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

共 16713 条记录，以下是301-310 订阅

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Achieving High Accuracy and Fast Speed for Sketch compression

Achieving High Accuracy and Fast Speed for Sketch Compressio...

引用

ieee International conference on Communications (ieee ICC)

作者： Ye, Jin Shan, Yuanchao Zhang, Wenlu Li, Lin Li, Sitan Shao, Jing Huang, Jiawei Guangxi Univ Sch Comp Elect & Informat Nanning 530004 Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Guangxi Key Lab Multimedia Commun & Network Techn Nanning 530004 Peoples R China

ISBN: (纸本)9781538674628

To reduce the communication overhead in distributed sketch system, it is desirable to compress sketches before uploading. However, current sketch compression approaches hardly achieve high speed of compression procedure and low error of compressed sketches at the same time. In this paper, we take a clean slate approach to design a sketch compression scheme called as Fast-Mapping that achieves both fast compression speed and high accuracy. Based on the prior statistics knowledge of bucket data distribution, Fast-Mapping compresses the similar buckets to obtain high accuracy. We also theoretically derive the compression error bound of Fast-Mapping. The experimental results show that, Fast-Mapping achieves higher speed and lower error than the-state-of-art works.

关键词： sketch compression network measurement compression speed network management

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Visual data compression Approaches for Edge-based ORB-VSLAM Systems 25

Visual Data Compression Approaches for Edge-based ORB-VSLAM ...

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25th International Carpathian Control conference (ICCC)

作者： Salih, Omar M. Vasarhelyi, Jozsef Univ Miskolc Inst Automat & Infocommun Miskolc Hungary Northern Tech Univ Tech Coll Kirkuk Mosul Iraq

ISBN: (纸本)9798350350708;9798350350715

Visual Simultaneous Localization And Mapping (VSLAM) is a technique that enables robots to localize and estimate their pose by reconstructing a map structure of the surrounding environment. However, VSLAM encounters significant computational loads, resource consumption, and bandwidth constraints. Due to these issues, offloading the entire VSLAM architecture onto a mobile device could be impractical. To reduce weight, lower energy consumption, and keep the size of mobile devices small, VSLAM can be efficiently deployed by offloading computationally intensive tasks to cloud platforms. This requires efficient visual data compression methods to reduce the data load, bandwidth, and latency. This paper proposes two implementation approaches for integrating visual data compression and decompression with ORB-VSLAM systems. The proposed systems are compared to counterpart JPEG-based systems, and the results show significant improvement for both approaches.

关键词： Visual Simultaneous Localization And Mapping (VSLAM) mobile robots image compression edge-assisted computing machine vision

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A Du-Octree based Cross-Attention Model for LiDAR Geometry compression

A Du-Octree based Cross-Attention Model for LiDAR Geometry C...

引用

ieee International conference on Robotics and Automation (ICRA)

作者： Cui, Mingyue Feng, Mingjian Long, Junhua Hu, Daosong Zhao, Shuai Huang, Kai Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Sun Yat Sen Univ Shenzhen Inst Guangzhou 510275 Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Point cloud compression is an essential technology for efficient storage and transmission of 3D data. Previous methods usually use hierarchical tree data structures for encoding the spatial sparseness of point clouds. However, the node context within the tree is not fully discovered since the feature space among nodes varies significantly. To address this problem, we innovatively represent the LiDAR points in a two-octree structure instead of using traditional single-octree coding, and then design the cross-attention model to capture the hierarchical features between different octrees, of which each octree incorporates a transformer-based deep entropy model and an arithmetic encoder. Besides, we introduce the untied cross-aware position encoding with principal component analysis and different projection matrices, which enhances the correlations over two octrees' attention feature embeddings. Experimental results show that our method outperforms the previous state-of-the-art works, achieving up to 8.2% Bpp savings on point cloud benchmark datasets with different lasers.

关键词： Encoding (symbols)

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Energy Consumption Minimization for a data compression Based NOMA-MEC System 5

Energy Consumption Minimization for a Data Compression Based...

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5th Information Communication Technologies conference (ICTC)

作者： Tu, WeiZheng Liu, Xuan Dept State Grid Jiuquan Power Supply Co State Grid Jiuquan Peoples R China Xian Univ Posts & Telecommun Dept Commun Engn Xian Shannxi Peoples R China

ISBN: (纸本)9798350373530;9798350373523

In this paper, we study the energy consumption minimization problem in a data compression based non-orthogonal multiple access-mobile edge computing (NOMA-MEC) system. Considering the partial compression and offloading schemes, we formulate a non-convex system energy consumption minimization optimization problem by jointly optimizing the users' data compression and offloading time, transmit power, as well as task compression and offloading ratios. Then we derive the closed-form expression of the optimal transmit power for each user and propose a successive convex approximation (SCA) based iterative algorithm to solve the optimization problem. Computer results verify the advantages of the proposed scheme via computer simulations and show that compared with other benchmark schemes, the proposed scheme can effectively reduce the system energy consumption.

