readable media may include: non-volatile memory, such as semiconductor memory devices (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)) and flash memory devices; magnetic or other phase-change or state-change memory circuits; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0057] Instructions 924 may also be sent or received over a communications network 926 using a transmission medium via the network interface device 920 using any of a number of transmission protocols (e.g., Frame Relay, Internet Protocol (IP), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), etc.). Example communications networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), a mobile telephone network (e.g., a cellular network), a plain old telephone (POTS) network, and a wireless data network (e.g., a wireless network known as a cellular network). The Institute of Electrical and Electronics Engineers (IEEE) 802.22 family of standards, known as 26 family of standards, IEEE 802.25.4 family of standards, peer-to-peer (P2P) networks, etc.). In an example, the network interface device 920 may include one or more physical jacks (e.g., Ethernet, coaxial, or telephone jacks) or one or more antennas for connecting to the communication network 926. In an example, the network interface device 920 may include multiple antennas to communicate wirelessly using at least one of single-input multiple-output (SIMO) technology, multiple-input multiple-output (MIMO) technology, or multiple-input single-output (MISO) technology. The term "transmission medium" should be taken to include any intangible medium that can store, encode, or carry instructions for execution by the machine 900, including digital or analog communication signals, or other intangible media for facilitating the communication of such software.

[0058] Various annotations

[0059] Each of the above non-limiting aspects can stand on its own or can be combined in various permutations or combinations with one or more of the other aspects or other subject matter described herein.

[0060] The above detailed description includes reference to the accompanying drawings, which form a part of the detailed description. The accompanying drawings show specific implementations that can be put into practice of the present invention by way of illustration. These implementations are also generally referred to as "examples". Such examples may include elements other than those shown or described. However, the inventors also contemplate examples in which only those elements shown or described are provided. In addition, the inventors also contemplate examples (or one or more aspects thereof) of any combination or arrangement of those elements shown or described with respect to the specific examples (or one or more aspects thereof) shown or described herein or with respect to other examples (or one or more aspects thereof).

[0061] In the event of any inconsistent usage between this document and any document incorporated by reference, the usage in this document controls.

[0062] Herein, as is common in patent literature, the terms "a" or "an" are used to include one or more than one, independent of any other instance or usage of "at least one of" or "one or more." Herein, unless otherwise indicated, the term "or" is used to represent a non-exclusive or, such that "A or B" includes "A but not B," "B but not A," and "A and B." Herein, the terms "including" and "in which" are used as the plain English equivalents of the respective terms "comprising" and "wherein." In addition, in the appended claims, the terms "including" and "comprising" are open-ended, that is, systems, devices, articles, combinations, formulations, or processes that include elements other than those listed after such terms in the claim are still considered to fall within the scope of the claim. In addition, in the appended claims, the terms "first," "second," and "third," etc. are used merely as labels and are not intended to impose numerical requirements on their objects.

[0063] The method examples described herein may be at least partially machine or computer-implemented. Some examples may include a computer-readable medium or machine-readable medium encoded with instructions that can operate to configure an electronic device to perform the methods described in the above examples. The implementation of such methods may include code, such as microcode, assembly language code, higher-level language code, etc. Such code may include computer-readable instructions for performing various methods. The code may form a part of a computer program product. In addition, in an example, for example, during execution or at other times, the code may be tangibly stored on one or more volatile, non-transient or non-volatile tangible computer-readable media. Examples of these tangible computer-readable media may include, but are not limited to, hard disks, removable disks, removable optical disks (e.g., compact disks and digital video disks), magnetic tape cassettes, memory cards or memory sticks, random access memories (RAMs), read-only memories (ROMs), etc.

