Electric power system networks are facing major challenges because of the rapid increase in penetration of distributed energy resources (DERs). Reliability evaluation plays an important role in system analysis, design, upgrades, and operations, especially in bulk power systems. The research presented in this thesis focuses on the evaluation of the composite system reliability under steady state conditions, and also goes a step further towards assessing operational risks in real-time system operations using direct probabilistic analysis techniques. The thesis also examines the reliability and risk improvements that are accrued from penetration of DERs into the power system. The challenge of using analytical methods in reliability evaluation of composite power systems is the large computational burden involved, to examine all the possible outage events. This thesis presents the mathematical foundations, evaluation procedures, and reliability indices associated with composite power system reliability evaluation using the minimal cut set calculations. The objective of this approach is to evaluate reliability and risk indices for the system and for every load bus in the system. The performance of the system under outage condition of generators, transmission lines, or both, is examined by conducting an appropriate power flow study. An optimal power flow (OPF) is solved to find the system minimal cut sets which are then used to evaluate reliability and operational risk. DER units are incorporated to investigate the enhancement in system reliability and operational risk. The concepts and developed model are illustrated by application to the 24-bus IEEE Reliability Test System.

With the advance of data acquisition techniques, massive solid geometries are being collected routinely in scientific tasks, these complex and unstructured data need to be effectively correlated for various processing and analysis. Volumetric mapping solves bijective low-distortion correspondence between/among 3D geometric data, and can serve as an important preprocessing step in many tasks in compute-aided design and analysis, industrial manufacturing, medical image analysis, to name a few. This dissertation studied two important volumetric mapping problems: the mapping of heterogeneous volumes (with nonuniform inner structures/layers) and the mapping of sequential dynamic volumes. To effectively handle heterogeneous volumes, first, we studied the feature-aligned harmonic volumetric mapping. Compared to previous harmonic mapping, it supports the point, curve, and iso-surface alignment, which are important low-dimensional structures in heterogeneous volumetric data. Second, we proposed a biharmonic model for volumetric mapping. Unlike the conventional harmonic volumetric mapping that only supports positional continuity on the boundary, this new model allows us to have higher order continuity $C^1$ along the boundary surface. This suggests a potential model to solve the volumetric mapping of complex and big geometries through divide-and-conquer. We also studied the medical applications of our volumetric mapping in lung tumor respiratory motion modeling. We were building an effective digital platform for lung tumor radiotherapy based on effective volumetric CT/MRI image matching and analysis. We developed and integrated in this platform a set of geometric/image processing techniques including advanced image segmentation, finite element meshing, volumetric registration and interpolation. The lung organ/tumor and surrounding tissues are treated as a heterogeneous region and a dynamic 4D registration framework is developed for lung tumor motion modeling and tracking. Compared to the previous 3D pairwise registration, our new 4D parameterization model leads to a significantly improved registration accuracy. The constructed deforming model can hence approximate the deformation of the tissues and tumor.

The volatile world economy has greatly affected fuel prices, while pollution and gas emissions are increasing to negatively impact global warming. Rising fuel costs have made drivers more concerned about how much of their monthly budgets are allocated for gasoline. In terms of the air pollution problem, greenhouse gas (GHG) emissions from vehicles are considered to be one of the main contributing sources. Carbon dioxide (CO₂) is the largest component of GHG emissions. As a result, it is important to develop and implement effective strategies to reduce fuel expenditure and prevent the expected increase of CO₂ emission from vehicles. Vehicular networks offer a promising approach that can be applied in transportation systems to reduce fuel consumption and emissions. One of the major applications of vehicular networks is intelligent transportation systems (ITS). To exchange and distribute messages, geocast routing protocols have been proposed for ITS applications. Most of these protocols focus on improving network-centric performance measures (e.g., message delay, packet delivery ratio, etc.) instead of focusing on improving the performance measures that are meaningful to both the scientific community and the general public (e.g., fuel consumption and CO₂ emission). Stop-and-go conditions, high acceleration, and unnecessary speed are uneconomical and environmentally unfriendly (UEU) actions that increase the amount of vehicle fuel consumed and the CO₂ emission. These actions can happen frequently for vehicles approaching a traffic light signal (TLS). This thesis proposes a new protocol named Economical and Environmentally Friendly Geocast (EEFG), which focuses on minimizing CO₂ emission and fuel consumption from vehicles approaching a TLS. The goal of this protocol is to deliver useful information to approaching vehicles inside the regions of interest (ROIs). Based on the information sent, the vehicle receiving the message adapts its speed to a recommended speed (Sʀ), which helps the vehicle reduce its UEU actions. To determine the value of Sʀ, a comprehensive optimization model that is applicable in both vehicle-to-vehicle (V2V) communication and traffic light signal-to-vehicle (TLS2V) communication is developed. The objective function is to minimize fuel consumption by and emissions from vehicles. The speed that can achieve this goal is the optimum Sʀ (Sʀ*). The thesis also proposes efficient heuristic expressions to compute the optimum or near-optimum value of Sʀ. An extensive performance study of the EEFG protocol is performed. It shows the impact of using EEFG in a modeled real-world network for urban and suburban areas in the city of Waterloo, Ontario, Canada. Four case studies have been considered: (1) a suburban environment at the maximum traffic volume hour of the day; (2) a suburban environment at the minimum traffic volume hour of the day; (3) an urban environment at the maximum traffic volume hour of the day; (4) an urban environment at the minimum traffic volume hour of the day. The results show that EEFG saves fuel and CO₂ emission in all four cases. In addition, the thesis studies the effect of communication parameters (e.g., transmission range, packet delay, and packet dropping rate) on vehicle fuel consumption and CO₂ emission. Having high transmission range, low packet delay, and low packet dropping rate, can save more fuel and CO₂ emission.