关键词： Partial compression Resource allocation Energy consumption minimization NOMA

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Task-aware compression for Wearable Sensor data

Task-aware Compression for Wearable Sensor Data

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2023 ieee Biomedical Circuits and Systems conference, BioCAS 2023

作者： Wu, Di Yang, Jie Sawan, Mohamad Westlake University CenBRAIN Neurotech School of Engineering Hangzhou310024 China

ISBN: (纸本)9798350300260

The progress made in the field of neuroscience, along with the advancement of deep learning (DL), has led to notable achievements with wearable biomedical sensors. However, the large amount of data generated by wearable sensors poses a significant challenge for data transmission due to the limited resource capacity of wearable and implantable devices. We present in this paper presents a task-aware signal compression framework, dubbed task-aware compression (TAC), for wearable sensor data that considers the specific task being performed and the data reconstruction process. The proposed algorithm uses a learnable compression matrix that is trained by a neural network to extract task-related features to reduce the dimension of the data. The compressed data is utilized for task-related analysis and high-fidelity reconstruction. The proposed methodology is evaluated with the task of seizure prediction using electroencephalogram (EEG) data, and experimental results illustrate that with a compression ratio of 1/16, the method is able to reduce maximum transmission and data analysis related power by more than 90%, and the prediction accuracy degradation is below 3%. © 2023 ieee.

关键词： data compression Deep neural network Energy efficient Task-aware Wearable sensors

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Autoencoder-based data compression Model Experiment for Semantic Communication 38

Autoencoder-based Data Compression Model Experiment for Sema...

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38th International conference on Information Networking (ICOIN)

作者： Oh, Jinyoung Choi, Yunkyung Oh, Chanyoung Na, Woongsoo Kongju Natl Univ Dept Software Cheonan South Korea

ISBN: (纸本)9798350330946;9798350330953

In recent years, the convergence of neural network architectures and semantic communication has led to innovative strides in representation learning. This paper explores the application of autoencoders, a subset of neural networks designed for unsupervised learning, in encoding and decoding data to capture semantic nuances. Additionally, we discuss future research directions, proposing a semantic communication model learned from time series data and presenting experimental results. Our experiments involve encoding and decoding time series data using autoencoders, evaluating the feasibility of integrating autoencoder technology into semantic communication. Following the experiment, our proposed model exhibited a loss rate of approximately 0.15-0.2% for the time series data. This outcome represents a notable result, especially when compared to the compression rate of transmission data. It hints at the potential for a future autoencoder-based semantic communication model.

关键词： Semantic communication autoencoder encoding/decoding data compression

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Attention Based Machine Learning Methods for data Reduction with Guaranteed Error Bounds

Attention Based Machine Learning Methods for Data Reduction ...

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2024 ieee International conference on Big data, Bigdata 2024

作者： Li, Xiao Lee, Jaemoon Rangarajan, Anand Ranka, Sanjay University of Florida Department of Computer and Information Science and Engineering GainesvilleFL32611 United States

ISBN: (纸本)9798350362480

Scientific applications in fields such as high energy physics, computational fluid dynamics, and climate science generate vast amounts of data at high velocities. This exponential growth in data production is surpassing the advancements in computing power, network capabilities, and storage capacities. To address this challenge, data compression or reduction techniques are crucial. These scientific datasets have underlying data structures that consist of structured and block structured multidimensional meshes where each grid point corresponds to a tensor. It is important that data reduction techniques leverage strong spatial and temporal correlations that are ubiquitous in these applications. Additionally, applications such as CFD, process tensors comprising hundred plus species and their attributes at each grid point. Reduction techniques should be able to leverage interrelationships between the elements in each *** this paper, we propose an attention-based hierarchical compression method utilizing a block-wise compression setup. We introduce an attention-based hyper-block autoencoder to capture inter-block correlations, followed by a block-wise encoder to capture block-specific information. A PCA-based post-processing step is employed to guarantee error bounds for each data block. Our method effectively captures both spatiotemporal and inter-variable correlations within and between data blocks. Compared to the state-of-the-art SZ3, our method achieves up to 8× higher compression ratio on the multi-variable S3D dataset. When evaluated on single-variable setups using the E3SM and XGC datasets, our method still achieves up to 3× and 2× higher compression ratio, respectively. © 2024 ieee.

关键词： Spatio-temporal data

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Adaptive Lossless compression for Genomics data by Multiple (s, k)-mer Encoding and XLSTM

Adaptive Lossless Compression for Genomics Data by Multiple ...

