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

共 16713 条记录，以下是451-460 订阅

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Collaborative DNNs Inference with Joint Model Partition and compression in Mobile Edge-Cloud Computing Networks 25

Collaborative DNNs Inference with Joint Model Partition and ...

引用

ieee Wireless Communications and Networking conference (ieee WCNC)

作者： Tang, Yaxin Li, Xiuhua Li, Hui Yang, Zhengyi Wang, Xiaofei Leung, Victor C. M. Chongqing Univ Sch Big Data & Software Engn Chongqing Peoples R China Tianjin Univ Coll Intelligence & Comp TKLAN Tianjin Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada

ISBN: (纸本)9798350303582;9798350303599

Mobile edge-cloud computing utilizes the computing resources of edge devices and cloud servers to execute complex deep neural networks (DNNs) for collaborative inference. However, many existing collaborative inference methods do not fully consider the limited resources of edge devices, resulting in high inference latency. In this paper, we design an integrated computational framework that combines model partition and compression to reduce inference latency. Specifically, we partition a DNN model at the middle layer and deploy the previous layer on the edge device and the subsequent layer on the cloud server respectively. We propose a collaborative dual-agent reinforcement learning algorithm called CPCDRL to determine partition point and compression ratios. It enables adaptive adjustments of compression ratios based on various partition points, with the overarching goal of minimizing the inference latency across the entire DNN model. The proposed algorithm can significantly reduce computational latency while minimizing accuracy loss compared to the baseline schemes.

关键词： Deep neural networks

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Development of an Algorithm-Based Analysis-compression Integrated Communication Tracking Management Information System (iCTMIS) 11

Development of an Algorithm-Based Analysis-Compression Integ...

引用

11th ieee Open conference of Electrical, Electronic and Information Sciences, eStream 2024

作者： Abuda, Carlo Jude P. Villafuerte, Ritchell S. Visayas State University Alangalang Department of Arts and Sciences Alangalang Leyte Philippines Eastern Visayas State University Main Campus Information Technology Department Tacloban City Philippines

ISBN: (纸本)9798350352412

This study addresses the challenges of administrative tasks and communication tracking at Visayas State University Alangalang (VSUA), highlighting the inefficiencies in the current manual tracking of communication processes. The objective is to develop an Integrated Communication Tracking Management Information System (iCTMIS) that enhances operational efficiency by integrating Analysis Optical Character Recognition (OCR) and Lempel-Ziv-Welch (LZW) Lossless and Zlib compression Algorithms. By employing a developmental research design and ADDIE model, the system significantly proves that there is an improvement on the data analysis and reduce disk space through efficient compression. Significant findings reveal that OCR achieves up to 90% accuracy in text conversion, while LZW techniques in compressions substantially deflates among data and file sizes. This was evaluated against ISO 9126 Software Quality Characteristics, the iCTMIS has substantially shown to optimize storage and address VSUA's operational challenges effectively. This research therefore concludes that the systematic integration of combined algorithmic frameworks analysiscompression to iCTMIS pointedly enhances organizational communication and administrative workflows efficiency. © 2024 ieee.

关键词： Quality control

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x3: Lossless data Compressor

x3: Lossless Data Compressor

引用

data compression conference (DCC)

作者： Barina, David Klima, Ondrej Brno Univ Technol Brno Czech Republic

ISBN: (纸本)9781665478939

x31 is a lossless optimizing dictionary-based data compressor. The algorithm uses a combination of a dictionary, context modeling, and arithmetic coding. Optimization adds the ability to find the most appropriate parameters for each file. Even without optimization, x3 can compress data with a compression ratio comparable to the best dictionary compression methods like LZMA, zstd, or Brotli.

关键词： Dictionaries data compression Encoding Optimization Context modeling Arithmetic

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Research on Seismic data compression and Error Correction System 35

Research on Seismic Data Compression and Error Correction Sy...

引用

35th Chinese Control and Decision conference (CCDC)

作者： Sun, Hong-zhi Sun, Yi-mei Luo, Fei Zhao, Lei Wang, Qi Gong, Fei Liaoning Earthquake Agcy Shenyang 110034 Liaoning Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Tianyuan Times Automat instrumentat CO Ltd Harbin 150040 Peoples R China

ISBN: (纸本)9798350334722

This paper introduces a seismic data compression and error correction system suitable thy digital seismic stations and networks. The transmitter attic system pertorms intra-frame compression and inter-frame redundancy on seismic data, and sends the processed data Return to the seismic monitoring center through wired or wireless channels. After receiving the data, the real-time compression and error correction equipment of seismic data in the seismic monitoring center perfomis inverse processing to realize the error collection function and outputs the seismic data in 24 -digit fonnat to the computer. This paper describes the working principL hardwire structure and main functions of the instmment.

