24 results about "Hadamard matrix" patented technology

The invention provides a two-dimensional coding and decoding method for ultrasonic synthetic aperture imaging. The method comprises the following steps of: generating an order Hadamard matrix, which is a multiple of 8, for an ultrasonic transducer array with an array element; according to the requirements of the axial resolution and the near-field dead zone range, a level serial coding matrix is generated; a Hadamard matrix and a serial coding matrix are used to generate a two-dimensional coding matrix through matrix multiplication, and common coding emission and acquisition are carried out; performing dual decoding on the acquired echo data according to the two-dimensional coding matrix; and performing synthetic aperture imaging by using the decoded data. According to the invention, the depth and contrast of ultrasonic synthetic aperture imaging can be improved.

The application discloses a dynamic cross-modal hash retrieval method and system based on concept vector learning, and the method comprises the following steps: constructing a concept vector matrix based on current label information and a Hadamard matrix; the concept vectors in the concept vector matrix are used for representing that samples with the same label have the same and invariable vectors; constructing a data similarity matrix based on current label information and historical label information; obtaining target hash codes based on the concept vector matrix and the data similarity matrix; obtaining a target hash function based on the target hash codes; analyzing the to-be-queried data according to the target hash function to obtain to-be-queried hash codes corresponding to the to-be-queried data; and performing similarity calculation on the to-be-queried hash codes and the target hash codes in a database to obtain target retrieval results. The application can overcome the defects of the concept drift problem in a dynamic data environment, improve the adaptability of a cross-modal hash model to the concept drift, improve the dynamic cross-modal hash retrieval performance, and can be widely applied to the technical field of information processing.

The application belongs to the technical field of quantum imaging of computational imaging, and specifically discloses a correlation imaging reconstruction algorithm based on a deep convolutional hybrid neural network, which uses a Hadamard matrix gray scale coding sequence to replace a Gaussian matrix speckle sequence of original correlation imaging, and adjusts a digital micromirror device with a fixed sequence of speckle sequences in data acquisition and processing of correlation imaging. Under the same sampling rate, the algorithm can reconstruct a high-quality target object image, and has higher PSNR and SSIM values. Deep learning realizes end-to-end image recovery of an image in the algorithm implementation of correlation imaging, and reduces the steps of image calculation.

The application discloses a kind of ghost imaging reconstruction and classification method and equipment based on hybrid network, belong to the field of computational imaging technology, will be based on Hadamard matrix generation light speckle sequence formed by fixed light speckle sequence;Then, based on this light speckle sequence sampling expanded MNIST data set, generate light intensity sequence, light speckle sequence data;Then, construct and train GRU-CNN network model;Finally, using the model trained to calculate correlation imaging reconstruction and target identification classification.The conventional ghost imaging technique is often faced with the problem of poor image quality under low sampling conditions.In order to solve this problem, the application proposes a GRU-CNN network (GCGI) model combined with deep learning, which can efficiently reconstruct high-quality images and accurately identify and classify targets under very low sampling rates by fusing light intensity sequence and light speckle information, and has strong practical value.

The invention provides an interference communication integrated signal design method based on MIMO-OCDM (Multiple Input Multiple Output-Optical Code Division Multiplexing). The implementation steps are as follows: constructing an application scene of an interference communication integrated signal and a binary communication data matrix; performing OCDM on the binary communication data matrix, and obtaining an MIMO-OCDM communication signal and a frequency shift interference signal; and finally, an MIMO-OCDM interference communication integrated signal is obtained. According to the method, Hadamard matrix coding is carried out on the time domain signal matrix obtained after discrete Fresnel inverse transformation is carried out on the frequency domain QPSK symbol matrix so as to obtain the MIMO-OCDM communication signal matrix, it is guaranteed that multiple time domain signals have the high anti-interference capacity, interference of signals between MIMO transmitting channels is reduced, and the communication performance of the MIMO-OCDM communication signal matrix is improved. Moreover, a non-partner needs to obtain the interference communication integrated signals transmitted by all MIMO transmission channels and correctly analyze and recombine the interference communication integrated signals to obtain the communication data, thereby improving the anti-interference difficulty of the radar of the opposite side.

