Master of Science in Engineering (MSEgr), Wright State University, 2008, Biomedical Engineering

Our challenge was to develop a semi-automatic target detection algorithm to aid human operators in locating potential targets within images. In contrast to currently available methods, our approach is relatively insensitive to image brightness, image contrast and object orientation. Working on overlapping image blocks, we used a sliding difference method of histogram matching. Incrementally sliding the histograms of the known object template and the image region of interest (ROI) together, the sum of absolute histogram differences was calculated. The minimum of the resultant array was stored in the corresponding spatial position of a response surface matrix. Local minima of the response surface suggest possible target locations. Because the template contrast will rarely perfectly match the contrast of the actual image contrast, which can be compromised by illumination conditions, background features, cloud cover, etc., we perform a random contrast manipulation, which we term ‘wobble', on the template histogram. Our results have shown improved object detection with the combination of the sliding histogram difference and wobble.

Committee: Julie Skipper PhD (Advisor); Daniel Repperger PhD (Committee Member); Thomas Hangartner PhD (Committee Member); S. Narayanan PhD (Other); Joseph F. Thomas, Jr. PhD (Other) Subjects: Biomedical Research; Engineering; Scientific Imaging

Master of Science (MS), Ohio University, 2011, Electrical Engineering (Engineering and Technology)

Human safety is one of the highest priorities in the automotive industry. The demands made for reliable safety systems have been increasing tremendously in the past decade. The radar sensors used for safety systems should be capable of detecting not only automobiles but also motorcycles, bicycles, pedestrians, roadside objects and any other obstacles the vehicle may come in contact with. This thesis investigates several performance aspects and test procedures for a 77 GHz long range radar sensor with different test target objects. This assessment helps to investigate the potential to use these radar sensors for obstacle detection and/or avoidance for smaller objects like bicycles, humans, traffic barrels, 4” poles, metal sheets, and also for bigger objects like vans, motorcycles, aircraft, etc. For these purposes, different test cases were developed to evaluate the performance. The different test cases used to test a 77 GHz radar sensor includes: finding maximum range, range accuracy, finding maximum field of view, detection (& separation) of two target objects (similar & different) at different radial distances, and maximum range for detecting an aircraft. Observations were made with the radar sensor mounted on a moving cart and the measurements were logged. The results from these tests will provide insight into analyzing the possibilities and limitations of these radar sensors for different applications. The tests were successfully conducted on a flat, open field at Ohio University Airport, Albany, OH.

Committee: Chris Bartone PhD, P.E. (Advisor); Jeffrey Dill PhD (Committee Member); Bryan Riley PhD, PMP (Committee Member); William Kaufman PhD (Committee Member) Subjects: Automotive Engineering; Electrical Engineering

MS, University of Cincinnati, 2019, Engineering and Applied Science: Mechanical Engineering

Deep learning networks for semantic segmentation are the core of modern computer vision applications involving target identification and scene extraction. Tremendous research in the area combined with the increased computation power and abundance of labelled datasets allowed the rise of deep neural networks to provide solutions for the long lasting problems. However, for the real time applications, huge parameter count and computational efficiency of the algorithms have taken significance. The novel method presented here will tackle the above mentioned problems for the effective real time segmentation and color extraction to identify the missing person.

Committee: Janet Jiaxiang Dong Ph.D. (Committee Chair); Daniel Humpert M.S. (Committee Member); Anca Ralescu Ph.D. (Committee Member) Subjects: Computer Science

Doctor of Philosophy (PhD), Wright State University, 2019, Human Factors and Industrial/Organizational Psychology PhD

