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A 65-nm CMOS Lossless Bio-Signal compression Circuit With 250 FemtoJoule Performance Per Bit

IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2019年第5期13卷 1087-1100页

作者： Crispin-Bailey, Christopher Dai, Chenglaing Austin, Jim Univ York Dept Comp Sci York YO10 5GH N Yorkshire England Imperial Coll London London SW7 2AZ England

A 65nm CMOS integrated circuit implementation of a bio-physiological signal compression device is presented, reporting exceptionally low power, and extremely low silicon area cost, relative to state-of-the-art. A novel 'xor-log2-sub-band' data compression scheme is evaluated, achieving modest compression, but with very low resource cost. With the intent to design the simplest useful compression algorithm', the outcome is demonstrated to be very favourable where power must be saved by trading off compression effort against data storage capacity, or data transmission power, even where more complex algorithms can deliver higher compression ratios. A VLSI design and fabricated Integrated Circuit implementation are presented, and estimated performance gains and efficiency measures for various bio-medical use-cases are given. Power costs as low as 1.2 pJ per sample-bit are suggested for a 10kSa/s data-rate, whilst utilizing a power-gating scenario, and dropping to 250fJ/bit at continuous conversion data-rates of 5MSa/sec. This is achieved with a diminutive circuit area of 155um(2). Both power and area appear to be state-of-the-art in terms of compression versus resource cost, and this yields benefit for system optimization.

关键词： Encoding Integrated circuits Data compression compression algorithms Biomedical measurement Hardware ECG EEG lossless data compression power efficiency VLSI design wearable sensors

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compression of Data Streams Down to Their Information Content

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IEEE TRANSACTIONS ON INFORMATION THEORY 2019年第7期65卷 4471-4485页

作者： Barmpalias, George Lewis-Pye, Andrew Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100190 Peoples R China London Sch Econ Dept Math Columbia House London WC2A 2AE England

According to the Kolmogorov complexity, every finite binary string is compressible to a shortest code its information content from which it is effectively recoverable. We investigate the extent to which this holds for the infinite binary sequences (streams). We devise a new coding method that uniformly codes every stream X into an algorithmically random stream Y, in such a way that the first n bits of X are recoverable from the first 1(X vertical bar n) bits of Y, where I is any partial computable information content measure that is defined on all prefixes of X, and where X vertical bar n is the initial segment of X of length n. As a consequence, if g is any computable upper bound on the initial segment prefix-free complexity of X, then X is computable from an algorithmically random Y with oracle-use at most g. Alternatively (making no use of such a computable bound g), one can achieve an the oracle-use hounded above by K(X X vertical bar n)+ log n. This provides a strong analogue of Shannon's source coding theorem for the algorithmic information theory.

关键词： Source coding algorithmic information theory compression algorithms Kolmogorov complexity prefix-free codes

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Context-Aware Encoding and Delivery in the Web 20th

Context-Aware Encoding and Delivery in the Web

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20th International Conference on Web Engineering (ICWE)

作者： Wollmer, Benjamin Wingerath, Wolfram Ritter, Norbert Univ Hamburg Hamburg Germany Baqend GmbH Hamburg Germany

ISBN: (纸本)9783030505783;9783030505776

While standard HTTP caching has been designed for static resources such as files, different conceptual extensions have made it applicable to frequently changing data like database query results or server-generated HTML content. But even though caching is an indispensable means to accelerate content delivery on the web, whether or not cached resources can be used for acceleration has always been a binary decision: a cached response is either valid and can be used or has been invalidated and must be avoided. In this paper, we present an early-stage PhD project on a novel scheme for content encoding and delivery. Our primary goal is minimizing the payload for client requests in the web by enabling partial usage of cached resources. We discuss related work on the topic and analyze why existing approaches have not been established in practice so far, despite significant gains such as reduced bandwidth usage and loading times for end users. We then present open challenges, derive our research question, and present our research goals and agenda.

