38 results about "Distributed source coding" patented technology

The present invention is a method and apparatus for protection of various items against counterfeiting using physical unclonable features of item microstructure images. The protection is based on the proposed identification and authentication protocols coupled with portable devices. In both cases a special transform is applied to data that provides a unique representation in the secure key-dependent domain of reduced dimensionality that also simultaneously resolves performance-security-complexity and memory storage requirement trade-offs. The enrolled database needed for the identification can be stored in the public domain without any risk to be used by the counterfeiters. Additionally, it can be easily transportable to various portable devices due to its small size. Notably, the proposed transformations are chosen in such a way to guarantee the best possible performance in terms of identification accuracy with respect to the identification in the raw data domain. The authentication protocol is based on the proposed transform jointly with the distributed source coding. Finally, the extensions of the described techniques to the protection of artworks and secure key exchange and extraction are disclosed in the invention.

The invention provides a compression sensing technology-based method for distributed type information source coding. The advantages of the CS technology and the sparse characteristics of video images are adopted in a process of realizing the distributed type information source coding DSC to form a novel distributed type information source coding method, namely in the corresponding operational steps of the DSC process, the CS technology is adopted to process the video image data and execute the corresponding recovery processing which comprises using the CS operation and the sparse reconstruction of the CS to replace the data sampling and the DCT transformation operation and the DCT inverse transformation in the conventional information source coding respectively so as to use much less measuring data to reconstruct a video image source, reduce a sampling speed ratio and memory burden of the system, reinforce the robustness of the system and realize the construction of the distributed type information source coding in three different structures. The method has the advantages of reducing the sampling rate and the operating complexity of the system, obviously reducing the workload of the data sampling and relative processing and the necessary memory space, improving the robustness of the system and reducing the speed rate of data transmission.

Characters of the invention are that video sequence codes of each viewpoint are divided into I frame, P frame and SW frame. In each image set, first frame is I frame, and then multiple P frames and SW frames. H.264 standard is adopted to encode and decode I frame, and P frames, and SW frames are as viewpoint switch frames. When encoding SW frames, the invention carries out motion estimation and entropy coding for SW frame and previous frame in sequence so as to obtain bit stream of motion vectors. Carrying out displacement estimation and entropy coding for SW frame and video frame at same position of adjacent viewpoint, the invention obtains bit stream of displacement vectors between viewpoints. After carrying out transforms, quantification, dividing into bit planes, and channel encoding, the invention obtains bit stream of verification information. At decoding end, SW frames are rebuilt by the invention.

The invention discloses a distributed compressed forwarding system of a Gaussian source and an optimization method of the system. In the system, the source sends analog Gaussian signals, distributed compressed coding is carried out in relays, and digital transmission is carried out. A theoretical analysis frame of the system is provided by considering that reception signal to noise ratios of different relays satisfy certain proportional relation and a reception end has different receiving signal to noise ratios for different relay signals in an additive white Gaussian noise channel. For a multi-relay distributed source coding problem, a CEO theory is used to establish a rate distortion function of a multi-relay network, transmission rates of the multiple relays are obtained, a Shannon channel capacity theory is combined, the compressed transmission rates of the relay are linked to the capacity of the channel from the relays to the channel, an optimized design equation of the system is provided, and an optimal solving algorithm is provided. Under the condition that the total power is limited, power distribution is carried out between the source and the relay network based on the signal to noise ratios, and the signal to noise ratio performance of the reception end of the system is maximized.

The invention relates to hyperspectral image compressing and restructuring device and method. The method includes steps of firstly, subjecting each spectrum range of a hyperspectral image to three-level two-dimensional 9/7 integer discrete and lifting wavelet transformation; organizing wavelet coefficients to sections and blocks through CCDSD-IDC, encoding section header information and DC (direct current) coefficient initializing and AC (alternating current) coefficient depth of each block, extracting AC coefficient and residual DC coefficient bit plane simultaneously, and finally subjecting the AC coefficient and residual DC coefficient bit plane to distributed signal source encoding by a Slepian-Wolf encoder based on LDPC (low density parity check). The hyperspectral image compressing and restructuring device and method have the advantages that by adopting distributed signal source encoding strategy, complexity of the integral encoder is lowered, and the method is easy to implement through processors such as FPGA (field programmable gate array) and DSP (digital signal processor).

