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

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LogDAC: A Universal Efficient Parser-based Log compression Approach

LogDAC: A Universal Efficient Parser-based Log Compression A...

引用

ieee International conference on Communications (ICC)

作者： Tian, Ran Diao, Zulong Jiang, Haiyang Xie, Gaogang Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci lnst Comp Technol Beijing Peoples R China Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China Purple Mt Labs Nanjing Peoples R China

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

A large volume of logs provides a reliable data source for online services. For instance, 5G, Big 5G or Beyong 5G cloud core network and its User Plane Functions require analysis built on massive log storage. The storage cost of log data remains a problem for the industry. Compressing the logs before archives remains the most popular way to reduce it. However, structured logs, which usually present in tabular format, and unstructured logs, which build on variable templates, have to use different compression strategies. Using log parsers can eliminate this difference by converting unstructured logs into structured ones. Based on this idea, we introduce our universal efficient parser-based log compression approach LogDAC. It also uses a divide-and-conquer preprocess strategy to improve the compatibility with successive dictionary-based general compressors. The evaluation against both structured and unstructured public datasets show a solid improvement up to 45.4% and 489.53% of compression ratio respectively, compared with existing parser-based compressors.

关键词： log compression logs log management

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Phase Identification of Smart Meters Using a Fourier Series compression and a Statistical Clustering Algorithm

Phase Identification of Smart Meters Using a Fourier Series ...

引用

ieee Electrical Power and Energy conference (EPEC)

作者： Chiu, Jeremy Wong, Albert Park, James Mahony, Joe Ferri, Michael Berson, Tim Langara Coll Math & Stat Vancouver BC Canada Harris SmartWorks Res & Dev Ottawa ON Canada

ISBN: (纸本)9781665463188

Accurate labeling of phase connectivity in electrical distribution systems is important for maintenance and operations but is often erroneous or missing. In this paper, we present a process to identify which smart meters must be in the same phase using a hierarchical clustering method on voltage time series data. Instead of working with the time series data directly, we apply the Fourier transform to represent the data in their frequency domain, remove 98% of the Fourier coefficients, and use the remaining coefficients to cluster the meters are in the same phase. Result of this process is validated by confirming that cluster (phase) membership of meters does not change over two monthly periods. In addition, we also confirm that meters that belong to the same feeder within the distribution network are correctly classified into the same cluster, that is, assigned to the same phase.

关键词： Phase identification clustering Fourier series Fourier series compression

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Supervised compression for Resource-Constrained Edge Computing Systems 22

Supervised Compression for Resource-Constrained Edge Computi...

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22nd ieee/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Matsubara, Yoshitomo Yang, Ruihan Levorato, Marco Mandt, Stephan Univ Calif Irvine Dept Comp Sci Irvine CA 92717 USA

ISBN: (纸本)9781665409155

There has been much interest in deploying deep learning algorithms on low-powered devices, including smartphones, drones, and medical sensors. However, full-scale deep neural networks are often too resource-intensive in terms of energy and storage. As a result, the bulk part of the machine learning operation is therefore often carried out on an edge server, where the data is compressed and transmitted. However, compressing data (such as images) leads to transmitting information irrelevant to the supervised task. Another popular approach is to split the deep network between the device and the server while compressing intermediate features. To date, however, such split computing strategies have barely outperformed the aforementioned naive data compression baselines due to their inefficient approaches to feature compression. This paper adopts ideas from knowledge distillation and neural image compression to compress intermediate feature representations more efficiently. Our supervised compression approach uses a teacher model and a student model with a stochastic bottleneck and learnable prior for entropy coding (Entropic Student). We compare our approach to various neural image and feature compression baselines in three vision tasks and found that it achieves better supervised rate-distortion performance while maintaining smaller end-to-end latency. We furthermore show that the learned feature representations can be tuned to serve multiple downstream tasks.

关键词： Deep learning Training Image coding Computational modeling Stochastic processes Rate-distortion Entropy coding

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FedRS-Net: A Federated Learning Approach for Collaborative Multi-Modal Maritime Analytics 27

FedRS-Net: A Federated Learning Approach for Collaborative M...

