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5th IEEE World Forum on Internet of Things (IEEE WF-IoT)

作者： Sarbishei, Omid Motsai Res Dept Res & Dev St Bruno PQ Canada

ISBN: (纸本)9781538649800

Lightweight Temporal compression (LTC) is an energy-efficient lossy compression algorithm that maintains a memory usage and per-sample computational cost in O(1). The method provides a trade-off between compression ratio and accuracy using an error bound. In this paper, we present the Refined LTC (RLTC) algorithm, which uses a binning approach to widen the search space and increase the LTC's compression ratio and reduce its dynamic energy consumption, which is characterized by CPU computations and radio transmissions, without compromising the error bound. The proposed RLTC algorithm adds negligible overhead to the memory usage and latency of LTC. Experimental results on an environmental sensor dataset have shown that the LTC's compressed byte stream can be further reduced in size by up to 18%, while the dynamic energy consumption is reduced by 9.5% on average.

关键词： Internet of Things Indexes compression algorithms Energy consumption Energy efficiency Heuristic algorithms Complexity theory"

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Bank Select Method for Reducing Symbol Search Operations on Stream-based Lossless Data compression

Bank Select Method for Reducing Symbol Search Operations on ...

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Data compression Conference (DCC)

作者： Yamagiwa, Shinichi Morita, Ryuta Marumo, Koichi Univ Tsukuba Fac Engn Informat & Syst 1-1-1 Tennodai Tsukuba Ibaraki Japan Univ Tsukuba Dept Comp Sci 1-1-1 Tennodai Tsukuba Ibaraki Japan

ISBN: (纸本)9781728106571

Dictionary-based lossless data compression algorithms mainly replace a frequent data pattern in the inputted data to a compressed symbol, and to decompress vice versa. The mechanism potentially has an overhead problem regarding the number of symbol matchings in the table. This work focuses on a technique to reduce the number of searches in the dictionary using a bank separation technique. This poster presentation shows design and implementation of the technique applied to LCT-DLT.

关键词： Data compression Bandwidth Software Computer science Multiprocessor interconnection Data communication compression algorithms

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Exploiting Adaptive Data compression to Improve Performance and Energy-efficiency of Compute Workloads in Multi-GPU Systems 33

Exploiting Adaptive Data Compression to Improve Performance ...

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33rd IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Tavana, Mohammad Khavari Sun, Yifan Agostini, Nicolas Bohm Kaeli, David Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA

ISBN: (纸本)9781728112466

Graphics Processing Unit (GPU) performance has relied heavily on our ability to scale of number of transistors on chip, in order to satisfy the ever-increasing demands for more computation. However, transistor scaling has become extremely challenging, limiting the number of transistors that can be crammed onto a single die. Manufacturing large, fast and energy-efficient monolithic GPUs, while growing the number of stream processing units on-chip, is no longer a viable solution to scale performance. GPU vendors are aiming to exploit multi-GPU solutions, interconnecting multiple GPUs in the single node with a high bandwidth network (such as NVLink), or exploiting Multi-Chip Module (MCM) packaging, where multiple GPU modules are integrated in a single package. The inter-GPU bandwidth is an expensive and critical resource for designing multi-GPU systems. The design of the inter-GPU network can impact performance significantly. To address this challenge, in this paper we explore the potential of hardware-based memory compression algorithms to save bandwidth and improve energy efficiency in multi-GPU systems. Specifically, we propose an adaptive inter-GPU data compression scheme to efficiently improve both performance and energy efficiency. Our evaluation shows that the proposed optimization on multi-GPU architectures can reduce the inter-GPU traffic up to 62%, improve system performance by up to 33%, and save energy spent powering the communication fabric by 45%, on average.

