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检索条件"主题词=Compression Algorithms"

共 2082 条记录，以下是361-370 订阅

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Data compression and decompression Integrated Circuit for AC Power Quality data

Data compression and decompression Integrated Circuit for AC...

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Annual IEEE Northeast Workshop on Circuits and Systems (NEWCAS)

作者： Marcos Hervé Cristiano Thiele Diógenes Machado Guilherme Wojichowski Cesar Crovato Sergio Bampi Altamiro Susin Embrasul Indústria Eletrônica LTDA Porto Alegre Brazil Electronic Engineering Department FEEVALE Hamburgo Brazil UFRGS PGMICRO - Informatics Institute Porto Alegre Brazil

ISBN: (纸本)9781509049929

This paper presents the integration of a Power Quality (PQ) data compression/decompression Application Specific Integrated Circuit (ASIC) into an AC power quality analyzer. The integration of the ASIC improves substantially the system data storage capabilities. The paper describes the compression algorithms implemented in dedicated CMOS circuitry. compression results obtained from real power line measurements are herein shown to prove the effectiveness of our compression/decompression ASIC. This chip has a die area of 15mm 2 , more than 400k 2-input logic gates equivalent, and 10 SRAM modules, all integrated into a 180nm digital CMOS technology. To the best of our knowledge this is the first lossy compression/decompression ASIC design for registering power quality trends over time, which is also compliant with the IEC 61000-4-30 standard accuracy requirements, fully tested and functional in a Class A system hardware PQ analyzer.

关键词： Power quality Voltage measurement Data compression compression algorithms Power measurement Application specific integrated circuits Harmonic analysis

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IP core for efficient zero-run length compression of CNN feature maps

IP core for efficient zero-run length compression of CNN fea...

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Telecommunications Forum (TELFOR)

作者： Andrea Erdeljan Bogdan Vukobratović Rastislav Struharik Faculty of Technical Sciences University of Novi Sad Novi Sad Frobas GmbH Forstern Germany

Convolutional Neural Networks (CNNs) are becoming a fundamental tool for machine learning. High performance and energy efficiency are of great importance for deployments of CNNs in many embedded applications. Energy consumption during CNN processing is dominated by memory access and since large networks do not fit on on-chip storage, they require expensive DRAM access. This paper introduces an universal Output Stream Manager (OSM) which can be used to compress and format data coming from a CNN accelerator and reduce external memory access. The OSM exploits the sparsity of data and implements two Zero-Run Length encoding algorithms and can be easily reconfigured to optimize usage for different CNN layers.

关键词： Random access memory Memory management Field programmable gate arrays Encoding compression algorithms IP networks

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Effective Lossless compression of Sensor Information in Manufacturing Industry

Effective Lossless Compression of Sensor Information in Manu...

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Conference on Local Computer Networks (LCN)

作者： Hagen Sparka Roman Naumann Stefan Dietzel Björn Scheuermann Humboldt-Universität zu Berlin Germany

In recent years, many visions for hitherto considered-futuristic computing applications gained momentum. The vision of smart factories is one example for these trends, which all share a common requirement: the prolific dissemination of sensor information. As wireless communication in smart factories needs to cope with harsh environments, the amount of sensor information produced by sources will likely surpass the communication channel's available capacity. This discrepancy calls for efficient communication and filtering protocols, as well as compression mechanisms, as a foundation for dependable applications. We propose such a compression algorithm that is lossless and tailored towards the requirements of the manufacturing industry. Our algorithm employs a two-step stochastic model that uses lossy compression to extract an approximation from the signal and a separate noise model to accommodate the remaining error. Evaluation results validate that our algorithm achieves better compression rates than existing approaches for several types of real world sensor data from the industry.

关键词： Wireless sensor networks Production facilities Discrete cosine transforms Entropy Wireless communication compression algorithms

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Elastic Data compression with Improved Performance and Space Efficiency for Flash-Based Storage Systems

Elastic Data Compression with Improved Performance and Space...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Bo Mao Hong Jiang Suzhen Wu Yaodong Yang Zaifa Xi Software School of Xiamen University China Department of Computer Science & Engineering at University of Texas-Arlington USA Computer Science Department of Xiamen University China Microsoft Redmond WA USA

