Compression Algorithms

An antidictionary code is a lossless compression algorithm using an antidictionary which is a set of minimal words that do not occur as substrings in an input string. The code was proposed by Crochemore et al. in 2000, and its asymptotic optimality has been proved with respect to only a specific information source, called balanced binary source that is a binary Markov source in which a state transition occurs with probability 1/2 or 1. In this paper, we prove the optimality of both static and dynamic antidictionary codes with respect to a stationary ergodic Markov source on finite alphabet such that a state transition occurs with probability p (0 < p ≤ 1).

This paper reports a comparative study of lossless compression algorithms for MODIS data. MODIS, The Moderate Resolution Imaging Spectroradiometer, is a 36 band Visible and IR multispectral imager aboard the Terra and Aqua satellites, having spatial resolution ranging from 0.250 to 1 kilometer and spectral resolution ranging from 0.405 -0.420 to 4.482-4.549 microns. MODIS data rates are 10.6 Mbps (peak daytime); and 6.1 Mbps (orbital average). Faced with such an enormous volume of data on a current generation imager, this study provides a comparison of current compression algorithms as a baseline for future work. The Hierarchical Data Format (HDF) is standard format selected for data archiving and distribution within the Earth Observing System Data and Information System (EOSDIS). Currently this system handles over one terabyte of data daily, and this volume continues to increase over time. With growing satellite Earth science multispectral imager volume data compression, it becomes increasingly important to evaluate which compression algorithms are most appropriate for data management in transmission and archiving. This comparative compression study uses a wide range standard implementations of the leading lossless compression algorithms. Examples include image compression algorithms such as PNG and JPEG2000, and widely-used file compression formats such as BZIP2 and 7z. This study includes a comparison with the Consultative Committee for Space Data Systems (CCSDS) most recent recommended compression standard. by a significant margin. Research sponsored by NOAA/NESDIS under Roger Heymann (OSD), Tim Schmit (STAR) Compression Group.

Generally, textual data sets are represented by using different models. But, sometimes it does not capture the text arrangement as it is. Vector space model is also recognized as the bag of word model. To represent textual document using vector space model is based on most text mining methods. This model cannot maintain the text structure as it is. Compression distances are the most widely used technique for the purpose of knowledge discovery and mining of data also to improve the performance metric. Compression distance technique is for measuring the similarity between two documents. By applying Distortion technique which is for purpose of destroys the text structure. A distortion technique removes nonrelevant words as well as maintains the text structure. The NCD i.e. normalized compression distance technique gives the structural similarity between text documents. By applying a detailed word removal method clustering accuracy can be enhanced. In proposed system, JPG encoding is th...

The original model (on the left, courtesy of Cyberware) containing 134,074 triangles requires 4,100,000 bytes, when stored as a WRL file. A dense partitioning of its surface into deformed triangles, called "triangloids" (second), was produced by SwingWrap per. The corresponding retiled mesh (third) was generated by flattening the triangloids. Its L 2 distortion is about 0.007% of the bounding box diagonal and its 13642 triangles were encoded with 3.5 bits per triangle using Edgebreaker's connectivity compression combined with a novel geometry predictor. The resulting total of 6042 bytes represents a 678-to-1 compression ratio. A coarser partitioning (fourth) decomposes the original surface into 1505 triangloids. The distortion of the corresponding retiled mesh (last) is about 0.15%, and the encoded model requires 980 bytes: A 4000-to-1 compression. Colors are associated to the EdgeBreaker's triangle labeling.

Nomadic computing often refers to people using computer support working anywhere, anytime and not necessarily attached to a specific location or time of the day. Mobile computing and wireless ad-hoc networks are important elements in this kind of scenarios. Nowadays it is common to see architects, engineers, geologists and/or designers working on the field and sharing ideas on a collaborative media using sketches and freehand writing. On this scenario, data overflow among the peer applications often happens, since the sketches have to be distributed among all participants in real time. This paper presents a work on characterization and compression algorithms for sketches, with and without loss of information, to be used on mobile devices, in order to reduce the data traffic. We focused our study on three parameters of the algorithms: time required characterizing and compressing the sketch, size of the resulting information and human perceived of lost information. Our results present algorithms with a compression ratio lower than 1% the size of the original image without information loosing under human perception. Nomadic computing, mobile computing, peer-to-peer collaborative systems I.

