Along with the proliferating research interest in semantic communication(Sem Com),joint source channel coding(JSCC)has dominated the attention due to the widely assumed existence in efficiently delivering information ...Along with the proliferating research interest in semantic communication(Sem Com),joint source channel coding(JSCC)has dominated the attention due to the widely assumed existence in efficiently delivering information semantics.Nevertheless,this paper challenges the conventional JSCC paradigm and advocates for adopting separate source channel coding(SSCC)to enjoy a more underlying degree of freedom for optimization.We demonstrate that SSCC,after leveraging the strengths of the Large Language Model(LLM)for source coding and Error Correction Code Transformer(ECCT)complemented for channel coding,offers superior performance over JSCC.Our proposed framework also effectively highlights the compatibility challenges between Sem Com approaches and digital communication systems,particularly concerning the resource costs associated with the transmission of high-precision floating point numbers.Through comprehensive evaluations,we establish that assisted by LLM-based compression and ECCT-enhanced error correction,SSCC remains a viable and effective solution for modern communication systems.In other words,separate source channel coding is still what we need.展开更多
Tracking moving wideband sound sources is one of the most challenging issues in the acoustic array signal processing which is based on the direction of arrival(DOA) estimation. Compressive sensing(CS) is a recent theo...Tracking moving wideband sound sources is one of the most challenging issues in the acoustic array signal processing which is based on the direction of arrival(DOA) estimation. Compressive sensing(CS) is a recent theory exploring the signal sparsity representation, which has been proved to be superior for the DOA estimation. However, the spatial aliasing and the offset at endfire are the main obstacles for CS applied in the wideband DOA estimation. We propose a particle filter based compressive sensing method for tracking moving wideband sound sources. First, the initial DOA estimates are obtained by wideband CS algorithms. Then, the real sources are approximated by a set of particles with different weights assigned. The kernel density estimator is used as the likelihood function of particle filter. We present the results for both uniform and random linear array. Simulation results show that the spatial aliasing is disappeared and the offset at endfire is reduced. We show that the proposed method can achieve satisfactory tracking performance regardless of using uniform or random linear array.展开更多
Source and mask joint optimization(SMO)is a widely used computational lithography method for state-of-the-art optical lithography process to improve the yield of semiconductor wafers.Nowadays,computational efficiency ...Source and mask joint optimization(SMO)is a widely used computational lithography method for state-of-the-art optical lithography process to improve the yield of semiconductor wafers.Nowadays,computational efficiency has become one of the most challenging issues for the development of pixelated SMO techniques.Recently,compressive sensing(CS)theory has be explored in the area of computational inverse problems.This paper proposes a CS approach to improve the computational efficiency of pixel-based SMO algorithms.To our best knowledge,this paper is the first to develop fast SMO algorithms based on the CS framework.The SMO workflow can be separated into two stages,i.e.,source optimization(SO)and mask optimization(MO).The SO and MO are formulated as the linear CS and nonlinear CS reconstruction problems,respectively.Based on the sparsity representation of the source and mask patterns on the predefined bases,the SO and MO procedures are implemented by sparse image reconstruction algorithms.A set of simulations are presented to verify the proposed CS-SMO methods.The proposed CS-SMO algorithms are shown to outperform the traditional gradient-based SMO algorithm in terms of both computational efficiency and lithography imaging performance.展开更多
Source localization by matched-field processing （MFP） can be accelerated by building a database of Green＇s functions which however requires a bulk-storage memory. According to the sparsity of the source locations i...Source localization by matched-field processing （MFP） can be accelerated by building a database of Green＇s functions which however requires a bulk-storage memory. According to the sparsity of the source locations in the search grids of MFP, compressed sensing inspires an approach to reduce the database by introducing a sensing matrix to compress the database. Compressed sensing is further used to estimate the source locations with higher resolution by solving the β -norm optimization problem of the compressed Green＇s function and the data received by a vertieal/horizontal line array. The method is validated by simulation and is verified with the experimental data.展开更多
