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

共 16713 条记录，以下是331-340 订阅

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Research on Virtual Widening of Power Information Acquisition Channel Based on LoRa and data compression 12

Research on Virtual Widening of Power Information Acquisitio...

引用

ieee 12th data Driven Control and Learning Systems conference (DDCLS)

作者： Jiang, Jing Wang, Mingjia Shandong Foreign Trade Vocat Coll Dept Informat Engn Qingdao 266100 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Peoples R China

ISBN: (纸本)9798350321050

Limited by the channel bandwidth and information rate of the communication network of the electric energy information acquisition system, the energy data of the power system terminal lacks a suitable access method response to the above problems, this article combines the wide coverage characteristics and data compression technology achieved by LoRa (Long Range Radio, LoRa) spread spectrum communication, and proposes a new method of access communication based on LoRa spread spectrum and improved data compression coding for embedded applications. This technology can not only take into account the advantages of low design cost, strong coverage, anti-interference, etc. But also increases the virtual bandwidth of the channel, optimizes the comlink, and lay a solid foundation for high-frequency data collection and large-scale data transmission.

关键词： Electric energy information acquisition system LoRa spread spectrum technology data compression technology Virtual bandwidth

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Limits of the current H.264 video crypto-compression methods 13

Limits of the current H.264 video crypto-compression methods

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13th International conference on Image Processing Theory Tools and Applications

作者： Reinders, Erwan Puteaux, Pauline Brau, Samuel Puechl, William Univ Montpellier CNRS LIRMM Montpellier France DroneGeofencing Nimes France Univ Lille Cent Lille CNRS UMR CRIStAL 9189 F-59000 Lille France

ISBN: (纸本)9798331541859;9798331541842

In video surveillance scenarios, it is vital to secure the visual content, both during transmission and storage. As these video sequences can represent large volumes of data, it is also necessary to compress them, in order to transmit the most data with the available bandwidth, or reduce their final size. Moreover, video sensors are constantly evolving, offering videos at ever-higher resolutions. Codec H.264 is a video compression standard widely used in video surveillance, even today. In this paper, we propose an analysis of the various crypto-compression techniques based on the codec H.264, in light of this evolution in video resolution. We show that it is quantitatively difficult to find differences between low-resolution and high-resolution crypto-compressed video, while visually, more of the original video content is recognizable at high resolution.

关键词： Multimedia security Video crypto-compression analysis Resolution evolution Visual security

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GEOMETRY-CORRECTED GEODESIC MOTION MODELING WITH PER-FRAME CAMERA MOTION FOR 360-DEGREE VIDEO compression 49

GEOMETRY-CORRECTED GEODESIC MOTION MODELING WITH PER-FRAME C...

引用

49th ieee International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Regensky, Andy Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg Multimedia Commun & Signal Proc Cauerstr 7 D-91058 Erlangen Germany

ISBN: (纸本)9798350344868;9798350344851

The large amounts of data associated with 360-degree video require highly effective compression techniques for efficient storage and distribution. The development of improved motion models for 360-degree motion compensation has shown significant improvements in compression efficiency. A geodesic motion model representing translational camera motion proved to be one of the most effective models. In this paper, we propose an improved geometry-corrected geodesic motion model that outperforms the state of the art at reduced complexity. We additionally propose the transmission of per-frame camera motion information, where prior work assumed the same camera motion for all frames of a sequence. Our approach yields average Bjontegaard Delta rate savings of 2.27% over H.266/VVC, outperforming the original geodesic motion model by 0.32 percentage points at reduced computational complexity.

关键词： 360-degree geodesic motion model inter prediction video compression

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Comparison of Imaging and data Prediction compression Methods for Implanted Real-Time Peripheral Nervous System 3

Comparison of Imaging and Data Prediction Compression Method...