We present a novel approach that integrates occlusion culling within the view-dependent rendering framework. View-dependent rendering provides the ability to change level of detail over the surface seamlessly and smoothly in real-time. The exclusive use of viewparameters to perform level-of-detail selection causes even occluded regions to be rendered in high level of detail. To overcome this serious drawback we have integrated occlusion culling into the level selection mechanism. Because computing exact visibility is expensive and it is currently not possible to perform this computation in real time, we use a visibility estimation technique instead. Our approach reduces dramatically the resolution at occluded regions.

Απαγορεύεται η αντιγραφή, αποθήκευση και διανομή της παρούσας εργασίας, εξ ολοκλήρου ή τμήματος αυτής, για εμπορικό σκοπό. Επιτρέπεται η ανατύπωση, αποθήκευση και διανομή για σκοπό μη κερδοσκοπικό, εκπαιδευτικής ή ερευνητικής φύσης, υπό την προϋπόθεση να αναφέρεται η πηγή προέλευσης και να διατηρείται το παρόν μήνυμα. Ευχαριστίες. Θα ήθελα καταρχήν να ευχαριστήσω τους γονείς μου για την καθοδήγηση και την ηθική συμπαράσταση που μου προσέφεραν όλα αυτά τα χρόνια.

Digital high-definition TV (HDTV) signals are generally compressed to reduce transmission bandwidth requirements. This paper presents a compression algorithm for the bit rate reduction of an HDTV image using the wavelet transform. The major problems related to the transmission of a compressed HDTV signal are analyzed. "kansmission is examined both on a noisy channel and an asynchronous transfer mode (ATM) network. The effects of channel noise on the reconstructed image are determined, and a solution to mitigate the degradation of the image quality is presented. Lastly, a model for the output bit rate of the HDTV coder is derived and used to simulate the transmission of an ATM multiplexer so that the network's main performance parameters can be determined.

Software defined radios (S D R) are highly configurable hardware platforms that provide the technology for realizing the rapidly expanding third (and future) generation digital wireless communication infrastructure. Many sophisticated signal processing tasks are performed in a SDR, including advanced compression algorithms, power control, channel estimation, equalization, forward error control and protocol management. While there is a plethora of silicon alternatives available for implementing the various functions in a S D R , field programmable gate arrays (FPGAs) are a n attractive option for m a n y of these tasks for reasons of performance, power consumption and configurability. Amongst the more complex tasks performed in a high data rate wireless system is synchronization. This paper is about carrier and timing synchronization in SDRs using FPGA based signal processors. W e describe and examine a QPSK Costas loop for performing coherent demodulation, and report o n the implications of a n FPGA mechanization. Symbol timing recovery is addressed using a differential matched filter control system. A tutorial style approach is adopted to describe the operation of the timing recovery loop and considerations for FPGA implementation are outlined. 195

In automatic fractal image compression, most of the encoding time is spent on finding the best match between each range block and the domain blocks. We propose the use of the local complexity of the image domain blocks to reduce the number of pairs to be tested on this search. Indexing the contracted domain pools according to their local fractal dimension and using multiple contraction factors we can confine all potential matches to a relatively small number of possibilities. By selecting an appropriate criterion for close match, the compression time can be shortened without image quality degradation. The performance of the proposed algorithm, evaluated by means of fidelity versus encoding time and compression ratio, is compared with five approaches. Future developments can also improve the compression ratio by using fractal dimension in the definition of the size of range blocks.

A compression algorithm takes a finite structure of a class K as input and produces a finite structure of a different class K' as output. Given a property P on the class K defined in a logic L, we study the definability of property P on the class K'. We consider two compression schemas on unary ordered structures (words), compression by runlength encoding and the classical Lempel-Ziv. First-order properties of strings are first-order on runlength compressed strings, but this fails for images, i.e. 2dimensional strings. We present simple first-order properties of strings which are not first-order definable on strings compressed with the Lempel-Ziv compression schema. We show that all properties of strings that are firstorder definable on strings are definable on Lempel-Ziv compressed strings in FO(TC), the extension of first-order logic with the transitive closure operator. We define a subclass F of the first-order properties of strings such that if L is defined by a property in F, it is also first-order definable on the Lempel-Ziv compressed strings. Monadic second-order properties of strings are dyadic second order definable on Lempel-Ziv compressed strings.

