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检索条件"任意字段=IEEE Data Compression Conference"

共 16713 条记录，以下是271-280 订阅

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Hardware-accelerated compression Core on RISC-V for Online-BCG data Reduction 10

Hardware-accelerated Compression Core on RISC-V for Online-B...

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10th Nordic Circuits and Systems conference

作者： Rahman, Kazi Mohammad Abidur Ahmed, Abdelrahman Noshy Abdelalim Fey, Goerschwin Kulau, Ulf Hamburg Univ Technol Smart Sensors Grp Hamburg Germany Hamburg Univ Technol Inst Embedded Syst Hamburg Germany

ISBN: (纸本)9798331517663;9798331517670

Efficient compression of Ballistocardiography (BCG) data is critical for wearable and Ultra-Low-Power (ULP) applications, particularly in space-bound missions. This paper presents a hardware accelerator for data compression integrated into a NEORV32 RISC-V system specifically designed to enhance the performance of BCG sensors in resource-constraint environments. Utilizing hardware-accelerated Modified-Delta encoding with a software-based simple bit-packing compression can significantly improve real-time data transmission and storage efficiency. Implemented on the Lattice iCE40UP5K FPGA, the proposed method can achieve up to 13x speed improvement over traditional software solutions while maintaining a minimal footprint of 3719 Look-Up Tables (LUTs). This technique paves the way for ultra-efficient, wearable BCG sensors, which are crucial for, e.g. deep-space missions where energy and storage are at a premium.

关键词： BCG SCG data compression RISC-V FPGA

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Lightweight Image Crypto-compression Using Haar Transform and Selective Encryption for Grayscale IoT Images 20

Lightweight Image Crypto-Compression Using Haar Transform an...

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20th ieee International Wireless Communications and Mobile Computing (ieee IWCMC) - Green and Intelligent Communications

作者： Azar, Joseph Noura, Hassan Couturier, Raphael Univ Franche Comte UFC FEMTO ST Inst CNRS Belfort France

ISBN: (纸本)9798350361261;9798350361278

With the advent of the Multimedia Internet of Things (MIoT), many image compression techniques have been proposed to address the network's considerable challenges related to performance and security. However, many MIoT devices, such as the nRF52832 SoC with 64Kb RAM or even less, have significant memory constraints, making conventional methods unsuitable. MIoT networks face considerable challenges related to performance and security due to limitations in the power, computation, and memory of MIoT devices. These limitations result in difficulties in handling high image volumes. Multimedia compression is a potential solution to reduce data size. As MIoT devices often rely on wireless connections, they are also vulnerable to diverse security attacks (passive and active). This work introduces a secure and efficient image crypto-compression technique dedicated to devices having limited memory. It also proposes using denoising and a super-resolution deep learning model to reduce the overhead of the compression process and a lightweight cipher scheme that requires a single round of simple operations to reduce the overhead of the encryption process. The proposed approach effectively addresses the mentioned challenges with minimal overhead on the MIoT device, especially in terms of computational and communication delays, and extensive experimentation underscores its suitability in both effectiveness and robustness.

关键词： Multimedia Internet of Things (MIoT) Cryptocompression scheme DWT Selective encryption Denoising superresolution model

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An Innovative data compression Architecture for General Electronic Test Instruments Using Compressed Sensing Theory 67

An Innovative Data Compression Architecture for General Elec...

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67th ieee AUTOTESTCON conference

作者： Yan, Yanjun Liu, Chuanrong Chen, Kai Zhao, Yijiu Wang, Houjun Qian, Lei Zhang, Xiaotian Univ Elect Sci & Technol China Sch Automat Engn Chengdu Peoples R China

ISBN: (纸本)9798350349436;9798350349443

General Electronic Test Instruments (GETI) include digital storage oscilloscopes, power analyzers, and spectrum analyzers, which can test signals from most military or civilian electronic equipment. GETI is a reconfigurable, integrated system based on the architecture of digital storage oscilloscopes, consisting of a mainframe and multiple boards. Different boards are assembled into different test systems. Its boards include a high-speed acquisition board, temperature acquisition board, rotational speed acquisition board, etc. Other physical quantities are converted into unified electrical parameters through different sensors. Oscilloscope recorders expand storage space to record and save information, thus posing problems with massive storage, transfer, and processing speeds. Compressive sensing (CS) demonstrates that a priori sparse signals can be accurately reduced in sparse domains with fewer samples. Therefore, this letter proposes a data compression Processing Architecture (DCPA) for GETI based on the theory of compressive sensing. This solution will greatly reduce the storage and transmission bandwidth of GETI and improve the real-time processing efficiency and the probability of capturing abnormal signals. First, we pre-store sparse binary matrices in a parallel logic array FPGA, and the matrix elements are multiplied with the input quantized signal and then accumulated. Since the Fourier convolution theorem, the baseband of the muxed quantized signal will obtain the full-band information of the original quantized signal. After accumulation and downsampling, the architecture accomplishes the integration and filtering of the integrating and filtering. It is worth noting that the configurable compression rate in the FPGA is jointly determined by the accumulation length and variable sparse matrix coefficients, so we pre-store the sparse matrix coefficients corresponding to a fixed compression rate (4, 8, 16) in the FPGA. We recommend using an integer multiple of

关键词： General Electronic Test Instruments Oscilloscope Recorders compression Sensing FPGAs data compression Orthogonal Matching Pursuit Algorithms

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Multiobjective Image compression based on Tensor Decomposition 8

Multiobjective Image Compression based on Tensor Decompositi...