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3rd ieee International conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Coviello, Antonio Mangiagalli, Letizia Spagnolini, Umberto Magarini, Maurizio Politecnico di Milano Department of Electronics Information and Bioengineering Italy

ISBN: (纸本)9798350378009

This study focuses on the problem of reducing the transmission rate of electroneurographic (ENG) signals for implantable medical devices. Such devices represent a significant innovation in the healthcare sector. We examine seven compression algorithms by implementing a variety of techniques that includes, among the others, image compression and predictors. Our results show that Fractal compression (FC) and Vector Quantization (VQ) algorithms are the most effective, with a low value of percentage root difference (PRD), acceptable compression Ratio (CR), and temporal redundancy. The analysis suggests that the optimal time window for compression should be between 10 ms and 50 ms. Our findings indicate that the FC and VQ algorithms could be suitable for real-time application, opening promising avenues for future research in the field of neural interfaces. © 2024 ieee.

关键词： Vector quantization

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A Precise Decompression Method for Intel FPGA Bitstream 3

A Precise Decompression Method for Intel FPGA Bitstream

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3rd International conference on Electronics and Information Technology, EIT 2024

作者： Ren, Lingrui Zhang, Xingcan Wang, Jian University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China

ISBN: (纸本)9798350369151

FPAG bitstream is a binary file containing chip configuration information. It is often compressed to conserve storage space and reduce configuration time. In this paper, we propose a precise decompression method for commercial FPGA bitstreams, which is a crucial step for assessing the security of FPGAs. In detail, by comparing the structure of compressed and uncompressed bitstream files, we identify the compression region, extract the compressed data, and construct a database with the mapping information. Then, we introduce a decoding algorithm that utilizes a sliding window approach in terms of the characteristics of the compression data. Finally, our decompression framework is validated on 24 different types of Intel FPGAs from 6 families, with a perfect accuracy rate of 100%. © 2024 ieee.

关键词： data compression

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DECO: Dynamic Energy-aware compression and Optimization for In-Memory Neural Networks 67

DECO: Dynamic Energy-aware Compression and Optimization for ...

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67th ieee International Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Gaire, Rebati Tabrizchi, Sepehr Najafi, Deniz Angizi, Shaahin Roohi, Arman Univ Nebraska Sch Comp Lincoln NE USA New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

ISBN: (纸本)9798350387186;9798350387179

This paper introduces DECO, a framework that combines model compression and processing-in-memory (PIM) to improve the efficiency of neural networks on IoT devices. By integrating these technologies, DECO significantly reduces energy consumption and operational latency through optimized data movement and computation, demonstrating notable performance gains on CIFAR-10/100 datasets. The DECO learning framework significantly improved the performance of compressed network modules derived from MobileNetV1 and VGG16, with accuracy gains of 1.66% and 0.41%, respectively, on the intricate CIFAR-100 dataset. DECO outperforms the GPU implementation by a significant margin, demonstrating up to a two-order-of-magnitude increase in speed based on our experiment.

关键词： Model compression feature extraction processing in-memory

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Classification with PPMd compression in Few-Shot Learning: The Case of Eye-Tracking Dyslexia Detection

Classification with PPMd Compression in Few-Shot Learning: T...

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2024 ieee Ural-Siberian conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2024

作者： Abrosimova, Marina Rebak, Anna Novikov, Roman Markov, Nikita Ural Federal University Yekaterinburg Russia

ISBN: (纸本)9798350362893

Eye-tracking is an evolving field of medical technology that provides valuable information about cognitive processes and visual patterns of an individual. Dyslexia detection is known to be one of the most reliable applications of eye-tracking. However, eye-tracking requires an active screening process, meaning that available data is limited in scope. Therefore, there is interest in methods that can use a limited amount of data for classification, i.e. few-shot learning. In this paper, we applied a compression-based text classification method with PPMd algorithm to the task of dyslexia detection. We propose a signal processing and quantization procedure that enables classification with compression for eye-tracking data. By representing the signal as interpolated series of differences between gaze shifts, the entire dataset can be classified with 86-92.2% accuracy using one to ten shots for learning. The magnitude spectrum representation of the same signal can be used to achieve 95.1% of accuracy in more conservative 10-fold setting, close to the best published results for the dataset. Our results highlight the potential of classification with compression in data-constrained problems and further demonstrate the suitability of the method for quantized biomedical data. © 2024 ieee.