In recent years, the accident rate related to traffic is high. Analyzing the crash data and extracting useful information from it can help in taking respective measures to decrease this rate or prevent the crash from happening. Related research has been done in the past which involved proposing various measures and algorithms to obtain interesting crash patterns from the crash records. The main problem is that large numbers of patterns were produced and vast number of these patterns would be obvious or not interesting. A deeper analysis of the data is required in order to get the interesting patterns. In order to overcome this situation, we have proposed a new approach to detect the most associated sequential patterns in the crash data. We also make use of the technique, Association Rule Mining to mine interesting traffic accident patterns from the crash records. The main goal of this research is to detect the most associated sequential patterns (MASP) and mine patterns within the data sets generated by MASP using a modified FP-growth approach in regular association rule mining. We have designed and implemented data structures for efficient implementation of algorithms. The results extracted can be further queried for pattern analysis to get a deeper understanding. Efficient memory management is one of the main objectives during the implementation of the algorithms. Linked list based tree structures have been used for searching the patterns. The results obtained seemed to be very promising and the detected MASPs contained most of the attributes which gave a deeper insight into the crash data and the patterns were found to be very interesting. A prototype application is developed in C# .NET.

Fault observability as well as fault location algorithms in distributed power system are studied in this thesis. The importance of finding the fault location in a distribution system with the purpose of increasing reliability and decreasing the maintenance time and cost is discussed. Then, different existing fault location algorithms and approaches in the literature are introduced and compared. Subsequently, a new strategy to achieve fault observability of power systems while aiming minimum required number of Phasor Measurement Units (PMUs) in the network is proposed. The method exploits the nodal voltage and mesh current analyses where the impedance and admittance matrices of the network and its dual circuit are developed and utilized for fault location. The criterion of determining the number and the places of PMUs is that the fault location and impedance can be obtained in a unique manner without multi estimation. In addition, the method considers faults along the lines as opposed to the faults only on system buses available in the literature. The proposed approach provides an economical solution to decrease measurement costs for large power networks, distributed generation networks, and micro grids. Simulation results for IEEE 7-bus, 14-bus, and 30-bus systems verify the effectiveness of the proposed approach.