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8th International conference on Cloud Computing and Big data Analytics (ICCCBDA)

作者： Cao, Bin Yang, Xingyi Li, Ziming Fu, Yunjian Lv, Zhihan Hebei Univ Technol Tianjin Peoples R China Uppsala Univ Uppsala Sweden

ISBN: (纸本)9781665455336

Most of the traditional image compression methods vectorize the data contained in the image and then compress it. However, this approach does not take into account the high-dimensional information inside the image. To solve this problem, this paper regards the color image as a third-order tensor, and proposes a method of color image compression based on Tucker decomposition and multiobjective optimization. The tensor size compression ratio and Hu invariant moment similarity are proposed to measure the image compression quality. And to more comprehensively consider the sensitivity of human visual system to different visual signals, the five-objective optimization model of image compression is constructed. The five-objective optimization model includes: the above two indexes, information content weighted structure similarity index, color image feature similarity and information fidelity criterion. In addition, an angle-aware opposition-based learning strategy is proposed to improve the reference vector guided selection strategy of RVEA*. In the experiments, this method could effectively solve the problem of color image compression.

关键词： multiobjective optimization Tucker decomposition image compression Hu invariant moment opposition-based learning

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Research and Application of LZ-Hf Algorithm in compression of Automotive CAN Message data 4

Research and Application of LZ-Hf Algorithm in Compression o...

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4th International conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2024

作者： Liu, Chuanbo Zhu, Bin Zhang, Jun Jiang, Tiancheng College of Mechanical and Electrical Engineering Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350378542

With the increasing richness of the functions carried on top of the car, the amount of data generated on the nodes carried on the vehicle CAN bus is getting bigger and bigger, and it takes more and more time to upload the data of the collected CAN messages to the server side. Aiming at this problem, this paper proposes a LZ-Hf algorithm for message compression by combining the data characteristics of automotive CAN messages, and designs a data acquisition system so as to obtain CAN message data to verify the algorithm. After several tests and analysis of files of different sizes, the algorithm drastically reduces the time needed to upload the data from the TF card to the cloud. © 2024 ieee.

关键词： data compression

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Channel selection and wavelet transformation-based data compression preserve motor unit information 46

Channel selection and wavelet transformation-based data comp...

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46th Annual International conference of the ieee Engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Siyu Quinn, Kiara N. Slepyan, Ariel Qin, Lang Thakor, Nitish V. Johns Hopkins Whiting School of Engineering Department of Biomedical Engineering BaltimoreMD21218 United States Johns Hopkins Whiting School of Engineering Department of Electrical and Computer Engineering BaltimoreMD21218 United States

ISBN: (纸本)9798350371499

Both motor unit-based control algorithms and implantable interfaces are promising approaches to improve current prosthetic limbs by allowing control of more movements. However, combining the two methods poses a challenge due to the current wireless transmission capabilities which limits the amount of data that can be processed. Capturing the motor unit requires a higher sampling rate and higher channel counts. Thus, this paper investigates a data compression strategy comprised of two independent stages: recording data reduction by selecting fewer channels to be 'active' and minimization of the transmitting data through compression with wavelet transformation. We have demonstrated a promising wavelet compression pipeline using db4, which can compress the raw EMG signal 0.63% of its original size while maintaining low WMSE error (0.109) and high heatmap reconstruction correlation coefficient (0.9741±0.972). In addition, placing high-density electrodes along the muscle fibers during channel selection while utilizing wavelet transformation can compress 99.82% of transmitted data while preserving a MCC of 0.706 ± 0.169. © 2024 ieee.

关键词： data compression

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Power Quality Disturbance Detection Algorithm Based on Distributed compression Sensing 4

Power Quality Disturbance Detection Algorithm Based on Distr...

