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

共 16774 条记录，以下是581-590 订阅

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LOSSY LIDAR POINT CLOUD compression VIA CYLINDRICAL 3D CONVOLUTION NETWORKS 30

LOSSY LIDAR POINT CLOUD COMPRESSION VIA CYLINDRICAL 3D CONVO...

引用

30th ieee International conference on Image Processing (ICIP)

作者： Gao, Yelang Zhang, Pingping Wang, Xu Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9781728198354

Compared with object-level and human-level point clouds, LiDAR point clouds have larger data scales and are more sparse, posing a challenge for the existing learning-based lossy compression scheme. In this paper, we resolve this issue by transforming the point cloud into a cylindrical coordinate system. In this way, we can better retain points close to the sensor with a high density while extending the receptive field of convolution in areas of low point density. Following cylindrical quantization, an autoencoder is utilized to progressively downsample voxels. The coordinates and latent features are compressed by G-PCC and hyperprior-based entropy encoding respectively. The results demonstrate that our approach performs better than PCGCv2. The visualization results also show that our algorithm can better retain the shape of objects. Ablation studies further prove the efficiency of the cylindrical coordinates. The code is publicly available at https: //***/AirManH/cylindrical_pcc.

关键词： cylindrical coordinates LiDAR point cloud lossy geometry compression

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Real-Time Adaptive data compression for Permanent Magnet Motor Control System 6

Real-Time Adaptive Data Compression for Permanent Magnet Mot...

引用

6th ieee International Electrical and Energy conference, CIEEC 2023

作者： Duan, Bing Xu, Wei Wang, Jiyao Southeast University - Monash University Joint Graduate School Southeast University Suzhou China

ISBN: (纸本)9798350346671

Permanent magnet synchronous motor (PMSM) control system processes massive electrical and mechanical data sets which are important for various data-driven methods of big data analysis or failure data recording. However, as the control frequency going up, motor data processing faces more challenges of the limited computational resource of a motor controller. This paper proposes a real-time data compression method based on connected piecewise linear regression (CPLR) and compressed sensing (CS), so the motor operational data can be recorded with a minimum memory storage space. The study demonstrates the parameter boundary of CPLR under the condition that samples obtained by digital sensors. On this basis, adaptive-CPLR is proposed, which tunes parameter automatically with the given active function and has parameter reset mechanism. Further, CS is implemented using sparsity binary matrix (SBM) for space and time saving and triggered by adaptive-CPLR aimed to preserve frequency domain information conditionally. Experiment results is presented to demonstrate the real-time characteristic of the proposed compression method in resource limited motor controller STM32F407. Adaptive-CPLR and CS are also evaluated, showing that the proposed method successfully achieves desired performance of high compression ratio and low percentage root mean square residues. © 2023 ieee.

关键词： Permanent magnets

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Research on the Optimization of Storage Strategies for EMU On-Board data 2

Research on the Optimization of Storage Strategies for EMU O...

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2nd ieee International conference on Electrical, Automation and Computer Engineering, ICEACE 2024

作者： Yang, Chunhui Zhang, Weijiao Sun, Chaoyuan Zhao, Shiwei China Academy of Railway Sciences Corporation Limited Institute of Computing Technologies Beijing100081 China

ISBN: (纸本)9798350368208

This paper conducts an optimization study on the storage strategy of EMU on-board data, proposing a performance ratio-based weighted calculation method to address storage challenges in a big data environment. EMU on-board data is crucial for ensuring the safety and efficiency of train operations, yet the surge in data volume presents significant storage and processing difficulties. By evaluating two prominent big data storage solutions, Hbase and Hive, and analyzing the performance impact of snappy and gzip compression methods, the experiment reveals that Hive-snappy demonstrates superior query performance, while Hbase-gzip shows a clear advantage in space efficiency. The paper introduces time efficiency and space efficiency indicators and combines them with a comprehensive weighted performance ratio (WPR) to provide a scientific basis for data storage strategy in the railway industry. Future research will delve into data compression technology, storage strategy optimization, real-time data processing, data security and durability, and cross-domain applications to promote technological advancement in related industries. © 2024 ieee.

关键词： Storage efficiency

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A Memory-Efficient Edge Inference Accelerator with XOR-based Model compression 60

A Memory-Efficient Edge Inference Accelerator with XOR-based...