This thesis presents a study of the bias-induced threshold voltage metastability phenomenon of the hydrogenated amorphous silicon (a-Si:H) thin film transistors (TFTs). An application of gate bias stress shifts the threshold voltage of a TFT. After the bias stress is removed, the threshold voltage eventually returns to its original value. The underlying physical mechanisms for the shift in threshold voltage during the application of the bias and after the removal of the bias stress are investigated. <br /><br /> The creation of extra defect states in the band gap of a-Si:H close to the gate dielectric interface, and the charge trapping in the silicon nitride (SiN) gate dielectric are the most commonly considered instability mechanisms of threshold voltage. In the first part of this work, the defect state creation mechanism is reviewed and the kinetics of the charge trapping in the SiN is modelled assuming a simplified mono-energetic and a more realistic Gaussian distribution of the SiN traps. The charge trapping in the mono-energetic SiN traps was approximated by a logarithmic function of time. However, the charge trapping with a Gaussian distribution of SiN traps results in a more complex behavior. <br /><br /> The change in the threshold voltage of a TFT after the gate bias has been removed is referred to threshold voltage relaxation, and it is investigated in the second part of this work. A study of the threshold voltage relaxation sheds more light on the metastability mechanisms of a-Si:H TFTs. Possible mechanisms considered for the relaxation of threshold voltage are the annealing of the extra defect states and the charge de-trapping from the SiN gate dielectric. The kinetics of the charge de-trapping from a mono-energetic and a Gaussian distribution of the SiN traps are analytically modelled. It is shown that the defect state annealing mechanisms cannot explain the observed threshold voltage relaxation, but a study of the kinetics of charge de-trapping helps to bring about a very good agreement with the experimentally obtained results. Using the experimentally measured threshold voltage relaxation results, a Gaussian distribution of gap states is extracted for the SiN. This explains the threshold voltage relaxation of TFT after the bias stress with voltages as high as 50V is removed. <br /><br /> Finally, the results obtained from the threshold voltage relaxation make it possible to calculate the total charge trapped in the SiN and to quantitatively distinguish between the charge trapping mechanism and the defect state creation mechanisms. In conclusion, for the TFTs used in this thesis, the charge trapping in the SiN gate dielectric is shown to be the dominant threshold voltage metastability mechanism caused in short bias stress times.

Non-linear programming has been extensively used in wireless telecommunication systems design. An important criterion in optimization is the minimization of mean square error. This thesis examines two applications: peak to average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems and wireless airtime traffic estimation. These two applications are both of interests to wireless service providers. PAPR reduction is implemented in the handheld devices and low complexity is a major objective. On the other hand, exact traffic prediction can save a huge cost for wireless service providers by better resource management through off-line operations. <br /><br /> High PAPR is one of the major disadvantages of OFDM system which is resulted from large envelope fluctuation of the signal. Our proposed technique to reduce the PAPR is based on constellation shaping that starts with a larger constellation of points, and then the points with higher energy are removed. The constellation shaping algorithm is combined with peak reduction, with extra flexibilities defined to reduce the signal peak. This method, called MMSE-Threshold, has a significant improvement in PAPR reduction with low computational complexity. <br /><br /> The peak reduction formulated into a quadratic minimization problem is subsequently optimized by the semidefinite programming algorithm, and the simulation results show that the PAPR of semidefinite programming algorithm (SDPA) has noticeable improvement over MMSE-Threshold while SDPA has higher complexity. Results are also presented for the PAPR minimization by applying optimization techniques such as hill climbing and simulated annealing. The simulation results indicate that for a small number of sub-carriers, both hill climbing and simulated annealing result in a significant improvement in PAPR reduction, while their degree of complexity can be very large. <br /><br /> The second application of non-linear optimization is in airtime data traffic estimation. This is a crucial problem in many organizations and plays a significant role in resource management of the company. Even a small improvement in the data prediction can save a huge cost for the organization. Our proposed method is based on the definition of extra parameters for the basic structural model. In the proposed technique, a novel search method that combines the maximum likelihood estimation with mean absolute percentage error of the estimated data is presented. Simulated results indicate a substantial improvement in the proposed technique over that of the basic structural model and seasonal autoregressive integrated moving average (SARIMA) package. In addition, this model is capable of updating the parameters when new data become available.

This thesis presents RF front-end system design for both WLAN (Wireless Local Area Network) and SDARS (Satellite Digital Audio Radio System) applications using adaptive smart antenna technique. It is important to understand that smart antenna system with adaptive beam-forming provides the following advantages: 1. It can concentrate radio transmission on specific directions by modifying transmit/receive phase and amplitude characteristics, and therefore provides greater coverage and increased capacity. 2. It can greatly reduce multi-path fading effect and co-channel interference. This thesis first presents a complete 4-element smart antenna system for IEEE 802.11b/g WLAN applications. The prototype presented can interconnect with an of-the-shelf WLAN network card. The system is controlled via a microcontroller that adjusts the array pattern to capture the best signal. Measurements show that the system can increase the RSSI (Received Signal Strength Indicator) level significantly and therefore the SNR (Signal to Noise Ratio). This prototype is the first step towards a novel DBF (Digital Beam-Forming) smart antenna architecture. The second part of the thesis presents a 2-element smart antenna system for SDARS application. This prototype can be connected with an of-the-shelf SDARS digital receiver. The system was tested in a chamber and measurements show a controlled beam and increased gain. Ansoft HFSS was used to simulate the 2-element smart antenna and validate the testing results.

