Still image and image sequence compression plays an important role in the development of digital television. Although various still image and image sequence compression algorithms have already been developed, it is very difficult for them to achieve both compression performance and coding efficiency simultaneously due to the complexity of the compression process itself. As a results, improvements in the forms of hybrid coding, coding procedure refinement, new algorithms and even new coding concepts have been constantly tried, some offering very encouraging results.In this thesis, Block Adaptive Classified Vector Quantisation (BACVQ) has been developed as an alternative algorithm for still image compression. It is found that BACVQ achieves good compression performance and coding efficiency by combining variable block-size coding and classified VQ. Its performance is further enhanced by adopting both spatial and transform domain criteria for the image block segmentation and classification process. Alternative algorithms have also been developed to accelerate normal codebook searching operation and to determine the optimal sizes of classified VQ sub-codebooks.For image sequence compression, an adaptive spatial/temporal compression algorithm has been developed which divides an image sequence into smaller groups of pictures (GOP) using adaptive scene segmentation before BACVQ and variable block-size motion compensated predictive coding are applied to the intraframe and interframe coding processes. It is found the application of the proposed adaptive scene segmentation algorithm, an alternative motion estimation strategy and a new progressive motion estimation algorithm enables the performance and efficiency of the compression process to be improved even further.Apart from improving still image and image sequence compression algorithms, the application of parallel ++ / processing to image sequence compression is also investigated. Parallel image compression offers a more effective approach than the sequential counterparts to accelerate the compression process and bring it closer to real-time operation. In this study, a small scale parallel digital signal processing platform has been constructed for supporting parallel image sequence compression operation. It consists of a 486DX33 IBM/PC serving as a master processor and two DSP (PC-32) cards as parallel processors. Because of the independent processing and spatial arrangement natures of most image processing operations, an effective parallel image sequence compression algorithm has been developed on the proposed parallel processing platform to significantly reduce the processing time of the proposed parallel image compression algorithms.

Cardiologists can gain useful insight into a patient's condition when they are able to correlate the patent's symptoms and activities. For this purpose, a Holter Monitor is often used - a portable electrocardiogram (ECG) recorder worn by the patient for a period of 24-72 hours. Preferably, the monitor is not cumbersome to the patient and thus it should be designed to be as small and light as possible; however, the storage requirements for such a long signal are very large and can significantly increase the recorder's size and cost, and so signal compression is often employed. At the same time, the decompressed signal must contain enough detail for the cardiologist to be able to identify irregularities. "Lossy" compressors may obscure such details, where a "lossless" compressor preserves the signal exactly as captured.<p>The purpose of this thesis is to develop a platform upon which a Holter Monitor can be built, including a hardware-assisted lossless compression method in order to avoid the signal quality penalties of a lossy algorithm. <p>The objective of this thesis is to develop and implement a low-complexity lossless ECG encoding algorithm capable of at least a 2:1 compression ratio in an embedded system for use in a Holter Monitor. <p>Different lossless compression techniques were evaluated in terms of coding efficiency as well as suitability for ECG waveform application, random access within the signal and complexity of the decoding operation. For the reduction of the physical circuit size, a System On a Programmable Chip (SOPC) design was utilized. <p>A coder based on a library of linear predictors and Rice coding was chosen and found to give a compression ratio of at least 2:1 and as high as 3:1 on real-world signals tested while having a low decoder complexity and fast random access to arbitrary parts of the signal. In the hardware-assisted implementation, the speed of encoding was a factor of between four and five faster than a software encoder running on the same CPU while allowing the CPU to perform other tasks during the encoding process.

Cardiologists can gain useful insight into a patient's condition when they are able to correlate the patent's symptoms and activities. For this purpose, a Holter Monitor is often used - a portable electrocardiogram (ECG) recorder worn by the patient for a period of 24-72 hours. Preferably, the monitor is not cumbersome to the patient and thus it should be designed to be as small and light as possible; however, the storage requirements for such a long signal are very large and can significantly increase the recorder's size and cost, and so signal compression is often employed. At the same time, the decompressed signal must contain enough detail for the cardiologist to be able to identify irregularities. "Lossy" compressors may obscure such details, where a "lossless" compressor preserves the signal exactly as captured.<p>The purpose of this thesis is to develop a platform upon which a Holter Monitor can be built, including a hardware-assisted lossless compression method in order to avoid the signal quality penalties of a lossy algorithm. <p>The objective of this thesis is to develop and implement a low-complexity lossless ECG encoding algorithm capable of at least a 2:1 compression ratio in an embedded system for use in a Holter Monitor. <p>Different lossless compression techniques were evaluated in terms of coding efficiency as well as suitability for ECG waveform application, random access within the signal and complexity of the decoding operation. For the reduction of the physical circuit size, a System On a Programmable Chip (SOPC) design was utilized. <p>A coder based on a library of linear predictors and Rice coding was chosen and found to give a compression ratio of at least 2:1 and as high as 3:1 on real-world signals tested while having a low decoder complexity and fast random access to arbitrary parts of the signal. In the hardware-assisted implementation, the speed of encoding was a factor of between four and five faster than a software encoder running on the same CPU while allowing the CPU to perform other tasks during the encoding process.

