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检索条件"主题词=compression algorithms"

共 2082 条记录，以下是281-290 订阅

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Light Field compression Using Fourier Disparity Layers

Light Field Compression Using Fourier Disparity Layers

引用

IEEE International Conference on Image Processing

作者： Elian Dib Mikaël Le Pendu Christine Guillemot Campus Universitaire de Beaulieu Rennes France V-SENSE Dublin Ireland

In this paper, we present a compression method for light fields based on the Fourier Disparity Layer representation. This light field representation consists in a set of layers that can be efficiently constructed in the Fourier domain from a sparse set of views, and then used to reconstruct intermediate viewpoints without requiring a disparity map. In the proposed compression scheme, a subset of light field views is encoded first and used to construct a Fourier Disparity Layer model from which a second subset of views is predicted. After encoding and decoding the residual of those predicted views, a larger set of decoded views is available, allowing us to refine the layer model in order to predict the next views with increased accuracy. The procedure is repeated until the complete set of light field views is encoded. Following this principle, we investigate in the paper different scanning orders of the light field views and analyse their respective efficiencies regarding the compression performance.

关键词： Encoding Calibration Decoding Fourier transforms Image reconstruction compression algorithms Prediction algorithms

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Batched Trajectory compression Algorithm Based on Hierarchical Grid Coordinates

Batched Trajectory Compression Algorithm Based on Hierarchic...

引用

IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Lin Li XueZhi Xia XiaoLong Liu Yu An Department of System Architecture Wuhan Digital Engineering Institute Wuhan Hubei Province China

ISBN: (数字)9781728109459

ISBN: (纸本)9781728109466

In the visual analysis application of large-scale off-line trajectory data, users need fast user-interface response with low latency. For such demand, we established a hierarchical grid coordinate system and proposed a batched trajectory compression algorithm. We projected the original track points into the discrete grid coordinates at the corresponding layer according to the trajectory visualization needs. Then, the original trajectory points were clustered by using the approximation of transformation to form a preliminary data set of compressed trajectory points in the corresponding layer. The data set was processed by the Douglas-Poker algorithm to get the final compressed trajectory data; we also accelerated the algorithm by using GPU. The analysis and experiments show that our algorithm can quickly generate the compressed trajectory data and improve the displaying efficiency of massive trajectory data on the basis of maintaining the invariance of the trajectory data visualization.

关键词： Trajectory compression algorithms Graphics processing units Image resolution Sensors Meters Computational complexity

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Exploring Lossy compression of Gene Expression Matrices

Exploring Lossy Compression of Gene Expression Matrices

引用

IEEE/ACM International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-5)

作者： Coleman B. McKnight Alexandra L. Poulos M. Reed Bender Jon C. Calhoun F. Alex Feltus Department Genetics and Biochemistry Clemson University Clemson South Carolina Clemson University Clemson SC USA Biological Data Science and Informatics Clemson University Clemson South Carolina Holcombe Department of Electrical and Computer Engineering Clemson University Clemson South Carolina

ISBN: (纸本)9781728160184

Gene Expression Matrices (GEMs) are a fundamental data type in the genomics domain. As the size and scope of genomics experiments increase, researchers are struggling to process large GEMs through downstream workflows with currently accepted practices. In this paper, we propose a methodology to reduce the size of GEMs using multiple approaches. Our method partitions data into discrete fields based on data type and employs state-of-the-art lossless and lossy compression algorithms to reduce the input data size. This work explores a variety of lossless and lossy compression methods to determine which methods work the best for each component of a GEM. We evaluate the accuracy of the compressed GEMs by running them through the Knowledge Independent Network Construction (KINC) workflow and comparing the quality of the resulting gene co-expression network with a lossless control to verify result fidelity. Results show that utilizing a combination of lossy and lossless compression results in compression ratios up to 9.77× on a Yeast GEM, while still preserving the biological integrity of the data. Usage of the compression methodology on the Cancer Cell Line Encyclopedia(CCLE) GEM resulted in compression ratios up to 9.26×. By using this methodology, researchers in the Genomics domain may be able to process previously inaccessible GEMs while realizing significant reduction in computational costs.

关键词： Gene expression Genomics Bioinformatics DNA compression algorithms Sequential analysis

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An Efficient Lossless compression Algorithm for Electrocardiogram Signals 26

An Efficient Lossless Compression Algorithm for Electrocardi...

