Digital radiographic images are created by a variety of diagnostic imaging modalities. A multi-modality workstation, known as the Arizona Viewing Console (AVC), was designed and built by the University of Arizona Radiology Department to support research in radiographic image processing and image display. Two specially designed VMEbus components, the base image memory and the video display memory, were integrated into the AVC and are the subject of this thesis. The base image memory is a multi-ported, 8 megabyte memory array based on random access memory used for raw image storage. It supports a 10 megapixel per second image processor and can interface to a 320 megabit per second network. The video display memory utilizes video memories and is capable of displaying two independent high resolution images, each 1024 pixels by 1536 lines, on separate video monitors. In part, these two memory designs have allowed the AVC to excel as a radiographic image workstation.

The information transfer efficiency of any quantum detection imaging system can be described by a unique measure: the detective quantum efficiency {DQE(f)}, which is a function of the statistically independent frequency channels. The DQE(f) is a combined descriptor which takes into account the signal transfer as well as noise transfer properties of a complete system. For a complicated multistage imaging system, each system component contributes noise. In this dissertation, physical and mathematical models for noise analysis are developed and verified experimentally with an x-ray image intensifier (XRII)-based imaging system. In such a system, the DQE at low frequency range is primarily determined by the x-ray detection and scintillation processes at the CsI layer of the XRII. The effects of x-ray photon energy and sensor layer thickness on DQE are measured in detail. Numerical calculations based on a physical model of x-ray interactions show a general agreement with the experimental data. At higher frequencies, the DQE behavior becomes more complicated. A mathematical model which combines the micro-image properties and noise statistics is formulated to analyze the noise power spectrum (NPS) of a linear n-stage imaging system. Measurement of NPS components of an XRII system verifies the validity of this analytical prediction. The associated image transfer properties are also measured with emphasis on the effect of signal-induced background on the image information transfer. The low frequency data derived from these image property measurements show further agreement with the numerical calculations based on the physical model. As a result of this predictability of information transfer efficiency, system gain and recording capacity are emphasized in the design consideration of a projected high performance XRII radiographic system.

Professional imaging systems, particularly motion picture cameras, usually employ larger photosites and lower pixel counts than many amateur cameras. This results in the desirable characteristics of improved dynamic range, signal to noise and sensitivity. However, high performance optics often have frequency response characteristics that exceed the Nyquist limit of the sensor, which, if not properly addressed, results in aliasing artifacts in the captured image. Most contemporary still and video cameras employ various optically birefringent materials as optical low-pass filters (OLPF) in order to minimize aliasing artifacts in the image. Most OLPFs are designed as optical elements with a frequency response that does not change even if the frequency responses of the other elements of the capturing systems are altered. An extended evaluation of currently used birefringent-based OLPFs is provided. In this work, the author proposed and demonstrated the use of a parallel optical window p ositioned between a lens and a sensor as an OLPF. Controlled X- and Y-axes rotations of the optical window during the image exposure results in a manipulation of the system's point-spread function (PSF). Consequently, changing the PSF affects some portions of the frequency components contained in the image formed on the sensor. The system frequency response is evaluated when various window functions are used to shape the lens' PSF, such as rectangle, triangle, Tukey, Gaussian, Blackman-Harris etc. In addition to the ability to change the PSF, this work demonstrated that the PSF can be manipulated dynamically, which allowed us to modify the PSF to counteract any alteration of other optical elements of the capturing system. There are several instances presented in the dissertation in which it is desirable to change the characteristics of an OLPF in a controlled way. / In these instances, an OLPF whose characteristics can be altered dynamically results in an improvement of the image quality. / by Branko Petljanski. / Thesis (Ph.D.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