关键词： Classification (of information)

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ACD: A Lossless compression Based on Dimensionality Reduction for Multi-dimensional Time Series data 6

ACD: A Lossless Compression Based on Dimensionality Reductio...

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6th International conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Chen, Haolong Liu, Liang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798331541750

Multi-dimensional time series data are generated daily in lots of domains. Compressing these data to reduce storage overhead is an important topic. Existing compression algorithms resort to converting multi-dimensional time series data into multiple separate one-dimensional data to compress. However, these algorithms fail to exploit both intra- and inter-correlation among dimensions and the compression is not efficient. To solve the problem, this paper introduces a novel multi-dimensional time series compression framework called ACD, a lossless compression based on dimensionality reduction. Initially, the n-dimensional time series data undergo dimensionality reduction via space-filling curves(SFCs) encoding. Subsequently, we partition the encoded data heuristically into several segments based on distinctive patterns in different time windows. Finally, suitable compression is conducted according to segment patterns. We implement our algorithm in an out-of-box commercial time series database engine VictoriaMetrics. Our experiments demonstrate that ACD achieves at least 40% improvement over existing methods such as Chimp, Gorilla, and DeltaZstd as evaluated through the Time Series Benchmark Suite(TSBS). © 2024 ieee.

关键词： data compression

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Secure transmission of colored images using a sparse matrix-based compression-then-encryption technique 5

Secure transmission of colored images using a sparse matrix-...

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5th ieee International conference on Recent Trends in Computer Science and Technology, ICRTCST 2024

作者： Bose, Sayantan Biswas, Satarupa Hazra, Joydev Heritage Institute of Technology Department of Computer Science and Business Systems Kolkata India Heritage Institute of Technology Department of Information Technology Kolkata India

ISBN: (纸本)9798350351378

In the realm of modern communication, the transmission of substantial data, predominantly in the form of images, necessitates a delicate balance between data capacity optimization and ensuring secure transmission. This paper addresses this challenge through the introduction of a novel compression-then-Encryption (CtE) scheme. The scheme incorporates a tailored luminance quantization matrix, optimizing luminance preservation, and employs sparse matrices to efficiently compress chrominance components. To fortify the security of image transmission, the CtE scheme integrates the standard AES-256 encryption algorithm. Through a synergistic approach, this scheme aims to enhance compression efficiency in the context of contemporary communication channels. © 2024 ieee.

关键词： Cryptography

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Resource Allocation for Semantic Relay Aided Wireless Networks with Probability Graph 59

Resource Allocation for Semantic Relay Aided Wireless Networ...

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59th Annual ieee International conference on Communications (ieee ICC)

作者： Zhao, Jiahui Chen, Ming Yang, Zhaohui You, Changsheng Chen, Mingzhe Southeast Univ Natl Mobile Commun Res Lab Nanjing 211111 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China Zhejiang Prov Key Lab Info Proc Commun Netw IPCAN Hangzhou Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 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

ISBN: (纸本)9781728190549

In this paper, we introduce a novel uplink semantic relay (SemRelay)-aided wireless communication system, catering to multiple users by leveraging a shared probability graph between the SemRelay and the base station (BS). In this system, users transmit text information to the SemRelay through conventional bit transmission, and the SemRelay compresses this information using a knowledge based characterized by probability graph before transmitting it to the BS through semantic communication. Then, the BS recovers the information based on the shared probability graph. While the semantic information compression incurs computational resource consumption, it significantly reduces communication resource usage. This paper addresses the challenge of minimizing overall system latency through jointly optimizing communication and computation resource allocation, considering limited wireless resources and the system's energy budget. To address this problem, we introduce an efficient iterative algorithm, which employs block coordinate descent for communication resource allocation and exhaustive searching for determining the optimal data compression scheme. In particular, both power allocation subproblem and bandwidth allocation subproblem are proved to be convex. The complexity analysis of the proposed algorithm are also provided. Numerical results validate the effectiveness of the proposed algorithm and the superior performance of semantic communication compared to the conventional bit transmission.

关键词： Semantic communication semantic relay probability graph semantic information compression

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