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Title: DEVELOPMENT OF AN IMPLICITLY COUPLED ELECTROMECHANICAL AND ELECTROMAGNETIC TRANSIENTS SIMULATOR FOR POWER SYSTEMS
Creator: Abhyankar, Shrirang
Date: 2011-11, 2011-11
Description: The simulation of electrical power system dynamic behavior is done using tran- sient stability simulators (TS) and electromagnetic transient...
Show moreThe simulation of electrical power system dynamic behavior is done using tran- sient stability simulators (TS) and electromagnetic transient simulators (EMT). A Transient Stability simulator, running at large time steps, is used for studying rela- tively slower dynamics e.g. electromechanical interactions among generators and can be used for simulating large-scale power systems. In contrast, an electromagnetic transient simulator models the same components in finer detail and uses a smaller time step for studying fast dynamics e.g. electromagnetic interactions among power electronics devices. Simulating large-scale power systems with an electromagnetic transient simulator is computationally inefficient due to the small time step size in- volved. A hybrid simulator attempts to interface the TS and EMT simulators which are running at different time steps. By modeling the bulk of the large-scale power system in a transient stability simulator and a small portion of the system in an electromagnetic transient simulator, the fast dynamics of the smaller area could be studied in detail, while providing a global picture of the slower dynamics for the rest of power system. In the existing hybrid simulation interaction protocols, the two simulators run independently, exchanging solutions at regular intervals. However, the exchanged data is accepted without any evaluation, so errors may be introduced. While such an explicit approach may be a good strategy for systems in steady state or having slow variations, it is not an optimal or robust strategy if the voltages and currents are varying rapidly, like in the case of a voltage collapse scenario. This research work proposes an implicitly coupled solution approach for the combined transient stability and electromagnetic transient simulation. To combine the two sets of equations with their different time steps, and ensure that the TS and EMT solutions are consistent, the equations for TS and coupled-in-time EMT equations are solved simultaneously. While computing a single time step of the TS equations, a simultaneous calculation of several time steps of the EMT equations is proposed. Along with the implicitly coupled solution approach, this research work also proposes to use a three phase representation of the TS network instead of using a positive-sequence balanced representation as done in the existing transient stability simulators. Furthermore a parallel implementation of the three phase transient stability simulator and the implicitly coupled electromechanical and electromagnetic transients simulator, using the high performance computing library PETSc, is presented. Re- sults of experimentation with different reordering strategies, linear solution schemes, and preconditioners are discussed for both sequential and parallel implementation.
Ph.D. in Electrical Engineering, December 2011
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Title: IMPROVED SPATIAL-TEMPORAL RECONSTRUCTION FOR CARDIAC AND RESPIRATORY GATED SPECT
Creator: Qi, Wenyuan
Date: 2014, 2014-12
Description: Myocardial perfusion single photon emission computed tomography (SPECT) is an important imaging technique for evaluating coronary artery...
Show moreMyocardial perfusion single photon emission computed tomography (SPECT) is an important imaging technique for evaluating coronary artery disease. It can provide information of both myocardial perfusion and ventricular function. However, SPECT images su er from both cardiac and respiratory motion blur. In order to reduce the motion degrading, cardiac and respiratory gated SPECT imaging is used. In gated SPECT imaging, due to the lowered counts, the gated images will be more noisy than the ungated ones. Spatiotemporal (4D) processing is often used to reduce the noise level in gated images. In this thesis, we aim to investigate spatial and temporal processing techniques for improving the quality in cardiac and respiratory gated SPECT imaging. First, we will investigate a piecewise spatial smoothing prior based on totalvariation (TV) in 4D cardiac SPECT image reconstruction. In previous studies, it was found that spatial smoothing could adversely a ect the accuracy of 4D reconstruction in cardiac gated SPECT when temporal smoothing was applied, even though it could suppress the noise level. Our goal is to explore whether a piecewise spatial smoothing prior will improve the image accuracy while reducing the noise. Toward this goal, we will compare TV based piecewise spatial smoothing with quadratic spatial smoothing with simulated imaging, in which we will evaluate the lesion detectability. Clinical data will also be used to compare the results as a preliminary test. Motion-compensated temporal smoothing is known to play a key role in 4D cardiac gated SPECT reconstruction. Next, we will investigate whether better motion estimation could further improve the accuracy of reconstructed images. We will consider two di erent motion estimation models and the known motion in simulated experiments. The motion estimation methods are the classic optical ow estimation (OFE) and a periodic motion estimation method. We will evaluate the reconstruction from di erent motion models using several numerical quanti cation metrics. Furthermore, we will demonstrate reconstruction with the two motion estimation models using clinical acquisitions. Respiratory motion is known to cause motion blur in SPECT image reconstruction, and respiratory gated SPECT imaging can be e ective to combat its e ect. We will develop reconstruction techniques in respiratory gated SPECT. We will consider two reconstruction schemes for respiratory gated SPECT. The rst scheme is a post motion compensated reconstruction, in which images at di erent respiratory phases are reconstructed seperately, and afterwards are averaged over all the respiratory gates by motion compensation. The second scheme is a model based motion compensated reconstruction approach, in which one reference gate is used to describe the acquisition data of all the respiratory gates. Due to irregular respiratory motion, the data acquisition in each respiratory gate is not uniformly distributed among the acquisition angles, which would lead to limited-angle artifacts. To correct such artifacts, we propose an angle compensation method in the reconstruction. In order to deal with both cardiac and respiratory motion, we will investigate a 4D reconstruction approach for dual cardiac-respiratory gated SPECT reconstruction. This approach can accommodate the acquired data simultaneously from di erent cardiac and respiratory gates. It can exploit the correlation in the signal component among both the cardiac and respiratory phases. Both simulated experiments and clinical reconstruction will be used for evaluating this reconstruction approach. Due to the radiation risk of myocardial perfusion imaging (MPI) scans, there is an urgent need to lower the radiation dose used in SPECT. However, lower radiation dose will lead to more noisy reconstruction, which is even more serious in gated SPECT. We would explore the potential of using 4D reconstruction for lowering the dose in dual cardiac-respiratory gated SPECT.
