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Sensor nodes are embodiment of IoT systems in microscopic level. As the volume of sensor data increases exponentially, data compression is essential for storage, transmission and in-network processing. The compression performance to realize significant gain in processing high volume sensor data cannot be attained by conventional lossy compression methods. In this paper, we propose ASDC (Adaptive Sensor Data Compression), an adaptive compression scheme that caters various sensor applications and achieve high performance gain. Our approach is to exhaustively analyze the sensor data and adapt the parameters of compression scheme to maximize compression gain while optimizing information loss. We apply robust statistics and information theoretic techniques to establish the adaptivity criteria. We experiment with large sets of heterogeneous sensor datasets to prove the efficacy. Nonlinear lossy compression (Chebyshev) is extensively considered as the standard technique as well as experimental result with frequency domain compression like Discrete Fourier Transform (DFT) is shown as future scope of further improvement.

In the last few years, due to the growing use of stereoscopic images, much effort has been spent by the scientific community to develop algorithms for stereoscopic image compression. Stereo images represent the same scene from two different views, and therefore they typically contain a high degree of redundancy. It is then possible to implement some compression strategies devoted to exploit the intrinsic characteristics of the two involved images that are typically embedded in a MPO (Multi Picture Object) data format. MPO files represents a stereoscopic image by building a list of JPEG images. Our previous work introduced a simple block-matching approach to compute local residual useful to reconstruct during the decoding phase, stereoscopic images that maintain high perceptual quality; this allows to the encoder to force high level of compression at least for one of the two involved images. On the other hand the matching approach, based only on the similarity of the blocks, results rather inefficient. Starting from this point, the main contribution of this paper focuses on the improvement of both matching step effectiveness and its computational cost. Such alternative approach aims to greatly enhance matching step by exploiting the geometric properties of a pair of stereoscopic images. In this way we significantly reduce the complexity of the method without affecting results in terms of quality.

Розглянуто особливості формування числової моделі джерела даних. Досліджено вплив на коефіцієнт ущільнення довжини блоків даних, на які розбивається ущільнюваний файл. Запропоновано і досліджено два методи адаптивного ущільнення даних на основі лінійної форми Фібоначчі, які передбачають використання набору числових моделей джерела даних., Рассмотрены особенности формирования числовой модели источника данных. Исследовано влияние на коэффициент сжатия длины блоков данных, на которые разбивается сжимаемый файл. Предложено и исследовано два метода адаптивного сжатия данных на основе линейной формы Фибоначчи, предусматривающие использование набора числовых моделей источника данных., A fundamentally new data compression approach, which is based on the optimizing properties of Fibonacci numbers lies in the fact that the figures are considered as positive whole numbers and presented by a linear Fibonacci form, was investigated. Formation features of the numerical data source model were examined. The effect of the length of data blocks of the compressed file on the compression ratio was studied. Changing the number of bytes in the block provides the formation of different data source models. Ability to change the data source model allows to choose a model that provides the greatest compression ratio for a given encoding rule. Analysis of the results has shown that the effect of the data block length on the compression ratio is different for different file types. For some types, the greatest compression ratio is achieved when the block length is 100 bytes, and the ratio decreases with the increased length. For other file types, the effect of the data block length has a "wave" pattern (the ratio repeatedly increases and decreases with the increased length), and for certain types of files, the dependence of the transformed data on the data source model used is negligible. Low compression ratios and no compression for certain file types are caused by the fact that the data modeling used does not ensure the formation of numbers that are compactly presented by the linear Fibonacci form. To eliminate this shortcoming, two adaptive data compression methods, based on the linear Fibonacci form, which involve using a set of numerical data source models were proposed and investigated. These models are based on the maximum value of the numerical equivalents of the ASCII codes of bytes that make up the block. Adaptation enhances the compression ratio compared to non-adaptive compression method based on the linear Fibonacci form.

We present a system called the adaptive compression environment (ACE) that automatically and transparently applies compression (on-the-fly) to a communication stream to improve network transfer performance. ACE uses a series of estimation techniques to make short-term forecasts of compressed and uncompressed transfer time at the 32 Kb block level. ACE considers underlying networking technology, available resource performance, and data characteristics as part of its estimations to determine which compression algorithm to apply (if any). Our empirical evaluation shows that, on average, ACE improves transfer performance given changing network types and performance characteristics by 8 to 93 percent over using the popular compression techniques that we studied (Bzip, Zlib, LZO, and no compression) alone.

We present a new algorithm for mining maximal frequent itemsets from a transactional database. The search strategy of the algorithm integrates a depth-first traversal of the itemset lattice with effective pruning mechanisms that significantly improve mining performance. Our implementation for support counting combines a vertical bitmap representation of the data with an efficient bitmap compression scheme. In a thorough experimental analysis, we isolate the effects of individual components of MAFIA including search space pruning techniques and adaptive compression. We also compare our performance with previous work by running tests on very different types of data sets. Our experiments show that MAFIA performs best when mining long itemsets and outperforms other algorithms on dense data by a factor of three to 30.

