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Title: COMPUTER AIDED DIAGNOSIS IN MAMMOGRAPHY WITH CONTENT-BASED IMAGE RETRIEVAL
Creator: Jing, Hao
Date: 2011-11, 2011-12
Description: Computer-aided diagnosis (CAD) for breast cancer, a common form of cancer in women, has been an active research area. This work aims to...
Show moreComputer-aided diagnosis (CAD) for breast cancer, a common form of cancer in women, has been an active research area. This work aims to investigate and develop CAD techniques for clustered microcalcifications (MCCs), which can be an important early sign of breast cancer. The contributions of this work include development of a database of cancer cases and algorithms for detection and classification of MCCs. First, a database consisting of a large number of cases is built from different sources. To support the merging of cases from different data sources, a feature comparison study is conducted between mammograms from screen film and full field digital mammography (FFDM) systems. It is demonstrated that the features extracted from film and FFDM are highly correlated and there is no adverse effect on a CAD task of classification when used together. Second, a spatial point process (SPP) approach is proposed to exploit the spatial distribution among different MCs in a mammogram directly during the detection process. This is different from the conventional approach in which detection algorithms are employed to first identify individual MCs in a mammogram, which are subsequently grouped into clusters by a clustering algorithm. The performance of the proposed approach is demonstrated to be superior to an existing method based on the support vector machine (SVM). Third, in observation of the emerging of large databases from the picture archiving and communication (PAC) systems in the clinics, a retrieval driven approach is proposed for classification of MCCs. In this approach, for a case to be diagnosed (i.e., query), a set of similar cases is retrieved from a database and subsequently is used to train xii an adaptive classifier specifically for the query case using the technique of logistic regression. The proposed approach is demonstrated to lead to significant improvement in classification accuracy. Moreover, the proposed adaptive classification approach is further developed using regularization techniques, where a prior is first derived from a baseline classifier and then used to regularize the adaptive classifier trained with the retrieved cases. The regularized adaptive classifier can be more computationally efficient, and is demonstrated to achieve further improvement in performance.
Ph.D. in Electrical Engineering, December 2011
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Title: POLARIZATION COUPLING IN SEMICONDUCTOR NANO-DIMERS IN THE TERAHERTZ RANGE
Creator: Hu, Zhijing
Date: 2017, 2017-05
Description: Surface plasmon resonance (SPR) occurs at the interface of a semiconductor and a dielectric when certain conditions are satisfied. SPR is...
Show moreSurface plasmon resonance (SPR) occurs at the interface of a semiconductor and a dielectric when certain conditions are satisfied. SPR is impetus to new sensor and device development in the optical range, with nanoparticles of noble metals taking up major roles. Typical conduction band electron concentrations in semiconductors lead to resonance frequencies in the terahertz and infrared bands. While the response strength is weaker than those exhibited by metals, it can be made up for by the formation of aggregates. The added degree of freedom by doping or carrier injection further enhances the versatility of semiconductor nanoclusters. To obtain a first principle solution to the coupled set of equations for charge carrier transport and electrodynamics in a conductive cluster is a formidable task with a high computational cost. Employing a finite-element based tool, the COMSOL Multiphysics Simulation Software, the interaction inside and outside some elementary semiconductor structures such as slab and sphere have been solved, which revealed the screening of the internal field while displaying dispersion and absorptions effects. The study of semiconductor dimer also showed a significant field enhancement and frequency shift. Under strong applied field, asymmetric polarization within the particles is revealed. The accompanying nonlinear polarization response can be employed to develop new devices. These model structures can serve to provide insight to the analysis and synthesis more complex structures.
Ph.D. in Electrical Engineering, May 2017
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Title: SELF-HEALING IN MICROGRID OPERATION AND MESSAGE-PASSING BASED DEMAND RESPONSE
Creator: Barati, Masoud
Date: 2013, 2013-12
Description: Electrical energy can be more efficiently and reliably generated, transmitted, and consumed over electricity grids as smart grids evolve....
Show moreElectrical energy can be more efficiently and reliably generated, transmitted, and consumed over electricity grids as smart grids evolve. Through the two-way flow of information between suppliers and consumers, the grids can encourage and adapt more easily to the increased consumer participation in energy management through demand response. This dissertation studies the transactive energy management on the residential side via microgrid operation and message-passing based demand response. A microgrid is an independent section of the electrical distribution grid with capabilities to transmit, produce, and distribute power within a localized area. The implementation of the microgrid increases the reliability and quality of power supply through various means including the self-healing paradigm. A microgrid operating under a self-healing paradigm can automatically and intelligently detect and reroute the power flow around an unexpected line fault. This dissertation presents the formulations and the methodologies of the self-healing process, which is incorporated into the microgrid operation for real-time scheduling. The self-healing process tries to find the best topology of the microgrid including radial and closed-loop configurations that minimize the total operation cost while respecting all security constraints. The dissertation also considers the AC solution of optimal power flow for self-healing applications, which enhances the reactive power flow for mitigating any bus voltage violations and for alleviating any real and reactive loop flows. The message-passing based demand response scheme relies on dynamic pricing of electricity to regulate electricity consumption. To achieve this goal, load serving entities via messages-passing gather the information such as consumers’ usage of electricity from xv ii smart meters, and set the dynamic price level appropriately in order to reduce the peak electricity demand through the cooperation of customers. In response to the dynamic price signals, customers can shift their demands automatically, with the help of a home energy management system, or manually to the off-peak hours so as to minimize their electricity payment and maximize its utility function. The message-passing based demand response scheme is applied in this dissertation to residential household scheduling, which is a key component of a future smart grid that can help reduce peak loads and adjust elastic demands to provide economic and emergency demand responses. A decentralized and iterative message-passing method is developed for solving the residential household scheduling problem. Under the context of a competitive retail electricity market, this dissertation analytically models load serving entities’ production function based on wellknown economic theory, analytically models household behaviors based on the ordinal and cardinal concepts of the utility function and using a static game strategy, and efficiently calculates retail electricity price with pure ex-ante or combinatorial pricing strategies. A simple yet effective price stabilization strategy for retail electricity price is proposed to mitigate the potential price and consumption spike caused by uncertainties in wholesale electricity prices.