关键词： Multi-GPU system Multi-Chip-Module compression algorithms Bandwidth management Performance

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Light field image compression with random access

Light field image compression with random access

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Data compression Conference (DCC)

作者： Amirpour, Hadi Pinheiro, Antonio Pereira, Manuela Lopes, Fernando J. P. Ghanbari, Mohammad Inst Telecomunicacoes Covilha Portugal Univ Beira Interior Covilha Portugal Polytech Inst Coimbra ISEC Coimbra Portugal Univ Tehran Sch Elect & Comp Engn Tehran Iran Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England

ISBN: (纸本)9781728106571

In light field compression, besides coding efficiency, providing random access to individual views is also a very significant factor. Highly efficient compression methods usually lack random access. Similarly, random access methods usually reduce the compression efficiency. To address this trade-off, a light field image encoding method is proposed in this paper which favors random access. In the proposed scheme 15x15 view images are divided into 25 independent 3x3 view images which are called Macro View Image (MVI). To encode MVIs, the central view image is used to compress its immediate neighboring view images using a hierarchical reference structure. To encode the central view of each MVI, the most central view image, along with the center of at most three MVIs, are used as the reference images for the disparity estimation. In addition, the proposed method enables the use of parallel computation to improve encoding/decoding time complexity. To reduce memory footprint in case a Region of Interest (ROI) is required, HEVC tile partitioning is used.

关键词： Image coding Data compression Rate-distortion Computer science compression algorithms Rendering (computer graphics) Decoding

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MR-RePair: Grammar compression based on Maximal Repeats

MR-RePair: Grammar Compression based on Maximal Repeats

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Data compression Conference (DCC)

作者： Furuya, Isamu Takagi, Takuya Nakashima, Yuto Inenaga, Shunsuke Bannai, Hideo Kida, Takuya Hokkaido Univ Grad Sch IST Sapporo Hokkaido Japan Kyushu Univ Dept Informat Fukuoka Japan

ISBN: (纸本)9781728106571

We analyze the grammar generation algorithm of the RePair compression algorithm and show the relation between a grammar generated by RePair and maximal repeats. We reveal that RePair replaces step by step the most frequent pairs within the corresponding most frequent maximal repeats. Then, we design a novel variant of RePair, called MR-RePair, which substitutes the most frequent maximal repeats at once instead of substituting the most frequent pairs consecutively. We implemented MR-RePair and compared the size of the grammar generated by MR-RePair to that by RePair on several text corpora. Our experiments show that MR-RePair generates more compact grammars than RePair does, especially for highly repetitive texts.

关键词： Maintenance engineering Grammar Production Data compression compression algorithms Informatics Upper bound

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Multidimensional compression with Pattern Matching

Multidimensional Compression with Pattern Matching

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Data compression Conference (DCC)

作者： Del Guercio, Olivia Orozco, Rafael Sim, Alex Wu, Kesheng Scripps Coll Claremont CA 91711 USA Lawrence Berkeley Natl Lab Berkeley CA USA Bucknell Univ Lewisburg PA 17837 USA

ISBN: (纸本)9781728106571

Sensors typically record their measurements using more precision than the accuracy of the sensing techniques. Thus, experimental and observational data often contains noise that appears random and cannot be easily compressed. This noise increases storage requirement as well as computation time for analyses. In this work, we describe a line of research to develop data reduction techniques that preserve the key features while reduce the storage requirement. Our core observation is that the noise in such cases could be characterized by a small number of patterns based on statistical similarity. In earlier tests, this approach was shown to reduce the storage requirement by over 100-fold for one-dimensional sequences. In this work, we explore a set of different similarity measures for multidimensional sequences. During our tests with standard quality measures such as PSNR, we see that the new compression methods reduce the storage requirements over 100-fold while maintaining relatively low errors in peak signal-to-noise ratio. Thus, we believe that this is a new and effective way of constructing data reduction techniques.

关键词： Pattern matching Time measurement Data compression Dictionaries Heuristic algorithms compression algorithms Runtime

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A Low Complexity Image compression Algorithm for IoT Multimedia Applications 27

A Low Complexity Image Compression Algorithm for IoT Multime...

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27th European Signal Processing Conference (EUSIPCO)

作者： Campobello, Giuseppe Segreto, Antonino Univ Messina Dept Engn Messina Italy

ISBN: (纸本)9789082797039

This paper focuses on a novel lossless image compression algorithm which can be efficiently used for Internet of Things (IoT) multimedia applications. The proposed algorithm has low memory requirements and relies on a simple and efficient encoding scheme. Thus it can be easily implemented even in low-cost microcontrollers as those commonly used in several IoT platforms. Despite its simplicity, comparison results on different image datasets show that the proposed algorithm achieves compression ratios comparable with other more complex state-of-the-art solutions.