Capacitance measurement accuracy is affected by phase conformity between signal and reference. This work describes phase detection scheme which is necessary for phase synchronization in tomography application. Core processing for calculating phase and amplitude of the detected signal was built on FPGA (Field-Programmable Gate-Array) platform. Phase shift demodulation algorithm employs IP core provided by Xilinx FPGA. Direct digital synthesizer (DDS), multiplier, accumulator, and CORDIC (coordinate rotation digital computer) modules were used as excitation-reference signal generator, signal multiplication, accumulation, and conversion to polar coordinate in order to conduct trigonometric operation respectively. Hardware design was emulated on MATLAB-Xilinx System Generator to observe its performance. Phase detection range 0-114.58 o and mean absolute error 0.58 o have been achieved. Data processing rate solely at digital signal stage was approximately 100data/s suitable for 32-channel electrical capacitance volume tomography (ECVT) system.

The multimedia signal processing community has recently identified the need to design depth map compression algorithms which preserve depth discontinuities in order to improve the rendering quality of virtual views for Free Viewpoint Video (FVV) services. This paper adopts contour detection with surround suppression on the color video to approximate the foreground edges present in the depth image. Displacement estimation and compensation is then used to improve this prediction and reduce the amount of side information required by the decoder. Simulation results indicate that the proposed method manages to accurately predict around 64% of the blocks. Moreover, the proposed scheme achieves a Peak Signal-to-Noise Ratio (PSNR) gain of around 4.9-6.6 dB relative to the JPEG standard and manages to outperform other state of the art depth map compression algorithms found in literature.

In the past several years, organizations have been changing their storage methods as the volume of data they managed has increased. The cloud computing paradigm offers new ways of storing data based on scalability and on good conditions of reliability and accessibility. This paper proposes the design of HPS3, a service that uses compression and concurrency techniques in order to reduce storage costs and data processing times in public storage providers. Different strategies for compressing and uploading data depending on differential characteristics of the datasets are also explained. The evaluation of HPS3 shows, in comparison with the default use of the cloud storage provider (Amazon S3), an average reduction of 61.6% in data transfer volume, 85.5% in upload time and 73.2% in incurred costs. Compared to Dropbox, HPS3 shows an average improvement of 27.4% in data transfer volume and 93.6% in upload time.

This paper presents a compensation technique for the amplitude and phase imbalance between the I and the Q components of a quadrature demodulator. The proposed scheme allows the implementation of a fully integrated self-aligned tuner for digital modulation systems such as QAM demodulation and Zero-IF Receivers. It is based on a mixed signal analog/digital integrated circuit approach to automatically correct amplitude and phase imbalances resulting from mismatch of integrated circuit components. The approach presented uses the original digital signal information to create both a control signal for the amplitude and phase correction. It uses digital circuitry to compensate for mismatch errors, by means of an analog feedback mechanism, and perform high accuracy phase and amplitude quadrature correction.

This paper presents a compensation technique for the amplitude and phase imbalance between the I and the Q components of a quadrature demodulator. The proposed scheme allows the implementation of a fully integrated self-aligned tuner for digital modulation systems such as QAM demodulation and Zero-IF Receivers. It is based on a mixed signal analog/digital integrated circuit approach to automatically correct amplitude and phase imbalances resulting from mismatch of integrated circuit components. The approach presented uses the original digital signal information to create both a control signal for the amplitude and phase correction. It uses digital circuitry to compensate for mismatch errors, by means of an analog feedback mechanism, and perform high accuracy phase and amplitude quadrature correction.

This paper presents a compensation technique for the amplitude and phase imbalance between the I and the Q components of a quadrature demodulator. The proposed scheme allows the implementation of a fully integrated self-aligned tuner for digital modulation systems such as QAM demodulation and Zero-IF Receivers. It is based on a mixed signal analog/digital integrated circuit approach to automatically correct amplitude and phase imbalances resulting from mismatch of integrated circuit components. The approach presented uses the original digital signal information to create both a control signal for the amplitude and phase correction. It uses digital circuitry to compensate for mismatch errors, by means of an analog feedback mechanism, and perform high accuracy phase and amplitude quadrature correction.

A new split-approach based method for approximating a boundary curve by a polygon is proposed in this paper. The proposed method recursively splits boundary curve into smaller segment with the help of small eigenvalue of covariance matrix of the boundary curve. Set of boundary points at which boundary curve is split into smaller segments are considered as vertices of the approximating polygon. Experimental results show that the proposed method is robust and efficient.

