Field programmable gate array based target detection and gesture recognition

The move from Standard Definition (SD) to High Definition (HD) represents a six times increases in data, which needs to be processed. With expanding resolutions and evolving compression, there is a need for high performance with flexible architectures to allow for quick upgrade ability. The technology advances in image display resolutions, advanced compression techniques, and video intelligence. Software implementation of these systems can attain accuracy with tradeoffs among processing performance (to achieve specified frame rates, working on large image data sets), power and cost constraints. There is a need for new architectures to be in pace with the fast innovations in video and imaging. It contains dedicated hardware implementation of the pixel and frame rate processes on Field Programmable Gate Array (FPGA) to achieve the real-time performance. ^ The following outlines the contributions of the dissertation. (1) We develop a target detection system by applying a novel running average mean threshold (RAMT) approach to globalize the threshold required for background subtraction. This approach adapts the threshold automatically to different environments (indoor and outdoor) and different targets (humans and vehicles). For low power consumption and better performance, we design the complete system on FPGA. (2) We introduce a safe distance factor and develop an algorithm for occlusion occurrence detection during target tracking. A novel mean-threshold is calculated by motion-position analysis. (3) A new strategy for gesture recognition is developed using Combinational Neural Networks (CNN) based on a tree structure. Analysis of the method is done on American Sign Language (ASL) gestures. We introduce novel point of interests approach to reduce the feature vector size and gradient threshold approach for accurate classification. (4) We design a gesture recognition system using a hardware/ software co-simulation neural network for high speed and low memory storage requirements provided by the FPGA. We develop an innovative maximum distant algorithm which uses only 0.39% of the image as the feature vector to train and test the system design. Database set gestures involved in different applications may vary. Therefore, it is highly essential to keep the feature vector as low as possible while maintaining the same accuracy and performance^

Dynamic image precompensation for improving visual performance of computer users with ocular aberrations

With the progress of computer technology, computers are expected to be more intelligent in the interaction with humans, presenting information according to the user's psychological and physiological characteristics. However, computer users with visual problems may encounter difficulties on the perception of icons, menus, and other graphical information displayed on the screen, limiting the efficiency of their interaction with computers. In this dissertation, a personalized and dynamic image precompensation method was developed to improve the visual performance of the computer users with ocular aberrations. The precompensation was applied on the graphical targets before presenting them on the screen, aiming to counteract the visual blurring caused by the ocular aberration of the user's eye. A complete and systematic modeling approach to describe the retinal image formation of the computer user was presented, taking advantage of modeling tools, such as Zernike polynomials, wavefront aberration, Point Spread Function and Modulation Transfer Function. The ocular aberration of the computer user was originally measured by a wavefront aberrometer, as a reference for the precompensation model. The dynamic precompensation was generated based on the resized aberration, with the real-time pupil diameter monitored. The potential visual benefit of the dynamic precompensation method was explored through software simulation, with the aberration data from a real human subject. An "artificial eye'' experiment was conducted by simulating the human eye with a high-definition camera, providing objective evaluation to the image quality after precompensation. In addition, an empirical evaluation with 20 human participants was also designed and implemented, involving image recognition tests performed under a more realistic viewing environment of computer use. The statistical analysis results of the empirical experiment confirmed the effectiveness of the dynamic precompensation method, by showing significant improvement on the recognition accuracy. The merit and necessity of the dynamic precompensation were also substantiated by comparing it with the static precompensation. The visual benefit of the dynamic precompensation was further confirmed by the subjective assessments collected from the evaluation participants.

Dynamic Image Precompensation for Improving Visual Performance of Computer Users with Ocular Aberrations

With the progress of computer technology, computers are expected to be more intelligent in the interaction with humans, presenting information according to the user's psychological and physiological characteristics. However, computer users with visual problems may encounter difficulties on the perception of icons, menus, and other graphical information displayed on the screen, limiting the efficiency of their interaction with computers. In this dissertation, a personalized and dynamic image precompensation method was developed to improve the visual performance of the computer users with ocular aberrations. The precompensation was applied on the graphical targets before presenting them on the screen, aiming to counteract the visual blurring caused by the ocular aberration of the user's eye. A complete and systematic modeling approach to describe the retinal image formation of the computer user was presented, taking advantage of modeling tools, such as Zernike polynomials, wavefront aberration, Point Spread Function and Modulation Transfer Function. The ocular aberration of the computer user was originally measured by a wavefront aberrometer, as a reference for the precompensation model. The dynamic precompensation was generated based on the resized aberration, with the real-time pupil diameter monitored. The potential visual benefit of the dynamic precompensation method was explored through software simulation, with the aberration data from a real human subject. An "artificial eye'' experiment was conducted by simulating the human eye with a high-definition camera, providing objective evaluation to the image quality after precompensation. In addition, an empirical evaluation with 20 human participants was also designed and implemented, involving image recognition tests performed under a more realistic viewing environment of computer use. The statistical analysis results of the empirical experiment confirmed the effectiveness of the dynamic precompensation method, by showing significant improvement on the recognition accuracy. The merit and necessity of the dynamic precompensation were also substantiated by comparing it with the static precompensation. The visual benefit of the dynamic precompensation was further confirmed by the subjective assessments collected from the evaluation participants.