PH.D in Electrical Engineering, July 2013
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Title: OPTIMAL ROUTING ALGORITHMS IN ENERGY-HARVESTING WIRELESS SENSOR NETWORKS
Creator: Martinez, Gina
Date: 2014, 2014-12
Description: Harnessing energy from environmental sources such as solar and wind is an attractive solution to the critical energy limitation problem in...
Show moreHarnessing energy from environmental sources such as solar and wind is an attractive solution to the critical energy limitation problem in wireless sensor networks. Energy harvesting can potentially provide the network with perpetual and sustainable operation, or it can prolong network lifetime even for high consumption applications so as to justify the high cost of deployment. However, in order to efficiently utilize harvested energy, the energy source dynamics need to be incorporated into the network design. One way to do so is to make the network layer routing algorithm energy-harvest-aware. One common property of environmental energy sources is that they are generally only intermittently available. To address this, a storage unit such as a rechargeable battery can be introduced into the system. However, this is only a partial solution due to finite buffer storage capacities that cause harvested energy to be wasted when full. In this work, we aim to maximize the network lifetime by optimizing the energy availability and consumption alignment. To realize this objective, we first show that the minimization of energy wastage is a necessary condition to the maximization of available network energy. We then propose an on-demand routing algorithm that maximizes the total residual network energy by minimizing the energy consumption and wastage. Next, we illustrate the tradeoff between the two objectives of maximizing the total network energy and maximizing the minimum network energy in prolonging network lifetime. Then, we propose a linear-programming routing solution that maximizes a utility objective function based on this tradeoff. Although these routing approaches are shown to achieve high energy utilization, they are still based on deterministic harvest and consumption models. In the last part of this work, we propose a routing algorithm by applying the Semi-Markov Decision Process. Using this method, we are able to incorporate a comprehensive consideration of stochastic solar availability and traffic models, heterogeneous network properties such as non-uniform energy buffer capacities and consumption rates, and the optimization of an analytical formulation for network lifetime.
Ph.D. in Electrical and Computer Engineering, December 2014
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Title: DESIGN AND OPTIMIZATION OF CONFIGURABLE PASSIVE COMPONENTS FOR CMOS MILLIMETER-WAVE INTEGRATED CIRCUITS
Creator: Liu, Gui
Date: 2011-05-10, 2011-05
Description: With the rapid growth of wireless communications, there is an increasing demand for low cost, low power consumption, high data rates and high...
Show moreWith the rapid growth of wireless communications, there is an increasing demand for low cost, low power consumption, high data rates and high density integrated circuits. The continuous scaling of CMOS technologies promises to achieve higher frequencies of operation in the millimeter-wave (mm-wave) frequency regime. To enable lager bandwidth for higher data rates wireless applications, many efforts have been focused on the design of mm-wave CMOS integrated circuits. The emerging mm-wave wireless commercial applications such as Wireless Hi-definition Video (60 GHz), automotive radar (77 GHz) and mm-wave imaging system (94 GHz) have brought new challenges in devices technology and systems. There is an ever increasing demand for multi-band and multi-mode integrated wireless communication systems which have the advantages of power and area savings. Therefore, flexible and configurable mm-wave on-chip components and circuits are needed to accommodate a wide variety of wireless communication standards. On the other hand, the first silicon success of the challenging mm-wave integrated circuits requires superior and robust design capabilities in cutting-edge technologies. To satisfy customers by providing them with the fastest time-to-market and the lowest total cost, the configurable multiband mm-wave solution is preferred. Design of on-chip passive components operating at millimeter wave frequencies presents several challenges due to the ohmic loss, parasitic inductance and capacitance. Therefore, it requires both an accurate model and electromagnetic (EM) simulation tools to characterize the passive components. The other challenge of design of mm-wave on-chip passives is process variations which can have a significant effect on the robustness of the passive components and circuits. Methodology to compensate and adjust for process variations is needed. Passives that can be configured after fabrication would be an attractive way to obtain accurate parameters and overcome effects of process variations. The configurable Multilayer Coplanar Waveguide (MCPW) based transmission lines offer convenient method to alleviate the problem of process variations and obtain accurate inductor values. This dissertation focuses on the design of mm-wave passive components and their applications. Model, EM simulation, and optimization of several novel MCPW-based configurable inductors are presented. A 77-GHz voltage controlled oscillator (VCO) and a 77-GHz receiver employing the configurable inductors have been realized. The 77-GHz VCO with MCPW-based configurable inductor exhibits low phase noise and wide frequency tuning range. The 77-GHz receiver achieves low power and state-of-the-art performance. The successful implementations of several individual configurable passive components, a 77-GHz VCO, and a 77-GHz receiver demonstrate the feasibility to achieve good performance and robust design with configurable passive components.
Ph.D. in Electrical Engineering, May 2011
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Title: HARDWARE-EFFICIENT VLSI IMPLEMENTATION FOR PARALLEL LINEAR-PHASE DIGITAL FIR FILTER
Creator: Tsao, Yu-chi
Date: 2011-11, 2011-12
Description: Along with the explosive growth of multimedia applications, the number of gates required and the area consumed in very-large-scale integration...
