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检索条件"主题词=compression algorithms"

共 2082 条记录，以下是521-530 订阅

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Optimization of the compression Ratio of the Modified Algorithm of Decomposition Electromyographic Signals by a Superimposed Coding

Optimization of the Compression Ratio of the Modified Algori...

引用

International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Aimé Joseph Oyobé-Okassa Pierre Elé EPM of Masuku Franceville Gabon

The purpose of the compression is the minimization of the data storage space, the transmission time or the bandwidth. If the wavelets and specifically wavelets packets bases provide an appropriate framework for the selection of an optimal representation of electromyographic (EMG) signals, the choice of encoding method optimizes the compression ratio without altering the compressed data. In compression, it is usual to fix as goal either a compression ratio to achieve or a minimum acceptable quality of the reconstructed signal. In this work, the novelty is that we have obtained the compression ratio without fixing anything, and the results obtained are better than those given by the modified compression algorithm proposed in [6] as association of coding methods was not used there.

关键词： Electromyography Discrete wavelet transforms Channel coding compression algorithms

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Entropy Adaptive On-Line compression

Entropy Adaptive On-Line Compression

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IEEE International Symposium on Network Computing and Applications

作者： Shlomi Dolev Sergey Frenkel Marina Kopeetsky Department of Computer Science Ben-Gurion University of the Negev Beer-Sheva Israel Institute of Informatics Problems Russian Academy of Sciences Moscow Russia Department of Software Engineering Shamoon College of Engineering Beer-Sheva Israel

Self-Organization is based on adaptivity. Adaptivity should start with the very basic fundamental communication tasks such as encoding the information to be transmitted or stored. Obviously, the less signal transmitted the less energy in transmission used. In this paper we present a novel on-line and entropy adaptive compression scheme for streaming unbounded length inputs. The scheme extends the window dictionary Lempel-Ziv compression, is adaptive and is tailored to on-line compress inputs with non stationary entropy. Specifically, the window dictionary size is changed in an adaptive manner to fit the current best compression rate for the input. On-line Entropy Adaptive compression scheme (EAC), that is introduced and analyzed in this paper, examines all possible sliding window sizes over the next input portion to choose the optimal window size for this portion, a size that implies the best compression ratio. The size found is then used in the actual compression of this portion. We suggest an adaptive encoding scheme, which optimizes the parameters block by block, and base the compression performance on the optimality proof of Lempel Ziv algorithm when applied to blocks. The EAC scheme was tested over files of different types (docx, ppt, jpeg, xls) and over synthesized files that were generated as segments of homogeneous Markov Chains. Our experiments demonstrate that the EAC scheme typically provides a higher compression ratio than LZ77 does, when examined in the scope of on-line per-block compression of transmitted (or compressed) files.

关键词： Entropy Dictionaries Encoding Data compression compression algorithms Training Heuristic algorithms

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Improving I/O Performance with Adaptive Data compression for Big Data Applications

Improving I/O Performance with Adaptive Data Compression for...

引用

IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Hongbo Zou Yongen Yu Wei Tang Hsuanwei Michelle Chen Georgia Institute of Technology Atlanta GA USA Illinois Institute of Technology Chicago IL USA Argonne National Laboratory Argonne IL USA San Jose State University CA USA

ISBN: (纸本)9781479941155

Increasingly larger scale simulations are generating an unprecedented amount of data. However, the increasing gap between computation and I/O capacity on High End Computing machines makes a severe bottleneck for data analysis. As a solution, in-situ analytics processes output data while simulations are running and before placing data on disk. Data movement between simulation and analytics, however, incurs overheads of in-situ analytics at scale. This paper tries to answer the following question: can we use compression technology to reduce the data movement cost and improve the performance of in-situ analytics for peta-scale applications? In particular, we explore when, where, how to use the compression techniques to reduce data movement cost between simulation and analytics. To find out the best algorithm and place to compress data in given situation, we introduce an adaptive data compression algorithm in this paper. The adaptive compression service is developed and analyzed for the in-situ analytics middleware. Experimental results demonstrate that compression service increases data transition bandwidth and improve the application End-to-End transfer performance.

