Harmonic distortions are inevitably caused by a rectifier and an inverter due to their inherent nonlinearities. An AC-DC-AC converter, configured by the series connection of a rectifier, DC link, and an inverter, induces harmonic distortions at both AC sides and at the DC link. These harmonics can nonlinearly interact or modulate the fundamental frequencies at the AC sides to cause interharmonic distortions. Harmonic and interharmonic distortions can seriously hamper the normal operation of the power system by means of side effects such as excitation of undesirable electrical and/or mechanical resonances, misoperation of control devices, and so forth. This dissertation presents effective methodologies to mitigate harmonic and interharmonic distortions by applying dithered pulse-width modulated (PWM) signals to a voltage-sourced inverter (VSI) type adjustable speed drive (ASD). The proposed methods are also efficient because the dithering applications are performed on control signals without the need for additional devices. By the help of dithering, the rejection bandwidth of a harmonic filter can be relaxed, which enables a lower-order configuration of harmonic filters. First, this dissertation provides a dithering application on gating signals of a sinusoidal PWM (SPWM) inverter in the simulated VSI-ASD model. The dithering is implemented by adding intentional noise into the SPWM process to randomize rising and falling edges of each pulse in a PWM waveform. As a result of the randomized edges, the periodicity of each pulse is varied, which result in mitigated harmonic tones. This mitigation of PWM harmonics also reduces associated interharmonic distortions at the source side of the ASD. The spectral densities at harmonic and interharmonic frequencies are quanti fied by Fourier analysis. It demonstrates approximately up to 10 dB mitigation of harmonic and interharmonic distortions. The nonlinear relationship between the mitigated interharmonics and harmonics is confirmed by cross bicoherence analysis of source- and DC-side current signals. Second, this dissertation proposes a dithered sigma-delta modulation (SDM) technique as an alternative to the PWM method. The dithering method spreads harmonic tones of the SD M bitstream into the noise level. The noise-shaping property of SDM induces lower noise density near the fundamental frequency. The SDM bitstream is then converted into SDM waveform after zero-order interpolation by which the noise-shaping property repeats at every sampling frequency of the bitstream. The advantages of SDM are assessed by comparing harmonic densities and the number of switching events with those of SPWMs. The dithered SD M waveform bounds harmonic and noise densities below approximately -30 dB with respect to the fundamental spectral density without increasing the number of switching events. Third, this dissertation provides additional validity of the proposed method via hardware experiments. For harmonic assessment, a commercial three-phase inverter module is supplied by a DC voltage source. Simulated PWM signals are converted into voltage waveforms to control the inverter. To evaluate interharmonic distortions, the experimental configuration is extended to a VSI-ASD model by connecting a three-phase rectifier to the inverter module via a DC link. The measured voltage and current waveforms are analyzed to demonstrate coincident properties with the simulation results in mitigating harmonics and interharmonics. The experimental results also provide the efficacy of the proposed methods; the dithered SPWM method effectively mitigates the fundamental frequency harmonics and associated interharmonics, and the dithered SDM reduces harmonics with the desired noise-shaping property. / text

Neurological diseases in newborns are usually first revealed by seizures, which are characterised by a synchronous discharge of a large number of neurons. Failure to control seizures may lead to brain damage or even death. The importance of this problem prompted many researchers to look for accurate automatic methods for seizure detection. Nonstationarity and multicomponent behaviour of newborn EEG signals made this task very challenging. The significant overlap in the characteristic of background and seizure activities in newborn EEG signals added to the difficulty of seizure detection. This research uses time-frequency based methods for automatic seizure detection. Since time-frequency signal analysis methods use joint representation in both time and frequency domains, they proved to be very suitable for analysis and processing of nonstationary and multicomponent signals such as newborn EEG. Before using any seizure detector, the EEG data is pre-processed in order to reduce the noise effects using a time-frequency based technique. The proposed method is based on the singular value decomposition (SVD) technique applied to the matrix representing the time-frequency distribution (TFD) of the EEG signal. It has been shown that by appropriately filtering the singular vectors associated with the TFD, one can effectively enhance the desired information embedded in