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

共 16713 条记录，以下是291-300 订阅

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A New Offloading Method in Cloud Platforms: Reducing Latency Based on data compression 7

A New Offloading Method in Cloud Platforms: Reducing Latency...

引用

7th International conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Yang, Zhiqin Zhou, Zhennan Dong, Han Feng, Xiaoning Southampton Ocean Engineering Joint Institute Harbin Engineering University Harbin China College Of Computer Science And Technology Harbin Engineering University Harbin China

ISBN: (纸本)9798350361445

This paper introduces a novel approach to reduce computational latencies in cloud platforms by integrating data compression with Non-Orthogonal Multiple Access (NOMA) technologies. We propose a computational task offloading scheme that optimizes resource allocation to minimize maximum user task completion latency by utilizing an adaptive step size Artificial Fish Swarm Algorithm. Experiments conducted using Python simulation demonstrate that our approach significantly lowers task completion latencies compared to existing methods that rely solely on either data compression or NOMA technologies. © 2024 ieee.

关键词： data compression

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ConvAE-512: Bandwidth-Efficient Image compression in Edge with Auto-Encoders 4

ConvAE-512: Bandwidth-Efficient Image Compression in Edge wi...

引用

4th ieee Asian conference on Innovation in Technology, ASIANCON 2024

作者： Kotha, Aditya Sankepally, Sainath Reddy Vasanth, Murukuri S. V. S. V. Kosaraju, Nishoak Lovanshi, Mayank Ingle, Rajesh Iiit Naya Raipur India Iiit Naya Raipur India

ISBN: (纸本)9798350354218

The exponential growth in data creation and transmission, particularly high-resolution images, poses a significant challenge to network bandwidth, necessitating advanced compression techniques. This paper introduces ConvAE-512, a novel convolutional neural network-based encoder-decoder architecture optimized for edge computing environments. Our approach significantly outperforms existing lossy compression algorithms in image fidelity while efficiently reducing data transmission size and network utilization. ConvAE-512 achieves a compression rate of 6x, outperforming traditional Direct compression with a higher Structural Similarity Index (SSIM) of 0.9150 and a Peak Signal-to-Noise Ratio (PSNR) of 27.15 dB, underscoring its efficiency in preserving image quality despite compression. These results establish ConvAE-512 as an advanced alternative for efficient image compression, adeptly balancing quality retention and compression ratio. © 2024 ieee.

关键词： Bandwidth compression

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ALICE: Adapt your Learnable Image compression modEl for variable bitrates

ALICE: Adapt your Learnable Image Compression modEl for vari...

引用

2024 conference on Visual Communications and Image Processing

作者： Spadaro, Gabriele Ali, Muhammad Salman Presta, Alberto Pilo, Giommaria Bae, Sung-Ho Giraldo, Jhony H. Fiandrotti, Attilio Grangetto, Marco Tartaglione, Enzo Inst Polytech Paris Telecom Paris LTCI Paris France Univ Turin Turin Italy Kyung Hee Univ Yongin South Korea

ISBN: (纸本)9798331529543;9798331529550

When training a Learned Image compression model, the loss function is minimized such that the encoder and the decoder attain a target Rate-Distorsion trade-off. Therefore, a distinct model shall be trained and stored at the transmitter and receiver for each target rate, fostering the quest for efficient variable bitrate compression schemes. This paper proposes plugging Low-Rank Adapters into a transformer-based pre-trained LIC model and training them to meet different target rates. With our method, encoding an image at a variable rate is as simple as training the corresponding adapters and plugging them into the frozen pre-trained model. Our experiments show performance comparable with state-of-the-art fixed-rate LIC models at a fraction of the training and deployment cost. We publicly released the code at https://***/EIDOSLAB/ALICE.

关键词： Image compression

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MQ-CODER INSPIRED ARITHMETIC CODER FOR SYNTHETIC DNA data STORAGE 30

MQ-CODER INSPIRED ARITHMETIC CODER FOR SYNTHETIC DNA DATA ST...

引用

30th ieee International conference on Image Processing (ICIP)

作者： Pic, Xavier Dimopoulou, Melpomeni San Antonio, Eva Gil Antonini, Marc Cote Azur Univ I3S Lab Nice France CNRS UMR 7271 Sophia Antipolis France

ISBN: (纸本)9781728198354

Over the past years, the ever-growing trend on data storage demand, more specifically for "cold" data (i.e. rarely accessed), has motivated research for alternative systems of data storage. Because of its biochemical characteristics, synthetic DNA molecules are now considered as serious candidates for this new kind of storage. This paper introduces a novel arithmetic coder for DNA data storage, and presents some results on a lossy JPEG 2000 based image compression method adapted for DNA data storage that uses this novel coder. The DNA coding algorithms presented here have been designed to efficiently compress images, encode them into a quaternary code, and finally store them into synthetic DNA molecules. This work also aims at making the compression models better fit the problematic that we encounter when storing data into DNA, namely the fact that the DNA writing, storing and reading methods are error prone processes. The main take away of this work is our arithmetic coder and it's integration into a performant image codec.