Real-world visual search differs significantly from the laboratory task. One distinct feature is that most targets in real-world visual search are low prevalence. Considering the important practical connections between the laboratory study and applied research, there has been a resurgence in exploring prevalence effects on visual search performance, especially the effect that targets are more likely to be missed when they have low prevalence. Though there is a consensus that target misses are due to a liberal criterion, previous studies failed to consider the potentiality of optimal performance from the perspective of Signal Detection Theory, which also predicts a the liberal criterion shift. Moreover, previous decision making literature has demonstrated that observers subjectively weighted the probability based on the information communications they were given (i. e. the description-experience gap), motivates the current study to explore how target probability communications influence search performance. To explore the hypothesis of optimal performance and the influence of probability communications, the current research assessed observers' performance from two aspects: behavioral performance and eye movements. The results indicated that with a high penalty on miss errors, observers' criteria were more liberal toward “target-present” responses. However, the performance was not optimal as expected. The manipulation of information indicated visual search was affected by the way the target prevalence information was given to observers. Specifically, when target prevalence was low, learning prevalence from experience resulted in the belief in more targets and longer search time before quitting compared to the contexts in which observers had been explicitly informed about the target probability. The observed discrepancy narrowed with increased prevalence and reversed when target prevalence was high. There was no clear evidence for the same discrepancy in item fix (open full item for complete abstract)

Committee: Joseph W. Houpt Ph.D. (Committee Chair); Valerie Shalin Ph.D. (Committee Member); Scott Watamaniuk Ph.D. (Committee Member); Christopher Myers Ph.D. (Committee Member) Subjects: Psychobiology

Doctor of Philosophy, The Ohio State University, 2018, Neuroscience Graduate Studies Program

Cognitive deficits in executive functions such as attention and cognitive control form a core symptom cluster in schizophrenia that is most predicative of functional outcomes for patients, such as the ability to return to work. Unfortunately this class of symptoms is poorly treated with currently available neuroleptics and so far adjunctive treatment with potential pro-cognitive compounds has not yielded improvements in global cognition. Not only are alternative treatment strategies necessary, but there is a need for better validated preclinical tasks and animal models. The current work seeks to validate the rodent Target Detection Task (rTDT) and the embryonic kynurenine (EKYN) model as a platform for assessing the efficacy of cognitive training via prior experience in a cognitively demanding task. The central hypothesis guiding the experiments in this dissertation is that gestational elevations of kynurenine will induce a profile of translationally relevant attentional deficits in the rTDT and these deficits can be reversed with cognitive training. The first aim consisted of a validation of the rTDT. It was found that rTDT acquisition follows a stable and repeatable pattern. Additionally, rTDT performance is sensitive to manipulations of stimulus parameters including the reduction of stimulus duration and contrast. These manipulations result in predictable impairments in sensitivity, or the ability to discriminate between target and non-target stimuli. The rTDT was also shown to be sensitive to pharmacological challenges with agents that impair glutamatergic and cholinergic neurotransmission. These neurotransmitter systems are known to be essential for intact attentional processing. The second aim consisted of a validation of the EKYN model. EKYN animals, compared to control animals, showed disruptions of attentional processing and cognitive control. These deficits did not present during task acquisition but emerged upon challenge with task parameters that enhance (open full item for complete abstract)

Committee: Bruno John (Advisor); Golomb Julie (Committee Member); Lenz Kathryn (Committee Member); Lindquist Derick (Committee Member) Subjects: Behavioral Psychology; Neurobiology; Neurosciences

Master of Science (M.S.), University of Dayton, 2015, Electrical Engineering

Hyperspectral remote sensing has become a popular topic of research due to the numerous applications stemming from the high dimensionality of the recorded spectral data. From the design perspective, hyperspectral sensors are generally more complex than standard color or infrared imaging systems because there are more optical components in the system. The quality of each of these components directly affects the target detection performance of the system. In addition to the integrity of optical components, target detection performance is also affected by signal variations due to sensor noise. This research addresses the design of an end-to-end hyperspectral imaging system performance model that incorporates the optical design of the system as well as the stochastic nature of data collected by electronic remote sensing. A system transmission model is presented that calculates the camera signal as a function of input radiance and accounts for each individual optical element in the imaging system. This model can be used to analyze the performance sensitivities of a specific component for a variety of target detection scenarios. The accuracy of the system transmission model is assessed using calibrated hyperspectral data. In addition to the system transmission model, a realistic statistical data model is proposed. Many data models currently account for sensor noise with an additive, stationary variance. This research expands upon this by implementing an additive, signal-dependent sensor noise model that more accurately represents the true phenomena driving the sensor noise. The same data set is used to test target detection performance using the signal-dependent noise model. The results are analyzed to investigate the possible benefits of using the proposed noise model. The data used for this research was collected at Wright Patterson Air Force Base 25-26 June 2014. The scene consists of a grassy background with eight painted wooden panel targets. Data collections (open full item for complete abstract)

