This dissertation addresses the advantage of using multispectral narrow-band images over conventional broad-band images for improved face recognition under varying illumination. To verify the effectiveness of multispectral images for improving face recognition performance, three sequential procedures are taken into action: multispectral face image acquisition, image fusion for multispectral and spectral band selection to remove information redundancy. Several efficient image fusion algorithms are proposed and conducted on spectral narrow-band face images in comparison to conventional images. Physics-based weighted fusion and illumination adjustment fusion make good use of spectral information in multispectral imaging process. The results demonstrate that fused narrow-band images outperform the conventional broad-band images under varying illuminations. In the case where multispectral images are acquired over severe changes in daylight, the fused images outperform conventional broad-band images by up to 78%. The success of fusing multispectral images lies in the fact that multispectral images can separate the illumination information from the reflectance of objects which is impossible for conventional broad-band images. To reduce the information redundancy among multispectral images and simplify the imaging system, distance-based band selection is proposed where a quantitative evaluation metric is defined to evaluate and differentiate the performance of multispectral narrow-band images. This method is proved to be exceptionally robust to parameter changes. Furthermore, complexity-guided distance-based band selection is proposed using model selection criterion for an automatic selection. The performance of selected bands outperforms the conventional images by up to 15%. From the significant performance improvement via distance-based band selection and complexity-guided distance-based band selection, we prove that specific facial information carried in certain narrow-band spectral images can enhance face recognition performance compared to broad-band images. In addition, both algorithms are proved to be independent to recognition engines. Significant performance improvement is achieved by proposed image fusion and band selection algorithms under varying illumination including outdoor daylight conditions. Our proposed imaging system and image processing algorithms lead to a new avenue of automatic face recognition system towards a better recognition performance than the conventional peer system over varying illuminations.

With the most advanced and mature technology for electronic devices, silicon (Si) based devices can be processed with practically no material defects. However, Si technology has difficulty meeting the demand for some high-power, high-speed, and high-temperature applications due to limitations in its intrinsic properties. Wide bandgap semiconductors have greater prospects compared to Si based devices. The wide band gap material system shows higher breakdown voltage, lower leakage, higher saturation velocity, larger thermal conductivity and better thermal stability suitable for high-power, high-speed, and high-temperature operations of the devices. In recent years, GaN based devices have drawn much research attention due to their superior performances compared to other wide bandgap semiconductor (SiC) devices. Specifically, implementation of AlGaN/GaN high electron mobility transistor (HEMT) based power amplifiers have become very promising for applications in base stations or radar. With the increase in device power, channel temperature rises. This introduces high-temperature effects in the device characteristics. In addition, high-power, high-frequency and high-temperature operation of AlGaN/GaN HEMT is required for telemetry in extreme environment. AlGaN/GaN HEMT also shows great potential as chemically selective field-effect transistor (CHEMFET). Due to simpler imprint technique and amplification advantages CHEMFET based detection and characterization of bio-molecules has become very popular. AlGaN/GaN HEMT has high mobility two-dimensional electron gas (2 DEG) at the hetero-interface closer to the surface and hence it shows high sensitivity to any surface charge conditions. The primary objective of this research is to develop a temperature dependent physics based model of AlGaN/GaN HEMT to predict the performance for high-power and high- speed applications at varying temperatures. The physics based model has also been applied to predict the characteristics of AlGaN/GaN HEMT based CHEMFET for the characterization of bio-molecular solar batteries - Photosystem I reaction centers. Using the CHEMFET model, the number of reaction centers with effective orientation on the gate surface of the HEMT can be estimated.

This dissertation research is motivated by the potential and promise of 3D sensing technologies in safety and security applications. With specific focus on unmanned robotic mapping to aid clean-up of hazardous environments, under-vehicle inspection, automatic runway/pavement inspection and modeling of urban environments, we develop modular, multi-sensor, multi-modality robotic 3D imaging prototypes using localization/navigation hardware, laser range scanners and video cameras. While deploying our multi-modality complementary approach to pose and structure recovery in dynamic real-world operating conditions, we observe several data fusion issues that state-of-the-art methodologies are not able to handle. Different bounds on the noise model of heterogeneous sensors, the dynamism of the operating conditions and the interaction of the sensing mechanisms with the environment introduce situations where sensors can intermittently degenerate to accuracy levels lower than their design specification. This observation necessitates the derivation of methods to integrate multi-sensor data considering sensor conflict, performance degradation and potential failure during operation. Our work in this dissertation contributes the derivation of a fault-diagnosis framework inspired by information complexity theory to the data fusion literature. We implement the framework as opportunistic sensing intelligence that is able to evolve a belief policy on the sensors within the multi-agent 3D mapping systems to survive and counter concerns of failure in challenging operating conditions. The implementation of the information-theoretic framework, in addition to eliminating failed/non-functional sensors and avoiding catastrophic fusion, is able to minimize uncertainty during autonomous operation by adaptively deciding to fuse or choose believable sensors. We demonstrate our framework through experiments in multi-sensor robot state localization in large scale dynamic environments and vision-based 3D inference. Our modular hardware and software design of robotic imaging prototypes along with the opportunistic sensing intelligence provides significant improvements towards autonomous accurate photo-realistic 3D mapping and remote visualization of scenes for the motivating applications.