Ph.D. in Electrical and Computer Engineering, December 2014
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Title: Load Redistribution Attacks and Protection Strategy Design in Electric Power Systems
Creator: Yuan, Yanling
Date: 2012-04-27, 2012-05
Description: Electric power systems have evolved over the past century to the largest and the most complex cyber-physical systems. With the development of...
Show moreElectric power systems have evolved over the past century to the largest and the most complex cyber-physical systems. 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 develops the concept of load redistribution attack, a special type of false data injection attack. The physical and economic impact of load redistribution (LR) attacks is quantitatively analyzed in this thesis. Since LR attacks can successfully bypass bad data detection and manipulate the state estimation outcome, security constrained economic dispatch (SCED) based on the false state estimation would lead the system into a non-optimal or insecure operation state. Based on the consequence analysis, two different attacking goals are differentiated from the adversary’s perspective, i.e., immediate attacking goal and delayed attacking goal. For the immediate attacking goal, a max-min attacker-defender model is proposed to identify the most damaging immediate LR attack. Two different algorithms are used to solve this bi-level optimization problem. For the delayed attacking goal, a tri-level model is proposed to identity the most damaging delayed LR attack. This thesis studies the economic impact of LR attacks on power market operation. A convex model is proposed under the mechanism of virtual bidding to compute the optimal injection of LR attack, which gains the most profit from the attackers’ perspective. The quantitative analysis of LR attacks provides an in-depth insight on effective attack prevention with limited protection resource budget. This thesis proposes a new protection strategy, which intends to mitigate the most damaging effect of LR attacks on power system operation. The criterion of determining effective protections against the most damaging LR attack, considering the existence of stochastic measurement errors, is deduced.
Ph.D. in Electrical Engineering, May 2012
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Title: NEURAL ADAPTIVE CONTROL STRATEGY FOR HYBRID ELECTRIC VEHICLES WITH PARALLEL POWERTRAIN
Creator: Gurkaynak, Yusuf
Date: 2011-04-20, 2011-05
Description: In a hybrid electric vehicle (HEV) with parallel powertrain, the system can be controlled by splitting the required power between the electric...
Show moreIn a hybrid electric vehicle (HEV) with parallel powertrain, the system can be controlled by splitting the required power between the electric propulsion machine and internal combustion engine (ICE) to meet specific goals related to fuel consumption, efficiency, performance, and/or emissions. This power splitting scenario, which is of great hybridization importance, is in fact the control strategy or energy management of the hybrid vehicle. Performance of the system depends on the control strategy, which needs to be robust, stable, reliable, and independent from uncertainties. This Ph.D. research is focused on model based control strategies, which are proposed for parallel hybrid powertrains, showing significant advantages in performance and fuel economy. If a model based control strategy is used to develop the hybrid power management algorithm, the accuracy of the model data needs to be high for proper control. Therefore, this type of management method is parameter sensitive. Implementing system identification features into this algorithm reduces the effect. As a result, the proposed controller algorithm learns the existing component parameters while operating. Furthermore, combining the base controller with an online tuner, which simultaneously optimizes the controller for current conditions, will improve the performance of the power management. In addition, this Ph.D. thesis presents a novel neural adaptive equivalent consumption minimization strategy (ECMS) and applies it to a hybrid representative sport utility vehicle (SUV) with parallel powertrain. The ECMS is a model based optimal control strategy and is based on the minimization of both fuel consumption and battery charge usage by introducing the equivalent coefficient between them. Proper operation of the controller depends on the accuracy of the model. It also depends on the correct selection of the equivalent coefficient. In this Ph.D. thesis, specific neural network structures are proposed for both coefficient selections by drive cycle recognition and for precise model building by system identification. This thesis also presents a novel fast solution method of ECMS algorithm for real time applications.
Ph.D. in Electrical Engineering, May 2011
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Title: NETWORK CODING BASED COOPERATIVE PEER-TO-PEER REPAIR IN WIRELESS NETWORKS
Creator: Liu, Yu
Date: 2012-07-11, 2012-07
Description: Multimedia Broadcast/Multicast Service (MBMS) in cellular networks has emerged recently as a promising distribution model to provide rich...
Show moreMultimedia Broadcast/Multicast Service (MBMS) in cellular networks has emerged recently as a promising distribution model to provide rich content distribution where a batch of content is broadcast to a large number of peers simultaneously. However, ensuring efficient error-free message delivery in such a scenario is a challenge, since packet loss is inevitable due to the time-varying nature of wireless transmissions, and the server is probably overwhelmed by floods of individual retransmission requests from peers. Cooperative Peer-to-Peer (P2P) information repair has been proposed to mitigate the packet loss among peers during Base Station (BS) broadcast, by allowing peers to cooperate on information exchange among themselves, rather than asking the BS to rebroadcast the lost packets for the peers. Network Coding, a fairly recent transmission paradigm with the potential network throughput improvement and high reliability advantage, has been widely recognized as a promising information dissemination approach for wireless networks. In this research, we study the network coding based cooperative P2P information repair in wireless networks. We first propose our initial work - a connected dominating set (CDS) based P2P information repair (PPIR) protocol with network coding which utilize the clustering idea, to minimize the total repair latency as well as alleviate the congestion and burden of BS’s downlink channels. Then the decision making problem for P2P repair with densely distributed nodes is studied and two approaches are provided. Later on, the NC based P2P information repair protocol with tunable parameter (NC-PIRTP) which evolved from PPIR protocol is proposed to further reduce transmission collisions and total repair latency. At last, P2P information repair under mobile network environment with pedestrian speed is studied and three efficient protocols are illustrated which are suitable to different specific cases. Extensive simulation results are provided for performance evaluation and comparisons, and to demonstrate the effectiveness and efficiency of our proposed protocols in terms of the total repair latency. Furthermore, an analytical model is developed, based on which theoretical results are derived. These results validate our protocol models and provide useful protocol design guideline for the cooperative P2P information repair problem in wireless networks.
