56results about How to "Easy to implement in parallel" patented technology

The invention provides a gray scale image fitting enhancement method based on local histogram equalization. The gray scale image fitting enhancement method has advantages of improving gray scale image contrast and detail information and eliminating block effect and over-enhancement. The gray scale image fitting enhancement method comprises the steps of performing segmental linear transformation on a gray scale image in an overwide dynamic range, obtaining the gray scale image in an appropriate dynamic range, dividing an image gray scale distribution interval to two segments to multiple segments, adjusting the gradient of a segmenting point and a transformation line of each image gray scale distribution interval, performing expansion or compression on a random gray scale interval; performing subblock part overlapping histogram equalization on a transformation result, obtaining the transformation function of the current subblock through performing weighted summation on a subblock transform function in the neighborhood, performing histogram equalization processing on the current subblock by means of the transformation function; and performing nonlinear fitting on the gray scale map after histogram equalization, and performing histogram distribution correction on the gray scale image after subblock part overlapping histogram equalization.

The invention relates to a transmission line audible noise prediction method based on BP neural network optimization. The method includes acquiring and preprocessing data parameters, establishing a BP neural network prediction model of the data parameters, performing optimization training on the BP neural network by the ant colony algorithm, and utilizing the network to predict the transmission line audible noise. The method has high prediction accuracy, fine convergence and high stability, the problem that the BP neural network falls into local minima is avoided, and great guidance significance is provided for transmission line audible noise prediction and audible noise reducing development.

The invention relates to a method for predicting radio disturbance of an electric transmission line by using an improved BP (back propagation) neural network. The method comprises the following steps of acquiring and preprocessing data parameters; establishing a BP neural network predicting model of the data parameters; performing optimal training on the BP neural network by using a genetic algorithm and a simulated annealing algorithm; and predicting radio disturbance of the electric transmission line by using the network. The method is high in prediction precision, high in convergence and high in stability; the problem that the BP neural network is caught in a local minimum point is solved; and the method has a great guiding significance on prediction of radio disturbance of the electric transmission line and research on reduction of radio disturbance.

The invention discloses an H.264 / AVC standard-based intra-frame prediction mode selection method which mainly solves the problems of complex intra-frame mode selection and difficulty in realizing hardware in parallel of the traditional H.264 / AVC standard. The H.264 / AVC standard-based intra-frame prediction mode selection method comprises the steps of: firstly, carrying out primary selection on all prediction modes of a 4*4 subblock and a 8*8 subblock by using an SATD (Sum Of Absolute Transformed Difference) to obtain four candidate prediction modes; carrying out rate distortion optimization (RDO) on the four candidate modes to select an optimal prediction mode; then replacing DCT with KL transformation during transformation; and finally, carrying out code rate estimation by using CAVLC (Context-based Adaptive Variable Length Coding) instead of CABAC (Context-Based Adaptive Binary Arithmetic Coding). According to the invention, the speed of intra-frame mode selection of the H.264 / AVC standard is increased, hardware is easily realized rapidly and in parallel in the intra-frame mode selection of the H.264 / AVC standard, and the H.264 / AVC standard-based intra-frame prediction mode selection method can be used in an intra-frame prediction process in the H.264 / AVC standard.

A gold code generator is described comprising two pairs of linear feedback shift registers, the seed values for the second pair of linear feedback shift registers are different from the seed values for the first pair of linear feedback shift registers. The second seed values are calculated from the first seed values. The use of this second pair of linear feedback shift registers prevents the need to use a wide span of taps to the linear feedback shift register to produce output bits. By using two pairs of linear feedback shift registers, a parallel output implementation can be produced in which multiple output bits are produced in a single clock cycle.

The invention discloses an FPGA-based thin film rolling tension control system and method. The system comprises a tension signal collection module, a thin film rolling tension control module, a thin film rolling module and an upper computer operation panel. A programmable logic device FPGA controls a chip to achieve a fuzzy PID control algorithm, the speed controlled quantity of a brushless direct current motor is obtained, a double closed-loop control algorithm of the brushless direct current motor is combined, then signals are controlled to be correspondingly changed and adjusted when tension changes in the rolling process of a thin film, and constant control over the tension in the rolling process of the thin film can be achieved through real-time collection of tension of the thin film by utilization of the fuzzy PID control algorithm; the development cost is low, the integration level of the system is high, the steady state performance is good, the response speed is high, the control precision is high, and adaptability is high.

