A systems approach to device-circuit interaction in electrical power processing Annual status report, 1 Jun. 1967 - 31 May 1968

Initial research on switched and modulated networks, tunable and bandwith-adjustable filter and FET current density for device circuit interaction in power processin

A Nonstationary Model of Newborn EEG

The detection of seizure in the newborn is a critical aspect of neurological research. Current automatic detection techniques are difficult to assess due to the problems associated with acquiring and labelling newborn electroencephalogram (EEG) data. A realistic model for newborn EEG would allow confident development, assessment and comparison of these detection techniques. This paper presents a model for newborn EEG that accounts for its self-similar and non-stationary nature. The model consists of background and seizure sub-models. The newborn EEG background model is based on the short-time power spectrum with a time-varying power law. The relationship between the fractal dimension and the power law of a power spectrum is utilized for accurate estimation of the short-time power law exponent. The newborn EEG seizure model is based on a well-known time-frequency signal model. This model addresses all significant time-frequency characteristics of newborn EEG seizure which include; multiple components or harmonics, piecewise linear instantaneous frequency laws and harmonic amplitude modulation. Estimates of the parameters of both models are shown to be random and are modelled using the data from a total of 500 background epochs and 204 seizure epochs. The newborn EEG background and seizure models are validated against real newborn EEG data using the correlation coefficient. The results show that the output of the proposed models has a higher correlation with real newborn EEG than currently accepted models (a 10% and 38% improvement for background and seizure models, respectively)

Investigation of target detection in noncoherent systems with colored noise

Design efforts concerning the problem of detecting moving ground targets from an airborne platform with a noncoherent radar have been concentrated in the area of video filter design. The filter formulation generally follows an empirical path with no generally acceptable criterion for an optimum processor. This Thesis considers several problem formulations which are based on a Neyman-Pearson detection criteria. A square-law second detector is assumed and the resulting likelihood ratio shown to be too complex for closed form solution. The problem is reformulated in terms of sequences using complex random variable representations and the likelihood ratio is investigated. A test statistic is derived and discussed in terms of a practical implementation. A suboptimum receiver is implemented in the video frequency region and compared with existing MTI processors by using computer simulation programs. A clutter rejection video filter shaped in accordance with the optimum receiver derivation is shown to have some advantage over conventional shaping with which it is compared --Abstract, page ii

Analysis and design of space vehicle flight control systems. Volume VI - Stochastic effects

Statistical methods for analyzing stochastic effects influencing design of space vehicle control system

New ultrasonic signal processing techniques for NDE applications

New ultrasonic signal processing techniques have been developed for nondestructive evaluation (NDE) applications. This dissertation has two parts. The first part is about the application of the wavelet transform to ultrasonic flaw detection. Wavelet transform is a newly developed signal analysis tool that handles time-localized signals such as an ultrasonic flaw signal quite well. A wavelet transform based signal processing technique has been developed which uses only partial knowledge of the flaw signal waveform that may be obtained from a reference experiment. The detection performance of the proposed technique is found to be comparable to that of the matched filter which requires exact knowledge of the flaw signal waveform and the noise autocorrelation function to obtain good detection performance. The proposed technique based on the wavelet transform can therefore be quite useful in situations where the flaw signal waveform is unknown or partially known. The detection performance of the proposed technique which was evaluated for hard-alpha detection in titanium samples using experimentally obtained grain noise data and simulated flaw data was very close to that of the matched filter;The second part of this dissertation describes a Kalman filter based deconvolution algorithm for ultrasonic signals and its application to material characterization and hard-alpha detection. The Kalman filter based deconvolution algorithm is based on state-space modeling of the ultrasonic measurement system. Since the Kalman filter can handle time-varying systems and non-stationary statistics quite naturally, it is better suited for such situations than the Wiener filter approach. A signal processing technique using Kalman filter based deconvolution algorithm has been developed and applied to characterize materials with different grain sizes and to detect inclusions from host material. The proposed method was tested using experimentally obtained ultrasonic data from pure titanium samples with different grain sizes. The results showed good detection performance for detecting inclusions larger that 4 mm

Stochastic reaction networks with input processes: Analysis and applications to reporter gene systems

Stochastic reaction network models are widely utilized in biology and chemistry to describe the probabilistic dynamics of biochemical systems in general, and gene interaction networks in particular. Most often, statistical analysis and inference of these systems is addressed by parametric approaches, where the laws governing exogenous input processes, if present, are themselves fixed in advance. Motivated by reporter gene systems, widely utilized in biology to monitor gene activation at the individual cell level, we address the analysis of reaction networks with state-affine reaction rates and arbitrary input processes. We derive a generalization of the so-called moment equations where the dynamics of the network statistics are expressed as a function of the input process statistics. In stationary conditions, we provide a spectral analysis of the system and elaborate on connections with linear filtering. We then apply the theoretical results to develop a method for the reconstruction of input process statistics, namely the gene activation autocovariance function, from reporter gene population snapshot data, and demonstrate its performance on a simulated case study