关键词： Micro Control Unit compression Redundance Correction

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VOLUMETRIC 3D POINT CLOUD ATTRIBUTE compression: LEARNED POLYNOMIAL BILATERAL FILTER FOR PREDICTION 49

VOLUMETRIC 3D POINT CLOUD ATTRIBUTE COMPRESSION: LEARNED POL...

引用

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

作者： Do, Tam Thuc Chou, Philip A. Cheung, Gene York Univ Toronto ON Canada

ISBN: (纸本)9798350344868;9798350344851

We extend a previous study on 3D point cloud attribute compression scheme that uses a volumetric approach: given a target volumetric attribute function f : R-3 (sic) R, we quantize and encode parameters theta that characterize f at the encoder, for reconstruction f((theta) over cap) (x) at known 3D points x at the decoder. Specifically, parameters (theta) over cap. are quantized coefficients of B-spline basis vectors Phi(l) (for order p >= 2) that span the function space F-l((p)) at a particular resolution l, which are coded from coarse to fine resolutions for scalability. In this work, we focus on the prediction of finer-grained coefficients given coarser-grained ones by learning parameters of a polynomial bilateral filter (PBF) from data. PBF is a pseudo-linear filter that is signal-dependent with a graph spectral interpretation common in the graph signal processing (GSP) field. We demonstrate PBF's predictive performance over a linear predictor inspired by MPEG standardization over a wide range of point cloud datasets.

关键词： 3D point cloud compression deep learning

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Synergized data Efficiency and compression (SEC) Optimization for Large Language Models 4

Synergized Data Efficiency and Compression (SEC) Optimizatio...

引用

4th International conference on Electronic Information Engineering and Computer Science, EIECS 2024

作者： Li, Xinjin Ma, Yu Huang, Yangchen Wang, Xingqi Lin, Yuzhen Zhang, Chenxi Columbia University New York NY United States Carnegie Mellon University Pittsburgh PA United States Johns Hopkins University MD United States University College London United Kingdom

ISBN: (纸本)9798331531409

The rapid advancements in large language models (LLMs) have propelled natural language processing but pose significant challenges related to extensive data requirements, high computational demands, and more training times. While current approaches have demonstrated powerful capabilities, they often fall short of achieving an optimal balance between model size reduction and performance preservation, limiting their practicality in resource-constrained settings. We propose Synergized Efficiency and compression (SEC) for Large Language Models, a novel framework that integrates data utilization and model compression techniques to enhance the efficiency and scalability of LLMs without compromising performance. Inside our framework, Synergy Controller could balance data optimization and model compression automatically during the training. The SEC could reduce data requirements by 30%, compress model size by 67.6%, and improve inference speed by 50%, with minimal performance degradation. Our results demonstrate that SEC enables high-performing LLM deployment with reduced resource demands, offering a path forward toward more sustainable and energy-efficient AI models in diverse applications. © 2024 ieee.

关键词： Natural language processing systems

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A Review on Different Level data Encryption through a compression Techniques 7

A Review on Different Level Data Encryption through a Compre...

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7th International conference on Inventive Computation Technologies, ICICT 2024

作者： Gadhiya, Niravkumar Tailor, Shrinath Degadwala, Sheshang Sri Balaji College of Engineering & Technology Benad Road Jaipur India Sri Balaji College of Engineering & Technology Computer Science Engineering Benad Road Jaipur India Sigma University Dept. of Comp. Eng Gujarat India

ISBN: (纸本)9798350359299

In the rapidly evolving digital landscape, ensuring the secure transmission and storage of sensitive data is paramount. This paper introduces a multilevel data encryption-compression approach, leveraging Base64 encoding, Fernet symmetric encryption, and zlib compression. The methodology explores related works, detailing the step-by-step implementation of the proposed approach. By combining these techniques, the method enhances data security while minimizing storage and transmission overhead. Performance analysis focuses on encryption/decryption time and compression ratio, comparing the approach with existing methods. The conclusion provides a succinct summary, emphasizing the approach's significance in data security. The paper suggests avenues for future research, underscoring the dynamic nature of cybersecurity. In essence, this review offers a practical solution to data security challenges and lays the foundation for ongoing advancements in the broader landscape of securing sensitive information. © 2024 ieee.