As photoelectrically detected ^(252)Cf-source-driven neutron signals always contain noise, a denoising algorithm is proposed based on compressive sensing for the noised neutron signal. In the algorithm, Empirical Mode...As photoelectrically detected ^(252)Cf-source-driven neutron signals always contain noise, a denoising algorithm is proposed based on compressive sensing for the noised neutron signal. In the algorithm, Empirical Mode Decomposition(EMD) is applied to decompose the noised neutron signal and then find out the noised Intrinsic Mode Function(IMF) automatically. Thus, we only need to use the basis pursuit denoising(BPDN) algorithm to denoise these IMFs. For this reason, the proposed algorithm can be called EMDCSDN(Empirical Mode Decomposition Compressive Sensing Denoising). In addition, five indicators are employed to evaluate the denoising effect. The results show that the EMDCSDN algorithm is more effective than the other denoising algorithms including BPDN. This study provides a new approach for signal denoising at the front-end.展开更多
It is known by entropy theory that image is a source correlated with a certain characteristic of probability. The entropy rate of the source and ε- entropy (rate-distortion function theory) are the information conten...It is known by entropy theory that image is a source correlated with a certain characteristic of probability. The entropy rate of the source and ε- entropy (rate-distortion function theory) are the information content to identify the characteristics of video images, and hence are essentially related with video image compression. They are fundamental theories of great significance to image compression, though impossible to be directly turned into a compression method. Based on the entropy theory and the image compression theory, by the application of the rate-distortion feature mathematical model and Lagrange multipliers to some theoretical problems in the H.264 standard, this paper presents a new the algorithm model of coding rate-distortion. This model is introduced into complete test on the capability of the test model of JM61e (JUT Test Model). The result shows that the speed of coding increases without significant reduction of the rate-distortion performance of the coder.展开更多
To compress hyperspectral images, a low complexity discrete cosine transform （DCT）-based distributed source coding （DSC） scheme with Gray code is proposed. Unlike most of the existing DSC schemes, which utilize tr...To compress hyperspectral images, a low complexity discrete cosine transform （DCT）-based distributed source coding （DSC） scheme with Gray code is proposed. Unlike most of the existing DSC schemes, which utilize transform in spatial domain, the proposed algorithm applies transform in spectral domain. Set-partitioning-based approach is applied to reorganize DCT coefficients into waveletlike tree structure and extract the sign, refinement, and significance bitplanes. The extracted refinement bits are Gray encoded. Because of the dependency along the line dimension of hyperspectral images, low density paritycheck-（LDPC）-based Slepian-Wolf coder is adopted to implement the DSC strategy. Experimental results on airborne visible/infrared imaging spectrometer （AVIRIS） dataset show that the proposed paradigm achieves up to 6 dB improvement over DSC-based coders which apply transform in spatial domain, with significantly reduced computational complexity and memory storage.展开更多
An efficient chaotic source coding scheme operating on variable-length blocks is proposed. With the source message represented by a trajectory in the state space of a chaotic system, data compression is achieved when ...An efficient chaotic source coding scheme operating on variable-length blocks is proposed. With the source message represented by a trajectory in the state space of a chaotic system, data compression is achieved when the dynamical system is adapted to the probability distribution of the source symbols. For infinite-precision computation, the theoretical compression performance of this chaotic coding approach attains that of optimal entropy coding. In finite-precision implementation, it can be realized by encoding variable-length blocks using a piecewise linear chaotic map within the precision of register length. In the decoding process, the bit shift in the register can track the synchronization of the initial value and the corresponding block. Therefore, all the variable-length blocks are decoded correctly. Simulation results show that the proposed scheme performs well with high efficiency and minor compression loss when compared with traditional entropy coding.展开更多