关键词： Data compression compression algorithms Data models Computational modeling Analytical models Bandwidth Data transfer

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Depth stream compression for enhanced real time fall detection by multiple sensors

Depth stream compression for enhanced real time fall detecti...

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IEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin)

作者： Enea Cippitelli Samuele Gasparrini Ennio Gambi Susanna Spinsante Department of Information Engineering Universita' Politecnica delle Marche Ancona Italy

A fall detection system using depth data acquired from a single Kinect sensor could be expanded by addition of more devices. The idea consists in equipping each sensor with an embedded board, in order to stream the collected depth data over a network, towards a central system, able to retrieve the data and process them. A preliminary evaluation of a low-complexity compression algorithm for depth streams is performed, and some encouraging results are presented.

关键词： compression algorithms Sensor systems PSNR Bandwidth Streaming media Transform coding

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High-fidelity compression of extracellular recordings from motor cortex

High-fidelity compression of extracellular recordings from m...

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International Joint Conference on Neural Networks

作者： Rachel Zhang Gang Pan Yueming Wang Zhenfang Hu Department of Computer Science Zhejiang University

ISBN: (纸本)9781479914821

In invasive brain-machine interfaces (BMI), the recorded high-quality neural signals produce a large data volume. This calls for effective compression. In this paper, we focus on extracellular recording of motor cortex. First the characteristics of the signals are studied, one of which is that peaks of DCT coefficients at high frequency may correspond to spike firing patterns. Based on these characteristics, we propose a high-fidelity compression framework for these signals. The DCT coefficients of the signal are divided into two parts according to amplitude, rather than frequency. The Low-Amplitude-Component (LAC) is encoded by a phase called Symbol Encoding, which helps to reduce overall distortion. The High-Amplitude-Component (HAC), containing major information and spikes, is encoded by another phase called Hybrid Encoding. It combines the Huffman encoding and a novel Zero-Length-Encoding. Experiments show that the algorithm achieves a compression ratio of 18% without obvious distortion. Moreover, spikes are reserved more than 92%, outperforming existing work. Our algorithm enables low-cost storage devices to store long-time neural signals.

关键词： Encoding Discrete cosine transforms Extracellular Correlation Quantization (signal) Neurons compression algorithms

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Adaptive Online Feedback Controlled Message compression

Adaptive Online Feedback Controlled Message Compression

引用

IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Marcus Jägemar Sigrid Eldh Andreas Ermedahl Björn Lisper Ericsson AB Sweden Mälardalen University

ISBN: (纸本)9781479935765

Communication is a vital part of computer systems today. One current problem is that computational capacity is growing faster than the bandwidth of interconnected computers. Maximising performance is a key objective for industries, both on new and existing software systems, which further extends the need for more powerful systems at the cost of additional communication. Our contribution is to let the system selectively choose the best compression algorithm from a set of available algorithms if it provides a better overall system performance. The online selection mechanism can adapt to a changing environment such as temporary network congestion or a change of message content while still selecting the optimal algorithm. Additionally, is autonomous and does not require any human intervention making it suitable for large-scale systems. We have implemented and evaluated this autonomous selection and compression mechanism in an initial trial situation as a proof of concept. The message round trip time were decreased by 7.1%, while still providing ample computational resources for other co-existing services.

关键词： compression algorithms Prediction algorithms Computers Bandwidth Equations Mathematical model Time measurement

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A lossless compression algorithm for video frames

A lossless compression algorithm for video frames

引用

International Conference on Industrial and Information Systems, ICIIS

作者： K.V. Arya Ngali Tato ABV-Indian Institute of Information Technology and Management Gwalior India

In this paper a scheme has been proposed for lossless compression of video sequences. In the proposed scheme the number of video frames is calculated in input file. Then quantitatively, the characteristics of display frame data are analysed. All video pixels are classified into two categories (Dictionary and Non Dictionary Pixels) based on the adjacency matrix of the frame under consideration. The code words are generated separately for dictionary and non-dictionary pixels using the combination of dictionary coding and Huffman coding. The code words are converted into the bit streams which is then compressed with the help of predefine set of rules referred to as union function. Through experimental results it is observed that the proposed lossless compression scheme achieves 19.77% data compression without degrading system performance.