We introduce a novel method to analyse complete genomes and recognise some distinctive features by means of an adaptive compression algorithm, which is not DNA-oriented. We study the Information Content as a function of the number of symbols encoded by the algorithm. Preliminar results are shown concerning regions having a sublinear type of information growth, which is strictly connected to the presence of highly repetitive subregions that might be supposed to have a regulatory function within the genome., Comment: 30 pages, 13 figures, submitted (Oct. 2003)

As mobile and wearable health technologies have evolved, constant monitoring of bio-signals from patients has become important for accurate diagnosis using mobile and remote health services. As the need for constant monitoring of bio-signals increases, the amount of bio-signal data also increases, therefore an efficient compression method for specific bio-signals should be developed. In contrast to other types of signals, bio-signals contain important diagnostic information that should not be removed during compression. An effective compression algorithm for phonocardiogram (PCG) signals that contain important diagnostic information (murmur) is proposed. In this algorithm, the murmur is first estimated, then an adaptive thresholding scheme is applied in the wavelet domain to the normal portions and the murmur portions of PCGs depending on the murmur estimates during compression. Although other conventional compression methods result in substantial loss of murmur information, the proposed method is able to keep most of the murmur information in compressed PCGs.

In this paper, we will investigate an adaptive compression scheme for tracking time-varying sparse signals with possibly varying sparsity patterns and/or order. In particular, we will focus on sparse sensing, which enables a completely distributed compression and simplifies the sampling architecture. The sensing matrix is designed at each time step based on the entire history of measurements and known dynamics such that the information gain is maximized. We illustrate the developed theory with a target tracking example. Finally, we provide a few extensions of the proposed framework to include a richer class of sparse signals, e.g., structured sparsity and smoothness.

The Minimal-Pair ABX (MP-ABX) paradigm has been proposed as a method for evaluating speech features for zeroresource/unsupervised speech technologies. We apply it in a phoneme discrimination task on the Articulation Index corpus to evaluate the resistance to noise of various speech features. In Experiment 1, we evaluate the robustness to additive noise at different signal-to-noise ratios, using car and babble noise from the Aurora-4 database and white noise. In Experiment 2, we examine the robustness to different kinds of convolutional noise. In both experiments we consider two classes of techniques to induce noise resistance: smoothing of the time-frequency representation and short-term adaptation in the time-domain. We consider smoothing along the spectral axis (as in PLP) and along the time axis (as in FDLP). For short-term adaptation in the time-domain, we compare the use of a static compressive non-linearity followed by RASTA filtering to an adaptive compression scheme.

Self-Organization is based on adaptivity. Adaptivity should start with the very basic fundamental communication tasks such as encoding the information to be transmitted or stored. Obviously, the less signal transmitted the less energy in transmission used. In this paper we present a novel on-line and entropy adaptive compression scheme for streaming unbounded length inputs. The scheme extends the window dictionary Lempel-Ziv compression, is adaptive and is tailored to on-line compress inputs with non stationary entropy. Specifically, the window dictionary size is changed in an adaptive manner to fit the current best compression rate for the input. On-line Entropy Adaptive Compression scheme (EAC), that is introduced and analyzed in this paper, examines all possible sliding window sizes over the next input portion to choose the optimal window size for this portion, a size that implies the best compression ratio. The size found is then used in the actual compression of this portion. We suggest an adaptive encoding scheme, which optimizes the parameters block by block, and base the compression performance on the optimality proof of Lempel Ziv algorithm when applied to blocks. The EAC scheme was tested over files of different types (docx, ppt, jpeg, xls) and over synthesized files that were generated as segments of homogeneous Markov Chains. Our experiments demonstrate that the EAC scheme typically provides a higher compression ratio than LZ77 does, when examined in the scope of on-line per-block compression of transmitted (or compressed) files.

Dynamic and active pixel vision sensors (DAVISs) are a new type of sensor that combine a frame-based intensity readout with an event-based temporal contrast readout. This paper demonstrates that these sensors inherently perform high-speed, video compression in each pixel by describing the first decompression algorithm for this data. The algorithm performs an online optimization of the event decoding in real time. Example scenes were recorded by the 240×180 pixel sensor at sub-Hz frame rates and successfully decompressed yielding an equivalent frame rate of 2kHz. A quantitative analysis of the compression quality resulted in an average pixel error of 0.5DN intensity resolution for non-saturating stimuli. The system exhibits an adaptive compression ratio which depends on the activity in a scene; for stationary scenes it can go up to 1862. The low data rate and power consumption of the proposed video compression system make it suitable for distributed sensor networks.

International audience; We combine a mask motion detection algorithm with both the WAAVES adaptive compression algorithm (resulting into MMWaaves) and a JPEG2000 coder (resulting into MMJPEG2000) for the compression of medical images sequences. Several images were compressed using Waaves, MMWaaves, and MMJPEG2000 to observe which tool provided the best visual quality while maintaining a high compression ratio. Compared to Waaves, the MMWaaves achieved compression gains up to 40% for CT scans and 50 % for MRI. In addition, the SSIM values attributed to the compressed images were between 0.96 and 0.988 while the PSNR values were higher than 42. In addition MMWaaves attained a superior performance than MMJPEG2000.