by Lai Wai Kuen. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves [119-125]). / Abstract --- p.i / Acknowledgement --- p.ii / Table of Contents --- p.iii / Chapter Chapter 1 --- Introduction / Chapter 1.1 --- Background - The Need for Nonlinear Filtering --- p.1.1 / Chapter 1.2 --- Nonlinear Filtering --- p.1.2 / Chapter 1.3 --- Goal of the Work --- p.1.4 / Chapter 1.4 --- Organization of the Thesis --- p.1.5 / Chapter Chapter 2 --- An Overview of Robust Estimator Based Filters Morphological Filters / Chapter 2.1 --- Introduction --- p.2.1 / Chapter 2.2 --- Signal Representation by Sets --- p.2.2 / Chapter 2.3 --- Robust Estimator Based Filters --- p.2.4 / Chapter 2.3.1 --- Filters based on the L-estimators --- p.2.4 / Chapter 2.3.1.1 --- The Median Filter and its Derivations --- p.2.5 / Chapter 2.3.1.2 --- Rank Order Filters and Derivations --- p.2.9 / Chapter 2.3.2 --- Filters based on the M-estimators (M-Filters) --- p.2.11 / Chapter 2.3.3 --- Filter based on the R-estimators --- p.2.13 / Chapter 2.4 --- Filters based on Mathematical Morphology --- p.2.14 / Chapter 2.4.1 --- Basic Morphological Operators --- p.2.14 / Chapter 2.4.2 --- Morphological Filters --- p.2.18 / Chapter 2.5 --- Chapter Summary --- p.2.20 / Chapter Chapter 3 --- Multi-Structuring Element Erosion Filter / Chapter 3.1 --- Introduction --- p.3.1 / Chapter 3.2 --- Problem Formulation --- p.3.1 / Chapter 3.3 --- Description of Multi-Structuring Element Erosion Filter --- p.3.3 / Chapter 3.3.1 --- Definition of Structuring Element for Multi-Structuring Element Erosion Filter --- p.3.4 / Chapter 3.3.2 --- Binary multi-Structuring Element Erosion Filter --- p.3.9 / Chapter 3.3.3 --- Selective Threshold Decomposition --- p.3.10 / Chapter 3.3.4 --- Multilevel Multi-Structuring Element Erosion Filter --- p.3.15 / Chapter 3.3.5 --- A Combination of Multilevel Multi-Structuring Element Erosion Filter and its Dual --- p.3.21 / Chapter 3.4 --- Chapter Summary --- p.3.21 / Chapter Chapter 4 --- Properties of Multi-Structuring Element Erosion Filter / Chapter 4.1 --- Introduction --- p.4.1 / Chapter 4.2 --- Deterministic Properties --- p.4.2 / Chapter 4.2.1 --- Shape of Invariant Signal --- p.4.3 / Chapter 4.2.1.1 --- Binary Multi-Structuring Element Erosion Filter --- p.4.5 / Chapter 4.2.1.2 --- Multilevel Multi-Structuring Element Erosion Filter --- p.4.16 / Chapter 4.2.2 --- Rate of Convergence of Multi-Structuring Element Erosion Filter --- p.4.25 / Chapter 4.2.2.1 --- Convergent Rate of Binary Multi-Structuring Element Erosion Filter --- p.4.25 / Chapter 4.2.2.2 --- Convergent Rate of Multilevel Multi-Structuring Element Erosion Filter --- p.4.28 / Chapter 4.3 --- Statistical Properties --- p.4.30 / Chapter 4.3.1 --- Output Distribution of Multi-Structuring Element Erosion Filter --- p.4.30 / Chapter 4.3.1.1 --- One-Dimensional Statistical Analysis of Multilevel Multi-Structuring Element Erosion Filter --- p.4.31 / Chapter 4.3.1.2 --- Two-Dimensional Statistical Analysis of Multilevel Multi-Structuring Element Erosion Filter --- p.4.32 / Chapter 4.3.2 --- Discussions on Statistical Properties --- p.4.36 / Chapter 4.4 --- Chapter Summary --- p.4.40 / Chapter Chapter 5 --- Performance Evaluation / Chapter 5.1 --- Introduction --- p.5.1 / Chapter 5.2 --- Performance Criteria --- p.5.2 / Chapter 5.2.1 --- Noise Suppression --- p.5.5 / Chapter 5.2.2 --- Subjective Criterion --- p.5.16 / Chapter 5.2.3 --- Computational Requirement --- p.5.20 / Chapter 5.3 --- Chapter Summary --- p.5.23 / Chapter Chapter 6 --- Recapitulation and Suggestions for Further Work / Chapter 6.1 --- Recapitulation --- p.6.1 / Chapter 6.2 --- Suggestions for Further Work --- p.6.4 / Chapter 6.2.1 --- Probability Measure Function for the Two-Dimensional Filter --- p.6.4 / Chapter 6.2.2 --- Hardware Implementation --- p.6.5 / References / Appendices