The invention provides a fluid-solid coupling method based on smoothed-particle hydrodynamics (SPH) and nonlinear finite elements. The method comprises the following six steps: at a collision detection stage, detecting collision information of fluid particles with a finite element network; at an agent particle generation stage, generating collision agent particles presenting a finite element model according to the collision information for processing the collision between a fluid and a solid; at a coupling force calculation stage, calculating force generated by collision between agent particles and fluid particles according to the position and speed relations of the agent particles and the fluid particles; at a coupling force allocation stage, controlling the position relation of the agent particles and the finite element model and allocating the coupling force to a finite element stress model; at a position and speed updating stage, driving the position and speed update of the finite element model and a fluid particle model according to the calculated coupling force; at a non-penetration modification stage, modifying penetration fluid particles according to an updated position.

The invention discloses a target tracking method of a passive multi-sensor based on layered particle filtering. A sub-state including an azimuth angle, a change ratio of the azimuth angle, a change ratio of a logarithm radial distance and auxiliary parameters and a sub-state Psi including the logarithm radial distance are constructed to realize a structure of hierarchical filtering through rewriting a system equation of a logarithm polar coordinate and adding the auxiliary parameters indicative of process noise intensity and radial distance ratio, wherein a first layer updates the auxiliary parameters in a second layer by means of sequential importance sampling method according to observation information of various sensors; Psi is iterated and updated; and the auxiliary parameters are combined to obtain estimation of the target state; finally a fusion output result of the target state is obtained according to an optimal information fusion method. Values of the auxiliary parameters can be estimated in real time by using a method of the layered particle filtering, and errors introduced by a maximum value of the noise intensity in the use process of the filtering is avoided, such that the problem of the target tracking can be effectively solved under the conditions of unknown process noise intensity and unmeasured distance.

The invention discloses a motor imagery EEG pattern recognition method based on the time-frequency parameter optimization of an artificial bee colony. The method comprises the steps of conducting the leads selection based on the linear decision rule, selecting time-domain and frequency-domain optimal parameters based on the artificial bee colony algorithm, extracting features based on the common spacial pattern algorithm, and finally classifying features based on the linear discriminant analysis algorithm. The result of the method shows that, a lead channel of larger inter-class distinction degree can be effectively selected based on the lead selection algorithm. At the same time, based on the time-frequency parameter optimization algorithm of the artificial bee colony, a time window and a frequency band of larger inter-class distinction degree can be automatically selected, so that a better classification effect is obtained. The method is capable of effectively recognizing different motor imagery modes. Compared with the traditional parameter manual selection method and the frequency-domain parameter automatic selection algorithm, global optimal parameters can be automatically searched in both time domain and frequency domain at the same time based on the above method. Therefore, the feature extraction and feature classification effect for motor imagery EEG signals is improved.

The invention discloses a multi-dimensional parameter pre-sorting method for radar pulse signals, and aims at providing a method which is fast and rapid, has small computing amount and can realize efficient pulse pre-sorting. The multi-dimensional parameter pre-sorting method is implemented by the technical schemes of: dividing a pulse into multiple groups of pulse trains for dense overlapping pulses output by a receiver, and counting a pulse number corresponding to each CF, PW and DOA to draw a histogram; utilizing the multi-dimensional histogram to count parameter distribution characteristics, and automatically grouping pulse data, so as to realize pre-sorting of radar pulses; and utilizing orthogonality of the radar pulses in DOA, CF, and PW pre-sorting spaces to convert the multi-dimensional histogram statistics into a plurality of one-dimensional histogram when making multi-dimensional histogram statistic, and carrying out parameter estimation successively; and looking for discontinuous regions in parameter distribution according to the parameter statistical result, and regarding GAP regions as break points of the corresponding parameters, thereby realizing automatic classification of different parameters.

