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Dynamic Alternation of Huffman Codebooks for Sensor Data compression

IEEE EMBEDDED SYSTEMS LETTERS 2017年第3期9卷 81-84页

作者： Yunge, Daniel Park, Sangyoung Kindt, Philipp Chakraborty, Samarjit Tech Univ Munich Chair Real Time Comp Syst D-80290 Munich Germany

Signal compression is crucial for reducing the amount of communication, and hence power consumption of wireless sensors. Lossless compression techniques, such as Huffman coding, are often used in healthcare applications since they do not compromise the integrity of vital signals. Techniques that adapt to changing signal patterns have been proposed. However, most of them involve significant computation overhead or are too simple to maintain high compression rates under changing signal patterns. In this letter, we propose a technique that makes use of multiple codebooks, which are generated offline based on the signal context. In the applications we study, we observe that the symbols that compose a big variety of signals follow Laplacian distributions in which the spread changes over time. This can be effectively utilized to generate a set of codebooks. Then, appropriate codebooks are selected online depending on the currently measured spread, which ensures high compression efficiency and the adaptability to changing signal patterns. Our experiments on real-world medical datasets show that our approach is computationally very efficient, and exhibits competitive compression rates. Our proposed technique outperforms a state-of-the-art compression algorithm, FAS-LEC, in terms of average data reduction by 4.3%, while consuming a similar amount of energy. Compared to the adaptive Huffman method, which achieves near-optimal compression rates, our results indicate energy savings of 19% due to the reduced computational complexity, while the compression rate is improved by 0.6%.

关键词： Bluetooth body sensor networks codecs compression algorithms Huffman coding maximum likelihood estimation meteorology telemedicine wearable computers

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Optimal Rule Caching and Lossy compression for Longest Prefix Matching

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IEEE-ACM TRANSACTIONS ON NETWORKING 2017年第2期25卷 850-864页

作者： Rottenstreich, Ori Tapolcai, Janos Princeton Univ Dept Comp Sci Princeton NJ 08544 USA Budapest Univ Technol & Econ MTA BME Future Internet Res Grp H-1117 Budapest Hungary

Packet classification is a building block in many network services, such as routing, monitoring, and policy enforcement. In commodity switches, classification is often performed by memory components of various rule matching patterns (longest prefix match, ternary matches, exact match, and so on). The memory components are fast but expensive and power-hungry with power consumption proportional to their size. In this paper, we study the applicability of rule caching and lossy compression to create packet classifiers requiring much less memory than the theoretical size limits of the semantically-equivalent representations, enabling significant reduction in their cost and power consumption. This paper focuses on the longest prefix matching. Our objective is to find a limited-size longest prefix match classifier that can correctly classify a high portion of the traffic, so that it can be implemented in commodity switches with classification modules of restricted size. While for the lossy compression scheme a small amount of traffic might observe classification errors, a special indication is returned for traffic that cannot be classified in the rule caching scheme. We develop optimal dynamic-programming algorithms for both problems and describe how to treat the small amount of traffic that cannot be classified. We generalize our solutions for a wide range of classifiers with different similarity metrics. We evaluate their performance on real classifiers and traffic traces and show that in some cases we can reduce a classifier size by orders of magnitude while still classifying almost all traffic correctly.

关键词： compression algorithms classification algorithms cache memory software defined networking

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From Imitation to Prediction, Data compression vs Recurrent Neural Networks for Sentiment Analysis and Automatic Text Generation

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INTELIGENCIA ARTIFICIAL-IBEROAMERICAL JOURNAL OF ARTIFICIAL INTELLIGENCE 2018年第61期21卷 30-46页

作者： Andres Laura, Juan Omar Masi, Gabriel Argerich, Luis Univ Buenos Aires Fac Ingn Dept Comp Buenos Aires DF Argentina Univ Nacl Tres Febrero Saenz Pena Buenos Aires Argentina

In recent studies [9] [26] [2] Recurrent Neural Networks were used for generative processes and their surprising performance can be explained by their ability to create good predictions. In addition, Data compression is also based on prediction. What the problem comes down to is whether a data compressor could be used to perform as well as recurrent neural networks in the natural language processing tasks of sentiment analysis and text generation. If this is possible, then the problem comes down to determining if a compression algorithm is even more intelligent than a neural network in such tasks. In our journey, a fundamental difference between a Data compression Algorithm and Recurrent Neural Networks has been discovered.

