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检索条件"主题词=Compression Algorithms"

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Reconfiguration of environmental data compression parameters through cognitive IoT technologies

Reconfiguration of environmental data compression parameters...

引用

IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Massimo Vecchio Swaytha Sasidharan Francesco Marcelloni Raffaele Giaffreda CREATE-NET Trento Italy Department of Information Engineering University of Pisa Italy

Internet of Things (IoT) is expected to substantially support sustainable developments of future smart cities. However, the heterogeneity among connected objects (i.e., things) and the unreliable nature of their associated services may prevent IoT from playing this crucial role. To enable the possibility of dynamically (re-)configuring real world objects at run-time, we propose to represent them as Virtual Objects (VOs). VOs are semantic descriptions of physical objects and of the phenomena they observe and include software modules to expose the object functionalities as IoT services. In order to provide self configuration functionality at VO level, we propose to use a cognitive management framework that wisely tunes key application parameters. Finally, we present a practical environmental monitoring application exploiting wireless sensor nodes, to show how the proposed cognitive framework can be useful to select the most appropriate compression algorithm so as to reduce the overall energy consumption of the sampling devices.

关键词： compression algorithms Data compression Wireless sensor networks Energy consumption Tin Machine learning algorithms Quantization (signal)

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Minability through compression

Minability through Compression

引用

International Symposium on Symbolic and Numeric algorithms for Scientific Computing (SYNASC)

作者： Dan A. Simovici Department of Computer Science University of Massachusetts Boston Boston MA UAS

We offer an experimental proof that the application of compression to data files can be used as a evaluation technique for minability of the data. This is based on the fact that the presence of patterns embedded in da... 详细信息

关键词： Entropy Correlation Random variables compression algorithms Probability distribution Association rules

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Efficient Data Communication Using Dynamic Switching of compression Method

Efficient Data Communication Using Dynamic Switching of Comp...

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International Symposium on Computing and Networking (CANDAR)

作者： Masayuki Omote Kanemitsu Ootsu Takeshi Ohkawa Takashi Yokota Utsunomiya Daigaku Utsunomiya Tochigi JP Graduate School of Engineering Utsunomiya University Utsunomiya Tochigi Japan

As a means for high-speed communication on the network with limited communication bandwidth such as WAN or wireless LAN environment, we can use the online high-speed data compression/decompression method. Since it is necessary to reduce the transfer time including the time of the data compression process, it is difficult to adopt the advanced but time-consuming compression algorithm. However, it is possible to mitigate this constraint, if we take advantage of the multiple cores of multi-core processor that is significantly spread in recent years. By performing data transfer and compression on different processor cores in parallel, it is possible to use the advanced algorithm for higher compression ratio than conventional online algorithm, as long as the compression process catches up with the process of data transmission to network. Thus, it is possible to transfer the data faster, by transferring the more compressed data than conventional. In this paper, we investigate the method of highly efficient data transfer in the network of limited communication bandwidth. The method tries to select the highest compression algorithm dynamically as long as it catches up with the actual speed of sending data, in order to fully utilize the network bandwidth. Our evaluation result shows that the proposed method can select the nearly best compression method, and achieve up to 3 times speed-up maximum in various situations where both communication bandwidth and input data vary with time.

关键词： Bandwidth Switches Data transfer Data compression compression algorithms Heuristic algorithms

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On Board EMG Signal compression for Portable Devices - A Study

On Board EMG Signal Compression for Portable Devices - A Stu...

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International Conference on Machine Intelligence and Research Advancement (ICMIRA)

作者： Sushant Sharma Mandeep Singh ACSD Centre for Development of Advanced Computing Mohali India Division Centre for Development of Advanced Computing Engineer Digital Electronics and Communication Mohali India

Electromyography signals are used for biomedical applications and clinical diagnosis. This paper presents a system to acquire high quality EMG (electromyography) signal and compress it for high data transfer rate. For acquiring EMG signals we are using off-the-shelf integrated circuit AFE which generates 24 bit data stream output. After acquiring the signal from front end amplifier, the signal is processed (filtration, compression). This is achieved by making use of MSP430 MCU (micro controller). We are using AFE and MSP430 because it is less power consuming, reliable, and both are compatible with each other. The detail of the hardware and software is explained in the further section of the paper. This paper also describes the compression algorithm. compression is done so that whenever the data is send wirelessly using Bluetooth, Zigbee etc, it requires less bandwidth.