Since its discovery in 1881, Benford's law has been verified in several application fields related to economics, physics and even in number theory. In particular, a random dataset obtained from physical measurements seems to be the best empirical example of its validity. Following this reasoning, we present a novel and fast source coding algorithm for wireless sensor measurements. In more details, the Benford's probability density distribution is exploited for Huffmann coding of measured values, considering the Benford probability density of the first digit. This results may be fast obtained assuming the Benford's law validity, without directly computing the a priori probabilities of the measured digits. Performance evaluation is provided by system simulations, comparing the obtained results with the computed a posteriori source entropy.

With the increasing deployment of wireless mesh net-works, the usage of a number of services in such networks increased in the last few years. Real time services like VoIP (Voice over IP) are included, despite of their particular be-havior. Wireless mesh networks has been ...

Header compression techniques can improve the speech quality of VoIP applications. However, its deployment over wireless mesh environments may not offer much improvement, since the wireless and multi-hop characteristics of such a network impose a high packet loss rate. In this work, we propose a new approach for header compression, using packet aggregation to cooperate with the header compression scheme.

Packets originated from an information source in the network can be highly correlated. These packets are often routed through different paths, and compressing them requires to process them individually. Traditional universal compression solutions would not perform well over a single packet because of the limited data available for learning the unknown source parameters. In this paper, we define a notion of correlation between information sources and characterize the average redundancy in universal compression with side information from a correlated source. We define the side information gain as the ratio between the average maximin redundancy of universal compression without side information to that with side information. We derive a lower bound on the side information gain, where we show that the presence of side information provides at least 50% traffic reduction over traditional universal compression when applied to network packet data confirming previous empirical studies.

Facultat d'Informàtica de Barcelona Computer Graphics and Virtual Reality Master in Investigation and Research in Informatics Compressed Representation of 3D Models for 3D Printing by J. Hugo BALLESTEROS In this work we tackle the issue of large file sizes in disk of volume data representations of 3D models when used for 3D printing purposes. In the pipeline of 3D printing software, the representation, in disk, of the model go from the initial representation, which normally is a polygonal mesh, to volume data and later into instructions for the printer. Each of these stages has a cost in terms of size of disk and memory usage, and with large models with high resolutions, the size of the representations can become unfeasible to save and the memory usage may be too large. For that reason, a better encoding of the representation of volume data is necessary, while maintaining the efficiency of the processing of slices and not impacting or reducing the memory usage of the creating of such encoding. We describe several methods with different configurations utilised to reduce the size of the representation in disk, such as Run Length encoding and Modulus Encoding, while also compressing them with known lossless compression algorithms such as LZMA, used by 7Zip, and DEFLATE, used by GZip. We utilise several models to show that our results greatly reduce the size of the representation without impacting any other stage of the pipeline, and therefore providing a better alternative to current market techniques. v Acknowledgements I would like to firstly thank my supervisor, Antonio Chica, of the Visualization, Virtual Reality, and Graphical Interaction group, for his help, dedication and patience in times where social interaction needed to be different. He was always available to help when required, and, no matter the question, he kept his advice objective, providing any clarification necessary. Without his dedication and expertise, this work could not have been completed. To my friends in Barcelona, a huge thank you for the support in making me feel welcome in a new place, for not only including me but making me a part of their environment while studying, laughing, and sharing experiences together. Finally, I would also like to express my profound gratitude to my parents, for allowing me to study where I wanted, for pushing me to learn, have no boundaries and advising me when tough decisions come my way. It is thanks to them that I arrived where I am today.