The invention discloses a biquadratic B-spline local interpolation-based image scaling algorithm. The core of the algorithm is to perform interpolation description on 16 pixels in a local 4*4 area of a source image by adopting a biquadratic B-spline function, and realizes the scaling of an image by performing resampling on the function. The algorithm avoids the problem of phase distortion caused by the conventional quadratic interpolation method, and can achieve an effect similar to a bicubic algorithm on vision effect. The algorithm is a localized algorithm, each processing unit and the utilized pixel resource are mutually independent, and the processing processes thereof have very good parallelism, so the algorithm can be accelerated in a CPU. Therefore, the efficiency of the algorithm is improved on the basis of ensuring the effect of the bicubic algorithm, and the real-time requirement on image processing is met.

The invention provides a novel multi-angle SAR image fusion imaging algorithm being a multi-angle SAR image fusion method with local image index optimization. According to binary segmentation results of a layover zone and a non-layover zone of image pixels, paired crossed classification and fusion processing, like larger value taking, smaller value taking, and criterion interchanging, is carried out on a layover zone image and a non-layover zone image, thereby eliminating the adverse influence on SAR image fusion by layover and shadow phenomena effectively. Meanwhile, image quality evaluation is carried out on each image after fusion processing, a local image index of each image is calculated by using a sliding window by taking each pixel as center, and a final fusion processing result Iopt based on a local image index optimization criterion. Therefore, the image index of the fusion processing result Iopt can be guaranteed not only to be optimal globally but also to be optimal in a neighbor region by using any pixel as center.

The invention discloses a 192 bit key expansion system and method based on an AES (Advanced Encryption Standard), which are used for mainly solving the problems of low efficiency and high power consumption of the traditional 192 bit AES encryption algorithm in a key expansion process. The method comprises the steps of: storing an initial key in a first turn of key expansion, taking the front four lines as turn keys of the turn of key expansion, carrying out word circulation, byte replacement and bitwise XOR operation; using results as the turn keys and storing the results in a local register and an external memory unit, reading by an encryption process; and finally, repeating the operation for the turn keys obtained in the former turn in each turn until all 12 turn keys are obtained, and ending the key expansion. The 192 bit secret key expansion system and method can be compatible with the instantaneity of the key expansion and the reusability of the turn keys, realize high-efficiency and low-consumption key expansion, and is suitable for a 192 bit key expansion process of the AES encryption algorithm.

The invention discloses a parallel matrix inversion method for a multi-antenna system, which is used for detection and self-adaptive transmission of a receiving end in the multi-antenna system. The parallel matrix inversion method comprises the following steps: solving a rotation matrix; replacing a conventional Givens rotation matrix with a high-speed Givens rotation matrix to halve the multiplication operand, and further reducing the square-root complexity by adopting limit and Tayior expansion methods; achieving rotation transformation; adopting an iterative updating method to calculate the mold value of column vector so as to remarkably improve the algorithm efficiency in comparison with the method of directly calculating the vector mold value; conducting repeated iteration; and achieving matrix inversion. The parallel matrix inversion method has very high efficiency and excellent parallelism, and can be used for matrix inversion in the fields of wireless communication, signal processing, numerical calculation and the like.

The invention discloses a 128 bit secret key expansion system and a method based on an AES (Advanced Encryption Standard), which mainly solve the problems of low efficiency and high power consumption in a 128 bit secret key expansion process of an existing AES encryption algorithm. A realization process comprises the following steps of: storing an initial secret key at a first round of secret key expansion; using the stored initial secret key as a round secret key of the round; carrying out word circulation, byte substitution and bitwise XOR operation on the round secret key; storing the round secret key in a local register and an external storage unit simultaneously so as to be read by an encryption process; repeating the operation on the round secret key obtained through the former round; and finishing secret key expansion until 10 round secret keys are obtained. According to the 128 bit secret key expansion system and method based on the AES, the instantaneity of secret key expansion and the reusability of the round secret key can be ensured, the high efficiency and the low power consumption of secret key expansion are realized; and the system and the method can be applied to the 128 bit secret key expansion process of the AES encryption algorithm.

