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

共 16774 条记录，以下是721-730 订阅

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Design and Evaluation of ECG compression Techniques Using FPGA 8

Design and Evaluation of ECG Compression Techniques Using FP...

引用

8th International conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Reddy, Palagiri Veera Satyanarayana Tallapragada, V.V. Mohan Babu University Department of Ece Tirupati India

ISBN: (纸本)9798350367904

Electrocardiogram (ECG) signals are crucial for diagnosing and monitoring heart health but can produce significant data files that create challenges for storage, transmission, and analysis in eHealth systems. This paper presents various ECG compression techniques implemented on Field-Programmable Gate Arrays (FPGAs) to mitigate these challenges. The study covers lossless methods like Run-Length Encoding (RLE) and Differential Pulse Code Modulation (DPCM) as well as lossy methods such as Wavelet Transform and Singular Value Decomposition (SVD). Performance metrics, including compression Ratio (CR), Percent Root Mean Square Difference (PRD), Signal-to-Noise Ratio (SNR), Power Consumption, and Latency, are analyzed to assess their effectiveness. The findings indicate that while SVD on PYNQ-Z2 FPGA achieves the highest compression ratio, methods like SPIHT and hybrid approaches provide a balanced trade-off between compression efficiency and signal fidelity. Future work is suggested to focus on optimizing power consumption, improving compression ratios, and reducing latency. This paper provides insights into developing efficient, real-time ECG compression solutions suitable for resource-constrained eHealth applications. © 2024 ieee.

关键词： Differential pulse code modulation

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Wireless Point Cloud Streaming Experiment using ieee 802.11ax 21

Wireless Point Cloud Streaming Experiment using IEEE 802.11a...

引用

ieee 21st Consumer Communications and Networking conference (CCNC)

作者： Yamazaki, Taku Maeda, Naoki Miyoshi, Takumi Asaka, Takuya Shibaura Inst Technol Coll Syst Engn & Sci Saitama Japan Tokyo Metropolitan Univ Fuculty Syst Design Tokyo Japan

ISBN: (纸本)9798350304572

Wireless point cloud streaming is a key technology in digital twin-enabled smart cities for maintaining city-scale spatial information. Various point cloud coding and compression algorithms have been studied to facilitate efficient transmission. However, real-field experiments are required to reveal the characteristics using small devices, which support the city from the edges. In this paper, we conducted field experiments to analyze the characteristics of wireless point cloud streaming. First, we proposed a middleware to mediate between the robot operating system and the network interface to easily configure the transmission protocol. Thereafter, we implemented the middleware on edge devices and analyzed the performance using ieee 802.11ax based on the following perspectives: processing time versus transmission frequency, point cloud and packet transmission success rates versus distance between nodes, and point cloud transmission success rate versus mobility of nodes. The first experiment revealed that small edge devices with the proposed middleware could handle high-frequency streaming without exceeding the maximum time of every period. The second experiment revealed the relationship between data rate and transmission success rates of point clouds and packets. Finally, the third experiment revealed that the mobility of the nodes caused variations in the transmission success rate.

关键词： Point cloud streaming wireless transmission ieee 802.11ax transmission frequency

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DeepMapping: Learned data Mapping for Lossless compression and Efficient Lookup 40

DeepMapping: Learned Data Mapping for Lossless Compression a...

引用

40th ieee International conference on data Engineering, ICDE 2024

作者： Zhou, Lixi Candan, K. Selçuk Zou, Jia Arizona State University Tempe United States

ISBN: (纸本)9798350317152

Storing tabular data to balance storage and query efficiency is a long-standing research question in the database community. In this work, we argue and show that a novel DeepMapping abstraction, which relies on the impressive memorization capabilities of deep neural networks, can provide better storage cost, better latency, and better run-time memory footprint, all at the same time. Such unique properties may benefit a broad class of use cases in capacity-limited devices. Our proposed DeepMapping abstraction transforms a dataset into multiple key-value mappings and constructs a multi-tasking neural network model that outputs the corresponding values for a given input key. To deal with memorization errors, DeepMapping couples the learned neural network with a lightweight auxiliary data structure capable of correcting mistakes. The auxiliary structure design further enables DeepMapping to efficiently deal with insertions, deletions, and updates even without retraining the mapping. We propose a multi-task search strategy for selecting the hybrid DeepMapping structures (including model architecture and auxiliary structure) with a desirable trade-off among memorization capacity, size, and efficiency. Extensive experiments with a real-world dataset, synthetic and benchmark datasets, including TPC-H and TPC-DS, demonstrated that the DeepMapping approach can better balance the retrieving speed and compression ratio against several cutting-edge competitors. © 2024 ieee.

关键词： data compression

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A Comparative Study of Lossless compression Technique in Quantum Secure Direct Communication 1

A Comparative Study of Lossless Compression Technique in Qua...