In this paper, we study the problem of lossless universal source coding for stationary memoryless sources on countably infinite alphabets. This task is generally not achievable without restricting the class of sources over which universality is desired. Building on our prior work, we propose natural families of sources characterized by a common dominating envelope. We particularly emphasize the notion of adaptivity, which is the ability to perform as well as an oracle knowing the envelope, without actually knowing it. This is closely related to the notion of hierarchical universal source coding, but with the important difference that families of envelope classes are not discretely indexed and there is no well-ordered nesting. Our contribution is to extend the classes of envelopes over which adaptive universal source coding is possible, namely by including max-stable (heavy-tailed) envelopes which are excellent models in many applications, such as natural language modeling. We derive a minimax lower bound on the redundancy of any code on such envelope classes, including an oracle that knows the envelope. We then propose a constructive code that does not use knowledge of the envelope. The code is computationally efficient and is structured to use an \underline{e}xpanding \underline{t}hreshold for \underline{a}uto-\underline{c}ensoring, and we therefore dub it the \textsc{ETAC}-code. We prove that the \textsc{ETAC}-code achieves the lower bound on the minimax redundancy within a factor logarithmic in the sequence length, and can be therefore qualified as a near-adaptive code over families of heavy-tailed envelopes. For finite and light-tailed envelopes the penalty is even less, and the same code follows closely previous results that explicitly made the light-tailed assumption. Our technical results are founded on methods from regular variation theory and concentration of measure.

International audience; This paper sheds light on universal coding with respect to classes of memoryless sources over a countable alphabet defined by an envelope function with finite and non-decreasing hazard rate. We prove that the auto-censuring AC code introduced by Bontemps (2011) is adaptive with respect to the collection of such classes. The analysis builds on the tight characterization of universal redundancy rate in terms of metric entropy % of small source classes by Opper and Haussler (1997) and on a careful analysis of the performance of the AC-coding algorithm. The latter relies on non-asymptotic bounds for maxima of samples from discrete distributions with finite and non-decreasing hazard rate.

Formal Concept Analysis (FCA) decomposes a matrix into a set of sparse matrices capturing its underlying structure. A similar task for real-valued data, transform coding, arises in image compression. Existing cosine transform coding for JPEG image compression uses a fixed, decorrelating transform; however, compression is limited as images rarely consist of pure cosines. The question remains whether an FCA adaptive transform can be applied to image compression. We propose a multi-layer FCA (MFCA) adaptive ordered transform and Sequentially Sifted Linear Programming (SSLP) encoding pair for adaptive image compression. Our hypothesis is that MFCA’s sparse linear codes (closures) for natural scenes, are a complete family of ordered, localized, oriented, bandpass receptive fields, predicted by models of the primary visual cortex. Results on real data demonstrate that adaptive compression is feasible. These initial results may play a role in improving compression rates and extending the applicability of FCA to real-valued data.

Coarse Grained Reconfigurable Architecture (CGRA) achieves high performance by exploiting instruction-level parallelism with software pipeline. Large instruction memory is, however, a critical problem of CGRA, which requires large silicon area and power consumption. Code compression is a promising technique to reduce the memory area, bandwidth requirements, and power consumption. We present an adaptive code compression scheme for CGRA instructions based on dictionary-based compression, where compression mode and dictionary contents are adaptively selected for each execution kernel and compression group. In addition, it is able to design hardware decompressor efficiently with two-cycle latency and negligible silicon overhead. The proposed method achieved an average compression ratio 0.52 in a CGRA of 16-functional unit array with the experiments of well-optimized applications.

A locally adaptive data compression strategy can be efficiently applied to a text data file when characters are used frequently over short intervals. Some data files, such as program files, image files, or exposition files of application software contain some commonly used sequences of characters which occur consecutively. We can use their common characteristics to develop a simple heuristic method of compression. Among all kinds of compression schemes based upon a locally adaptive data compression strategy, the MTF (Move-To-Front) method is a superior one that can be easily implemented and can obtain a higher compression rate for most data files. This article uses the idea of a locally adaptive compression strategy and proposes a new data compression mechanism for Chinese and English data files. Some experimental results show that our proposed method can achieve a higher compression rate than using multigroup Huffman encoding, which is a method recently proposed by Chang and Tsai for Chinese data files.

In view of the different demands on quality of service of different user activities in the 3D Tele-immersive (3DTI) environment, we combine activity recognition and real-time morphing-based compression and present the Activity-Aware Adaptive Compression. We implement this scheme on our 3DTI platform: the TEEVE Endpoint, which is a runtime engine to handle the creation, transmission and rendering of 3DTI data. User study as well as objective evaluation of the scheme show that it can achieve 25% more bandwidth saving compared to conventional 3D data compression as zlib without perceptible degradation in the user experience.

Genomics data is being produced at an unprecedented rate, especially in the context of clinical applications and grand challenge questions. There are various types of data in genomics research, most of which are stored as plain text tables. A data compression framework tailored to this file type is introduced in this paper, featuring a combination of generic compression algorithms, GPU acceleration, and column-major storage. This approach is the first to achieve both compression and decompression rates of around 100MB/s on commodity hardware without compromising compression ratio. By selecting appropriate compression schemes for each column of data, this framework efficiently exploits data redundancy while remaining applicable to a wide range of formats. The GPU-accelerated implementation also properly exploits the parallelism of compression algorithms. Finally, this paper presents a novel first-order Markov model based transformation, with evidence that it is at least as effective as Burrows-Wheeler and Move-To-Front in some contexts.

