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A novel family of compression algorithms for ECG and other semiperiodical, one-dimensional, biomedical signals

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 1996年第8期43卷 820-828页

作者： Barlas, GD Skordalakis, ES NATL TECH UNIV ATHENS DIV COMP SCI GR-15310 ATHENS GREECE

In this paper, a novel family of compression algorithms is presented, which is designed to exploit the redundancy of one-dimensional (1-D) semiperiodical biomedical signals resulting from the cyclic nature of the underlying physical process, The basic idea is that a pool of past-seen cycles is maintained and cycles to be encoded can be stored as transformed versions of those residing in the pool, Conceptually, this approach is an extension of dictionary-based coding schemes used for text compression to signal patterns residing in an n-dimensional space. A cycle transformation method is introduced in order to render the pattern matching process practical and to enable cycle substitution, Based on the principles of the algorithmic family and this transformation method, an electrocardiogram (ECG)-oriented algorithm is implemented and thoroughly tested. The performance of this implementation is examined theoretically and deductions about the optimal algorithm settings are made, The ECG compression algorithm is superior to the average beat subtraction algorithm as proposed by Hamilton and Tompkins in cases where high compression ratios are required.

关键词： compression algorithms Electrocardiography Signal processing Signal analysis Computer science Signal design Algorithm design and analysis Pattern matching Testing Parameter extraction

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Bootstrap Analysis of compression algorithms

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IEEE LATIN AMERICA TRANSACTIONS 2020年第9期18卷 1639-1645页

作者： Beserra, Antonio A. R. Souza, Leandro C. Souza, Daniel F. L. Univ Sao Paulo Sao Carlos SP Brazil Univ Fed Paraiba Joao Pessoa Paraiba Brazil Univ Fed Rural Semi Arido Mossoro RN Brazil

compression algorithms have been proposed with the technology advance. However, there are not objective analysis procedures to guide a future choice of an algorithm directed for the type of data in the system they are intended for. This paper introduces a statistical framework, based on the bootstrap method, to execute the analysis of compression algorithms using an objective comparison parameter as a criterion. A case study using the compression ratio as the parameter and file samples of 4 different types was analyzed. The proposed scheme allowed us to infer which algorithm is better to be used for each data type. RLE has proven more suitable to image, audio and video files with Huffman obtaining comparable performance. For text files, LZW has remarkably outperformed all other algorithms.

关键词： Image coding Media Encoding compression algorithms Transform coding Security IEEE transactions compression Algorithm Statistical Analysis Bootstrap

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A Comparison of Trajectory compression algorithms Over AIS Data

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IEEE ACCESS 2021年 9卷 92516-92530页

作者： Makris, Antonios Kontopoulos, Ioannis Alimisis, Panagiotis Tserpes, Konstantinos Harokopio Univ Athens Dept Informat & Telemat Athens 17778 Greece Natl Tech Univ Athens Dept Elect & Comp Engn Athens 15773 Greece

Today's industry is flooded with tracking data originating from vessels across the globe that transmit their position at frequent intervals. These voluminous and high-speed streams of data has led researchers to develop novel ways to compress them in order to speed-up processing without losing valuable information. To this end, several algorithms have been developed that try to compress streams of vessel tracking data without compromising their spatio-temporal and kinematic features. In this paper, we present a wide range of several well-known trajectory compression algorithms and evaluate their performance on data originating from vessel trajectories. Trajectory compression algorithms included in this research are suitable for either historical data (offline compression) or real-time data streams (online compression). The performance evaluation is three-fold and each algorithm is evaluated in terms of compression ratio, execution speed and information loss. Experiments demonstrated that each algorithm has its own benefits and limitations and that the choice of a suitable compression algorithm is application-dependent. Finally, considering all assessed aspects, the Dead-Reckoning algorithm not only presented the best performance, but it also works over streaming data, which constitutes an important criterion in maritime surveillance.

关键词： Trajectory compression algorithms Artificial intelligence Clustering algorithms Loss measurement Heuristic algorithms Shape Error metrics lossy compression techniques similarity measures simplifying trajectory algorithms trajectory compression algorithm trajectory similarity

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PAC: A monitoring framework for performance analysis of compression algorithms in Spark

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 157卷 237-249页

作者： Zhu, Changpeng Han, Bo Li, Gang Chongqing Univ Technol Dept Data Sci & Big Data Pufu Ave Chongqing 401135 Peoples R China Jiaotong Univ Sch Cyber Sci & Engn Xian 710049 Peoples R China Chongqing Univ Posts & Telecommun Dept Software Engn Chongqing Peoples R China