[0064] The above description is intended to be illustrative and not restrictive. For example, the examples described above (or one or more aspects of the examples) can be used in combination with each other. For example, a person of ordinary skill in the art may use other implementations after consulting the above description. An abstract is provided to enable the reader to quickly determine the nature of the disclosure of this technology. The abstract is submitted with the following understanding: the abstract will not be used to interpret or limit the scope or meaning of the claims. In addition, in the above specific embodiments, various features can be combined together to streamline the disclosure. This should not be interpreted as meaning that the disclosed features that are not claimed for protection are necessary for any claim. Instead, the subject matter of the invention may lie in less than all the features of a particular disclosed implementation. Therefore, the appended claims are incorporated into the specific embodiments as examples or implementations, each claim existing independently as a separate implementation, and it is expected that such implementations can be combined with each other in various combinations or permutations. The scope of the present invention should be determined with reference to the appended claims and the full scope of equivalents enjoyed by such claims.

Claims

1. A method for compressed sampling, comprising: obtaining a signal representing one or more acoustic waves received using a matrix of sensing elements, wherein N samples define a full matrix representation of the signal, the full matrix representation comprising N elements; downsampling the obtained signal by a subset of M samples, where N is greater than M; and Reconstruct an image based on the M samples, Wherein, reconstructing the image comprises: Applying weights to the M samples; Obtaining an inverse sparse matrix of N samples of the signal; Establishing a model of full matrix representation based on the weights and the inverse sparse matrix; converting the M samples into a reconstructed full matrix representation of the signal based on the model; and The image is reconstructed based on the reconstructed full matrix representation.

2. The method according to claim 1, wherein The M samples are randomly selected.

3. The method according to claim 1, wherein The model includes an inverse sparse matrix.

4. The method according to claim 1, wherein The coefficients of the inverse sparse matrix are based on characteristics of the sensing element matrix.

5. The method according to claim 1, wherein The coefficients of the inverse sparse matrix are based on characteristics of the object being measured.

6. A machine storage medium comprising instructions that, when executed by a machine, cause the machine to perform operations comprising: A signal representing one or more acoustic waves received using a matrix of sensing elements is obtained, wherein: N samples define a full matrix representation of the signal, the full matrix representation comprising N elements; downsampling the obtained signal by a subset of M samples, where N is greater than M; and Reconstruct an image based on the M samples, Wherein, reconstructing the image comprises: Applying weights to the M samples; Obtaining an inverse sparse matrix of N samples of the signal; Establishing a full matrix representation model based on the weights and the inverse sparse matrix; converting the M samples into a reconstructed full matrix representation of the signal based on the model; and The image is reconstructed based on the reconstructed full matrix representation.

7. The machine storage medium according to claim 6, wherein: The M samples are randomly selected.

8. The machine storage medium according to claim 6, wherein: The model includes an inverse sparse matrix.

9. The machine storage medium according to claim 6, wherein: The coefficients of the inverse sparse matrix are based on characteristics of the sensing element matrix and / or characteristics of the object under test.

10. A system for compressive sampling, comprising: one or more processors of the machine; as well as a memory storing instructions that, when executed by the one or more processors, cause the machine to perform operations comprising: obtaining a signal representing one or more acoustic waves received using a matrix of sensing elements, wherein N samples define a full matrix representation of the signal, the full matrix representation comprising N elements; downsampling the obtained signal by a subset of M samples, where N is greater than M; and Reconstruct an image based on the M samples, Wherein, reconstructing the image comprises: Applying weights to the M samples; Obtaining an inverse sparse matrix of N samples of the signal; Establishing a model of full matrix representation based on the weights and the inverse sparse matrix; converting the M samples into a reconstructed full matrix representation of the signal based on the model; and The image is reconstructed based on the reconstructed full matrix representation.

11. The system according to claim 10, wherein: The M samples are randomly selected.

12. The system according to claim 10, wherein: The model includes an inverse sparse matrix.

13. The system according to claim 10, wherein: The coefficients of the inverse sparse matrix are based on characteristics of the sensing element matrix.

14. The system according to claim 10, wherein: The coefficients of the inverse sparse matrix are based on characteristics of the object being measured.

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