A new class of accelerating structures employing a uniformly twisted waveguide is investigated. Twisted waveguides of various cross-sectional geometries are considered and analyzed. It is shown that such a twisted waveguide can support waves that travel at a speed slower than the speed of light c. The slow-wave properties of twisted structures are of interest because these slow-wave electromagnetic fields can be used in applications such as electron traveling wave tubes and linear particle accelerators. Since there is no exact closed form solution for the electromagnetic fields within a twisted waveguide or cavity, several previously proposed approximate methods are examined, and more efficient approaches are developed. It is found that the existing perturbation theory methods yield adequate results for slowly twisted structures; however, our efforts here are geared toward analyzing rapidly twisted structures using modified finite difference methods specially suited for twisted structures. Although the method can handle general twisted structures, three particular cross sections are selected as representative cases for careful analysis. First, a slowly twisted rectangular cavity is analyzed as a reference case. This is because its shape is simple and perturbation theory already gives a good approximate solution for such slow twists rates. Secondly, a symmetrically notched circular cross section is investigated, since its longitudinal cross section is comparable to the well known disk-loaded cavity (used in many practical accelerator designs, including SLAC). Finally, a "dumbbell" shaped cross section is analyzed because of its similarity to the well-known TESLA-type accelerating cavity, which is of great importance because of its wide acceptance as a superconducting cavity. To validate the results of the developed theory and our extensive simulations, the newly developed numerical models are compared to commercial codes. Also, several prototypes are developed employing the three basic shapes discussed previously. Bench measurements are performed on the prototype cavities to evaluate dispersion by measuring the field distribution along these cavities. The measurement results are compared to the simulations and theoretical results, and good agreement is shown. Once validated, the developed models are used to design twisted accelerating structures with specific phase velocities and good accelerating performance.

A precise localization system in an indoor environment has been developed. The developed system is based on transmitting and receiving picosecond pulses and carrying out a complete narrow-pulse, signal detection and processing scheme in the time domain. The challenges in developing such a system include: generating ultra wideband (UWB) pulses, pulse dispersion due to antennas, modeling of complex propagation channels with severe multipath effects, need for extremely high sampling rates for digital processing, synchronization between the tag and receivers’ clocks, clock jitter, local oscillator (LO) phase noise, frequency offset between tag and receivers’ LOs, and antenna phase center variation. For such a high precision system with mm or even sub-mm accuracy, all these effects should be accounted for and minimized. In this work, we have successfully addressed many of the above challenges and developed a stand-alone system for positioning both static and dynamic targets with approximately 2 mm and 6 mm of 3-D accuracy, respectively. The results have exceeded the state of the art for any commercially available UWB positioning system and are considered a great milestone in developing such technology. My contributions include the development of a picosecond pulse generator, an extremely wideband omni-directional antenna, a highly directive UWB receiving antenna with low phase center variation, an extremely high data rate sampler, and establishment of a non-synchronized UWB system architecture. The developed low cost sampler, for example, can be easily utilized to sample narrow pulses with up to 1000 GS/s while the developed antennas can cover over 6 GHz bandwidth with minimal pulse distortion. The stand-alone prototype system is based on tracking a target using 4-6 base stations and utilizing a triangulation scheme to find its location in space. Advanced signal processing algorithms based on first peak and leading edge detection have been developed and extensively evaluated to achieve high accuracy 3-D localization. 1D, 2D and 3D experiments have been carried out and validated using an optical reference system which provides better than 0.3 mm 3-D accuracy. Such a high accuracy wireless localization system should have a great impact on the operating room of the future.