Ph.D. in Electrical Engineering, July 2012
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Title: POWER SYSTEM VOLTAGE STABILITY AND AGENT BASED DISTRIBUTION AUTOMATION IN SMART GRID
Creator: Nguyen, Cuong Phuc
Date: 2011-04-25, 2011-05
Description: Our interconnected electric power system is presently facing many challenges that it was not originally designed and engineered to handle. The...
Show moreOur interconnected electric power system is presently facing many challenges that it was not originally designed and engineered to handle. The increased interarea power transfers, aging infrastructure, and old technologies, have caused many problems including voltage instability, widespread blackouts, slow control response, among others. These problems have created an urgent need to transform the present electric power system to a highly stable, reliable, efficient, and self-healing electric power system of the future, which has been termed “smart grid”. This dissertation begins with a discussion on the voltage stability issue in bulk transmission networks. A new continuation power flow tool for studying the impacts of generator merit order based dispatch on inter-area transfer capability and static voltage stability is presented. In using this tool, it is realized that all distribution systems are represented by only a single lumped load model. While this representation is acceptable in traditional power system analysis, it may not be valid in the future smart grid where the distribution system will be integrated with intelligent and quick control capabilities to mitigate voltage problems before they propagate into the entire system. Therefore before analyzing the operation of the whole smart grid, it is important to understand the distribution system first. The second part of this dissertation presents a new platform for studying and testing emerging technologies in advanced Distribution Automation (DA) within smart grids. Due to the key benefits over the traditional centralized approach, namely flexible deployment, scalability, and avoidance of single-point-of-failure, a new distributed approach is employed to design and develop all elements of the platform. The multi-agent system (MAS), which has the three key characteristics of autonomy, local view, and decentralization, is selected to implement the advanced DA functions. The intelligent agents utilize the communication network for cooperation and negotiation. Communication latency is modeled using a user-defined probability density function. Failure-tolerant communication strategies are developed for agent communications. Major elements of advanced DA are developed in a completely distributed way and successfully tested for several IEEE standard systems, including: Fault Detection, Location, Isolation, and Service Restoration (FLISR); Coordination of Distributed Energy Storage Systems (DES); Distributed Power Flow (DPF); Volt-VAR Control (VVC); and Loss Reduction (LR).
Ph.D. in Electrical Engineering, May 2011
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Title: VIBRATION IN TRACTION MOTORS FOR ELECTRIC AND HYBRID ELECTRIC VEHICLES
Creator: Yang, Zhi
Date: 2014, 2014-12
Description: Due to increased fuel efficiency and lower cost/mile feature, electric vehicle (EV) and hybrid electric vehicle (HEV) are becoming more and...
Show moreDue to increased fuel efficiency and lower cost/mile feature, electric vehicle (EV) and hybrid electric vehicle (HEV) are becoming more and more popular. It is estimated that the sale of electric vehicles will reach 3.8 million by 2020, while hybrid vehicles will grow to 4% by 2020 from their current share of 2%. To meet this target, EV an HEV motors, the core energy conversion components, should not only satisfy specific requirements in performance and efficiency but also constrain vibration. This necessitates the analysis of vibration in traction motors for EV/HEV application. The primary objective of this dissertation is to characterize and compare the electromagnetic and vibrational behavior of typical traction motors (PMSM with distributed winding, PMSM with concentrated winding, IM, and SRM) over a wide torque speed range. For this purpose, weak-coupled analysis of electromagnetic force and structure are performed in ANSYS environment. The secondary aim of this dissertation is to develop a rotor position related variable switching frequency pulse width modulated (PWM) strategy to ameliorate the acoustic noise due to high frequency harmonic current. Switching frequency is modified online to adapt current ripple and vibration requirement, thus ameliorate the acoustic noise. It is expected that this strategy with variable switching frequency has the advantages of spreading the vibration spectrum and reducing switching losses. Experimentally verification is also performed. At the end of this dissertation, characterization of vibration behavior of switch reluctance machine with higher number of rotor poles than stator poles is performed.
Ph.D. in Electrical and Computer Engineering, December 2014
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Title: CMOS POLAR DIGITAL POWER AMPLIFIER FOR HIGH DATA RATE WIRELESS COMMUNICATIONS
Creator: Zhu, Qiuyao
Date: 2016, 2016-12
Description: Power amplifier (PA) is the most important circuit block in an RF transmitter. It typically consumes more than 80% of the power taken by the...
