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检索条件"主题词=compression algorithms"

共 2082 条记录，以下是81-90 订阅

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Palindromes compression and Retrieval

Palindromes Compression and Retrieval

引用

Data compression Conference (DCC)

作者： Michael Itzhaki Bar-Ilan University Israel

ISBN: (数字)9798331534714

ISBN: (纸本)9798331534721

Palindromes are sequences of characters that read the same forward and backward and have fascinated computer scientists for centuries due to their unique properties. The exploration of palindromes sheds light on fundamental principles of pattern recognition, sequence alignment, and combinatorics, making them a crucial concept in theoretical and applied disciplines. We introduce a novel method for compressing palindromic structures in strings, establishing upper and lower bounds for their efficient representation. We do so by presenting a data structure capable of storing all maximal palindromes (Manacher array) in sublinear space with near-optimal access time. Our approach reduces the memory overhead of storing palindromes, offering a new avenue for optimizing compression algorithms in text processing applications.

关键词： Lower bound Data compression Pattern recognition Arrays compression algorithms Text processing

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Training Swinging Door Trending compression Algorithm for IoT devices

Training Swinging Door Trending Compression Algorithm for Io...

引用

IEEE World Forum on Internet of Things (WF-IoT)

作者： Juan David Arias Correa Alex Sandro Roschildt Pinto Carlos Montez Post-graduate Program of Automation Systems Engineering Federal University of Santa Catarina (UFSC) Florianópolis Brazil Dept. of Informatics and Statistic Dept. of Automation and Systems Engineering Federal University of Santa Catarina (UFSC) Florianópolis Brazil

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

The communication system is essential for the devices used in IoT. Nevertheless, the consumption caused by transmitting and receiving data is a concern in many scenarios. That is due to these devices commonly having limited energy and computational capacities. Transmission of redundant or irrelevant samples frequently wastes the device’s resources. Furthermore, Storing a large amount of redundant data could consume storage space and not offer any benefit. Data compression (DC) methods are a potential solution. DC could reduce communication usage and storage demand. This research proposes the Training Swing Door Trending (TSDT) for being implemented in IoT Devices. TSDT is a new algorithm that improves the classic Swing Door Trending (STD). They represent the data by trend lines and have a constant computational complexity. TSDT has a training step for the automatic configuration of its parameters. This article additionally presents the compression Factor (C-Score), a new quality metric to analyze the compression results in lossy DC methods. C-Score takes as a basis the F-Score, a measure of predictive performance. The C-Score uses the compression and Error metrics to evaluate the compression performance in Lossy algorithms.

关键词： Training Performance evaluation Communication systems Data compression Prediction algorithms Market research Internet of Things Computational complexity compression algorithms

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compression Algorithm of Convolutional Neural Network Model

Compression Algorithm of Convolutional Neural Network Model

引用

Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe Conference on

作者： Yanchun Liu Lishuang Zhao College of Information Science and Technology Bohai University Jinzhou China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

The exponential growth of data requires powerful analytical methods and processing tools, and deep learning demonstrates powerful analytical and predictive capabilities. CNN is excellent model in the field of deep learning, enabling powerful feature abstraction and image understanding that goes beyond the accuracy of recognition using the human eye. However, while the CNN model is constantly approaching the precision limit, the network depth and model parameters have multiplied, and the huge memory consumption and excessive storage occupation cannot be applied on many embedded and mobile devices. This paper constructs a compression algorithm model, including convolution layer, filter and pruning algorithm, and focuses on structured pruning and filter importance calculation. Using MNIST data set, the LeNet-5 model was trained by parameter pruning according to different pruning ratios, and the model compression was realized. The application of the research results in this paper can significantly reduce the redundant parameters in the network model, thus achieving the purpose of reducing the computing cost and storage space, and promoting the development of deep learning.

关键词： Deep learning Training Costs Accuracy Computational modeling Memory management Filtering algorithms Mobile handsets Convolutional neural networks compression algorithms

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A Novel Artistic Image compression Algorithm Based on Multi-Scale Adaptive Discrete Wavelet Transform

A Novel Artistic Image Compression Algorithm Based on Multi-...

引用

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Yijun Li China Academy of Art Yuhang District Hangzhou Zhejiang China

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

How to compress information is a challenge in the digital age, and the image compression has a wide range of application scenarios. In order to solve the challenges of low compression efficiency and high loss rate of the traditional algorithms, this study proposes a compression algorithm based on multi-scale adaptive discrete wavelet transform. The core of the proposed algorithm is to make reasonable use of the multiresolution analysis characteristics of discrete wavelet transform. Based on the feature extraction, the image information is decomposed into high-frequency and low-frequency parts. This operation can stably and effectively implement frequency domain threshold processing and reduce redundant data. The proposed algorithm optimizes the encoding efficiency of image features to the greatest extent by introducing block difference features and designing a new iterative function system. In view of the high redundancy and high complexity of the artistic images, the traditional wavelet analysis is improved and optimized, and a multi-scale adaptive model is introduced. In the experiment, the test used multiple sets of artistic image test sets and compared them with mainstream algorithms (JPEG, VAE, etc.). In PSNR analysis, the proposed algorithm achieves better performance.