This work explores the process of model-based classification of speech audio signals using low-level feature vectors. The process of extracting low-level features from audio signals is described along with a discussion of established techniques for training and testing mixture model-based classifiers and using these models in conjunction with feature selection algorithms to select optimal feature subsets. The results of a number of classification experiments using a publicly available speech database, the Berlin Database of Emotional Speech, are presented. This includes experiments in optimizing feature extraction parameters and comparing different feature selection results from over 700 candidate feature vectors for the tasks of classifying speaker gender, identity, and emotion. In the experiments, final classification accuracies of 99.5%, 98.0% and 79% were achieved for the gender, identity and emotion tasks respectively. / by Chris Thoman. / Thesis (M.S.C.S.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.

by Tsang Yick Man, Steven. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 94-99). / ABSTRACT --- p.i / ACKNOWLEDGMENT --- p.ii / LIST OF GLOSSARY --- p.iii / Chapter 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- What is the jitter ？ --- p.3 / Chapter 2 --- WHY DOES JITTER OCCUR IN DIGITAL AUDIO ？ --- p.4 / Chapter 2.1 --- Poorly-designed Phase Locked Loop ( PLL ) --- p.4 / Chapter 2.1.1 --- Digital data problem --- p.7 / Chapter 2.2 --- Sampling jitter or clock jitter ( Δti) --- p.9 / Chapter 2.3 --- Waveform distortion --- p.12 / Chapter 2.4 --- Logic induced jitter --- p.17 / Chapter 2.4.1 --- Digital noise mechanisms --- p.20 / Chapter 2.4.2 --- Different types of D-type flop-flip chips are linked below for ease of comparison --- p.21 / Chapter 2.4.3 --- Ground bounce --- p.22 / Chapter 2.5 --- Power supply high frequency noise --- p.23 / Chapter 2.6 --- Interface Jitter --- p.25 / Chapter 2.7 --- Cross-talk --- p.28 / Chapter 2.8 --- Inter-Symbol-Interference (ISI) --- p.28 / Chapter 2.9 --- Baseline wander --- p.29 / Chapter 2.10 --- Noise jitter --- p.30 / Chapter 2.11 --- FIFO jitter reduction chips --- p.31 / Chapter 3 --- JITTER REDUCTION TECHNIQUES --- p.33 / Chapter 3.1 --- Why using two-stage phase-locked loop (PLL ) ？ / Chapter 3.1.1 --- The PLL circuit components --- p.35 / Chapter 3.1.2 --- The PLL timing specifications --- p.36 / Chapter 3.2 --- Analog phase-locked loop (APLL ) circuit usedin second stage --- p.38 / Chapter 3.3 --- All digital phase-locked loop (ADPLL ) circuit used in second stage --- p.40 / Chapter 3.4 --- ADPLL design --- p.42 / Chapter 3.4.1 --- "Different of K counter value of ADPLL are listed for comparison with M=512, N=256, Kd=2" --- p.46 / Chapter 3.4.2 --- Computer simulated results and experimental results of the ADPLL --- p.47 / Chapter 3.4.3 --- PLL design notes --- p.58 / Chapter 3.5 --- Different of the all digital Phase-Locked Loop (ADPLL ) and the analogue Phase-Locked Loop (APLL ) are listed for comparison --- p.65 / Chapter 3.6 --- Discrete transistor oscillator --- p.68 / Chapter 3.7 --- Discrete transistor oscillator circuit operation --- p.69 / Chapter 3.8 --- The advantage and disadvantage of using external discrete oscillator --- p.71 / Chapter 3.9 --- Background of using high-precision oscillators --- p.72 / Chapter 3.9.1 --- The temperature compensated crystal circuit operation --- p.73 / Chapter 3.9.2 --- The temperature compensated circuit design notes --- p.75 / Chapter 3.10 --- The discrete voltage reference circuit operation --- p.76 / Chapter 3.10.1 --- Comparing the different types of Op-amps that can be used as a voltage comparator --- p.79 / Chapter 3.10.2 --- Precaution of separate CMOS chips Vdd and Vcc --- p.80 / Chapter 3.11 --- Board level jitter reduction method --- p.81 / Chapter 3.12 --- Digital audio interface chips --- p.82 / Chapter 3.12.1 --- Different brand of the digital interface receiver (DIR) chips and clock modular are listed for comparison --- p.84 / Chapter 4. --- APPLICATION CIRCUIT BLOCK DIAGRAMS OF JITTER REDUCTION AND CLOCK RECOVERY --- p.85 / Chapter 5 --- CONCLUSIONS --- p.90 / Chapter 5.1 --- Summary of the research --- p.90 / Chapter 5.2 --- Suggestions for further development --- p.92 / Chapter 5.3 --- Instrument listing that used in this thesis --- p.93 / Chapter 6 --- REFERENCES --- p.94 / Chapter 7 --- APPENDICES --- p.100 / Chapter 7.1.1 --- Phase instability in frequency dividers / Chapter 7.1.2 --- The effect of clock tree on Tskew on ASIC chip / Chapter 7.1.3 --- Digital audio transmission----Why jitter is important? / Chapter 7.1.4 --- Overview of digital audio interface data structures / Chapter 7.1.5 --- Typical frequency Vs temperature variations curve of Quartz crystals / Chapter 7.2 --- IC specification used in these research project

