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检索条件"主题词=Compression Algorithms"

共 2082 条记录，以下是241-250 订阅

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Software and Hardware Integrated Accelerators for Hadoop Appliance

Software and Hardware Integrated Accelerators for Hadoop App...

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International Conference on Applied Machine Learning (ICAML)

作者： Yuan Meng Jun Yang Jun Li Shengkai Wang Xiao Zhang School of Information Science and Engineering Yunnan University Kunming Yunnan China

ISBN: (纸本)9781665421263

With the development of the Internet, the data center for next-generation must be capable of data processing at PB/s level, ensuring a high throughput of the storage and network. This paper displays the design of the hardware accelerator for tuning the throughput of the storage and network. First, we illustrate the importance of a lossless data compression algorithm for data-intense applications based on the analysis of the characteristics of the Hadoop distributed system. Second, we effectively implement Deflate compression algorithm on the FPGA. The experimental results show that the compression speed of the Deflate compression algorithm hardware accelerator can reach 2.44 Gb/S, and the compression ratio is 2.08 on the Calgary standard evaluation set. Finally, we propose a hardware/software integration architecture to combine Deflate compression algorithm hardware accelerator and Hadoop distributed system. This architecture also incorporated the unique design of semaphore and shared-memory mechanism. The overall design can double the performance of a single mapping process and retain good scalability.

关键词： Scalability Computer architecture Throughput Software compression algorithms Hardware acceleration Field programmable gate arrays

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RIFD Fibonacci Zeckendorf Hybrid Encoding and Decoding Algorithm for Medical Image compression and Reconstruction

RIFD Fibonacci Zeckendorf Hybrid Encoding and Decoding Algor...

引用

Multimedia Computing, Networking and Applications (MCNA), International Conference on

作者： Nema Salem Fathy Elnaggar Electrical and Computer Engineering Effat University Jeddah KSA Mechanical Engineering Alexandria University Alexandria Egypt

ISBN: (数字)9781728183732

ISBN: (纸本)9781728183749

Digital medical images are an important source of information that help doctors in diagnosis and treatment. The raw form of a digital image requires a tremendous amount of storage memory and a longer time for transmission from one node to another through a limited bandwidth network. Thus, many algorithms are developed and implemented for image compression that eliminates the redundant information while keeping the essential ones. The decoding algorithms are able to extract this essential information and reconstruct the original images without losing the original image quality. Rounding a pixel's Intensity Followed by a Division process is called RIFD algorithm. It minimizes the information redundancy of the images and maintains the image visual quality with insignificant distortion. The integration of Fibonacci sequence and Zeckendorf's theorem produces a simple and fast variable length code for representing integer data. This article proposes a hybrid encoding and decoding algorithm for medical image compression by merging the RIFD and the suffix variable length second order Fibonacci-Zeckendorf codes. Performance metrics such as number of bits per pixel, compression ratio, memory saving percentage, run time, mean square error, peak signal to noise ratio and similarity structure index are used in evaluating the efficiency of the proposed algorithm on nine medical images. The simulation results shows the efficiency of the proposed algorithm especially in compressing images with non uniform distributed histograms.

关键词： Image coding Channel coding Decoding Signal processing algorithms XML compression algorithms Medical diagnostic imaging

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Efficient compression Algorithm for Multimedia Data

Efficient Compression Algorithm for Multimedia Data

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IEEE International Conference on Multimedia Big Data (BigMM)

作者： Rameshwar Pratap Karthik Revanuru Ravi Anirudh Raghav Kulkarni SCEE IIT Mandi India IIIT Bangalore India Chennai Mathematical Institute India

ISBN: (数字)9781728193250

ISBN: (纸本)9781728193267

In this work, we consider the problem of Cosine Similarity preserving dimensionality reduction (compression) for the sparse binary dataset. [18] suggested a compression algorithm for high dimensional, sparse, binary data for preserving Inner product and Hamming distance. In this work, we show that their proposed algorithm also works well for Cosine Similarity. We present a theoretical analysis of the dimension reduction bound and complement it with rigorous experimentation on real-world datasets. We compare our results with the state-of-the-art for the considered problem - SimHash [8], MinHash [21], Circulant Binary Embedding [25], and Densified one Permutation Hashing [20], and show that our result offers a significant saving in the compression time and the number of random bits required for the compression, and simultaneously provides comparable performance.

