Engineering Commons^™

Bulk Micromachining Of Silicon For Moems Prototype, Sung-Dong Suh

Doctoral Dissertations

In this dissertation, the optical application of silicon micromachining technology was investigated in order to create the three-dimensional microstructures that can be used as the components for the MOEMS prototype. These microstructures were designed and fabricated by utilizing corner compensation techniques and silicon bulk micromachining technologies. The fabricated microstructures are silicon mirror arrays that have a 1250 μm etch depth and through-holes across the OE-MCM substrate that has sixteen-fan-out OCDN on front side and a 1mm thickness.

Guided-wave OCDN on MCMs are designed and fabricated to meet the high-speed clocking requirements of next-generation digital systems through a realization of superior …

Kinetics Of Laser Chemical Vapor Deposition Of Carbon And Refractory Metals, Feng Gao

Doctoral Dissertations

Three-dimensional laser chemical vapor deposition (3D-LCVD) has been used to grow rods of carbon, tungsten, titanium, and hafnium from a variety of hydrocarbons and metal halide-based precursors. A novel computerized 3D-LCVD system was designed and successfully used in the experiments. A focused Nd:Yag laser beam (λ = 1.06 μm) was utilized to locally heat up a substrate to deposition temperature. The rods, which grew along the axis of the laser beam, had a typical diameter of 30–80 μm and a length of about 1 mm. The precursors for carbon deposition were the alkynes: propyne, butyne, pentyne, hexyne, and octyne. Propyne …

Cramer-Rao Bound And Optimal Amplitude Estimator Of Superimposed Sinusoidal Signals With Unknown Frequencies, Shaohui Jia

Doctoral Dissertations

This dissertation addresses optimally estimating the amplitudes of superimposed sinusoidal signals with unknown frequencies. The Cramer-Rao Bound of estimating the amplitudes in white Gaussian noise is given, and the maximum likelihood estimator of the amplitudes in this case is shown to be asymptotically efficient at high signal to noise ratio but finite sample size. Applying the theoretical results to signal resolutions, it is shown that the optimal resolution of multiple signals using a finite sample is given by the maximum likelihood estimator of the amplitudes of signals.

Bottom-Up Design Of Artificial Neural Network For Single-Lead Electrocardiogram Beat And Rhythm Classification, Srikanth Thiagarajan

Doctoral Dissertations

Performance improvement in computerized Electrocardiogram (ECG) classification is vital to improve reliability in this life-saving technology. The non-linearly overlapping nature of the ECG classification task prevents the statistical and the syntactic procedures from reaching the maximum performance. A new approach, a neural network-based classification scheme, has been implemented in clinical ECG problems with much success. The focus, however, has been on narrow clinical problem domains and the implementations lacked engineering precision. An optimal utilization of frequency information was missing. This dissertation attempts to improve the accuracy of neural network-based single-lead (lead-II) ECG beat and rhythm classification. A bottom-up approach defined …