Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1990. / Vita. Abstract. Includes bibliographical references (leaves 251-253). Also available via the Internet.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1991. / Vita. Abstract. Includes bibliographical references (leaves 63-68). Also available via the Internet.

Thesis (Ph. D. in Mechanical Engineering)--Naval Postgraduate School, September 2002. / Dissertation supervisor: Indranath Dutta. Includes bibliographical references (p. 119-124). Also available online.

Thesis (M.S.)--Wisconsin. / Includes bibliographical references (leaves 118-122).

Thesis (M.S.) -- University of Texas at Dallas, 2006. / Includes vita. Includes bibliographical references (leaves 57-61)

During the last ten to fifteen years, a new class of electronic imaging devices has been created. Closely related to the visible-light Charge-Coupled Device, or CCD, this new class of components has extended electronic imaging capability into the near, middle, and long-wave infrared regions of the electromagnetic spectrum. Most notable among these new components are the IRCCD, a monolithic device, and the hybrid imaging device, which employs separately optimized detector and readout assemblies. Since the creation of the first infrared imaging device, there has been a continual effort to improve performance. One of the many problems faced by the designers of such devices is that of spatial response nonuniformity. This investigation considers the impact of spatial response nonuniformity on thermal imaging. The analysis presented assumes the use of a platinum silicide hybrid imaging device intended to operate in the 3-5 μm middle-wave IR band. Both linear and nonlinear models for its operation are developed. Using these models, estimates of system performance are made. Post-correction spatial noise is estimated for two popular nonuniformity correction schemes. To demonstrate the validity of these concepts, results obtained from actual device testing are presented. Upper bounds are established for the amount of nonuniformity present in the tested device. To complete the investigation, conventional detector figures of merit are then modified to include the effects of nonuniformity.

No description available.

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, in ful llment of the requirements for the degree of Master of Science. October 31, 2016. / Brain Computer Interface (BCI) technology is a promising new channel for communication between humans and computers, and consequently other humans. This technology has the potential to form the basis for a paradigm shift in communication for people with disabilities or neuro-degenerative ailments. The objective of this work is to create an asynchronous BCI that is based on a commercial-grade electroencephalography (EEG) sensor. The BCI is intended to allow a user of possibly low income means to issue control signals to a computer by using modulated cortical activation patterns as a control signal. The user achieves this modulation by performing a mental task such as imagining waving the left arm until the computer performs the action intended by the user. In our work, we make use of the Emotiv EPOC headset to perform the EEG measurements. We validate our models by assessing their performance when the experimental data is collected using clinical-grade EEG technology. We make use of a publicly available data-set in the validation phase. We apply signal processing concepts to extract the power spectrum of each electrode from the EEG time-series data. In particular, we make use of the fast Fourier transform (FFT). Specific bands in the power spectra are used to construct a vector that represents an abstract state the brain is in at that particular moment. The selected bands are motivated by insights from neuroscience. The state vector is used in conjunction with a model that performs classification. The exact purpose of the model is to associate the input data with an abstract classification result which can then used to select the appropriate set of instructions to be executed by the computer. In our work, we make use of probabilistic graphical models to perform this association. The performance of two probabilistic graphical models is evaluated in this work. As a preliminary step, we perform classification on pre-segmented data and we assess the performance of the hidden conditional random fields (HCRF) model. The pre-segmented data has a trial structure such that each data le contains the power spectra measurements associated with only one mental task. The objective of the assessment is to determine how well the HCRF models the spatio-spectral and temporal relationships in the EEG data when mental tasks are performed in the aforementioned manner. In other words, the HCRF is to model the internal dynamics of the data corresponding to the mental task. The performance of the HCRF is assessed over three and four classes. We find that the HCRF can model the internal structure of the data corresponding to different mental tasks. As the final step, we perform classification on continuous data that is not segmented and assess the performance of the latent dynamic conditional random fields (LDCRF). The LDCRF is used to perform sequence segmentation and labeling at each time-step so as to allow the program to determine which action should be taken at that moment. The sequence segmentation and labeling is the primary capability that we require in order to facilitate an asynchronous BCI protocol. The continuous data has a trial structure such that each data le contains the power spectra measurements associated with three different mental tasks. The mental tasks are randomly selected at 15 second intervals. The objective of the assessment is to determine how well the LDCRF models the spatio-spectral and temporal relationships in the EEG data, both within each mental task and in the transitions between mental tasks. The performance of the LDCRF is assessed over three classes for both the publicly available data and the data we obtained using the Emotiv EPOC headset. We find that the LDCRF produces a true positive classification rate of 82.31% averaged over three subjects, on the validation data which is in the publicly available data. On the data collected using the Emotiv EPOC, we find that the LDCRF produces a true positive classification rate of 42.55% averaged over two subjects. In the two assessments involving the LDCRF, the random classification strategy would produce a true positive classification rate of 33.34%. It is thus clear that our classification strategy provides above random performance on the two groups of data-sets. We conclude that our results indicate that creating low-cost EEG based BCI technology holds potential for future development. However, as discussed in the final chapter, further work on both the software and low-cost hardware aspects is required in order to improve the performance of the technology as it relates to the low-cost context. / LG2017

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Thesis--M. Phil., University of Hong Kong.