关键词： Web caching Efficiency compression algorithms Delta encoding Benchmarking Runtime optimization User experience

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Pattern Matching Based algorithms for Graph compression 4

Pattern Matching Based Algorithms for Graph Compression

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4th IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)

作者： Chatterjee, Amlan Shah, Rushabh Jitendrakumar Sen, Soumya Calif State Univ Dominguez Hills Dept Comp Sci Carson CA 90747 USA Univ Calcutta AK Choudhury Sch Informat Technol Kolkata WB India

ISBN: (纸本)9781538676387

Graphs can be used to represent a wide variety of data belonging to different domains. Graphs can capture the relationship among data in an efficient way, and have been widely used. In recent times, with the advent of Big Data, there has been a need to store and compute on large data sets efficiently. However, considering the size of the data sets in question, finding optimal methods to store and process the data has been a challenge. Therefore, we study different graph compression techniques and propose novel algorithms to do the same in this paper. Specifically, given a graph G = (V, E), where V is the set of vertices and E is the set of edges, and vertical bar V vertical bar = n, we propose techniques to compress the adjacency matrix representation of the graph. Our algorithms are based on finding patterns within the adjacency matrix data, and replacing the common patterns with specific markers. All the techniques proposed here are lossless compression of graphs. Based on the experimental results, it is observed that our proposed techniques achieve almost 70% compression as compared to the adjacency matrix representation. The results show that large graphs can be efficiently stored in smaller memory and exploit the parallel processing power of compute nodes as well as efficiently transfer data between resources.

关键词： Graph compression Patterns Quadtree Big Data compression compression algorithms

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Partial Gated Feedback Recurrent Neural Network for Data compression Type Classification

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IEEE ACCESS 2020年 8卷 151426-151436页

作者： Song, Hyewon Kwon, Beom Yoo, Hoon Lee, Sanghoon Yonsei Univ Dept Elect & Elect Engn Seoul 03722 South Korea Sangmyung Univ Dept Elect Seoul 03016 South Korea

Owing to the widespread use of digital devices such as mobile phones and tablet PCs that are capable of easily viewing contents, the number of digital crimes committed using these digital devices has increased. One of the most common digital crimes is to hide the header information of the compressed data, which makes the user's data unusable. It is difficult to restore original data without the header because header contains the compression type. In this paper, we propose a Partial Gated Feedback Recurrent Neural Network (PGF-RNN) for the identification of lossless compression algorithms. We modify the gated recurrent units to improve the correlation of layers by grouping the fully-connected layers to effectively determine the characteristics of the compressed data. We emphasize on the temporal features, which consider a wide range of data, and spatial features from fully-connected layers to extract the feature vectors of each compression type. To improve the performance of the proposed PGF-RNN, we apply post-processing that considers the frequency of bit sequences on some compression types with similar compressed data. The proposed method is evaluated on 31 well-known lossless compression algorithms of the Association for Computational Linguistics dataset. The average top 1 accuracy of the proposed method is 92.63%.

关键词： Feature extraction compression algorithms Logic gates Recurrent neural networks Encoding Data mining Image coding compression type classification deep learning gated recurrent unit lossless compression partial gated feedback recurrent neural network recurrent neural network

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Studying the Effects of compression in EEG-Based Wearable Sleep Monitoring Systems

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IEEE ACCESS 2020年 8卷 168486-168501页

作者： Liu, Deland Hu Imtiaz, Syed Anas Univ Texas Austin Dept Elect & Comp Engn Austin TX 78705 USA Imperial Coll London Dept Elect & Elect Engn Wearable Technol Lab London SW7 2AZ England

Long-term sleep monitoring through the use of wearable EEG-based systems generates large volumes of data that need to be either locally stored or wireless transmitted. compression of data can play a vital role to reduce the power consumption of these already resource-constrained systems. While compression methods can result in significantly reduced data storage and transmission requirements, the loss in signal information can have an impact on the algorithms used to extract the key sleep parameters. This paper studies the impact of six different state-of-the-art compression methods, including wavelet, SPIHT, filter and predictor-based methods, analysing their effects on the reconstructed signal quality particularly for automatic sleep staging applications. It looks at how the overall sleep staging accuracy as well as the detection accuracy of different sleep stages is reduced as a result of different EEG compression methods. It shows that the SPIHT and predictor-based compression methods outperform wavelet and filter-based methods in preserving the relevant signal features. It also shows that compression ratios of up to 65 can be achieved using the QSPIHT method with less than 10% loss in overall sleep staging accuracy.