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

作者： Sun, Hui Ding, Yanfeng Yi, Liping Ma, Huidong Xie, Haonan Wang, Gang Liu, Xiaoguang College of Computer Science NBJL ICIC TMCC SysNet DISSec GTIISC Nankai University Tianjin300350 China Nanyang Technological University Nanyang639798 Singapore School of Electrical Engineering Guangxi University Nanning530004 China

ISBN: (纸本)9798350368741

Learning-based lossless compressors have been validated to have competitive advantages in genomics data (GD) compression. However, learning-based GD-dedicated compressors typically need to be pre-trained on multi-source data and then are directly used to compress another target data, we denote them as static compressors, and they often face two challenges: limited compression ratios and bad-performed generalization due to data distribution variations. To solve these problems, we propose AGDLC, a novel Adaptive Genomics data Lossless Compressor. It includes two critical designs: 1) We design a multiple (s, k)-mer mixer for extracting GD redundancy from multiple dimensions to improve compression ratios. 2) We introduce a recently popular XLSTM model as the backbone, which adaptively compresses GD while updating parameters, without pre-training, improving compression ratios and compression generalization at the same time. We compare AGDLC with 13 baselines on 7 real-world datasets, and the experimental results demonstrate that it achieves the best compression ratio with an average improvement of 2.162%-69.436%. The codes can be found at https://***/dingyanfeng/AGDLC. © 2025 ieee.

关键词： (s, k)-mer encoding genomics data learning-based compressor lossless compression XLSTM

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HBC: Combining Lossy and Lossless Hybrid Bilayer compression Framework on Time-Series data 21

HBC: Combining Lossy and Lossless Hybrid Bilayer Compression...

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21st ieee International Symposium on Parallel and Distributed Processing with Applications, 13th ieee International conference on Big data and Cloud Computing, 16th ieee International conference on Social Computing and Networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Lu, Wanying Liu, Liang Zhai, Wenbin Chen, Haoyuan Liu, Yulei Nanjing University of Aeronautics and Astronautics China

ISBN: (纸本)9798350329223

The popularization and application of the Internet of Things (IoT) technology has brought massive time series data, which puts forward higher requirements for data compression technology. At present, most existing compression methods use only a single lossy or lossless compression algorithm to perform data compression. Furthermore, traditional lossy compression methods usually adopt a fixed error threshold. However, in practical applications, users have different accuracy requirements for time series data in different numerical ranges. In this paper, we design a Hybrid Bilayer compression (HBC) framework, which consists of a data accuracy-aware lossy compression layer and a data feature-aware lossless compression layer. First, the original time series data is lossy compressed on the top layer of HBC, where the error threshold can be adaptively adjusted according to the user's accuracy requirements. Then, based on the features of lossy-compressed data, we use supervised learning to select the optimal lossless compression algorithm from the algorithm pool to further compress the data. Experimental results show that compared with three state-of-the-art compression algorithms, HBC reduces the storage space by 52.1%, 91.66%, and 92.27%, respectively. © 2023 ieee.

关键词： Digital storage

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Computationally and Memory-Efficient Robust Predictive Analytics Using Big data 2

Computationally and Memory-Efficient Robust Predictive Analy...

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2nd ieee conference on Artificial Intelligence (CAI)

作者： Menges, Daniel Rasheed, Adil Norwegian Univ Sci & Technol Dept Engn Cybernet Trondheim Norway

ISBN: (纸本)9798350354102;9798350354096

In the current data-intensive era, big data has become a significant asset for Artificial Intelligence (AI), serving as a foundation for developing data-driven models and providing insight into various unknown fields. This study navigates through the challenges of data uncertainties, storage limitations, and predictive data-driven modeling using big data. We utilize Robust Principal Component Analysis (RPCA) for effective noise reduction and outlier elimination, and Optimal Sensor Placement (OSP) for efficient data compression and storage. The proposed OSP technique enables data compression without substantial information loss while simultaneously reducing storage needs. While RPCA offers an enhanced alternative to traditional Principal Component Analysis (PCA) for high-dimensional data management, the scope of this work extends its utilization, focusing on robust, data-driven modeling applicable to huge data sets in real-time. For that purpose, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network, are applied to model and predict data based on a low-dimensional subset obtained from OSP, leading to a crucial acceleration of the training phase. LSTMs are feasible for capturing long-term dependencies in time series data, making them particularly suited for predicting the future states of physical systems on historical data. All the presented algorithms are not only theorized but also simulated and validated using real thermal imaging data mapping a ship's engine.

关键词： Big data Robust PCA Optimal Sensor Placement LSTM Thermal Imaging Ship Engine

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