For demanding applications, for example image or video processing, there may be computations that aren’t very suitable for digital signal processors. While a DSP processor is appropriate for some tasks, the instruction set could be extended in order to achieve higher performance for the tasks that such a processor normally isn’t actually design for. The platform used in this project is flexible in the sense that new hardware can be designed to speed up certain computations. This thesis analyzes the computational complex parts of JPEG2000. In order to achieve sufficient performance for JPEG2000, there may be a need for hardware acceleration. First, a JPEG2000 decoder was implemented for a DSP processor in assembler. When the firmware had been written, the cycle consumption of the parts was measured and estimated. From this analysis, the bottlenecks of the system were identified. Furthermore, new processor instructions are proposed that could be implemented for this system. Finally the performance improvements are estimated.

Society is increasingly concerned about the environmental impact of energy systems, and prefers to locate power lines underground. In future, certain socially/environmentally sensitive overhead transmission feeders will need to include underground cable sections. Fault location, especially when using travelling waves, become complicated when the combined transmission line includes a number of discontinuities, such as junction points, teed points and fault points. Consequently, a diverse range of fault locators were developed in this thesis, and the performance of the proposed fault locators investigated. For a combined transmission line (CTL), consisting of one or more overhead line sections and one or more underground cable sections, a hybrid fault location scheme is proposed. This utilises the robustness of an impedance based distance algorithm and the accuracy, but stability concerns, of a travelling wave based fault locator, to determine the faulted section. The distance algorithm can determine the approximate fault location, but if the fault is located near an “underground-overhead” junction point, the accuracy is not sufficient to decide whether the fault is located on the overhead or the underground section. This thesis proposes utilizing a single end travelling wave fault locator to improve the accuracy of the fault location decision. The single end travelling wave fault locator can determine the fault section according to the permutation of the polarity of the “special surges”, which is especially important when the fault is close to a junction point. However, this single end fault locator fails in certain “blind” areas, wand these require the use of a distance relay to help determine fault section. Simulation results demonstrated that this hybrid fault locator can reliably determine which section of the feeder is faulty. For all types of CTL, including teed networks, the multiple-end travelling wave fault locator, utilising the arrival time at the feeder ends of the first fault instigated surges, can estimate the fault location. One of the main features of the proposed fault locator is the classification of the time difference between the arrivals of a fault instigated surge at the feeder ends as standard values, when the fault is located at each of the junction points or teed points. Comparing the time differences measured during an actual fault with these standard values allows the faulted feeder section to be estimated. The simulation results show this multiple-end travelling wave fault locator is highly reliable and suitable for application on combined overhead and underground transmission lines.

Fault location within electric power systems is an important topic that helps reduce outage duration and increases reliability of the system. This paper explores the topic of fault location using traveling waves generated by fault conditions and the discrete wavelet transform used for time-frequency analysis. The single-ended and double-ended traveling wave methods are presented and evaluated on a single circuit and double circuit 500kV system modeled using MATLAB SIMULINK. Results are compared on the basis of wavelet used for analysis, sampling rate, and fault resistance.

Stethoscopes are the most commonly used medical devices for diagnosing heart conditions because they are inexpensive, noninvasive, and light enough to be carried around by a clinician. Auscultation with a stethoscope requires considerable skill and experience, but the introduction of digital stethoscopes allows for the automation of this task. Auscultation waveform segmentation, which is the process of determining the boundaries of heart sound and murmur segments, is the primary challenge in automating the diagnosis of various heart conditions. The purpose of this thesis is to improve the accuracy and efficiency of established techniques for detecting, segmenting, and classifying heart sounds and murmurs in digitized phonocardiogram audio files. Two separate segmentation techniques based on the discrete wavelet transform (DWT) and the simplicity transform are integrated into a MATLAB software system that is capable of automatically detecting and classifying sound segments. The performance of the two segmentation methods for recognizing normal heart sounds and several different heart murmurs is compared by quantifying the results with clinical and technical metrics. The two clinical metrics are the false negative detection rate (FNDR) and the false positive detection rate (FPDR), which count heart cycles rather than sound segments. The wavelet and simplicity methods have a 4% and 9% respective FNDR, so it is unlikely that either method would not detect a heart condition. However, the 22% and 0% respective FPDR signifies that the wavelet method is likely to detect false heart conditions, while the simplicity method is not. The two technical metrics are the true murmur detection rate (TMDR) and the false murmur detection rate (FMDR), which count sound segments rather than heart cycles. Both methods are equally likely to detect true murmurs given their 83% TMDR. However, the 13% and 0% respective FMDR implies that the wavelet method is susceptible to detecting false murmurs, while the simplicity method is not. Simplicity-based segmentation, therefore, demonstrates superior performance to wavelet-based segmentation, as both are equally likely to detect true murmurs, but only the simplicity method has no chance of detecting false murmurs.

This thesis deals with analysis of dynamic forces and vibrations created during cutting with saw. The analysis is done on both simulation and experimental level. Acquired signals are evaluated with signal tools as STFT, CWT and DWT.

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This paper demonstrates the implementation of R-CNN in terms of electromyography-related signals to recognize hand gestures. The signal acquisition is implemented using electrodes situated on the forearm, and the biomedical signals are generated to perform the signals preprocessing using wavelet packet transform to perform the feature extraction. The R-CNN methodology is used to map the specific features that are acquired from the wavelet power spectrum to validate and train how the architecture is framed. Additionally, the real-time test is completed to reach the accuracy of 96.48% compared to the related methods. This kind of result proves that the proposed work has the highest amount of accuracy in recognizing the gestures.

No description available.

Despite being the prefered approach for still-image compression for nearly a decade, wavelet-based coding for video has been slow to emerge, due primarily to the fact that the shift variance of the discrete wavelet transform hinders motion estimation and compensation crucial to modern video coders. Recently it has been recognized that a redundant, or overcomplete, wavelet transform is shift invariant and thus permits motion prediction in the wavelet domain. In this dissertation, other uses for the redundancy of overcomplete wavelet transforms in video coding are explored. First, it is demonstrated that the redundant-wavelet domain facilitates the placement of an irregular triangular mesh to video images, thereby exploiting transform redundancy to implement geometries for motion estimation and compensation more general than the traditional block structure widely employed. As the second contribution of this dissertation, a new form of multihypothesis prediction, redundant wavelet multihypothesis, is presented. This new approach to motion estimation and compensation produces motion predictions that are diverse in transform phase to increase prediction accuracy. Finally, it is demonstrated that the proposed redundant-wavelet strategies complement existing advanced video-coding techniques and produce significant performance improvements in a battery of experimental results.

Because of the vast data volume of hyperspectral imagery, compression becomes a necessary process for hyperspectral data transmission, storage, and analysis. Three-dimensional discrete wavelet transform (DWT) based algorithms are particularly of interest due to their excellent rate-distortion performance. This thesis investigates several issues surrounding efficient compression using JPEG2000. Firstly, the rate-distortion performance is studied when Principal Component Analysis (PCA) replaces DWT for spectral decorrelation with the focus on the use of a subset of principal components (PCs) rather than all the PCs. Secondly, the algorithms are evaluated in terms of data analysis performance, such as anomaly detection and linear unmixing, which is directly related to the useful information preserved. Thirdly, the performance of compressing radiance and reflectance data with or without bad band removal is compared, and instructive suggestions are provided for practical applications. Finally, low-complexity PCA algorithms are presented to reduce the computational complexity and facilitate the future hardware design.