Show moreAlong with the explosive growth of multimedia applications, the number of gates required and the area consumed in very-large-scale integration (VLSI) for digital signal processing (DSP) is getting higher and higher. Therefore, the demand for low-complexity and low-cost VLSI architectures for DSP, which occupies area as small as possible while remaining high performance, is imperatively needed. Finite impulse response (FIR) digital filter is one of the most widely used fundamental devices performed in DSP systems, ranging from wireless communications to video and image processing. Furthermore, when narrow transition band characteristics are required, the much higher order in the FIR filter is unavoidable. For instance, a 576-tap digital filter is used in video ghost canceller for broadcast television, which reduces the effect of multi-path signal echoes. On the other hand, parallel and pipelining processing are two techniques used in DSP applications, which both can be exploited to reduce the power consumption. Parallel processing applied on FIR digital filter can either increase the throughputs for high-speed processing or reduce the power consumption of the original sequential filter by lower supply voltage. Hence, there have been several architectures for parallel FIR digital filter proposed in the past. However, for symmetric convolutions, linear phase digital FIR filter, these proposed architectures are not beneficial in terms of hardware consumption. In this dissertation, new parallel FIR digital filter architectures which can save significant hardware cost by exploiting the inherent nature of symmetric convolutions, namely linear phase digital FIR filter, are presented. This dissertation is mainly divided into four chapters. In chapter one, some prerequisites as the background related to parallel finite impulse response (FIR) digital xi filter design and implementation are introduced, including conventional parallel FIR digital filter architectures, parallel FIR digital filter structures based on fast FIR algorithm (FFA), previously proposed architectures for parallel FIR filter, maximum absolute difference (MAD) quantization algorithm, canonic signed digit (CSD) representation and CSD multipliers for constant multiplications, are introduced and reviewed. In chapter two, the proposed parallel FIR digital filter structures for symmetric convolutions of even length are presented, in which the complexity and experimental results are analyzed and compared with FFA-based parallel FIR digital filter and other previously proposed structures. In chapter three, the proposed parallel FIR digital filter architectures for symmetric convolutions of odd length are presented, in which the complexity and the benefit of structures are analyzed and compared with FFA-based parallel FIR digital filter and other previously proposed structures The last chapter, chapter four, gives the summary and the conclusion of the proposed parallel linear-phase FIR digital filter.
Ph.D. in Electrical Engineering, December 2011
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Title: PARTICLE FILTERING ESTIMATION APPROACH IN ADVANCED DIGITAL COMMUNICATION SYSTEMS
Creator: Huang, Lun
Date: 2013, 2013-05
Description: The ever-increasing volume of users and the demand for more communication services bring about many advanced modulation and demodulation...
Show moreThe ever-increasing volume of users and the demand for more communication services bring about many advanced modulation and demodulation technologies which are developed to increase the spectrum efficiency and cope with challenging transmission conditions in digital communications. However, it is difficult to improve the performance of those traditional modulation and demodulation approaches without increasing transmit power and lowering spectrum efficiency. This thesis studies the application of powerful Particle Filtering methods to the problems associated with the interference cancellation, equalization, demodulation, and decoding of the signals over communication channel. In this thesis, theoretic models of using particle filtering approaches in digital communications are investigated, and several specific algorithms and schemes are considered as applications of the theoretic models. First, the application of particle filtering in delayed decision feed-back sequence estimation equalization is addressed. The particle filtering approach is then introduced to an efficient particle filtering receiver for inter-carrier interference cancellation and demodulation of M-ary modulated signals in OFDM/OFDMA system under time-variant Rayleigh fading channels. Subsequently, an efficient sequential Monte Carlo (SMC) demodulation approach for Polynomial Phase Modulation (PPM) is discussed. The interference cancellation and demodulation algorithm for MIMO-PPM scenario is then derived. The analysis of performance and computational complexity for SMC particle filtering approach is also provided. Comprehensive simulation results confirm that the proposed sequential Monte Carlo particle filtering approaches have better performance than the conventional methods.
PH.D in Electrical Engineering, May 2013
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Title: HARDWARE/SOFTWARE CO-DESIGN PARTITIONING ALGORITHM FOR MACHINE VISION APPLICATIONS
Creator: Gonnot, Thomas
Date: 2017, 2017-05
Description: Advancements in FPGA technologies now allows the implementation of machine vision using hardware component rather than processors for...
Show moreAdvancements in FPGA technologies now allows the implementation of machine vision using hardware component rather than processors for increased efficiency. The combination of hardware and software implementations, however, can provide even more efficient results by combining the advantages of both technologies. This leads to the problem of partitioning the machine vision algorithms between hardware and software. The hardware/software partition problem is NP-hard, which means that a solution to the problem can be checked in polynomial time, but the time to find the solution is not predictable. Automated methods based on a genetic algorithm or discrete particle swarm optimization algorithm allow a designer to implement computer vision algorithms without concerns for the hardware/software partitioning. Their reliance on randomness to explore different partitioning selections, however, means that the optimum result might not be reached and that the processing time cannot be predicted. This dissertation introduces a model for image processing and computer vision algorithms in a set of elementary blocks, each of which is assigned one or more configuration. This configuration can be either hardware or software and is linked to the corresponding resource utilization and performance. A procedure is also introduced to allocate the different blocks to either hardware or software, and a cost function is defined to evaluate the relevance of the generated design. The implementation of the model and procedure allows for the partitioning of any image processing in polynomial time by checking various implementations and selecting the optimum solution. This thesis includes two test cases used to test the efficiency of the method. The shift-invariant features transform is used to demonstrate the viability of the partitioning results on an algorithm containing multiple image convolution operations in parallel. The neural network, on the other hand, is used to demonstrate the performances of the procedure when machine vision algorithm contains many blocks. Finally, this dissertation present a set of machine vision applications, such as object tracking, object recognition, optical character recognition, facial recognition, and visually impaired assistance. The proposed model and procedure could be included in the design flow of hardware/software co-design tools and provide a library of image processing blocks ready to be implemented. This would allow image processing and computer vision designers would be able to implement any algorithm efficiently in hardware/software co-design without the need to know how to partition it.
Ph.D. in Electrical Engineering, May 2017
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Title: CAPACITY ANALYSIS OF MULTI-RADIO MULTI-CHANNEL (MR-MC) WIRELESS NETWORK
Creator: Li, Hongkun
Date: 2012-11-15, 2012-12
Description: The multi-radio multi-channel (MR-MC) networking provides a generic computing platform for a wide range of next-generation wireless networks,...