关键词： compression algorithms Dimensionality reduction Task analysis Inference algorithms Hash functions Hamming distance Data mining

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PhishZip: A New compression-based Algorithm for Detecting Phishing Websites

PhishZip: A New Compression-based Algorithm for Detecting Ph...

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IEEE Conference on Communications and Network Security (CNS)

作者： Rizka Purwanto Arindam Pal Alan Blair Sanjay Jha School of Computer Science and Engineering University of New South Wales Australia Data61 CSIRO Sydney New South Wales Australia

ISBN: (数字)9781728147604

ISBN: (纸本)9781728147611

Phishing has grown significantly in the past few years and is predicted to further increase in the future. The dynamics of phishing introduce challenges in implementing a robust phishing detection system and selecting features which can represent phishing despite the change of attack. In this study, we propose PhishZip which is a novel phishing detection approach using a compression algorithm to perform website classification and demonstrate a systematic way to construct the word dictionaries for the compression models using word occurrence likelihood analysis. PhishZip outperforms the use of best-performing HTML-based features in past studies, with a true positive rate of 80.04%. We also propose the use of compression ratio as a novel machine learning feature which significantly improves machine learning based phishing detection over previous studies. Using compression ratios as additional features, the true positive rate significantly improves by 30.3% (from 51.47% to 81.77%), while the accuracy increases by 11.84% (from 71.20% to 83.04%).

关键词： Phishing Dictionaries Feature extraction compression algorithms Machine learning Machine learning algorithms

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Comparison and Model of compression Techniques for Smart Cloud Log File Handling

Comparison and Model of Compression Techniques for Smart Clo...

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2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)

作者： Josef Spillner Zurich University of Applied Sciences Winterthur Switzerland

ISBN: (数字)9781728173153

ISBN: (纸本)9781728173160

compression as data coding technique has seen approximately 70 years of research and practical innovation. Nowadays, powerful compression tools with good trade-offs exist for a range of file formats from plain text to rich multimedia. Yet in the dilemma of cloud providers to reduce log data sizes as much as possible while having to keep as much as possible around for regulatory reasons and compliance processes, many companies are looking for smarter solutions beyond brute compression. In this paper, comprehensive applied research setting around network and system logs is introduced by comparing text compression ratios and performance. The benchmark encompasses 13 tools and 30 tool-configuration-search combinations. The tool and algorithm relationships as well as benchmark results are modelled in a graph. After discussing the results, the paper reasons about limitations of individual approaches and suitable combinations of compression with smart adaptive log file handling. The adaptivity is based on the exploitation of knowledge on format-specific compression characteristics expressed in the graph, for which a proof-of-concept advisor service is provided.

关键词： Tools Benchmark testing compression algorithms Runtime Random access memory Adaptation models Memory management

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Variable Sampling Domain and Map compression Based on Greedy RRT Algorithm for Robot Path Planning

Variable Sampling Domain and Map Compression Based on Greedy...

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Chinese Control Conference (CCC)

作者： Zhiqiang Wang Liang Shan Lu Chang Bo Qiu Zhidong Qi School of Automation Nanjing University of Science and Technology Nanjing P. R. China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

The traditional rapidly-exploring random tree (RRT) algorithm and the improved algorithms use a fixed sampling domain in each iteration. This way of sampling will result in much repeated exploration. Also, a fixed step causes the inefficiency of expansion in open areas and the incremental algorithm consumes much time in collision detection. In order to solve these problems, this paper proposes an improved RRT algorithm based on a variable sampling domain and a map compression algorithm along with a strategy of greedy growing (VSDGC-RRT). A variable sampling domain and the greedy strategy increase the utilization of the sample node and reduce the number of iterations. The map compression algorithm reduces time consumption in collision detection. These improvements can make the tree expand more rapidly to the target. The simulation results indicate that the improved algorithm can effectively reduce the quantity of iterations and take less time while retaining the probabilistic integrity of the traditional RRT.

关键词： Collision avoidance Path planning compression algorithms Planning Robots Simulation Probabilistic logic

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An Enhanced Data compression Algorithm

An Enhanced Data Compression Algorithm

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Emerging Trends in Information Technology and Engineering (ic-ETITE), Conference on

作者： Manak Pandey Shreya Shrivastava Sagar Pandey S Shridevi Department of Computer Science Vellore Institute of Technology Chennai India

ISBN: (数字)9781728141428

ISBN: (纸本)9781728141435

Data in today's world is the most important asset one can have, but with it comes the issue of handling it properly. To increase data portability, facilitated by reducing the size of data to be stored due to limited storage space, a strong need for data compression has arisen. A lot of ways have been defined for data compression; since the 1950's we have been digging up and devising many algorithms to modify data compression techniques and over the years, many seemingly powerful algorithms have been developed. With a wide range of compression techniques available, there comes the need to choose the right method for text compression, which gives good results in lesser time. In this paper, we present a hybrid approach to compress text data. This hybrid approach encompasses various methodologies like Run Length Encoding (RLE), Infix Encoding and Bit reduction techniques. We aim to overlay different techniques to achieve the best possible compression ratio for large text files and dictionary data.

