359 results about "Block matrix" patented technology

A system for delivering broadcast and communications services, wherein said services comprise provision of one or more Internet media streams; the system comprising one or more gateways which provide connections to external communications networks and nodes and internal loopbacks from which said services are obtained, each of said gateways comprising: a selection system to selectively establish communication channels with said external communication nodes networks and said loopbacks to establish an individual bidirectional channel between each node network and allow recipients to obtain the communication channel of their choice; a processing system comprising non-blocking matrix switching or routing system, buffering, packeting, and addressing systems; processing said channels containing services into digitized packaged data format and said addressing systems identifying, storing and updating the location of each recipient, and applying routing information to each packet of digitized packaged data to enable packets to be correctly routed through the system.

An LDPC code encoding apparatus includes: a code matrix generator for generating and transmitting a parity-check matrix comprising a combination of square matrices having a unique value on each row and column thereof; an encoding means encoding block LDPC codes according to the parity-check matrix received from the code matrix generator; and a codeword selector for puncturing the encoded result of the encoding means to generate an LDPC codeword. The code matrix generator divides an information word to be encoded into block matrices having a predetermined length to generate a vector information word. The encoding means encodes the block LDPC codes using the parity-check matrix divided into the block matrices and a Tanner graph divided into smaller graphs in correspondence to the parity-check matrix.

The invention provides a method for enhancing microphone array voice based on combined inhibition. The method comprises the following steps of: structuring a microphone array for receiving external signals; analyzing the signals and obtaining time delays of different array signals relative to benchmark array signals in the microphone arrays opposite to a target voice source; respectively performing time delay compensation on digital signals corresponding to the two microphones, obtaining the compensated signals; respectively performing subband decomposition on the compensated array signals, and then forming fixed beams on each subband; meanwhile, respectively using blocking matrixes on each subband to obtain noise reference signals on each subband; and then respectively removing the noiseirrelative to the target voice form the fixed beam forming device on corresponding subband through an adaptive filtering processing algorithm, and then merging the subbands, thereby forming an initial gain signal; and meanwhile, making use of the previously compensated any two array signals to obtain a filter for inhibiting the noise signal related to the target voice through a recursive mutual power spectral density, thereby obtaining the final target voice signal through combining the initial gain signal.

The invention discloses a voice signal enhancement system and a voice signal enhancement method. The method comprises the following steps of: filtering an input signal by an adaptive filter to obtain a voice reference signal and performing noise extraction processing on the input signal by using a block matrix to obtain a noise reference signal; filtering the noise reference signal and the voice reference signal through multi-channel Wiener filtering to obtain a denoised voice signal; performing voice enhancement processing on the denoised voice signal according to the noise reference signal to obtain an enhanced voice signal and a voice existence probability signal; and updating a filter coefficient of the adaptive matching filter, the block matrix and a filter coefficient and a learning step length of a multi-channel Wiener filter according to the voice existence probability signal. By the voice signal enhancement system and the voice signal enhancement method, under the condition that a microphone array is relatively simple, the problems that convergence cannot be guaranteed, the speed is low and the real-time enhancement distortion is high during real-time tracking of a target source in a practical application environment can be solved.

The invention discloses a low-rank structure-based sparse compressive sensing MRI (Magnetic Resonance Imaging) image reconstruction method, which mainly solves the problem of difficulty in accurate recovery of an MRI image existing in the conventional technology. The method comprises the following implementation steps: initially recovering the MRI image by using the conventional compressive sensing and looking for a similar block matrix from the image to form an index set; performing singular value decomposition on the similar block matrix and calculating a threshold, and performing threshold calculation on a singular value by using the threshold to obtain an after-threshold singular value; and optimizing the MRI image by using the after-threshold singular value, i.e., circularly performing processes of updating of the similar block matrix and the index thereof, similar block matrix singular value decomposition and threshold and singular value threshold calculation on the MRI image to obtain a final recovery image. The recovered MRI image is clearer and has sharper edges; and the method can be used for processing a medical image.

A noise suppression system includes plural microphones, a fixed beam former, a blocking matrix, plural adaptive filters, and a direction of arrival circuit coupled to the adaptive filters that prevents the filters from adapting in the presence of a signal in the look direction. The direction of arrival circuit causes the filters to adapt more quickly in the absence of a signal in the look direction. A pair of adjustable gain circuits is coupled to each microphone. A first adjustable gain circuit from each pair is calibrated during the presence of a desired signal and a second adjustable gain circuit from each pair is calibrated during the presence of an interfering signal. A fixed null-forming circuit is coupled to a first pair of variable gain circuits and an adaptive null forming circuit is coupled to a second pair of adjustable gain circuits. The ratio of the gains of the null forming circuits is used as a control signal. Successive ratios are averaged with a variable smoothing constant and a control signal is derived from the averaged ratios.