Nowadays the growing availability of stereo cameras for common applications is becoming a commodity. This paper addresses the problem of stereoscopic images data compression proposing an innovative algorithm for compressing Multi Picture Object coded stereopairs. By means of self organizing reconstruction algorithm based on image redundancy we are able to reduce the size of the enclosed JPEG images. The overall perceived (and measured) quality is managed by considering that a stereoscopic image represents the same scene acquired from two different perspectives. In particular we achieve some compression gain just encoding the two images with different quality factors. The reported results and test benchmarks show the robustness and efficiency of the proposed algorithm.

Objective Non-speech stimuli typically are used to estimate the electroacoustic characteristics of a hearing aid. At present, there is no consensus as to what type of input stimulus will best represent the gain for real speech. The purpose of this study was to measure hearing aid gain using continuous discourse and to compare these values with gain measured with five different types of simpler stimuli. Design Hearing aid gain as a function of frequency was measured in a 2 cm3 coupler for 20 commercially available hearing aids. Circuitry included features such as linear peak clipping, compression limiting, 1-, 2-, and 3-channel full dynamic range compression, and adaptive compression. Input stimuli were a) 15 sec of continuous discourse, b) swept pure tones (SPTs), c) speech weighted composite noise (SWCN), d) simulated speech, e) speech weighted warble tones, and f) speech modulated noise. Input levels ranged from 50 to 80 dB SPL. Results In general, both SPTs and SWCN tended to underestimate the high-frequency gain for real speech. These discrepancies increased as a function of input intensity. On average, the SPT produced the greatest departure from the gain for real speech, producing differences for individual hearing aids as large as 10 to 14 dB. An analysis by circuit type revealed that discrepancies most likely occurred when a hearing aid was operating in a nonlinear mode. Of the five non-speech stimuli used, speech modulated noise and simulated speech seemed to provide the closest approximation to the gain measured with continuous discourse. Conclusions When a hearing aid is operating in a nonlinear mode, non-speech stimuli will tend to underestimate the gain for real speech, particularly in the high frequencies. Under some conditions, these discrepancies may impact clinical decisions during the hearing aid fitting process. Additional studies are needed to elucidate the factors that contribute to the gain discrepancies observed in this study and to explore the use of additional stimuli (including short speech samples), which may result in better predictions of the gain for speech.

In this paper, a classification-based adaptive compression method for computer screen image is presented. This method firstly divides the computer Screen Image into 16x16 non-overlapping blocks, and then every block is classified into three types: text/graphic, pictorial and hybrid blocks based on the characteristics of histogram distribution and the number of colors. For complex text/graphic block, - clustering method is used to reduce the number of colors to improve compression performance, finally the text/graphic block is coded by our proposed loss less coding method, hybrid block is coded by hybrid coding method and the pictorial block is coded by H.264-like intra coding method. Experiment results show that our proposed block classification method exactly distinguishes three block types, color clustering method can effectively reduce the number of colors for complex text/graphic. The compression performance and subjective image quality of our proposed method can outperform JPEG and JP2k.

Pu and Singaravelu presented Fine-Grain Mixing, an adaptive compression system which aimed to maximize CPU and network utilization simultaneously by splitting a network stream into a mixture of compressed and uncompressed blocks. Blocks were compressed opportunistically in a send buffer, they compressed as many blocks as they could without becoming a bottleneck. They successfully utilized all available CPU and network bandwidth even on high speed connections. In addition, they noted much greater throughput than previous adaptive compression systems. Here, we take a different view of FG-Mixing than was taken by Pu and Singaravelu and give another explanation for its high performance: that fine-grain mixing of compressed and uncompressed blocks enables off-the-shelf compressors to scale down their degree of compression linearly with decreasing CPU usage. Exploring the scaling behavior in-depth allows us to make a variety of improvements to fine-grain mixed compression: better compression ratios for a given level of CPU consumption, a wider range of data reduction and CPU cost options, and parallelized compression to take advantage of multi-core CPUs. We make full compatibility with the ubiquitous deflate decompress or (as used in many network protocols directly, or as the back-end of the gzip and Zip formats) a primary goal, rather than using a special, incompatible protocol as in the original implementation of FG-Mixing. Moreover, we show that the benefits of fine-grain mixing are retained by our compatible version.

Motion saliency detection has an important impact on further video processing tasks, such as video segmentation, object recognition and adaptive compression. Different to image saliency, in videos, moving regions (objects) catch human beings' attention much easier than static ones. Based on this observation, we propose a novel method of motion saliency detection, which makes use of the low-rank and sparse decomposition on video slices along X-T and Y-T planes to achieve the goal, i.e. separating foreground moving objects from backgrounds. In addition, we adopt the spatial information to preserve the completeness of the detected motion objects. In virtue of adaptive threshold selection and efficient noise elimination, the proposed approach is suitable for different video scenes, and robust to low resolution and noisy cases. The experiments demonstrate the performance of our method compared with the state-of-the-art.