关键词： data compression run-length encoding prefix encoding bit reduction techniques Data compression run length encoding portability （data） Text files Text compression compression algorithms storage spaces

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A Deep Gradient compression Model Based on Tensor-Train Decomposition for Federated Learning

A Deep Gradient Compression Model Based on Tensor-Train Deco...

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Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC)

作者： Xuyong Qiu Dong Lin Xinxin Feng Youjia Chen Jinsong Hu Haifeng Zheng College of Physics and Information Engineering Fuzhou University Fuzhou China

ISBN: (数字)9781728181813

ISBN: (纸本)9781728181820

Federated Learning (FL) is an emerging artificial intelligence (AI) technology where the goal is to train a high-quality centralized model. However, one of challenges that FL faces is that it requires a large communication cost since it needs to exchange model parameters at each model update. In this paper, we propose a gradient compression algorithm with Tensor-Train (TT) decomposition to reduce the communication cost, where it uses low-di-mensional matrices or tensors to represent high-dimensional weight parameters. Simulation results show that our method can obtain a higher compression ratio while still achieving better accuracy compared with the other compression algorithms for FL.

关键词： Wireless communication Tensors Simulation Collaborative work Matrix decomposition Artificial intelligence compression algorithms

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Group-Scheme: SIMD-based compression algorithms for web text data

Group-Scheme: SIMD-based compression algorithms for web text...

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IEEE International Conference on Big Data

作者： Xudong Zhang Wayne Xin Zhao Dongdong Shan Hongfei Yan Department of Computer Science and Technology Peking University Beijing China

compression algorithms have been quite important for data oriented tasks, especially in the era of Big Data. The rapid development of modern processors facilitates us with powerful SIMD instruction sets, which provides an opportunity for better performance. Although SIMD based optimization on compression have been explored in some studies [2, 7], these studies usually focus on modifying the existing algorithms to fit into the SIMD instruction. In this paper, we propose a compression framework with a novel storage layout format, which aims to improve instruction-level parallelizability of compression algorithms. By instantiating the framework, we design a novel compression algorithm family, called Group-Scheme, and present a parallelized version of Group-Scheme, called SIMD-Group-Scheme. We evaluate the proposed algorithms on two public TREC data sets. With very competitive performance on compression ratio and encoding speed, SIMD-Group-Scheme significantly outperforms the implementation without SIMD instructions and state-of-the-art algorithm (i.e. SIMD-G8IU [7]), w.r.t decoding speed.

关键词： Encoding Decoding Layout compression algorithms Arrays Indexes Program processors

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A Multi-UAVs’ Trajectory data compression Method Based on 3D-SPM Algorithm

A Multi-UAVs’ Trajectory data Compression Method Based on 3...

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Chinese Control Conference (CCC)

作者： Dongdong Yuan Yankai Wang School of Aerospace Engineering Beijing Institute of Technology Beijing China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

With the widespread use of the Global Positioning System (GPS) in engineering fields such as UAVs' military operations, environmental reconnaissance, aerial photography, traffic monitoring, target tracking, long-range strike, circuit inspection, and geographic mapping, collected UAVs' trajectory data grows exponentially. However, massive UAVs' trajectory data will bring huge challenges to storage, transmission, query, analysis and mining. At the same time, the cost of data and trajectory processing limit their applications. Therefore, this paper designs a multi-UAVs' trajectory data compression method based on the 3D-SPM algorithm. On the basis of the trajectory data, the 3D-SPM algorithm is used to compress the trajectory data to reduce the storage space, which can realize the use of the obtained trajectory data to complete the UAVs' trajectory data compression. Simulation experiments show that the above trajectory compression algorithm maintains the important characteristics of the original path (adding compression rate, length loss rate, most Optimization index), can achieve fast compression, simplify the trajectory, and greatly reduce the storage space.

关键词： Trajectory Data compression compression algorithms Prediction algorithms Optimization Indexes Heuristic algorithms

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