The paper deals with factorial experimental design models decoding.For the ease of calculation of the experimental mathematical models,it is convenient first to code the independent variables.When selecting independen...The paper deals with factorial experimental design models decoding.For the ease of calculation of the experimental mathematical models,it is convenient first to code the independent variables.When selecting independent variables,it is necessary to take into account the range covered by each.A wide range of choices of different variables is presented in this paper.After calculating the regression model,its variables must be returned to their original values for the model to be easy recognized and represented.In the paper,the procedures of simple first order models,with interactions and with second order models,are presented,which could be a very complicated process.Models without and with the mutual influence of independent variables differ.The encoding and decoding procedure on a model with two independent first-order parameters is presented in details.Also,the procedure of model decoding is presented in the experimental surface roughness parameters models’determination,in the face milling machining process,using the first and second order model central compositional experimental design.The simple calculation procedure is recommended in the case study.Also,a large number of examples using mathematical models obtained on the basis of the presented methodology are presented throughout the paper.展开更多
How to identify topological entities during rebuilding features is a critical problem in Feature-Based Parametric Modeling System (FBPMS). In the article, authors proposes a new coding approach to distinguish differen...How to identify topological entities during rebuilding features is a critical problem in Feature-Based Parametric Modeling System (FBPMS). In the article, authors proposes a new coding approach to distinguish different entities. The coding mechanism is expatiated,and some typical examples are presented. At last, the algorithm of decoding is put forward based on set theory.展开更多
Adaptive inverse control system can improve the performance of turbo decoding,and modeling turbo decoder is one of the most important technologies. A neural network model for the inverse model of turbo decoding is pro...Adaptive inverse control system can improve the performance of turbo decoding,and modeling turbo decoder is one of the most important technologies. A neural network model for the inverse model of turbo decoding is proposed in this paper. Compared with linear filter with its revi-sion,the general relationship between the input and output of the inverse model of turbo decoding system can be established exactly by Nonlinear Auto-Regressive eXogeneous input (NARX) filter. Combined with linear inverse system,it has simpler structure and costs less computation,thus can satisfy the demand of real-time turbo decoding. Simulation results show that neural network in-verse control system can improve the performance of turbo decoding further than other linear con-trol system.展开更多
Software fault positioning is one of the most effective activities in program debugging. In this paper, we propose a model-based fault positioning method to detect the faults of embedded program without source code. T...Software fault positioning is one of the most effective activities in program debugging. In this paper, we propose a model-based fault positioning method to detect the faults of embedded program without source code. The system takes the machine code of embedded software as input and translates the code into high-level language C with the software reverse engineering program. Then, the static analysis on the high-level program is taken to obtain a control flow graph（CFG）, which is denoted as a node-tree and each node is a basic block. According to the faults found by the field testing, we construct a fault model by extracting the features of the faulty code obtained by ranking the Ochiai coefficient of basic blocks. The model can be effectively used to locate the faults of the embedded program. Our method is evaluated on ST chips of the smart meter with the corresponding source code. The experiment shows that the proposed method has an effectiveness about 87% on the fault detection.展开更多
This paper presents an efficient VLSI architecture of the contest-based adaptive variable length code （CAVLC） decoder with power optimized for the H.264/advanced video coding （AVC） standard. In the proposed design...This paper presents an efficient VLSI architecture of the contest-based adaptive variable length code （CAVLC） decoder with power optimized for the H.264/advanced video coding （AVC） standard. In the proposed design, according to the regularity of the codewords, the first one detector is used to solve the low efficiency and high power dissipation problem within the traditional method of table-searching. Considering the relevance of the data used in the process of runbefore＇s decoding, arithmetic operation is combined with finite state machine （FSM）, which achieves higher decoding efficiency. According to the CAVLC decoding flow, clock gating is employed in the module level and the register level respectively, which reduces 43% of the overall dynamic power dissipation. The proposed design can decode every syntax element in one clock cycle. When the proposed design is synthesized at the clock constraint of 100 MHz, the synthesis result shows that the design costs 11 300 gates under a 0.25 μm CMOS technology, which meets the demand of real time decoding in the H.264/AVC standard.展开更多
