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

共 16774 条记录，以下是751-760 订阅

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data Security Using compression Methods in the Area of Plumbing Problems in a Cloud Environment 9

Data Security Using Compression Methods in the Area of Plumb...

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9th International conference on Smart Computing and Communications, ICSCC 2023

作者： Radhakrishnan, Meera Ramya, R. Mahesh, A.S. Amrita Vishwa Vidyapeetham School of Computing Department of Computer Science and It Kerala Kochi India

ISBN: (纸本)9798350314090

In this research, we compare and contrast lossless and lossy picture compression methods. Both lossy and lossless compression techniques each use one algorithm. By using these compression techniques, we can give the systems data security. This approach will assist in resolving the plumbing problems. We used run-length encoding and the DCT technique to compress the png file, comparing the compression rates, and deciding which one was superior. We will demonstrate which one is superior based on the results. To determine whether an approach is better for compression, both are applied to an identical set of photos. © 2023 ieee.

关键词： Image compression

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Optimizing Federated Learning Efficiency: Exploring Learning Rate Scheduling in Sparsified Ternary compression

Optimizing Federated Learning Efficiency: Exploring Learning...

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2024 International conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Nithyaniranjana Murthy, C. Manjula, Sh. University of Visvesvaraya College of Engineering Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350359688

Federated Learning facilitates the collaborative training of a deep learning model by leveraging the combined data of multiple entities, with a focus on maintaining the privacy of individual datasets. The Sparse Ternary compression (STC) method improves gradient sparsification by integrating downstream compression, ternarization, and optimal Golomb encoding of weight updates. Although STC enhances compression techniques in federated learning, the broader scope of Federated Learning lies within the domain of machine learning. Researchers investigating improvements in STC compression techniques must consider neural network concepts and the impact of hyperparameters, such as learning rate, on convergence speed and accuracy. Increased communication across multiple devices poses challenges in achieving desired accuracy levels. Our research underscores the importance of selecting appropriate learning rate schedules when implementing algorithms like STC. Incorrect choice of learning rate schedules can hinder convergence, even if the algorithm functions effectively, emphasizing the crucial role of hyperparameters in reducing communication costs and achieving desired accuracy levels © 2024 ieee.

关键词： Federated learning

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A Physics-Informed Vector Quantized Autoencoder for data compression of Turbulent Flow

A Physics-Informed Vector Quantized Autoencoder for Data Com...

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data compression conference (DCC)

作者： Momenifar, Mohammadreza Diao, Enmao Tarokh, Vahid Bragg, Andrew D. Duke Univ Dept Civil & Environm Engn Durham NC 27701 USA Duke Univ Dept Elect & Comp Engn Durham NC 27701 USA

ISBN: (纸本)9781665478939

Analyzing large-scale data from simulations of turbulent flows is memory intensive, requiring significant resources. This major challenge highlights the need for data compression techniques. In this study, we apply a physics-informed Deep Learning technique based on vector quantization to generate a discrete, low-dimensional representation of data from simulations of three-dimensional turbulent flows. The deep learning framework is composed of convolutional layers and incorporates physical constraints on the flow, such as preserving incompressibility and global statistical characteristics of the velocity gradients. The accuracy of the model is assessed using statistical, comparison-based similarity and physics-based metrics. The training data set is produced from Direct Numerical Simulation of an incompressible, statistically stationary, isotropic turbulent flow. The performance of this lossy data compression scheme is evaluated not only with unseen data from the stationary, isotropic turbulent flow, but also with data from decaying isotropic turbulence, and a Taylor-Green vortex flow. Defining the compression ratio (CR) as the ratio of original data size to the compressed one, the results show that our model based on vector quantization can offer CR = 85 with a mean square error (MSE) of O(10(-3)), and predictions that faithfully reproduce the statistics of the flow, except at the very smallest scales where there is some loss. Compared to the recent study based on a conventional autoencoder where compression is performed in a continuous space, our model improves the CR by more than 30 percent, and reduces the MSE by an order of magnitude. Our compression model is an attractive solution for situations where fast, high quality and low-overhead encoding and decoding of large data are required.

关键词： Deep learning Solid modeling Computational modeling Vector quantization Computational fluid dynamics data compression Training data

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Combining Haar transform and row-column directional RLE in image compression 8

Combining Haar transform and row-column directional RLE in i...