引用

26th European Signal Processing Conference (EUSIPCO)

作者： Campobello, Giuseppe Segreto, Antonino Zanafi, Sarah Serrano, Salvatore Univ Messina Dept Engn Messina Italy Univ Abdelmalek Essaadi Fac Sci Tetouan Tetouan Morocco

ISBN: (纸本)9789082797015

This paper focuses on a novel lossless compression algorithm which can be efficiently used for compression of electrocardiogram (ECG) signals. The proposed algorithm has low memory requirements and relies on a simple and efficient encoding scheme which can be implemented with elementary counting operations. Thus it can be easily implemented even in resource constrained microcontrollers as those commonly used in several low-cost ECG monitoring systems. Despite its simplicity, simulation results carried out on real-world ECG signals show that the proposed algorithm achieves higher compression ratios as even compared to other more complex state-of-the-art solutions.

关键词： compression algorithms Signal processing algorithms Encoding Electrocardiography Microcontrollers Prediction algorithms Matrix decomposition

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A Specific compression Algorithm Used for Data Acquisition of Focal Plane Arrays

A Specific Compression Algorithm Used for Data Acquisition o...

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International Conference on Computer and Communications (ICCC)

作者： Yuan Liu Kong Fanlin Zhou Xiaoyan Ke Zungui Southwest Institute of Technical Physics Chengdu China

ISBN: (数字)9781728147437

ISBN: (纸本)9781728147444

Focal plane arrays (FPA) fabricated with the state-of-the-art technology are widely used in many fields such as target detection and three-dimensional imaging, but how to handle the big data acquired by FPAs is a challenging problem. Based on the statistical distribution of output values of FPA, a specific compression algorithm is presented in this paper. By setting the template, mapping original values to symbols and adopting the lossless Huffman coding, original data can be significantly compressed. Experiments were performed to verify the performance of the proposed algorithm, resulting in compression ratios ranging from 33.4% to 37.3% and mean squared errors ranging from 2.59×10 -9 to 1.99×10 -7 . To the best of our knowledge, this is the first study on the compression algorithm for big data acquired from FPAs.

关键词： Statistical distributions Big Data compression algorithms Data acquisition Dictionaries Image coding Huffman coding

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Multi-Thread Concurrent compression Algorithm for Genomic Big Data

Multi-Thread Concurrent Compression Algorithm for Genomic Bi...

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IEEE International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)

作者： Yimu Ji Shuai Chen Haichang Yao Houzhi Fang Kui Li Shangdong Liu Zhengyuan Xie Kairui Wang School of Computer Science Nanjing University of Posts and Telecommunications Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing Center of HPC China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications School of Computer and Software Nanjing Institute of Industry Technology Nanjing China University of Toronto Toronto Canada BSc Mathematics with Finance University of Liverpool Liverpool United Kingdom

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

At present, there are many excellent genome compression algorithms with high genome compression ratio. However, there is a lack of highly efficient compression algorithms for simultaneous compression of a large number of genomes. This manuscript presents an algorithm, which is called FastLNGC, for simultaneous compression of a large amount of genome data based on multi-thread concurrency. This algorithm is based on the LNGC (Large Number of Genomes Compressor) algorithm, and adopts multi-thread technology to achieve concurrent processing of genome data compression. A large number of experiments show that FastLNGC has better performance on compression of a large number of genes. The source code of FastLNGC is available at https://***/APandaThief/FastLNGC.

关键词： Bioinformatics Genomics compression algorithms Instruction sets Telecommunications Computer science Concurrent computing

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Dictionary based compression Type Classification using a CNN Architecture

Dictionary based Compression Type Classification using a CNN...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Hyewon Song Beom Kwon Seongmin Lee Sanghoon Lee Yonsei University Seoul South Korea

ISBN: (数字)9781728132488

ISBN: (纸本)9781728132495

As digital devices which are capable of viewing contents easily such as mobile phones and tablet PCs have become widespread, the number of digital crimes using these digital contents also increases. Usually, the data which can be the evidence of crimes is compressed and the header of data is damaged to conceal the contents. Therefore, it is necessary to identify the characteristics of the entire bits of the compressed data to discriminate the compression type not using the header of the data. In this paper, we propose a method for distinguishing 16 dictionary-based compression types. We utilize 5-layered Convolutional Neural Network (CNN) for classification of compression type using Spatial Pyramid Pooling (SPP) layer. We evaluate our proposed method on the Wikileaks Dataset, which is a text file database. The average accuracy of 16 dictionary-based compression algorithms is 99%. We expect that our proposed method will be useful for providing evidence for Digital Forensics.