关键词： Image coding Signal processing algorithms compression algorithms Prediction algorithms Channel coding Internet of Things Gray-scale

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Geometric inhomogeneous random graphs

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THEORETICAL COMPUTER SCIENCE 2019年 760卷 35-54页

作者： Bringmann, Karl Keusch, Ralph Lengler, Johannes Max Planck Inst Informat Saarbrucken Germany Swiss Fed Inst Technol Inst Theoret Comp Sci Zurich Switzerland

Real-world networks, like social networks or the internet infrastructure, have structural properties such as large clustering coefficients that can best be described in terms of an underlying geometry. This is why the focus of the literature on theoretical models for real-world networks shifted from classic models without geometry, such as Chung-Lu random graphs, to modern geometry-based models, such as hyperbolic random graphs. With this paper we contribute to the theoretical analysis of these modern, more realistic random graph models. Instead of studying directly hyperbolic random graphs, we use a generalization that we call geometric inhomogeneous random graphs (GIRGs). Since we ignore constant factors in the edge probabilities, GIRGs are technically simpler (specifically, we avoid hyperbolic cosines), while preserving the qualitative behavior of hyperbolic random graphs, and we suggest to replace hyperbolic random graphs by this new model in future theoretical studies. We prove the following fundamental structural and algorithmic results on GIRGs. (1) We provide a sampling algorithm that generates a random graph from our model in expected linear time, improving the best-known sampling algorithm for hyperbolic random graphs by a substantial factor O (root n). (2) We establish that GIRGs have clustering coefficients in Omega(1), (3) we prove that GIRGs have small separators, i.e., it suffices to delete a sublinear number of edges to break the giant component into two large pieces, and (4) we show how to compress GIRGs using an expected linear number of bits. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Real-world networks Random graph models Hyperbolic random graphs Sampling algorithms compression algorithms Clustering coefficient

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Angle estimation of coherent targets via Toeplitz induced compressed matrix method for the Bistatic MIMO radar

Angle estimation of coherent targets via Toeplitz induced co...

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European Conference on Microwave

作者： Evans Baidoo Jurong Hu Bilguun Batbaatar Benjamin Danso Kwakye College of Computer and Information Engineering Hohai University Nanjing China College of Science Hohai University Nanjing China

ISBN: (数字)9782874870590

ISBN: (纸本)9781728170398

A computationally efficient preprocessing technique for the estimation of coherent angles of targets for the bistatic MIMO radar centered on Toeplitz based Matrix compression algorithm is presented. The proposed scheme first resolves the coherent source problem by exploiting the diagonalization structure of the received data and reconstructing the output to formulate a Toeplitz matrix. By applying a Nyström transformation, a compressed matrix is derived to enhance speedy computation. The proposed method, aside from being robust to coherent target signals with a much lower estimation error, presents a faster computational time than its competitors.

关键词： MIMO radar Radar Microwave theory and techniques Robustness Computational efficiency compression algorithms Signal resolution

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Cross-Content Evaluations in the Subjective Quality Assessment of Light Field Images

Cross-Content Evaluations in the Subjective Quality Assessme...

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International Conference on Radioelektronika

作者： Adam Zizien Karel Fliegel Faculty of Electrical Engineering Czech Technical University in Prague Technická 2 166 27Prague 6 Czech Republic

Plenoptic modalities, in general, have seen a steady increase in research activity in recent years. Advances in this field pose additional demands on the development of new compression algorithms, which could be used for efficient delivery and storage of the acquired data. Subjective assessments are a key part of the development process. In this work, we examine the differences between two passive methodologies for the subjective assessment of light field images; a rating methodology, introduced in our previous work, and a ranking methodology. Additionally, we analyse the impact and viability of cross-content evaluations on the results of the assessment. The resulting data are made publicly available and can be used to design new compression algorithms, subjective assessment methodologies, or objective quality metrics.

关键词： Measurement Light fields Quality assessment compression algorithms

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