The invention proposes an OTFS anti-interference transmission method and system and a computer readable storage medium, and the system carries out the sparse arrangement of user service data in a time delay Doppler domain, carries out the homogenization processing through the multiplication with a Hadamard matrix, carries out the processing according to a conventional OTFS signal transmission process, and carries out the transmission of an OTFS signal. A receiving end firstly converts received OTFS signals into a time-frequency domain, then selects signals lower than an interference threshold value to form a dimensionality-reduced observation signal matrix, and then reconstructs a sparse signal matrix in an original time delay Doppler domain by using a compressed sensing algorithm, thereby obtaining user service signals. According to the method, interference is filtered in the time-frequency domain, interference is smoothed in the time delay Doppler domain, the capability of an OTFS system for resisting external narrow-band intentional interference is greatly improved, and dual-robustness transmission for time-varying fading and external interference is achieved.

The present invention provides a method for processing EEG signals with a high compression rate, comprising the following steps: first, obtaining the original EEG signal, then converting it into a sparse domain through a sparse transform such as a discrete cosine transform and extracting sparse coefficients. Next, constructing an initial correlation matrix, and generating an extended matrix through vertical and horizontal expansion and nesting operations to meet the requirements of compressed sensing. Finally, the sparse coefficients are compressed using the extended matrix to obtain a compressed signal representation. The method of the present invention improves the compression rate and reduces the hardware power consumption by designing an extended measurement matrix based on a combination of Hadamard matrices, solving the problem of resource consumption in the online generation of random sparse matrices in the prior art, and is particularly suitable for low-power demand scenarios, thereby improving the hardware performance of the EEG signal processing solution.

The invention relates to a deep semantic embedding method and device based on a cross-environment separated architecture, and the method comprises the steps: a cloud side generates global semantic embedding through a pre-training language model and a Hadamard matrix based on a class label text, mines a label hierarchical structure through a double-block attention mechanism, and obtains a classification label; generating a high-level semantic hash code containing intra-layer similarity and cross-layer correlation, and constructing an anchor point library; the edge device receives an anchor point library and global semantic embedding, learns a low-level semantic hash code through a discrete cyclic coordinate descent algorithm, and introduces an instance-level fine-grained weight to dynamically distribute a multi-modal data fusion weight; a cloud and an edge device record a corresponding relation of high-layer and low-layer hash codes through a dynamic semantic mapping table, a bit-by-bit calibration algorithm corrects cross-environment deviation, and the edge device only transmits hash code bits different from an anchor point library and compresses semantic information in combination with category-level high-level codes, so that deep semantic embedding with consistent cross-environment semantics is realized. Cloud precision and edge real-time performance are balanced, and efficient retrieval of multi-modal data is supported.

The present application belongs to the technical field of calibration of phased array radar, and relates to a two-stage grouping asynchronous rotating electric vector radar array antenna calibration method, and the specific process is as follows: an attenuator and a phase shifter are configured to each element channel of the array antenna to be measured, and signals received by all channels are synthesized through a power divider; the phased array radar to be measured is equally grouped by channel number in a first stage; a grouping matrix is constructed based on a Hadamard matrix, and the channels grouped in the first stage are divided into rotating groups and fixed groups according to elements in the grouping matrix; a phase shift matrix for phase shift measurement of each row in different grouping matrices is constructed based on a discrete Fourier transform matrix with an order of N, and phase shift is performed; linear equations are constructed according to measurement results and solved, and the ambiguity coefficient, the amplitude of each channel and the phase difference between each channel are obtained, and each field normalization amplitude and phase is solved; amplitude and phase compensation of the channels is completed based on each field normalization amplitude and phase, and finally the amplitude and phase of all channels are consistent.

A computer-implemented method for simulation of a given quantum circuit, the method comprising separating the quantum circuit into layers of quantum gates of a common gate class; decomposing each of the layers of quantum gates of the common gate class into a sequence of base operations, the base operations being selected from a diagonal matrix operation based on unitary diagonal operations applied in parallel to qubits of the quantum circuit, a permutation matrix operation expressible as a matrix with exactly one entry of 1 in every row and column, with the rest being zero, and a Hadamard matrix operation based on Hadamard gates applied in parallel to qubits of the quantum circuit that transforms a plurality of the qubits between two orthogonal bases; and evolving a quantum state vector according to the layers decomposed into the sequence of base operations.