Show morePower amplifier (PA) is the most important circuit block in an RF transmitter. It typically consumes more than 80% of the power taken by the entire transmitter. Therefore, a highly efficient PA is the key to a successful RF front-end system. The polar transmitter architecture is studied herein in order to take advantage of the highly efficient switching-mode PA. However, due to the large expanded bandwidth from the nonlinear IQ to polar conversion and the sensitive amplitude/phase delay impairment, hardly any reported polar design is able to transmit high data rate wireless communication signals. In this work, an extensive research on the digital polar transmitter system for high data rate signals is presented. An integrated CMOS digital power am- plifier (DPA) design is demonstrated afterwards. This DPA consists of 9-bit fully thermometer-coded uniform cells to achieve high linearity for wide bandwidth OFDM signals. By analyzing the amplitude and phase paths impairment, which will cause both in-band and out-of-band distortions, a 960 MHz digital delay tuner is designed for precise amplitude and phase alignment. Furthermore, two digital pre-distortion algorithms for DPA are implemented and compared. Importantly, an on-chip DC- DC converter is included for direct battery connection and power control. A boosted cascode gate bias improves PA efficiency at the low power region. The proposed design is fabricated using a 55 nm RF CMOS technology. The DPA with several peripheral blocks occupies only 0.63 mm2 active silicon area. This DPA including the digital AM filtering achieves a peak output power of +21.9 dBm with 41% efficiency. It achieves EVM of 2.9% with 20 MHz IEEE 802.11ac compliance of 256-QAM OFDM signals, and also achieves EVM of 4.5% (CC0) / 4.8% (CC1) with 2 x 20 MHz 3GPP LTE-Advanced carrier aggregation compliance of 64-QAM OFDM signals. This highly linear DPA has demonstrated high flexibility, high efficiency, and small area. To the author's knowledge, this is the first reported DPA that meets either the linearity requirements of 256-QAM OFDM signals or the signal bandwidth of 40 MHz, paving the path for wideband high data rate wireless applications using digital polar architecture. At the same time, aiming at a higher average efficiency, a two-level class- G supply modulator is investigated to dynamically switch the DPA VDD. It has successfully demonstrated an average efficiency of 34.6% for this class-G modulated DPA in a complete circuit simulation using the IEEE 802.11b signal.
Ph.D. in Electrical Engineering, December 2016
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Title: IMAGE PROCESSING ALGORITHMS FOR PROSTATE CANCER LOCALIZATION WITH MULTISPECTRAL MAGNETIC RESONANCE IMAGING
Creator: Xin, Liu
Date: 2011-11, 2011-12
Description: In this thesis, we develop a series of image processing algorithms to localize prostate cancer with multispectral magnetic resonance (MR)...
Show moreIn this thesis, we develop a series of image processing algorithms to localize prostate cancer with multispectral magnetic resonance (MR) images to guide biopsy, surgery and minimally invasive therapy. Besides, we develop a new method to for evaluation of image classification algorithms considering correlation between neighboring pixels. Prostate cancer is one of the most prevalent cancer types and one of the leading causes of cancer death among men in the United States. High-resolution MRI has shown higher accuracy than trans-rectal ultrasound (TRUS) to ascertain the presence of prostate cancer. In this work, three different types of MR techniques are employed to provide both morphological and functional information about the benign and malignant tissues of the prostate. These are T2-weighted (T2w) MRI, diffusionweighted imaging MRI (DWI) and dynamic contrasted enhanced MRI (DCE MRI). In the first chapter of this thesis, we briefly describe the fundamentals of different MR techniques, and the multispectral MR dataset used in our experiment. Then, we focus on two tasks of the prostate cancer localization problem: prostate gland extraction and prostate tumor localization. For each topic, we review the previous studies available in the literature, and present our methods with their advantages. Finally, the new image evaluation method considering correlation between pixels is presented. Our prostate segmentation method is fully unsupervised and extracts the prostate gland from DWI MRI in 3D by fusing the active contour model and shape prior information. For tumor localization, we develop an unsupervised approach which is based on fuzzy Markov random field (fuzzy MRF) model, a new scheme based on relative intensity values which can be combined with supervised segmentation classifiers to mimic the cancer localization procedures performed by human readers and a new feature named location map which incorporates the spatial inforx mation of the tumors to remove the need for manual peripheral zone extraction. The proposed image evaluation algorithm is based on receiver operating characteristics (ROC) analysis and it considers the correlation between neighboring pixels. This method could replace the conventional ROC analysis and offers a more accurate evaluation of the test image. Our algorithms are tested on 20 patients’ multispectral MR images, and the qualitative as well as quantitative experimental results demonstrate the efficacy of our segmentation methods and show that the proposed segmentation methods outperform the currently available used approaches. The evaluation method has been tested on computer simulated images and shows very promising results. The summary and future work is also described at the end of the thesis.
Ph.D. in Electrical Engineering, December 2011
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Title: SOFT ERROR TOLERANT LATCH CIRCUIT DESIGN FOR DEEPLY SCALED CMOS TECHNOLOGY
Creator: Nan, Haiqing
Date: 2012-01-25, 2012-05
Description: As CMOS technology keeps scaling down, circuit designers face variety of challenges. Due to the scaling of supply voltage and node capacitance...