The invention provides a speech recognition method and device based on artificial intelligence. The method comprises steps of acquiring a microphone array and acquiring multiple paths of first speech signals; based on the WPE algorithm, removing reverberation signals in each path of the first speech signal so as to obtain each path of second speech signal, and passing each path of the second speech signal through an MVDR wave beam former to acquire one path of third speech signal; inputting the third speech signal into an adaptive blocking matrix module and an adaptive interference elimination module; carrying out noise extraction based on the third speech signal and each path of first speech signal in the adaptive blocking matrix module to obtain each path of first noise signal; and filtering each path of the first speech signal and carrying out superposition in the adaptive interference elimination module to obtain one path of second noise signal and subtracting the third speech signal from the second noise signal to obtain a target speech signal. According to the invention, by carrying out de-reverberation, enhancement and de-noising processing on the input signal, far-field speech recognition rate is improved.

The invention discloses a sparse representation and low-rank double restraints-based nonlocal denoising method. The problem of a poor effect in an existing denoising method is mainly solved. The method comprises the following implementation steps: (1) inputting a noise image; (2) estimating a noise standard deviation; (3) setting parameters; (4) obtaining a pixel block sample set; (5) building a similar block matrix; (6) obtaining a coefficient matrix of the similar block matrix; (7) carrying out singular value filtering on the coefficient matrix; (8) obtaining the denoised coefficient matrix; (9) obtaining the denoised image matrix; (10) judging whether the maximal iterative times is achieved, if so, carrying out a step (11), or else, carrying out residuals recovery, and carrying out the step (2); and (11) outputting the denoised image matrix. The method has mating constraints on the sparsity and the constitutive property of an image signal. Compared with the prior art, the method disclosed by the invention has the advantages that structural texture information of a natural image can be kept and recovered when the noise is well smoothed.

The invention provides a gait recognition method based on a partitioned matrix. Firstly, extracting single-frame images from a video, then carrying out grey scale transformation on the single-frame images, using the background subtraction method to extract person body targets, using mathematical morphology to fill the holes of binary images, and extracting profiles of the person by means of single connection analysis so that the person bodies are positioned in the middle and are uniformly in the size of 64 * 64 pixels; observing the periodic change of the gait according to elliptical short axis and eccentricity fitted in image regions after the standard centralization of each frame image in a gait video sequence; using a gait energy diagram to extract the integral characteristic of the gait in the one period, dividing GEI into sub-blocks by means of the partitioned matrix, eliminating the sub-blocks which are useless to classification in a self-adapting manner, and adopting the method, which combines the two-dimensional principal component analysis of a sub-block mode with the two-dimensional linear discriminant analysis, to further extract local characteristics; and integrating the characteristics of each effective sub-block into a whole during the classification recognition, and adopting a nearest neighbor classifier to perform identification judgment. The method is effective for the recognition of the gait of knapsack change.

A method for modeling a sparse function over sequences is described. The method includes inputting a set of sequences that support a function. A set of prefixes and a set of suffixes for the set of sequences are identified. A sub-block of a full matrix is identified which has the full structural rank as the full matrix. The full matrix includes an entry for each pair of a prefix and a suffix from the sets of prefixes and suffixes. A matrix for the sub-block is computed. A minimal non-deterministic weighted automaton which models the function is computed, based on the sub-block matrix. Information based on the identified minimal non-deterministic weighted automaton is output.

A block matrix multiplication vectorization method supporting a vector processor with multiple MAC (multiply accumulate) operational units includes the steps of (1), determining the optimum block size of submatrix, the quantity of lines and rows of the submatrix of a multiplier matrix B and the quantity of lines and rows of the submatrix of a multiplier matrix A according to the quantity p of vector processing elements (VPE) of the vector processor, the quantity m of the MAC operational units in the VPE, the capacity s of a vector memory and data size d of matrix elements, (2) equally dividing the capacity s of the vector memory into two storage areas of Buffer 0 and Buffer 1, and realizing multiplication of the submatrix in the Buffer 0 and the Buffer 1 in a Pingpong mode until completing the multiplication operation of the whole matrix. The block matrix multiplication vectorization method has the advantages of easiness in implementing, convenience in operation, capabilities of improving parallelism of the vector processor and increasing operation efficiency thereof, and the like.