The invention discloses a calculation ghost imaging reconstruction method based on a Vision Transform and a U-Net network, and the method comprises the following steps: 1, enabling a Hadamard matrix of a cut cake sequence to serve as a phase mask, sequentially embedding the Hadamard matrix into a spatial light modulator, sequentially projecting the Hadamard matrix to a target image, and collecting a total light intensity sequence through a barrel detector; 2, partitioning the Hadamard matrix of the cake cutting sequence and the light intensity sequence, performing linear projection on each block to obtain a high-dimensional vector token, adding a position code to each high-dimensional vector token, inputting the high-dimensional vector token into a Transform encoder for processing, and converting the output into a two-dimensional matrix; and step 3, inputting the two-dimensional matrix obtained in the step 2 into the U-Net network to enhance transmission and fusion of feature information until a loss function of the ViT-U-Net network is kept stable, and obtaining a result of calculating ghost imaging. The image reconstructed by the method is high in quality and high in reconstruction efficiency.

A signal depth compression and direct positioning method, system, device and medium, the method is: by arranging at least three observation stations in two-dimensional space, synchronously observing the signal emitted by the target, using Hadamard matrix for sampling compression and 1-bit quantization means for quantization compression, realizing the depth compression processing of the observed signal; using the characteristics of Hadamard matrix and the phase invariance of 1-bit compression, avoiding the complex signal reconstruction process, without extracting position parameters such as time delay, directly establishing a cost function from the depth compressed data using the retained time delay relationship to determine the position of the target; the system, device and medium can realize signal depth compression and direct positioning based on the above method; the present application can significantly shorten the data amount of signal transmission and simplify the processing process, and is suitable for positioning scenes with limited communication bandwidth and the need for fast response; on the premise of ensuring positioning accuracy, the signal data amount is greatly compressed, and the communication bandwidth requirement of each station receiving signal transmission to the center station is reduced.

The invention discloses a spatial channel feature identification method based on compression measurement, and belongs to the technical field of electromagnetic information and multi-antenna wireless communication. According to the method, the random singular value decomposition and compressed sensing theories are combined, programmable array antennas are configured at a transmitting end and a receiving end respectively, the orthogonality of a Hadamard matrix is utilized to realize double-side compressed projection measurement, and a low-dimensional observation matrix is obtained only through a small amount of coding measurement. The main mode of the original space can be deduced by performing singular value decomposition on the matrix, and high-precision estimation of key features such as channel degree of freedom is realized. Compared with a traditional full-amount measurement method, the method has the advantages that the measurement complexity is reduced, the test time and resource consumption are remarkably reduced, and the method has the advantages of being friendly in hardware, high in robustness, high in engineering practicability and the like, and is suitable for scenes such as a large-scale MIMO system, millimeter wave channel modeling and intelligent wireless network optimization.

This application provides a two-dimensional encoding and decoding method for ultrasonic synthetic aperture imaging. The method includes: generating an order Hadamard matrix for an ultrasonic transducer array with n elements, where is a multiple of 8; generating an order serial encoding matrix according to the requirements of axial resolution and near-field dead zone; performing co-encoded transmission and acquisition using n two-dimensional encoding matrices generated by matrix multiplication of the Hadamard matrix and the serial encoding matrix, where ; performing dual decoding of the acquired echo data based on the two-dimensional encoding matrices; and using the decoded data for synthetic aperture imaging. This application can improve the depth and contrast of ultrasonic synthetic aperture imaging.

The invention discloses a cross-border data dynamic management and control method and system based on flow analysis, and relates to the technical field of data transmission, and the method comprises the following steps: encrypting a data stream image according to a Hadamard product matrix and a diagonal algorithm of a matrix vector product, carrying out the first verification of a new data stream according to an encryption verification method, and carrying out the second verification of the new data stream; and performing second verification on the new data stream according to the decryption verification method, and judging whether the new data stream is compliant or not based on the first verification result and the second verification result. According to the method, PPMI weighted co-occurrence and sliding step length minimum Hash position are used, a globally unique and sequenced natural number sequence number is generated for each byte graph, a data stream image and a random Hadamard matrix are subjected to Hadamard product, then diagonalization product is carried out, matrix vector diagonal coefficients are written in an encryption process, secondary confirmation independent of a rule base is provided, and an encryption result is obtained. The sequence number sorting space and the encryption matrix are both updated online along with flow distribution, and the corresponding encryption graph template can be quickly issued to the edge node.