WiMAX is one of the most important broadband wireless technologies and is anticipated to be a viable alternative to traditional wired broadband techniques due to its cost efficiency. Being an emerging technology, WiMAX supports multimedia applications such as voice over IP (VoIP), voice conference and online gaming. It is necessary to provide Quality of Service (QoS) guaranteed with different characteristics, quite challenging, however, for Broadband Wireless Access (BWA) networks. Therefore, an effective scheduling is critical for the WiMAX system. Many traffic scheduling algorithms are available for wireless networks, e.g. Round Robin, Proportional Fairness (PF) scheme and Integrated Cross-layer scheme (ICL). Among these conventional schemes, some cannot differentiate services, while some can fulfill the service differentiation with a high-complexity implementation. This thesis proposes a novel scheduling algorithm for Orthogonal Frequency Division Multiplex/Time Division Multiple Access (OFDM/TDMA)-based systems, which extends the PF scheme to multiple service types with diverse QoS requirements. The design objective is to provide differentiated services according to their QoS requirements, while the objective can be achieved by adjusting only one unique parameter, the time window for evaluating the average throughput. By extensive simulation, it is shown that the proposed scheduling algorithm exploits the advantage of the PF scheme, enhancing the throughput, and distinguishes the services in terms of the average delay. Afterward, we prove the superiority of the new scheme over the conventional ones by showing simulation results.

The revolutionary concept of space-time coding introduced in the last decade has demonstrated that the deployment of multiple antennas at the transmitter allows for simultaneous increase in throughput and reliability because of the additional degrees of freedom offered by the spatial dimension of the wireless channel. However, the use of antenna arrays is not practical for deployment in some practical scenarios, e. g. , sensor networks, due to space and power limitations. <br /><br /> A new form of realizing transmit diversity has been recently introduced under the name of user cooperation or cooperative diversity. The basic idea behind cooperative diversity rests on the observation that in a wireless environment, the signal transmitted by the source node is overheard by other nodes, which can be defined as "partners" or "relays". The source and its partners can jointly process and transmit their information, creating a "virtual antenna array" and therefore emulating transmit diversity. <br /><br /> Most of the ongoing research efforts in cooperative diversity assume frequency flat channels with perfect channel knowledge. However, in practical scenarios, e. g. broadband wireless networks, these assumptions do not apply. Frequency-selective fading and imperfect channel knowledge should be considered as a more realistic channel model. The development of equalization and channel estimation algorithms play a crucial element in the design of digital receivers as their accuracy determine the overall performance. <br /><br /> This dissertation creates a framework for designing and analyzing various time and frequency domain equalization schemes, i. e. distributed time reversal (D-TR) STBC, distributed single carrier frequency domain (D-SC-FDE) STBC, and distributed orthogonal frequency division multiplexing (D-OFDM) STBC schemes, for broadband cooperative communication systems. Exploiting the orthogonally embedded in D-STBCs, we were able to maintain low-decoding complexity for all underlying schemes, thus, making them excellent candidates for practical scenarios, such as multi-media broadband communication systems. <br /><br /> Furthermore, we propose and analyze various non-coherent and channel estimation algorithms to improve the quality and reliability of wireless communication networks. Specifically, we derive a non-coherent decoding rule which can be implemented in practice by a Viterbi-type algorithm. We demonstrate through the derivation of a pairwise error probability expression that the proposed non-coherent detector guarantees full diversity. Although this decoding rule has been derived assuming quasi-static channels, its inherent channel tracking capability allows its deployment over time-varying channels with a promising performance as a sub-optimal solution. As a possible alternative to non-coherent detection, we also investigate the performance of mismatched-coherent receiver, i. e. , coherent detection with imperfect channel estimation. Our performance analysis demonstrates that the mismatched-coherent receiver is able to collect the full diversity as its non-coherent competitor over quasi-static channels. <br /><br /> Finally, we investigate and analyze the effect of multiple antennas deployment at the cooperating terminals assuming different relaying techniques. We derive pairwise error probability expressions quantifying analytically the impact of multiple antenna deployment at the source, relay and/or destination terminals on the diversity order for each of the relaying methods under consideration.