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4th Power System and Green Energy conference (PSGEC)

作者： Cao Jianwei Wang Xiaojian Zhang Lei Liu Ying State Grid Zhejiang Elect Power Co Ltd Huzhou Power Supply Co Huzhou Peoples R China

ISBN: (数字)9798350365573

ISBN: (纸本)9798350365573;9798350365580

With the increasing diversification of new energy generation and the trend of load equipment towards power electronics, the issue of power quality in data volume is becoming increasingly complex in the distribution network. In this paper, a compression storage method for power quality data based on distributed compression sensing and edge computing is proposed. An adaptive joint reconstruction algorithm is proposed in combination with a fast principal component analysis algorithm. In this paper, typical single and composite perturbation data are modelled, combining the sparse features of the perturbation data, the standard deviation and the number of principal frequencies as the classification features of the perturbation data. The compressed perception algorithm is used to detect various types of perturbations. Simulation results show that the detection accuracy of the algorithm is higher than existing algorithms and meets the accuracy requirements of M-class PMU measurements.

关键词： Interharmonic compression sensing (CS) Multi-frequency dynamic phasor

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Butterfly: Multiple Reference Frames Feature Propagation Mechanism for Neural Video compression

Butterfly: Multiple Reference Frames Feature Propagation Mec...

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data compression conference (DCC)

作者： Wang, Feng Ruan, Haihang Xiong, Fei Yang, Jiayu Li, Litian Wang, Ronggang Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350347951

Using more reference frames can significantly improve the compression efficiency in neural video compression. However, in low-latency scenarios, most existing neural video compression frameworks usually use the previous one frame as reference. Or a few frameworks which use the previous multiple frames as reference only adopt a simple multi-reference frames propagation mechanism. In this paper, we present a more reasonable multi-reference frames propagation mechanism for neural video compression, called butterfly multi-reference frame propagation mechanism (Butterfly), which allows a more effective feature fusion of multi reference frames. By this, we can generate more accurate temporal context conditional prior for Contextual Coding Module. Besides, when the number of decoded frames does not meet the required number of reference frames, we duplicate the nearest reference frame to achieve the requirement, which is better than duplicating the furthest one. Experiment results show that our method can significantly outperform the previous state-of-the-art (SOTA), and our neural codec can achieve -7.6% bitrate save on HEVC Class D dataset when compares with our base single-reference frame model with the same compression configuration.

关键词： Image compression

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TSARN: A Joint Temporal-Spatial-Angular Reconstruction Network for Light Field Lenslet Video compression

TSARN: A Joint Temporal-Spatial-Angular Reconstruction Netwo...

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2024 conference on Visual Communications and Image Processing

作者： Li, Huan Huang, Xinpeng Li, Yongjie An, Ping Shanghai Univ Sch Commun & Informat Engn Shanghai Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Light Field (LF) lenslet videos are capable of capturing the aggregation of light rays in dynamic scenes, providing a more immersive experience. However, the immense data volume of LF lenslet videos poses significant challenges for storage and transmission. Existing methods that utilize traditional video codecs to encode all views fail to meet the demands for efficient compression. This paper proposes a compression framework based on down-sampling, which focuses on designing an efficient reconstruction method at the decoder side. In LF lenslet videos, pixels that are invisible in the current view may be visible in adjacent views angularly and/or in adjacent frames temporally. Therefore, we propose a joint temporal-spatial-angular reconstruction network. In this network, Spatial-Angular Convolutional Module utilizes different forms of convolutions to fully extract spatial-angular features for synthesizing LF views initially;Deformable Convolutional Temporal Fusion Module employs deformable convolutions to align information from different frames and aggregate temporal features;View Refinement Module is finally adopted to further refine the features and reconstruct high-quality LF lenslet videos. Experimental results demonstrate that our proposed LF lenslet video compression framework achieves superior performance, significantly reducing the bitstream.

关键词： light field lenslet video compression temporal-spatial-angular reconstruction

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GENERALIZED NESTED LATENT VARIABLE MODELS FOR LOSSY CODING APPLIED TO WIND TURBINE SCENARIOS 31

GENERALIZED NESTED LATENT VARIABLE MODELS FOR LOSSY CODING A...

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2024 International conference on Image Processing

作者： Perez-Gonzalo, Raul Espersen, Andreas Agudo, Antonio CSIC UPC Inst Robot & Informat Ind Barcelona Spain Wind Power LAB Copenhagen Denmark

ISBN: (纸本)9798350349405;9798350349399

Rate-distortion optimization through neural networks has accomplished competitive results in compression efficiency and image quality. This learning-based approach seeks to minimize the compromise between compression rate and reconstructed image quality by automatically extracting and retaining crucial information, while discarding less critical details. A successful technique consists in introducing a deep hyperprior that operates within a 2-level nested latent variable model, enhancing compression by capturing complex data dependencies. This paper extends this concept by designing a generalized L-level nested generative model with a Markov chain structure. We demonstrate as L increases that a trainable prior is detrimental and explore a common dimensionality along the distinct latent variables to boost compression performance. As this structured framework can represent autoregressive coders, we outperform the hyperprior model and achieve state-of-the-art performance while reducing substantially the computational cost. Our experimental evaluation is performed on wind turbine scenarios to study its application on visual inspections.

关键词： Image compression Rate-distortion Loss Nested Models Wind Turbine Blade Inspections

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