Hyperspectral sensors generate useful information about climate and the earth surface in numerous contiguous narrow spectral bands, and are widely used in resource management, agriculture, environmental monitoring, etc. Compression of the hyperspectral data helps in long-term storage and transmission systems. Lossless compression is preferred for high-detail data, such as hyperspectral data. Due to high redundancy in neighboring spectral bands and the tendency to achieve a higher compression ratio, using adaptive coding methods for hyperspectral data seems suitable for this purpose. This paper introduces two new compression methods. One of these methods is adaptive and powerful for the compression of hyperspectral data, which is based on separating the bands with different specifications by the histogram and Binary Particle Swarm Optimization (BPSO) and compressing each one a different manner. The new proposed methods improve the compression ratio of the JPEG standards and save storage space the transmission. The proposed methods are applied on different test cases, and the results are evaluated and compared with some other compression methods, such as lossless JPEG and JPEG2000.

This paper presents a portable optimization for MPI communications, called PRAcTICaL-MPI (Portable Adaptive Compression Library- MPI). PRAcTICaL-MPI reduces the data volume exchanged among processes by using lossless compression and offers two main advantages. Firstly, it is independent of the MPI implementation and the application used. Secondly, it allows for turning the compression on and off and selecting the most appropriate compression algorithm at run-time, depending on the characteristics of each message and on network performance. We have validated PRAcTICaL-MPI in different MPI implementations and HPC clusters. The evaluation shows that compressing MPI messages with the best algorithm and only when it is worthwhile, we obtain a great reduction in the overall execution time for many of the scenarios considered.

Future space missions are based on a new generation of instruments. These missions find a serious constraint in the telemetry system, which cannot download to ground the large volume of data generated. Hence, data compression algorithms are often mandatory in space, despite the modest processing power usually available on-board. We present here a compact solution implemented in hardware for such missions. FAPEC is a lossless compressor which typically can outperform the CCSDS 121.0 recommendation on realistic data sets. With efficiencies higher than 90% of the Shannon limit in most cases - even in presence of noise or outliers - FAPEC has been successfully validated in its software version as a robust low-complexity alternative to the recommendation. This work describes the FAPEC implementation on an FPGA, targeting the space-qualified Actel RTAX family. We prove that FAPEC is hardwarefriendly and that it does not require external memory. We also assess the correct operation of the prototype for an initial throughput of 32 Mbits/s with very low power consumption (about 20 mW). Finally, we discuss further potential applications of FAPEC, and we set the basis for the improvements that will boost FAPEC performance beyond the 100 Mbit/s level.

International audience; This paper deals with the problem of universal lossless coding on a countable infinite alphabet. It focuses on some classes of sources defined by an envelope condition on the marginal distribution, namely exponentially decreasing envelope classes with exponent $\alpha$. The minimax redundancy of exponentially decreasing envelope classes is proved to be equivalent to $\frac{1}{4 \alpha \log e} \log^2 n$. Then a coding strategy is proposed, with a Bayes redundancy equivalent to the maximin redundancy. At last, an adaptive algorithm is provided, whose redundancy is equivalent to the minimax redundancy

In this paper, we use morphology to improve the performance of the object oriented coding scheme. The object oriented compression algorithm is popular in recent years. However, due to the high frequency components of the edges, the reconstructed image is always seriously disturbed by the object edges (i.e., the ring effect) when using the algorithm. In this paper, we use the morphological operation to separate an object into the interior and the contour sub-regions before applying the shape-adaptive DCT. Since the contour subregion is separated apart, the high frequency components in it will not disturb the interior region and the reconstructed image will have much better quality. Simulation results show that the proposed algorithm outperforms the conventional object oriented compression algorithm.

This paper presents a new adaptive compression method for ECG's. The method represents each R-R interval by an optimally time-warped polynomial. It achieves a high-quality approximation at less than 250 bits/s. The article shows that the corresponding rates for other transform based schemes (the DCT and the DLT) are always higher. Also, the new method is less sensitive to errors in QRS detection and it removes more (white) noise from the signal. The reconstruction errors are distributed more uniformly in the new scheme and the peak error is usually lower. The reconstruction method is also useful for adaptive filtering of noisy ECG signals.