Show moreAs CMOS technology keeps scaling down, circuit designers face variety of challenges. Due to the scaling of supply voltage and node capacitance, digital circuits are more aware of noise and variations, which cause reliability issues such as soft error. Traditionally the soft error aware VLSI design is limited to applications which require high reliability and operated in high radiation environment such as avionics applications, medical equipments, space industry and military applications. However, with CMOS technology scales down to nanometer region, the VLSI circuits can also be affected by soft errors at ground level which features low radiation energy. In this thesis, totally 5 soft error tolerant latch designs are proposed including HLR-1, HLR-2, HLR-CG1, HLR-CG2, and HLR-CG3. All the proposed designs protect internal nodes as well as output node for soft error regardless the radiation energy. The proposed HLR-1 and HLR-2 latch circuits tolerate soft error for non-CG systems. Since the proposed HLR-1 and HLR-2 designs take advantages of floating node to tolerate soft error, these two designs cannot be applied with clock gating techniques and the minimum clock frequency of these two designs should be greater than 16MHz in order to maintain correct logic at the floating node. The power consumption and circuit delay between the proposed HLR-1 and HLR-2 designs are very close. The proposed HLR-1 design achieves a small amount of benefits in terms of power and delay compared with the proposed HLR-2 design. But the proposed HLR-2 circuit reduces area 3.5% compared to the proposed HLR-1 circuit. The proposed HLR-CG1, HLR-CG2 and HLR-CG3 latch designs fully tolerate soft error regardless of radiation energy for both CG and non-CG systems. Due to the auto correction mechanism embedded in the proposed HLR-CG1, HLR-CG2 and HLRCG3 designs, any soft error at any location will be automatically corrected without generating any floating nodes. The proposed HLR-CG3 features the smallest power consumption and delay but it has the largest area overhead compared to HLR-CG1 and HLR-CG2 circuits. The proposed HLR-CG1 design features the smallest area compared with HLRCG2 and HLR-CG3 designs. The design cost of HLR-CG2 design is between the proposed HLR-CG1 and HLR-CG3 designs. All the proposed designs achieve faster speed and smaller PDP compared to previous hardening techniques. Compared to the proposed HLR-1 design, previous designs increases power 3.77% on average, delay 272.74% on average, PDP 300.29% on average and decreases area 7.09% on average. Compared to the proposed HLR-2 design, previous designs increases power 3.77% on average, delay 272.40% on average, PDP 299.89% on average and decreases area 3.93% on average. Compared to the proposed HLR-CG1 design, previous designs increases area 19.65% on average, delay 213.14% on average, PDP 203.78% on average and decreases power 5.82% on average. Compared to the proposed HLR-CG2 design, previous designs increase area 6.49% on average, delay 193.28% on average, PDP 223.45% on average and power 6.51% on average. Compared to the proposed HLR-CG3 design, previous designs increases delay 272.18% on average, PDP 314.38% on average, power 8.01% on average and area 2.93% on average.
Ph.D. in Electrical and Computer Engineering, May 2012
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Title: DEVELOPMENT AND ANALYSIS OF A SECURE AND EFFICIENT VEHICULAR AD HOC NETWORK
Creator: Hao, Yong
Date: 2012-07-06, 2012-07
Description: Vehicular ad hoc networks (VANETs) enable vehicles to communicate with each other by equipping every vehicle with an on board unit (OBU). Many...
Show moreVehicular ad hoc networks (VANETs) enable vehicles to communicate with each other by equipping every vehicle with an on board unit (OBU). Many interesting and promising functionalities can be achieved in the VANETs, such as safety related application and data downloading application. In this thesis, we focus on the security and privacy provision as well as efficiency improvement of above two applications in the VANETs. In the safety related application, each vehicle periodically broadcasts messages including its current position, direction and velocity (which can be generated by a global positioning system (GPS) device) to inform its geographic data to its neighbors. Privacy is an important issue in VANETs. Meanwhile, some important security functionalities such as message authentication, integrity and non-repudiation should be integrated into the VANETs. In this thesis, we propose a distributed key management protocol based on group signature to provide security and privacy for vehicles. Distributed key management is expected to facilitate the revocation of malicious vehicles, verification efficiency, maintenance of the system and heterogeneous security policies, compared with the centralized key management assumed by the existing group signature schemes. In our framework, each road side unit (RSU) acts as the key distributor for the group, where a new issue incurred is that the semi-trust RSUs may be compromised. Therefore, we develop security protocols which are able to detect compromised RSUs and their malicious accomplices. Moreover, we address the issue of large computation overhead due to the group signature implementation. A practical cooperative message authentication protocol (CMAP) is thus proposed to alleviate the verification burden for vehicles. In the CMAP, on average, each vehicle just needs to verify a very small amount of received geographic messages. Compared with the existing probabilistic verification protocol, CMAP can save at least 40 % computation resource for vehicles. In the data downloading application, we propose a secure cooperative data downx loading framework for payment services in VANETs. In our framework, vehicles download data when they pass by an RSU and then share the data after they travel out of the RSU’s coverage. A fundamental issue of our framework is how vehicles share data with each other. Thus, we develop an application layer data sharing protocol (DSP) in which vehicles share their downloaded data one by one in sequence according to their positions. A better performance can be achieved by the proposed protocol because it is able to avoid medium access control (MAC) layer collisions and the hidden terminal effect. Analytical models are derived to quantitatively evaluate the impact of the distance between RSUs on the amount of data that vehicles can download in a drive through. The simulation results show that our protocol can download 87.4% more data for vehicles than the existing scheme “VC-MAC” when the distance between two consecutive RSUs reaches 10 kilometers. Moreover, we also address security and privacy issues in the process of data downloading and sharing. Both applicants’ exclusive access to the applied data and vehicles’ privacy are ensured by our framework. Compared with the communication overhead in the intuitive method, the communication overhead in our framework will be reduced to 50%. We also propose a security protocol to detect sybil attacks in privacy preserved VANETs. In the above two applications, vehicles’ location information is utilized to facilitate the efficiency. However, if malicious vehicles launch the sybil attack by forging several fake entities and claim they are at some certain positions. The overall performance of the applications will be compromised greatly. So, we propose a security protocol to detect sybil attacks by examining the rationality of vehicles’ positions. The attack detection utilizes the characteristics of communication. No extra hardware and little communication and computation overhead will be introduced to vehicles. Moreover, a smart attacker scenario in which a malicious vehicle may adjust its communication range to avoid detection and the malicious vehicles’ collusion scenario are also considered.
Ph.D. in Computer Engineering, July 2012
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Title: OPERATION AND PLANNING OF COORDINATED NATURAL GAS AND ELECTRICITY INFRASTRUCTURES
Creator: Zhang, Xiaping
Date: 2015, 2015-07
Description: Natural gas is becoming rapidly the optimal choice for fueling new generating units in electric power system driven by abundant natural gas...
