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IEEE International Conference on Electronics, Circuits, and Systems

作者： Matej Bartik Sven Ubik Pavel Kubalik CTU FIT & CESNET CESNET CTU FIT

ISBN: (纸本)9781509002474

This paper describes analysis and implementation of a LZ4 compression algorithm. LZ4 is derived from a standard LZ77 compression algorithm and is focused on the compression and decompression speed. The LZ4 lossless compression algorithm was analyzed regarding its suitability for hardware implementation. The first step of this research is based on software implementation of LZ4 with regard to the future hardware implementation. As a second step, a simple hardware implementation of LZ4 is evaluated for bottlenecks in the original LZ4 code. Xilinx Virtex-6 and 7-Series FPGAs are used to obtain experimental results. These results are compared to the industry competitor.

关键词： compression algorithms Random access memory Streaming media Field programmable gate arrays Computer architecture Resource management Standards

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Template Matching compression Algorithm for Wearable ECG Measurement System Based on Multi Wireless Transmission Platform

Template Matching Compression Algorithm for Wearable ECG Mea...

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International Conference on Advanced Information Networking and Applications Workshops (AINAW)

作者： Tae-Young Kim Yun-Hong Noh Do-Un Jeong Dept. Ubiquitous IT engineering Graduate School Dongseo University Busan Republic of Korea Division of Computer & Information Engineering Dongseo University Busan Republic of Korea

ISBN: (纸本)9781479917761

In this paper, a template matching based ECG compression algorithm for wearable ECG measurement system is proposed and implemented. First of all, R-peak is detected in the ECG signal. Then, from the point of R-peak, we extract the information of QRS complex, PQ, and ST section to create a template for each of the interval. Template code is saved for the use of comparing real time input ECG. R-peak is detected in the pre-processing step from a normalized signal in 0 and 1 basis, and adaptive threshold is applied for good R-peak detection accuracy. Template for PQ interval, QRS complex, ST interval is created. Only template code that closely matched with the input signal is saved. The determining factor for "closely matched" is based on the calculation of MSE value after comparing the template with each of the intervals (PQ interval, QRS complex and ST interval) with real time input ECG signal. The performance of the algorithm is evaluated by using the Massachusetts Institute of Technology-Boston's Beth Israel Hospital Normal Sinus Rhythm Database and different sampling record. The highest compression ratio is 15.2:1. The accuracy of the R-peak detection is 100%.

关键词： Electrocardiography compression algorithms Transceivers Real-time systems Zigbee Bluetooth Biomedical monitoring

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Optimizing compression for power efficient wireless multimedia sensor networks

Optimizing compression for power efficient wireless multimed...

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IEEE International Conference on Computational Intelligence and Computing Research

作者： Avnish Kumar Brahmjit Singh Electronics and Communication Engineering National Institute of Technology Kurukshetra Kurukshetra India

Main focus of our study here is how to reach optimum compression in the wireless Multimedia Sensor Network (WMSN) so as to improve power allocation between compression and transmission for maximizing the battery life of sensors. We know that there exists a large redundancy in sensor's data. So data need to be compressed before being transmitted to the sink/receiver/customer for reducing the heavy amount of energy consumed in transmission, but wireless sensor are very limited to computational ability, memory and energy. Due to these limitations, it becomes a sincere issue while processing any compression algorithm on the data because this also requires a significant amount of energy to perform these operations but even so, for a single bit, the energy used for compression is usually less than that of transmission. But if algorithm is highly complex and requires execution of a very large number of operations then the high degree of compression may also consume more energy than that of transmission. Here we can clearly see that there must be balance between power consumption of data transmission and that of compression. The power of compression depends on many factors such as amount of data to be reduced, compression algorithm, compression ratio, processor's architecture, etc. Our scheme is primarily focused on the video sensor networks which optimizes the balance between compression and transmission; and consequently, the overall power consumption will be reduced. In our results, we have discussed the variation of optimum compression ratio with respect to various model parameters and shown the power consumption behaviour for a typical model and achieved the minimum power consumption on optimum value of compression.

关键词： Power demand Multimedia communication compression algorithms Wireless sensor networks Wireless communication Resource management Streaming media

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Optimizing response time and synchronization in User Virtualization via adaptive memory compression

Optimizing response time and synchronization in User Virtual...

