The application of neural networks in the area of signal processing is examined. Two major areas are identified and suitable neural networks are developed. In the first area, neural
networks are used as a tool for the design of digital filters. In the second area, neural
networks are used for processing bathymetric data.
The field of artificial neural networks is first introduced with an emphasis on Hopfield
networks. The optimizing capabilities of such networks are noted. Based on these networks,
a feedback neural network is developed for the design of 1-D finite-duration impulse response
(FIR) filters on the basis of given amplitude responses. A suitable cost function is formulated
first and an associated network is developed. This work is then extended to the design of two
more networks for the design of FIR filters based on given amplitude and phase responses
and prescribed specifications. The idea is extended to the design of 2-D FIR filters. Two
networks are presented for designing 2-D FIR filters on the basis of a given amplitude
response and prescribed specifications. The design of 1-D infinite-duration impulse response
(IIR) filters is studied next and two networks are developed. The first one is to design filters
with prescribed specifications in the magnitude-squared domain. The other network designs
IIR filters for a given frequency response. A network for designing equiripple 1-D FIR filters
based on the weighted least-squares technique is presented next. A new updating algorithm
is developed for this network.
Two different neural networks are proposed for classifying lidar waveforms into various
categories. A single-layer network is developed for classifying lidar waveforms representing
milt of varied densities. A fast version of the supervised learning algorithm is presented.
A threshold term is also introduced in the recall phase to give the user flexibility to accept
or reject any waveform. A two-stage, multi-layer network is presented next which uses
waveform characteristics to assign a signature number to the waveform. This network
extracts various ocean parameters from the waveforms as well.
The issue of implementing the feedback neural network is addressed next. Basic building
blocks for implementing such networks are identified and a network is constructed from
circuits existing in the literature. The network is simulated in Cadence using 0.8 μ BICMOS
technology. The results show that these networks have a high potential to be implemented
in analog VLSI for real-time signal processing. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/9693 |
| Date | 13 July 2018 |
| Creators | Bhattacharya, Dipankar |
| Contributors | Antoniou, Andreas |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
Page generated in 0.0017 seconds