Engineering Commons^™

Efficient Algorithms And Implementations For Signal Processing, Changbai Xiao

Doctoral Dissertations

A scheme is presented to regain a finite number of lost samples from a Nyquist-rate-sampled band-limited signal f of finite energy by replenishing new sample values of the same number. The result can also be viewed as the solution to a special non-uniform sampling problem.

A scheme is also presented to recover a band-limited function f of finite energy from its sampling values on real sequences with an accumulation point. The result given here can also be viewed as an approach to the extrapolation problem of determination a band-limited function in terms of its given values on a finite interval. …

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 …

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 …