Facultat d'Informàtica de Barcelona Computer Graphics and Virtual Reality Master in Investigation and Research in Informatics Compressed Representation of 3D Models for 3D Printing by J. Hugo BALLESTEROS In this work we tackle the issue of large file sizes in disk of volume data representations of 3D models when used for 3D printing purposes. In the pipeline of 3D printing software, the representation, in disk, of the model go from the initial representation, which normally is a polygonal mesh, to volume data and later into instructions for the printer. Each of these stages has a cost in terms of size of disk and memory usage, and with large models with high resolutions, the size of the representations can become unfeasible to save and the memory usage may be too large. For that reason, a better encoding of the representation of volume data is necessary, while maintaining the efficiency of the processing of slices and not impacting or reducing the memory usage of the creating of such encoding. We describe several methods with different configurations utilised to reduce the size of the representation in disk, such as Run Length encoding and Modulus Encoding, while also compressing them with known lossless compression algorithms such as LZMA, used by 7Zip, and DEFLATE, used by GZip. We utilise several models to show that our results greatly reduce the size of the representation without impacting any other stage of the pipeline, and therefore providing a better alternative to current market techniques. v Acknowledgements I would like to firstly thank my supervisor, Antonio Chica, of the Visualization, Virtual Reality, and Graphical Interaction group, for his help, dedication and patience in times where social interaction needed to be different. He was always available to help when required, and, no matter the question, he kept his advice objective, providing any clarification necessary. Without his dedication and expertise, this work could not have been completed. To my friends in Barcelona, a huge thank you for the support in making me feel welcome in a new place, for not only including me but making me a part of their environment while studying, laughing, and sharing experiences together. Finally, I would also like to express my profound gratitude to my parents, for allowing me to study where I wanted, for pushing me to learn, have no boundaries and advising me when tough decisions come my way. It is thanks to them that I arrived where I am today.

Wireless sensor networks are typically characterized by large network size and sensor nodes with low energy capacity and a limited bandwidth for data transmission. Over-activity of the sensor nodes will therefore cause many issues to the network, such as an increase in the network depletion rate and poor data transmission. Data prediction methods that exploit the interrelationship between sensor nodes can be used to reduce data traffic across wireless networks. Several related work in data prediction do not consider the time-series distribution of the sensing data. However, exploiting sequential features of the historical observations can improve prediction accuracy, and increase the number of sensing data predicted per sequence. This work propose a sequence to sequence data prediction model, using a one dimensional layer convolutional neural network to extract spatial features from the pre-processed sensing data, and an encoder-decoder model to predict the next two outputs in the sequence by exploiting the temporal distribution of the data. The aforementioned approach has the capacity of generating more accurate information, which can reduce network traffic and energy expenditure in WSNs. Furthermore, the experimental results reveals that, based on a suitable choice of nodes, our proposed model perform accurate predictions, with reduced root mean squared error as compared to related work. We also propose an approach to regulate and control the data traffic toward the base station. INDEX TERMS Wireless sensor network, data prediction, lifetime extension, deep learning.

In this paper, a new image compression method is presented using the Distance Transform on Curved Space (DTOCS) and derivative information in nding positions for control points. In previous work it has been shown that the control points are not in exactly optimal positions. This paper presents theoretical considerations according to which the new method enhances the decompressed image quality particularly in the areas of rapid changes. The obtained results shown verify the correctness of the theoretical considerations. The reconstructed image quality is clearly better measured by error criteria. Also visually the di erence is signi cant.

Mosquito noise is a time dependent video compression impairment in which the high frequency spatial detail in video images having crisp edges is aliased intermittently. A new synthetic test pattern of moving spirals or circles is described which generates mosquito noise (MN) under Motion Pictures Expert Group (MPEG) compression. The spiral pattern is one of several NIST-developed patterns designed to stress specific features of compression based on motion estimation and quantization. The "Spirals" pattern has several spirals or circles superimposed on a uniform background. The frames are filtered to avoid interline flicker which may be confounded with MN. Motion of the spirals and changing luminance of the background can be included to reduce the correlation between successive frames. Unexpectedly, even a static pattern of spirals can induce mosquito noise due to the stochastic character of the encoder. We consider metrics which are specific to the impairment being measured. For mosquito noise, we examine two separable detectors: each consists of a temporal (frame-to-frame) computation applied to the output of a spatial impairment detector which is applied to each frame. The two spatial detectors are: FLATS, which detects level 8x8-pixel image blocks; and the root-mean-square (RMS) applied to the image differences between original and compressed frames. The test patterns are encoded at low bit rates. We examine the measured mosquito noise as a function of the Group-of-Pictures (GOP) pattern in the MPEG-2 encoding and find the GOP structure defines the periodicities of the MN.

