Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Analysis and Design of Low-Cost Waveguide Filters for Wireless Communications

The area of research of this thesis is built around advanced waveguide filter structures. Waveguide filters and the waveguide technology in general are renowned for high power capacity, low losses and excellent electromagnetic shielding. Waveguide filters are important components in fixed wireless communications as well as in satellite and radar systems. Furthermore, their advantages and utilization become even greater with increase in frequency, which is a trend in modern communication systems because upper frequency bands offer larger channel capacities. However, waveguide filters are relatively bulky and expensive. To comply with more and more demanding miniaturization and cost-cutting requirements, compactness and economical design represent some of the main contemporary focuses of interest. Approaches that are used to achieve this include use of planar inserts to build waveguide discontinuities, additive manufacturing and substrate integration. At the same time, waveguide filters still need to satisfy opposed stringent requirements like small insertion loss, high selectivity and multiband operation. Another difficulty that metal waveguide components face is integration with other circuitry, especially important when solid-state active devices are included. Thus, improvements of interconnections between waveguide and other transmission interfaces are addressed too. The thesis elaborates the following aspects of work: Further analysis and improved explanations regarding advanced waveguide filters with E-plane inserts developed by the Wireless Communications Research Group, using both cross coupled resonators and extracted pole sections (Experiments with higher filter orders, use of tuning screws, degrees of freedom in design, etc. Thorough performance comparison with competing filter technologies) - Proposing novel E-plane filter sections with I-shaped insets - Extension of the E-plane filtering structures with metal fins to new compact dual band filters with high frequency selectivity and miniaturized diplexers. - Introduction of easy-to-build waveguide filters with polymer insert frames and high-performance low-profile cavity filters, taking advantage of enhanced fabrication capabilities when using additive manufacturing - Developing new substrate integrated filters, as well as circuits used to transfer signals between different interfaces Namely, these are substrate integrated waveguide to metal waveguide planar transitions that do not require any modifications of the metal waveguides. Such novel transitions have been designed both for single and orthogonal signal polarizations

Target Recognition Using Late-Time Returns from Ultra-Wideband, Short-Pulse Radar

The goal of this research is to develop algorithms that recognize targets by exploiting properties in the late-time resonance induced by ultra-wide band radar signals. A new variant of the Matrix Pencil Method algorithm is developed that identifies complex resonant frequencies present in the scattered signal. Kalman filters are developed to represent the dynamics of the signals scattered from several target types. The Multiple Model Adaptive Estimation algorithm uses the Kalman filters to recognize targets. The target recognition algorithm is shown to be successful in the presence of noise. The performance of the new algorithms is compared to that of previously published algorithms

Multiscale aeroelastic modelling in porous composite structures

Driven by economic, environmental and ergonomic concerns, porous composites are increasingly being adopted by the aeronautical and structural engineering communities for their improved physical and mechanical properties. Such materials often possess highly heterogeneous material descriptions and tessellated/complex geometries. Deploying commercially viable porous composite structures necessitates numerical methods that are capable of accurately and efficiently handling these complexities within the prescribed design iterations. Classical numerical methods, such as the Finite Element Method (FEM), while extremely versatile, incur large computational costs when accounting for heterogeneous inclusions and high frequency waves. This often renders the problem prohibitively expensive, even with the advent of modern high performance computing facilities. Multiscale Finite Element Methods (MsFEM) is an order reduction strategy specifically developed to address such issues. This is done by introducing meshes at different scales. All underlying physics and material descriptions are explicitly resolved at the fine scale. This information is then mapped onto the coarse scale through a set of numerically evaluated multiscale basis functions. The problems are then solved at the coarse scale at a significantly reduced cost and mapped back to the fine scale using the same multiscale shape functions. To this point, the MsFEM has been developed exclusively with quadrilateral/hexahedral coarse and fine elements. This proves highly inefficient when encountering complex coarse scale geometries and fine scale inclusions. A more flexible meshing scheme at all scales is essential for ensuring optimal simulation runtimes. The Virtual Element Method (VEM) is a relatively recent development within the computational mechanics community aimed at handling arbitrary polygonal (potentially non-convex) elements. In this thesis, novel VEM formulations for poromechanical problems (consolidation and vibroacoustics) are developed. This is then integrated at the fine scale into the multiscale procedure to enable versatile meshing possibilities. Further, this enhanced capability is also extended to the coarse scale to allow for efficient macroscale discretizations of complex structures. The resulting Multiscale Virtual Element Method (MsVEM) is originally applied to problems in elastostatics, consolidation and vibroacoustics in porous media to successfully drive down computational run times without significantly affecting accuracy. Following this, a parametric Model Order Reduction scheme for coupled problems is introduced for the first time at the fine scale to obtain a Reduced Basis Multiscale Virtual Element Method. This is used to augment the rate of multiscale basis function evaluation in spectral acoustics problems. The accuracy of all the above novel contributions are investigated in relation to standard numerical methods, i.e., the FEM and MsFEM, analytical solutions and experimental data. The associated efficiency is quantified in terms of computational run-times, complexity analyses and speed-up metrics. Several extended applications of the VEM and the MsVEM are briefly visited, e.g., VEM phase field Methods for brittle fracture, structural and acoustical topology optimization, random vibrations and stochastic dynamics, and structural vibroacoustics

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described