the signal. Neonatal EEG seizures can have signatures in both low frequency (lower than 10 Hz) and high frequency (higher than 70 Hz) areas. The seizure detection techniques proposed in the literature concentrated on using either low frequency or high frequency signatures but not both simultaneously. These methods tend to miss the seizures that reveal themselves only in one of the two frequency areas. In this research, we propose a detection method that uses seizure features in both low and high frequency areas. To detect EEG seizures using the low frequency signatures, an SVD-based technique is employed. The technique uses the estimated distribution function of the singular vectors associated with the time-frequency distribution of EEG epochs to discriminate between seizure and nonseizure patterns. The high frequency signatures of seizures are mostly the result of spike events in the EEG signals. To detect these spike events, the signal is mapped into the TF domain. The high instantaneous energy of spikes is reflected as a localised energy in the high frequency area of the TF domain. Consequently, a spike can be seen as a ridge in this area of the TF domain. It has been shown that during seizure activity there is regularity in the distribution of the interspike intervals. This feature has been used as the basis for discriminating between seizure and nonseizure patterns. The performance results obtained by applying the proposed methods on EEG signals extracted from a number of newborns show the superiority of these methods over the existing ones.

This thesis deals with the problem of estimating the velocity of a mobile station (MS)in a mobile communication system using the instantaneous frequency (IF) of the received signal at the MS antenna. This estimate is essential for satisfactory handover performance, effective dynamic channel assignment, and optimisation of adaptive multiple access wireless receivers. Conventional methods for estimating the MS velocity are based either on the statistics of the envelope or quadrature components of the received signal. In chapter 4 of the thesis, we show that their performance deteriorates in the presence of shadowing. Other velocity estimators have also been proposed which require prior estimation of the channel or the average received power. These are generally difficult to obtain due to the non-stationary nature of the received signal. An appropriate window which depends on the unknown MS velocity must first be applied in order to accurately estimate the required quantities. Using the statistics of the IF of the received signal at the MS antenna given in chapter 3, new velocity estimators are proposed in chapter 4 of this thesis. The proposed estimators are based on the moments, zero-crossing rate, and covariance of the received IF. Since the IF of the received signal is not affected by any amplitude distortion, the proposed IF-based estimators are robust to shadowing and propagation path-loss. The estimators for the MS velocity in a macro- and micro-cellular system are presented separately. A macro-cell system can be considered as a special case of a micro-cell in which there is no line-of-sight component at the receiver antenna. It follows that those estimators which are derived for micro-cells can be used in a macro-cell as well. In chapter 4, we analyse the performance of the proposed velocity estimators in the presence of additive noise, non-isotropic scattering, and shadowing. We also prove analytically that the proposed velocity estimators outperform the existing methods in the presence of shadowing and additive noise. The proposed IF-based estimators need prior estimation of both the IF of the received signal and Ricean K-factor. The IF estimation in a typical wireless environment, can be considered as a special case of a general problem of IF estimation in the presence of multiplicative and additive noise. In chapter 5, we show that current time-frequency approaches to this problem which are based on the peak of a time-frequency distribution (TFD) of the signal, fail because of the special shape of the power spectral density of the multiplicative noise in a wireless environment. To overcome this drawback, the use of the first-order moment of a TFD is studied in chapter 5. Theoretical analysis and simulations show that the IF estimator based on the first-order moment of a TFD exhibits negligible bias when the signal-to-additive noise ratio is more than 10 dB. The Ricean K-factor is not only necessary for velocity estimation in micro-cells, but also is a measure of the severity of fading and a good indicator of the channel quality. Two new methods for estimating the Ricean K-factor based on the first two moments of the envelope of the received signal, are proposed in chapter 6. Performance analysis presented in chapter 6, prove that the proposed K estimators are robust to non-isotropic scattering. Theoretical analysis and simulations which are presented in chapters 4 and 7 of this thesis, prove that the proposed velocity and K estimators outperform existing estimators in the presence of shadowing and additive noise.