Data compression has become a commodity feature for space efficiency and reliability in flash-based storage systems by reducing write traffic and space capacity demand. However, it introduces noticeable processing overheads on the critical I/O path, which degrades the system performance significantly. Existing data compression schemes for flash-based storage systems use fixed compression algorithms for all the incoming write data, failing to recognize and exploit the significant diversity in compressibility and access patterns of data and missing an opportunity to improve the system performance, the space efficiency or both. To achieve a reasonable trade-off between these two important design objectives, in this paper we introduce an Elastic Data compression scheme, called EDC, which exploits the data compressibility and access intensity characteristics by judiciously matching data of different compressibility with different compression algorithms while leveraging the access idleness. Specifically, for compressible data blocks EDC exploits the compression diversity of the workload, and employs algorithms of higher compression rate in periods of lower system utilization and algorithms of lower compression rate in periods of higher system utilization. For non-compressible (or very lowly compressible) data blocks, it will write them through to the flash storage directly without any compression. The experiments conducted on our lightweight prototype implementation of the EDC system show that EDC saves storage space by up to 38.7%, with an average of 33.7%. In addition, it significantly outperforms the fixed compression schemes in the I/O performance measure by up to 61.4%, with an average of 36.7%.

关键词： Data compression System performance compression algorithms Time factors Market research Reliability Algorithm design and analysis

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Less is more: Increasing the scope of hardware debugging with compression

Less is more: Increasing the scope of hardware debugging wit...

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Panhellenic Conference on Electronics and Telecommunications (PACET)

作者： Fotis Kostarelos George Charitopoulos Dionisios N. Pnevmatikatos School of Electrical and Computer Engineering Technical University of Crete Chania Greece

ISBN: (纸本)9781538622889

In this work we consider the slow and tedious phase of hardware debugging in FPGAs. The process of hardware debugging is normally done via Internal Logic Analyzer (ILA) circuits, which add user observability in internal FPGA signals. The user first defines the target for debugging signal and a triggering condition. Then the ILA stores traces of it in trace buffers, these traces are finally transferred to a host PC for the user to observe. The user also has to consider the limited FPGA memory resources, which result in small-sized trace buffers, an important restriction of hardware debugging. In this paper, we attempt to increase the scope of hardware tracing, i.e. the number of samples written on the trace buffers, with the use of compression. We use the LZW algorithm and find that we can increase the debugging signals number of recorded samples by 90%, i.e. have double the amount of useful data with the same memory usage. On the memory plane we conclude that, for the same number of recorded samples, our architecture uses less than half the resources compared to the standard Xilinx ILA block for signal tracing.

关键词： Debugging Hardware Dictionaries compression algorithms Field programmable gate arrays Data compression Memory management

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RAKE: a Simple and Efficient Lossless compression Algorithm for the Internet of Things

RAKE: a Simple and Efficient Lossless Compression Algorithm ...

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European Signal Processing Conference

作者： Giuseppe Campobello Antonino Segreto Sarah Zanafi Salvatore Serrano Department of Engineering University of Messina Italy Faculty of Science Tetouan University Abdelmalek Essaadi Morocco

In this paper we propose a new lossless compression algorithm suitable for Internet of Things (IoT). The proposed algorithm, named RAKE, is based only on elementary counting operations and has low memory requirements, and therefore it can be easily implemented in low-cost and low-speed micro-controllers as those used in IoT devices. Despite its simplicity, simulation results show that, in the case of sparse sequences, the proposed algorithm outperforms well-known lossless compression algorithms such as rar, gzip and bzip2. Moreover, in the case of real-world data, RAKE achieves higher compression ratios as even compared to IoT-specific lossless compression algorithms.

关键词： compression algorithms Signal processing algorithms Dictionaries Prediction algorithms Encoding Europe Signal processing

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A novel compression algorithm decision method for spark shuffle process

A novel compression algorithm decision method for spark shuf...

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IEEE International Conference on Big Data

作者： Shanshan Huang Jungang Xu Renfeng Liu Husheng Liao Faculty of Information Technology Beijing University of Technology Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

With the wide application of Spark big data platform, some problems in practical application are exposed, and one of the main problems is performance optimization. The Shuffle module of Spark is one of the core modules of Spark, and it is also an important module of some other distributed big data computing frameworks. The design of Shuffle module is the key factor that directly determines the performance of big data computing framework. The main optimization parameters of Shuffle process involve the CPU utilization, I/O literacy rate, network transmission rate, and one of these factors is likely to be the bottleneck during the execution of application. The network data transmission time consumption, I/O read and write time, and the CPU utilization are closely related with the size of the data processing. As a result, Spark provides compression configuration options and different compression algorithms for users to select. Different compression algorithms have different effects in compression rate and compression ratio, but the default configuration is usually selected by all users even though they run different applications, so the optimal configuration cannot be achieved. In order to achieve the optimal configuration of compression algorithm for the Shuffle process, one cost optimization model for Spark Shuffle process is proposed in this paper, which enables users to get the best compression configuration before application execution. The experimental results show that the prediction model for compression configuration has an accuracy of 58.3%, and the proposed cost optimization model can improve the performance by 48.9%.

