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检索条件"主题词=compression algorithms"

共 2082 条记录，以下是421-430 订阅

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CSI compression and Feedback for Network MIMO 81

CSI Compression and Feedback for Network MIMO

引用

81st IEEE Vehicular Technology Conference (VTC Spring)

作者： Kurras, Martin Jaeckel, Stephan Thiele, Lars Braun, Volker Heinrich Hertz Inst Nachrichtentech Berlin GmbH Fraunhofer Inst Telecommun Einsteinufer 37 D-10587 Berlin Germany Alcatel Lucent Bell Labs Stuttgart Germany

ISBN: (纸本)9781479980888

We consider the downlink of a multi-cell system where base stations and users are equipped with multiple antennas. Therein, a cluster of base stations employ joint transmission with linear zero-forcing beamforming based on compressed channel state information (CSI) feedback. For propagation we assume a realistic 3D channel model including antenna patterns. We discuss in detail a novel compression algorithm exploiting the spatial and frequency domain and also present a feedback data format. We evaluate the proposed compression algorithm in the presence of channel estimation error in terms of throughput and feedback rate and compare it with the perfect CSI case. The proposed algorithm and feedback scheme is shown to reduce the feedback rate to less than 1 % of uncompressed feedback and at the same time increasing the sum throughput significantly.

关键词： Feedback compression algorithms BASE STATION antenna radiation patterns channel model Throughput multiple antenna multiple input multiple output systems channel estimation

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A Map-Matching Aware Framework For Road Network compression 16

A Map-Matching Aware Framework For Road Network Compression

引用

IEEE 16th International Conference on Mobile Data Management MDM

作者： Hendawi, Abdeltawab M. Khot, Amruta Rustum, Aqeel Basalamah, Anas Teredesai, Ankur Ali, Mohamed Univ Washington Tacoma Inst Technol Ctr Data Sci Tacoma WA USA Univ Minnesota Dept Comp Sci & Engn St Paul MN USA Umm Al Qura Univ Dept Comp Engn Mecca Saudi Arabia Umm Al Qura Univ KACST GIS Technol Innovat Ctr Mecca Saudi Arabia

ISBN: (纸本)9781479999729

We demonstrate a novel location aware services framework termed COMA for efficient compression and map-matching of road-network graph data. Key innovations include working demonstration of a new compression algorithm to eliminate nodes and edges that do not affect graph connectivity while ensuring object location map-matching accuracy. The demonstration features: (1) Algorithm to leverage compressed versions of road-network graphs for map-matching of objects locations to correct road edges without decompression;(2) Upwards of 75% compression ratio implying significant savings for road-network data transmission costs;a key constraint for internet of things (IOT) devices, and (3) Use of a new controllable parameter;termed conflict factor C, whereby location aware services can trade the compression efficiency with map-matching accuracy at varying granularity. In addition to above features the demonstration features an extensible framework that enables experimentation and comparison between various compression and map-matching algorithms in a rich interactive interface. In this paper we outline data management challenges for location aware services using various scenarios for compression of a real road-network map of a large region of United States, along with both real and synthetic moving object trajectories distributed over this map. We describe the COMA framework through its map-based Graphical User Interface, ability to select the area of interest from the road-network map, and submit a compression request. COMA can export and save the compact versions of the selected area in different formats and plot the compressed graph over the original map for visual inspection to study the differences between compressed and uncompressed versions and various related statistics.

关键词： Coma comatic aberration compression algorithms network map compression Awareness road network maps compression EFFICIENCY COMPRESSED Internet of things compression ratio Frameworks Data management MAPPING(MATHEMATICAL)

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Comparison of Wavelet Thresholding Methods for Industrial Data compression 8

Comparison of Wavelet Thresholding Methods for Industrial Da...

引用

IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing System-Technology and Applications (IDAACS)

作者： Sztyber, Anna Pobocha, Marcin Warsaw Univ Technol Fac Mechatron Inst Automat Control & Robot Boboli 8 PL-02525 Warsaw Poland Enerbit PL-00714 Warsaw Poland

ISBN: (纸本)9781467383615

Nowadays in industry, data acquisition system signals are often compressed by piecewise linear trending. Many authors have indicated that compression with use of a wavelet transform provides better results. In this paper, the threshold selection algorithms, such as VisuShrink, SureShrink, BayesShrink, and NeighCoeffs are compared experimentally using real industrial data from the Damadics benchmark. The impact of the compression on the decoded signal is analysed. The results of the calculations are compared with a standard piecewise linear trending approach.