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The operational life of wireless sensor network based distributed sensing systems is limited by the energy provided by a portable battery pack. Owing to the inherently resource constrained nature of wireless sensor networks and nodes, a major research thrust in this field is the search for energy-aware methods of operation. Communication is among the most energy-intensive operations on a wireless device. It is therefore, the focus of our efforts to develop an energy-aware method of communication and to introduce a degree of reconfigurability to ensure autonomous operation of such devices. Given this background, three research tasks have been identified and investigated during the course of this research. 1) Devising an energy-efficient method of communication in a framework of reconfigurable operation: The dependence of the energy consumed during communication on the number of bits transmitted (and received) was identified from prior research work. A novel method of data compression was designed to exploit this dependence. This method uses the time-limited, orthonormal Walsh functions as basis functions for representing signals. The L2 norm of this representation is utilized to further compress the signals. From Parseval’s relation, the square of the L2 norm represents the energy content of a signal. The application of this theorem to our research makes it possible to use the L2 norm as a control knob. The operation of this control knob makes it possible to optimize the number of terms required to represent signals. The time-limited nature of the Walsh functions was leveraged to inject dynamic behaviour into our coding method. This time-limited nature allows decomposition of finite time-segments, without attendant limitations like loss of resolution that are inherent to derived, discrete transforms like the discrete Fourier transform or the discrete time Fourier transform. This decomposition over successive, finite time-segments, coupled with innovative operation of the previously mentioned control knob on every segment, gives us a dynamic scaling technique. The amount of data to be transmitted is in turn based on the magnitude of the coefficients of decomposition of each time-segment, leading to the realization of a variable word length coding method. This dynamic coding method can identify evolving changes or events in the quantity being sensed. The coefficients of decomposition represent features present in successive time-segments of signals and therefore enable identification of evolving events. The ability to identify events as they occur enables the algorithm to react to events as they evolve in the system. In other words the data transmission and the associated energy consumption are imparted a reconfigurable, event-driven nature by implementation of the coding algorithm. Performance evaluation of this method via simulations on machine generated (bearing vibration) and biometric (electro-cardio gram) signals shows it be a viable method for energy-aware communication. 2) Developing a framework for reconfigurable triggering: A framework for completely autonomous triggering of the coding method has been developed. This is achieved by estimating correlations of the signal with the representative Walsh functions. The correlation coefficient of a signal segment with a Walsh function gives a picture of the amount of energy localized by the function. This information is used to autonomously tune the abovementioned control knob or, in more proper terms, the degree of thresholding used in compression. Evaluation of this framework on bearing vibration and electro-cardio gram signals has shown results consistent with those of previous simulations. 3) Devising a computationally compact method of feature classification: A method of investigating time series measurements of dynamic systems in order to classify features buried in the signal measurements was investigated. The approach involves discretizing time-series measurements into strings of pre-defined symbols. These strings are transforms of the original time-series measurements and are a representation of the system dynamics. A method of statistically analyzing the symbol strings is presented and its efficacy is studied through representative simulations and experimental investigation of vibration signals recorded from a rolling bearing element. The method is computationally compact because it obviates the need for local signal processing tasks like denoising, detrending and amplification. Results indicate that the method can effectively classify deteriorating machine health, changing operating conditions and evolving defects. In addition to these major foci, another research task was the design and implementation of a wireless network testbed. This testbed consists of a network of netbooks, connected together wirelessly and was utilized for experimental verification of the variable word length coding method.