The invention discloses a harmonic noise suppression method based on waveform morphology sparse modeling. The harmonic noise suppression method includes the steps of 1) constructing Chirplet transform according to waveform morphological characteristics of harmonic noise in seismic records acquired via vibroseis slip sweep, and constituting an over-complete dictionary with continuous wavelet transform; 2) quickly implementing Chirplet positive and inverse transforms; 3) determining Chirplet transform parameters based on time-frequency distribution characteristics of correlated data; and 4) according to the starting frequency of a reference scanning signal, determining the filter cutoff frequency of the harmonic noise to achieve fidelity separation of an effective signal from the harmonic noise. The invention solves the problem of harmonic noise interference in the seismic data acquired via vibroseis slip sweep, and achieves the purpose of improving the signal-to-noise ratio of the seismic data. The method of the invention determines the Chirplet transform according to the time-frequency distribution characteristics of the harmonic noise, and ensures the sparseness; the fast implementation of the Chirplet transform guarantees the efficiency of transform, and the correction coefficient guarantees the accuracy of inverse transform reconstruction; and the Chirplet transform parameters are automatically determined according to the data drive, a strong adaptability is gained and single-channel calculation facilitates parallel processing.

The invention discloses KPCA-LA-RBM-based transmission and transformation project cost prediction method, and belongs to the electric digital data processing technology field. The method includes suchsteps as selecting and preprocessing data of each sample and obtaining key influencing factor set; the training set is composed of several groups of pre-processed sample data, and the training set isused to train KPCA-LA-RBM combination model; using the remaining data as the test set, according to the trained combination model, using the test set to predict the trained model, the final prediction results are obtained. The invention selects the first four principal components as input vectors of the combination model according to the principal component accumulative variance contribution rate, which improves the calculation efficiency of the model under the condition of guaranteeing the prediction accuracy. The combined model proposed by the invention can effectively reduce the error caused by a single model, improve the prediction accuracy, improve the generalization ability and robustness, and is suitable for the cost prediction of transmission and transformation projects.

The invention provides a lightweight license plate recognition method and system based on a full convolutional network, and the method comprises the steps: collecting and marking license plate samplepictures, and dividing the pictures into a training set and a test set according to a preset proportion; building a lightweight license plate recognition network model based on full convolution; determining a multi-task learning framework and a set loss function; training a full convolution-based lightweight license plate recognition network model by using the license plate sample pictures of thetraining set until the error of the loss function is smaller than a preset value; selecting full-convolution-based lightweight license plate recognition network model parameters stored in different stages in the training process, testing the comprehensive performance of a full-convolution-based lightweight license plate recognition network under different parameters by using license plate sample pictures in a test set, and fixing the parameter with the highest accuracy as a final parameter of the model. According to the method, the sequence information is modeled by adopting the full convolutional network, so that the model is easy to realize in parallel, fewer computing resources are required in the reasoning stage, and the time delay is lower.

The invention relates to a coding technology of low-density parity-check codes LDPC, in particular to a coding method and a coding device of LDPC codes for carrying out staggered shift change of source bit vectors. The coding method and the coding device deform a circulating storage matrix into a staggered storage matrix which is fixed in a read-only memory, and the operation for generating the matrix is changed to the staggered shift operation of the original source bits, thereby being conductive to the parallel realization of hardware. The coding method and the coding device can improve the processing speed of a decoder of the LDPC codes and can reduce the complexity of the realization, etc.

The invention discloses the designing of current-statistical-model-based probability hypothesis density particle filter and the current-statistical-model-based probability hypothesis density particle filter. An observed value of the filter is connected with the first input end of an updating circuit. The first input end of a prediction circuit is connected with the first output end of a state estimation circuit, and the output end of the prediction circuit is connected with the second input end of the updating circuit. The output end of the updating circuit is connected with the input end of the resampling circuit. The first output end of the resampling circuit is connected with the second input end of the prediction circuit, and the second output end of the resampling circuit is connected with the input end of the state estimation circuit. By the invention, a hardware circuit realization scheme for the current-statistical-model-based probability hypothesis density particle filter is designed based on the theory of the current-statistical-model-based probability hypothesis density particle filter, and simulation results show that the tracking performance of the designing of the current-statistical-model-based probability hypothesis density particle filter and the current-statistical-model-based probability hypothesis density particle filter is similar to that of theoretical analysis and can be used for tracking problems about maneuvering multi-target movement in a clutter environment.