Show moreThe multi-radio multi-channel (MR-MC) networking provides a generic computing platform for a wide range of next-generation wireless networks, e.g., wireless mesh networks based on the IEEE 802.16, 4G cellular networks based on the long term evolution (LTE), and cognitive radio networks based on the dynamic spectrum sharing. Multiple radio interfaces and channels allow more flexible network configuration to achieve higher network capacity. However, the capacity of the MR-MC networking is not well studied due to the lack of effective tool addressing the complex interactions of the channel assignment and radio interface allocation problem. Moreover, how to efficiently utilize multiple interfaces and channels is unexplored. Generally, we have the four main contributions in this work. 1) an efficient methodology is proposed to compute the optimal capacity of MR-MC network and the concept of critical set is revealed. 2) two sufficient conditions are developed for the flow assignment, which can be constructed and verified in distributed manner. Both conditions achieve a provable portion of the optimal capacity region. 3) a novel framework and efficient algorithms are developed for the dynamic network control in the MR-MC network. 4) a new routing metric is proposed to consider both delay and interference for path selection, and a routing protocol is designed correspondingly. Specifically, we originally construct a new multi-dimensional conflict graph (MDCG) to describe all the interference relationship in the MR-MC network. Based on MDCG, we formulate a multi-commodity flow (MCF) problem augmented with maximal independent set (MIS) constraint to compute the optimal capacity, so that the optimal capacity planning in MR-MC networks can be transformed from integer programming regime to linear programming regime. We further provide the new concept of critical MIS set, and estimate the upper bound of the size of critical MIS set. Therefore, a heuristic algorithm is designed to systematically compute those MISs, which are more likely to be involved in critical set. Moreover, we develop the sufficient conditions for the flow rate assignment, which achieve viii a provable portion of the optimal capacity region, termed as efficiency ratio. These sufficient constraints could be constructed and verified in the distributed and localized manner. We develop new method to compute the efficiency ratio for each sufficient condition by exploring the disruptively different geometric property of MR-MC networks compared with single radio single channel network. Then we develop a new framework to systematically study the resource allocation problem considering the dynamic network control in the MRMC network. The framework not only facilitates the formulation of throughput-optimal scheduling for the MR-MC network, but also allows us optimally solving the joint resource allocation problem, including routing, channel/interface assignment, flow allocation and scheduling. At last, a new routing metric is proposed to consider not only the transmission delay also queuing delay. In addition, in the MR-MC context, the inter-flow interference and intra-flow interference are taken into account. An AODV-based routing protocol is designed to implement the new metric.
PH.D in Electrical and Computer Engineering, December 2012
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Title: CYBER ATTACKS AGAINST STATE ESTIMATION IN POWER SYSTEMS: VUNERABILITY ANALYSIS AND PROTECTION STRATEGIES
Creator: Liu, Xuan
Date: 2015, 2015-07
Description: Power grid is one of the most critical infrastructures in a nation and could suffer a variety of cyber attacks. With the development of Smart...
Show morePower grid is one of the most critical infrastructures in a nation and could suffer a variety of cyber attacks. With the development of Smart Grid, cyber security has become an area of growing concern. False data injection attack has recently attracted wide research interest. This thesis proposes a false data attack model with incomplete network information and develops optimal attack strategies for attacking load measurements and the real-time topology of a power grid. The impacts of false data on the economic and reliable operations of power systems are quantitatively analyzed in this thesis. To mitigate the risk of cyber attacks, a distributed protection strategies are also developed. It has been shown that an attacker can design false data to avoid being detected by the control center if the network information of a power grid is known to the attacker. In practice, however, it is very hard or even impossible for an attacker to obtain all network information of a power grid. In this thesis, we propose a local load redistribution attacking model based on incomplete network information and show that an attacker only needs to obtain the network information of the local attacking region to inject false data into smart meters in the local region without being detected by the state estimator. A heuristic algorithm is developed to determine a feasible attacking region by obtaining reduced network information. This thesis investigates the impacts of false data on the operations of power systems. It has been shown that false data can be designed by an attacker to: 1) mask the real-time topology of a power grid; 2) overload a transmission line; 3) disturb the line outage detection based on PMU data. To mitigate the risk of cyber attacks, this thesis proposes a new protection strategy, which intends to mitigate the most damaging effect of LR attacks on power system operation. The objective is to mitigate the damage effects of false data injection attacks by increasing the attacking cost of an attacker. This is achieved by protecting a small set of critical measurements. To further reduce the computation complexity, we also propose a mixed integer linear programming approach to separate the power grid into several subnetworks, then distributed protection strategy is applied to each subnetwork. The results of this thesis reveal the mechanism of local false data injection attacks and highlight the importance and complexity of defending power systems against false data injection attacks.
Ph.D. in Electrical and Computer Engineering, July 2015
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Title: TEMPORAL AND SPATIOTEMPORAL MODELS FOR SHORT-CRIME PREDICTION
Creator: Liu, Xiaomu
Date: 2017, 2017-07
Description: One of the most important aspects of predictive policing is identifying the likely time and place of crime occurrences so as to prevent future...