关键词： Electromyography Electrodes Noise Muscles Conferences compression algorithms Quantization (signal)

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FPGA implementation of a lossy compression algorithm for hyperspectral images with a high-level synthesis tool

FPGA implementation of a lossy compression algorithm for hyp...

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NASA/ESA Conference on Adaptive Hardware and Systems

作者： Lucana Santos Jose Fco. Lopez Roberto Sarmiento Raffaele Vitulli Institute for Applied Microelectronics University of Las Palmas de Gran Canaria European Space Agency (ESA) ESTEC

ISBN: (纸本)9781467363822

In this paper, we present an FPGA implementation of a novel adaptive and predictive algorithm for lossy hyperspectral image compression. This algorithm was specifically designed for on-board compression, where FPGAs are the most attractive and popular option, featuring low power and high-performance. However, the traditional RTL design flow is rather time-consuming. High-level synthesis (HLS) tools, like the well-known CatapultC, can help to shorten these times. Utilizing CatapultC, we obtain an FPGA implementation of the lossy compression algorithm directly from a source code written in C language with a double motivation: demonstrating how well the lossy compression algorithm would perform on an FPGA in terms of throughput and area;and at the same time showing how HLS is applied, in terms of source code preparation and CatapultC settings, to obtain an efficient hardware implementation in a relatively short time. The P&R on a Virtex 5 5VFX130 displays effective performance terms of area (maximum device utilization at 14%) and frequency (80 MHz). A comparison with a previous FPGA implementation of a lossless to near-lossless algorithm is also provided. Results on a Virtex 4 4VLX200 show less memory requirements and higher frequency for the LCE algorithm.

关键词： Field programmable gate arrays compression algorithms lossy compression Source code high level synthesis hyperspectral imagery hue lightness saturation memory requirements C language

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Integrating Online compression to Accelerate Large-Scale Data Analytics Applications

Integrating Online Compression to Accelerate Large-Scale Dat...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Tekin Bicer Jian Yin David Chiu Gagan Agrawal Karen Schuchardt Computer Science and Engineering Ohio State University USA Pacific Northwest National Laboratories USA Washington State University USA

ISBN: (纸本)9781467360661

Compute cycles in high performance systems are increasing at a much faster pace than both storage and wide-area bandwidths. To continue improving the performance of large-scale data analytics applications, compression has therefore become promising approach. In this context, this paper makes the following contributions. First, we develop a new compression methodology, which exploits the similarities between spatial and/or temporal neighbors in a popular climate simulation dataset and enables high compression ratios and low decompression costs. Second, we develop a framework that can be used to incorporate a variety of compression and decompression algorithms. This framework also supports a simple API to allow integration with an existing application or data processing middleware. Once a compression algorithm is implemented, this framework automatically mechanizes multi-threaded retrieval, multi-threaded data decompression, and the use of informed prefetching and caching. By integrating this framework with a data-intensive middleware, we have applied our compression methodology and framework to three applications over two datasets, including the Global Cloud-Resolving Model (GCRM) climate dataset. We obtained an average compression ratio of 51.68%, and up to 53.27% improvement in execution time of data analysis applications by amortizing I/O time by moving compressed data.

关键词： Prefetching compression algorithms Data models Middleware Meteorology Computational modeling

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Evaluating Memory compression and Deduplication

Evaluating Memory Compression and Deduplication

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International Conference on Networking, Architecture, and Storage (NAS)

作者： Yuhui Deng Liangshan Song Xinyu Huang Key Laboratory of Computer System and Architecture Chinese Academy of Sciences Beijing PR China Computer science Department Jinan University Guangzhou PR China

Many programs require more RAM to hold their data than a typical computer has. Theoretically, both the compression and deduplication can trade the rich computing capacity for more available RAM space. This paper comprehensively evaluates the performance behaviour of memory compression and memory deduplication by using seven real memory traces. The experimental results give two implications: (1) Memory deduplication greatly outperforms memory block compression. (2) Fixed-size partition (FSP) achieves the best performance in contrast to Content-defined Chunking (CDC) and Sliding Block (SB). The optimal chunking size of FSP is equal to the size of a memory page. The analysis results in this paper should be able to provide useful insights for designing or implementing systems that require abundant memory resources to enhance the system performance.