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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Mayank Kulkarni Rushikesh Ghatpande Nikunj Karnani Jibi Abraham Department of Computer Engineering and Information Technology College of Engineering Pune (COEP) Pune India

In this paper, we address the problem of prolonged latency periods for user log-in and log-off in User Virtualization using Virtual Pooled Desktops. These latency periods are caused due to large chunks of User Profile data transferred over the network in order to achieve User Virtualization. Thus we require efficient and quick `synchronization' of data from the client to the server. Data transfer time and file characteristics are factors that assist us to determine how synchronization can be improved. File type and File size are the key characteristics used to determine which compression algorithms will be applicable. Our contribution in this paper is the development of a new system for synchronization, using various compression algorithms and a `learning' based model. The learning model helps to determine whether or not to compress the file and plays an integral part in building a ranking system of compression algorithms, based on their performance on a specific type and size of a file.

关键词： Synchronization compression algorithms Algorithm design and analysis Virtualization Virtual machining Servers Supervised learning

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Front-haul compression using scheduling side information for Cloud Radio Access Networks

Front-haul compression using scheduling side information for...

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IEEE Global Communications Conference

作者： Sameer Pawar Huaning Niu Apostolos Papathanassiou Next Generation and Standards (NGS) Intel Corporation

ISBN: (纸本)9781479959532

Cloud Radio Access Network (C-RAN) is a novel mobile network architecture that allows for efficient and economic building, upgrading and maintenance of RAN networks to cope with increasing mobile traffic data. The main idea behind C-RAN is to maintain distributed remote radio head (RRH) units for transmission and reception of radio signals, while pool the Baseband Units (BBUs) from multiple base stations into a centralized location for statistical multiplexing gain. The BBUs are connected to RRH via front-haul links. Although the C-RAN architecture enjoys significant advantages in terms of both the capital expenditure (CAPEX) and the operational expenditure (OPEX) over traditional networks, it also exerts considerable burden on the capacity-constrained front-haul links of transporting the high bandwidth complex baseband data between RRH and BBUs. Fortunately, the LTE signal is inherently redundant and compressible. In this paper, we propose a low-complexity, frequency domain, front-haul compression algorithm, that leverages the user scheduling side-information available at the BBU pool, in addition to the LTE signal redundancy, to achieve a compression ratio of 10X for a full-buffer traffic model. We also provide extensive link-level and system-level simulations to validate the performance of our proposed front-haul compression algorithm on simulated LTE signal.

关键词： RRH gene Wireless networks BASE STATION compression ratio Local thermodynamic equilibrium Long Term Evolution MULTIPLEXOR compression algorithms RADIO SIGNALS Traffic data side information Baseband reception CLOUDS Clouds

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Using compression models for filtering troll comments

Using compression models for filtering troll comments

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Jorge de-la-Peña-Sordo Iker Pastor-López Igor Santos Pablo G. Bringas DeustoTech - Computing University of Deusto Bilbao Spain

Internet is evolving. How the content is generated has changed and currently, users and readers of a site can create content. They can express themselves showing their feelings or opinions commenting diverse stories or other users' comments in social news websites. This fact has led to negative side effects: the appearance of troll users and their contents seeking deliberately controversy. In this paper we propose a new method to filter trolling comments using compression models. Normally, Vector Space Model representation use is quite common but these filters can be attacked. To this end, we validate our approach with data from `Menéame', a popular Spanish social news site, training several compression models, showing that our method can maintain high accuracy rates whilst making such filters difficult to defeat.

关键词： Dictionaries Training Mathematical model compression algorithms Government Algorithm design and analysis Probabilistic logic

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Better compression through Better List Update algorithms

Better Compression through Better List Update Algorithms

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Data compression Conference (DCC)

作者： Shahin Kamali Alejandro López Ortiz Cheriton School of Computer Science University of Waterloo Canada

ISBN: (纸本)9781479934188

List update is a key step during the Burrows-Wheeler transform (BWT) compression. Previous work has shown that careful study of the list update step leads to better BWT compression. Surprisingly, the theoretical study of list update algorithms for compression has lagged behind its use in real practice. To be more precise, the standard model by Sleator and Tarjan for list update considers a 'linear cost-of-access' model while compression incurs a logarithmic cost of access, i.e. accessing item i in the list has cost Theta(i) in the standard model but Theta(log i) in compression applications. These models have been shown, in general, not to be equivalent. This paper has two contributions: (1) We give the first theoretical proof that the commonly used Move-To-Front (MTF) has good performance under the compression logarithmic cost-of-access model. This has long been known in practice but a formal proof under the logarithmic cost compression model was missing until now, (2) we further refine the online compression model to reflect its use under compression by applying the recently developed 'online algorithms with advice' model. This advice model was initially a purely theoretical construct in which the online algorithm has access to an all powerful oracle during the computation. We show that surprisingly, this seemingly unrealistic model can be used to produce better multi-pass compression algorithms. More precisely, we introduce an 'almost-online' list update algorithm, which we term BIB which results in a compression scheme which is superior to schemes using standard online algorithms, in particular those of MTF and TIMESTAMP. For example, for the files in the standard Canterbury Corpus, the compression ratio of the scheme that uses BIB is 33.66 on average, while the compression ratios for the schemes that use MTF and TIMESTAMP are respectively 34.25 and 36.30.