The invention discloses a distributed compression forwarding system of a multi-relay network under a Gaussian source and a design method. A source sends a simulated Gaussian signal, performs distributed source coding at a relay node, and performs the digital transmission. Since the simulated signal is transmitted, the index for system performance evaluation is the signal-to-noise ratio. A theoretical analysis frame of the system is proposed in consideration that the sending power of each relay node is the same and all noises powers are the same under an additive Gaussian white noise channel, a rate-distortion function of the multi-relay network is established by use of the CEO theory, and the connection between a sensing network and a digital communication network is established in combination with a Shannon channel capacity theory. An optimal design theory method of the system is proposed, and the power distribution is performed between the sensing network and a communication network based on the signal-to-noise rate under a condition that the total power is limited, thereby enabling the signal-to-noise rate to achieve the maximum. The theoretical analysis and the simulation result show that the optimal compression forwarding system provided by the invention can provide better signal-to-noise ratio in comparison with an amplification forwarding system.

The invention discloses a distributed source code detection system and the method based on the serialized intermediate representation, the system functions in the the following steps: coordinating the nodes, determining the programming language and the edition of compiler used for the source code to be detected, using the integrated compiler to compile the source code to be detected and generating the intermediate representation, serializing the intermediate representation on the basis of code translation unit to create the relevant intermediate representation files and independent intermediate representation files; representing the detection node, selecting at least one rule bank from multiple rule banks based on the detection type, and using at least one rule bank to detect at least one independent intermediate representation file to confirm the detection result of at least one independent code; using the relevant detection nodes based on the relationships of detection relevance and the detection results of at least one independent code to detect the relevant intermediate representation file in order to determine the detection results of the relevant code.

The invention provides a hyperspectral image compression method based on deep learning and distributed information source coding. The hyperspectral image compression method comprises the following steps: step 1, constructing a hyperspectral image saliency detection deep learning network model; step 2, extracting spectral segment groups and key frames of a to-be-compressed hyperspectral image; 3, extracting the spectral band group local significance characteristics of the to-be-compressed hyperspectral image; 4, obtaining a global saliency mapping graph of the spectral segment group; step 5, obtaining a region of interest of the spectral band group of the hyperspectral image to be compressed; step 6, performing distributed compression on the region of interest of the spectrum segment group;7, obtaining a compressed code of the hyperspectral image. According to the method, the defect that the scene saliency deep representation problem is difficult to solve in the prior art is overcome,and the method has the advantage of accurately compressing useful information; the method overcomes the defect of low hyperspectral image compression efficiency in the prior art, and has the advantageof quickly realizing compression.

The embodiment of the invention discloses a signal decoding and coding method and a device; the signal coding method comprises a Wyner-Ziv frame and a key frame K for obtaining the original signal flow, wherein the frame K is transmitted to a signal receiving terminal and is decoded and reconstructed into a frame Y at the receiving terminal, and the frame Y is used as side information for decoding process of signals; a signal Xc is obtained according to the Wyner-Ziv frame, the correlation modeling between Xc and the frame Y is a virtual related channel, falling in with the formula Xc=Y+E, E is generated by a hidden Markov model (HMM) and is independent of Y; obtaining X through the quantization of a signal Xc; carrying out Raptor coding to X to obtain the coded signal Zm, so as to transmit to the signal receiving terminal. In the invention, the distribution type information source coding technology is adopted to transfer the coding complexity of a WSNs (wireless sensor networks) nodeto a decoding end, so as to reduce the compression computing energy consumption of the WSNs node.

Systems and methods are provided for transcompiling non-distributed source code for a nondistributed software program into a distributed software package for implementation on a distributed computing system. A transcompiler can identify loops within non-distributed source code written in a data-driven language. The transcompiler can generate MapReduce jobs using mapper keys based on grouping indicators associated with each of the loops. The MapReduce jobs can be linked together based on input-output connections of the loops in the non-distributed source code. Then, the transcompiler can generate a distributed software package including the generated MapReduce jobs to implement the same functionality as the non-distributed source code on the distributed computing system, thereby improving the speed of execution over very large datasets. The distributed software package can be optimized using machine learning searching algorithms. The distributed software package can also be optimized based on execution usage statistics.

