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 presents a wavelet Galerkin scheme for the fast solution of boundary integral equations. Wavelet Galerkin schemes employ appropriate wavelet bases for the discretization of boundary integral operators. This yields quasi-sparse system matrices which can be compressed to O(N_J) relevant matrix entries without compromising the accuracy of the underlying Galerkin scheme. Herein, O(N_J) denotes the number of unknowns. The assembly of the compressed system matrix can be performed in O(N_J) operations. Therefore, we arrive at an algorithm which solves boundary integral equations within optimal complexity. By numerical experiments we provide results which corroborate the theory.

The wavelet Galerkin scheme for the fast solution of boundary integral equations produces approximate solutions within discretization error accuracy offered by the underlying Galerkin method at a computational expense that stays proportional to the number of unknowns. In this paper we present an adaptive version of the scheme which preserves the super-convergence of the Galerkin method.

This dissertation considers an approximation strategy using a wavelet reconstruction scheme for solving elliptic problems. The foci of the work are on (1) the approximate solution of differential equations using multiresolution analysis based on wavelet transforms and (2) the homogenization process for solving one and two-dimensional problems, to understand the solutions of second order elliptic problems. We employed homogenization to compute the average formula for permeability in a porous medium. The structure of the associated multiresolution analysis allows for the reconstruction of the approximate solution of the primary variable in the elliptic equation. Using a one-dimensional wavelet reconstruction algorithm proposed in this work, we are able to numerically compute the approximations of the pressure variables. This algorithm can directly be applied to elliptic problems with discontinuous coefficients.We also implemented Java codes to solve the two dimensional elliptic problems using our methods of solutions. Furthermore, we propose homogenization wavelet reconstruction algorithm, fast transform and the inverse transform algorithms that use the results from the solutions of the local problems and the partial derivatives of the pressure variables to reconstruct the solutions.

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.

The ongoing development and increased affordability of hyperspectral sensors are increasing their utilization in a variety of applications, such as agricultural monitoring and decision making. Hyperspectral Automated Target Recognition (ATR) systems typically rely heavily on dimensionality reduction methods, and particularly intelligent reduction methods referred to as feature extraction techniques. This dissertation reports on the development, implementation, and testing of new hyperspectral analysis techniques for ATR systems, including their use in agricultural applications where ground truthed observations available for training the ATR system are typically very limited. This dissertation reports the design of effective methods for grouping and down-selecting Discrete Wavelet Transform (DWT) coefficients and the design of automated Wavelet Packet Decomposition (WPD) filter tree pruning methods for use within the framework of a Multiclassifiers and Decision Fusion (MCDF) ATR system. The efficacy of the DWT MCDF and WPD MCDF systems are compared to existing ATR methods commonly used in hyperspectral remote sensing applications. The newly developed methods’ sensitivity to operating conditions, such as mother wavelet selection, decomposition level, and quantity and quality of available training data are also investigated. The newly developed ATR systems are applied to the problem of hyperspectral remote sensing of agricultural food crop contaminations either by airborne chemical application, specifically Glufosinate herbicide at varying concentrations applied to corn crops, or by biological infestation, specifically soybean rust disease in soybean crops. The DWT MCDF and WPD MCDF methods significantly outperform conventional hyperspectral ATR methods. For example, when detecting and classifying varying levels of soybean rust infestation, stepwise linear discriminant analysis, results in accuracies of approximately 30%-40%, but WPD MCDF methods result in accuracies of approximately 70%-80%.

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Yes / Recent studies suggest that multicarrier systems using wavelets outperform conventional OFDM systems using the FFT, in that they have well-contained side lobes, improved spectral efficiency and BER performance, and they do not require a cyclic prefix. Here we study the wavelet packet and discrete wavelet transforms, comparing the BER performance of wavelet transform-based multicarrier systems and Fourier based OFDM systems, for multipath Rayleigh channels with AWGN. In the proposed system zero-forcing channel estimation in the frequency domain has been used. Results confirm that discrete wavelet-based systems using Daubechies wavelets outperform both wavelet packet transform- based systems and FFT-OFDM systems in terms of BER. Finally, Alamouti coding and maximal ratio combining schemes were employed in MIMO environments, where results show that the effects of multipath fading were greatly reduced by the antenna diversity.

Yes / New orthogonal wavelet transforms can be designed by changing the wavelet basis functions or by constructing new low-pass filters (LPF). One family of wavelet may appeal, in use, to a particular application than another. In this study, the wavelet transform based on raisedcosine spectrum is used as an independent orthogonal wavelet to study multicarrier modulation behaviour over multipath channel environment. Then, the raised-cosine wavelet is compared with other well-known orthogonal wavelets that are used, also, to build multicarrier modulation systems. Traditional orthogonal wavelets do not have side-lobes, while the raised-cosine wavelets have lots of side-lobes; these characteristics influence the wavelet behaviour. It will be shown that the raised-cosine wavelet transform, as an orthogonal wavelet, does not support the design of multicarrier application well like the existing well-known orthogonal wavelets.