Abstract An improvement and more accuracy method for spectrum analysis has been achieved in this thesis. There are three major parts in this thesis: the signal parameter estimation, the optimization of spectrum analysis, and the supervision to electric machinery. All these parts suggest the improvement ways to theories and applications of signal process. Parameter estimation is the base of dynamic designs, controls, and supervisions. This thesis infers the complete method to estimate parameters. The method estimates signal parameters in frequency domain. In electric machinery analysis, the most signals can consist of complex exponents. The component parameters include frequency, damping, amplitude, and phase. Basing on the damping existed or not, signals can be classified into two parts: periodic and non-periodic. Each complex exponent component will produce its band on spectrum. This method references the scales with highest amplitudes to estimate exact parameters. In suitable conditions, these mathematical equations can be simplified substantially to save computing time. The developed technologies of spectrum analysis take FFT to deal with the time-frequency transform work extensively. However, the sample of discrete signal is at random, and FFT suffers specific restrictions. When FFT transforms signal into frequency domain, the signal will cause errors on spectrum inevitably. This thesis corrects the errors by the optimization method. When frequency scales can match with signal characteristics, the picket-fence effect and leakage effect that the signal caused on spectrum will decrease to minimum. This method consists of three new technologies: parameter estimation, selection for optimal scale parameters, and adjustable spectrum. The method not only displays signal parameters on spectrum exactly and clearly, but also keeps the ability of fast process. When analyzing the more complex signal, the result of optimization will be restricted. Under this condition, the method can focus on the partial components and analyze them, then the result will keep accurate. This thesis combines supervisory technologies via a signal measurement. The signal sampling of these technologies is more convenient and simple. The system monitors operating conditions and fault conditions of the electric machinery with sound signal analysis. This signal analysis not only keeps normal measurement in the place which other signals can¡¦t be detected, but also can expand the monitoring ability. In operation conditions, the system monitors the speed and the input power of electric machinery through sound signal analysis. In fault conditions, the system recognizes type of fault under variation loads successfully. The recognition system is established by artificial neural network. The improvement of recognition ability is also discussed in this thesis. The methods discussed in the thesis give powerful estimation method for the signal analysis accurately and practically.

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Measuring physiological parameters or vital sign using camera has become popular in recent years. Contact-less monitoring and extraction of vital signs can be important source of information in situations like medical care system and safety control system. This paper presents the implementation of real-time, non-contact method for extraction of vital signs, heart rate in this case. A better face tracking method is used for efficient face detection. This study extends some of the previous works done and have a comparison study with several methods. The developed system used filtering with window over the green channel of the signal and then Converted to frequency domain to analyze the signal to detect heart rate. The developed system achieved high correlation and showed small error while referencing with actual heart signal from ECG. This method delivers better result in better light condition but gives fairly good result on lower light as well.

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published_or_final_version / Physics / Master / Master of Philosophy

The parameter estimation problem or the inverse problem of ordinary differential equations is prevalent in many process models in chemistry, molecular biology, control system design and many other engineering applications. It concerns the re-construction of auxillary parameters by fitting the solution from the system of ordinary differential equations( from a known mathematical model) to that of measured data obtained from observing the solution trajectory. Some of the traditional techniques (for example, initial value technques, multiple shooting, etc.) used to solve this class of problem have been discussed. A new algorithm, motivated by algorithms proposed by Childs and Osborne(1996) and Z.F.Li's dissertation(2000), has been proposed. The new algorithm inherited the advantages exhibited in the above-mentioned algorithms and, most importantly, the parameters can be transformed to a form that are convenient and suitable for computation. A statistical analysis has also been developed and applied to examples. The statistical analysis yields indications of the tolerance of the estimates and consistency of the observations used.

