Zolotarev polynomials utilization in spectral analysis

Tato práce je zaměřena na vybrané problémy Zolotarevových polynomů a jejich vyuľití ke spektrální analýze. Pokud jde o Zolotarevovy polynomy, jsou popsány základní vlastnosti symetrických Zolotarevových polynomů včetně ortogonality. Rovněľ se provádí prozkoumání numerických vlastností algoritmů generujících dokonce Zolotarevovy polynomy. Pokud jde o aplikaci Zolotarevových polynomů na spektrální analýzu, je implementována aproximovaná diskrétní Zolotarevova transformace, která umoľňuje výpočet spektrogramu (zologramu) v reálném čase. Aproximovaná diskrétní zolotarevská transformace je navíc upravena tak, aby lépe fungovala při analýze tlumených exponenciálních signálů. A nakonec je navrľena nová diskrétní Zolotarevova transformace implementovaná plně v časové oblasti. Tato transformace také ukazuje, ľe některé rysy pozorované u aproximované diskrétní Zolotarevovy transformace jsou důsledkem pouľití Zolotarevových polynomů.This thesis is focused on selected problems of symmetrical Zolotarev polynomials and their use in spectral analysis. Basic properties of symmetrical Zolotarev polynomials including orthogonality are described. Also, the exploration of numerical properties of algorithms generating even Zolotarev polynomials is performed. As regards to the application of Zolotarev polynomials to spectral analysis the Approximated Discrete Zolotarev Transform is implemented so that it enables computing of zologram in real–time. Moreover, the Approximated Discrete Zolotarev Transform is modified to perform better in the analysis of damped exponential signals. And finally, a novel Discrete Zolotarev Transform implemented fully in the time domain is suggested. This transform also shows that some features observed using the Approximated Discrete Zolotarev Transform are a consequence of using Zolotarev polynomials

Theoretical and Experimental Improvements for Fast Microdialysis

Microdialysis is the most widely used technique for intracranial sampling. Its versatility prompts important applications, from bedside traumatic brain injury monitoring to psychological disorder treatment. However, historically microdialysis methods have a time resolution of 5-30 mins, and thus are not well suited to obtain physiological information, such as rates of extracellular processes or the relationship between neurochemical levels and behavior. Building from recent work in our laboratory, we developed instrumentation for making dopamine measurements in awake and freely moving rats over extended periods at one-minute time resolution using microdialysis sampling analyzed with online HPLC (Fast Microdialysis). Fast Microdialysis was used to investigate the beneficial effects of the anti-inflammatory drug dexamethasone (DEX) to penetration injury caused by dialysis probe implantation. Retrodialysis of DEX was found to potentiate both basal levels and stimulated release of striatal DA. Applying Fast Microdialysis to behavioral studies necessitated creation of a rotating operant chamber. Using this device, both trained- and untrained- animal’s behavior correlated with DA release, however with different characteristics. Robust and fast determination of DA allowed for creation of new microdialysis techniques in the non-steady state regime to investigate morphology and neurotransmitter regulation. Thus, a comprehensive mathematical model was created to analyze transient responses measured by Fast Microdialysis. A robust, adaptive random sampling simplex approach was used to fit the model to transient data. Striatal tissue tortuosity, porosity and the reuptake rate constant of DA were determined from a single transient response on awake and freely moving rats

Theoretical Developments and Practical Aspects of Dynamic Systems in Wind Energy Applications

The availability of offshore wind resources in coastal regions along with a high concentration of load centers in these areas makes offshore wind energy an attractive opportunity. Infrastructure costs and operation and maintenance costs for offshore wind technology, however, are significant obstacles that need to be overcome to make offshore wind a viable option. Vertical-axis wind turbines (VAWTs) are potentially ideal candidates for large offshore wind energy applications, and may provide a means to significantly reduce life-cycle costs associated with offshore wind energy. This has motivated the development of a flexible and extensible modular analysis framework for investigating VAWT designs. The Offshore Wind Energy Simulation toolkit contains a modular analysis framework that provides a general interface to external modules such as aerodynamics, hydrodynamics/platform dynamics, and generator/drive-train modeling software. Theoretical developments in dynamic systems are also presented in this work. Implicit time integration methods are investigated for their applicability to Gyric systems (flexible systems undergoing general rotational motion). An energy conserving integration method for conventional flexible systems are considered and proven to be energy preserving for Gyric systems. A new, efficient procedure for developing linearized representation of discrete dynamic systems is also presented. Two existing approaches for developing linear representations are combined to arrive at a new, more efficient linearization procedure that overcomes the pitfalls of the individual approaches alone. Furthermore, aeroelastic stability is a known issue for large, flexible structures under aerodynamic loads, and aeroelastic analysis was considered in the development of wind energy design tools. Finally, an investigation of the structural dynamics of offshore VAWT structure is conducted. A fundamental understanding of a resonance in VAWT configurations is sought, and the effects of support conditions on dynamic response of VAWT configurations is explored