The invention discloses an expansion system and a method for 256-bit keys based on an advanced encryption standard (AES), and mainly solves the problems of low efficiency and high power consumption in a key expansion process in the existing 256-bit AES. The process includes storing an initial key in the first round of key expansion, taking the first four rows as a round key of the key expansion in the first round, and performing word cycle, byte replacement, and bitwise XOR operations; executing a new byte replacement and a secondary bitwise XOR operation according to serial number n; taking results of the two bitwise XOR operations as a round key and storing in a local register and an external storage cell for the usage of encryption process reading; and repeating the operations for round keys obtained in a former round in following rounds until 14 round keys are obtained, and finishing key expansion. According to the expansion system and the method for the 256-bit keys, instantaneity of key expansion and reusability of round keys are achieved simultaneously, key expansion with high efficiency and low power consumption is achieved, and the expansion system and the method are suitable for key expansion processes of the 256-bit AES.

The invention discloses a code rate estimation method for video coding. The code rate estimation method for video coding comprises S1, dividing a transform unit into a plurality of 4*4 coefficient subblocks and extracting feature parameters of the coefficient subblocks; S2, grouping the coefficient subblocks according to the feature parameters; S3, calculating the coding length Rcoeff generated by the coefficient subblocks according to grouping; S4, calculating an optimal coding total length Ropt of the coefficient subblocks according to the number of coefficients utilizing Golomb coding; S5, calculating a Golomb coding compensation value Rc according to the greatest coefficient utilizing the Golomb coding; S6, repeating S1-S5 and calculating the coding total length Rtotal of all the coefficient subblocks; S7, calculating LastXY coding lengh Rlastxy; S8, calculating the auxiliary information coding total length Rside; S9, calculating a code rate estimated value R of the transform unit according to the Rtotal, Rlastxy and the Rside.

The invention relates to a decoding method used for low-density parity-check codes in wireless communicating system, in particular to a method for improving the code performance of standard belief propagation. The invention carries out parallel and serial hybrid decoding based on check nodes and bit nodes and traverses the bit nodes in each group in turn after initializing the posterior information of the bit nodes through grouping the bit nodes of variables / .m, MNM and grouping parallel to choose a check node set connected with the currently traversed bit nodes, then finds out a bit node set connected with the check node sets, transfers the information of all the bit nodes besides the currently traversed bit nodes in the bit node set to the currently traversed bit nodes through the check node set firstly and then transfers the information of the currently traversed bit nodes that have been updated to all other bit nodes through the check node set. The method intensifies the broadcasting speed and extent of new information, reaches a reasonable decoding time lag through the parallel between the check nodes and reaches an ideal decoding performance through the serialization between the bit nodes.

The invention belongs to the technical field of integrated circuits, and relates to a method and a device for solving multi-core parallel minimum cost flow applied in integrated circuit design automation. The method and device solves the minimum cost flow based on a nondeterministic affair model, makes realization of algorithm design and parallelization easy and ensures the accuracy of an algorithm in theory; and the method reduces the expenditure of the creation and release of a thread and thread dispatching by utilizing thread pool and thread binding technology, and improves the parallel efficiency. The invention comprises an input unit, an output unit, a program storage unit, an external bus, a memory, a storage management unit, an input / output bridging unit, a system bus and a multi-core processor. In the invention, the speed of solving the minimum cost flow is increased by utilizing the technology of the multi-core processor, and the device is used for devices with any numbers of processor cores and has the excellent flexibility. The method and the device can be used for realizing multi-core parallelization for solving problems on integrated circuit design automation.

The present invention provides a novel multi-angle SAR (Synthetic Aperture Radar) image fusion imaging algorithm, in particular, a visibility classification-based multi-angle SAR image fusion method. According to the method, the multi-angle of view visibility LI of non-overlap pixels of each azimuth angle-of-view image is calculated according to the overlap region and non-overlap region of image pixels; and sophisticated classification of the image pixels is automatically realized according to the visibility indicator; and the contributions of different angle-of-view observation results to the image pixels can be distinguished, and therefore, angles making contributions are utilized to realize fusion, and the defect of incapability of a simple fusion method to obtain an optimal fusion result can be eliminated. The image fusion method of the invention has the advantages of simple principles, good fusion quality and simple calculation of the visibility indicator, high classification accuracy and simplicity in engineering realization.