Show moreOne of the most important aspects of predictive policing is identifying the likely time and place of crime occurrences so as to prevent future crimes. The ability to make short-term predictions may be of particular importance for optimizing police resource allocation. The goal of this study is to investigate the temporal and spatiotemporal pattern of crime in the city of Chicago and to build corresponding predictive models. First, a temporal model for forecasting citywide violent crime time count is proposed. This model is composed of a long-term trend and short-term variations using data of time, weather and crime. The importance of model reproducibility is addressed in this study to produce low-complexity models. We introduce an approach that provides a way to extend the model selection criterion to both prediction accuracy and model reproducibility. The experimental results show that models produced by this approach outperform several simple time-series models. It is also found that these models typically include fewer variables; therefore, they are more interpretable, and may provide superior generalization error. Next we develop a framework that provides predictions for tomorrow’s violent crime counts at the level of a police district. The procedures include citywide daily violent crime count prediction, violent crime density estimation, and distributing citywide predictions to districts according to the estimated densities. In order to estimate the crime spatial densities, we use mesh modeling and demonstrate that a mesh model can be used as the structure for modeling the spatial variation of crime rate since it is well adapted to the inhomogeneous crime distribution. The experimental results show that our method provides more-accurate forecasts than those given by historical crime statistics. One aspect of studying spatial pattern of crimes is identifying geographical regions with similar crime characteristics. Specifically, we illustrate applying unsupervised clustering techniques to segment the city into sub-regions. We explore the use of Gaussian mixture models combined with a Markov random field for the purpose of regularization. We also propose a framework for the evaluation of clustering models without knowing the ground truth, which can present a more-complete picture for model selection in unsupervised clustering problems. Finally, we develop a spatiotemporal prediction method that predicts the locations where violent crimes or property crimes are most likely to occur tomorrow. Crime incidents are rasterized by a spatiotemporal grid. Other factors that affect the time and location preferences of criminal activities are also leveraged and represented by that grid. Each spatiotemporal grid cell is treated as an example for training and testing our models. We also explore whether pooling data from various sub-regions based on spatial clustering can improve model performance. The experimental results show that our models are more accurate than conventional hot-spot models. It is found that the effects of using different training samples are not consistent, which depends on target crime type.
Ph.D. in Electrical Engineering, July 2017
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Title: PEDESTRIAN DETECTION AND TRACKING FOR ADVANCED DRIVER ASSISTANCE SYSTEMS
Creator: Mesmakhosroshahi, Maral
Date: 2017, 2017-05
Description: In an effort to reduce driver errors in being the major cause of traffic accidents, there is a lot of research being conducted into the...
Show moreIn an effort to reduce driver errors in being the major cause of traffic accidents, there is a lot of research being conducted into the development of advanced driver assistance systems (ADAS). ADAS is a system aimed at helping the driver in tasks such as pedestrian and vehicle detection, traffic sign recognition and lane detection. Pedestrian detection is one of the major tasks in advanced driver assistance systems (ADAS). Most of the stereo based pedestrian detection algorithms include three major steps: 1. Ground plane estimation 2. Region of interest (ROI) generation 3. Pedestrian classification In this thesis, we present a stereo-based pedestrian detection framework for advanced driver assistance systems by exploiting both color and depth information obtained from a stereo camera installed in a vehicle. In our proposed framework, we first use the vertical gradient of the dense depth map to estimate and discard the ground plane. The boundaries of the ground plane are then searched to detect the pedestrians and the depth values of the boundaries are used to compute the size of the detection windows for detecting pedestrians at different scales. In addition, a depth-based multi-scale ROI extraction method has been proposed to reduce the computation time of ROI extraction. For classifying ROIs to pedestrian and non-pedestrian classes, Histogram of Oriented Gradients (HOG)/Linear support vector machine (SVM) and Integral Channel Features (ICF)/Adaboost are used. To recover the missed pedestrians and improve the detection rate, an ROI tracking algorithm is proposed which incorporates the ROIs extracted from the current frame with theROIs tracked from a reference frame. For additional reduction in search space, we propose a novel algorithm to reduce the number of candidate windows extracted as ROI by taking advantage of the temporal correlation between the adjacent frames. We also propose a method to improve the accuracy of the pedestrian classifi- cation using the aggregated channel features. In this approach, we use the aggregated channel features as our baseline detector and improve it's accuracy using the tanh normalization and Gabor filter. After classification using Adaboost, we use a posi- tive subset of the bounding boxes to classify them again using Convlutional Neural Network to finalize the detection.
Ph.D. in Electrical Engineering, May 2017
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Title: LOAD ANALYSIS BASED ON MACHINE LEARNING IN POWER SYSTEMS
Creator: Lu, Dan
Date: 2017, 2017-05
Description: The dissertation is composed by four parts, first, load sampling for SCUC based on Principal Component Analysis (PCA) and Kernel Density...
Show moreThe dissertation is composed by four parts, first, load sampling for SCUC based on Principal Component Analysis (PCA) and Kernel Density Estimation (KDE); second, load forecasting based on PCA and Bayesian ridge regression; third, anomalies detection based on Machine Learning methodology; fourth the long-term planning of Battery-based Energy Storage Transportation (BEST) in power system. Mathematical models are constructed to fulfill the research of the three targets, and numerical examples are used to test the models. The first three parts are based on PCA, which reduced the load dimensions. In the first part, a robust power system Unit Commitment (UC) is the aim to fulfil the possible load. In the second part, a novel short-term nodal load forecasting is raised to give better prediction of the next day load to improve the next data UC scheduling. In the third part, anomalies are detected in the reduced power flow space based on the pattern identified in the lower dimensional space. The purpose of the fourth part is to find ways of better utilizing the existing resources from integrating the frontier technology, the mobility of more compact and higher capacity batteries. Mix-integer programming (MIP) is used in the formulation.
Ph.D. in Electrical Engineering, May 2017
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Title: STEREO-BASED DEPTH MAP PROCESSING: ESTIMATION AND REFINEMENT
Creator: Loghman, Maziar
Date: 2016, 2016-12
Description: During the past decade, research in 3D video has become a hot topic owing to advancements in both hardware and software. Amongst different...
Show moreDuring the past decade, research in 3D video has become a hot topic owing to advancements in both hardware and software. Amongst different methods proposed for representing 3D data, multi-view video plus depth (MVD) format has gained a lot of attention. Most of such 3D algorithms rely on a per-pixel depth representation of the scene called a depth map. Depth maps are very useful for rendering virtual views and have lead to advancements in 3D compression algorithms. Generating an accurate and dense depth map is one of the important prerequisite for many 3D video applications. In this thesis, we highlight the following major problems in MVD. * Depth map estimation * Depth map refinement * Depth map coding In order to generate an accurate depth map, we propose a method based on Census transform with adaptive window patterns and semi-global optimization. A modified cross-based cost aggregation technique is proposed which helps to calculate a more reliable depth map. In order to further enhance the quality of the generated depth map, a novel multi-resolution anisotropic diffusion based algorithm is presented. The proposed depth refinement algorithm computes a dense depth map in which the holes have been filled and the object boundaries are sharpened. The next part of the research is based on depth map coding. In depth map coding, a considerable amount of time is required to investigate the mode decision pro- cess for every block of depth pixels. However, in real-time purposes, we can partially skip the mode selection step. In this thesis, we propose a novel depth intra-coding scheme for 3D video coding based on HEVC standard. The core idea of the proposed method is motivated by the fact that depth maps have specific characteristics that distinguish them from those of color images. By analyzing the reference depth maps based on homogeneousness of different regions, for some particular blocks, the DMM full-RD search is skipped and the mode is selected based on the previous similar tree- blocks. By this means, the time complexity of the encoding process is significantly reduced.