Application of constrained optimisation techniques in electrical impedance tomography

A Constrained Optimisation technique is described for the reconstruction of temporal resistivity images. The approach solves the Inverse problem by optimising a cost function under constraints, in the form of normalised boundary potentials. Mathematical models have been developed for two different data collection methods for the chosen criterion. Both of these models express the reconstructed image in terms of one dimensional (I-D) Lagrange multiplier functions. The reconstruction problem becomes one of estimating these 1-D functions from the normalised boundary potentials. These models are based on a cost criterion of the minimisation of the variance between the reconstructed resistivity distribution and the true resistivity distribution. The methods presented In this research extend the algorithms previously developed for X-ray systems. Computational efficiency is enhanced by exploiting the structure of the associated system matrices. The structure of the system matrices was preserved in the Electrical Impedance Tomography (EIT) implementations by applying a weighting due to non-linear current distribution during the backprojection of the Lagrange multiplier functions. In order to obtain the best possible reconstruction it is important to consider the effects of noise in the boundary data. This is achieved by using a fast algorithm which matches the statistics of the error in the approximate inverse of the associated system matrix with the statistics of the noise error in the boundary data. This yields the optimum solution with the available boundary data. Novel approaches have been developed to produce the Lagrange multiplier functions. Two alternative methods are given for the design of VLSI implementations of hardware accelerators to improve computational efficiencies. These accelerators are designed to implement parallel geometries and are modelled using a verification description language to assess their performance capabilities

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Extraction of anthropological data with ultrasound

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.Human body scanners used to extract anthropological data have a significant drawback, the subject is required to undress or wear tight fitting clothing. This thesis demonstrates an ultrasonic based alternative to the current optical systems, that can potentially operate on a fully clothed subject. To validate the concept several experiments were performed to determine the acoustic properties of multiple garments. The results indicated that such an approach was possible. Beamforming is introduced as a method by which the ultrasonic scanning area can be increased, the concept is thoroughly studied and a clear theoretical analysis is performed. Additionally, Matlab has been used to demonstrate graphically, the results of such analysis, providing an invaluable tool during the simulation, experimental and results stages of the thesis. To evaluate beamfoming as a composite part of ultrasonic body imaging, a hardware solution was necessary. During the concept phase, both FPGA and digital signal processors were evaluated to determine their suitability for the role. An FPGA approach was finally chosen, which allows highly parallel operation, essential to the high acquisition speeds required by some beamforming methodologies. In addition, analogue circuitry was also designed to provide an interface with the ultrasonic transducers, which, included variable gain amplifiers, charge amplifiers and signal conditioning. Finally, a digital acquisition card was used to transfer data between the FPGA and a desktop computer, on which, the sampled data was processed and displayed in a coherent graphical manner. The beamforming results clearly demonstrate that imaging multiple layers in air, with ultrasound, is a viable technique for anthroplogical data collection. Furthermore, a wavelet based method of improving the axial resolution is also proposed and demonstrated

Low-Cost Paper-Based Assays for Multiplexed Genetic Analysis using Surface Enhanced Raman Spectroscopy

In order to improve human health it is critical to develop low-cost sensors for chemical detection and healthcare applications. Low-cost chemical detectors can enable pervasive monitoring to identify health threats. Rapid yet accessible infectious disease diagnostics have the potential to improve patient quality of care, reduce healthcare costs and speed recovery. In both cases, when multiple targets can be detected with a single test (multiplexing), accessibility is improved through lowered costs and simplicity of operation. In this work we have investigated the practical considerations and applications of ink-jet printed paper surface enhanced Raman spectroscopy (SERS) devices. SERS enables specific simultaneous detection of numerous analytes using a single excitation source and detector. Sensitive detection is demonstrated in several real-world applications. We use a low-cost portable spectrometer for detection, further emphasizing the potential for on-site detection. These ink-jet printed devices are then used to develop a novel DNA detection assay, in which the multiplexing capabilities of SERS are combined with DNA amplification through polymerase chain reaction (PCR). In this assay, the chromatographic properties of paper are leveraged to perform discrimination within the substrate itself. As a test case, this assay is then used to perform duplex detection of the Methicillin-resistant Staphylococcus aureus (MRSA) genes mecA and femB, two genes which confer antibiotic resistance on MRSA. Finally, we explore statistical multiplexing methods to enable this assay to be applied to perform highly-multiplexed detection gene targets (5+), and demonstrate the differentiation of these samples using partial least-squares regression (PLS). By averaging the signal over a region of the SERS substrate, substrate variability was mitigated allowing effective identification and differentiation, even for the complex spectra from highly multiplexed samples which were impossible to visually analyze

Smart antennas for strategic environment

The objective of this Thesis consists in presenting in a concise and effective way the results achieved during researches and studies on satellite systems interference sources, advanced antenna arrays for satellite systems to mitigate the increasing anti-interference needs and on an innovative way to generates inhomogeneous wave in lossless media for contributing to the design of a novel type of antenna for deep penetrating lossy media