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

共 16713 条记录，以下是401-410 订阅

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MAGIC: Memory Advanced General Index compression for Learned Indexes 6

MAGIC: Memory Advanced General Index Compression for Learned...

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6th International conference on Next Generation data-Driven Networks, NGDN 2024

作者： Guo, Na Wang, Yaqi Xia, Xiufeng Shenyang Aerospace University School of Computer Science Shenyang China Northeastern University School of Computer Science and Engineering Shenyang China

ISBN: (纸本)9798350388374

With the continuous evolution of data-driven networks, storage optimization has emerged as a critical challenge for achieving efficient data management and real-time queries. Storage optimization, employing techniques such as data compression, deduplication, and redundancy elimination, aims to reduce storage requirements, thereby enhancing overall storage efficiency. Learned indexes play a pivotal role in data-driven networks, utilizing learning algorithms to identify frequently queried data and hotspots. This optimization contributes to the refinement of index design, ultimately boosting query performance. While learned indexes have achieved lower memory consumption compared to traditional indexes, their unstable memory overhead limits their applicability in large-scale *** address this challenge, this paper introduces a framework for learned index memory compression. The framework presents both lossy and lossless parameter compression strategies tailored to two types of learned index structures: data-based and range-based. Lossless parameter compression seeks to reduce the memory consumption of the index with minimal impact on query performance. In contrast, lossy parameter compression further diminishes memory consumption by sacrificing a certain degree of query performance. These two strategies provide flexible choices for storage optimization, allowing for an ideal balance in different application scenarios. Extensive experiments on synthetic and real datasets demonstrate that both lossless and lossy compression techniques can significantly reduce the memory consumption of learned indexes by up to 98.88% and 96.3%, respectively, with a maximum loss of query performance limited to 2.081% and 20.6%. © 2024 ieee.

关键词： Storage management

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Classification and Statistics of Application of Measurement Information compression Methods 27

Classification and Statistics of Application of Measurement ...

引用

27th International conference on Soft Computing and Measurements, SCM 2024

作者： Raimzhanova, Adel B. Semenov, Konstantin K. Peter The Great St. Petersburg Polytechnic University Higher School of Computer Tech. and Information Systems St. Petersburg Russia

ISBN: (纸本)9798350363708

The paper proposes a classification of the main groups of measurement information compression methods. We carried out an analysis of existing gaps in the range of algorithms and approaches used in practice. The paper presents the study results for statistics on the use of major groups of measurement information compression methods based on the sample of scientific publications indexed in the Google Scholar scientometric database. We identified the need in the field for algorithms that provide control of the error introduced during the measurement data compression. © 2024 ieee.

关键词： data compression

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Advancements in VLSI implementation of lossless ECG signal compression schemes

Advancements in VLSI implementation of lossless ECG signal c...

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2024 ieee International conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Dhanusha, A. Singh, Vipula RNSIT Dept of ECE Bangalore India

ISBN: (纸本)9798350376135

This paper analyzes various prediction methods of ECG signal compression and its hardware implementation. The comparative study of various prediction techniques of ECG compression both for single channel and multichannel ECG signals demonstrate significant compression ratios while maintaining acceptable reconstruction quality and low computational overhead. The paper examines recent progress in VLSI architectures customized for integrating prediction-based lossless ECG compression techniques. Various techniques like adaptive linear prediction, Golomb rice coding and Huffman coding are analyzed for ECG signals from different database. After the comparative study, it shows that the lossless ECG signal compression technique with Golomb rice coding has improved the compression ratio. For ECG signals from MIT-BIH database, with Golomb rice coding compression ratio has become 2.89. For multi-channel ECG signals from PTB database, compression ratio has become 4.07 using Golomb rice coding. For PTB database ECG signals, the gate count is 476K and for single channel ECG signals its 3.1K by using Golomb rice coding. © 2024 ieee.

关键词： Electrocardiography

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Conservative compression of Information Matrices using Event-Triggering and Robust Optimization 27

Conservative Compression of Information Matrices using Event...