Ph.D. in Electrical Engineering, December 2016
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Title: NON-INTRUSIVE LOAD MONITORING AND DEMAND RESPONSE FOR RESIDENTIAL ENERGY MANAGEMENT
Creator: Iwayemi, Abiodun
Date: 2016, 2016-05
Description: Compared to cellphone bills which itemize billing into local, international, text messaging, and data, todays electricity bills are opaque....
Show moreCompared to cellphone bills which itemize billing into local, international, text messaging, and data, todays electricity bills are opaque. Residential electricity customers receive a monthly bill detailing their aggregate energy usage, without any insight into which appliances are responsible for what proportions of their bill. We therefore created a Non-intrusive load monitoring framework that uses only data available from smart meters and the price signals from the Electric utility, and combine it with Optimal Stopping Rule-based schedulers to create a framework to equip residents with the information they need to be more energy efficient while balancing their costs and comfort. Non-intrusive load monitoring provides homeowners with detailed feedback on their electricity usage, but an open area is automated appliance labeling and the creation of generalizable appliance models that can be trained in one home, and deployed in another. Manually labeling such events to use them for disaggregating residential appliances is a costly and tedious task, and we developed two approaches for semisupervised learning of appliance signatures. The first approach uses 1-Nearest neighbor semi-supervised learning, and we developed a stopping criterion which reduces the likelihood of mislabeling appliance instances. This approach was extended to a cluster-then-label semi-supervised learning approach which can use only 3 labeled samples of each appliance to label and classify similar appliances within the home. Our approach enables the comparison of unequal length time series, and incorporates additional features extracted from the appliance time series. Finally, we develop a hybrid framework that combines detailed appliance models learned via Non-intrusive load monitoring with optimal stopping rule schedulers. We evaluated the performance of these models in terms of cost and delay, and explored the effect that errors in the real-time price and appliance models have on appliance running costs to demonstrate how our approach outperforms scheduling using only day head prices.
Ph.D. in Electrical Engineering, May 2016
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Title: SECURE AND RESILIENT OPERATION OF CYBER-PHYSICAL POWER SYSTEMS
Creator: Li, Zhiyi
Date: 2017, 2017-07
Description: For economic reasons, modern power systems are commonly operated close to their secure limits so that they are vulnerable to unexpected severe...
Show moreFor economic reasons, modern power systems are commonly operated close to their secure limits so that they are vulnerable to unexpected severe disruptions such as disastrous cyberattacks and extreme weather events. This thesis is aimed at enhancing the security and resilience of power supplies for facilitating the development of a Smart Grid, when power systems in various parts of the world have been undergoing transitions toward cyber-physical systems. First, this thesis discusses common cybersecurity vulnerabilities in modern power systems and presents physical implications of cyberattacks on power system operations. In particular, this thesis analyzes a specifc type of coordinated cyberphysical attacks that could lead to undetectable line outages. Coordinated with physical attacks causing line outages, cyberattacks comprising topology preserving and load redistribution attacks could mask and potentially exasperate the outages to trigger cascading failures. Such coordinated cyber-physical attacks are analyzed in a bi-level optimization model which is then transformed into a mixed-integer linear programming problem. The proposed model and the two-stage solution algorithm are examined by case studies based on the IEEE 14-bus and 118-bus test systems. Second, this thesis offers the pertinent studies on quantifying the risk of cybersecurity vulnerabilities in power system operations. A type of locally coordinated cyber-physical attacks is analyzed in detail, which would cause undetectable line outages in local areas without the need for complete network information. A risk-based optimization model in the mixed-integer linear programming form is presented for analyzing physical implications resulting from the power ow redistribution. An efficient greedy search-based heuristic method is then developed to offer satisfactory solutions for real-time applications, which are verified by case studies based on a six-bus system and the two-area IEEE RTS-96 system. Third, this thesis studies security measures for mitigating the cybersecurity risk in power system operations. A game-theoretic framework is built for determining the optimal combination of security measures based on the minimax-regret decision rule. The resulting multi-level optimization model is reformulated as a bilevel mixed-integer linear programming problem. An implicit enumeration algorithm is then developed to achieve an exact solution to this complex problem. Acceleration techniques are also provided to improve the computation efficiency for large-scale power system applications. The proposed model and solution methods are validated by case studies based on a six-bus test system and the two-area RTS-96 system. Fourth, this thesis extends the discussion of cybersecurity vulnerabilities to the operation of distributed power systems like microgrids. Since microgrids are regarded as building blocks of a Smart Grid, they strive for cyber-secure operations for sustaining power services to local customers. The assessment and mitigation of the cybersecurity risk in microgrid operations is then presented in depth. Additional opportunities provided by software-defined networking technologies to enhance the microgrid cybersecurity are also realized by the proposed defense-in-depth framework that comprises three lines of defense against cyberattacks. Last, this thesis investigates the role of networked microgrids in enhancing the power system resilience against extreme events. Since resilience is an intrinsically complex property which requires deep understanding of power system operations, a generic simulation-based framework is developed for power system operators to analyze the resilience comprehensively and respond effectively in emergency conditions. The notion that the deployment of networked microgrids catalyzes the resilience enhancement in a Smart Grid is discussed in detail. Besides, the management of networked microgrids for achieving a higher degree of resilience, reliability, and efficiency of power supplies is discussed based on the proposed hierarchical control framework.