Recent advancements in healthcare monitoring equipments and wireless communication technologies have led to the integration of specialized medical technology with the pervasive wireless networks. Intensive research has been focused on the development of medical wireless networks (MWN) for telemedicine and smart home care services. Wireless technology also shows potential promises in surgical applications. Unlike conventional surgery, an expert surgeon can perform the surgery from a remote location using robot manipulators and monitor the status of the real surgery through wireless communication link. To provide this service each surgical tool must be facilitated with smart electronics to accrue data and transmit the data successfully to the monitoring unit through wireless network. To avoid unwieldy wires between the smart surgical tool and monitoring units and to reap the benefit of excellent features of wireless technology, each smart surgical tool must incorporate a low-power wireless transmitter. Low-power transmitter with high efficiency is essential for short range wireless communication. Unlike conventional transmitters used for cellular communication, injection-locked transmitter shows greater promises in short range wireless communication. The core block of an injection-locked transmitter is an injection-locked oscillator. Therefore, this research work is directed towards the development of a low-voltage low-power injection-locked oscillator which will facilitate the development of a low-power injection-locked transmitter for MWN applications. Structure of oscillator and types of injection are two crucial design criteria for low-power injection-locked oscillator design. Compared to other injection structures, body-level injection offers low-voltage and low-power operation. Again, conventional NMOS/PMOS-only cross-coupled LC oscillator can work with low supply voltage but the power consumption is relatively high. To overcome this problem, a self-cascode LC oscillator structure has been used which provides both low-voltage and low-power operation. Body terminal coupling is used with this structure to achieve injection-locking. Simulation results show that the self-cascode structure consumes much less power compared to that of the conventional structure for the same output swing while exhibiting better phase noise performance. Usage of PMOS devices and body bias control not only reduces the flicker noise and power consumption but also eliminates the requirements of expensive fabrication process for body terminal access.

For about the last ten years, stereo matching in computer vision has been treated as a combinatorial optimization problem. Assuming that the points in stereo images form a Markov Random Field (MRF), a variety of combinatorial optimization algorithms has been developed to optimize their underlying cost functions. In many of these algorithms, the MRF parameters of the cost functions have often been manually tuned or heuristically determined for achieving good performance results. Recently, several algorithms for statistical, hence, automatic estimation of the parameters have been published. Overall, these algorithms perform well in labeling, but they lack in performance for handling discontinuity in labeling along the surface borders. In this dissertation, we develop an algorithm for optimization of the cost function with automatic estimation of the MRF parameters – the data and smoothness parameters. Both the parameters are estimated statistically and applied in the cost function with support of adaptive neighborhood defined based on color similarity. With the proposed algorithm, discontinuity handling with higher consistency than of the existing algorithms is achieved along surface borders. The data parameters are pre-estimated from one of the stereo images by applying a hypothesis, called noise equivalence hypothesis, to eliminate interdependency between the estimations of the data and smoothness parameters. The smoothness parameters are estimated applying a combination of maximum likelihood and disparity gradient constraint, to eliminate nested inference for the estimation. The parameters for handling discontinuities in data and smoothness are defined statistically as well. We model cost functions to match the images symmetrically for improved matching performance and also to detect occlusions. Finally, we fill the occlusions in the disparity map by applying several existing and proposed algorithms and show that our best proposed segmentation based least squares algorithm performs better than the existing algorithms. We conduct experiments with the proposed algorithm on publicly available ground truth test datasets provided by the Middlebury College. Experiments show that results better than the existing algorithms’ are delivered by the proposed algorithm having the MRF parameters estimated automatically. In addition, applying the parameter estimation technique in existing stereo matching algorithm, we observe significant improvement in computational time.

This work investigates the prediction of electricity price and power transmission network congestions under load variation and uncertainty in deregulated power systems. The study is carried out in three stages. In the first stage, the mathematical programming models, which produce the generation dispatch solution, the Locational Marginal Price (LMP), and the system statuses such as transmission congestions, are reviewed. These models are often referred to as Optimal Power Flow (OPF) models, and can be categorized into two major groups: Alternating Current OPF (ACOPF) and Direct Current OPF (DCOPF). Due to the convergence issue with the ACOPF model and the concern of inaccuracy with the DCOPF model, a new DCOPF-based algorithm is proposed, using a fictitious nodal demand (FND) model to represent power losses at each individual line. This is an improvement over the previous work that assigns losses to a few user-defined buses, and is capable of achieving a better tradeoff between computational effectiveness and the accuracy of the results. In the second stage, the solution features are explored for each of the three OPF models to predict critical load levels where a step change of LMP occurs due to the change of binding constraints. After careful examinations of the mathematical relationship of the OPF solutions, nodal prices, and congestions, with respect to load variation, simplex-like method, quadratic interpolation method, and variable substitution method are proposed for each of the three OPF models respectively in order to predict price changes and system congestion. In the last stage, the probabilistic feature of the forecasted LMP is investigated. Due to the step change characteristic of the LMP and uncertainty in load forecasting, the forecasted LMP represents only a certain possibility in a lossless DCOPF framework. Additional possible LMP values exist, other than the deterministically forecasted LMP. Therefore, the concept of Probabilistic LMP is introduced and a systematic approach to quantify the probability of the forecasted LMP, with respect to load variation, is proposed. Similar concepts and methodology have been applied to the ACOPF and FND-based DCOPF frameworks, which can be useful for power market participants in making financial decisions.