Committee: Russell Hardie Ph.D. (Advisor); Joseph Meola Ph.D. (Committee Member); Eric Balster Ph.D. (Committee Member) Subjects: Electrical Engineering; Remote Sensing; Statistics

Doctor of Philosophy (PhD), Wright State University, 2010, Engineering PhD

We integrated a sensor hardware test-bed using scientific grade, commercial off-the-shelf (COTS) technology and developed supporting software to enable rapid prototyping. The validity of this test-bed and associated software was demonstrated through the delivery of a ground-based multispectral visual surveillance prototype for improvised explosive devices (IED) detection using electro-optical (EO) and short-wave infrared (SWIR) cameras. Software developed to support the test-bed included modules for image acquisition, preconditioning, segmentation, feature extraction, data regularization and pattern recognition. To provide spatially co-aligned data, we optimized a mutual information-based image registration algorithm to improve its convergence rate and benchmarked it against the established simplex method. For four different multimodal test image sets, our algorithm convergence success improved by 15 to 40% as compared to the downhill simplex method, albeit with an approximately four times higher computational cost. Additional strategies, such as bit-depth reduction, image down-sampling and gradient-based regions of interest (ROI) selection, were systematically evaluated and led to the registration of high resolution images at nearly 60 times faster than the standard approach. To automatically identify IED in the acquired multi-spectral imagery, four different pattern classifiers were tested; Bayes, k-nearest neighbor (knn-NN), support vector machines (SVM) and our novel piece-wise linear convex-hull classifier. Initial tests with the convex-hull classifier using simulated data indicated significant reduction in error rates of up to 89% (p=3e-6) when compared to the Bayes classifier. Subsequently, each of the four classifiers was tested using the IED data set that consisted of 154 different intensity-based and content-based features extracted from the EO and SWIR imagery. Salient features were selected using receiver operating characteristic (ROC) curve analysis an (open full item for complete abstract)

Committee: Julie A. Skipper PhD (Advisor); Thomas N. Hangartner PhD (Committee Member); Lang Hong PhD (Committee Member); S. Narayanan PhD (Committee Member); Mark E. Oxley PhD (Committee Member) Subjects: Engineering; Remote Sensing; Scientific Imaging; Systems Design

Master of Science, Miami University, 2012, Computational Science and Engineering

This research proposes using the multispectral radar imaging methodology for target detection in complicated scenarios. The investigation proposes to extend this concept to ultrawideband radar by means of employing multi-carrier waveforms based upon Orthogonal Frequency Division Multiplexing (OFDM) modulation. Individual sub-bands of an OFDM waveform can be processed separately to yield range and cross-range reconstruction of a target scene containing both useful targets and clutter. An image based automatic target recognition algorithm has been developed to provide decision making within each sub-band. Target detection in resultant images will be performed and contrasted with the detection performance of a traditional xed-waveform Synthetic Aperture Radar system. The adaptive removal of clutter from these selected images will result in a system that looks to improve detection performance.

Committee: Dmitriy Garmatyuk PhD (Advisor); Donald Ucci PhD (Committee Member); Chi-Hao Cheng PhD (Committee Member) Subjects: Electrical Engineering; Engineering

Master of Science, Miami University, 2009, Computer Science and Systems Analysis

Modern radar imaging requires advanced Synthetic Aperture Radar (SAR) techniques in order to compensate for the low resolution typically found on airborne radars. However, the techniques used to process the data collected during SAR operation require a great deal of computation. This paper proposes a novel efficient algorithm and approach to processing the data collected to quickly get high-resolution approximations for the locations of non-moving targets.

Committee: William Brinkman PhD (Advisor); Dmitriy Garmatyuk PhD (Advisor); Keith Frikken PhD (Committee Member); Jade Morton PhD (Committee Member) Subjects: Computer Science; Electrical Engineering