Ph.D. in Electrical Engineering, July 2017
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Title: DESIGN AND ANALYSIS OF AN UNCONVENTIONAL PERMANENT MAGNET LINEAR MACHINE FOR ENERGY HARVESTING
Creator: Zeng, Peng
Date: 2011-11, 2011-12
Description: The rise of global energy consumption and the growing trend to utilize clean energy stress the demand to harvest the untapped green energy...
Show moreThe rise of global energy consumption and the growing trend to utilize clean energy stress the demand to harvest the untapped green energy existing in every day life of human kind. Among the commonly over-looked energy resources, the kinetic motions including the vibration generated by linear motion and the vibration existent in ambient environment prove to own strong energy potentials. Though so far a number of such kinetic energy harvesters have already been studied, these existent energy harvesting devices can be improved on multiple aspects from power density, usage efficiency of expensive permanent magnetic material, to optimization of interface power electronics. This Ph.D. dissertation proposes an unconventional high power density linear electromagnetic kinetic energy harvester, and a high-performance two-stage interface power electronics to maintain maximum power abstraction from the energy source and charge the Li-ion battery load with constant current of low ripple at the same time. The proposed machine architecture is composed of a double-sided flat type silicon steel stator with winding slots, a permanent magnet mover, coil windings, a linear motion guide and an adjustable spring bearing. The unconventional design of the machine is that NdFeB magnet bars in the mover are placed with magnetic fields in horizontal direction instead of vertical direction and the same magnetic poles are facing each other. The derived magnetic equivalent circuit model proves the average air-gap flux density of the novel topology is as high as 0.73 T with 17.7% improvement over that of the conventional topology at the given geometric dimensions of the proof-of-concept machine. Subsequently, the improved output voltage and power are achieved. The dynamic model of the linear generator is also developed, and the analytical equations of output maximum xv power are derived for the case of driving vibration with amplitude that is equal, smaller and larger than the relative displacement between the mover and the stator of the machine respectively. Furthermore, the finite element analysis (FEA) model has been built and simulated to prove the derived analytical results and the improved power generation capability. Also, an optimization framework is explored to extend the dynamic system modeling method of the proposed single-Degree-of-Freedom (1-DOF) linear generator to the multi-Degree-of-Freedom (n-DOF) vibration based linear energy harvesting devices with multi proof masses and springs. Moreover, a boost-buck cascaded switch mode converter with current controller is designed to extract the maximum power from the harvester and charge the Li-ion battery with trickle current. Meanwhile, a maximum power point tracking (MPPT) algorithm is proposed and optimized for low frequency driving vibrations. Finally, a proof-of-concept unconventional PM linear generator is prototyped and tested to verify the simulation results of the FEA model. For the coil windings of 33, 66 and 165 turns, the output power of the machine is tested to have the output power of 65.6 mW, 189.1 mW, and 497.7 mW respectively with the maximum power density of 2.486 mW/cm3.
Ph.D. in Electrical Engineering, December 2011
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Title: HIGH GAIN HIGH EFFICIENCY RESONANT DC-DC CONVERTER
Creator: Shang, Fei
Date: 2016, 2016-12
Description: Low voltage power sources such as batteries, solar panels, and fuel cells have played an important role in applications such as automotive...
Show moreLow voltage power sources such as batteries, solar panels, and fuel cells have played an important role in applications such as automotive system, renewable energy power generation and so on. These applications of the low voltage power sources require a high gain DC-DC step-up converter. Research in this area shows great improvements for the converter topologies. As the power requirements keep increasing, the converter is going to sustain a very high input current. This high current can bring many design challenges in the existing topologies, such as high component current stress and power loss, complex and costly design for magnetic components, high input current ripple, etc. To address these challenges, a new topology of high gain DCDC step-up converter is needed. Evaluation of current high gain DC-DC converter topologies introduces the idea of the new topology which combines the advantages of different topologies and techniques. The new topology of high gain DC-DC converter suitable for low-voltage-high-current application is proposed in this dissertation. It consists of interleaved step-up topology, resonant circuit, and high frequency transformer. The topology has many merits such as high gain capability, high efficiency, low components stress and requirement of the transformer, simple topology with less number of active switching device, and easy to control. The dissertation carries out theoretical analysis of the proposed topology under different operating modes and the voltage gain has been deduced for each mode. The high voltage gain capability comes from 3 parts, which are interleaved step-up function, transformer turns-ratio and output voltage doubler circuit. Some variants of the topology make it more practical in many applications. In order to realize the design of the proposed converter, the design guidelines of major circuit components have been well studied in this dissertation. The switching power devices current stress and power loss are discussed in detailed to show the trend of their variation under different operating modes. The selection of transformer turns-ratio with the consideration of its impact to the component stress and power loss has been fully analyzed. The design method of the resonant tank is also well studied based on the resonant component value selection and its influence to the other components. Input inductor design is related to the current ripple requirement and this relationship is discussed thoroughly. These guidelines can be used to support the practical design of the proposed converter for different specifications. An effective output voltage regulation of the converter is essential for the proposed converter. To design a proper controller of the converter, the system transfer function is needed. The methods of system dynamic modeling have been fully studied in this dissertation. System dynamic state-space models are acquired by using generalized averaging method and the results validate the effectiveness of the method. Small signal model of the converter is achieved by linearization of the dynamic model around the operating points and system transfer functions are available at di↵erent operating points. The stability study indicates that the system is stable at all operating points, though there are several transfer functions at some operating points containing RHP zeros which can cause system unstable if the closed-loop controller is poorly designed. The parameter sensitivity study shows that the system transfer function is not greatly affected by the variation of the leakage inductance and load resistance. A design of PI controller is introduced in the dissertation and closed loop control of the converter is implemented to achieve the output voltage regulation. Simulations in PSIM and MATLAB Simulink have been carried out to validate the circuit operation and support the design analysis. A 2kW prototype has been built for experimental testing. The experimental results are in a good agreement with the theoretical analysis and efficiency of over 95% has been achieved for the nominal operating point.