A range of signal processing techniques have been adopted and developed as a methodology which can be used in developing an intelligent surface electromyography (SEMG) signal classifier. An intelligent SEMG signal classifier would be used for recognising and treatment of musculoskeletal pain and some neurological disorders by physiotherapists and occupational therapists. SEMG signals displays the electrical activity from a skeletal muscle which is detected by placing surface electrodes placed on the skin over the muscle. The key factors of this research were the investigation into digital signal processing using various analysis schemes and the use of the Artificial Neural Network (ANN) for signal classification of normal muscle activity. The analysis schemes explored for the feature extraction of the signals were the Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) and Discrete Wavelet Packet Transform (DWPT).Traditional analysis methods such as FFT could not be used alone, because muscle diagnosis requires time-based information. CWT, which was selected as the most suitable for this research, includes time-based information as well as scales, and can be converted into frequencies, making muscle diagnosis easier. CWT produces a scalogram plot along with its corresponding frequency-time based spectrum plot. Using both of these plots, overviewed extracted features of the dominant frequencies and the related scales can be selected for inputs to train and validate an ANN. The purpose of this research is to classify (SEMG) signals for normal muscle activity using different extracted features in an ANN. The extracted features of the SEMG signals used in this research using CWT were the mean and median frequencies of the average power spectrum and the RMS values at scales 8, 16, 32, 64 and 128. SEMG signals were obtained for a 10 second period, sampled at 2048 Hz and digitally filtered using a Butterworth band pass filter (5 to 500 Hz, 4th order). They were collected from normal vastus lateralis and vastus medialis muscles of both legs from 45 male subjects at 25%, 50%, and 75% of their Maximum Voluntary Isometric Contraction (MVIC) force of the quadriceps. The ANN is a computer program which acts like brain neurons, recognises, learns data and produces a model of that data. The model of that data becomes the target output of an ANN. Using the first 35 male subjects' data sets of extracted features, the ANN was trained and then validated with the last 10 male subjects' data sets of the untrained extracted features. The results showed how accurate the untrained data were classified as normal muscle activity. This methodology of using CWT for extracting features for analysing and classifying by an ANN for SEMG signals has shown to be sound and successful for the basis implementation in developing an intelligent SEMG signal classifier.

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As more and more multimedia applications are integrated in mobile devices, a significant amount of energy is devoted to digital signal processing (DSP). Thus, reducing energy consumption for DSP systems has become an important design goal for battery operated mobile devices. Since supply voltage scaling is one of the most effective methods to reduce power/energy consumption, this study examines aggressive voltage scaling to achieve significant energy savings by allowing some output quality degradation for error tolerant image processing system. The objective of proposed research is to explore ultra-low energy image processing system design methodologies based on efficient accuracy (quality)-energy tradeoffs. This dissertation presents several new analyses and techniques to achieve significant energy savings without noticeable quality degradation under aggressive voltage scaling. In the first, this work starts from accurate error analysis and a model based on input sequence dependent delay estimation. Based on the analysis, we explain the dependence of voltage scalability on input image types, which may be used for input dependent adaptive control for optimal accuracy-energy tradeoffs. In addition, this work includes the system-level analysis of the impact of aggressive voltage scaling on overall energy consumption and a low-cost technique to reduce overall energy consumption. Lastly, this research exploits an error concealment technique to improve the efficiency of accuracy-energy tradeoffs. For an image compression system, the technique minimizes the impact of delay errors on output quality while allowing very low voltage operations for significant energy reduction.

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