Ο κυματιδικός μετασχηματισμός αποτελεί το πλέον σύγχρονο μαθηματικό εργαλείο για την ανάλυση σήματος σε βάση συναρτήσεων. Σε σχέση με άλλες παρόμοιες τεχνικές (π.χ. Fourier) παρουσιάζει εμφανή πλεονεκτήματα με κυρίοτερο την τοπικότητα στο χρόνο των συναρτήσεων βάσης. Η δύναμη του κυματιδιακού μετασχηματισμού βρίσκεται στη διακριτή του έκδοση (Discrete Wavelet Transform), που υπολογίζεται με τη βοήθεια διατάξεων FIR φίλτρων ακολουθούμενων από υποδειγματοληψία. Η ταχύτερη και πιο σύγχρονη τεχνική υπολογισμού του DWT ονομάζεται σχήμα lifting και βασίζεται στην παραγοντοποίηση των πινάκων μετασχηματισμού σε γινόμενο αραιών πινάκων. Στο πλαίσιο της εργασίας σχεδιάστηκε και υλοποιήθηκε σε γλώσσα VHDL μία VLSI αρχιτεκτονική ικανή να εκτελεί οποiοδήποτε φίλτρο (ευθύ και αντίστροφο) του DWT τροποποιημένο με τη μέθοδο lifting. Τα φίλτρα είναι αποθηκευμένα σαν μικροπρογράμματα σε μνήμη ελέγχου για ευκολία στη σχεδίαση και δυνατότητα επαναπροσδιορισμού του συστήματος. Το σύστημα εξομοιώθηκε για ορθή λειτουργία κατά την εκτέλεση των φίλτρων του προτύπου JPEG2000, ενώ έγινε και σύνθεση σε FPGA. / The wavelet transform is the most powerful mathematical tool for analysing signals into function bases. Comparing with other such technics (e.g. Fourier transform), wavelets show obvious advantages, with the most important being the spatial locality of the basis functions. The real power of wavelet transform is the Discrete Wavelet Tranfsorm (DWT), which is a filtering operation followed by downsampling. Recently, a new, fast approach for calculating these filter banks has been developed, named the lifting scheme. This method is based on the factorization of the transform matrices into a product of some sparse matrices. Α VLSI architecture that executes wavelet filters (forward and inverse) modified by the lifting scheme is designed and implemented in VHDL code. The filters are considered as microprogramms placed in the system

En raison de la grande quantité d’EEG acquise sur plusieurs journées, une technique de compression efficace est nécessaire. Le manque des experts et la courte durée des crises encouragent la détection automatique des convulsions. Un affichage uniforme est obligatoire pour assurer l’interopérabilité et la lecture des examens EEG transmis. Le codeur certifié médical WAAVES fournit des CR élevés et assure une qualité de diagnostic d’image. Durant nos travaux, trois défis sont révélés : adapter WAAVES à la compression des signaux, détecter automatiquement les crises épileptiques et assurer l’interopérabilité des afficheurs EEG. L’étude du codeur montre qu’il est incapable de supprimer la corrélation spatiale et de compresser des signaux monodimensionnels. Par conséquent, nous avons appliqué l’ICA pour décorréler les signaux, la mise en échelle pour redimensionner les valeurs décimales et la construction d’image. Pour garder une qualité de diagnostic avec un PDR inférieur à 7%, nous avons codé le résidu. L’algorithme de compression EEGWaaves proposé a atteint des CR de l’ordre de 56. Ensuite, nous avons proposé une méthode d’extraction des caractéristiques des signaux EEG basée sur un nouveau modèle de calcul de la prédiction énergétique (EAM) des signaux. Ensuite, des paramètres statistiques ont été calculés et les Réseaux de Neurones ont été appliqués pour détecter les crises épileptiques. Cette méthode nous a permis d’atteindre de meilleure sensibilité allant jusqu’à 100% et une précision de 99.44%. Le dernier chapitre détaille le déploiement de notre afficheur multi-plateforme des signaux physiologiques. Il assure l’interopérabilité des examens EEG entre les hôpitaux. / Due to the large amount of EEG acquired over several days, an efficient compression technique is necessary. The lack of experts and the short duration of epileptic seizures require the automatic detection of these seizures. Furthermore, a uniform viewer is mandatory to ensure interoperability and a correct reading of transmitted EEG exams. The certified medical image WAAVES coder provides high compression ratios CR while ensuring image quality. During our thesis, three challenges are revealed : adapting WAAVES coder to the compression of the EEG signals, detecting automatically epileptic seizures in an EEG signal and ensure the interoperability of the displays of EEG exams. The study of WAAVES shows that this coder is unable to remove spatial correlation and to compress directly monodimensional signals. Therefore, we applied ICA to decorrelate signals, a scaling to resize decimal values, and image construction. To keep a diagnostic quality with a PDR less than 7%, we coded the residue. The proposed compression algorithm EEGWaaves has achieved CR equal to 56. Subsequently, we proposed a new method of EEG feature extraction based on a new calculation model of the energy expected measurement (EAM) of EEG signals. Then, statistical parameters were calculated and Neural Networks were applied to classify and detect epileptic seizures. Our method allowed to achieve a better sensitivity up to 100% and an accuracy of 99.44%. The last chapter details the deployment of our multiplatform display of physiological signals by meeting the specifications established by doctors. The main role of this software is to ensure the interoperability of EEG exams between healthcare centers.