Ph.D. in Electrical Engineering, December 2016
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Title: MARKET DOMINANT PLUG-IN HYBRID ELECTRIC VEHICLES OPTIMAL CONTROL FOR MINIMUM CHARGING COST AND V2G REGULATION SERVICE
Creator: Li, Zhihao
Date: 2012-07-23, 2012-07
Description: Plug-in hybrid electric vehicles (PHEVs) share the characteristics of hybrid electric vehicles (HEVs) and electric vehicles (EVs), employing...
Show morePlug-in hybrid electric vehicles (PHEVs) share the characteristics of hybrid electric vehicles (HEVs) and electric vehicles (EVs), employing electric motors and internal combustion engines (ICEs) for propulsion as well as large capacity batteries for energy storage. With ICEs and fuel tank on board, PHEVs do not have the range limitations posed by EVs; large capacity battery promises long distance all-electric range (AER) and fuel efficiency improvement. PHEVs will play a vital role in future as a sustainable transportation system, promising for environment, energy solution, and economy. It is estimated that by 2015, the total number of PHEVs in the world will be approximately 1.7 million with the U.S. marking leading the industry with about one million PHEVs. Growing penetration of PHEVs will place significant impacts on the grid, either as additional electric loads or potential assets which could provide various vehicleto- grid (V2G) services. There are four potential grid services that PHEVs can provide: base load generation, peak load shaving, spinning reserve, and regulation. PHEVs are not competitive in base load or peak load markets due to limited battery capacities. In addition, PHEVs are not real generating units and the energy stored in batteries is absorbed from grid. V2G support is taken into account as frequency regulation by participating in ancillary service markets. However, if implemented without proper control, large scale PHEVs will cause increases of peak load and destabilize the grid. This paper proposes an optimization strategy to maximize V2G profits as well as to minimize charging costs. The optimization strategy is based on a forecast of future electricity price for both residential electricity and regulation market. Due to the stochastic nature of electricity price, final prices cannot be deterministically calculated. Therefore, the addressed problem is solved by stochastic dynamic programming to find the economically optimal solution with price uncertainties. Constraints caused by vehicle utilization as well as technical limitations are taken into account. Additional costs arising from discharging batteries for ancillary service can be partially or completely compensated by V2G profits. In this Ph.D. research work, economical impacts of PHEV fleet are examined in Pennsylvania Jersey Maryland (PJM) regulation market. The major contributions of this paper are: Mathematically model the optimal control of PHEV with comprehensive transition function and cost function; A full study of battery life and cost that considers different ageing factors; A stochastic study of uncertainty and volatility in electricity price; Include battery degradation and price uncertainty in the comprehensive function for optimal control.
Ph.D. in Electrical Engineering, July 2012
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Title: BANDWIDTH ENHANCEMENT OF A COMPACT ANTENNA BY PARASITIC ELEMENTS
Creator: Celebi, Adem
Date: 2015, 2015-05
Description: Antenna structures are playing a major role in wireless systems, including communication systems, radars, satellite systems, information...
Show moreAntenna structures are playing a major role in wireless systems, including communication systems, radars, satellite systems, information networks and medical diagnostic systems. In these systems, external antennas are widely used, especially for devices with metallic enclosures. One of the common requirements for the ex- ternal antennas is the need for achieving small dimension while keeping the antenna performance optimized. In this respect, electrically small antennas (ESAs) are em- ployed. Realizing the design requirements of an ESA has been a challenge for antenna designers because some of the parameters such as large bandwidth and reasonably high input impedance are in conflict with the small antenna dimensions. A compact antenna with parasitic elements for bandwidth enhancement is proposed to address these issues for use with mobile devices. Loading the known antenna topologies with other structures could improve the design parameters such as gain, bandwidth, impedance, and beamwidth for antenna topologies which are impossible to achieve with the conventional antennas. In this respect, a parasitic element with a smaller scale is placed within the structure to obtain a second resonance close to the main antennas resonance for an increased bandwidth without increasing the overall dimen- sions of the antenna. This composite antenna is expected to have relatively high input impedances for each of the resonances, thus minimizing the need for an input matching network. A review of related prior work of antenna structures is presented to gain insight into the recent developments and methods in the field. The mini- mum quality factor (Q) and maximum gain of the ESA designs and their theoretical Antenna structures are playing a major role in wireless systems, including communication systems, radars, satellite systems, information networks and medical diagnostic systems. In these systems, external antennas are widely used, especially for devices with metallic enclosures. One of the common requirements for the ex- ternal antennas is the need for achieving small dimension while keeping the antenna performance optimized. In this respect, electrically small antennas (ESAs) are em- ployed. Realizing the design requirements of an ESA has been a challenge for antenna designers because some of the parameters such as large bandwidth and reasonably high input impedance are in conflict with the small antenna dimensions. A compact antenna with parasitic elements for bandwidth enhancement is proposed to address these issues for use with mobile devices. Loading the known antenna topologies with other structures could improve the design parameters such as gain, bandwidth, impedance, and beamwidth for antenna topologies which are impossible to achieve with the conventional antennas. In this respect, a parasitic element with a smaller scale is placed within the structure to obtain a second resonance close to the main antennas resonance for an increased bandwidth without increasing the overall dimen- sions of the antenna. This composite antenna is expected to have relatively high input impedances for each of the resonances, thus minimizing the need for an input matching network. A review of related prior work of antenna structures is presented to gain insight into the recent developments and methods in the field. The mini- mum quality factor (Q) and maximum gain of the ESA designs and their theoretical limitations are then discussed. The features of the antenna designs discussed in the review are used as foundation of the developed antenna structures. The properties and a fabrication method of the proposed antenna are then discussed. A commer- cially available software package based on finite element method is employed to aid in the antenna design. Several antenna prototypes are constructed to verify the design and the accuracy of the simulations. The prototypes are then tested using network analyzers and an RF anechoic chamber with the aim of characterizing the antenna performance in terms of antenna bandwidth and input impedance.
Ph.D. in Electrical and Computer Engineering, May 2015
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