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Peripheral coupled waveguide (PCW) design has been deployed in InGaAsP multiple quantum-well (MQW) electroabsorption modulator (EAM) at 1.55-mum wavelength. PCW enhances the optical saturation power and reduces the optical insertion loss and the equivalent V-pi simultaneously. A radio-frequency link using a 1.3-mm-long lumped-element PCW EAM has achieved experimentally a link gain of -3 dB, at 500 MHz and at input optical power of 80 mW. The corresponding two-tone multioctave spurious-free dynamic range (SFDR) at the same bias is measured at 118 dB (.) Hz(2/3). The single-octave SFDR at the third-order null bias is 132 dB (.) Hz(4/5).

We first model and analyze distributed systems including distributed sensors and actuators. We then consider identification of distributed systems via adaptive wavelet neural networks (AWNNs) by taking advantage of the multiresolution property of wavelet transforms and the parallel computational structure of neural networks. A new systematic approach is developed in this dissertation to construct an optimal discrete orthonormal wavelet basis with compact support for spanning the subspaces employed for system identification and signal representation. We then apply a backpropagation algorithm to train the network to approximate the system. Filter banks for parameterizing wavelet systems are studied. An analog VLSI implementation architecture of the AWNN is also given in this dissertation. This work is applicable to signal representation and compression under optimal orthonormal wavelet bases in addition to progressive system identification and modeling. We anticipate that this work will find future applications in signal processing and intelligent systems.

We study the problem of choosing the optimal wavelet basis with compact support for signal representation and provide a general algorithm for computing the optimal wavelet basis. We first briefly review the multiresolution property of wavelet decomposition and the conditions for generating a basis of compactly supported discrete wavelets in terms of properties of quadrature mirror filter (QMF) banks. We then parametrize the mother wavelet and scaling function through a set of real coefficients. We further introduce the concept of information measure as a distance measure between the signal and its projection onto the subspace spanned by the wavelet basis in which the signal is to be reconstructed. The optimal basis for a given signal is obtained through minimizing this information measure. We have obtained explicitly the sensitivity of dilations and shifts of the mother wavelet with respect to the coefficient set. A systematic approach is developed here to derive the information gradient with respect to the parameter set for a given square integrable signal and the optimal wavelet basis. A gradient based optimazation algorithm is developed in this paper for computing the optimal wavelet basis.

We study the problem of choosing the optimal wavelet basis with compact support for signal representation and provide a general algorithm for computing the optimal wavelet basis. We first briefly review the multiresolution property of wavelet decomposition and the conditions for generating a basis of compactly supported discrete wavelets in terms of properties of quadrature mirror filter (QMF) banks. We then parametrize the mother wavelet and scaling function through a set of real coefficients. We further introduce the concept of decomposition entropy as an information measure to describe the distance between the given signal and its projection onto the subspace spanned by the wavelet basis in which the signal is to be reconstructed. The optimal basis for a given signal is obtained through minimizing this information measure. We have obtained explicitly the sensitivity of dilations and shifts of the mother wavelet with respect to the coefficient set. A systematic approach is developed in this paper to derive the information gradient with respect to the parameter set from a given square integrable signal and a discrete basis of wavelets. The existence of the optimal basis for the wavelets has been proven in this paper. a gradient based optimization algorithm is developed for computing the optimal wavelet basis.

This paper introduces the principles and architectures of high definition televisions (HDTV) and analyzes the functions of HDTV's digital components. It compares the current analog HDTV systems with the prototypes of digital HDTVs. It then summarizes the latest developments in digital HDTV technologies and identifies current technology targets. Finally, this paper predicts future research directions in digital HDTVs.

We describe a design technique for optimal control in active structural vibration damping using smart materials. The vibration of a cantilever beam is stabilized by using distributed sensors and actuators. We model the beam by the Timoshenko beam model together with the distributed sensors and actuators. A control law using the weighted integration of vibration velocity is incorporated in the closed loop system. We propose a method to find the optimal layout design of the smart material so as to maximize the damping effect. An objective functional is defined based on the vibration energy of the system. The optimal shapes of the sensor and actuator are determined through minimizing the energy functional of the beam over the admissible shaped function space subject to certain geometric constraints. An algorithm has been developed to determine the optimal sensor and actuator layout. This method can be generalized to the plate damping problem and more complicated structures as well.

We consider identification of distributed systems via adaptive wavelet neural networks (AWNNs). We take advantage of the multiresolution property of wavelet systems and the computational structure of neural networks to approximate the unknown plant successively. A systematic approach is developed in this paper to find the optimal discrete orthonormal wavelet basis with compact support for spanning the subspaces employed for system identification. We then apply backpropagation algorithm to train the network with supervision to emulate the unknown system. This work is applicable to signal representation and compression under the optimal orthonormal wavelet basis in addition to autoregressive system identification and modeling. We anticipate that this work be intuitive for practical applications in the areas of controls and signal processing.