Show moreNatural gas is becoming rapidly the optimal choice for fueling new generating units in electric power system driven by abundant natural gas supplies and environmental regulations that are expected to cause coal-fired generation retirements. The growing reliance on natural gas as a dominant fuel for electricity generation throughout North America has brought the interaction between the natural gas and power grids into sharp focus. The primary concern and motivation of this research is to address the emerging interdependency issues faced by the electric power and natural gas industry. This thesis provides a comprehensive analysis of the interactions between the two systems regarding the short-term operation and long-term infrastructure planning. Natural gas and renewable energy appear complementary in many respects regarding fuel price and availability, environmental impact, resource distribution and dispatchability. In addition, demand response has also held the promise of making a significant contribution to enhance system operations by providing incentives to customers for a more flat load profile. We investigated the coordination between natural gas-fired generation and prevailing nontraditional resources including renewable energy, demand response so as to provide economical options for optimizing the short-term scheduling with the intense natural gas delivery constraints. As the amount and dispatch of gas-fired generation increases, the long-term interdependency issue is whether there is adequate pipeline capacity to provide sufficient gas to natural gas-fired generation during the entire planning horizon while it is widely used outside the power sector. This thesis developed a co-optimization planning model by incorporating the natural gas transportation system into the multi-year resource and transmission system planning problem.This consideration would provide a more comprehensive decision for the investment and accurate assessment for system adequacy and reliability. With the growing reliance on natural gas and widespread utilization of highly efficient combined heat and power (CHP), it is also questionable that whether the independent design of infrastructures can meet potential challenges of future energy supply. To address this issue, this thesis proposed an optimization framework for a sustainable multiple energy system expansion planning based on an energy hub model while considering the energy efficiency, emission and reliability performance. In addition, we introduced the probabilistic reliability evaluation and flow network analysis into the multiple energy system design in order to obtain an optimal and reliable network topology.
Ph.D. in Electrical and Computer Engineering, July 2015
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Title: OPTIMAL DESIGN OF PERMANENT MAGNET SYNCHRONOUS MACHINES BASED ON MAGNETIC FIELD DISTRIBUTION ASSESSMENT AND PERFORMANCE ANALYSIS
Creator: Jiang, Yong
Date: 2015, 2015-05
Description: The detailed magnetic field distribution of a permanent magnet electric motor is very important for the accurate prediction of performance...
Show moreThe detailed magnetic field distribution of a permanent magnet electric motor is very important for the accurate prediction of performance parameters such as back electromotive force (back-EMF), rotor and stator losses, winding inductances, noise and vibration, torque profiles, etc. Although finite element analysis is a good option for accurately calculating magnetic field distribution in electrical machines, it is typically timeconsuming and does not provide closed form solutions. Alternatively, analytical calculation of magnetic field distribution can be conducted in Fourier series, which is more suitable for a design tool to predict the motor performance. This dissertation presents a novel numerical technique for calculating exact magnetic field distribution in the air gap of a surface mounted permanent magnet machine. This solution can be obtained via a two-dimension analytical solution with Laplacian and quasi-Poisonian equations, assuming that the iron is infinitely permeable and the air gap is slotless. Slot effects can be added in the model by using relative air gap permeance calculated by the conformal transformation of slot geometry. This technique is constructed by multiplying the relative permeance function expressed in an infinite Fourier series with the distribution of magnetic field in the slotless air gap. This method shows a very good alignment with finite element method for a surface mounted permanent magnet machine with radial magnetization. It can also be extended to calculating magnetic field distribution of interior permanent magnet motors including consideration of magnetic saturation, crosssaturation between d-axis and q-axes, affecting both inductances and flux linkages, as well as localized effects due to rotor bridges. Furthermore, this approach can be used to create a closed-form solution, which is the first step towards inverse modeling of electric machines. This is a complete paradigm shift in the design process for electric machines, which can reduce the time taken to design an electric machine, while reducing the active material content to make them power dense with significant reduction in cost. Furthermore, availability of analytical description of the field components will aid in the designer’s ability to distinguish between the control and magnetic design aspects.
Ph.D. in Electrical and Computer Engineering, May 2015
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Title: DEVELOPMENT OF COMPUTER-AIDED DIAGNOSIS METHODS IN MAMMOGRAPHY
Creator: Wang, Juan
Date: 2015, 2015-12
Description: Computer-aided diagnosis (CAD) is developed as a diagnostic aid to provide a “second opinion” in diagnosis of breast cancer in early stage....
Show moreComputer-aided diagnosis (CAD) is developed as a diagnostic aid to provide a “second opinion” in diagnosis of breast cancer in early stage. Clustered microcalcifications (MCs) can be an important early sign of breast cancer. The goal of this work is to develop automatic CAD methods in mammography for breast cancer. Its contribution consists of both development of machine learning algorithms and study of related issues in detection and diagnosis of breast cancer with clustered MCs. First, a bi-thresholding scheme is proposed for reduction of false-positives (FPs) associated with linear structures in MC detection. An unified classifier with dummy variable modeling is further developed to reduce the FPs caused by both linear structures and MC-like noise patterns. It is demonstrated that both of the proposed algorithms can reduce FPs in MC detection, and thus, improve the detection accuracy significantly. Second, a spatial density modeling approach is investigated to quantify the spatial distribution of the MCs in a cluster when the MC detection is inaccurate. A spatial density function (SDF) is defined such that the extracted features are more robust to the presence of FPs and false-negatives (FNs) in MC detection. The results show that the features extracted from the SDF can achieve better class separation while being robust to the variations in MC detection when compared with those extracted from a traditional region-based method. Third, a retrieval-boosted approach is studied to discriminate between the benign and malignant MC lesions. A retrieval strategy is proposed to boost the classification performance by taking into account the similarity both in image features and in pathology. An adaptive Adaboost classifier, which can be adapted to the retrieved cases at a low computational cost, is applied to demonstrate the benefit of the retrieval strategy. The results show that the retrieval-boosted approach can signifishow that the features extracted from the SDF can achieve better class separation while being robust to the variations in MC detection when compared with those extracted from a traditional region-based method. Third, a retrieval-boosted approach is studied to discriminate between the benign and malignant MC lesions. A retrieval strategy is proposed to boost the classification performance by taking into account the similarity both in image features and in pathology. An adaptive Adaboost classifier, which can be adapted to the retrieved cases at a low computational cost, is applied to demonstrate the benefit of the retrieval strategy. The results show that the retrieval-boosted approach can significantly outperform its baseline classifier and that inclusion of pathology information in the retrieval can further improve the classification accuracy. Fourth, the perceptual similarity of MC lesions by radiologists is studied. The issues investigated include the degree of variability in the similarity ratings, the impact of this variability on agreement between readers in retrieval of similar lesions, and the factors contributing to the readers’ similarity ratings. The results indicate that perceptually similar lesions could be of diagnostic value in diagnosis for clustered MCs. Fifth, the feasibility of modeling the perceptual similarity of MC lesions is investigated. A support vector regression (SVR) is applied to model the perceptual similarity of clustered MCs, and a feature saliency analysis derived from SVR is used to determine the most relevant image features among a large set of candidate features. The results demonstrate that the relevant features are consistent in radiologists’ similarity ratings among different MC lesions, indicating that the perceptual similarity of MC lesions by radiologists can be effectively modeled. Finally, whether retrieval of similar images can effectively assist radiologists in diagnosis of clustered MCs is investigated. A retrieval system for relevant images is designed by considering both perceptually similar image features and the likelihood of malignancy of the lesion under consideration. An observer study is conducted to evaluate the diagnostic value of the proposed retrieval system. The results indicate that the proposed retrieval system has the potential to improve the reader’s ability in diagnosis of breast cancer with clustered MCs.