In this PhD thesis we present the results we obtained in three linked fields: data compression for infinite alphabets; infinite-dimensinal Bayesian Statistics; multivariate multinomial mixture models. The first point deals with the problem of universal lossless coding on a countable infinite alphabet. It focuses on some classes of stationary memoryless sources defined by an envelope condition on the marginal distribution, namely exponentially decreasing envelope classes. An equivalent of the minimax redundancy of such classes is obtained. Then an approximately maximin prior distribution is provided and an adaptive algorithm is proposed, whose maximum redundancy is equivalent to the minimax redundancy. The next works deal with the asymptotic normality of a-posteriori distributions (Bernstein-von Mises theorems) in several nonparametric and semiparametric frameworks. First, in Gaussian linear regression models when the number of regressors increases with the sample size. Two kinds of Bernstein-von Mises Theorems are obtained in this framework: nonparametric theorems for the parameter itself, and semiparametric theorems for functionals of the parameter. We apply them to the Gaussian sequence model and to the regression of functions in Sobolev and Hölder regularity classes, in which we get the minimax convergence rates. Adaptivity is reached for the Bayesian estimators of functionals in our applications. We also get a nonparametric Bernstein-von Mises theorem for increasing-dimensional exponential models. In the last part of our work we consider the problem of estimating the number of components and the relevant variables in a multivariate multinomial mixture, in order to perform an unsupervised classification. Such models arise in particular when dealing with multilocus genotypic data. A new penalized maximum likelihood criterion is proposed, and a non-asymptotic oracle inequality is obtained. The criterion used in practice needs a calibration thanks to the slope heuristics, in an automatic data-driven procedure. Using simulated data, we found that this procedure improves the performances of the selection procedure with respect to classical criteria such as BIC and AIC. The procedures are implemented in a free-of-charge software., Dans cette thèse de doctorat, nous présentons les travaux que nous avons effectués dans trois directions reliées : la compression de données en alphabet infini, les statistiques bayésiennes en dimension infinie, et les mélanges de distributions discrètes multivariées. Dans le cadre de la compression de données sans perte, nous nous sommes intéressé à des classes de sources stationnaires sans mémoire sur un alphabet infini, définies par une condition d'enveloppe à décroissance exponentielle sur les distributions marginales. Un équivalent de la redondance minimax de ces classes a été obtenue. Un algorithme approximativement minimax ainsi que des a-priori approximativement les moins favorables, basés sur l'a-priori de Jeffreys en alphabet fini, ont en outre été proposés. Le deuxième type de travaux porte sur la normalité asymptotique des distributions a-posteriori (théorèmes de Bernstein-von Mises) dans différents cadres non-paramétriques et semi-paramétriques. Tout d'abord, dans un cadre de régression gaussienne lorsque le nombre de régresseurs augmente avec la taille de l'échantillon. Les théorèmes non-paramétriques portent sur les coefficients de régression, tandis que les théorèmes semi-paramétriques portent sur des fonctionnelles de la fonction de régression. Dans nos applications au modèle de suites gaussiennes et à la régression de fonctions appartenant à des classe de Sobolev ou de régularité hölderiennes, nous obtenons simultanément le théorème de Bernstein-von Mises et la vitesse d'estimation fréquentiste minimax. L'adaptativité est atteinte pour l'estimation de fonctionnelles dans ces applications. Par ailleurs nous présentons également un théorème de Bernstein-von Mises non-paramétrique pour des modèles exponentiels de dimension croissante. Enfin, le dernier volet de ce travail porte sur l'estimation du nombre de composantes et des variables pertinentes dans des modèles de mélange de lois multinomiales multivariées, dans une optique de classification non supervisée. Ce type de modèles est utilisé par exemple pour traiter des données génotypiques. Un critère du maximum de vraisemblance pénalisé est proposé, et une inégalité oracle non-asymptotique est obtenue. Le critère retenu en pratique comporte une calibration grâce à l'heuristique de pente. Ses performances sont meilleurs que celles des critères classiques BIC et AIC sur des données simulées. L'ensemble des procédures est implémenté dans un logiciel librement accessible.

Researchers have classically addressed the problem of universal compression using two approaches. The first approach has been to develop adaptive compression algorithms, where the system changes its behaviour during the compression to fit the encoding situation of the given data. The second approach has been to use the composition of multiple compression algorithms. Recently, however, a third approach has been adopted by researchers in order to develop compression systems: the application of computational intelligence paradigms. This has shown remarkable results in the data compression domain improving the decision making process and outperforming conventional systems of data compression. This paper reviews some of the previous attempts to address the universal compression problem within conventional and computational intelligence techniques.

In this article a scheme for image transmission over Wireless Sensor Networks (WSN) with an adaptive compression factor is introduced. The proposed control architecture affects the quality of the transmitted images according to: (a) the traffic load within the network and (b) the level of details contained in an image frame. Given an approximate transmission period, the adaptive compression mechanism applies Quad Tree Decomposition (QTD) with a varying decomposition compression factor based on a gradient adaptive approach. For the initialization of the proposed control scheme, the desired a priori maximum bound for the transmission time delay is being set, while a tradeoff among the quality of the decomposed image frame and the time needed for completing the transmission of the frame should be taken under consideration. Based on the proposed control mechanism, the quality of the slowly varying transmitted image frames is adaptively deviated based on the measured time delay in the transmission. The efficacy of the adaptive compression control scheme is validated through extended experimental results.

In high definition video decoders for the standard like H.264, bandwidth requirement is a critical design issue due to the overwhelming amount of memory data access. This paper proposes a new adaptive reference frame compression scheme to reduce external memory bandwidth consumption. An efficient variable length coding is proposed to compress each processing unit efficiently. Moreover, an adaptive compression mode decision unit is proposed to adaptively choose the best compression mode according to the image characteristic and bandwidth requirement. As a result, the proposed scheme achieves efficient bandwidth reduction with little image quality loss. The experimental results show that, at the same bandwidth reduction ratio the proposed algorithm achieves up-to 0.7 dB gain compared to the existing approaches.