关键词： Image coding Wavelet transforms Transforms Discrete wavelet transforms Feature extraction Wavelet analysis Wavelet domain compression algorithms Frequency-domain analysis Collaborative intelligence

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Optimizing Video compression Quality Using AI-Boosted HEVC

Optimizing Video Compression Quality Using AI-Boosted HEVC

引用

International Conference on Intelligent Computing in Data Sciences (ICDS)

作者： Hajar Hardi Imade Fahd Eddine Fatani Sciences and Techniques for the Engineer Laboratory National School of Applied Science University Sultan Moulay Slimane Khouribga Morocco

ISBN: (数字)9798350351200

ISBN: (纸本)9798350351217

There has been increased demand for more immersive video content to enhance the Quality of Service. This prompted the need for newer compression algorithms which would retain or even improve the original video quality. This research specifically delves into the implementation of a lossless Generative Adversarial Network (GAN)-based H.265 video compression. The main employed methods and applied treatments are briefly described. Subsequently, this work's findings are presented in the results sections. The conclusion indicates the effectiveness of (GAN)-based algorithm utilized in the video compression process.

关键词： Visualization Quality of service Streaming media Video compression Generative adversarial networks Signal reconstruction Quality assessment compression algorithms Video recording Optimization

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Research on the Influence of Point Cloud Geometric Features on compression Algorithm

Research on the Influence of Point Cloud Geometric Features ...

引用

Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Gang Zhao Tao Xu Xijun Cao Wei Lu Hao Zhu Wenlai Ji Jiangsu Hydraulic Research Institute Nanjing China Water Resources Department of Jiangsu Province Nanjing China Nantong Jianghai Surveying and Mapping Institute Co. Ltd. Nantong China Xuzhou Water Conservancy Project Construction Co. Ltd. Xuzhou China Architectural Design and Research Institute Nanjing Tech University Nanjing China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Although point cloud has abundant information and can accurately describe objects' surface shape and structure, the amount of data has always been an essential factor restricting its wide application. In order to retain adequate information at the same time, it is necessary to reduce the amount of point cloud data as much as possible, so a variety of point cloud compression algorithms have appeared. This paper mainly studies the effect of geometric features of point clouds on compression algorithms. First, we choose four classical algorithms based on different compression strategies as the primary research objects and explain their compression principles. Then, we take the underwater multi-beam point cloud in the riprap area as the experimental object, use four algorithms to test, and compare the compression effect. Finally, combined with the experimental results, we analyze the adaptive effects of different feature attributes on the compression algorithm. The experimental results show that the uniform compression algorithm can maintain the overall features of the point cloud but will lose more details. The curvature compression algorithm can keep the details but dramatically impacts data integrity. The octree compression algorithm can maintain the whole data but will lose local details. Although the information content compression algorithm has a good compression effect, its execution efficiency is relatively low.

关键词： Point cloud compression Control engineering Shape Data integrity Octrees compression algorithms

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An Efficient Lossless Gradient compression Technique for Distributed Training Systems

An Efficient Lossless Gradient Compression Technique for Dis...

引用

Data compression Conference (DCC)

作者： Shiyan Yi Xinlin Wei Libo Lin Peng Zhang Yibo Fan State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China Peng Cheng Laboratory Shenzhen China Peking University Advanced Institute of Information Technology Hangzhou China

ISBN: (数字)9798331534714

ISBN: (纸本)9798331534721

Distributed training improves the accuracy of large models by scaling parameter sizes. However, this scalability introduces communication bottlenecks between nodes. Gradient compression can alleviate these bottlenecks, but most existing methods are lossy, potentially compromising convergence and accuracy. This paper proposes a novel lossless compression algorithm for the floating-point gradient data, significantly improving compression efficiency compared to traditional lossless float compression algorithms while overcoming the shortcomings of lossy compression. First, new distance-run-length encoding and exponent prediction encoding using dynamic Huffman coding (***.) are designed based on the distinct statistical characteristics of zero and non-zero gradient data, effectively representing data values and zero locations. Second, an innovative hybrid Huffman coding (***.) is introduced, combining the advantages of dynamic and static Huffman coding. It enhances compression efficiency via local data adaptability, and simultaneously improves computational efficiency and hardware implementation friendliness with multiple fixed Huffman code tables. We also present a K-means-based probability vector clustering algorithm to effectively construct multiple prebuilt Huffman code tables. To evaluate the effectiveness of the proposed compression method, we selected two different deep neural network (DNN) learning tasks: (1) image classification, using ResNet50 and VGG16 for CIFAR-100 training, and ViT for ImageNet; and (2) language modeling, employing BERT on the IMDB dataset. The results are presented in Table 1. Furthermore, we tested mixed-precision training compression using the BF16 format. Experimental results demonstrate that our method achieves a lossless compression ratio of 0.4931 on typical gradient data, representing a significant 16.61% improvement over the state-of-the-art lossless float data compression approach, ndzip.