引用

1st International conference on Cyber Security and Computing, CyberComp 2024

作者： Ishak, Izlin Zuriani Wooi, Liew Seow Harun, Nur Ziadah Azahari, Nur Shahirah Ramli, Sofia Najwa Ghazali, Osman Politeknik Seberang Perai Department of Information Technology and Communication Pulau Pinang Malaysia Universiti Putra Malaysia Faculty of Computer Science and Information Technology Department of Computer Science Selangor Malaysia Raffles University Faculty of AI and Robotics Johor Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Department of Information Security and Web Technology Johor Malaysia School of Computing Universiti Utara Malaysia Kedah Malaysia

ISBN: (纸本)9798350387728

This is a comparative study to explore the performance of three lossless compression techniques, namely Run Length Encoding (RLE), Shannon-Fano Coding (SFC), and Huffman Coding (HC) in the context of a Hybrid M-Ary in Braided Single Stage (HMBSS) protocol communication scheme. The previous study on HMBSS using HC found that HC's compression technique has drawbacks, i.e., it is sensitive to errors and noise in the transmission channel and limited to certain data types. These drawbacks did affect the value of the compression rate limit. In this study, we evaluate the effectiveness of RLE, SFC, and HC in terms of data size reduction, coding efficiency, and data transmission time by comparing their results arising from the simulation experiments using Python coding in Jupiter Notebook and developed based on the HMBSS protocol algorithm. This simulation experiment starts with an initial six randomly generated datasets. Each dataset is tested using three compression techniques, which resulted in 18 different datasets. The results from the experiments reveal that SFC and HC consistently achieved a higher compression ratio and coding efficiency than RLE. These two compression techniques' algorithms effectively reduce the data size while preserving the original information. In our study, we also find that the data transmission time is increased with larger text sizes for all three compression techniques. This indicates that the larger the data size, the more transmission time is required with regard to what the compression technique's algorithm is. © 2024 ieee.

关键词： Quantum communication

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Psychological Dimensions of data compression & Source Coding for Networking Applications 15

Psychological Dimensions of Data Compression & Source Coding...

引用

15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Juneja, Bhanu Anbarasan, P. Rongali, Aruna Sri Mishra, Isa Dhabliya, Dharmesh Shiju, Christina Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Agricultural Extension and Economics Vellore India University of The Cumberlands United States Kiit Deemed to be University Kiit School of Management Bhubaneswar India Vishwakarma Institute of Technology Department of Information Technology Maharashtra Pune India Siescoms Department of Management College India

ISBN: (纸本)9798350370249

Records compression and supply coding are powerful strategies for optimizing records communication and garage in networking programs. Those strategies reduce the number of facts that desires to be transmitted or stored, central to faster speeds and extra green use of resources. As such, psychological dimensions of information compression and source coding are vital issues for hit implementations. The principal psychological drivers of performance in those structures are: the inherent complexity of the records the consumer belief of the decreased records length the perceived intuitiveness of the compression or coding gadget Studies have proven that customers come to be less satisfied as the scale of the information turns smaller, and they can be more likely to assess the effectiveness of a coding device primarily based on its usability and learnability. Additionally, complicated principles, pix, and tasks can be more challenging and much less gratifying to compress because of the elevated problem of discerning and representing the underlying statistics. Due to these psychological elements, complete know-how of the consumer needs and interface plays an essential function within the design and implementation of records compression and supply coding structures. © 2024 ieee.

关键词： data compression

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Variable Rate compression for Raw 3D Point Clouds 39

Variable Rate Compression for Raw 3D Point Clouds

引用

ieee International conference on Robotics and Automation (ICRA)

作者： Al Muzaddid, Md Ahmed Beksi, William J. Univ Texas Arlington Dept Comp Sci & Engn Arlington TX 76019 USA

ISBN: (纸本)9781728196817

In this paper, we propose a novel variable rate deep compression architecture that operates on raw 3D point cloud data. The majority of learning-based point cloud compression methods work on a downsampled representation of the data. Moreover, many existing techniques require training multiple networks for different compression rates to generate consolidated point clouds of varying quality. In contrast, our network is capable of explicitly processing point clouds and generating a compressed description at a comprehensive range of bitrates. Furthermore, our approach ensures that there is no loss of information as a result of the voxelization process and the density of the point cloud does not affect the encoder/decoder performance. An extensive experimental evaluation shows that our model obtains state-of-the-art results, it is computationally efficient, and it can work directly with point cloud data thus avoiding an expensive voxelized representation.

关键词： RGB-D Perception Deep Learning for Visual Perception Big data in Robotics and Automation

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Converter Transformer Fault Diagnosis Based on Domain Adaptive Semi-Supervision, Lightweight and Model compression 4

Converter Transformer Fault Diagnosis Based on Domain Adapti...