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27th International conference on Information Fusion (FUSION)

作者： Tienin, Bole Wilfried Cui, Guolong Nana, Yannick Abel Talla Ukwuoma, Chiagoziem Chima Esidang, Roldan Mba Senouci, Mohammed Raouf Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu Peoples R China Chengdu Univ Technol Coll Nucl Technol & Automat Engn Chengdu Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China

ISBN: (纸本)9798350371420;9781737749769

Ensuring the safety and security of our oceans demands a comprehensive Maritime Situational Awareness (MSA) strategy. However, this task has several challenges, including using multi-modal data, data sharing among agencies, privacy concerns, and bandwidth limitations. To address these challenges, this research introduced FedRS-Net. FedRS-Net is a federated deep learning framework that trains multi-modal remote sensing data without exposing client data. The system employs a communication-efficient federated averaging algorithm and a novel convolutional neural network architecture called Redesigned Skip Connection. It integrates synthetic aperture radar (SAR) and optical satellite imageries to achieve remarkable results. Extensive experiments were conducted on the maritime vessel datasets, resulting in a testing accuracy of 99.8%. Further, applying secure aggregation and momentum-based gradient compression reduced communication costs by 7%. FedRS-Net overcomes privacy concerns and facilitates agency collaboration by enabling collective maritime monitoring through decentralized data. This research provides a robust federated learning solution tailored for multi-modal remote sensing analytics applications.

关键词： Federated Learning Maritime Situational Awareness (MSA) Remote Sensing Synthetic Aperture Radar (SAR) Optical Satellite Imagery Multi-modal Classification Collaborative data Analysis

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Martin: Mobility-Aware Reputation Mechanism for Federated Learning

Martin: Mobility-Aware Reputation Mechanism for Federated Le...

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2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Lixin Chang, Xin Yang, Hanqing Wang, Jingyu Zhang, Xiaolin Shi, Cuiyun School of Information Renmin University of China Beijing China School of Digital and Intelligence Industry Inner Mongolia University of Science and Technology BaoTou China

ISBN: (纸本)9798350368741

The rapid development of the Internet of Things (IoT) has resulted in an increasing volume of data, much of which contains sensitive private information. Federated Learning (FL) allows clients to train models without sharing raw data, showing significant potential for privacy protection. However, device mobility presents challenges for applying FL in IoT, including high communication costs, data quality fluctuations and intermittent device participation. We propose a mobility-aware reputation mechanism for federated learning (named Martin) to address these issues. First, we designed a quality evaluation method based on sparse gradient similarity, which evaluates the data quality of devices while reducing communication costs. Additionally, we designed a dynamic reputation mechanism to quantify the quality evaluation records of devices, enabling the correct selection of intermittently participating devices from a dynamically changing set of devices, thereby improving model training quality under limited communication bandwidth. Experimental results validate its effectiveness. © 2025 ieee.

关键词： dynamic reputation federated learning gradient compression quality assessment

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2024 ieee International conference on Real-Time Computing and Robotics, RCAR 2024

2024 IEEE International Conference on Real-Time Computing an...

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2024 ieee International conference on Real-Time Computing and Robotics, RCAR 2024

ISBN: (纸本)9798350372601

The proceedings contain 105 papers. The topics discussed include: design of a water surface robot photographer with the optimized interface photography algorithm;motion control of training robot with enhanced defecation control ability based on active and passive;data-driven discovery of unmanned aerial vehicles dynamics;a light waveguide bending sensor with a high sensing range and high sensitivity;haptic rendering of arthroscopic meniscus examination in SOFA;enhanced small ship segmentation with optimized ScatYOLOv8+CBAM on embedded systems;robotic assistance for standardized compression ultrasound elastography;learning capability to enhance locomotion control and planning for legged robots;deformability-aware stiffness-guided robotic grasping strategy based on tactile sensing;and a building information modeling (BIM)-enabled robotic system for automated indoor construction progress monitoring.

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The High-Resolution Medical Picture compression Using Segmentation-Based Lossless Image Coding 3

The High-Resolution Medical Picture Compression Using Segmen...

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3rd International conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Garg, Avni Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

Medical imaging serves a crucial role in the diagnosis of illness. The amount of local storage capacity and transfer bandwidth needed by remote medical devices to aid in patient diagnosis and treatment is closely related to the losslessness of the compression used. Losslessness and similarity are two amazing features of medical imaging. compression centres on learning to take use of these two features to decrease the amount of data that must be stored to describe a picture. In this study, we use large dataset mining to create an image catalogue. That is, to identify pictures' elemental parts. For multi-component medical pictures, we offer a soft compression approach that faithfully preserves the images' underlying structure. Our findings demonstrate that our newly developed soft compression approach outperforms the industry standards JPEG2000, and PNG and we accompany this with thorough representation architecture for image compression. © 2023 ieee.

关键词： DCT DWT Huffman Coding Image compression Soft compression

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Deep Learning and its Applications: A Novel Approach to Machine Learning with Encoding Algorithms

Deep Learning and its Applications: A Novel Approach to Mach...

