The importance of spectral efficiency in mobile communications often requires the use of non-constant-envelop linear digital modulation schemes. These modulation techniques carry signal information in both magnitude and phase, thus they must be linearly amplified to avoid nonlinear signal distortion which is not correctable in a typical receiver. A second difficulty in utilizing these modulation formats is that nonlinear amplification generates out-of-band power (spectral regrowth). Therefore, to achieve both high energy efficiency and spectral efficiency, some forms of linearization must be used to compensate for the nonlinearity of power amplifiers. One powerful technique that is amenable to monolithic integration is digital signal predistortion. Most predistorters try to achieve the inverse nonlinear characteristic of High Power Amplifier(HPA). In this thesis a new multi-stage digital adaptive signal predistorter is presented. The scheme is developed from the direct iterative method with low memory requirement proposed by Cavers [1] in combination with the multi-stage predistortion proposed by Stonick [2]. To make the predistorter more compact a very simple and fast method called the complementary method is proposed. The complementary method has prominent advantages over other digital predistorters in terms of stability of the algorithm, complexity of the algorithm and computational load. / Graduation date: 2001

Traditionally sinusoidal signal generation has been implemented with purely analog circuits such as phase-locked loops. The alternative of using a digital system to perform this signal generation has previously been unattractive due to limitations in clock frequency and size. However, recent advancements in sub-micron fabrication techniques have made the digital alternative tractable. The advantages of a digitally implemented signal frequency synthesizer include finer control of output frequency, reduced frequency drift due to part degradation over time, and faster response time for frequency change. Digital frequency synthesis has been previously realized using the Tierney, Rader, and Gold phase accumulator architecture. This method utilizes a variable-increment digital integrator that is input to a read-only memory. This memory then generates a quantized amplitude value. This thesis presents an alternative method for digital frequency synthesis based on circular interpolation and compares it to the performance of a comparable phase-accumulator structure for varying bit accuracies of phase. The comparison of transistor count and required die-size for each method reveals a lower requirement of both resources in the case of the new circle interpolator. Evaluation of the discrete-time spectral purity of synthesized signals also demonstrates less out of band noise in the new design. Finally, analysis of energy efficiency shows the new design to be generally optimal compared to the reference design. / Graduation date: 2001

A comparison of two novel demodulators. The first is a basic zero crossing demodulator, as introduced by Beards. The second is an approach proposed by Hovin. The two demodulators are compared to each other and to the conventional method of demodulation. / Graduation date: 1998

Thesis (Ph.D.)--University of Delaware, 2007. / Principal faculty advisor: Kenneth E. Barner, Dept. of Electrical and Computer Engineering. Includes bibliographical references.

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 107-113).

Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

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Although discriminative approaches like the support vector machine or logistic regression have had great success in many pattern recognition application, they have only achieved limited success in speech recognition. Two of the difficulties often encountered include 1) speech signals typically have variable lengths, and 2) speech recognition is a sequence labeling problem, where each spoken utterance corresponds to a sequence of words or phones. In this thesis, we present a framework for automatic speech recognition using logistic regression. We solve the difficulty of variable length speech signals by including a mapping in the logistic regression framework that transforms each speech signal into a fixed-dimensional vector. The mapping is defined either explicitly with a set of hidden Markov models (HMMs) for the use in penalized logistic regression (PLR), or implicitly through a sequence kernel to be used with kernel logistic regression (KLR). Unlike previous work that has used HMMs in combination with a discriminative classification approach, we jointly optimize the logistic regression parameters and the HMM parameters using a penalized likelihood criterion. Experiments show that joint optimization improves the recognition accuracy significantly. The sequence kernel we present is motivated by the dynamic time warping (DTW) distance between two feature vector sequences. Instead of considering only the optimal alignment path, we sum up the contributions from all alignment paths. Preliminary experiments with the sequence kernel show promising results. A two-step approach is used for handling the sequence labeling problem. In the first step, a set of HMMs is used to generate an N-best list of sentence hypotheses for a spoken utterance. In the second step, these sentence hypotheses are rescored using logistic regression on the segments in the N-best list. A garbage class is introduced in the logistic regression framework in order to get reliable probability estimates for the segments in the N-best lists. We present results on both a connected digit recognition task and a continuous phone recognition task.