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8th International conference on Informatics and Computing, ICIC 2023

作者： Emon, Anik Das Roy, Pronab Chandra Singh, Laiphrakpam Dolendro Saha, Anish Kumar National Institute of Technology Department of Computer Science and Engineering Assam Silchar788010 India

ISBN: (纸本)9798350342604

Haar transform, the simplest wavelet transformation, converts the data into wavelet coefficients. The output of the transformation generates larger numbers of Zeros. It acts as a basic step for data compression. The small values are further quantized to Zero for more appearance of Zeros in the data. Run-length encoding is a simple method of compression for repeating characters and is suitable here for repetitive Zeros. The quantized data are encoded in both horizontal and vertical direction run-length encoding. In this paper, we propose an algorithm that combines the Haar transform with the row-column directional run-length coding for a high rate of compression. The analysis of the results shows that the approximation of Haar coefficients increases the number of Zeros with a small increase in MSE in the transformed data. The visual or perceptual quality does not change so much. © 2023 ieee.

关键词： Image compression

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Efficient Semantic Segmentation for Compressed Video

Efficient Semantic Segmentation for Compressed Video

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ieee International conference on Robotics and Automation (ICRA)

作者： Cai, Jiaxin Li, Qi Shen, Yulin Pan, Jia Liu, Wenxi Fuzhou Univ Coll Comp & Data Sci Fuzhou Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Robots, constrained by limited onboard computing resources, often encounter situations wherein high-resolution and high-bit-rate videos captured by their cameras necessitate compression before further analysis. In this paper, we propose a novel video semantic segmentation paradigm for compressed video. Specifically, our framework draws the inspiration from the principle of Wavelet Transform, and thus we design the network structure, WTDecomNet, approximating the decomposition of high-resolution image into its low-resolution counterpart and axial details. The aim is to well preserve the image content through decomposition and maintain model efficiency by obtaining semantics from low-resolution image. To facilitate this purpose, we propose an efficient axial subband approximation module for extracting axial details and a lightweight temporal alignment module for associating keyframes and non-keyframes of compressed video. Through comprehensive experiments, we show that our model can achieve the state-of-the-art performance on public benchmarks. Especially on CamVid, comparing to baseline, our proposed model reduces the computational overhead by similar to 70% while improving mIoU by similar to 4%.

关键词： Semantic Segmentation

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Directed Acyclic Graph Based Online Multi-Dimensional Trajectory Simplification Algorithm 5

Directed Acyclic Graph Based Online Multi-Dimensional Trajec...

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5th International conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Tan, Shizhe Cao, Zhike Wang, Lizhi Gong, Runze Tian, Lu Ocean University of China Faculty of Information Science and Engineering Qingdao China

ISBN: (纸本)9798350309034

In response to the issues of disregarding turning and speed characteristics as well as excessive compression in traditional directed acyclic graph-based trajectory simplification (DOTS)algorithm, this study proposes a directed acyclic graphs based online multi-dimensional trajectory simplification (DOMTS)algorithm. The algorithm integrates real-time feature extraction methods and an improved DOTS algorithm. In the initial stage of trajectory processing, it dynamically identifies and labels turning and speed feature points in the trajectory, incorporating them into a comprehensive trajectory compression strategy. Subsequently, the trajectory undergoes compression using the enhanced DOTS algorithm. Experimental results unequivocally demonstrate that this algorithm effectively reduces trajectory data storage and transmission costs while maintaining a low global compression error. This lays a reliable foundation for further multi-dimensional feature-based trajectory analysis. © 2023 ieee.

关键词： data compression multi-dimensional trajectories steering characteristics velocity characteristics

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Heuristic Search based Test Pattern compression Scheme 1

Heuristic Search based Test Pattern Compression Scheme

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1st ieee International conference on Networking and Communications, ICNWC 2023

作者： Barath, M. Asha Pon, S. Jeyalakshmi, V. College of Engineering Department of ECE Guindy Tamil Nadu Chennai India

ISBN: (纸本)9798350336009

VLSI testing becomes a mandatory process with invention of system-on chip to ensure device reliability. To assure the effectiveness of system-on chip, larger test data volume are required. This leads to excessive power consumption and also increases the device testing time. Additionally, larger memory is required to store this enormous test data. Hence, to overcome these drawbacks test data volume has to be reduced. This paper proposes a test data compression scheme based on Genetic algorithm to minimize test power. Test power minimization is accomplished by reducing hamming distance among test patterns. Proposed algorithm is implemented in c17 ISCAS'85 benchmark combinational circuit. Experimental result shows that 67.74% reduction on test pattern count and 49.35% reduction in hamming distance is observed. © 2023 ieee.