93f. / Submitted by Suelen Reis (suziy.ellen@gmail.com) on 2013-04-10T19:28:26Z No. of bitstreams: 1 Dissertacao-Andrade2009.pdf: 2855290 bytes, checksum: 594e02d722d8945462d506683b9443b0 (MD5) / Approved for entry into archive by Rodrigo Meirelles(rodrigomei@ufba.br) on 2013-06-06T18:56:16Z (GMT) No. of bitstreams: 1 Dissertacao-Andrade2009.pdf: 2855290 bytes, checksum: 594e02d722d8945462d506683b9443b0 (MD5) / Made available in DSpace on 2013-06-06T18:56:16Z (GMT). No. of bitstreams: 1 Dissertacao-Andrade2009.pdf: 2855290 bytes, checksum: 594e02d722d8945462d506683b9443b0 (MD5) Previous issue date: 2009 / Wavelets são poderosas ferramentas matemáticas que, entre outras aplicações, visam melhorar a decomposição tempo-freqüência de sinais, avaliando-os em diferentes escalas, em uma abordagem multi-resolução que permite a analise dos diferentes aspectos da informação contida no respectivo sinal. Apesar de ser uma ferramenta relativamente nova, ela vem sendo aplicada em várias áreas do conhecimento humano, e é muito popular no processamento de sinais, particularmente na área compressão de sinais, tais como imagens, vídeo e áudio em computadores. A compressão de sinais está associada com a perda de informação, mas que pode ser aceitável para o usuário final. Com base em aplicações anteriores bem sucedidas e o compromisso com a qualidade dos resultados, este trabalho avalia a utilização da Transformada Wavelet Discreta (DWT), como uma técnica de compressão para reduzir a quantidade de dados coletados em sinais de carregamentos de pista (load history), que são utilizados por equipes de engenharia de durabilidade na indústria automotiva. Resultados preliminares mostram que com a utilização da DWT na compressão desses sinais, é possível reduzir o esforço computacional, tempo de processamento, espaço de armazenamento e tráfego de dados entre os computadores, melhorando o processo numérico computacional (CAE) de determinação de tensões, deformações e conseqüentemente da vida em relação a fadiga, ainda mantendo os parâmetros desejados de qualidade. / Salvador

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-04-26T11:21:06Z No. of bitstreams: 1 leandrorodriguesmansosilva.pdf: 2486503 bytes, checksum: f7db682d8ff4382dc706f061419d2a67 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-04-26T12:26:11Z (GMT) No. of bitstreams: 1 leandrorodriguesmansosilva.pdf: 2486503 bytes, checksum: f7db682d8ff4382dc706f061419d2a67 (MD5) / Made available in DSpace on 2017-04-26T12:26:11Z (GMT). No. of bitstreams: 1 leandrorodriguesmansosilva.pdf: 2486503 bytes, checksum: f7db682d8ff4382dc706f061419d2a67 (MD5) Previous issue date: 2016-02-24 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O presente trabalho apresenta o desenvolvimento de métodos de compressão e descompressão de sinais elétricos advindos de sistemas de potência. O método de compressão é baseada em três estágios: a Detecção de Novidades; um estágio de Compressão com Perdas, baseado na Transformada Wavelet Discreta; e finalmente, um estágio de Compressão sem Perdas, baseado no algoritmo Lempel-Ziv-Welch. Foi desenvolvido também, um método para a reconstrução dos sinais comprimidos, que é baseado na Transformada Wavelet Inversa, núcleos de Transformada Discreta de Fourier e na frequência estimada do sinal. A parte de compressão utiliza técnicas de processamento digital de sinais em tempo real, e foi desenvolvida de modo a ser implementada em plataforma FPGA, e a parte de descompressão é executada em um software offline. O trabalho apresenta também um estudo de técnicas de representação esparsa de sinais em dicionários redundantes, com o objetivo de avaliar seu desempenho quando aplicadas à compressão de sinais elétricos, e também, a viabilidade de implementá-las em tempo real, substituindo a Transformada Wavelet Discreta, no estágio de Compressão com Perdas no sistema mencionado anteriormente. / The present work presents the development of a method for power systems signal compression and decompression. The compression method is based in three stages: the novelty detection; the Lossy Compression based on the Discrete Wavelet Transform; and a Lossless Compression stage based on Lempel-Ziv-Welch algorithm. A decompression method was also developed to reconstruct the compressed signals, it is based on Inverse Wavelet Transform, Discrete Fourier Transform cores and the estimated frequency of the signal. The compression part uses digital signal processing techniques in real time, and it was developed to be implemented in FPGA platform. The decompression part runs offline in a PC software. This work also presents a study of sparse representation over redundant dictionaries techniques, in order to evaluate its performance when applied to electrical signal compression, and also the feasibility of implementing them in real time, replacing the Wavelet Transform compression stage in the system mentioned above.

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