关键词： image compression DNA JPEG 2000 arithmetic coder

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A Comprehensive Evaluation of Packet compression Techniques for NoC

A Comprehensive Evaluation of Packet Compression Techniques ...

引用

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

作者： John, Femy Sleeba, Simi Zerine APJ Abdul Kalam Technological University Kerala India Viswajyothi College of Eng. and Technology Kerala India Rajagiri School of Eng. and Technology Dept. of ECE Kerala India

ISBN: (纸本)9798350376135

Network-on-Chip (NoC) communication plays a vital role in modern multi-core processors. As the number of cores increases, so does the traffic on the NoC, leading to congestion and high latency. data compression techniques offer a promising approach to reduce network traffic and improve performance. This paper presents a comprehensive evaluation of various data compression methods suitable for NoC communication, focusing on their impact on network latency. We compare the different performance metrics like compression ratios achieved by different techniques and discuss their trade-offs with respect to complexity and latency overhead. © 2024 ieee.

关键词： Traffic congestion

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Efficient User Sequence Learning for Online Services via Compressed Graph Neural Networks

Efficient User Sequence Learning for Online Services via Com...

引用

ieee International conference on Web Services (ieee ICWS)

作者： Wu, Yucheng Chen, Liyue Cheng, Yu Chen, Shuai Xu, Jinyu Wang, Leye Peking Univ Key Lab High Confidence Software Technol Minist Educ Beijing Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China Alipay Hangzhou Informat Technol Co Ltd Hangzhou Peoples R China

ISBN: (纸本)9798350368567;9798350368550

Learning representations of user behavior sequences is crucial for various online services, such as online fraudulent transaction detection mechanisms. Graph Neural Networks (GNNs) have been extensively applied to model sequence relationships, and extract information from similar sequences. While user behavior sequence data volume is usually huge for online applications, directly applying GNN models may lead to substantial computational overhead during both the training and inference stages and make it challenging to meet real-time requirements for online services. In this paper, we leverage graph compression techniques to alleviate the efficiency issue. Specifically, we propose a novel unified framework called ECSeq, to introduce graph compression techniques into relation modeling for user sequence representation learning. The key module of ECSeq is sequence relation modeling, which explores relationships among sequences to enhance sequence representation learning, and employs graph compression algorithms to achieve high efficiency and scalability. ECSeq also exhibits plug-and-play characteristics, seamlessly augmenting pre-trained sequence representation models without modifications. Empirical experiments on both sequence classification and regression tasks demonstrate the effectiveness of ECSeq. Specifically, with an additional training time of tens of seconds in total on 100,000+ sequences and inference time preserved within 10(-4) seconds/sample, ECSeq improves the prediction R@P0.9 of the widely used LSTM by similar to 5%.

关键词： Sequence Representation Learning Graph Neural Network Graph compression Online Inference

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Spectral Efficiency Maximization for Probabilistic Semantic Communication with Rate Splitting 99

Spectral Efficiency Maximization for Probabilistic Semantic ...

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ieee 99th Vehicular Technology conference (VTC-Spring)

作者： Zhao, Zhouxiang Yang, Zhaohui Chen, Mingzhe Gan, Xu Huang, Chongwen Sun, Yao Yang, Qianqian Xu, Wei Zhang, Zhaoyang Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China Zhejiang Prov Key Lab Info Proc Commun & Netw IPC Hangzhou Peoples R China Univ Miami Dept Elect & Comp Engn Coral Gables FL 33146 USA Univ Miami Inst Data Sci & Comp Coral Gables FL 33146 USA Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Lanark Scotland Southeast Univ Natl Mobile Commun Res Lab Nanjing Peoples R China

ISBN: (纸本)9798350387414

In this paper, the problem of joint transmission and computation resource allocation for probabilistic semantic communication (PSC) network with rate splitting multiple access (RSMA) is investigated. In the considered model, the base station (BS) needs to transmit a large amount of data, which is represented by substantial knowledge graphs, to multiple users. Due to limited communication resource, the BS needs to utilize semantic communication techniques to compress the large-sized data. In this paper, the semantic communication is enabled by shared probability graphs between the BS and users. The process of semantic compression requires computation power at the BS, which has an impact on limited power budget. Therefore, it is necessary to balance the power between transmission and computation. Based on the probability graph, the semantic rate related to semantic compression ratio is first theoretically formulated. Then, the problem is formulated as an optimization problem with the aim of maximizing the sum semantic rate of all users under total power, semantic compression ratio, and rate allocation constraints. To tackle this problem, an iterative algorithm is accordingly proposed to obtain a suboptimal solution. Numerical results validate the effectiveness of the proposed scheme.

