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24th National Conference on Communications (NCC)

作者： Chittam, Dheeraj Kumar Bansal, Rakesh Srivastava, Ritvik Honeywell Tech Solut Lab Pvt Ltd Bangalore 560028 Karnataka India Indian Inst Technol Dept Elect Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)9781538612248

This paper focuses on universal compression of a piecewise stationary source using sequential change detection algorithms. The change detection algorithms that we have considered assume minimal knowledge of the source and make use of universal estimators of entropy. Here, data in each segment is characterized either by an I.I.D. random process or a first order Markov process. Simulation study of a modified sequential change detection test proposed by Jacob and Bansal [1] is carried out. Next, an algorithm to effectively compress a piece-wise stationary sequence using such change detection algorithms is proposed. Overall compression efficiency achieved with Page's Cumulative Sum (CUSUM) test and the modified change detection test proposed in [1] (JB-Page test) as part of the change detection schemes, are compared. Further, when JB-Page test is used for change detection, four different compression algorithms, namely, Lempel Ziv Welch (LZW), Lempel Ziv (LZ78), Burrows Wheeler Transform (BWT) and Context Tree Weighting (CTW) algorithms are compared based on their impact on overall compression.

关键词： compression algorithms Markov processes Probability distribution Detection algorithms Change detection algorithms Random processes Jacobian matrices

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Lossless compression of Time Series Data with Generalized Deduplication

Lossless Compression of Time Series Data with Generalized De...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Rasmus Vestergaard Qi Zhang Daniel E. Lucani DIGIT and Department of Engineering Aarhus University Denmark

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

To provide compressed storage for large amounts of time series data, we present a new strategy for data deduplication. Rather than attempting to deduplicate entire data chunks, we employ a generalized approach, where each chunk is split into a part worth deduplicating and a part that must be stored directly. This simple principle enables a greater compression of the often similar, non-identical, chunks of time series data than is the case for classic deduplication, while keeping benefits such as scalability, robustness, and on-the-fly storage, retrieval, and search for chunks. We analyze the method's theoretical performance, and argue that our method can asymptotically approach the entropy limit for some data configurations. To validate the method's practical merits, we finally show that it is competitive when compared to popular universal compression algorithms on the MIT-BIH ECG compression Test Database.

关键词： Dictionaries Time series analysis compression algorithms Correlation Mutual information Random variables Robustness

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A Memory Bandwidth-efficient Architecture for Lossless compression Using Multiple DPCM Golomb-rice Algorithm

A Memory Bandwidth-efficient Architecture for Lossless Compr...

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International Symposium on Multimedia and Communication Technology (ISMAC)

作者： Imjae Hwang Juwon Yun Cheong Ghil Kim Woo-Chan Park Sejong University Seoul Korea Namseoul University Cheonan Choongnam Korea

ISBN: (纸本)9781728119199

In this paper, we propose a lossless compression architecture with less memory bandwidth. For this purpose, the compression ratio is improved with the prediction function using multiple differential pulse-code modulation algorithm which performs the variable sign code Golomb-rice encoding algorithm with entropy coding. For performance evaluation, several experiments were performed on 4k image sequences on the proposed architecture. The results show that it achieved an average compression ratio of 1.67 on the benchmarks.

关键词： Pulse modulation Image coding Hardware Computer architecture compression algorithms Image sequences Prediction algorithms

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Bitcoin Blockchain compression Algorithm for Blank Node Synchronization

Bitcoin Blockchain Compression Algorithm for Blank Node Sync...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Xiaojiao Chen Sianjheng Lin Nenghai Yu University of Science and Technology of China Hefei China

The blockchain provides an immutable, transparent, and decentralized method for data storage. However, as the volume of data gradually increases, the public blockchain system requires significant storage space. Also the synchronization of blank nodes needs much transmission bandwidth. This paper introduces a method to compress the size of transactions in the Bitcoin blockchain by replacing hash pointers with index pointers. Simulations show that the proposed method can reduce the storage space of the Bitcoin blockchain by up to 12.71%.

关键词： Bitcoin Cryptography Data compression Storage management Blockchain compression algorithms

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Reordering Genomic Sequences for Enhanced Classification via compression Analytics

Reordering Genomic Sequences for Enhanced Classification via...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Christina Ting Renee Gooding Richard Field Jacob Caswell Sandia National Laboratories Albuquerque USA

The full implications of sharing genomic information are still largely unknown. Understanding what attributes can be inferred from available information is therefore a critical part of genomic privacy and security. We show that compression analytics are successful at classifying, or inferring, unknown attributes of genomic sequences without the need for a predefined feature set and with very little training data. compression analytics perform best when predictable elements within a sequence are local; however, long range dependencies are ubiquitous in the human genome. We therefore consider a variety of schemes to reorder genomic sequences so as to localize predictable elements and improve the performance of compression analytics. compression analytics on both native and reordered sequences are shown to outperform more traditional, feature-based machine learning approaches.

关键词： Genomics Bioinformatics Sociology Statistics compression algorithms Privacy Security

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EEG Signal compression using Wavelet Packet Transform

EEG Signal Compression using Wavelet Packet Transform

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IEEE Southeastcon

作者： Sharif M A Bhuiyan Jesmin F Khan Department of Electrical and Computer Engineering Tuskegee University Tuskegee Alabama

ISBN: (数字)9781728101378

ISBN: (纸本)9781728101385

A compression algorithm based on wavelet packet transform (WPT) is presented in this paper for electroencephalogram (EEG) signals. WPT is a tree algorithm, which is an expansion of wavelet transform (WT). WPT offers the advantage of selecting the best tree from a number of bases in order to provide a better signal representation by preserving integrity and removing redundancy to achieve higher compression with minimal degradation in signal fidelity. For the selection of the best tree from the full tree of WPT, Shannon entropy is employed to get an exact representation of the signal. The compression is achieved by the use of the threshold on the best tree coefficients, and finally the signal reconstruction by the use of the thresholded coefficients. The presented method, which is assessed using different EEG signals, achieves good compression ratios (CR) with minimum distortion.