关键词： Regulatory compliance

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compression of Power Quality Disturbance Using Wavelet-Atomic Decomposition for Grid-Connected Wind Farms 6

Compression of Power Quality Disturbance Using Wavelet-Atomi...

引用

6th International conference on Electrical Engineering and Green Energy (CEEGE)

作者： Hao, Wenbo Hu, Benran Zhao, Leilei State Grid Heilongjiang Elect Power Res Inst Harbin Peoples R China State Grid Heilongjiang Elect Power Co Ltd Harbin Peoples R China

ISBN: (纸本)9798350312669

The intermittency, volatility, and randomness of the power output of wind farms lead to serious power quality problems at the point of common coupling (PCC) of wind farms. Many waveform recording signals have high requirements for data transmission and storage, which is not conducive to wind power disturbance data analysis. Therefore, a power quality disturbance data compression method based on integrated wavelet and atomic decomposition is proposed to improve the compression ratio and computational complexity. According to the characteristics of power quality disturbance signal, a small-scale coherent atomic dictionary was established to characterize the disturbance components. Based on the small-scale atomic dictionary, the main components of the signal are represented in parametric atomics by atomic decomposition with the matching pursuit algorithm to achieve data compression. Then the irregular residuals of the signal are further decomposed by wavelet method to improve the effectiveness. Simulation results indicates that the proposed method inherits the high compression ratio of atomic decomposition, and further reduces the computational complexity of residual signal decomposition by wavelet transform, obtaining high compression ratios along with high signal-to-noise ratios.

关键词： Power quality data compression Atomic decomposition Matching pursuits Coherent dictionary

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Computational Linear Algebra to identify potential students with ADHD: pairing data compression and K-means 22

Computational Linear Algebra to identify potential students ...

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22nd ieee International conference on Advanced Learning Technologies (ICALT)

作者： Souza, Tain Silva, Monica Paixao, Joao Alves, Renato Bordalo, Roberta Moura, Waldir Fed Univ Rio de Janeiro UFRJ PPGI Rio De Janeiro Brazil

ISBN: (纸本)9781665495196

Attention deficit hyperactivity disorder (ADHD) causes several difficulties that can lead to academic failure. We aim to use information technology to improve the performance of these students. Our first step is to assist the teacher in identifying ADHD students in the classroom, which is the object of this work. We will use linear algebra techniques to achieve the objective: data compression and k-means. data analysis is done manually and is based on the theoretical framework. We got the necessary information by compressing it into one level, using two clusters to identify a binary response, making the information faster and visually accessible.

关键词： ADHD Diagnosis Linear Algebra K-means data compression

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Modified xLSTM for compression and Decompression of Multimodal Agricultural data in Low-Resource Settings

Modified xLSTM for Compression and Decompression of Multimod...

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2024 International conference on Decision Aid Sciences and Applications, DASA 2024

作者： Akbar, Nur Arifin Lenzitti, Biagio Tegolo, Domenico Dembani, Rahool Sullivan, Clare S. Universita Degli Studi di Palermo Dipartimento di Matematica e Informatica Palermo Italy SingularLogic Greece Agricultural University of Athens Greece

ISBN: (纸本)9798350369106

Efficient compression techniques are essential for handling large datasets, especially in low-resource agricultural settings where bandwidth and storage are limited. This paper introduces a novel approach that combines a modified extended Long Short-Term Memory (xLSTM) network with multiplicative LSTM (mLSTM) cells for compressing and decompressing multimodal agricultural data. The key contribution lies in tailoring the xLSTM-mLSTM architecture specifically for agricultural data, capturing unique patterns to enhance compression efficiency. Specifically, we focus on agricultural datasets comprising textual labels and images. The proposed method combines xLSTM with mLSTM cells for text data compression and employs a convolutional autoencoder for image data. We compare our approach with existing compression methods applied to agricultural data, demonstrating superior performance in terms of compression ratio and reconstruction quality. Experimental results demonstrate the effectiveness of the approach, achieving significant data size reduction while maintaining acceptable reconstruction quality, as evidenced by metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). © 2024 ieee.

关键词： data compression ratio

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