关键词： compression algorithms Discrete wavelet transforms Data acquisition

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Improvements to Online Distributed Monitoring Systems

Improvements to Online Distributed Monitoring Systems

引用

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Bo Wang Ying Song Yuzhong Sun Jun Liu SPKLSTN Lab Xi'an Jiaotong University Xi'an China State Key Laboratory of Computer Architecture Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509032068

Online monitoring, providing the real-time status information of servers, is indispensable for the management of distributed systems, e.g. failure detection and resource scheduling. The main design challenges for distributed monitoring systems include scalability, fine granularity, reliability and low overheads. And the challenges are growing with the increase of the scales of the distributed systems. To address the above problems, this paper studies improvements to online distributed monitoring systems (ODMSs) from three aspects: online compression algorithm, online compression reliability, and data representation for information interchanges. We summarize and classify the existing online compression algorithms to identify some research gaps that may represent opportunities for future research. A simple solution is proposed to address the problem that the inaccuracy of compression algorithms may be caused by some failures of distributed systems. A bitmap-like data format is presented to reduce the per-node overheads and the overheads of the management node in ODMSs, and compared with other existing formats used in the monitoring system both in mathematical analysis and practical experiment. The results show that the bitmap-like data format achieves best performance overall.

关键词： Peer-to-peer computing Monitoring Measurement compression algorithms Correlation Scalability Redundancy

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A novel fault detection method for flight data based on wavelet transform algorithm

A novel fault detection method for flight data based on wave...

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Prognostics and System Health Management Conference (PHM)

作者： Haoqiang Liu Hongbo Zhao Lei Chen Wenquan Feng School of Electronic and Information Engineering Beijing University of Aeronautics and Astronautics Beijing China

ISBN: (纸本)9781509027798

With the rapid increase of the flight parameters, fault detection for the aviation data tends to be more complex and less efficient. Therefore, it becomes prevalent to compress the flight data for transmitting and detection, which could reduce the information storage and make the fault detection more efficient. However, there are some limitations with the conventional compression algorithms, such as complicated compression process and low reconstruction accuracy, which would fail to retain the characteristic of data. To overcome such boundaries, a novel fault detection method for the flight data in Flight Data Recorder was proposed in this paper, which utilized the wavelet transform matrix for the compression. With the property of sparse matrix, the wavelet transform matrix could be efficient for fault detection, as it could detect the abnormal area without reconstruction. Also, to increase the reconstruction accuracy, the adaptive threshold function was used in this method. To confirm the proposed approach, Yaw angle parameter was taken as the trial data. Preliminary results show that the proposed method could achieve a low relative error with the same compression ratio, also it could reduce the computation burden of the fault detection. If Daubechies order 4 Wavelet for four levels decomposition is applied in compressing the flight data, with the adaptive threshold function, the compression ratio could exceed 13, whereas the relative error is still below 1%. Furthermore, the method could locate the detection area accurately and quickly, with the detection area falling to 3.71% of the raw data, therefore the performance of fault detection would improve significantly.

关键词： Wavelet transforms Fault detection Sparse matrices Data compression compression algorithms Image reconstruction

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CloudFC: Files Clustering for Storage Space Optimization in Clouds

CloudFC: Files Clustering for Storage Space Optimization in ...

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IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

作者： Haythem Yahyaoui Samir Moalla Department of Computer Science University of Tunis El Manar Tunis Tunisia

ISBN: (纸本)9781509014460

Nowadays Cloud environments are becoming a need for companies, research labs and also simple customers. Because of their several services, such a technology, has been succeeded to gather a high number of users around the world. One of the interesting services in Cloud computing is file storage, which consist on uploading our files to the Cloud's data centers and using them from anywhere at any time. Due to the highest number of users, an optimized management of the storage space around the data centers is required. Many studies have been focused on storage space optimization, researchers are trying to create some complex compression algorithms without considering their execution time and the user's behavior with a slow system. In this paper, we will try to save some storage space by classifying the customer files stored in the cloud's datacenter into three different clusters A, B and C, depending on the usage rate of each one. Cluster A represents the files which are the least frequently used. Cluster B represents the files which are the fair frequently used. And cluster C represents the files which are the high frequently used. Finally cluster A will be compressed by the algorithm which has the highest compression ratio and compression time results. And vice versa for cluster B. In the evaluation phase, our solution will be evaluated using a real dataset, and will be compared with some existing methods.

关键词： Cloud computing Clustering algorithms compression algorithms Classification algorithms Optimization Energy consumption Computer science

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A novel and efficient method to initialize FPGA embedded memory content in asymptotically constant time

A novel and efficient method to initialize FPGA embedded mem...