关键词： Electroencephalography Entropy Distortion compression algorithms Wavelet packets

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ICtCp Versus Y'CbCr

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IEEE CONSUMER ELECTRONICS MAGAZINE 2018年第3期7卷 38-47页

作者： Perrin, Anne-Flore Rerabek, Martin Husak, Walt Ebrahimi, Touradj Ecole Polytech Fed Lausanne Multimedia Signal Proc Grp Lausanne Switzerland Dolby Labs Inc San Francisco CA USA

New emerging technologies in the field of multimedia, such as high dynamic range (HDR) and wide color gamut (WCG), challenge the performance of existing image and video data processing algorithms, especially in terms of visual data compression and its faithful representation. HDR and WCG offer the technological capabilities to capture and represent a scene with an extended amount of details in both luminance and chrominance, respectively. This article presents and evaluates possible solutions to cope with such an extensive amount of data while delivering a faithful representation of visual information. More particularly, two approaches proposing novel color space, a constant intensity color difference signal representation called ICtCp and a novel compression optimization algorithm called the reshaper, are described and subjectively evaluated. The results of the conducted experiments show competitive performance of the two proposed approaches, ICtCp and reshaper, allowing a 10% bit rate reduction while preserving the perceived quality.

关键词： Image color analysis Multimedia communication Streaming media Dynamic range compression algorithms Quantization (signal) Data processing Heuristic algorithms

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Fast and Efficient compression of Next Generation Sequencing Data

Fast and Efficient Compression of Next Generation Sequencing...

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Data compression Conference (DCC)

作者： Constantinescu, Cornel Schmidt, Gero IBM Almaden Res Ctr San Jose CA 95120 USA

ISBN: (纸本)9781538648834

Despite the large number of research papers and compression algorithms proposed for com- pressing FASTQ genomic data generated by sequencing machines, by far the most commonly used compression algorithm in the industry for FASTQ data is gzip. The main drawback of the proposed alternative special-purpose compression algorithms is the slow speed of either compression or de- compression or both, and also their brittleness by making various limiting assumptions about the input FASTQ format (for example, the structure of the headers or fixed lengths of the records [5]) in order to further improve their specialized compression. In this paper we propose using a simple modeling improvement combined with fast general purpose encoders, achieving 4X-40X speed improvements and 6%-11% more efficienct compression ratios for the compression and decompression of FASTQ genomic data compared to gzip and also being 5X faster than special-purpose FASTQ compressors like DSRC v2 and slimfastq.

关键词： Genomics Bioinformatics Sequential analysis compression algorithms Robustness Data compression Industries

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The Application of the Internal Restructuring Method of the Information Resource Data According to the Sign of the Number of Series of Units to Improve the Statistical Coding Efficiency

The Application of the Internal Restructuring Method of the ...

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IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems

作者： Vladimir Barannik Ivan Tupitsya Valeriy Barannik Sergii Shulgin Alexander Musienko Roman Kochan Olga Veselska Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine Kharkov National University of Radio Electronics Kharkiv UKRAINE University of Bielsko-Biala Bielsko-Biala Poland

A fundamentally new approach to restructuring information resource data is considered - internal restructuring on a quantitative sign for a more favorable presentation of the encoded data. A technology has been developed for clustering information resource data based on the sign of number of series of units. The analysis of the application of the method of internal restructuring of information resource data on the sign of the number of series of units to improve the statistical coding efficiency from the position of reducing the length of the presentation of information.

关键词： Encoding Probability distribution Pattern classification compression algorithms Informations resources

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Dynamic Adaptive Network Edge Service Migration Method Based on a Docker Container

Dynamic Adaptive Network Edge Service Migration Method Based...

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IEEE International Conference on Computer and Communications

作者： Xiangjun Zhang Weiguo Wu Chi Zhang Wei Song Schoo of Couer Science nd Technoogy Xi'n Jioong UniversiySchoo of Couer Science nd Technoogy Xi'n Jioong UniversiySchoo of Couer Science nd Technoogy Xi'n Jioong UniversiySchoo of Couer Science nd Technoogy Xi'n Jioong Universiy

ISBN: (数字)9781728147437

ISBN: (纸本)9781728147444

With the development and maturity of 5G technology, edge networks have become more suitable for moving many low-latency cloud services to the edges of networks, thus significantly improving user service quality. Since mobile terminal users move with their location, the edge computing platform needs to seamlessly migrate services using users' mobile devices. However, the network environment of an edge network is complex, and service migration faces many challenges, such as how to overcome network delays and reduce the service interruption time. In this paper, we propose a service migration method based on Docker base image and design a compression algorithm for an adaptive network environment to compress images and other data. This method balances the multi-core processor computing power and network bandwidth resources. We implement pipeline parallel processing of each process and build a model to handle the situation. The results show that our method is more adaptable to changing network environments, and the migration time is reduced by 45.4%, on average, compared with the traditional uncompressed service switching method.

关键词： Containers Servers compression algorithms Cloud computing Bandwidth Adaptive systems File systems

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