关键词： Training Image coding Codes Accuracy Scalability Encoding Vectors Huffman coding compression algorithms Residual neural networks

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Development of Data compression Techniques for IoT Data Transmission in Isolated Aquaculture Using LoRa Through LoRaWAN Gateway Antares Telkom

Development of Data Compression Techniques for IoT Data Tran...

引用

International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Helmi Ahmad Khaderani Aldo Agusdian Iskandar School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (数字)9798331527686

ISBN: (纸本)9798331527693

Aquaculture in remote areas challenges significant monitoring due to limited internet access. This research develops and implements an IoT communication system using LoRa RFM95W module, ESP32 microcontroller, and Telkom's LoRaWAN Gateway Antares for efficient and reliable sensor data transmission. LoRa operates in the 920–923 MHz band which offers long-range communication with low power consumption and is ideal for remote IoT applications. Class A LoRaWANare selected for power efficiency and an optimal Spreading Factor (SF) is chosen to balance coverage, data rate and power consumption. Data compression techniques are applied to minimize data loss, reduce data size, optimize bandwidth usage and ensure real-time data integration. Experiments with various compression algorithms and distances showed that the unishox2 algorithm achieved the best compression ratio, capable of transmitting data sizes of up to 347 bytes with minimal signal loss on SF9.

关键词： Power demand Microcontrollers LoRaWAN Data compression Logic gates Propagation losses Telecommunications Internet of Things compression algorithms Aquaculture

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A Highly Scalable Parallel Design for Data compression

A Highly Scalable Parallel Design for Data Compression

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： S. Biplab Raut AMD India Private Limited Bangalore India

ISBN: (数字)9798350387131

ISBN: (纸本)9798350387148

With ever-increasing use of digital data, many applications rely on data compression for their needs related to processing, storage and communication over the network of large volumes of data. While compression saves the memory/disk space and decreases the communication time, there is a considerable runtime spent in this process. Parallel compression algorithms and solutions that are developed to speed up the operations do not scale well on the multi-core CPUs. The data parallel schemes implemented by the prior arts are inefficient in partitioning the data and scaling the performance on the multi-core processors. Another major drawback of the existing multi-threaded compression solutions is the non-compliance to the single-threaded compression format. In this paper, we propose a set of novel and high-performance parallel compression and decompression schemes. We introduce novel designs for dynamic threading based parallel compression and random access point based parallel decompression. With our solution, we mitigate both the scaling issues on multi-core x86 CPUs and format compliance issues encountered in multi-threading the compression operations. Our test results demonstrate massive speedups by manyfolds and performance scaling never seen before on x86 CPUs especially AMD's “ZZn”-based recent processors that come with very high core counts.

关键词： Runtime Multicore processing Instruction sets Scalability Data compression Benchmark testing Parallel processing Libraries compression algorithms Digital information

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An FPGA-Based Top-K Gradient compression Accelerator for Distributed Deep Learning Training

An FPGA-Based Top-K Gradient Compression Accelerator for Dis...

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International Conference on Solid-State and Integrated Circuit Technology

作者： Ziyao Wang Jiayu Zhang Kunyue Li Jialei Sun Feng Dong Ke Chen Yong Qiao Jianfei Jiang National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai China Beijing iQlYI Science & Technology Co.. Ltd.. Shanghai China

ISBN: (数字)9798350361834

ISBN: (纸本)9798350361841

In distributed neural network training with multiple machines and devices, communication limitations often create efficiency bottlenecks due to the frequent exchange of model parameters and gradient information between computing nodes. This paper proposes an FPGA-based accelerator leveraging the gradient compression algorithm Top-K sparsification, which enhances performance by offloading computationally intensive compression operations to the FPGA. Experimental results demonstrate that the FPGA compression accelerator designed in this study achieves superior computing performance and compression efficiency compared to compression algorithms implemented on CPUs and GPUs. Specifically, the FPGA compresses the same amount of data 3.3-3.7 times faster than parallel solutions on CPUs and 1.3-1.8 times faster than on GPUs.

关键词： Training Performance evaluation Integrated circuit technology Deep learning Computational modeling Neural networks Field programmable gate arrays compression algorithms

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