引用

4th International conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Yang, Xu Zhang, Min Zhang, Jing Zhou, Zhengqin Sun, Weihao Xu, Yongpeng Wuhan Nari Co Ltd. State Grid Electric Power Research Institute Wuhan China State Grid Corporation of China Equipment Management Department Beijing China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China

ISBN: (纸本)9798331534622

Aiming at the problem that deep learning cannot directly read one-dimensional dissolved gas data in oil, a wavelet transform data dimension conversion method based on the generalized Gaussian function is proposed to convert one-dimensional fault samples into two-dimensional The characteristic image is used to satisfy the data Diagnostic format of the converter transformer fault diagnosis method and at the same time, domain adaptive semi-supervised loss is introduced to optimize the model parameters. In view of the characteristics of existing deep learning models with many parameters and complex structures, a method based on lightweight neural networks and model compression is proposed to integrate and reduce deep learning models through convolutional layers and batch-normalization (BN) layers. The method of model compression by adjusting the bit-width of the parameters not only can effectively compress the deep learning model with almost no loss of model accuracy, but also improves the fault identification efficiency. In addition, the method can be successfully deployed on the edge computing platform, which realizes the effective utilization of resources and real-time response. © 2024 ieee.

关键词： Multilayer neural networks

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Adaptive Dictionary Learning data compression Algorithm for Distributed Photovoltaics 4

Adaptive Dictionary Learning Data Compression Algorithm for ...

引用

4th International conference on Information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Li, Bo Shi, Zhan Deng, XiaoZhi Wu, ZhenTian Wang, XiuZhu Power Dispatching Control Center of Guangdong Power Grid Co. Ltd. Guangdong Guangzhou510600 China

ISBN: (纸本)9798350337181

In order to promote the development of a low-carbon economy, many solar energy industries have emerged. The distributed photovoltaic power generation system can directly convert solar energy into electricity, which is a new type of clean and environmentally friendly system. The information monitoring network in the system provides photovoltaic power generation system data to help the power grid achieve collaborative optimization. In order to reduce the energy consumption in the data transmission process, an adaptive dictionary learning data compression algorithm based on compressed sensing theory is proposed. With the application of compressed sensing theory, it combines the sampling and compression process of signals, breaking the limitations of traditional Nyquis's theorem and greatly reducing the time of data processing. This paper combines adaptive dictionary learning with compressed sensing theory, first using input signal data for sparse coding, training adaptive dictionary to achieve rapid data compression, and finally using compressed sensing theory to reconstruct data. © 2023 ieee.

关键词： Compressed sensing

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Shock Wave compression of Porous Materials 25

Shock Wave Compression of Porous Materials

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25th ieee International conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Asylkaev, Arthur M. Ten, Konstantin A. Studennikov, Alexei A. Khalemenchuk, Viacheslav P. Tumanik, Alexander S. Glushak, Anastasia A. Department of Explosive Processes Lavrentyev Institute of Hydrodynamics Novosibirsk Russia Department of Electrophysical Installations and Accelerators Budker Institute of Nuclear Physics Novosibirsk Russia

ISBN: (纸本)9798350389234

The aim of the work was to measure the density distribution during shock compression of porous media. For this purpose, experiments were conducted using the VEPP-4 collider (located at the Institute of Nuclear Physics SB RAS). The shock wave was formed by the explosion of a compound based on triaminotrinitrobenzene. Intensity measurements were performed along the styrofoam/fluoroplastics axis after compression by a strong shock wave. The movement of the boundary between styrofoam/fluoroplast and explosion products is visible on X-ray images (dynamic «movie»). To calculate the densities of the compressed substances, calibrations of all the detector channels were carried out. Density distributions of styrofoam/fluoroplastics were obtained under shock wave compression. According to the results of the density distribution there is a monotonous decrease in the intensity of the shock wave in the styrofoam/fluoroplast. The results are consistent with the physical concepts of shock wave propagation in matter. According to the detector data, the dynamics of the shock wave velocity in styrofoam/fluoroplast was also determined. © 2024 ieee.

关键词： Wave propagation

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Converting RLBWT to LZ77 in smaller space

Converting RLBWT to LZ77 in smaller space

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data compression conference (DCC)

作者： Shigekuni, Masaki Tomohiro, I Kyushu Inst Technol 680-4 Kawazu Iizuka Fukuoka 8208502 Japan

ISBN: (纸本)9781665478939

Since highly repetitive strings, which can be compressed greatly, are ubiquitous nowadays, algorithms working on compressed space have gained much more attention. In particular, converting one compressed format to another without explicit decompression is of great interest as we can take advantage of various compressed formats with small cost of space. In this paper, we consider algorithms to convert run-length encoded Burrows-Wheeler Transform (RLBWT) to Lempel-Ziv 77 (LZ77). We implement a simplified version of the algorithm proposed by Nishimoto and Tabei and propose a two-pass algorithm to reduce its peak memory usage in practice. Experimental results show that the two-pass algorithm uses 23% to 37% less space with up to 10% increase of computational time when we use 8 threads in the second pass.

关键词： Costs Instruction sets Memory management data compression Transforms Throughput

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