Evolutionary Algorithms are search algorithms based on the Darwinian metaphor of “Natural Selection”. Typically these algorithms maintain a population of individual solutions, each of which has a fitness attached to it, which in some way reflects the quality of the solution. The search proceeds via the iterative generation, evaluation and possible incorporation of new individuals based on the current population, using a number of parameterised genetic operators. In this thesis the phenomenon of Self Adaptation of the genetic operators is investigated. A new framework for classifying adaptive algorithms is proposed, based on the scope of the adaptation, and on the nature of the transition function guiding the search through the space of possible configurations of the algorithm. Mechanisms are investigated for achieving the self adaptation of recombination and mutation operators within a genetic algorithm, and means of combining them are investigated. These are shown to produce significantly better results than any of the combinations of fixed operators tested, across a range of problem types. These new operators reduce the need for the designer of an algorithm to select appropriate choices of operators and parameters, thus aiding the implementation of genetic algorithms. The nature of the evolving search strategies are investigated and explained in terms of the known properties of the landscapes used, and it is suggested how observations of evolving strategies on unknown landscapes may be used to categorise them, and guide further changes in other facets of the genetic algorithm. This work provides a contribution towards the study of adaptation in Evolutionary Algorithms, and towards the design of robust search algorithms for “real world” problems.

Mathematical models are often used to describe and, in some cases predict, excitable cellular behaviour that is based on observed experimental results. With the increase of computational power, it is now possible to solve such models in a relatively short time. This, along with an increasing knowledge of cellular and subcellular processes, has led to the development of a large number of complex cellular models, capable of describing a broad range of excitable cell behaviour. But the use of complex models can also lead to problems. Most models can accurately reproduce results associated with the data on which the models are based. However, results from complicated models, with large numbers of variables and parameters, are less reliable if the model is not placed under the same physiological conditions as defined by the model author. In order to test a model??s suitability and robustness over a range of physiological conditions, one needs to fit model parameters against experimental data observed under those conditions. By using the modelling standard and repository offered by CellML, model users can easily select and adapt a large number of models to set up their own applications to fit model parameters against user-supplied experimental data. However, currently there is a lack of software that can utilise CellML model for parameter fitting. In this thesis, a Java-based utility has been developed, capable of performing least square parameter optimisation for a wide range of CellML models. Using the developed software, a number of parameter fits and identifiability analyses were performed on a selected group of CellML models. It was found that most of the models were ill-formed, with larger numbers of parameters worsening model identifiability. In some cases, the usage of multiple datasets and different objective functions can improve model identifiability. Finally, the developed software was used to perform parameter optimisation against two sets of action potentials from a sinoatrial node experiment, in the absence and presence of E9031, a specific ion channel blocker.

Mathematical models are often used to describe and, in some cases predict, excitable cellular behaviour that is based on observed experimental results. With the increase of computational power, it is now possible to solve such models in a relatively short time. This, along with an increasing knowledge of cellular and subcellular processes, has led to the development of a large number of complex cellular models, capable of describing a broad range of excitable cell behaviour. But the use of complex models can also lead to problems. Most models can accurately reproduce results associated with the data on which the models are based. However, results from complicated models, with large numbers of variables and parameters, are less reliable if the model is not placed under the same physiological conditions as defined by the model author. In order to test a model??s suitability and robustness over a range of physiological conditions, one needs to fit model parameters against experimental data observed under those conditions. By using the modelling standard and repository offered by CellML, model users can easily select and adapt a large number of models to set up their own applications to fit model parameters against user-supplied experimental data. However, currently there is a lack of software that can utilise CellML model for parameter fitting. In this thesis, a Java-based utility has been developed, capable of performing least square parameter optimisation for a wide range of CellML models. Using the developed software, a number of parameter fits and identifiability analyses were performed on a selected group of CellML models. It was found that most of the models were ill-formed, with larger numbers of parameters worsening model identifiability. In some cases, the usage of multiple datasets and different objective functions can improve model identifiability. Finally, the developed software was used to perform parameter optimisation against two sets of action potentials from a sinoatrial node experiment, in the absence and presence of E9031, a specific ion channel blocker.

Thesis (Ph. D.)--Virginia Polytechnic Institute, 1963. / Vita. Abstract. Includes bibliographical references (leaves 124-126). Also available via the Internet.