A three dimensional (3-D) capacitance extraction algorithm based on a kernel independent hierarchical method and geometric moments is described. Several techniques are incorporated, which leads to a better overall performance for arbitrary interconnect systems. First, the new algorithm hierarchically partitions the bounding box of all interconnect panels to build the partition tree. Then it uses simple shapes to match the low order moments of the geometry of each box in the partition tree. Finally, with the help of a fast matrix-vector product, GMRES is used to solve the linear system. Experimental results show that our algorithm reduces the linear system's size greatly and at the same time maintains a satisfying accuracy. Compared with FastCap, the running time of the new algorithm can be reduced more than a magnitude and the memory usage can be reduced more than thirty times.

A three dimensional (3-D) capacitance extraction algorithm based on a kernel independent hierarchical method and geometric moments is described. Several techniques are incorporated, which leads to a better overall performance for arbitrary interconnect systems. First, the new algorithm hierarchically partitions the bounding box of all interconnect panels to build the partition tree. Then it uses simple shapes to match the low order moments of the geometry of each box in the partition tree. Finally, with the help of a fast matrix-vector product, GMRES is used to solve the linear system. Experimental results show that our algorithm reduces the linear system's size greatly and at the same time maintains a satisfying accuracy. Compared with FastCap, the running time of the new algorithm can be reduced more than a magnitude and the memory usage can be reduced more than thirty times.

Research and development of Non-Photorealistic Rendering algorithms has recently moved towards the use of computer vision algorithms to extract image features. The feature representation capabilities of image moments could be used effectively for the selection of brush-stroke characteristics for painterly-rendering applications. This technique is based on the estimation of local geometric features from the intensity distribution in small windowed images to obtain the brush size, color and direction. This thesis proposes an improvement of this method, by additionally extracting the connected components so that the adjacent regions of similar color are grouped for generating large and noticeable brush-stroke images. An iterative coarse-to-fine rendering algorithm is developed for painting regions of varying color frequencies. Improvements over the existing technique are discussed with several examples.

A novel delamination identification technique based on a low-population genetic algorithm for the quantitative characterisation of a single delamination in composite laminated panels is developed, and validated experimentally The damage identification method is formulated as an inverse problem through which system parameters are identified. The input of the inverse problem, the central geometric moments (CGM), is calculated from the surface out-of-plane displacements measurements of a delaminated panel obtained from Digital Speckle Pattern Interferometry (DSPI). The output parameters, the planar location, size and depth of the flaw, are the solution to the inverse problem to characterise an idealised elliptical flaw. The inverse problem is then reduced to an optimisation problem where the objective function is defined as the L2 norm of the difference between the CGM obtained from a finite element (FE) model with a trial delamination and the moments computed from the DSPI measurements. The optimum crack parameters are found by minimising the objective function through the use of a low-population real-coded genetic algorithm (LARGA). DSPI measurements of ten delaminated T700/LTM-45EL carbon/epoxy laminate panels with embedded delaminations are used to validate the methodology presented in this thesis.

<p> Body Posture provides enough information regarding the current state of mind of a person. This idea is used to implement a system that provides feedback to lecturers on how engaging the class has been by identifying the attentive levels of students. This is carried out using the posture information extracted with the help of Mediapipe. A novel method of extracting features are from the key points returned by Mediapipe is proposed. Geometric moments aided features classification performs better than the general distances and angles features classification. In order to extend the single person pose classification to multi person pose classification object detection is implemented. Feedback is generated regarding the entire lecture and provided as the output of the system. </p>