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27th International conference on Information Fusion (FUSION)

作者： Funk, Christopher Noack, Benjamin Otto von Guericke Univ Inst Intelligent Cooperating Syst Autonomous Multisensor Syst Grp Magdeburg Germany

ISBN: (纸本)9798350371420;9781737749769

Distributed sensor fusion requires the transmission of intermediate fusion results, consisting of point estimates and associated error covariance or information matrices. Bandwidth constraints necessitate data compression techniques for error covariance and information matrices, which typically dominate data volume. To ensure the safe use of the fusion results for decision-making, these techniques must be conservative, i.e., not lead to the compressed error covariance or information matrices underestimating the true estimate error. This work introduces a novel approach for the conservative compressed transmission of information matrices, that builds on a previous event-based method for covariance matrices. The proposed method allows the entire sensor fusion pipeline to operate in 'information space', facilitating efficient fusion operations without the need to compute corresponding covariance matrices. Contributions include an event-trigger for information matrices and a robust-optimization-based bounding mechanism ensuring conservativeness. The proposed approach is evaluated in the context of transmitting error information matrices generated by extended information filter SLAM to a receiver for further processing.

关键词： Distributed data Fusion data Reduction Conservative data Fusion

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A Lightweight, Effective Compressibility Estimation Method for Error-bounded Lossy compression 25

A Lightweight, Effective Compressibility Estimation Method f...

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25th ieee International conference on Cluster Computing (CLUSTER)

作者： Ganguli, Arkaprabha Underwood, Robert Bessac, Julie Krasowska, David Calhoun, Jon C. Di, Sheng Cappello, Franck Argonne Natl Lab Lemont IL 60439 USA Natl Renewable Energy Lab Golden CO USA Northwestern Univ Evanston IL 60208 USA Clemson Univ Clemson SC 29631 USA Virginia Polytech Inst & State Univ Blacksburg VA 24061 USA

ISBN: (纸本)9798350307924

Error-bounded lossy compression turns more and more important for the data-moving intensive applications to deal with big datasets efficiently in HPC environments, which often requires knowing the compressibility of the datasets before performing the compression. However, the off-the-shelf state-of-the-art lossy compressors are often driven by error bounds, so the compression ratios cannot be forecasted until the completion of the compression operation. In this paper, we propose a lightweight, robust, easy-to-train model that estimates the compressibility of datasets for different lossy compressors accurately. Our approach combines novel predictors that measure various notions of spatial correlation and smoothness exploited by lossy compressors that are implemented efficiently on the GPU in a framework and that uses mixture model regression to improve robustness with conformal prediction to provide bounds on the estimates. We then use these models with a detailed analysis of speedup to understand the tradeoffs between high speed, consistent speed, and accuracy of the methods on real applications. We evaluate our approach in the context of 3 key applications where compression ratio estimation is highly required.

关键词： Lossy compression compression Estimation Rate Distortion Error Bounded Lossy Compressors

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compression and Decompression of Files Without Loss of Quality 1

Compression and Decompression of Files Without Loss of Quali...

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1st ieee International conference on Networking and Communications, ICNWC 2023

作者： Anand, K. Priyadharshini, M. Priyadharshini, K. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350336009

This paper proposes the Gzip algorithm for image and document compression and decompression. Gzip is a hybrid algorithm that combines Lz77 and Huffman. In document management and communication systems, picture and document compression and decompression are crucial. Image and document compression technologies are used to lower the amount of data required to represent the file. Image compression has proven to be the most advantageous and practical method in the field of digital image processing. The goal is to reduce the images' and documents' redundancy so that data may be stored or sent efficiently. In order to reduce data redundancy and conserve more hardware space and transmission bandwidth, the theory of data compression and decompression is therefore becoming more and more important. compression is beneficial because it makes use of less expensive resources like hard disc space and transmission bandwidth. When we evaluate the image quality, decompression is beneficial. In the proposed system, there is no reduction in data, but there is a decrease in data size without loss of quality. © 2023 ieee.

关键词： Bandwidth

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Smart Wildlife Security: Fog-Powered Video compression for Cloud Efficiency 2

Smart Wildlife Security: Fog-Powered Video Compression for C...

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2nd ieee International conference on Networking and Communications, ICNWC 2024

作者： Hore, Shayan Achari, Silpi Kartheek Pradeep, Chinta Deepa Thilak, K. SRM Institute of Science and Technology Dept of Networking and Communications Chengalpet Kattankulathur India

ISBN: (纸本)9798350365269

To address the pressing issue of wild animal incursions in rural areas, we propose an inventive solution to safeguard local communities, the project integrates fog computing and smart video compression. The system records continuous 24-hour video footage, employing intelligent algorithms to detect and transmit only relevant segments to the cloud. This reduction in data load enhances efficiency and conserves resources. The process generates a detailed document report, offering comprehensive documentation of wildlife activities for effective monitoring and analysis. The integration of these technologies not only improves security measures but also contributes to sustainable, resource-efficient solutions in wildlife protection. © 2024 ieee.