Ph.D. in Electrical Engineering, December 2015
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Title: POLARIZATION INDUCED BY A TERAHERTZ ELECTRIC FIELD ON A CONDUCTIVE PARTICLE
Creator: Shen, Tao
Date: 2013, 2013-05
Description: Interactions of an electromagnetic wave with an object of dimensions small compared to the wavelength can often be accounted for by...
Show moreInteractions of an electromagnetic wave with an object of dimensions small compared to the wavelength can often be accounted for by considering the dipole moments, which are effective in explaining the scattering characteristics in the frequency range referred to as the Rayleigh region. Dielectric functions derived from polarization processes due to molecular orientation or bound charge displacements have been employed over the years to account for the scattering properties of particles. In the presence of mobile charges, bulk conductivity may be incorporated with a complex dielectric function to explain the peak in absorption near the plasma frequency exhibited by metallic particles in the optical region. With the current interest in nanostructures, an investigation of the electromagnetic properties of a conductive particle with attention given to space-charge effects would appear timely. This can be accomplished by coupling the transport equations of the charge carriers to the Maxwell’s equations. Results of computations performed for elementary structures such as plates and particles revealed the screening of the internal field while dispersion and absorptions effects are shown by the complex dipole moments. To gain insight into the nature of charge-wave interactions, results based on quasi-static formulation for the electric field will be compared with those based on full-wave analysis, with special attention given to the charge and current distributions within the structure. By consideration of the physical process of charge carrier motion and lattice polarization, the equivalent circuit model for a conductive nanoparticle in the terahertz frequency range is developed. All circuit elements are of electrical nature and can be directly expressed in terms of material parameters. The equivalent circuit can serve as the basis of analysis for composite structures and aggregates of which the conductive nanoparticle is a constituent.
PH.D in Electrical Engineering, May 2013
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Title: VERIFICATION OF LARGE-SCALE ON-CHIP POWER GRIDS
Creator: Xiong, Xuanxing
Date: 2013, 2013-05
Description: As technology scaling continues, the performance and reliability of integrated circuits become increasingly susceptible to power supply noises...
Show moreAs technology scaling continues, the performance and reliability of integrated circuits become increasingly susceptible to power supply noises, such as IR drops and Ldi/dt noises in the on-chip power grids. Reduced supply voltage levels in the grid can increase the gate delay, leading to timing violations and logic failures. In order to ensure a reliable chip design, it is critical to verify that the power grid is robust, i.e., the power supply noises are acceptable for all possible runtime situations. Hence, power grid verification has become an indispensable step in modern design flow of integrated circuits. Nowadays, it is common practice to verify power grids by simulation. Typically, an equivalent RC/RLC circuit model of the grid is extracted from the layout, and designers perform simulations to evaluate the power supply noises based on the current waveforms drawn by the circuit. As power grid simulation can only be performed after the circuit design is done, vectorless power grid verification has been introduced to enable early power grid verification with incomplete current specifications, so that the power grid design can be better tuned and optimized at early design stages, thus reducing the design time. Due to the increasing complexity of modern chips, power grid verification has become very challenging. The broad goal of this dissertation is to explore efficient algorithms for verifying large-scale on-chip power grids. Specifically, we study parallel power grid transient simulation, vectorless steady-state verification and vectorless transient verification. Parallel forward and back substitution algorithms are designed for efficient transient simulation; a set of novel algorithms are developed to incrementally improve the runtime efficiency of vectorless steady-state verification; and an efficient approach is proposed for vectorless transient verification with novel constraint setting.
PH.D in Electrical Engineering, May 2013
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Title: SIMULATION AND DEVELOPMENT OF A CLINICAL ANALYZER-BASED IMAGING SYSTEM
Creator: Majidi, Keivan
Date: 2013, 2013-12
Description: The analyzer-based phase-sensitive X-ray imaging method (ABI) is emerging as a potential alternative to conventional radiography. ABI...