The embodiment of the invention provides an adaptive loop filter method in video coding and decoding. The method comprises: image block division is carried out on an inputted reconstruction video image, a similar block matrix is formed based on the image blocks, and singular value decomposition is carried out on the similar block matrix to obtain a singular value of the similar block matrix; a standard difference of the reconstruction video image is calculated, a shrinkage filter threshold value of the similar block matrix is determined according to the standard different, a coding mode, and a quantization parameter of the reconstruction video image, and shrinkage filtering is carried out on the singular value of the similar block matrix based on the shrinkage filter threshold value; and the image blocks in the similar block matrix are reconstructed by using the shrunk singular value to obtain an adaptive loop filter video image. According to the embodiment of the invention, an adaptive filter control parameter estimation method is put forward by using non-local similarity inside a video image and high-efficiency adaptive loop filtering in video coding and decoding is realized, so that the video compression performance is improved effectively.

A joint detection reception apparatus and method, which are utilized irrespective of a length of an orthogonal code in a TD-CDMA communication system. The joint detection reception method for creating a system matrix associated with a joint detection receiver in the same time slot includes the steps of a) repeating and partitioning individual channelization codes having variable lengths, and creating channelization code blocks having the same lengths, b) performing a convolution operation between the repeated and partitioned channelization code blocks and a channel impulse response, and acquiring combined impulse responses, c) grouping the combined impulse responses to construct sub-block matrices for a joint detection system, d) arranging the sub-block matrices for the joint detection system to be shifted by a predetermined column distance, and constructing a joint detection system matrix, and e) extending the joint detection system matrix to a squared-format matrix to create a block-circulant squared matrix.

The invention discloses a hybrid precoding method based on partial connection in millimeter wave large-scale MIMO. The method is characterized in that a receiving end adopts a full-digital combiner, achannel matrix is divided into block matrices which are equal to the number of radio frequency links, and the phase of an analog precoding matrix of each radio frequency link of a sending end is designed to be the phase of a maximum right singular value vector of the block matrix corresponding to the channel matrix decomposition; and after the analog precoding matrix is obtained, a digital precoding matrix of a base station end and a digital combination matrix of the receiving end are designed through SVD decomposition on the equivalent channel matrix. According to the method, the system reachable sum rate higher than that of the analog precoding can be obtained. Although the algorithm performance provided by the invention is lower than the optimal digital precoding, the hardware complexity and the power consumption of the base station are far lower than that of the optimal digital precoding scheme.

A high-resolution SAR image classification method based on a non-down-sampling contourlet full-convolution network is provided, which comprises: inputting a high-resolution SAR image to be classified; performing multi-layer non-down-sampling contourlet transform on each pixel in the image; obtaining the low-frequency coefficient and the high-frequency coefficient of each pixel; selecting and fusing the low-frequency coefficients and high-frequency coefficients to form a pixel-based characteristic matrix F; normalizing the element values in the characteristic matrix F to obtain a normalized characteristic matrix F1; dicing the normalized characteristic matrix F1 to obtain a characteristic block matrix F2 used as sample data; constructing a training data set characteristic matrix W1 and a testing data set characteristic matrix W2; constructing a classification model based on a full convolution neural network; training the classification model; utilizing the well-trained model to classify the testing data set T to obtain the category of each pixel in the testing data set T; comparing the obtained category of each pixel with a class diagram; and calculating the classification accuracy. With the method, the classification accuracy and speed are increased.

A touch display panel includes a substrate, a light-blocking matrix layer and a sensing circuit. The sensing circuit is disposed on a surface of the substrate and forms a single-layer structure and located at the light-blocking matrix layer. The sensing circuit includes a plurality of driving electrodes, a plurality of sensing electrodes and a plurality of signal wires. The driving electrodes and the sensing electrodes are insulated from each other. The signal wires are connected to the driving electrodes and the sensing electrodes. The sensing electrodes, the driving electrodes and the signal wires are disposed on the single-layer structure. A touch display apparatus is also disclosed.

The invention discloses a binary microphone microarray voice beam forming method, which comprises the following steps of: constructing a microphone microarray so as to receive external signals; analyzing the signals to obtain relative time delay of a target sound source relative to two microphones; respectively carrying out time delay correction on digital signals corresponding to the two microphones according to the time delay to obtain corrected signals; completing fixed beam forming according to the two corrected signals; obtaining a noise reference signal by using a block matrix according to the two corrected signals; by using a frequency domain self-adaption processing algorithm, converting the fixed beam forming into a primary gain signal and converting the noise reference signal into an estimated noise reference signal; and removing noise energy out of the primary gain signal to obtain a final beam forming signal. The invention has the advantages of good generality, low dependence on hardware, no requirement on angles of the target sound source and a noise source, application to a moving environment, a mixed-sound environment and a nonstationary noise environment and good denoising effect.

[Object] To provide an adaptive array control device, method, and program adapted to be able to output an enhanced target signal by precisely carrying out a coefficient update control of a plurality of signals input from a group of sensors arranged in an array, the coefficient update control being less influenced by the frequency characteristics of the signals and directions of a target signal and interference.