In Spark, a massive amount of immediate data inevitably leads to excessive I/O overhead. To mitigate this issue, Spark incorporates four compression algorithms to reduce the size of the data for better performance. However, compression and decompression only constitute a portion of the overall logical flows of Spark applications. This indicates a potential considerable interaction between compression algorithms and Spark applications regarding performance. Consequently, identifying factors that significantly impact the performance of compression algorithms in Spark, and subsequently, determining the actual performance benefits these algorithms provide to Spark applications, remains a significant challenge. To address the challenge, this paper presents a monitoring framework, named PAC, for conducting indepth and systematic p erformance a nalysis of c ompression algorithms in Spark. As the pioneer of such monitoring frameworks, PAC is built on top of Spark core and collaborates with multiple monitors to collect various types of performance metrics of compressors, correlates and integrates them into structured tuples by the data transformer in PAC. This makes it easier to diagnosis of factors that have a significant influence on the performance of compression algorithms in Spark. Upon utilizing PAC, our experiments reveal that new determinants include the input/output data sizes and types of compression/decompression invocations, CPU consumption for compressing a massive amount of data, and hardware utilization, besides traditional determinants. Moreover, these experiments demonstrate that ZSTD is more susceptible to performance issues when compressing and decompressing small data, despite the overall input and output data being huge. In terms of performance, LZ4 serves as a viable alternative to ZSTD. These findings not only benefit researchers and developers in making more informed decisions in terms of configuring and tuning Spark execution environments but also su

关键词： Spark compression algorithms Log analysis Performance comparison Root-cause analysis

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Explicit learning curves for transduction and application to clustering and compression algorithms

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JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH 2004年第0期22卷 117-142页

作者： Derbeko, P El-Yaniv, R Meir, R Technion Israel Inst Technol Dept Comp Sci IL-32000 Haifa Israel Technion Israel Inst Technol Dept Elect Engn IL-32000 Haifa Israel

Inductive learning is based on inferring a general rule from a finite data set and using it to label new data. In transduction one attempts to solve the problem of using a labeled training set to label a set of unlabeled points, which are given to the learner prior to learning. Although transduction seems at the outset to be an easier task than induction, there have not been many provably useful algorithms for transduction. Moreover, the precise relation between induction and transduction has not yet been determined. The main theoretical developments related to transduction were presented by Vapnik more than twenty years ago. One of Vapnik's basic results is a rather tight error bound for transductive classification based on an exact computation of the hypergeometric tail. While tight, this bound is given implicitly via a computational routine. Our first contribution is a somewhat looser but explicit characterization of a slightly extended PAC-Bayesian version of Vapnik's transductive bound. This characterization is obtained using concentration inequalities for the tail of sums of random variables obtained by sampling without replacement. We then derive error bounds for compression schemes such as (transductive) support vector machines and for transduction algorithms based on clustering. The main observation used for deriving these new error bounds and algorithms is that the unlabeled test points, which in the transductive setting are known in advance, can be used in order to construct useful data dependent prior distributions over the hypothesis space.

关键词： compression algorithms Support Vector Network Dataset Bound algorithms test points OBSERVATIONAL LEARNING Learning Curve Transduction Cluster Analysis

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A Mathematical Analysis of Clustering-Free Local SAR compression algorithms for MRI Safety in Parallel Transmission

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2024年第2期43卷 714-722页

作者： Gras, V. Boulant, N. Luong, M. Morel, L. Le Touz, N. Adam, J. -P. Joly, J. -C. Univ Paris Saclay CEA CNRS BAOBABNeuroSpin F-91191 Gif Sur Yvette France Univ Paris Saclay CEA DRF IRFUNeuroSpin F-91191 Gif Sur Yvette France CEA DAM F-46500 Gramat France

Parallel transmission (pTX) is a versatile solution to enable UHF MRI of the human body, where radiofrequency (RF) field inhomogeneity appears very challenging. Today, state of the art monitoring of the local SAR in pTX consists in evaluating the RF power deposition on specific SAR matrices called Virtual Observation Points (VOPs). It essentially relies on accurate electromagnetic simulations able to return the local SAR distribution inside the body in response to any applied pTX RF waveform. In order to reduce the number of SAR matrices to a value compatible with real time SAR monitoring (<< 10(3)) , a VOP set is obtained by partitioning the SAR model into clusters, and associating a so- called dominant SAR matrix to every cluster. More recently, a clustering-free compression method was proposed, allowing for a significant reduction in the number of SAR matrices. The concept and derivation however assumed static RF shims and their extension to dynamic pTX is not straightforward, thereby casting doubt on the strict validity of the compression approach for these more complicated RF waveforms. In this work, we provide the mathematical framework to tackle this problem and find a rigorous justification of this criterion in the light of convex optimization theory. Our analysis led us to a variant of the clustering-free compression approach exploiting convex optimization. This new compression algorithm offers computational gains for large SAR models and for high-channel count pTX RF coils.