关键词： Indexes Optimized production technology Standards Algorithm design and analysis Computational modeling Context compression algorithms

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Efficient algorithms for the compression of FASTQ files

Efficient algorithms for the compression of FASTQ files

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Subrata Saha Sanguthevar Rajasekaran Department of Computer Science and Engineering University of Connecticut Storrs

Since the introduction of the Sanger sequencing technology in 1977 by Frederic Sanger and his colleagues, we observe an explosion of sequence data. The cost of storage, processing, and analyzing the data is getting excessively high. As a result, it is extremely important that we develop efficient data compression and data reduction techniques. But standard data compression tools are not suitable to compress biological data since they contain many repetitive regions. There could exist high similarities among the sequences. In this context we need specialized algorithms to effectively compress biological data. In this paper we propose novel algorithms for compressing FASTQ files. We have done extensive and rigorous experiments that reveal that our proposed algorithm is indeed competitive and performs better than the best known algorithms for this problem.

关键词： Bioinformatics Genomics Sequential analysis Clustering algorithms compression algorithms Encoding Data compression

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Numerical compression Schemes for Proteomics Mass Spectrometry Data

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MOLECULAR & CELLULAR PROTEOMICS 2014年第6期13卷 1537-1542页

作者： Teleman, Johan Dowsey, Andrew W. Gonzalez-Galarza, Faviel F. Perkins, Simon Pratt, Brian Roest, Hannes L. Malmstroem, Lars Malmstrom, Johan Jones, Andrew R. Deutsch, Eric W. Levander, Fredrik Lund Univ Dept Immunotechnol S-22381 Lund Sweden Univ Manchester Fac Med & Human Sci Inst Human Dev Manchester M13 9WL Lancs England Univ Manchester CADET Manchester M13 9WL Lancs England Cent Manchester Univ Hosp NHS Fdn Trust Manchester Acad Hlth Sci Ctr Manchester M13 9WL Lancs England Univ Liverpool Inst Integrat Biol Liverpool L69 7ZB Merseyside England Univ Washington Dept Genome Sci Sch Med Seattle WA 98195 USA ETH Dept Biol Inst Mol Syst Biol CH-8093 Zurich Switzerland Lund Univ Fac Med Dept Clin Sci SE-22184 Lund Sweden Inst Syst Biol Seattle WA 98109 USA Lund Univ S-22100 Lund Sweden

The open XML format mzML, used for representation of MS data, is pivotal for the development of platform-independent MS analysis software. Although conversion from vendor formats to mzML must take place on a platform on which the vendor libraries are available (i.e. Windows), once mzML files have been generated, they can be used on any platform. However, the mzML format has turned out to be less efficient than vendor formats. In many cases, the naive mzML representation is fourfold or even up to 18-fold larger compared with the original vendor file. In disk I/O limited setups, a larger data file also leads to longer processing times, which is a problem given the data production rates of modern mass spectrometers. In an attempt to reduce this problem, we here present a family of numerical compression algorithms called MS-Numpress, intended for efficient compression of MS data. To facilitate ease of adoption, the algorithms target the binary data in the mzML standard, and support in main proteomics tools is already available. Using a test set of 10 representative MS data files we demonstrate typical file size decreases of 90% when combined with traditional compression, as well as read time decreases of up to 50%. It is envisaged that these improvements will be beneficial for data handling within the MS community.

关键词： Vendors Data Files compression algorithms binary data development platform read time proteomics Mass spectrometers Quadruple

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The Research on the compression algorithms for Vector Data

The Research on the Compression Algorithms for Vector Data

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International Conference on Multimedia Technology

作者： Huifeng Ji Yihe Wang

Vector data compression system carries important significance in studies such as geography environment simulation, map database establishment and GIS. But there are lots of redundant points in the digital topographic map. In order to reduce storage space and improve the speed, vector data must be compressed. The compression algorithms of the line element are researched in the paper. The line element is tracked by the digital software. The four algorithms are researched such as choosing interval points, limiting vertical distance, limiting angle, Douglas-Peucker. It is better that the number of nodes and the error are less in line elements by the compression algorithms. The result is that the Douglas - Peucker method is best, followed by the vertical distance method, the offset angle method and choosing interval points method.

关键词： Limiting Data compression Algorithm design and analysis Software algorithms Approximation algorithms compression algorithms Software

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