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60th ACM/ieee Design Automation conference (DAC)

作者： Lee, Hyunseung Hong, Jihoon Kim, Soosung Lee, Seung Yul Lee, Jae W. Seoul Natl Univ Seoul 08826 South Korea

ISBN: (纸本)9798350323481

Model compression is widely adopted for edge inference of neural networks (NNs) to minimize both costly DRAM accesses and memory footprints. Recently, XOR-based model compression has demonstrated promising results to maximize compression ratio and minimize accuracy drop. However, XOR-based decompression alone produces bit errors and requires auxiliary data for error correction. To minimize model size and hence DRAM traffic, we propose an enhanced decompression algorithm and a low-cost hardware accelerator for it. Since not all errors are equal, our algorithm selects only important errors to correct with no accuracy drop. Compared with the baseline XOR compression scheme correcting all errors, the compressed model size of ResNet-18 and VGG-16 is reduced by 23% and 27% respectively. We also present a low-cost hardware implementation of on-line XOR decompression and error-correction logic built on Gemmini, an open-source systolic array accelerator, at the cost of only a 0.39% and 0.46% increase in area and power.

关键词： Error correction

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Tucker Decomposition Based Lossless Image compression for 3D Medical Image 8

Tucker Decomposition Based Lossless Image Compression for 3D...

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8th ieee International conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Makandar, Aziz Biradar, Rekha Ksa Wuv Computer Science department India

ISBN: (纸本)9798331505462

The successful operation of a telecommunication system depends on the maintaining and transferring of high compression three-dimension medical images that produce reconstructions of excellent quality after decompression. To address this issue, we presented a Tucker Decomposition based lossless image compression for three-dimension medical image. Tucker decomposition, the extended form of Singular Value Decomposition (SVD), can handle high dimensional information;SVD is limited to two dimensions. Use the Selective Bounding volume (SBV) to find and retrieve the Volume of Interest (VOI). Tucker decomposition is used to break down the extracted volume of interest (VOI) into its component major tensor, coefficient matrices, and distinct parameters. subsequently, an Adaptive Binary Range Coder (ABRC) is utilized for compress these values. By inverting the compression procedure and integrating the uncompressed VOI with the surrounding area employing the bound volume coordinates connected to the compressed three-dimensional image, the final uncompressed VOI is produced. utilizing a range of 3D medical images obtained through different forms of imaging, the proposed approach's performance was assessed using analytical evaluation metrics like the compression Ratio (CR), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). The proposed compression methodology produces consistently high-quality image compressions and produces larger-scale results, as shown by the method's outcome. © 2024 ieee.

关键词： Voice/data communication systems

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Low-Power Lossless Image compression on Small Satellite Edge using Spiking Neural Network

Low-Power Lossless Image Compression on Small Satellite Edge...

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International Joint conference on Neural Networks (IJCNN)

作者： Kahali, Sayan Dey, Sounak Kadway, Chetan Mukherjee, Arijit Pal, Arpan Suri, Manan TCS Res Embedded Devices & Intelligent Syst Kolkata India Indian Inst Technol Delhi India

ISBN: (纸本)9781665488679

The emerging trend of small satellites for earth observation missions has enabled commercial organisations to exploit the horizon for various business applications related to weather forecasting/monitoring, Land Use Land Cover (LULC) classifications, disaster (such as oil spill or forest fire) monitoring etc. However, the limited power and computational capacity of these small satellites arising out of the size and weight restrictions have posed newer challenges, primarily related to low-power on-board data processing and transmission. One possible approach is to harness the capabilities of the evolving neuromorphic computing paradigm for such low-power computing requirements. One possible application can be lossless compression of high-resolution earth observation images before sending those downstream to ground stations for further analysis. In this paper, we propose a novel method of lossless image compression based on classical Arithmetic Encoding that exploits the low power computing capability of Spiking Neural Networks and neuromorphic platforms. We experimentally prove that our SNN approach achieves compression ratio at-par with state of the art ANN methods with an estimated 2.5x power efficiency and 50% lower latency with a much smaller model - thereby enabling on-board image compression and at the same time, saving on a corresponding amount of energy during transmission.

关键词： Weather forecasting

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Optimal Power Control for Over-the-Air Federated Learning with Gradient compression 30

Optimal Power Control for Over-the-Air Federated Learning wi...

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30th ieee International conference on Parallel and Distributed Systems, ICPADS 2024

作者： Ruan, Mengzhe Li, Yunhe Zhang, Weizhou Song, Linqi Xu, Weitao City University of Hong Kong Shenzhen Research Institute Hong Kong City University of Hong Kong Department of Computer Science Hong Kong Institute of Operations Research and Analytics National University of Singapore Singapore