关键词： Semantic communication rate splitting multiple access knowledge graph probability graph

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Less: Large-scale Workload Forecasting Model Based on Multiple Sequence compression

Less: Large-scale Workload Forecasting Model Based on Multip...

引用

ieee International conference on Web Services (ieee ICWS)

作者： Ding, Zeyuan Feng, Binbin Yu, Wangyang Zhang, Bolan Shaanxi Normal Univ Sch Comp Sci Xian Peoples R China Tongji Univ Dept Comp Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9798350368567;9798350368550

As application migration to the cloud becomes the mainstream way of application deployment, application run-time management presents a significant need for large-scale workload prediction technology. Existing large-scale workload approaches commonly construct training sets based on all the training samples generated from the original data to generate prediction models. However, due to the similar behavior between different container instances of the microservice application, training and modeling in this way results in a huge number of redundant samples, which produces a significant redundant training overhead. Therefore, this paper proposes Less, a large-scale workload forecasting model based on multiple sequence compression. First, based on the grouping results of similar containers, a container workload feature recognition algorithm is proposed to determine the common and individual features of container workloads in each prediction period, so as to guide the compression of workload sequences within each group;second, a fitness function that takes into account the common features, individual features, and the number of sequences are designed, and the optimal compressed sequences are solved by the Whale Optimization Algorithm to efficiently reduce the number of redundant training workload sequences, and then the Bidirectional Gated Recurrent Unit model is built and trained based on the compressed sequences, which effectively reduces the model complexity and overhead while ensuring the accuracy. Finally, we validate the comprehensive advantages of Less in terms of accuracy and overhead based on public datasets and verify the effectiveness of each subpart of our model through ablation experiments.

关键词： large-scale multiple sequence compression feature recognition common features individual features Whale Optimization Algorithm fitness function Bi-directional Gated Recurrent Unit

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Enhancing Lossy compression Through Cross-Field Information for Scientific Applications

Enhancing Lossy Compression Through Cross-Field Information ...

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2024 Workshops of the International conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Liu, Youyuan Jia, Wenqi Yang, Taolue Yin, Miao Jin, Sian Temple University PhiladelphiaPA United States University of Texas at Arlington ArlingtonTX United States

ISBN: (纸本)9798350355543

Lossy compression is one of the most effective methods for reducing the size of scientific data containing multiple data fields. It reduces information density through prediction or transformation techniques to compress the data. Previous approaches use local information from a single target field when predicting target data points, limiting their potential to achieve higher compression ratios. In this paper, we identified significant cross-field correlations within scientific datasets. We propose a novel hybrid prediction model that utilizes CNN to extract cross-field information and combine it with existing local field information. Our solution enhances the prediction accuracy of lossy compressors, leading to improved compression ratios without compromising data quality. We evaluate our solution on three scientific datasets, demonstrating its ability to improve compression ratios by up to 25% under specific error bounds. Additionally, our solution preserves more data details and reduces artifacts compared to baseline approaches. © 2024 ieee.

关键词： lossy compression machine learning scientific data compression

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Controller Dynamic Linearization Based data-Driven Adaptive Control for a Vapor-compression Refrigeration System 13

Controller Dynamic Linearization Based Data-Driven Adaptive ...

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13th ieee data Driven Control and Learning Systems conference, DDCLS 2024

作者： Fang, Jingyu Yu, Xian Xiong, Shuangshuang College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China School of Automation Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9798350361674

This paper applies a data-driven adaptive control method to a benchmark of vapor compression refrigeration system based on the controller dynamic linearization method. Two controllers, namely, a single-input single-output controller and an extended multiple-input multiple-output controller, are designed to track the exit temperature of the evaporator secondary flux and the refrigerant superheat at the evaporator exit, by controlling the valve opening of the evaporator, and the compressor speed. Both the controllers entirely rely on input-output data of the controlled system in the controller design. Comparative results between the applied data-driven adaptive control and the default PID control provided by the benchmark demonstrate the effectiveness of the two designed controllers. © 2024 ieee.

关键词： Vapor compression refrigeration

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