关键词： Natural language processing compression algorithms Neural networks Predictions

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compression of Dynamic Graphs Generated by a Duplication Model 56

Compression of Dynamic Graphs Generated by a Duplication Mod...

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56th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

作者： Turowski, Krzysztof Magner, Abram Szpankowski, Wojciech Purdue Univ Dept Comp Sci Ctr Sci Informat W Lafayette IN 47907 USA

ISBN: (纸本)9781538665961

We continue building up the information theory of non-sequential data structures such as trees, sets, and graphs. In this paper, we consider dynamic graphs generated by a full duplication model in which a new vertex selects an existing vertex and copies all of its neighbors. We ask how many bits are needed to describe the labeled and unlabeled versions of such graphs. We first estimate entropies of both versions and then present asymptotically optimal compression algorithms up to a constant term. Interestingly, for the full duplication model the labeled version needs Theta(n) bits while its unlabeled version (structure) can be described by Theta(log n) bits due to a significant amount of symmetry (i e., the cardinality of the automorphism group of graphs generated by this model is on average quite high).

关键词： Entropy Vehicle dynamics compression algorithms Evolution (biology) Biological system modeling Computational modeling Data models

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Compaction of RFID Devices Using Data compression

IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION

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IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION 2017年第3期1卷 202-207页

作者： Wiseman, Yair Bar Ilan Univ Comp Sci Dept IL-52900 Ramat Gan Israel

When a coding system used to represent letters or numbers in transmitting messages has many unused codewords, it will be more than reasonable to compress the messages. Electronic product code (EPC) which is widely used by many RFID devices has plentiful unused codewords. As a matter of fact, the unused codewords are most by far of the codewords. In this paper, we check the suitableness of a commonly used compression algorithm for EPC of RFID devices.

关键词： Huffman coding RFID tags compression algorithms Product codes Compaction Data compression RFID electronic product code (EPC) compression arithmetic coding

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The Implementation of Timestamp, Bitmap and RAKE Algorithm on Data compression and Data Transmission from IoT to Cloud

The Implementation of Timestamp, Bitmap and RAKE Algorithm o...

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International Conference on Science and Technology-Computer (ICST)

作者： Kartika Sari Mardhani Riasetiawan Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta Indonesia

In the Internet of Things (loT) system, the environmental data are collected by devices, but not always changed. If the gateway always returns all of data sensor in the process of data synchronization by platform, a lot of bandwidth would be wasted when most of the data collected by some devices. It is necessary to integrate between the cloud and IoT using data transmission as the limitations of IoT devices. However, for the loT system, the existing algorithms are not very efficient because the data in loT system have different characters such as real-time and mutability. To solve the problem, this paper proposes some methods to reduce the data transmission time using the Timestamp algorithm to record the last time of collected data by the device, sending data from the gateway if the data is different from the data on the platform, the Bitmap algorithm as the device status sync algorithm, and also the lossless RAKE compression algorithm to reducing memory. The results of this study are expected to reduce the time consumption of data transmission and reduce memory usage to increase the life of IoT devices. This study consider the data synchronization time, data compression and data decompression time, and also the compression ratio.

关键词： Synchronization Logic gates Cloud computing Data communication Data compression compression algorithms Servers

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Implementation of Pulse compression Algorithm Based on Multicore CPU

Implementation of Pulse Compression Algorithm Based on Multi...