关键词： Radio frequency Specific absorption rate Computational modeling Monitoring Magnetic resonance imaging Optimization compression algorithms MRI safety parallel transmission specific absorption rate virtual observation point ultra-high field

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Assessing compression algorithms on IoT sensor nodes 42

Assessing compression algorithms on IoT sensor nodes

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42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Guberovic, Emanuel Kristo, Fran Krivic, Petar Cavrak, Igor Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia

ISBN: (纸本)9789532330984

With the ever-growing presence of IoT devices in everyday applications, the sheer volume of data exchanged is increasing rapidly. Communication channels' bandwidth used in these applications often presents a bottleneck and limits the frequency at which data can be exchanged, making application of compression beforehand a viable approach. In this paper, we analyze some of the well known lossless compression algorithms capable of seamless integration as software modules on embedded devices used in (near) real-time applications. Low processing and memory resources available on them as well as unsteadiness of aforementioned communication channels were taken into consideration in algorithm selection. Attained compression ratio is evaluated on data harvested from smart meters, whilst time and energy consumed during processing were analyzed within a measurement framework purposefully built for this research. We show that considerable gains can be attained in the volume of data that can be sent from devices based on commonly used microcontrollers, with only a modest toll on time and overall energy used.

关键词： compression algorithms stream of data real-time systems sensor nodes internet of things smart meters

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Running compression algorithms in the encrypted domain: a case-study on the homomorphic execution of RLE 15

Running compression algorithms in the encrypted domain: a ca...

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15th Annual Conference on Privacy, Security and Trust (PST)

作者： Canard, Sebastien Carpov, Sergiu Kuate, Donald Nokam Sirdey, Renaud Orange Labs Appl Crypto Grp Paris France CEA LIST Paris France

ISBN: (纸本)9781538624876

This paper is devoted to the study of the problem of running compression algorithms in the encrypted domain, using a (somewhat) fully homomorphic encryption (FHE) scheme. We do so with a particular focus on conservative compression algorithms. Despite of the encrypted domain Turing-completeness which comes with the magic of FHE operators, we show that a number of subtleties crop up when it comes to running compression algorithms and, in particular, that guaranteed conservative compression is not possible to achieve in the FHE setting. To illustrate these points, we analyze the most elementary conservative compression algorithm of all, namely Run-Length Encoding (RLE). We first study the way to regularize this algorithm in order to make it (meaningfully) fit within the constraints of a FHE execution. Secondly, we analyze it from the angle of optimizing the resulting structure towards (as much as possible) FHE execution efficiency. The paper is concluded by concrete experimental results obtained using the Fan-Vercauteren cryptosystem as well as the Armadillo FHE compiler. It is also this paper intent to share the concrete return on experience we gained in attempting to run a simple yet practically significant algorithm over FHE.

关键词： compression algorithms Encryption Image coding Logic gates Encoding Video coding

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An introduction of compression algorithms into SSL/TLS and proposal of compression algorithms specialized for application 17

An introduction of compression algorithms into SSL/TLS and p...

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17th International Conference on Advanced Information Networking and Applications

作者： Okamoto, N Kimura, S Ebihara, Y Univ Tsukuba Inst Informat Sci & Elect Tsukuba Ibaraki 305 Japan

ISBN: (纸本)0769519067

The specification of SSL/TLS defines that data from the upper layer can be compressed in the record layer before they are encrypted Since only the no-compression is provided for the compression algorithm in the specification, nobody can compress transmission data at the SSL/TLS protocols unless communication peers have a special agreement to use a compression algorithm. However the compression mechanism should be useful for the users of narrower bandwidth communication lines such as the ordinary analog telephone lines and N-ISDN. In order to solve this problem, this paper first introduces general-purpose compression algorithms into SSL/TLS, and shows that the average transfer times for several Japanese text files via HTTP are much improved especially for narrow bandwidth communication lines. We also compare with the compression mechanism on HTTP and result the difference between the results of the SSL/TLS compression and HTTP compression is rather little. For improving the compression ratio, we next propose compression algorithms specialized for application protocols. As examples, we focus to HTTP and HTML, and construct the tree structures of the field names and field values. From the tree, the static dictionaries for the compression algorithms are defined. Finally, we implement the compression algorithms and show the compression ratios when the several HTML files are transfered.

关键词： Bandwidth compression algorithms Cryptography Dictionaries HTML Proposals Protocols Sockets Telephony Tree data structures

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Context tables: A tool for describing text compression algorithms

Context tables: A tool for describing text compression algor...

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Data compression Conference

作者： Yokoo, H Gunma Univ Dept Comp Sci Kiryu Gumma 3768515 Japan

ISBN: (纸本)0818684062

This paper introduces the notion of a context table, which is a common basis for describing and analyzing text compression algorithms. A context table stores all substrings in a text as their lexicographic orders. Examples of compression algorithms described in terms of context table concepts include LZ77 and the block-sorting algorithm. Since these algorithms are designed to work with an arbitrary source distribution, they can be expected to serve as an entropy estimator. A primal use of a context table is to reveal the capability of estimating the entropy. A context table makes it easy to understand several characteristic quantities including the recurrence time of substring, the conditional recurrence time, and the length of the shortest unique substring. By the help of these concepts, some relations among apparently independent algorithms are established.

关键词： substring Text compression algorithms compression algorithms

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