MobiMed: Framework for Rapid Application Development of Medical Mobile Apps

In the medical field images obtained from high definition cameras and other medical imaging systems are an integral part of medical diagnosis. The analysis of these images are usually performed by the physicians who sometimes need to spend long hours reviewing the images before they are able to come up with a diagnosis and then decide on the course of action. In this dissertation we present a framework for a computer-aided analysis of medical imagery via the use of an expert system. While this problem has been discussed before, we will consider a system based on mobile devices. Since the release of the iPhone on April 2003, the popularity of mobile devices has increased rapidly and our lives have become more reliant on them. This popularity and the ease of development of mobile applications has now made it possible to perform on these devices many of the image analyses that previously required a personal computer. All of this has opened the door to a whole new set of possibilities and freed the physicians from their reliance on their desktop machines. The approach proposed in this dissertation aims to capitalize on these new found opportunities by providing a framework for analysis of medical images that physicians can utilize from their mobile devices thus remove their reliance on desktop computers. We also provide an expert system to aid in the analysis and advice on the selection of medical procedure. Finally, we also allow for other mobile applications to be developed by providing a generic mobile application development framework that allows for access of other applications into the mobile domain. In this dissertation we outline our work leading towards development of the proposed methodology and the remaining work needed to find a solution to the problem. In order to make this difficult problem tractable, we divide the problem into three parts: the development user interface modeling language and tooling, the creation of a game development modeling language and tooling, and the development of a generic mobile application framework. In order to make this problem more manageable, we will narrow down the initial scope to the hair transplant, and glaucoma domains.

Market definition in industrial high-technology markets

This dissertation analyzes how marketers define markets in technology-based industries. One of the most important strategic decisions marketers face is determining the optimal market for their products. Market definition is critical in dynamic high technology markets characterized by high levels of market and technological uncertainty. Building on literature from marketing and related disciplines, this research is the first in-depth study of market definition in industrial markets. Using a national, probability sample stratified by firm size, 1,000 marketing executives in nine industries (automation, biotechnology, computers, medical equipment and instrumentation, pharmaceuticals, photonics, software, subassemblies and components, and telecommunications) were surveyed via a mail questionnaire. A 20.8% net response rate yielding 203 surveys was achieved. The market structure-conduct-performance (SCP) paradigm from industrial organization provided a conceptual basis for testing a causal market definition model via LISREL. A latent exogenous variable (competitive intensity) and four latent endogenous variables (marketing orientation, technological orientation, market definition criteria, and market definition success) were used to develop and test hypothesized relationships among constructs. Research questions relating to market redefinition, market definition characteristics, and internal (within the firm) and external (competitive) market definition were also investigated. Market definition success was found to be positively associated with a marketing orientation and the use of market definition criteria. Technological orientation was not significantly related to market definition success. Customer needs were the key market definition characteristic to high-tech firms (technology, competition, customer groups, and products were also important). Market redefinition based on changing customer needs was the most effective of seven strategies tested. A majority of firms regularly defined their market at the corporate and product-line level within the firm. From a competitive perspective, industry, industry sector, and product-market definitions were used most frequently.

Television Watching, Diet Quality, and Physical Activity and Diabetes among Three Ethnicities in the United States

Diabetes is a world-wide epidemic associated with multiple environmental factors. Prolonged television viewing (TV) time has been related to increased risk of obesity and type 2 diabetes in several studies. TV viewing has been positively associated with cardiovascular disease risk factors, lower energy expenditure, over-eating high-calorie and high-fat foods. The objective of this study was to assess the associations of hours of TV viewing with dietary quality, obesity and physical activity for three ethnic minorities with and without type 2 diabetes. Diet quality and physical activity were inversely related to prolonged TV viewing. African Americans and participants with type 2 diabetes were more likely to watch more than 4 hours of TV per day as compared to their counterparts. Diet quality was inversely associated with physical activity level. Future studies are needed to establish the risk factors of prolonged TV watching in adult populations for the development of diabetes or diabetes-related complications. Although strategies to reduce TV watching have been proven effective among children, few trials have been conducted in adults. Intervention trials aimed at reducing TV viewing targeting people with type 2 diabetes may be beneficial to improve dietary quality and physical activity, which may reduce diabetes complications.

Television Watching, Diet Quality, Physical Activity and Diabetes Among Three Ethnicities in the United States

Diabetes is a world-wide epidemic associated with multiple environmental factors. Prolonged television viewing (TV) time has been related to increased risk of obesity and type 2 diabetes in several studies. TV viewing has been positively associated with cardiovascular disease risk factors, lower energy expenditure, over-eating high-calorie and high-fat foods. The objective of this study was to assess the associations of hours of TV viewing with dietary quality, obesity and physical activity for three ethnic minorities with and without type 2 diabetes. Diet quality and physical activity were inversely related to prolonged TV viewing. African Americans and participants with type 2 diabetes were more likely to watch more than 4 hours of TV per day as compared to their counterparts. Diet quality was inversely associated with physical activity level. Future studies are needed to establish the risk factors of prolonged TV watching in adult populations for the development of diabetes or diabetes-related complications. Although strategies to reduce TV watching have been proven effective among children, few trials have been conducted in adults. Intervention trials aimed at reducing TV viewing targeting people with type 2 diabetes may be beneficial to improve dietary quality and physical activity, which may reduce diabetes complications.