关键词： Sparks compression algorithms Optimization Big Data Machine learning algorithms

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Experimental results in Prediction by Partial Matching and Star transformation applied in lossless compression of text files

Experimental results in Prediction by Partial Matching and S...

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International Symposium on Advanced Topics in Electrical Engineering (ATEE)

作者： Radu Rădescu Sever Paşca Faculty of Electronics Telecommunications and Information Technology

The PPM (Prediction by Partial Matching) methods are applied before the lossless compression algorithms, as a preprocessing procedure for the text in order to use efficiently the redundancy. This paper presents 4 compression algorithms preceded by 3 different PPM methods. The compression ratio is studied in relation with the file size, its content, and the context order (the prediction degree). Some useful recommendations are given in order to achieve the best compression for any kind of text files, both in English and in Romanian, as well as in natural or artificial languages. The paper also refers to the benefits provided in the lossless compression by using a preprocessing method in order to exploit better the redundancy of the source file. The procedure is known as the Star (*) Transform and it is applied for text files. The algorithm is briefly presented before emphasizing its benefic effects on a certain set of test files chosen from basic text corpora. Experimental results were performed and some interesting conclusions were driven on their basis.

关键词： Context Dictionaries Transforms compression algorithms Encoding History Redundancy

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Alternative algorithms involving alphabetical coding and limited length encoding in lossless data compression of text files

Alternative algorithms involving alphabetical coding and lim...

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International Symposium on Fundamentals of Electrical Engineering (ISFEE)

作者： Radu Rădescu Universitatea Politehnica din Bucuresti Bucuresti RO

Coding algorithms are generally aimed at minimizing the output code length encoding speed once the code has been designed. Moreover, most codes use a binary alphabet. This paper examines other issues related to coding, such as additional constraints imposed on the channel. Code generation will be considered where there is a limit on code words. Limits the application of such systems is a practical example of data compression where fast decoding is essential. When all code words correspond to a single word in memory (usually 32 bits, but there are situations that take 64-bit) can be used canonical decoding. If the deadline cannot be guaranteed, however, required the use of slower decoding methods. This paper also deals with the alphabetic code generation, where lexicographic arrangement of words by their code symbols must correspond to the original order in which the symbols were taken coding system. When an alphabetic code is used to compress a database that can be sorted in the same, order they would have had if the database records were first decompressed and then sorted. It also corresponds to alphabetic code trees binary search trees, which have applications in a wide variety of search problems. Assumption that the symbols are sorted by probability is not suitable for this scenario. The problem of finding codes for non-binary alphabets channel will be examined in detail. The subsequent experimental results cover the problem of alphabetic coding and of limited length coding.

关键词： Encoding compression algorithms Probability distribution XML Algorithm design and analysis Data compression Decoding

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Procedures of extending the alphabet in combined coding for prediction by partial string matching in text compression

Procedures of extending the alphabet in combined coding for ...

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International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Radu Rădescu Sever Paşca Applied Electronics and Information Eng. Dept University Politehnica of Bucharest Romania

The present work describes the PPM - Prediction by Partial (string) Matching algorithm for lossless compression of text. It studies the procedure of extending the source alphabet for the PPM encoding in order to allow using of symbols not yet present in the source alphabet at the very beginning of the process of text encoding. The work describes the procedure of extending the alphabet and presents the changes needed to be performed in the PPM algorithm so that it can use the extended generated alphabet. There are three ways proposed in extending the source alphabet: manual, running over the text, and adaptive. The arithmetical compression algorithm is used in order to encode the text words with the help of the statistic model derived from the PPM. Experimental results on different formats of text files and useful conclusions for text lossless compression are presented.

关键词： Encoding Decoding Binary trees Redundancy Prediction algorithms compression algorithms

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