Dysphagia (swallowing disorder) is a common health problem that degrades the quality of life of many people. The videofluoroscopic swallowing study (VFSS) is the current gold standard in dysphagia assessment but is associated with high cost, long wait times, and a lack of portability. As a result, there is a pining need for an alternative technique that can serve day-to-day monitoring of dysphagia as well as screening for VFSS referral. The primary objective of this thesis was to investigate three non-invasive signal modalities, namely dual-axis accelerometry, submental mechanomyography (MMG), and nasal airflow, for their potential as alternatives to VFSS. To this end, signals were acquired from 17 healthy individuals and 24 patients with dysphagia, with various stimuli. In a characterization study, the anterior-posterior (A-P) and superior-inferior (S-I) axes in dual-axis accelerometry were found to contain non-overlapping information about swallowing, justifying the extension of single-axis (A-P only) to dual-axis (A-P and S-I) accelerometry. Also, several dual-axis accelerometry signal features were found to be stimulus dependent, and the observed stimulus effects were linked to slower swallowing function with increasing bolus viscosity. Age and stimulus effects on submental MMG were scrutinized, as an analogy to previous electromyography (EMG) studies of similar design. Similarities to EMG confirmed the validity of MMG as a muscle activity measurement tool in swallowing research. Automatic swallow segmentation, which is a crucial precursory step to swallow diagnosis, was investigated with artificial neural networks. Segmentation performance was shown to improve as more signal modalities were included, verifying the value of multi-sensor fusion. When all signal modalities were utilized, an adjusted accuracy of 89.6% was achieved. Automatic discrimination between healthy and abnormal swallows was investigated in two studies. Using previously collected pediatric data, a radial basis classifier based only on A-P accelerometry resulted in an adjusted accuracy of 81.3% in aspiration detection. In an adult study, linear discriminant classifiers resulted in adjusted accuracies of 74.7%, 83.7%, and 84.2% for aspiration, valleculae residue, and pyriform sinus residue detection, respectively. It was concluded that the three signal modalities analyzed in this thesis possess promising potential for abnormal swallow detection.

Algorithms for analyzing laser interferometry signals were developed and adopted to the computer based processing of small displacement measurements. These methods, matrix operation approach and fixed parameters approach, are based on signal phase calculation and are able to replace complex fringe counting electronic circuits. The matrix operation provides an approach for instantaneously displaying the results. The computer fixed parameters analysis allows the laser intensity to vary arbitrarily during a measurement. Displacement caused by a piezoelectric crystal was measured. Second order polynomial curve fitting was performed. The root mean square error is found to be 0.0086 μm in this 8-bit data acquisition system. CTEs of a fused silica plate and a tube were measured by an interferometry system. Signals were analyzed by both manual chart approach and computer based fixed parameters approach. Results agree well with published data. The accuracy of the CTE measurement system was 4 μ€, one third of the reference NBS SRM 739 suggested standard deviation. Out-of-plane and in-plane displacements can be measured independently from speckle interferometry. Their resolutions are 0.3164 μm/cycle for the out-of-plane configuration and 0.224 μm/cycle for the in-plane configuration with light incident angle of 45°. Optical systems with Fast Fourier Transform data analysis showed that the minimum detectable vibration amplitudes were 0.0065 μm, 0.0038 μm, and 0.0010 μm for the out-of-plane speckle, the in-plane speckle, and Michelson interferometry systems respectively. Resonance frequency of a steel rod was found by the optical non-contact sensing system. The modulus of elasticity calculated from the resonance frequency was close to the literature data, 182 GPa vs. 200 GPa. / Graduation date: 1992