Wireless sensor nodes typically have limited processing capabilities and are powered by batteries. The amount of energy expended in transmitting a single data bit would be several orders of magnitude higher when compared to the energy needed for a 32 bit computation. Thus, to maximize network lifetime, data transmissions should be minimized without losing vital information. In this paper, a novel adaptive compression scheme using nonlinear estimation theory is proposed for data aggregation. Satisfactory performance of the proposed compression scheme in the presence of noise, distortion, and quantization errors is demonstrated using Lyapunov approach. The proposed scheme is contrasted with existing compression schemes using various metrics applicable to wireless sensor networks such as energy efficiency, distortion and compression ratio. Simulation and hardware experimental results demonstrate almost 50% energy savings with very low distortion (less than 5%) and overhead. By iteratively applying the proposed scheme at the cluster head nodes, higher energy savings are obtained with a tolerable level of distortion.

Energy is an important consideration in wireless sensor networks. In the current compression evaluations, traditional indices are still used, while energy efficiency is probably neglected. Moreover, various evaluation biases significantly affect the final results. All these factors lead to a subjective evaluation. In this paper, a new criterion is proposed and a series of tunable compression algorithms are reevaluated. The results show that the new criterion makes the evaluation more objective. Additionally it indicates the situations when compression is unnecessary. A new adaptive compression arbitration system is proposed based on the evaluation results, which improves the performance of compression algorithms.

We present a digital system for adaptive data compression for low power wireless transmission of Electroencephalography (EEG) data. The proposed system acts as a base-band processor between the EEG analog-to-digital front-end and RF transceiver. It performs a real-time accuracy energy trade-off for multi-channel EEG signal transmission by controlling the volume of transmitted data. We propose a multi-core digital signal processor for on-chip processing of EEG signals, to detect signal information of each channel and perform real-time adaptive compression. Our analysis shows that the proposed approach can provide significant savings in transmitter power with minimal impact on the overall signal accuracy.

This paper presents an adaptive compression algorithm for mobile devices to reduce the transmission energy of images through wireless networks. Our algorithm selects different compression schemes based on the amount of details in the images and the available transmission bandwidths. At low bandwidths, significant amounts of energy can be saved because the reduction in transmission energy outweighs the additional processing energy. We implement this adaptive algorithm on an HP iPAQ hw6945 Personal Digital Assistant (PDA). The algorithm is invoked before transmitting images and is transparent to users. The average energy reduction is 20% with an average delay of 1.1 seconds.respectively.

International audience; We introduce a new patch-based multi-resolution analysis of semi-regular mesh surfaces. This analysis brings patch-specific wavelet decomposition, quantization and encoding to the mesh compression process. Our underlying mesh partitioning relies on surface roughness (based on frequency magnitude variations), in order to produce patches, representative of semantic attributes of the object. With current compression methods based on wavelet decomposition, some regions of the mesh still have wavelet coefficients with a non negligible magnitude or polar angle (the angle with the normal vector), reflecting the high frequencies of the model. For each non-smooth region, our adaptive compression chain provides the possibility to choose the best prediction filter adjusted to its specificity. Our hierarchical analysis is based on a semi-regular mesh decomposition produced by second-generation wavelets. Apart from progressive compression, other types of applications can benefit from this adaptive decomposition, like error resilient compression, view-dependent reconstruction, indexation or watermarking. Selective refinement examples are given to illustrate the concept of ROI (Region Of Interest) decoding, which few people has considered, whereas it is possible with JPEG2000 for images.

Recent studies have shown that spectral loudness summation depends on duration. Modifications of a current loudness model were investigated with respect to their ability to predict this effect. The original version of the model could not simulate the duration dependence of spectral loudness summation. To reconcile the model with the loudness data, three different mechanisms accentuating temporal onsets of sounds were tested: (i) adaptive compression, (ii) adaptive auditory filters and (iii) bandwidth-dependent integration. A comparison between simulations and experimental data indicated that, in principle, all mechanisms lead to increased spectral loudness summation for short noise bursts, but bandwidth-dependent integration may be the most realistic approach. Such a modified model also predicts the spectral loudness summation of repeated noise bursts as a function of repetition rate.

