Digital image acquisition can be a time consuming process for situations where high spatial resolution is required. As such, optimizing the acquisition mechanism is of high importance for many measurement applications. Acquiring such data through a dynamically small subset of measurement locations can address this problem. In such a case, the measured information can be regarded as incomplete, which necessitates the application of special reconstruction tools to recover the original data set. The reconstruction can be performed based on the concept of sparse signal representation. Recovering signals and images from their sub-Nyquist measurements forms the core idea of compressive sensing (CS). In this work, a CS-based data-guided statistical sparse measurements method is presented, implemented and evaluated. This method significantly improves image reconstruction from sparse measurements. In the data-guided statistical sparse measurements approach, signal sampling distribution is optimized for improving image reconstruction performance. The sampling distribution is based on underlying data rather than the commonly used uniform random distribution. The optimal sampling pattern probability is accomplished by learning process through two methods - direct and indirect. The direct method is implemented for learning a nonparametric probability density function directly from the dataset. The indirect learning method is implemented for cases where a mapping between extracted features and the probability density function is required. The unified model is implemented for different representation domains, including frequency domain and spatial domain. Experiments were performed for multiple applications such as optical coherence tomography, bridge structure vibration, robotic vision, 3D laser range measurements and fluorescence microscopy. Results show that the data-guided statistical sparse measurements method significantly outperforms the conventional CS reconstruction performance. Data-guided statistical sparse measurements method achieves much higher reconstruction signal-to-noise ratio for the same compression rate as the conventional CS. Alternatively, Data-guided statistical sparse measurements method achieves similar reconstruction signal-to-noise ratio as the conventional CS with significantly fewer samples.

In modern power transmission systems, the double-circuit line structure is increasingly adopted. However, due to the mutual coupling between the parallel lines it is quite challenging to design accurate fault location algorithms. Moreover, the widely used series compensator and its protective device introduce harmonics and non-linearities to the transmission lines, which make fault location more difficult. To tackle these problems, this dissertation is committed to developing advanced fault location methods for double-circuit and series-compensated transmission lines. Algorithms utilizing sparse measurements for pinpointing the location of short-circuit faults on double-circuit lines are proposed. By decomposing the original network into three sequence networks, the bus impedance matrix for each network with the addition of the fictitious fault bus can be formulated. It is a function of the unknown fault location. With the augmented bus impedance matrices the sequence voltage change during the fault at any bus can be expressed in terms of the corresponding sequence fault current and the transfer impedance between the fault bus and the measured bus. Resorting to VCR the superimposed sequence current at any branch can be expressed with respect to the pertaining sequence fault current and transfer impedance terms. Obeying boundary conditions of different fault types, four different classes of fault location algorithms utilizing either voltage phasors, or phase voltage magnitudes, or current phasors, or phase current magnitudes are derived. The distinguishing charactristic of the proposed method is that the data measurements need not stem from the faulted section itself. Quite satisfactory results have been obtained using EMTP simulation studies. A fault location algorithm for series-compensated transmission lines that employs two-terminal unsynchronized voltage and current measurements has been implemented. For the distinct cases that the fault occurs either on the left or on the right side of the series compensator, two subroutines are developed. In additon, the procedure to identify the correct fault location estimate is described in this work. Simulation studies carried out with Matlab SimPowerSystems show that the fault location results are very accurate.

Digital image acquisition can be a time consuming process for situations where high spatial resolution is required. As such, optimizing the acquisition mechanism is of high importance for many measurement applications. Acquiring such data through a dynamically small subset of measurement locations can address this problem. In such a case, the measured information can be regarded as incomplete, which necessitates the application of special reconstruction tools to recover the original data set. The reconstruction can be performed based on the concept of sparse signal representation. Recovering signals and images from their sub-Nyquist measurements forms the core idea of compressive sensing (CS). In this work, a CS-based data-guided statistical sparse measurements method is presented, implemented and evaluated. This method significantly improves image reconstruction from sparse measurements. In the data-guided statistical sparse measurements approach, signal sampling distribution is optimized for improving image reconstruction performance. The sampling distribution is based on underlying data rather than the commonly used uniform random distribution. The optimal sampling pattern probability is accomplished by learning process through two methods - direct and indirect. The direct method is implemented for learning a nonparametric probability density function directly from the dataset. The indirect learning method is implemented for cases where a mapping between extracted features and the probability density function is required. The unified model is implemented for different representation domains, including frequency domain and spatial domain. Experiments were performed for multiple applications such as optical coherence tomography, bridge structure vibration, robotic vision, 3D laser range measurements and fluorescence microscopy. Results show that the data-guided statistical sparse measurements method significantly outperforms the conventional CS reconstruction performance. Data-guided statistical sparse measurements method achieves much higher reconstruction signal-to-noise ratio for the same compression rate as the conventional CS. Alternatively, Data-guided statistical sparse measurements method achieves similar reconstruction signal-to-noise ratio as the conventional CS with significantly fewer samples.