The application discloses a signal fluctuation extraction method combining a Hadamard matrix and mathematical morphology, and comprises the following steps: determining the order m of the Hadamard matrix and the specific number l of rows to be selected according to signal characteristics and sampling accuracy; taking the lth row of the selected Hadamard matrix as a structure element g to perform mathematical morphology operation on an input signal f to obtain a signal fluctuation part and complete the extraction operation. According to the application, a certain row in the Hadamard matrix is selected as the structure element according to signal characteristics, and the order of the matrix is determined; the characteristics that the Hadamard matrix only takes values of 1 and -1 are utilized; the fundamental component in the signal is suppressed through the morphological operation; and the fluctuation part is extracted. The orthogonality of the Hadamard matrix is fully utilized, the Hadamard matrix is used as the structure element for the morphological transformation, the signal can be processed simply and quickly, and the characteristic information is extracted. The application of the mathematical morphology can accurately analyze the signal in the time domain.

The invention relates to a multi-region compressed sensing imaging modulation matrix optimization method. According to the implementation mode, a coding matrix is only arranged at the position with a target to serve as a modulation mask, coefficients of 1 or-1 are given to different coding areas, coefficient sets are linearly independent, the optimized modulation matrix replaces a traditional full-coverage modulation Hadamard matrix to serve as an echo modulation mask, modulated echoes are received by a single-pixel detector, and the modulation efficiency is improved. Two-dimensional information of a target is reconstructed by using a compressed sensing algorithm, depth information is obtained by combining a distance measurement technology, and finally three-dimensional imaging is realized. The problem of spatial modulation redundancy is solved, the coefficient matrix is introduced, one-time imaging of multi-region scenes is supported, high-quality imaging can be completed only by using a low resolution and a small sampling rate, the detection efficiency of complex scenes such as multiple targets and small targets under large-scale terrains is remarkably improved, and the method is suitable for large-scale terrains and large-scale terrains. The method has potential application value in the fields of intelligent driving, high-resolution imaging, multi-target identification and the like.

The application discloses a TCM code parameter blind identification method and device and a storage medium, and belongs to the field of electronic signal reconnaissance and countermeasure. The method comprises the following steps: S1, acquiring binary code stream data of TCM coding, an initial code length and an initial constraint length; S2, constructing a binary linear equation group to obtain a mapping vector; S3, segmenting the mapping vector and then constructing a Hadamard matrix; S4, performing Walsh-Hadamard transformation on the segmented vector to obtain the value of a Walsh spectrum; S5, searching for a Walsh spectrum peak value outside the 0 point position, if the Walsh spectrum peak value is found, the correct code length and the correct constraint length are obtained according to the position of the Walsh spectrum peak, and S6 is performed; if the Walsh spectrum peak value is not found, the initial code length is increased by 1, and the subsequent steps are continued to be performed from S2; S6, constructing a new binary linear equation group, performing Walsh-Hadamard transformation according to the correct code length and the correct constraint length, obtaining a correct starting position and a check matrix, and outputting an identification result. The application has low calculation complexity, is easy to implement in engineering, and has high operation speed.

The invention discloses a distance Doppler sidelobe suppression method for joint design of a receiving end and a transmitting end of a pulse compression radar, which realizes good sidelobe suppression and improvement of signal detection performance by constructing a multi-objective function fusing ISL, PSL and SNR and adopting a discrete-continuous variable layered alternating iterative optimization framework. According to the method, a Gray-paired Hadamard matrix and a fuzzy function theory are fused, a sidelobe energy model is established, a dynamically weighted objective function is designed, and meanwhile, an integral sidelobe (ISL), a peak sidelobe (PSL) and a signal-to-noise ratio (SNR) are optimized. Finally, the method can suppress the maximum side lobe to-45dB under Doppler frequency shift, and significantly improves the detection performance of the radar in a complex scene.