关键词： Image compression

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A Sparsity Based Adaptive compression Algorithm for Model compression 6

A Sparsity Based Adaptive Compression Algorithm for Model Co...

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6th International conference on Electrical Engineering and Control Technologies, CEECT 2024

作者： Lv, Yifu Hu, Yueli Shanghai University School of Mechatronic Engineering and Automation Department of Electrical Engineering Shanghai China

ISBN: (纸本)9798331528096

With the development of convolutional neural networks, the scale of activation data has increased dramatically, posing significant challenges to model storage and computation. The insight indicates that the sparsity of activations is significantly different across network layers, due to the differences in activation functions. Most existing studies are limited to applying a single compression algorithm to the entire model. This work proposes an adaptive compression method based on sparsity. The optimization strategy includes an adaptive selector that chooses the appropriate compression algorithm based on the sparsity level of each layer, thereby achieving high compression ratios. In comparison to three state-of-the-art compression methods, the proposed algorithm achieves superior compression performance on four mainstream CNN models at different data accuracies. © 2024 ieee.

关键词： Convolutional neural networks

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Reliable ML-Based Image Processing and compression for an Onboard Imaging Pipeline 15

Reliable ML-Based Image Processing and Compression for an On...

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15th Space Computing conference

作者： Garrett, Tyler Wildenstein, Diego Wang, Eileen Gretok, Evan George, Alan Univ Pittsburgh NSF Ctr Space High Performance & Resilient Comp S Pittsburgh PA 15260 USA

ISBN: (纸本)9798350384543;9798350384536

Sensing and onboard-processing capabilities of next-generation spacecraft continue to evolve. Enabled by advances in avionic systems, large amounts of data can be collected and stored on orbit. Nevertheless, loss of signal, communication delays, and limited downlink rates remain a bottleneck for delivering data to ground stations or between satellites. This research investigates a multistage image-processing pipeline and demonstrates rapid collection, detection, and transmission of data using the Space Test Program - Houston 7 - Configurable and Autonomous Sensor Processing Research (STP-H7-CASPR) experiment aboard the International Space Station as a case study. Machine-learning (ML) models are leveraged to perform intelligent processing and compression of data prior to downlink to maximize available bandwidth. Moreover, to ensure accuracy and preserve data integrity, a fault-tolerant ML framework is employed to increase pipeline reliability. This pipeline fuses the fault-tolerant Resilient TensorFlow framework with ML-based tile classification and the CNNJPEG compression algorithm. This research shows that the imaging pipeline is able to alleviate the impact of limited communication bandwidth by using reliable, autonomous data processing and compression techniques to achieve reduced transfer sizes of essential data. The results highlight the benefits provided by resilient classification and compression including minimized storage use and reduced downlink time. Furthermore, the findings of this research are used to assess the feasibility of such a system for future space missions. The combination of these approaches enables the system to achieve up to a 98.67% reduction in data size and downlink time as well as the capacity to capture imagery up to 75.19x longer time period for a given storage size, respectively, while maintaining reconstruction quality and data integrity.

关键词： data compression Fault-Tolerant Computing Resilient Computing Image Processing Image Classification Machine Learning Deep Learning Onboard Processing

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Latency Minimization in Heterogeneous Federated Learning through Joint compression and Resource Allocation 100

Latency Minimization in Heterogeneous Federated Learning thr...

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100th ieee Vehicular Technology conference, VTC 2024-Fall

作者： Marnissi, Ouiame El Hammouti, Hajar Bergou, El Houcine College of Computing Ben Guerir Morocco

ISBN: (纸本)9798331517786

Federated Learning (FL) has emerged as a promising decentralized machine learning (ML) paradigm where distributed clients collaboratively train models without sharing their private data. However, the heterogeneous properties of the clients, combined with the high dimensions of ML models considerably slow down the wall-clock convergence time. To address these challenges, we propose FedHC, a framework that jointly optimizes resource allocations and uplink compression levels of the clients to minimize the overall latency while respecting the energy budget and convergence guarantees. To solve the formulated optimization problem, we first derive the required number of global training rounds, to achieve the target accuracy. Then we propose an iterative algorithm, where at each step optimal CPU levels and bandwidth along with compression levels are derived. Our numerical results show the performance -with time reduction up to 4×- and robustness to non-IID data of our approach, compared to the benchmarks. © 2024 ieee.

关键词： Federated learning

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