ISBN: (纸本)9798331515966

Federated Learning (FL) has emerged as a transformative approach in distributed machine learning, enabling the collaborative training of models using decentralized datasets from diverse sources such as mobile edge devices. This paradigm not only enhances data privacy but also significantly reduces the communication burden typically associated with centralized data aggregation. In wireless networks, Over-the-air Federated Learning (OTA-FL) has been developed as a communication-efficient solution, allowing for the simultaneous transmission and aggregation of model updates from numerous edge devices across the available bandwidth. Gradient compression techniques are necessary to further enhance the communication efficiency of FL, particularly in bandwidth-constrained wireless environments. Despite these advancements, OTA-FL with gradient compression encounters substantial challenges, including learning performance degradation due to compression errors, non-uniform channel fading, and noise interference. Existing power control strategies have yet to fully address these issues, leaving a significant gap in optimizing OTA-FL performance under gradient compression. This paper introduces a novel power control strategy that coordinately integrates gradient compression to optimize OTAFL performance by minimizing the impact of channel fading and noise. Our approach employs linear approximations to complex terms, ensuring the stability and effectiveness of each gradient descent iteration. Numerical results demonstrate that our strategy significantly enhances convergence rates compared to traditional methods like channel inversion and uniform power transmission. This research advances the OTA-FL field and opens new avenues for performance tuning in communication-efficient federated learning systems. © 2024 ieee.

关键词： Bandwidth

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Image compression and Decompression Framework Based on Latent Diffusion Model for Breast Mammography

Image Compression and Decompression Framework Based on Laten...

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International conference on Computational Science and Computational Intelligence (CSCI)

作者： Hwang, InChan Woo, MinJae Kennesaw State Univ Sch Data Sci & Analyt Kennesaw GA 30144 USA

ISBN: (纸本)9798350361513;9798350372304

This research presents a novel framework for the compression and decompression of medical images utilizing the Latent Diffusion Model (LDM). The LDM represents advancement over the denoising diffusion probabilistic model (DDPM) with a potential to yield superior image quality while requiring fewer computational resources in the image decompression process. A possible application of LDM and Torchvision for image upscaling has been explored using medical image data, such as mammography, serving as an alternative to traditional image compression and decompression algorithms. The experimental outcomes demonstrate that this approach surpasses a conventional file compression algorithm, and convolutional neural network (CNN) models trained with decompressed files perform comparably to those trained with original image files. This approach also significantly reduces dataset size so that it can be distributed with a smaller size, and medical images take up much less space in medical devices. The research implications extend to noise reduction in lossy compression algorithms and substitute for complex wavelet-based lossless algorithms.

关键词： Latent Diffusion Model Denoising Diffusion Probabilistic Model DICOM ResNet Breast Mammography

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Robust and efficient optimization scheme leading to KL transform

Robust and efficient optimization scheme leading to KL trans...

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data compression conference (DCC)

作者： Pankiv, Oleksandr Puchala, Dariusz Stokfiszewski, Kamil Lodz Univ Technol Inst Informat Technol Lodz Poland

ISBN: (纸本)9781665478939

The Karhunen-Loève transform (KLT), as a component of block scalar quanti-zation, is optimal among linear orthonormal transforms and allows to obtain the smallest value of mean squared error (MSE) for a given rate of data representation. In this paper we propose a novel and robust optimization scheme designed for arti-ficial neural networks that implies possibly minimal constraints and allows to obtain the KLT up to the permutation of basis vectors. The proposed scheme involves two optimization criteria: (i) minimization of the MSE of signal reconstruction and (ii) minimization of the entropy related criterion, see Fig. 1(a).

关键词： Neural networks data compression Transforms Minimization Signal reconstruction Entropy Optimization

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Compressed Cannon's Algorithm

Compressed Cannon's Algorithm

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2024 ieee High Performance Extreme Computing conference, HPEC 2024

作者： Jencka, Louis Bienz, Amanda University of New Mexico Department of Computer Science Albuquerque United States

ISBN: (纸本)9798350387131

Parallel matrix-matrix multiplication (GEMM) is a linear-algebra operation underpinning many applications in high-performance and scientific computing. With strong scaling, GEMM's communication latency among processes, nodes, and accelerators can be a leading bottleneck in performance. data-compression is a common solution for reducing transport costs, but is often not applied for this purpose in HPC, where low-latency & high-throughput networks are difficult for com-pressors to match. However, some parallel GEMM algorithms - such as Cannon's method - exhibit a communication pattern where data is rarely altered as it travels between processes, providing an opportunity for a simple compression strategy: compress submatrices once at the outset, and then circulate the compressed version. This minimizes compression, which is typically significantly slower than decompression, at a price of additional memory utilization. In this work, we have measured and modeled scaling studies of this strategy, using six existing and widely-used lossless compressors (NDZip, ZStandard, Zip, BloscLZ, ZFP, & LZ4). We find most of these compressors can improve communication latency, with speedups of up to 1.2 times observed. Additionally, we evaluate and discuss the accuracy of our model against our measured results. © 2024 ieee.

关键词： data compression

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