Digital high-definition TV (HDTV) signals are generally compressed to reduce transmission bandwidth requirements. This paper presents a compression algorithm for the bit rate reduction of an HDTV image using the wavelet transform. The major problems related to the transmission of a compressed HDTV signal are analyzed. "kansmission is examined both on a noisy channel and an asynchronous transfer mode (ATM) network. The effects of channel noise on the reconstructed image are determined, and a solution to mitigate the degradation of the image quality is presented. Lastly, a model for the output bit rate of the HDTV coder is derived and used to simulate the transmission of an ATM multiplexer so that the network's main performance parameters can be determined.

In this paper we present a system for the acquisition of color and shape of free form surfaces. We place the emphasis on the methods for the acquisition and reconstruction of surface color. We discuss algorithms to eliminate illumination effects such as shading and specular highlights. We will further describe a procedure to merge the color information of multiple range images, utilising redundancy in overlapping regions, thus enhancing the appearance of the object.

Great advancements in commodity graphics hardware have favored GPU-based volume rendering as the main adopted solution for interactive exploration of rectilinear scalar volumes on commodity platforms. Nevertheless, long data transfer times and GPU memory size limitations are often the main limiting factors, especially for massive, time-varying or multi-volume visualization, or for networked visualization on the emerging mobile devices. To address this issue, a variety of level-of-detail data representations and compression techniques have been introduced. In order to improve capabilities and performance over the entire storage, distribution and rendering pipeline, the encoding/decoding process is typically highly asymmetric, and systems should ideally compress at data production time and decompress on demand at rendering time. Compression and level-of-detail pre-computation does not have to adhere to real-time constraints and can be performed off-line for high quality results. In co...

Figure 1: Frames from real-time rendering of animated supernova data set (432 3 × 60, float-18GB), compressed using sparse coding of voxel blocks [GIM12] (block size 6, sparsity 4) at 0.10 bps, PSNR 46.57 dB. The full compressed dataset (184MB) fits in GPU memory and is available for low-latency local and transient decoding during rendering. Data made available by Dr.

This paper deals with the accuracy of algorithms for the detection of ventricular late potentials (VLP) in an electrocardiographic signal (ECG), being associated with malignant arrhytmias and possible cardiac death. VLP detection is strongly influenced by signal noise. The objective of this paper is to define a denoising algorithm improving the VLPs detection. The method uses wavelet denoising, subband coding, unfortunately introducing heavy linear distortions. Therefore, an equalisation filter has been properly designed, in order to cancel the distortions. The algorithm has been implemented and successfully verified using MATLAB. Then, it has been implemented on Altera's FPGA and then verified on the evaluation board DE1-SoC. On-board processed results and theoretical results are consistent, validating the algorithm. The results show that the algorithm capability to be implemented as programmable hardware. It also could be used for upgrading ECG devices reliability in the field of heart diseases prevention.

We study the problem of scalable monitoring of operational 3G wireless networks. Threshold-based performance monitoring in large 3G networks is very challenging for two main factors: large network scale and dynamics in both time and spatial domains. A fine-grained threshold setting (e.g., perlocation hourly) incurs prohibitively high management complexity, while a single static threshold fails to capture the network dynamics, thus resulting in unacceptably poor alarm quality (up to 70% false/miss alarm rates). In this paper, we propose a scalable monitoring solution, called threshold-compression that can characterize the location-and time-specific threshold trend of each individual network element (NE) with minimal threshold setting. The main insight is to identify groups of NEs with similar threshold behaviors across location and time dimensions, forming spatial-temporal clusters to reduce the number of thresholds while maintaining acceptable alarm accuracy in a large-scale 3G network. Our evaluations based on the operational experience on a commercial 3G network have demonstrated the effectiveness of the proposed solution. We are able to reduce the threshold setting up to 90% with less than 10% false/miss alarms.

This article describes lossless compression algorithms for multisets of sequences, taking advantage of the multiset's unordered structure. Multisets are a generalisation of sets where members are allowed to occur multiple times. A multiset can be encoded naïvely by simply storing its elements in some sequential order, but then information is wasted on the ordering. We propose a technique that transforms the multiset into an orderinvariant tree representation, and derive an arithmetic code that optimally compresses the tree. Our method achieves compression even if the sequences in the multiset are individually incompressible (such as cryptographic hash sums). The algorithm is demonstrated practically by compressing collections of SHA-1 hash sums, and multisets of arbitrary, individually encodable objects.