Multispectral time delay and integration charge coupled device （TDICCD） image compression requires a low- complexity encoder because it is usually completed on board where the energy and memory are limited. The Cons...Multispectral time delay and integration charge coupled device （TDICCD） image compression requires a low- complexity encoder because it is usually completed on board where the energy and memory are limited. The Consultative Committee for Space Data Systems （CCSDS） has proposed an image data compression （CCSDS-IDC） algorithm which is so far most widely implemented in hardware. However, it cannot reduce spectral redundancy in mukispectral images. In this paper, we propose a low-complexity improved CCSDS-IDC （ICCSDS-IDC）-based distributed source coding （DSC） scheme for multispectral TDICCD image consisting of a few bands. Our scheme is based on an ICCSDS-IDC approach that uses a bit plane extractor to parse the differences in the original image and its wavelet transformed coefficient. The output of bit plane extractor will be encoded by a first order entropy coder. Low-density parity-check-based Slepian-Wolf （SW） coder is adopted to implement the DSC strategy. Experimental results on space multispectral TDICCD images show that the proposed scheme significantly outperforms the CCSDS-IDC-based coder in each band.展开更多
面向高能同步辐射光源(High Energy Photon Source,HEPS)的高性能像素阵列探测器(HEPS-BPIX4)的数据获取系统(Data Acquisition,DAQ)需满足高实时性要求。通过在线压缩图像数据,可有效降低后续传输与存储的压力。针对传统压缩算法在压...面向高能同步辐射光源(High Energy Photon Source,HEPS)的高性能像素阵列探测器(HEPS-BPIX4)的数据获取系统(Data Acquisition,DAQ)需满足高实时性要求。通过在线压缩图像数据,可有效降低后续传输与存储的压力。针对传统压缩算法在压缩率和实时性方面的不足,本文提出了一种基于深度学习目标检测的图像数据在线压缩方法。采用端到端的YOLO(You Only Look Once)目标检测算法,对深度学习模型进行高效训练,并验证了其在HEPS-BPIX4 DAQ数据流中实现在线数据压缩的可行性。实验结果表明,该方法的图像数据在线压缩平均压缩比达到5.88。同时,设计了高效的部署方案,并对性能进行了测试,单线程下的压缩处理速率可达GB∙s^(−1)量级。此外,进一步提出了适用于HEPS-BPIX4 DAQ框架的多线程部署方案,以满足更高的压缩性能需求,为缓解HEPS-BPIX4 DAQ系统高带宽图像数据处理压力提供了新思路。展开更多
A new-type multiple-source heat pump cycle with two-stage compression was established on the basis of the problems of similarly existing heat pumps.The equivalent temperature levels of typical evaporators are applied ...A new-type multiple-source heat pump cycle with two-stage compression was established on the basis of the problems of similarly existing heat pumps.The equivalent temperature levels of typical evaporators are applied to the different heat sources of the proposed cycle,and the high-temperature heat sources are shown to enhance vapor injection.Then,the mathematical model and prototype are developed,and the results from experimental simulation and validation showed that the solar collector can improve the heating performance of the proposed heat pump system.In the middle-temperature heating period,the outdoor temperature is less than-25℃,and the average coefficient of performance(COP)value of the proposed heat pump was 4.2,which was greater than the COPs of conventional ground source heat pumps.展开更多
基金supported in part by the National Key Research and Development Program of China under Grant No.2024YFE0200600the Zhejiang Provincial Natural Science Foundation of China under Grant No.LR23F010005the Huawei Cooperation Project under Grant No.TC20240829036。
文摘Along with the proliferating research interest in semantic communication(Sem Com),joint source channel coding(JSCC)has dominated the attention due to the widely assumed existence in efficiently delivering information semantics.Nevertheless,this paper challenges the conventional JSCC paradigm and advocates for adopting separate source channel coding(SSCC)to enjoy a more underlying degree of freedom for optimization.We demonstrate that SSCC,after leveraging the strengths of the Large Language Model(LLM)for source coding and Error Correction Code Transformer(ECCT)complemented for channel coding,offers superior performance over JSCC.Our proposed framework also effectively highlights the compatibility challenges between Sem Com approaches and digital communication systems,particularly concerning the resource costs associated with the transmission of high-precision floating point numbers.Through comprehensive evaluations,we establish that assisted by LLM-based compression and ECCT-enhanced error correction,SSCC remains a viable and effective solution for modern communication systems.In other words,separate source channel coding is still what we need.