Context-based adaptive binary arithmetic coding(CABAC) is the major entropy-coding algorithm employed in H.264/AVC.In this paper,we present a new VLSI architecture design for an H.264/AVC CABAC decoder,which optimizes...Context-based adaptive binary arithmetic coding(CABAC) is the major entropy-coding algorithm employed in H.264/AVC.In this paper,we present a new VLSI architecture design for an H.264/AVC CABAC decoder,which optimizes both decode decision and decode bypass engines for high throughput,and improves context model allocation for efficient external memory access.Based on the fact that the most possible symbol(MPS) branch is much simpler than the least possible symbol(LPS) branch,a newly organized decode decision engine consisting of two serially concatenated MPS branches and one LPS branch is proposed to achieve better parallelism at lower timing path cost.A look-ahead context index(ctxIdx) calculation mechanism is designed to provide the context model for the second MPS branch.A head-zero detector is proposed to improve the performance of the decode bypass engine according to UEGk encoding features.In addition,to lower the frequency of memory access,we reorganize the context models in external memory and use three circular buffers to cache the context models,neighboring information,and bit stream,respectively.A pre-fetching mechanism with a prediction scheme is adopted to load the corresponding content to a circular buffer to hide external memory latency.Experimental results show that our design can operate at 250 MHz with a 20.71k gate count in SMIC18 silicon technology,and that it achieves an average data decoding rate of 1.5 bins/cycle.展开更多
Intelligent video coding(IVC),which dates back to the late 1980s with the concept of encoding videos with knowledge and semantics,includes visual content compact representation models and methods enabling structural,d...Intelligent video coding(IVC),which dates back to the late 1980s with the concept of encoding videos with knowledge and semantics,includes visual content compact representation models and methods enabling structural,detailed descriptions of visual information at different granularity levels(i.e.,block,mesh,region,and object)and in different areas.It aims to support and facilitate a wide range of applications,such as visual media coding,content broadcasting,and ubiquitous multimedia computing.We present a high-level overview of the IVC technology from model-based coding(MBC)to learning-based coding(LBC).MBC mainly adopts a manually designed coding scheme to explicitly decompose videos to be coded into blocks or semantic components.Thanks to emerging deep learning technologies such as neural networks and generative models,LBC has become a rising topic in the coding area.In this paper,wefirst review the classical MBC approaches,followed by the LBC approaches for image and video data.We also discuss and overview our recent attempts at neural coding approaches,which are inspiring for both academic research and industrial implementation.Some critical yet less studied issues are discussed at the end of this paper.展开更多
The throughput gain obtained by linear network coding （LNC） grows as the generation size increases, while the decoding complexity also grows exponentially. High decoding complexity makes the decoder to be the bottle...The throughput gain obtained by linear network coding （LNC） grows as the generation size increases, while the decoding complexity also grows exponentially. High decoding complexity makes the decoder to be the bottleneck for high speed and large data transmissions. In order to reduce the decoding complexity of network coding, a segment linear network coding （SLNC） scheme is proposed. SLNC provides a general coding structure for the generation-based network coding. By dividing a generation into several segments and restraining the coding coefficients of the symbols within the same segment, SLNC splits a high-rank matrix inversion into several low-rank matrix inversions, therefore reduces the decoding complexity dramatically. In addition, two coefficient selection strategies are proposed for both centrally controlled networks and distributed networks respectively. The theoretical analysis and simulation results prove that SLNC achieves a fairly low decoding complexity at a cost of rarely few extra transmissions.展开更多
文摘The paper deals with factorial experimental design models decoding.For the ease of calculation of the experimental mathematical models,it is convenient first to code the independent variables.When selecting independent variables,it is necessary to take into account the range covered by each.A wide range of choices of different variables is presented in this paper.After calculating the regression model,its variables must be returned to their original values for the model to be easy recognized and represented.In the paper,the procedures of simple first order models,with interactions and with second order models,are presented,which could be a very complicated process.Models without and with the mutual influence of independent variables differ.The encoding and decoding procedure on a model with two independent first-order parameters is presented in details.Also,the procedure of model decoding is presented in the experimental surface roughness parameters models’determination,in the face milling machining process,using the first and second order model central compositional experimental design.The simple calculation procedure is recommended in the case study.Also,a large number of examples using mathematical models obtained on the basis of the presented methodology are presented throughout the paper.
文摘How to identify topological entities during rebuilding features is a critical problem in Feature-Based Parametric Modeling System (FBPMS). In the article, authors proposes a new coding approach to distinguish different entities. The coding mechanism is expatiated,and some typical examples are presented. At last, the algorithm of decoding is put forward based on set theory.
文摘Adaptive inverse control system can improve the performance of turbo decoding,and modeling turbo decoder is one of the most important technologies. A neural network model for the inverse model of turbo decoding is proposed in this paper. Compared with linear filter with its revi-sion,the general relationship between the input and output of the inverse model of turbo decoding system can be established exactly by Nonlinear Auto-Regressive eXogeneous input (NARX) filter. Combined with linear inverse system,it has simpler structure and costs less computation,thus can satisfy the demand of real-time turbo decoding. Simulation results show that neural network in-verse control system can improve the performance of turbo decoding further than other linear con-trol system.