A weight quantification method and module of a large model, the large model, a device and a storage medium are applied to the field of model quantification, and the method comprises the following steps: obtaining a learnable search parameter having a preset dimension according to position information of model quantification; according to the search parameters and a preset rotation matrix search function capable of being micronized, discrete selection vectors are obtained; optimizing a Walsh-Hadamard matrix candidate library obtained in advance according to the selection vector to obtain a target matrix; performing rotation processing on the corresponding weight matrix based on the target matrix to obtain a rotated weight matrix; and quantizing the weight matrix based on a preset model parameter quantization algorithm to obtain a quantized weight matrix. According to the method, the cache can be successfully quantized to the low bit width, the quantization error is reduced, the quantization precision is improved, and almost all model performances can be reserved. And meanwhile, input and output operations are effectively reduced, the bit width of the Query vector is ensured, and efficient online quantization and calculation are realized.

Apparatus and methods for block-wise matrix multiplication are disclosed. An example processor includes vector registers for storing submatrices of two source matrices, and an execution circuit for performing a Hadamard matrix multiplication. A decoder interprets instructions that specify the source matrices and the operation. The execution circuit multiplies each combination of submatrices from the two source matrices - first with first, first with second, second with first, second with second - to generate four corresponding submatrices of a result matrix, which are stored in a third vector register. This architecture enables efficient parallel matrix computation for high performance processing tasks.

The invention discloses a programmable broadband transmission-type metasurface imager and method based on Hadamard matrix coding. The imager comprises a 1-bit programmable transmission-type metasurface which is controlled by an FPGA (Field Programmable Gate Array) and has polarization flipping and broadband characteristics, a vector network analyzer connected with a transceiving antenna, and a computer for data processing. During working, a computer outputs a coding sequence based on a Hadamard matrix to control the working state of the metasurface; the vector network analyzer measures the amplitude and phase information of the electromagnetic waves scattered twice by the to-be-measured target and the metasurface through the transceiving antenna; the measurement result is sent to the computer, and the image of the to-be-measured target is reconstructed through algorithm processing. The metasurface has the broadband and polarization conversion characteristics, the orthogonal coding sequence is combined, the imaging process has high robustness, and the method can be applied to the fields of microwave sensing, the Internet of Things, security check and the like.

The application discloses a Hadamard matrix multi-scale sequencing method and system, wherein the method comprises the following steps: initializing parameters, and constructing an M-row and M-column Hadamard matrix; extracting each row or column vector of the Hadamard matrix and then performing normalization, rearranging the elements of each row or column vector to form a 2 n Row 2 n Column square matrix H i ; performing n-layer two-dimensional Haar wavelet decomposition on the square matrix H i , obtaining a coefficient matrix H a , taking the absolute value |H a | of the coefficient matrix H a , summing up |H a |, and recording i into a sequence number value I(i); storing the values of Sa(i) and I(i) into a vector Sa and a vector I respectively; taking i=i+1, and judging whether i is greater than M; arranging the elements in the vector Sa in ascending order to obtain a new sequence R; extracting each row or column vector of the Hadamard matrix after normalization according to the new sequence R, and traversing M times to obtain M two-dimensional 2 n ×2 n square matrices RH i . The application solves the problem that the existing coding cannot simultaneously consider fast imaging and high signal-to-noise ratio imaging.

The invention discloses a unitary space-time modulation and demodulation method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the unitary space-time modulation of target data based on a unitary space-time modulation signal constellation set, and obtaining a unitary space-time modulation signal; a first signal in the modulation signal constellation set is generated based on a Hadamard matrix; and converting the unitary space-time modulation signal into an offset quadrature phase shift keying signal, and converting the offset quadrature phase shift keying signal into a continuous phase modulation signal. According to the unitary space-time modulation and demodulation method and device, the electronic equipment and the storage medium, the USTM constellation set head signal is constructed based on the orthogonal characteristic of the Hadamard matrix, the complete constellation set is generated through diagonal matrix design, finally, signal phase continuity is achieved through constant envelope processing, and the peak-to-average ratio can be remarkably reduced.