In this dissertation, our goal is to explore lossless DNA compression algorithms, experiment with predictive probabilistic models for DNA sequences, and perform a comprehensive analysis and comparison among different schemes. We omit lossy compressors as well as reference-based and pre-trained models to ensure that the algorithms are general and can also compress non-DNA files. The methods in this dissertation are based on techniques that cleanly separate the compression task into probabilistic models (predictors) and arithmetic coders (compressors). Existing methods based on arithmetic coding have become state-of-the-art in lossless compression in many domains. As a novel contribution, we implement probabilistic models ranging from traditional machine learning models, such as Random Forests, to deep neural network models which are not conventionally used with arithmetic coding. For each model, we supply a concise and robust mathematical formalisation. As background research, we ran...

Green computing is the process of reducing the power consumed by a computer and thereby reducing carbon emissions. The total power consumed by the computer excluding the monitor at its fully computative load is equal to the sum of the power consumed by the GPU in its idle state and the CPU at its full state. Recently, there have been tremendous interests in the acceleration of general computing applications using a Graphics Processing Unit (GPU). Now the GPU provides the computing powers not only for fast processing of graphics applications, but also for general computationally complex data intensive applications. On the other hand, power and energy consumptions are also becoming important design criteria. Consequently, software designs have to consider the power/energy consumptions together with performance when they are developing software.The GPU therefore does the 100% of the CPU work in its idle state .Hence the power consumed by the GPU will be low. Also when the GPU is doing all the work the CPU will remain at a load less than its idle load. Hence the power consumed will be equal to the power consumed by the CPU at a load less than its idle load plus the power consumed by a GPU.

In this paper, the seminal method proposed by Abraham Lempel and Jacob Ziv, aimed at the complexity analysis of sequences of symbols, was modified to compare similarities between two sequences. This modification allowed the creation of a new criterion which can replace likelihood in some pattern recognition applications. Moreover, to allow for analysis and comparison of multivariate continuously valued patterns, we also present a simple adaptation of the Lempel-Ziv's method to time-sampled signals. To illustrate the usefulness of these proposed tools, two sets of experimental results are presented, namely: one on speaker identity verification (biometrics) and another on healthcare signal detection. Both experiments yield promising performances. Moreover, as compared to a conventional pattern recognition method, the new approach provided better performances in terms of Equal Error Ratio in speaker verification experiments.

Introduction White [1967] proposed compressing text by ''replacing [a] repeated string by a reference to [an] earlier occurrence''. Ziv and Lempel [1977, 1978] implemented this idea by cleverly representing strings that occur in a relatively small sliding window. We extend the basic idea to represent long common strings that may appear far apart in the input text. On typical English text, our method provides little compression; there are few long common strings to be exploited. Some files, though, do contain repeated long strings. Baker [1995] documented significant repetition in the code of large software systems. In a mathematical subroutine library, we found many blocks of code repeated across functions of type float, double, complex, and double complex; our method combined with a standard compression algorithm reduced the file to half the size given by the standard algorithm alone. Corpora of real documents, such as correspondence, news articles, or netnews often contain long duplications due to quoting or republication, or even plagiarism. We begin by illustrating the opportunity for compressing very long strings. We then survey Karp and Rabin's [1987] algorithm for string matching, and apply it to data compression. Experiments show the efficacy of the new method on some classes of input, and analysis shows that it is efficient in run time. 2. The Opportunity Data compression schemes usually employ a sliding window, typically a few kilobytes long. Repetitions of strings are discovered and compressed only if they both appear in the window. This approach is efficient in bits to encode a position, in the space to store the window, and in the time required to search the window. Furthermore, the resulting dynamic codes reflect the locality intrinsic in many documents. Sliding windows do, however, present a significant drawback: they do not find repeated strings that occur far apart in the input text. An artificial experiment illustrates this point. We take two text files, the Constitution of the United States and the King James Bible, and compress both the original text and the text concatenated with itself. We use the GNU gzip implementation of LZ77 as a typical compression algorithm.

This paper describes a new algorithm for electrocardiogram (ECG) compression. The main goal of the algorithm is to reduce the bit rate while keeping the reconstructed signal distortion at a clinically acceptable level. It is based on the compression of the linearly predicted residuals of the wavelet coefficients of the signal. In this algorithm, the input signal is divided into blocks and each block goes through a discrete wavelet transform; then the resulting wavelet coefficients are linearly predicted. In this way, a set of uncorrelated transform domain signals is obtained. These signals are compressed using various coding methods, including modified run-length and Huffman coding techniques. The error corresponding to the difference between the wavelet coefficients and the predicted coefficients is minimized in order to get the best predictor. The method is assessed through the use of percent root-mean square difference (PRD) and visual inspection measures. By this compression method, small PRD and high compression ratio with low implementation complexity are achieved. Finally, we have compared the performance of the ECG compression algorithm on data from the MIT-BIH database.