The invention relates to a method and a device of bit collection and interlacing for a mixed automatic retransmission request. The method is characterized in that defining a bit collection and interlacing rectangle as a before data part and an after data part, dividing the data bit into eight sequences and respectively writing in eight memory units; and subsequently reckoning the eight memory units as an integral memory to process; and finally carrying out bit scrambling and physical signal channel mapping process. A device utilizing the method comprises a bit separation module, wherein the output end of the bit separation module is connected with a three-way rate adaption module; the output end of the three-way rate adaption module is connected to a bit collection module; the output end of the bit collection module is connected to the eight storage units; the control end of the eight storage units is connected with a bit collection and output control unit; and the output ends of the storage units are connected to the input end of a rear level bit scrambling and physical signal channel mapping processing unit. Hence, the cache between the rate adaptation module and the bit collection module is not required to increase to realize the bit collection and interlacing of the mixed automatic retransmission request.

The invention provides a rapid backward projection imaging method based on offline projection, and a rapid time-domain imaging idea based on an offline projection method is provided aimed at solving the problem that a time-domain algorithm is high is computational complexity and long in time consumption. Aimed at an application scene determined according to the geometric relation of imaging and known in advance, an offline calculation manner is used to calculate and store variables needed in the backward projection algorithm in advance, the flexibility and high precision of the time-domain algorithm are maintained, and in the imaging process, the calculation steps can be reduced substantially and time consumption of imaging is reduced.

The invention discloses an FPGA-based thin film rolling tension control system and method. The system comprises a tension signal collection module, a thin film rolling tension control module, a thin film rolling module and an upper computer operation panel. A programmable logic device FPGA controls a chip to achieve a fuzzy PID control algorithm, the speed controlled quantity of a brushless direct current motor is obtained, a double closed-loop control algorithm of the brushless direct current motor is combined, then signals are controlled to be correspondingly changed and adjusted when tension changes in the rolling process of a thin film, and constant control over the tension in the rolling process of the thin film can be achieved through real-time collection of tension of the thin film by utilization of the fuzzy PID control algorithm; the development cost is low, the integration level of the system is high, the steady state performance is good, the response speed is high, the control precision is high, and adaptability is high.

The invention provides a method for estimating top-n cardinal number data in high-speed data flow. The method comprises the following steps that a HyperLogLog Sketch matrix data structure is defined and set as S, the width is m, the height is n, each element is an HLL counter, and correspondingly, n mutually independent Harsh functions with the Harsh value being 1-m are set as f2, f2, ..., fn; when new data D appears, classification is carried out according to services, and the data D is set as the type X; xi is calculated to be equal to fi(X), wherein I is equal to 1, 2, ..., n; D is reckoned in the HLL counter in S(1, x1), S(2, x2), ..., S(n, xn), updated cardinal numbers, namely, Y1, Y2, ..., Yn are obtained, and an estimated cardinal number Y is obtained; the data type X and the estimated cardinal number Y are updated into top-n. The method is simple and convenient to use, is achieved parallel through hardware, can be used for calculating the cardinal number of the data type without storing the data type and has good safety.

The invention discloses realization of keyword optimization based on a fuzzy c-mean algorithm of an ant colony. A realization process comprises the steps of determining core keywords according to enterprise businesses, and searching for data items corresponding to the keywords, wherein the data items include a domestic monthly search volume, a competition degree, an estimated cost per click (CPC) and the like; performing dimension reduction processing on a keyword set, namely, increasing the number of home pages and the total number of search pages to reduce five dimensions to four dimensions, wherein each keyword is represented by a five-dimensional vector; performing clustering on the keywords based on the C-mean algorithm of the ant colony; and according to specific enterprise situations, selecting a proper keyword optimization policy. According to the realization process, an activity law of the ant colony and a k-means clustering algorithm are combined, so that data is more scientific and an obtained result is more intuitive; and meanwhile, the running time complexity is low, the processing speed is higher, the premature convergence is avoided, and the keyword ranking can be quickly increased, so that an ideal website optimization goal is achieved.