Suradnja i natjecanje je oblik međudjelovanja usluga u sustavima zasnovanim na uslugama. Kao i u ostalim oblicima međudjelovanja usluga, tako se i u suradnji i natjecanju usluga komunikacija usluga ostvaruje pomoću komunikacijskih poruka napisanih u meta jeziku XML. Opširnost meta jezika XML uzrokuje nezanemarivu potrošnju računalnih sredstava tijekom slanja, zaprimanja i obrade komunikacijskih poruka, te nezanemarivu potrošnju mrežnih sredstava tijekom mrežnog prijenosa komunikacijskih poruka. Zbog toga suradnja i natjecanje većeg broja usluga nema zadovoljavajuća radna svojstva. U doktorskoj disertaciji predložen je način poboljšavanja radnih svojstava suradnje i natjecanja usluga pomoću sredstvima uvjetovanog komprimiranja komunikacijskih poruka. Definiran i opisan je algoritam PrAKSiN, prilagodljivi algoritam za komprimiranje suradnje i natjecanja. Ovisno o vrijednostima sredstava sustava, algoritam mijenja komunikaciju usluga iz nekomprimirane u komprimiranu, i obratno. Algoritam je potpuno raspodijeljen i računalno nezahtjevan. Raspodjeljenost algoritma omogućava njegovu primjenu u sustavima zasnovanim na uslugama kao i općenitim mrežama ravnopravnih sudionika. Računalna nezahtjevnost algoritma omogućava rad algoritma bez utjecaja na međudjelovanje usluga. Radna svojstva algoritma eksperimentalno su vrednovana različitim scenarijima međudjelovanja usluga. Scenariji međudjelovanja usluga obuhvaćaju različite veličine komunikacijskih poruka te pobude računalnih i mrežnih sredstava. Eksperimentalno je također utvrđen utjecaj pojedinih sredstava sustava na rad algoritma. Coopetition is an approach for uncoupled service composition where services cooperate and compete in order to execute specified tasks. Like other approaches to service composition, coopetition is based on meta language XML. Verbosity and textual nature of XML causes computational performance degradation when coopetition messages are sent, received and processed. Also, network performance degradation is experienced when coopetition messages are sent through the network. Thus, coopetition of larger number of services doesn't have good performance results. This doctoral thesis proposes a solution for increasing performance of coopetition. The thesis defines and describes a resource-aware adaptive algorithm for compression of network communication. the algorithm changes communication between services from non-compressed to compressed, and viceversa, as a response to change of resources' values. The algorithm is fully distributed and computationally undemanding. Thus, the algorithm is suitable for use in service-oriented systems, as well as peer-to-peer networks. Also, execution of the algorithm causes no significant computational performance degradation for executing services. Performance of the algoritm is experimentally evaluated in different service composition scenarios. Scenarios include different sizes of coopetition messages and controlled stimulus of computational and network resources. Furthermore, the impact of certain resources on the algorithm is also experimentally evaluated.

This paper describes universal lossless coding strategies for compressing sources on countably infinite alphabets. Classes of memoryless sources defined by an envelope condition on the marginal distribution provide benchmarks for coding techniques originating from the theory of universal coding over finite alphabets. We prove general upper-bounds on minimax regret and lower-bounds on minimax redundancy for such source classes. The general upper bounds emphasize the role of the Normalized Maximum Likelihood codes with respect to minimax regret in the infinite alphabet context. Lower bounds are derived by tailoring sharp bounds on the redundancy of Krichevsky-Trofimov coders for sources over finite alphabets. Up to logarithmic (resp. constant) factors the bounds are matching for source classes defined by algebraically declining (resp. exponentially vanishing) envelopes. Effective and (almost) adaptive coding techniques are described for the collection of source classes defined by algebraically vanishing envelopes. Those results extend ourknowledge concerning universal coding to contexts where the key tools from parametric inference, 33 pages

Publisher Summary Compression algorithms are normally used to reduce the size of a file without removing information. This can increase their entropy and make the files appear more random because all of the possible bytes become more common. The compression algorithms can also be useful when they’re used to produce mimicry by running the compression functions in reverse. Compression algorithms generally produce data that look more random. Compressing generic information is also just a matter of finding the right formula that describes the data. It is often quite easy to find a good formula that works moderately well, but it can be very difficult to identify a very good formula that compresses the data very well. Compressing data is of great interest to anyone who wants to hide data for four reasons: Less data is easier to handle; compressed data is usually whiter; reversing compression can mimic data; and compression algorithms identify noise. A number of techniques for compressing data are used today. The field has expanded wildly over last several years because of the great economic value of such algorithms. Some of the more popular techniques are probability methods, dictionary methods, run-length encoding, wave methods, fractal methods, and adaptive compression schemes. All of these compression schemes are useful in particular domains. There is no universal algorithm that comes with a universal set of functions that adapts well to any data.

A compression technique which preserves edges in compressed pictures is developed. The proposed compression algorithm adapts itself to the local nature of the image. Smooth regions are represented by their averages and edges are preserved using quad trees. Textured regions are encoded using BTC (block truncation coding) and a modification of BTC using look-up tables. A threshold using a range which is the difference between the maximum and the minimum grey levels in a 4*4 pixel quadrant is used. At the recommended value of the threshold (equal to 18), the quality of the compressed texture regions is very high, the same as that of AMBTC (absolute moment block truncation coding), but the edge preservation quality is far superior to that of AMBTC. Compression levels below 0.5-0.8 b/pixel may be achieved. >

Compression of biomedical signals is of great interest in telemedicine applications for a fast and reliable online data transfer. In this paper, an adaptive method for compression of respiratory and swallowing sounds is proposed. The method is based on the transform coding of the signal and adaptively assigns two different bit allocations for encoding the stationary and non-stationary parts of the signal. The method was applied to data of 12 subjects and its performance was compared with other methods of bit allocation such as constant bit allocation. The results show that the proposed adaptive bit allocation method improves the SNR of the signal by 3 dB compared to that of constant bit allocation method. Furthermore, the possibility of removing the requirement of updating the bit allocation method for each subject was investigated. The results confirm that with a few training data, the proposed method can be used in a fully automated mode without the need to adjust the adaptive bit allocation for every signal separately; hence, it is faster for online coding applications.