This dissertation addresses automated correction and optimized contrast enhancement of multi-line CCD images for inspection and surveillance applications, focusing on three topics: multi-line CCD imaging systems setup, automated correction of multi-line CCD images, and automatic optimized image contrast enhancement. generation, low cost, etc. However, due to the physical separation of line CCD sensors for the red (R), green (G), blue (B) color channel, the color images acquired by multi-line CCD cameras intrinsically exhibit a color misalignment defect, which is expressed as that the edges of objects in the scene are separated by a certain number of pixels in the R, G, B color planes in the scan direction. This defect, if not corrected properly, can severely degrade the quality of multi-line CCD images and hence the applications of multi-line CCD cameras. We developed an algorithm to automatically correct the color misalignment problem in multi-line CCD images. generation, low cost, etc. However, due to the physical separation of line CCD sensors for the red (R), green (G), blue (B) color channel, the color images acquired by multi-line CCD cameras intrinsically exhibit a color misalignment defect, which is expressed as that the edges of objects in the scene are separated by a certain number of pixels in the R, G, B color planes in the scan direction. This defect, if not corrected properly, can severely degrade the quality of multi-line CCD images and hence the applications of multi-line CCD cameras. We developed an algorithm to automatically correct the color misalignment problem in multi-line CCD images. Contrast enhancement plays an important role in image processing applications. Conventional contrast enhancement techniques either often fail to produce satisfactory results for a broad variety of low-contrast images, or cannot be automatically applied to different images, because their processing parameters must be specified manually to produce a satisfactory result for a given image. However, the GLG technique doesn’t have the above drawbacks. The basic procedure of GLG is to first group the histogram components of a low-contrast image into a proper number of bins according to a new image contrast measure developed in this research, Average Pixel Distance on Grayscale (APDG); then remap these bins evenly over the grayscale, and finally ungroup the previously grouped gray-levels. Accordingly, this new technique is named gray-level grouping (GLG). GLG and its variations not only produce results superior to competing contrast enhancement techniques, but are also fully automatic in most circumstances, and are applicable to a broad variety of images.

In beginning the investigation of low noise current preamplification, noise-performance limitations of existing broadband current-amplifying stages are considered. Dominant noise sources of the general, shuntfeedback amplifier stage having both bipolar and field-effect transistor input devices are discussed. This discussion includes the reasons why optimum noise performance from this amplifier stage requires unavoidable signal integration. The integrating shunt-feedback configuration is commonly known as the charge-sensitive preamplifier. Criteria are developed for differentiating the output voltage pulse of the charge-sensitive preamplifier without degrading the signal-to-noise ratio. Subsequently, a new broadband, shunt-feedback amplifier is described having a current gain equal to the ratio of an RC feedback impedance to an RC load impedance. The value of the feedback resistance and capacitance can be made equal to that of conventional charge-sensitive preamplifiers. Basically, the configuration is similar to that of a charge-sensitive preamplifier, since a charge-proportional signal is present within the feedback network. However, differentiation is performed by the feedback network to allow a broadband current transfer function. The stage has a large bandwidth capability with linearity and noise performance comparable to that of the conventional charge-sensitive configuration. Equations predicting the bandwidth, input and output impedances, and noise-performance are derived. Also criteria are established for achieving the desired linearity and for cascading stages to achieve large current gains To facilitate experiments involving linear gating of the amplified detector current pulse, a review of related technology is presented and a simple Rte filter for use in a gated system is discussed. A three-stage preamplifier having a current gain of 8000 was constructed to experimentally verify the predicted preamplifier performance characteristics. Energy resolution experiments performed with a Ge(Li) semiconductor detector yielded noise line widths as low as 2.35 key FWHM for the integrated current output shaped by a 1.6 microsecond time constant RC-RC filter. The noise line width measured for the gated current pulse shaped by the RLC filter was 2.70 kev FWHM compared to 2.76 key FWHM obtained, without gating, from a 0.4 microsecond RC-RC filter having the same center frequency as the RLC filter. The preamplifier was not optimized for minimum noise line width. Leading edge timing experiments were performed with a 60Co gammaray source using Naton 136 as the detector for the standard timing channel. With the test channel consisting of the three-stage preamplifier coupled to a 1.7 cc. planar diode, a timing uncertainty of 1.6 x 10-9 seconds FWHM for a 13.3 to 1 dynamic energy range was measured. With a 34.1 cc true coacial Ge(Li) detector in the test channel, the timing uncertainty was 3.3 x 10-9 seconds FWHM for a 7.8 to 1 dynamic energy range. Rise times as low as 7 x 10-9 seconds were measured with the 9 x 10-12 farad planar detector connected to the preamplifier.