Wave propagation in unbounded media is a topic widely studied in different science and engineering fields. Global and local absorbing boundary conditions combined with the finite element method or the finite difference method are the usual numerical treatments. In this dissertation, an alternative is investigated based on the dynamic stiffness and the exponential window method in the space-wave number domain. Applying the exponential window in the space-wave number domain is equivalent to introducing fictitious damping into the system. The Discrete Fourier Transform employed in the dynamic stiffness can be properly performed in a damped system. An open boundary in space is thus created. Since the equation is solved by the finite difference formula in the time domain, this approach is in the time-wave number domain, which provides a complement for the original dynamic stiffness method, which is in the frequency-wave number domain. The approach is tested through different elasto-dynamic models that cover one-, two- and three-dimensional problems. The results from the proposed approach are compared with those from either analytical solutions or the finite element method. The comparison demonstrates the effectiveness of the approach. The incident waves can be efficiently absorbed regardless of incident angles and frequency contents. The approach proposed in this dissertation can be widely applied to the dynamics of railways, dams, tunnels, building and machine foundations, layered soil and composite materials.

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A wide range of dynamic systems can be approximated as linear and time invariant, for which a wealth of tools are available to characterize or modify their dynamic characteristics. Experimental modal analysis (EMA) is a procedure whereby the natural frequencies, damping ratios and mode shapes which parameterize vibratory, linear, time invariant systems are derived from experimentally measured response data. EMA is commonly applied in a multitude of applications, for example, to generate experimental models of dynamic systems, validate finite element models and to characterize dissipation in vibratory systems. Recent EMA has also been used to characterize damage or defects in a variety of systems. The Algorithm of Mode Isolation (AMI), presented by Drexel and Ginsberg in 2001, employs a unique strategy for modal parameter estimation in which modes are sequentially identified and subtracted from a set of FRFs. Their natural frequencies, damping ratios and mode vectors are then refined through an iterative procedure. This contrasts conventional multi-degree-of-freedom (MDOF) identification algorithms, most of which attempt to identify all of the modes of a system simultaneously. This dissertation presents a hybrid multi-input-multi-output (MIMO) implementation of the algorithm of mode isolation that improves the performance of AMI for systems with very close or weakly excited modes. The algorithmic steps are amenable to semi-automatic identification, and many FRFs can be processed efficiently and without concern for ill-conditioning, even when many modes are identified. The performance of the algorithm is demonstrated on noise contaminated analytical response data from two systems having close modes, one of which has localized modes while the other has globally responsive modes. The results are compared with other popular algorithms. MIMO-AMI is also applied to experimentally obtained data from shaker excited tests of the Z24 highway bridge, demonstrating the algorithm's performance on a data set typical of many EMA applications. Considerations for determining the number of modes active in the frequency band of interest are addressed, and the results obtained are compared to those found by other groups of researchers.

The main aim of this thesis is to introduce issue of averaging of biological signals. The first part of the thesis deals with the principles of individual averaging methods (constant, floating and exponential window) and describes their basic features. Moreover, the principle of filtered residue, detection of QRS complex, and stretching/shrinking the length of RR-interval to the standardized length are explicated. In the second part of the thesis the outcomes of practically realized (Matlab and GUI) methods of averaging (by final signal-to-noise ratio) are evaluated. Signals from MIT-BIH database are used.