基金supported by the NFSC Grants 51375385 and 51675425Natural Science Basic Research Plan in Shaanxi Province of China Grants 2016JZ013
文摘Tracking moving wideband sound sources is one of the most challenging issues in the acoustic array signal processing which is based on the direction of arrival(DOA) estimation. Compressive sensing(CS) is a recent theory exploring the signal sparsity representation, which has been proved to be superior for the DOA estimation. However, the spatial aliasing and the offset at endfire are the main obstacles for CS applied in the wideband DOA estimation. We propose a particle filter based compressive sensing method for tracking moving wideband sound sources. First, the initial DOA estimates are obtained by wideband CS algorithms. Then, the real sources are approximated by a set of particles with different weights assigned. The kernel density estimator is used as the likelihood function of particle filter. We present the results for both uniform and random linear array. Simulation results show that the spatial aliasing is disappeared and the offset at endfire is reduced. We show that the proposed method can achieve satisfactory tracking performance regardless of using uniform or random linear array.
基金the National Natural Science Foundation of China(NSFC)(61675021)the Fundamental Research Funds for the Central Universities(2018CX01025).
文摘Source and mask joint optimization(SMO)is a widely used computational lithography method for state-of-the-art optical lithography process to improve the yield of semiconductor wafers.Nowadays,computational efficiency has become one of the most challenging issues for the development of pixelated SMO techniques.Recently,compressive sensing(CS)theory has be explored in the area of computational inverse problems.This paper proposes a CS approach to improve the computational efficiency of pixel-based SMO algorithms.To our best knowledge,this paper is the first to develop fast SMO algorithms based on the CS framework.The SMO workflow can be separated into two stages,i.e.,source optimization(SO)and mask optimization(MO).The SO and MO are formulated as the linear CS and nonlinear CS reconstruction problems,respectively.Based on the sparsity representation of the source and mask patterns on the predefined bases,the SO and MO procedures are implemented by sparse image reconstruction algorithms.A set of simulations are presented to verify the proposed CS-SMO methods.The proposed CS-SMO algorithms are shown to outperform the traditional gradient-based SMO algorithm in terms of both computational efficiency and lithography imaging performance.
基金Supported by the National Natural Science Foundation of China under Grant Nos 11374271 and 11374270the Fundamental Research Funds for the Central Universities under Grant No 201513038
文摘Source localization by matched-field processing （MFP） can be accelerated by building a database of Green＇s functions which however requires a bulk-storage memory. According to the sparsity of the source locations in the search grids of MFP, compressed sensing inspires an approach to reduce the database by introducing a sensing matrix to compress the database. Compressed sensing is further used to estimate the source locations with higher resolution by solving the β -norm optimization problem of the compressed Green＇s function and the data received by a vertieal/horizontal line array. The method is validated by simulation and is verified with the experimental data.
基金Supported by the National Natural Science Foundation of China(Nos.61175005 and 61401049)
文摘As photoelectrically detected ^(252)Cf-source-driven neutron signals always contain noise, a denoising algorithm is proposed based on compressive sensing for the noised neutron signal. In the algorithm, Empirical Mode Decomposition(EMD) is applied to decompose the noised neutron signal and then find out the noised Intrinsic Mode Function(IMF) automatically. Thus, we only need to use the basis pursuit denoising(BPDN) algorithm to denoise these IMFs. For this reason, the proposed algorithm can be called EMDCSDN(Empirical Mode Decomposition Compressive Sensing Denoising). In addition, five indicators are employed to evaluate the denoising effect. The results show that the EMDCSDN algorithm is more effective than the other denoising algorithms including BPDN. This study provides a new approach for signal denoising at the front-end.
文摘It is known by entropy theory that image is a source correlated with a certain characteristic of probability. The entropy rate of the source and ε- entropy (rate-distortion function theory) are the information content to identify the characteristics of video images, and hence are essentially related with video image compression. They are fundamental theories of great significance to image compression, though impossible to be directly turned into a compression method. Based on the entropy theory and the image compression theory, by the application of the rate-distortion feature mathematical model and Lagrange multipliers to some theoretical problems in the H.264 standard, this paper presents a new the algorithm model of coding rate-distortion. This model is introduced into complete test on the capability of the test model of JM61e (JUT Test Model). The result shows that the speed of coding increases without significant reduction of the rate-distortion performance of the coder.