[Achieving Means] The invention includes: a gain control section 900 having an analyzing section 903 which analyzes characteristics of a target signal and a correcting section 905 which corrects a target signal power estimated value in response to the analysis result; and blocking matrix circuits 310 and 320 which receive and process signals from a plurality of sensors having different intervals.

The invention relates to a full-dimension and difference angle measurement method for zero setting conformal calibration of a planar phased array. The method comprises the following steps: evaluating to obtain an interference information matrix according to a block matrix and received data; obtaining a beam pointing Taylor sum weight vector and a direction/pitch Bayliss difference weight vector through Taylor and Bayliss functions; obtaining a direction/pitch full-dimension sum self-adaptive weight vector through a zero setting conformal calibration algorithm; obtaining a direction/pitch sum and difference beam directional diagram, and direction/pitch full-dimension sum beam output and difference output through the self-adaptive weight vector and the difference weight vector; obtaining a direction/pitch difference ratio sum resolvable angle curve and a direction/pitch difference ratio sum output value; counting the number of inflection points of the direction/pitch difference ratio sum resolvable angle curve, and adopting a nearest method to obtain a target direction/pitch angle estimation vector; and calculating to obtain a CAPON spectrum of a direction/pitch angle estimation value, searching a direction/pitch angle combination corresponding to a maximum value of the CAPON spectrum, and obtaining a target direction/pitch angle estimation value.

Methods, apparatuses, and systems are presented for performing data encoding involving receiving a sequence of data bits, encoding the sequence of data bits in accordance with a parity check matrix (H-matrix) to generate a sequence of encoded bits, wherein the H-matrix is capable of being partitioned into a first matrix and a second matrix, the first matrix being a dual-diagonal matrix, the second matrix comprising one or more vertically stacked sub-matrices, each sub-matrix consisting of a plurality of columns, each column having a column weight of no more than 1, wherein the second matrix is capable of being expressed as a product of a parity check matrix, an interleaver permutation matrix, and a repeat block matrix, and the interleaver permutation matrix satisfies a clash-free interleaver constraint, and outputting the sequence of encoded bits.

A system and method of cancelling downlink inter-channel interference between a first selected UE in one sector of a cell and a second selected UE in a second sector of said cell by using intra-eNB DL CoMP is described. UEs which are affected by ICI are identified in sectors which are adjacent to each other in the cell. A downlink CoMP operational set is formed for the identified UEs. Channel matrix blocks are estimated for the first and second selected UEs. The channel matrix blocks are representations of the channel characteristics between the first and second UE, in their respective sectors and the serving base station. The estimated channel matrix blocks are then loaded into a cross-sector channel block matrix. The inverse values of the channel matrix blocks are then calculated to form a sector specific cross-sector pre-coding matrix which is used to enable interference cancellation.

The invention discloses a sparse representation-based deblocking method, which mainly solves the problem of the presence of a blocking effect in a block discrete cosine transform (BDCT) compressed image. The method comprises the following implementation steps of: (1) selecting a clean training image set and training a general dictionary with a kernel singular value decomposition (KSVD) algorithm and a batch processing orthogonal matching pursuit algorithm; (2) compressing a test image by controlling a quality factor during joint photographic experts group (JPEG) compression so as to obtain a JPEG compressed image; (3) calculating the noise standard deviation of the JPEG compressed image; (4) automatically estimating an error threshold according to the quality factor and the noise standarddeviation; (5) constructing an image block matrix of the JPEG compressed image so as to obtain a de-noised sparse representation matrix; and (6) obtaining a deblocking result image by using the general dictionary and the sparse representation matrix. Compared with the prior art, the invention has the advantages that: a higher or similar peak signal to noise ratio can be obtained, the visual effect of a deblocked image is good, computation complexity is low, and a blocking effect in a BDCT compressed image can be eliminated.

The invention relates to an improved GSC self-adaptive speech enhancement method. An improved GSC self-adaptive speech enhancement system is used, a weight coefficient of a post filter is estimated based on the signal received by a microphone, incoherent noises in the signal are removed by using the post wiener filter, a self-adaptive blocking matrix is used for replacing a fixed blocking matrix in a conventional GSC structure so as to better block target signals and reduce cancellation of the target signals, the iterative mode of a self-adaptive algorithm is improved, the convergence rate and steady-state misadjustment signals and filter noise interference signals are balanced, and based on the characteristics of easy noise removal, small amount of calculation and the like of a spectral subtraction method, the possible residual noise signals can be further removed by using an improved spectral subtraction method. According to the invention, the method is capable of self-adaptive speech enhancement of signals at any angle and has a certain robustness for speech signal enhancement under a random environment, and the subsequent spectral subtraction method can be used for further removing the residual noises and improving the denoising capability of the entire system.