基金Supported by the National Natural Science Foundation of China(61303214)the Science and Technology Project of China State Grid Corp(KJ15-1-32)
文摘Software fault positioning is one of the most effective activities in program debugging. In this paper, we propose a model-based fault positioning method to detect the faults of embedded program without source code. The system takes the machine code of embedded software as input and translates the code into high-level language C with the software reverse engineering program. Then, the static analysis on the high-level program is taken to obtain a control flow graph（CFG）, which is denoted as a node-tree and each node is a basic block. According to the faults found by the field testing, we construct a fault model by extracting the features of the faulty code obtained by ranking the Ochiai coefficient of basic blocks. The model can be effectively used to locate the faults of the embedded program. Our method is evaluated on ST chips of the smart meter with the corresponding source code. The experiment shows that the proposed method has an effectiveness about 87% on the fault detection.
基金Project supported by the Applied Materials Shanghai Research and Development Foundation (Grant No.08700741000)the Foundation of Shanghai Municipal Education Commission (Grant No.2006AZ068)
文摘This paper presents an efficient VLSI architecture of the contest-based adaptive variable length code （CAVLC） decoder with power optimized for the H.264/advanced video coding （AVC） standard. In the proposed design, according to the regularity of the codewords, the first one detector is used to solve the low efficiency and high power dissipation problem within the traditional method of table-searching. Considering the relevance of the data used in the process of runbefore＇s decoding, arithmetic operation is combined with finite state machine （FSM）, which achieves higher decoding efficiency. According to the CAVLC decoding flow, clock gating is employed in the module level and the register level respectively, which reduces 43% of the overall dynamic power dissipation. The proposed design can decode every syntax element in one clock cycle. When the proposed design is synthesized at the clock constraint of 100 MHz, the synthesis result shows that the design costs 11 300 gates under a 0.25 μm CMOS technology, which meets the demand of real time decoding in the H.264/AVC standard.
基金Project supported by the National Natural Science Foundation of China(No.61100074)the Fundamental Research Funds for the Central Universities,China(No.2013QNA5008)
文摘Context-based adaptive binary arithmetic coding(CABAC) is the major entropy-coding algorithm employed in H.264/AVC.In this paper,we present a new VLSI architecture design for an H.264/AVC CABAC decoder,which optimizes both decode decision and decode bypass engines for high throughput,and improves context model allocation for efficient external memory access.Based on the fact that the most possible symbol(MPS) branch is much simpler than the least possible symbol(LPS) branch,a newly organized decode decision engine consisting of two serially concatenated MPS branches and one LPS branch is proposed to achieve better parallelism at lower timing path cost.A look-ahead context index(ctxIdx) calculation mechanism is designed to provide the context model for the second MPS branch.A head-zero detector is proposed to improve the performance of the decode bypass engine according to UEGk encoding features.In addition,to lower the frequency of memory access,we reorganize the context models in external memory and use three circular buffers to cache the context models,neighboring information,and bit stream,respectively.A pre-fetching mechanism with a prediction scheme is adopted to load the corresponding content to a circular buffer to hide external memory latency.Experimental results show that our design can operate at 250 MHz with a 20.71k gate count in SMIC18 silicon technology,and that it achieves an average data decoding rate of 1.5 bins/cycle.
基金supported by the National Natural Science Foundation of China(Grant No.62025101,62088102,62101007 and 61931014)the Young Elite Scientist Sponsorship Program by the Beijing Association of Science and Technology(Grant No.BYSS2022019).
文摘Intelligent video coding(IVC),which dates back to the late 1980s with the concept of encoding videos with knowledge and semantics,includes visual content compact representation models and methods enabling structural,detailed descriptions of visual information at different granularity levels(i.e.,block,mesh,region,and object)and in different areas.It aims to support and facilitate a wide range of applications,such as visual media coding,content broadcasting,and ubiquitous multimedia computing.We present a high-level overview of the IVC technology from model-based coding(MBC)to learning-based coding(LBC).MBC mainly adopts a manually designed coding scheme to explicitly decompose videos to be coded into blocks or semantic components.Thanks to emerging deep learning technologies such as neural networks and generative models,LBC has become a rising topic in the coding area.In this paper,wefirst review the classical MBC approaches,followed by the LBC approaches for image and video data.We also discuss and overview our recent attempts at neural coding approaches,which are inspiring for both academic research and industrial implementation.Some critical yet less studied issues are discussed at the end of this paper.
基金supported by the National Great Science Specific Project of China (2012ZX03001028)
文摘The throughput gain obtained by linear network coding （LNC） grows as the generation size increases, while the decoding complexity also grows exponentially. High decoding complexity makes the decoder to be the bottleneck for high speed and large data transmissions. In order to reduce the decoding complexity of network coding, a segment linear network coding （SLNC） scheme is proposed. SLNC provides a general coding structure for the generation-based network coding. By dividing a generation into several segments and restraining the coding coefficients of the symbols within the same segment, SLNC splits a high-rank matrix inversion into several low-rank matrix inversions, therefore reduces the decoding complexity dramatically. In addition, two coefficient selection strategies are proposed for both centrally controlled networks and distributed networks respectively. The theoretical analysis and simulation results prove that SLNC achieves a fairly low decoding complexity at a cost of rarely few extra transmissions.