关键词： Hamming distance

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Analysis of Energy Efficiency Improvement Using data compression Algorithms for Habitat Monitoring in WSNs Using Zigbee

Analysis of Energy Efficiency Improvement Using Data Compres...

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2024 International conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Sudha, G. Archana, M. Sharmila, S. Nandhini, K. Saranya, S. Sankari, S. Sri Sairam Engineering College Department Of Electronics And Communication Engineering Chennai India Sri Sairam Engineering College Department Of Information Technology Chennai India

ISBN: (纸本)9798350352689

Physical phenomena such as temperature and pressure are measured via Wireless Sensor Network (WSNs) using low-complexity and low-bandwidth data streams. WSN used in the armed forces, the environment, multimedia surveillance, and healthcare systems are specifically designed to offer excellent energy efficiency, since resource limitations make energy efficiency the most crucial parameter in WSN. An extensive analysis of the use of WSNs for habitat monitoring is discussed in this article. Seismic activity, the condition of civil structures, industrial process monitoring, and habitat and ecosystem monitoring can all be done with WSN. Our system collects data on the distant physical environment using fixed sensor nodes that are linked to a WSN. Different image compression algorithms are used to gather and compress the acquired datasets and exhibits a very good PSNR and compression Ratio. © 2024 ieee.

关键词： Sensor nodes

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Enhancing DFT Performance: The Power of Scan compression and Lockup Latches 4

Enhancing DFT Performance: The Power of Scan Compression and...

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4th International conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Rani, V. Rajitha Samson, Mamatha Jntu Department of Electronics and Communication Engineering Hyderabad India Nmrec Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331529635

With the miniaturization of transistors and the exponential growth in transistor count within integrated circuits, the challenges of controlling and observing internal nodes have become increasingly pronounced, particularly in sequential circuits. This has led to the complexities associated with fault localization and debugging. Design for Testability (DFT) offers a valuable solution by enabling efficient fault detection within the circuit under test, reducing simulation time and minimizing area overhead. Various DFT techniques have been proposed for pattern simulation, and this paper focuses on comparing two prominent approaches: scan compression and internal scan. The paper discusses the inclusion of lockup latches, which enhance performance by improving test coverage, reducing test data volume, and minimizing test time. Experiments conducted on a range of benchmark circuits demonstrate that both scan compression and lockup latch significantly reduce simulation time, and test data volume while achieving high fault coverage, with a modest increase in area overhead. © 2024 ieee.

关键词： Design for testability

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Lossless Telemetric data compression using Lempel-Ziv-Markov-Algorithm

Lossless Telemetric Data Compression using Lempel-Ziv-Markov...

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2023 ieee International conference on Next Generation Electronics, NEleX 2023

作者： Gummidela, Siri Chandana Bhupani, Sree Madhu Kiran Ikkurthi, Ganga Sridhar Bandaru, Thanmai Lakshmi Vankayalapati, Hima Deepthi Electronics and Communication Engineering Kalasalingam Academy Of Research and Education Tamil Nadu India

ISBN: (纸本)9798350319088

Missiles are used in warfare for various purposes. They are powerful weapons that can be used to attack a variety of targets. They can be launched from a platform, including aircraft, ships, submarines, and ground-based launchers. Missiles face several communication challenges, including the plasma layer's thickness, blackout region, etc. Plasma is an ionized gas layer created by the interaction of the missile's hot exhaust gases with the air/atmosphere which generates Electromagnetic interference (EMI), that interferes with the missile's communication guidance systems. The blackout region is caused by the scattering of radar waves by the plasma layer, making it difficult to track the missile, and reducing the effectiveness of air defense systems. Our objective is to overcome the data transmission problem during the communication blackout by using data compression techniques without losing the original information. In this work, the Lempel-Ziv-Markov-Algorithm (LZMA) and GZIP techniques are used for compressing the synthetic telemetric data as they provide greater output efficiency which was inferred by the analysis of different articles. The synthetically generated telemetric data, consisting of acceleration, temperature, velocity vector, pressure, and voltage measurements from real sensors, has been compressed. These techniques have improved transmission efficiency, data rate, and compression ratio. The compression ratio of LZMA outperforms the GZIP as the compression ratio of LZMA is 5.85 whereas GZIP is 1.41. © 2023 ieee.

关键词： Efficiency

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