基金supported by the National Natural Science Foundationof China (60702012)the Scientific Research Foundation for the Re-turned Overseas Chinese Scholars, State Education Ministry
文摘To compress hyperspectral images, a low complexity discrete cosine transform （DCT）-based distributed source coding （DSC） scheme with Gray code is proposed. Unlike most of the existing DSC schemes, which utilize transform in spatial domain, the proposed algorithm applies transform in spectral domain. Set-partitioning-based approach is applied to reorganize DCT coefficients into waveletlike tree structure and extract the sign, refinement, and significance bitplanes. The extracted refinement bits are Gray encoded. Because of the dependency along the line dimension of hyperspectral images, low density paritycheck-（LDPC）-based Slepian-Wolf coder is adopted to implement the DSC strategy. Experimental results on airborne visible/infrared imaging spectrometer （AVIRIS） dataset show that the proposed paradigm achieves up to 6 dB improvement over DSC-based coders which apply transform in spatial domain, with significantly reduced computational complexity and memory storage.
基金Project supported by the Research Grants Council of the Hong Kong Special Administrative Region,China (Grant No.CityU 123009)
文摘An efficient chaotic source coding scheme operating on variable-length blocks is proposed. With the source message represented by a trajectory in the state space of a chaotic system, data compression is achieved when the dynamical system is adapted to the probability distribution of the source symbols. For infinite-precision computation, the theoretical compression performance of this chaotic coding approach attains that of optimal entropy coding. In finite-precision implementation, it can be realized by encoding variable-length blocks using a piecewise linear chaotic map within the precision of register length. In the decoding process, the bit shift in the register can track the synchronization of the initial value and the corresponding block. Therefore, all the variable-length blocks are decoded correctly. Simulation results show that the proposed scheme performs well with high efficiency and minor compression loss when compared with traditional entropy coding.
基金supported by the National High Technology Research and Development Program of China (Grant No. 863-2-5-1-13B)
文摘Multispectral time delay and integration charge coupled device （TDICCD） image compression requires a low- complexity encoder because it is usually completed on board where the energy and memory are limited. The Consultative Committee for Space Data Systems （CCSDS） has proposed an image data compression （CCSDS-IDC） algorithm which is so far most widely implemented in hardware. However, it cannot reduce spectral redundancy in mukispectral images. In this paper, we propose a low-complexity improved CCSDS-IDC （ICCSDS-IDC）-based distributed source coding （DSC） scheme for multispectral TDICCD image consisting of a few bands. Our scheme is based on an ICCSDS-IDC approach that uses a bit plane extractor to parse the differences in the original image and its wavelet transformed coefficient. The output of bit plane extractor will be encoded by a first order entropy coder. Low-density parity-check-based Slepian-Wolf （SW） coder is adopted to implement the DSC strategy. Experimental results on space multispectral TDICCD images show that the proposed scheme significantly outperforms the CCSDS-IDC-based coder in each band.
基金supported by the Natural Science Foundation of Beijing Municipality(3172040)National Natural Science Foundation(51736008)Strategic Priority Research Program of the Chinese Academy of Sciences(XDA21050600)
文摘A new-type multiple-source heat pump cycle with two-stage compression was established on the basis of the problems of similarly existing heat pumps.The equivalent temperature levels of typical evaporators are applied to the different heat sources of the proposed cycle,and the high-temperature heat sources are shown to enhance vapor injection.Then,the mathematical model and prototype are developed,and the results from experimental simulation and validation showed that the solar collector can improve the heating performance of the proposed heat pump system.In the middle-temperature heating period,the outdoor temperature is less than-25℃,and the average coefficient of performance(COP)value of the proposed heat pump was 4.2,which was greater than the COPs of conventional ground source heat pumps.