关键词： Random access memory Memory management Dictionaries Encoding Computers compression algorithms Data compression

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Self-adjusting method for efficient GPS tracklog compression

Self-adjusting method for efficient GPS tracklog compression

引用

International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Marcell Feher Bertalan Forstner Budapest University of Technology and Economics Budapest Hungary

Data reduction algorithms operating on GPS tracklogs are widely used in cartographic applications, because the raw dataset usually includes a huge redundancy and unneccessarily high volume of measurement points. The existing methods for compressing tracklogs are either too simplistic or intended to be used on powerful computers, whereas our research focuses on mobile environments where both memory and computational power are very limited. In this paper we are introducing a modified version of a well-known line generalization algorithm, which aims to reach the best trade-off between complexity and accuracy in embedded systems, mostly smartphones.

关键词： Global Positioning System Trajectory Approximation methods Approximation algorithms Mobile communication Earth compression algorithms

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GPU-accelerated adaptive compression framework for genomics data

GPU-accelerated adaptive compression framework for genomics ...

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IEEE International Conference on Big Data

作者： GuiXin Guo Shuang Qiu ZhiQiang Ye BingQiang Wang Lin Fang Mian Lu Simon See Rui Mao BGI-Shenzhen Shenzhen P.R. China A *STAR Institute of HPC Singapore BGI-NVIDIA Joint Innovation Lab Shenzhen P.R. China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen University Shenzhen P.R. China

Genomics data is being produced at an unprecedented rate, especially in the context of clinical applications and grand challenge questions. There are various types of data in genomics research, most of which are stored as plain text tables. A data compression framework tailored to this file type is introduced in this paper, featuring a combination of generic compression algorithms, GPU acceleration, and column-major storage. This approach is the first to achieve both compression and decompression rates of around 100MB/s on commodity hardware without compromising compression ratio. By selecting appropriate compression schemes for each column of data, this framework efficiently exploits data redundancy while remaining applicable to a wide range of formats. The GPU-accelerated implementation also properly exploits the parallelism of compression algorithms. Finally, this paper presents a novel first-order Markov model based transformation, with evidence that it is at least as effective as Burrows-Wheeler and Move-To-Front in some contexts.

关键词： Graphics processing units Bioinformatics Genomics Markov processes Transforms compression algorithms Acceleration

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ON THE DESIGN OF AN EFFICIENT ENCRYPTION-THEN-compression SYSTEM

ON THE DESIGN OF AN EFFICIENT ENCRYPTION-THEN-COMPRESSION SY...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Jiantao Zhou Xianming Liu Oscar C. Au Department of Computer and Information Science University of Macau Macau China School of Computer Science and Technology Harbin Institute of Technology Harbin China Department of ECE Hong Kong University of Science and Technology Hong Kong China

ISBN: (纸本)9781479903573

In many practical scenarios, image encryption has to be conducted prior to image compression. This has led to the problem of how to design a pair of encryption and compression algorithms such that compressing the encrypted image can still be efficiently performed. In this work, we propose a permutation-based image encryption method conducted over the prediction error domain. We also design an arithmetic coding (AC)-based approach to efficiently compress the encrypted image. It can be shown that the proposed scheme can provide reasonably high level of security. More notably, the compression performance on the encrypted image is only slightly degraded, compared with that of compressing the original, un-encrypted one. In contrast, most of the existing approaches induce significant penalty on the compression performance.

关键词： cryptography Security level Imagery (Psychotherapy) Image images COMPRESSED compression algorithms Image compression Data compression arithmetic coding

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