关键词： Feature extraction Image coding Support vector machines Dictionaries Encoding compression algorithms Convolutional codes

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An Accelerometer Lossless compression Algorithm and Energy Analysis for IoT Devices

An Accelerometer Lossless Compression Algorithm and Energy A...

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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Pope, James Vafeas, Antonis Elsts, Atis Oikonomou, George Piechocki, Robert Craddock, Ian Univ Bristol Dept Elect & Elect Engn Bristol Avon England

ISBN: (纸本)9781538611548

The Internet of Things promises to enable numerous future applications spanning many domains, including health care, and is comprised of devices that are constrained in terms of computational and energy resources. A specific health care application is to ascertain patients' activity of daily living while at home using accelerometer data from non-invasive wearables. It is often necessary to store this data on the device to be retrieved later for analysis. However, the devices typically store far more data than can be transmitted with commonly used low power radios. To mitigate the problem, this paper proposes an energy efficient, lossless compression algorithm that uses an offline frequency distribution to create a symbol-code lookup table. Using an extensive set of data from a previous study, an analysis of the entropy of activities of daily living accelerometer data is presented. The compression algorithm is compared against this estimated entropy. Energy being critical for IoT devices, the trade-off between energy cost for compression versus energy saved during transmission is also analysed.

关键词： Entropy Accelerometers Conferences Medical services compression algorithms Biomedical monitoring Dictionaries

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Multidimensional compression with Pattern Matching

Multidimensional Compression with Pattern Matching

引用

Data compression Conference (DCC)

作者： Olivia Del Guercio Rafael Orozco Alex Sim Kesheng Wu Scripps College Bucknell University Lawrence Berkeley National Laboratory

Sensors typically record their measurements using more precision than the accuracy of the sensing techniques. Thus, experimental and observational data often contains noise that appears random and cannot be easily compressed. This noise increases storage requirement as well as computation time for analyses. In this work, we describe a line of research to develop data reduction techniques that preserve the key features while reduce the storage requirement. Our core observation is that the noise in such cases could be characterized by a small number of patterns based on statistical similarity. In earlier tests, this approach was shown to reduce the storage requirement by over 100-fold for one-dimensional sequences. In this work, we explore a set of different similarity measures for multidimensional sequences. During our tests with standard quality measures such as PSNR, we see that the new compression methods reduce the storage requirements over 100-fold while maintaining relatively low errors in peak signal-to-noise ratio. Thus, we believe that this is a new and effective way of constructing data reduction techniques.

关键词： Pattern matching Time measurement Data compression Dictionaries Heuristic algorithms compression algorithms Runtime

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A Grammar compression Algorithm based on Induced Suffix Sorting

A Grammar Compression Algorithm based on Induced Suffix Sort...

引用

Data compression Conference (DCC)

作者： Nogueira Nunes, Daniel Saad Louza, Felipe A. Gog, Simon Ayala-Rincon, Mauricio Navarro, Gonzalo Fed Inst Educ Sci & Technol Brasilia Brasilia DF Brazil Univ Brasilia Dept Comp Sci Brasilia DF Brazil Univ Sao Paulo Dept Comp & Math Sao Paulo Brazil Karlsruhe Inst Technol Inst Theoret Informat Karlsruhe Germany Univ Chile Dept Comp Sci Ctr Biotechnol & Bioengn Santiago Chile

ISBN: (纸本)9781538648834

We introduce GCIS, a grammar compression algorithm based on the induced suffix sorting algorithm SAIS, presented by Nong et al. in 2009. Our solution builds on the factorization performed by SAIS during suffix sorting. We construct a context-free grammar on the input string which can be further reduced into a shorter string by substituting each substring by its corresponding factor. The resulting grammar is encoded by exploring some redundancies, such as common prefixes between suffix rules, which are sorted according to SAIS framework. When compared to well-known compression tools such as Re-Pair and 7-zip under repetitive sequences, our algorithm is faster at compressing and achieves compression ratio close to that of Re-Pair, at the cost of being the slowest at decompressing.

关键词： Grammar Sorting Production Encoding Arrays compression algorithms Adaptive arrays

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