To deal with the large data volume of hyperspectral sensors the Canadian Space Agency (CSA) developed two near lossless compression algorithms. This paper analyzes compression errors introduced by the two compression algorithms and assesses their near lossless feature. Experimental results show that errors introduced by the two compression algorithms are smaller than the intrinsic noise of the original data caused by the instrument noise and other noise sources such as calibration and atmospheric correction errors. This level of compression errors is expected to have small to negligible impact on remote sensing applications compared to the intrinsic noise of the original data.

We coltsider the problem of finding an optimal path leading from the root of a tree to any of its leaees. 7he tree is known to be tmifonn, bh~ary, and of height IV, and each branch independently may haee a cost of i or 0 with probability p and l-p, reslx'ctit'ely. We show that for p < 1 the uniform cost algorithm can find a cheapest path in linear expected time. By contrast, when p > ~, every algorithm which guarantees finding an exact cheal~'St path, or even a path within a ~:ed cost ratio of the cheapest, mast rtttz in exponential average time. If, howct'er, we are willing to accept a near optimal solution alnzost always, then a pruning algorithm exists which finds such a solution in linear expected time. The algorithm empldys a depth-first strategy which stops at regular intervals to appraise its progress atzd, if the progress does not meet a criterion based on domain-specific knowledge, the current node is irrevocably pruned.

This paper proposes a new on-line antidictionary code with linear time. The proposed algorithm uses a subset of antidictionary which length of the elements is at most a given fixed length. It is proved that the time complexity of this algorithm is linear with respect to the string length. Its effectiveness is demonstrated by simulation results.

Luminance-based palette reordering is often considered less efficient than other more complex approaches, in what concerns improving the compression of color-indexed images. In this paper, we provide experimental evidence that, for color-quantized natural images, this may not be always the case. In fact, we show that, for dithered images with 128 colors or more, luminance-based reordering outperforms other more complex methods.

FPGA based signal processors are employed in a diverse range of signal processing applications for reasons of performance, economics, flexibility and power consumption. Software defined radios (SDR) are highly configurable hardware platforms that provide the technology for realizing the rapidly expanding futuregeneration digital wire less communication infrastructure. Many sophisticated signal processing tasks are performed in a SDR, including advanced compression algorithms, power control, channel estimation, equalization, forward error control, adaptive antennas, rake processing in a WCDMA system and protocol management. Field-programmable gate arrays (FPGA) outpace digital signal processors (DSPs), through hardware execution of a wide range of parallelizable algorithms. FPGAs are especially suited for embedded systems because, beside an area of reconfigurable logical elements, they can also incorporate large amounts of memory, high-speed DSP blocks, clock management circuitry, hi...

This paper analyses the performance of various lossless compression algorithms employed on uniformly quantized audio signals. The purpose of this study is to enlighten a new way of audio signal compression using lossless compression algorithms. The audio signal is first transformed into text by employing uniform quantization with different step sizes. This text is then compressed using lossless compression algorithms which include Run length encoding (RLE), Huffman coding, Arithmetic coding and Lempel-Ziv-Welch (LZW) coding. The performance of various lossless compression algorithms is analyzed based on mainly four parameters, viz., compression ratio, signal-tonoise ratio (SNR), compression time and decompression time. The analysis of the aforementioned parameters has been carried out after uniformly quantizing the audio files using different step sizes. The study exhibits that the LZW coding can be a potential alternative to the MP3 lossy audio compression algorithm to compress audio signals effectively.

Geographical Information System (GIS) is rapidly developing and then compression techniques for sensor image data are becomes an active research area in the field of GIS. Raster data images are very large size. So, it needs to compress these images for save storage space and effective transition on current network. A Digital Elevation Model (DEM) is any raster representation of a terrain surface. When it is important that the original and the decompressed data be identical, lossless compression is used. The purpose of this paper is to get best solution of GIS data compression for information collection. This paper mentions the compression approaches of block-encoding and quadtree. Compression techniques are very important for GIS.