关键词： Sleep Electroencephalography Biomedical monitoring Monitoring Discrete wavelet transforms compression algorithms Databases Sleep disorders electroencephalogram(EEG) compression lossy compression wearables brain monitoring sleep staging

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Sequential Change Detection Based on Universal compression for Markov Sources

Sequential Change Detection Based on Universal Compression f...

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IEEE International Symposium on Information Theory (ISIT)

作者： Verma, A. Bansal, R. K. Univ Calif San Diego Dept Elect & Comp Engn San Diego CA 92103 USA Indian Inst Technol Dept Elect Engn Kanpur Uttar Pradesh India Indian Inst Technol Kanpur Uttar Pradesh India

ISBN: (纸本)9781538692912

A universal compression code can act as an estimator for the distribution of a finite alphabet finite-order Markov source. This property of universal codes was exploited by Jacob and Bansal in [5] to propose a modification of the CUSUM test in order to solve the change detection problem when the post-change distribution is not known. The performance of this test was proven to be asymptotically optimal for a memoryless sources and class of sources with memory under Lorden's minimax formulation. This study was further extended in [9] where performance of the modified CUSUM for an i.i.d. setting under Lai's criterion involving a constraint on the probability of false alarm within a window (PFAW) was analyzed. In this paper, we introduce a modified version of the window limited CUSUM (WL-CUSUM) test by incorporating strongly universal code. We closely follow the work of Lai [8] in order to prove the asymptotic optimality for the test under the PFAW criterion. We further prove the asymptotic optimality of the modified WL-CUSUM test in the Bayesian setting.

关键词： Markov processes Delays compression algorithms Jacobian matrices Performance analysis Microsoft Windows Bayes methods

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Backward Weighted Coding

Backward Weighted Coding

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Data compression Conference (DCC)

作者： Aharon Fruchtman Yoav Gross Shmuel T. Klein Dana Shapira Ariel University Ariel Israel Bar Ilan University Ramat-Gan Israel

Extending recently suggested methods, a new dynamic compression algorithm is proposed, which assigns larger weights to characters that have just been coded by means of an increasing weight function. Empirical results present its efficient compression performance, which, for input files with locally skewed distributions, can improve beyond the lower bound given by the entropy for static encoding, at the price of slower running times for compression, and comparable time for decompression.

关键词： Heuristic algorithms Data compression Encoding Entropy compression algorithms

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Improving Run Length Encoding by Preprocessing

Improving Run Length Encoding by Preprocessing

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Data compression Conference (DCC)

作者： Sven Fiergolla Petra Wolf University Trier Germany

The binary representation of an arbitrary string does not contain long runs of repeating bits, but, first, reading all most significant bits of all bytes, then all second most significant bits and so on, results in much longer average runs. We use this observation in combination with several preprocessing steps to obtain a lossless RLE based compression algorithm comparable to ZIP: First, the uncompressed byte array is analyzed and for each byte its number of occurrences is counted. In parallel, a bijective Burrows-Wheeler-Scott Transform is applied, which produces a reversible permutation of the input byte array with long repetitions of similar symbols. Afterwards, each byte is remapped, where the most frequent byte values are mapped to the lowest binary values. The resulting byte array is interpreted in a specific way, known as Bit-Layers text representation, where all bits of same significance are read consecutively, starting with the most significant bits, resulting in long average runs of identical bits. On this representation, a run length encoding (RLE) is applied and the runs are counted to generate a Huffman tree. Then, the runs are output with a variable length code, together with the mapping needed to decompress the file.

关键词： Data compression Transforms Encoding compression algorithms

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On Random Editing in LZ-End

On Random Editing in LZ-End

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Data compression Conference (DCC)

作者： Daniel Roodt Ulrich Speidel Vimal Kumar Ryan K. L. Ko School of Computer Science University of Waikato Hamilton New Zealand School of Computer Science University of Auckland Auckland New Zealand School of ITEE University of Queensland Brisbane Australia

LZ-End is a variant of the LZ77 compression algorithm which allows random access to the compressed data. In this paper, we use the random-access capability of LZ-End to perform random edits on the compressed data.

关键词： Data compression compression algorithms

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