关键词： Dictionaries Streaming media Huffman coding Video compression compression algorithms Decoding

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Image compression via wavelets and row compression

Image compression via wavelets and row compression

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Asilomar Conference on Signals, Systems & Computers

作者： Mary Hudachek-Buswell Michael Stewart Saeid Belkasim College of Computing Georgia Institute of Technology Atlanta GA Department of Mathematics Georgia State University Atlanta GA Department of Computer Science Georgia State University Atlanta GA

This work exploits a stable row compression algorithm for decomposing a hierarchically or sequentially structured matrix to compress an n × n image represented by a wavelet transform. The multiresolution discrete wavelet transform is used to decompose an image. The row compression algorithm builds up a low rank approximation of the wavelet transform by applying orthogonal transformations and updating techniques. The cost is O(n 2 ) operations.

关键词： Image coding Matrix decomposition compression algorithms Discrete wavelet transforms Image resolution

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Lossless/near lossless compression using bilateral filter and symmetrical structure based bias cancellation

Lossless/near lossless compression using bilateral filter an...

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International Symposium on Communications Control and Signal Processing (ISCCSP)

作者： Vinit Jakhetiya Oscar C. Au Sunil Jaiswal Luheng Jia Manohar Kuse Department of Electronics and Computer Engineering Hong Kong University of Science and Technology Hong Kong

Recently, a few symmetrical predictor structure (SPS) based lossless / near lossless compression algorithms have been proposed, which can efficiently exploit the information from the neighboring pixels. Prediction stage of existing SPS algorithms uses least squares optimization, which is computationally expensive and only causal pixels are used for the bias cancellation stage. In this paper, we propose an efficient and computationally simple lossless / near-lossless compression algorithm using the bilateral filter. Moreover we propose to use both causal and non-causal pixels for bias cancellation. From extensive experiments, it is observed that the proposed algorithm has the capability of provide better prediction and compression performance.

关键词： Image coding Prediction algorithms Transform coding Context Gold compression algorithms Optimization

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Hybrid multichannel EEG compression scheme for tele-health monitoring

Hybrid multichannel EEG compression scheme for tele-health m...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Rashima Mahajan Dipali Bansal Dept. of EEE Manav Rachna International University Faridabad India Manav Rachna International Institute of Research and Studies Faridabad Haryana IN

Telemonitoring of Electroencephalogram (EEG) through wireless and information technology is very crucial as EEG is a primary clinical diagnostic tool for many neurological disorders. EEG signals are continuously recorded through many channels over prolonged time periods at extremely high resolution and thus possess enormous data size. This may lead to large storage space and more transmission bandwidth requirement for remote EEG signal analysis. An attempt has been made to compress multichannel EEG by exploiting neighboring channel and sample redundancy, present along with the information content. This paper presents a high performance hybrid multichannel EEG compression algorithm based on frequency transformation and parameter extraction techniques. It compares discrete cosine transform (DCT) with Fast Fourier transform (FFT) to compress the multichannel EEG. This compressed EEG is embedded with corresponding EEG sub band power information to obtain a high quality signal reconstruction for accurate brain state analysis. The performance of proposed hybrid compression scheme is quantified in terms of compression level achieved and reconstruction error introduced. A high compression ratio and considerably low percent root-mean-square difference (PRD) error is found thereby preserving diagnostic information while transmission/ recovery of real time multichannel EEG for efficient telehealth monitoring applications with reduced computational complexity. This research has also resulted in repeatability and thus reliability testing of an efficient hybrid compression technique developed earlier for ECG signals.

关键词： Electroencephalography Discrete cosine transforms Parameter extraction compression algorithms Signal reconstruction Real-time systems Fast Fourier transforms

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