引用

International Conference on Reconfigurable Computing and FPGAs (ReConFig)

作者： Matěj Bartík Sven Ubik Pavel Kubalík CTU FIT & CESNET a.l.e CESNET ale CTU FIT

This paper describes analysis and implementation of a new method for maintaining valid content of FPGA memory blocks with an asymptotically constant time synchronous clear ability, that can be useful for (re)initialization to one default value. A particular application can be for high-speed real-time LZ77 [1] lossless compression algorithms, where a dictionary has to be (re)initialized before each run of the implemented compression algorithm. The method is based on two most widely used techniques for clearing the memory content: a linear passage of the memory and clearing each cell by writing a default value and creating a register field providing an (in)valid bit for each memory cell. Our solution combines these two techniques together with the use of FPGA distributed memory blocks implemented in LUTs (Look-Up Tables) to overcome negative features of each previous method without losing the most of positive features. Our solution provides a balance between the two previous techniques and exceeds them in speed, resources utilization and latency of (re)initialization.

关键词： Field programmable gate arrays Dictionaries Table lookup Random access memory compression algorithms Hardware

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Analysis of multi scale features of compressed medical images

Analysis of multi scale features of compressed medical image...

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International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)

作者： Lalitha Kumari R. Pandian J. Aran Glenn Sathyabama University Chennai DMI College of Engineering Chennai

Image files contain considerable amount of redundant and irrelevant data and image compression algorithms can be used to overcome this. In this work, Wavelet Transform based compression algorithm is employed. Haar and daubechies wavelets are utilized in this proposed work for the decomposition of the medical image and the decomposed images are encoded using the schemes such as Embedded Zero wavelet, (EZW), Set Partitioning In Hierarchical Trees (SPIHT), Spatial orientation Tree Wavelet (STW), Wavelet Difference Reduction (WDR) and Adaptively Scanned Wavelet Difference Reduction (ASWDR). The computer tomography image is undergone the various developed compression techniques. The proposed compression schemes are evaluated in terms of PSNR, compression ratio, Means square error and bits per pixel. The optimum compression algorithm is also found based on the results. The results are an indicator to the promising application of this for medical image compression schemes. This paper provides the approach and analysis methodologies and the results obtained.

关键词： Image coding Wavelet transforms Image reconstruction Partitioning algorithms compression algorithms Biomedical imaging

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Improved frequency table adjusting algorithms for context-based adaptive lossless image coding

Improved frequency table adjusting algorithms for context-ba...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Jian-Jiun Ding I-Hsiang Wang Graduate Institute of Communication Engineering National Taiwan University

ISBN: (纸本)9781509020744

Lossless image compression can preserve all information of the original image and is useful for security and medical image processing. In this paper, we apply improved adaptive arithmetic coding to further improve the coding efficiency of context-based adaptive lossless image coding (CALIC), which is a well-known lossless image compression algorithm. We apply the techniques of mutual learning, initialization for the frequency table, and the increasingly adjusting step to improve adaptive arithmetic coding and apply them in the CALIC algorithm. Simulations show that, with the proposed coding techniques, the performance of CALIC for lossless image compression can be obviously improved.

关键词： Image coding Context Algorithm design and analysis Biomedical imaging Encoding Transform coding compression algorithms

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A Case Study of Optimizing Big Data Analytical Stacks Using Structured Data Shuffling

A Case Study of Optimizing Big Data Analytical Stacks Using ...

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IEEE International Conference on Services Economics (SE)

作者： Dixin Tang Taoying Liu Rubao Lee Hong Liu Wei Li Institute of Computing Technology Chinese Academy of Sciences Department of Computer Science and Engineering The Ohio State University

Current major big data analytical stacks often consist of a general-purpose, multi-staged cluster computation framework (e.g. Hadoop) and a SQL query execution system (e.g. Hive) on its top. In such stacks, a key factor of query execution performance is the efficiency of data shuffling between two execution stages (e.g. Map/Reduce). However, current stacks often execute data shuffling in a data-oblivious way, which means that for structured data processing, various useful information about the shuffled data and the queries on the data is simply wasted. Specifically, this problem makes two optimization opportunities lost: i) unnecessary records cannot be filtered in advance, ii) column-oriented compression algorithms cannot be applied. To solve the problem, in this paper, we have designed and implemented a novel data shuffling mechanism in Hadoop, called Structured Data Shuffling (S-Shuffle), which avoids the low efficiencies of traditional data shuffling by carefully leveraging the rich information in data and queries provided by Hive. Our experimental results with industry-standard TPC-H benchmark show that by using S-Shuffle, the performance of SQL query processing on Hadoop can be improved by up to 2.4x..

关键词： Optimization compression algorithms Big data Sorting Aggregates Computational modeling Sparks

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