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International Conference on Instrumentation, Measurement, Computer, Communication and Control (IMCCC)

作者： Nanyang Liu Yue Zhang Zengping Chen National University of Defense Technology Changsha China

ISBN: (数字)9781538682463

ISBN: (纸本)9781538682470

To realize real-timely detection of low altitude aircraft target, this paper introduces a parallel model based on multicore CPU aiming at pulse compression (PC). Eight cores are utilized to process parallel intensive task. The detailed implementation of pulse compression (PC) algorithm is introduced in this paper. The result indicates that this design meets the requirement of real-timely pulse compression with one echo sample.

关键词： Multicore processing Radar Instruction sets Matched filters Acceleration compression algorithms Signal processing algorithms

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Evaluation of Cache compression for Elbrus Processors

Evaluation of Cache Compression for Elbrus Processors

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Engineering and Telecommunication (EnT-MIPT)

作者： Aleksey S. Kozhin Alexander V. Surchenko AO “MCST” Moscow Institute of Physics and Technology (MIPT) Moscow Russian

ISBN: (纸本)9781728104331

Data compression in cache memory allows increasing the effective capacity, which improves the hit rate and only insignificantly affects power consumption and die area. In this paper, the results of the first research on cache compression in processors with Elbrus architecture are presented. Base+Delta (B+Δ) and Base-Delta-Immediate (BΔI) compression algorithms are selected for hardware implementation for their high efficiency and lower decompression latency compared to other algorithms. The modified versions of these algorithms, B+Δ* and BΔI*, which allow to reduce implementation complexity and further shorten the latency, are presented. Additionally, a new set of compression schemes for modified algorithms (labeled as BΔI*-HL algorithm) is proposed to account for width of the interfaces and internal data buses. The algorithms were implemented using Verilog HDL and evaluated on FPGA prototype of Elbrus-8C2 processor and SPEC CPU2000 benchmark suite. The results show that BΔI* demonstrates almost equal of greater compression ration as the original BÄI algorithm while has significantly lower implementation complexity. The algorithm BΔI*-HL is proposed as the most suitable for hardware implementation with average compressed lines share of 24.4% and mean compression ratio of 1.246.

关键词： compression algorithms Benchmark testing Program processors Cache memory Heuristic algorithms Hardware Hardware design languages

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Image compression using Shannon-Fano-Elias Coding and Run Length Encoding

Image Compression using Shannon-Fano-Elias Coding and Run Le...

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International Conference on Inventive Communication and Computational Technologies (ICICCT)

作者： M. Rajasekhar Reddy K. Akshaya R. Alice Infanta Seles RA. Dhivya K. S . Ravichandran School Of Computing SASTRA Deemed University Thanjavur

Image compression is a technique, which reduces the size of an image without much loss of information. In this paper we have put forward a new method for image compression that includes techniques such as Shannon-Fano - Elias coding followed by Run Length Encoding (RLE). By using parameters like Peak Signal to Noise Ratio(PSNR), Mean Square Error (MSE) the efficiency of our proposed algorithm is evaluated by giving square matrix image (256×256) as input.

关键词： Image coding Channel coding Gray-scale compression algorithms Data compression Probability Matrix converters

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The Original Method of Fixed Constraints Transform for Lossless Text compression

The Original Method of Fixed Constraints Transform for Lossl...

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International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Radu Rădescu Mihai Casandra Applied Electronics and Information Engineering University “Politehnica” of Bucharest Bucharest Romania Security Department 1&1 Internet Development SRL Bucharest Romania

ISBN: (纸本)9781538649022;9781538649015

The paper presents an original method of text transformation based on permutations with restrictions and elimination of repetitive letters within a word having the purpose of improving the performance of lossless compression algorithms, based on a new generated dictionary. The direct transformation algorithm and the reverse transformation algorithm are presented, as well as the impact on more compression techniques compared to other transforms used in text compression. This transformation has as purpose the performance improvement for existing text compression algorithms, including the use of neural networks. The main condition is that the transformation should be reversible for the text recovery after decompression.

关键词： Transforms Dictionaries Encoding Decoding Neural networks Extremities compression algorithms

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