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2024 International conference on Intelligent and Innovative Practices in Engineering and Management, IIPEM 2024

作者： Suresh Babu, Kamalesh Thoppaen Ashok Tingare, Bhagyashree Khedkar, Vijayshri Diwan, Tarun Dhar William, P. Badholia, Abhishek Globallogic Inc 2535 Augustine Drive Santa ClaraCA95054 United States D Y Patil College of Engineering Akurdi Department of Artificial Intelligence and Data Science Pune India Department of Computer Science & Engineering Pune India Atal Bihari Vajpayee University Bilaspur India Sanjivani College of Engineering Deaprtment of Information Technology Kopargaon India Amity University Dubai United Arab Emirates Shri Shankaracharya Institute of Professional Management and Technology Department of Data Science Raipur India

ISBN: (纸本)9798350390049

Deep learning has revolutionized data processing by recognizing complicated patterns using multi-layered neural networks. Encoding complex data like hyperspectral photos may help deep learning models perform better. Encoding shapes raw data for compression, dimensionality reduction, and feature extraction. These methods matter. This research found that higher-dimensional encodings can improve deep learning model accuracy and convergence. Complex encoding algorithms like FE-16D and BLOSUM-20D outperform simpler ones in validation accuracy and AUC. To prove this, extensive testing and comparison were done. The study emphasizes the necessity to combine deep learning frameworks with improved encoding techniques to improve machine learning and large-scale data processing. © 2024 ieee.

关键词： data compression Deep Learning Dimensionality Reduction Encoding Algorithms Feature Extraction Hyperspectral Images Model Performance Neural Networks

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Cellular liberality is measurable as Lempel-Ziv complexity of fastq files 22

Cellular liberality is measurable as Lempel-Ziv complexity o...

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ieee 22nd International conference on Bioinformatics and Bioengineering (BIBE)

作者： Ogata, Norichika Hosaka, Aoi Nihon BioData Corp Kawasaki Kanagawa Japan Tokyo Univ Agr & Technol Tokyo Japan Mfg Technol Assoc Biol Kobe Hyogo Japan Kihara Inst Biol Res Yokohama Kanagawa Japan

ISBN: (数字)9781665484879

ISBN: (纸本)9781665484879

Many studies used the Shannon entropy of transcriptome data to determine cell dedifferentiation and differentiation. The collection of evidence has strengthened the certainty that the transcriptome's Shannon entropy may be used to quantify cellular dedifferentiation and differentiation. Quantifying this cellular status is being justified, we propose the term "liberality" for the quantitative value of cellular dedifferentiation and differentiation. In previous studies, we must convert the raw transcriptome data into quantitative transcriptome data through mapping, tag counting, assembling, and more bioinforrnatic processing to calculate the liberality. If we could remove this conversion step from estimating liberality, we could save computing resources and time and remove technical difficulties in using the computer. In this study, we propose a method of calculating cellular liberality without those transcriptome data conversion processes. We could calculate liberality by measuring the compression rate of raw transcriptome data. This technique, independent of reference genome data, increased the generality of cellular liberality.

关键词： Lempel-Ziv complexity Shannon entropy liberality dedifferentiation differentiation transcriptome RNA-seq genomics compression classification clustering

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AMG: Automated Efficient Approximate Multiplier Generator for FPGAs via Bayesian Optimization 22

AMG: Automated Efficient Approximate Multiplier Generator fo...

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22nd International conference on Field Programmable Technology (ICFPT)

作者： Li, Zhen Zhou, Hao Wang, Lingli Zhou, Xuegong Fudan Univ State Key Lab ASIC & Syst Shanghai Peoples R China Fudan Univ Inst Big Data Shanghai Peoples R China

ISBN: (纸本)9798350359114

We propose AMG, an open-source automated approximate multiplier generator for FPGAs driven by Bayesian optimization (BO) with parallel evaluation. The proposed method simplifies the exact half adders (HAs) for the initial partial product (PP) compression in a multiplier while preserving coarsegrained additions for the following accumulation. The generated multipliers can be effectively mapped to lookup tables (LUTs) and carry chains provided by modern FPGAs. Compared with 1167 multipliers from previous works, our generated multipliers can form a Pareto front with 28.70%-38.47% improvements in terms of the product of hardware cost and error on average. All source codes, reproduced multipliers, and our generated multipliers are available at https://***/phyzhenli/*** propose AMG, an open-source automated approximate multiplier generator for FPGAs driven by Bayesian optimization (BO) with parallel evaluation. The proposed method simplifies the exact half adders (HAs) for the initial partial product (PP) compression in a multiplier while preserving coarsegrained additions for the following accumulation. The generated multipliers can be effectively mapped to lookup tables (LUTs) and carry chains provided by modern FPGAs. Compared with 1167 multipliers from previous works, our generated multipliers can form a Pareto front with 28.70%-38.47% improvements in terms of the product of hardware cost and error on average. All source codes, reproduced multipliers, and our generated multipliers are available at https://***/phyzhenli/AMG.

关键词： Approximate Multiplier Bayesian Optimization FPGA

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