A method for encoding a source image, wherein the source image includes a set of bitplanes of pixels, is disclosed. For each bitplane in a most to least significant order, the method include obtaining a list of significant pixels (LSP), a list of insignificant pixels (LIP), and a list of insignificant sets (LIS) according to a hierarchical ordering of the source image pixels; synchronizing the LSP, LIP and LIS of the source image with the LSP, LIP and LIS of a key image; constructing a temporary list of insignificant sets (TLIS) for the source image; and applying syndrome encoding to the LSP, LIP, and TLIS of the source image to obtain syndromes corresponding to magnitudes and signs of pixels in the source image, wherein the steps are performed in a processor.

The invention relates to an electric power system steady-state data compression method based on edge calculation, and the method comprises the steps: completing the steady-state data fusion of an electric power system under edge calculation through the stages of building a joint sparse model, building a sparse redundant dictionary, determining a measurement matrix, building a joint reconstruction algorithm and the like in combination with compressed sensing and distributed information source coding; and decomposing the obtained data fusion result to each scale level according to the resolution by utilizing a wavelet transform algorithm to obtain a high-frequency coefficient and a low-frequency coefficient, and outputting a compression result by adopting a lossless coding technology after processing the high-frequency coefficient through a threshold value. The steady-state data compression method is constructed in combination with an edge calculation method, the data of the power system are effectively compressed, and the storage space and the data transmission amount are reduced.

A method, apparatus, and system for providing distributed source coding techniques that improve data coding performance, such as video data coding, when channel errors or losses occur. Errors in the reconstruction of the data is eliminated or reduced by sending extra information. Correlation between a predicted sequence and an original sequence can be used to design codebooks and find the cosets required to represent the original image. This information may be sent over another channel, or a secondary channel.

The invention provides a method for designing LDPCA codes in an asymmetric structure distributed source coding system and relates to the technical field of source coding. Traditional LDPCA codes are still adopted for the compression rate ranging from 1/L to k/L, and the provided method is adopted for the compression rate ranging from (K+1)/L to 1. After the maximum degree of variable nodes is removed, a distribution character of redesigned optimum degree is as follows: lambda(x) = 0.3264x + 0.4254x2 + 0.1384x6 + 0.0794x7 + 0.0304x18. A belief propagation decoding method is adopted at the decoding end, and the maximum iteration number s 100. The difference of the method and a claude elwood Shannon can be still kept small in a high-speed region, and the LDPCA codes are greatly better than the traditional LDPCA codes. Good effect is obtained in a speed region Rx<[47/66,1] by adopting the method and approaches a Selpian-Wolf boundary, and the defect that the traditional LDPCA codes are only designed for the fixed speed is overcome. The method is characterized by selecting a maximum number of variable nodes and adopting high-compression-ratio optimum degree distribution design.

The invention relates to a method for anti-error encoding and decoding image based on the distributed source encodings, which includes: it decomposes the encoding image X by using Laplace pyramid to achieve a image with a low resolution and a residual image. Then, it encodes the image with a low resolution to achieve code stream B1 and directly discards the residual image. Then, it transforms X and divides the quantified value into sliced planes. Then, it carries out channel coding about each plane to achieve checking bit and multiplex them into checking bit flow B2. The decoding method is a reverse process of the encoding.

The invention discloses a system and method to optimize video performance in a wireless-dock with an ultra-high definition display. According to the invention, methods, devices, and computer program products are provided for using raw bitstreams and lossless distributed source coded (DSC) video to optimize video performance in wireless dock with ultra-high definition displays. In one aspect, a method of transmitting a video stream from a transmitting device to a wireless video display is described. The method includes determining a resolution of the wireless video display and a native resolution of a video stream, as well as a connection speed between the transmitting device and the wireless video display. Based on this information, the method selects a video compression format, choosing between raw video, DSC video, and high-efficiency video coded video. The method further transmits the video stream in the selected video compression format from the transmitting device to the wireless video display.