Show moreThe analyzer-based phase-sensitive X-ray imaging method (ABI) is emerging as a potential alternative to conventional radiography. ABI simultaneously generates a number of planar images containing information about scattering, refraction and absorption properties of the object. These parametric images are acquired by sampling the angular intensity profile (AIP) of an X-ray beam passing through the object at different positions of the analyzer crystal. Like many of the modern imaging techniques, ABI is a computed imaging method (meaning that the images are calculated from raw data). Therefore, the noise in ABI depends on the imaging conditions such as source flux, number of the analyzer positions, and the analyzer positions themselves as well as on the estimation method of the parameters. In the first part of this thesis, we use the Cramer-Rao lower bound to quantify the noise in ABI images and then investigate the effect of different analyzer-sampling strategies on this bound. The CRLB is the minimum bound for the variance of an unbiased estimator and defines the best noise performance that one can obtain regardless of which estimation method is used to estimate ABI parametric images. We will then use this bound to evaluate three ABI methods: Multiple-Image Radiography (MIR), Diffraction Enhanced Imaging (DEI) and Scatter Diffraction Enhanced Imaging (S-DEI). The proposed methodology can be used to evaluate any other ABI parametric image estimation technique. Synchrotron radiation has been the main source for experimental ABI and developing its methodologies, therefore the ABI application to clinical imaging has been very limited. It is inevitable to use conventional X-ray sources for ABI in order to utilize xii the technique in the clinical applications, however, due to the limited intensity of these sources and their finite source size, developing such systems is very challenging. In the second part of this thesis, we use computer simulations to understand the above challenges better. We measure the properties of this imaging system such as flux and point-spread function for various design parameters and discuss how to find an “optimal” setup based on these properties. The optimality of an imaging setup depends on the specific application that one wants to perform using the system; however, the results and discussions in this section layouts a design procedure for clinical ABI systems. In the last part of this thesis we review the steps we took in the Advanced X-ray Imaging Laboratory (AXIL) toward developing a clinical ABI system.
PH.D in Electrical Engineering, December 2013
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Title: SECURE DATA SERVICE OUTSOURCING IN CLOUD COMPUTING
Creator: Wang, Cong
Date: 2012-04-22, 2012-07
Description: Cloud computing economically enables a fundamental paradigm of data service outsourcing, which provides lower up-front capital costs and less...
Show moreCloud computing economically enables a fundamental paradigm of data service outsourcing, which provides lower up-front capital costs and less hands-on management. However, outsourcing data services to the commercial public cloud deprives customers' control over the systems that manage their data, raising security and privacy as the primary obstacles to the adoption of the cloud. To address these challenges, in this dissertation we explore the problem of secure and privacy-assured data service outsourcing in cloud computing. We aim at deploying the most fundamental data services including data storage, search, and sharing on the commercial public cloud, with built-in security and privacy assurance as well as high level service performance, usability, and scalability. Our contributions are as follows: Firstly, we focus on privacy-preserving secure cloud storage auditing to maintain strong storage correctness guarantee, given the di culty that data les are no longer locally possessed by data owners. We rst develop a random-masking sampling approach to allow a third party auditor to perform on-demand privacy-preserving storage correctness auditing on behalf of data owners, without violating owners' data privacy. For storage correctness assurance with data dynamics, we further investigate a novel sequence-enforced Merkle Hash Tree and manipulate it with the random sampling approach to support fully dynamic data operations. Secondly, we focus on privacy-assured and e ective cloud data search services with strong privacy-assurance, while enjoying high service-level performance inherently demanded by the large number of data users and huge amount data les. We rst investigate a widely applicable fuzzy/similarity keyword search problem, and develop a brand new symbol-based trie-traverse searching approach, where transformed fuzzy keywords extracted from data les are stored using a multi-way tree structure, while protecting keyword privacy. To enable search result relevance ranking, we further investigate secure ranked search, which facilitates e cient server-side result ranking without leaking any keyword related information. Thirdly, we study how to enable scalable and owner-controlled cloud data sharing services, given the challenge that data no longer resides on owners' trusted domain. We rst associate data with a set of meaningful attributes, use logical composition of attributes to re ect ne-grained data access, and enforce owner's control via attribute-based encryption. For the inherent scalability requirement of cloud system, we further leverage the cloud as a mediated proxy, to which data owners can delegate most cumbersome data/user management workload, without a ecting the underlying data con dentiality.
Ph.D. in Electrical Engineering, July 2012
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Title: EMBEDDED SYSTEM DESIGN FOR TRAFFIC SIGN RECOGNITION USING MACHINE LEARNING ALGORITHMS
Creator: Han, Yan
Date: 2016, 2016-12
Description: Traffic sign recognition system, taken as an important component of an intelligent vehicle system, has been an active research area and it has...
Show moreTraffic sign recognition system, taken as an important component of an intelligent vehicle system, has been an active research area and it has been investigated vigorously in the last decade. It is an important step for introducing intelligent vehicles into the current road transportation systems. Based on image processing and machine learning technologies, TSR systems are being developed cautiously by many manufacturers and have been set up on vehicles as part of a driving assistant system in recent years. Traffic signs are designed and placed in locations to be easily identified from its surroundings by human eyes. Hence, an intelligent system that can identify these signs as good as a human, needs to address a lot of challenges. Here, ―good‖ can be interpreted as accurate and fast. Therefore, developing a reliable, real-time and robust TSR system is the main motivation for this dissertation. Multiple TSR system approaches based on computer vision and machine learning technologies are introduced and they are implemented on different hardware platforms. Proposed TSR algorithms are comprised of two parts: sign detection based on color and shape analysis and sign classification based on machine learning technologies including nearest neighbor search, support vector machine and deep neural networks. Target hardware platforms include Xilinx ZedBoard FPGA and NVIDIA Jetson TX1 that provides GPU acceleration. Overall, based on a well-known benchmark suite, 96% detection accuracy is achieved while executing at 1.6 frames per seconds on the GPU board.
Ph.D. in Computer Engineering, December 2016
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Title: POWER GRID VERIFICATION ON CLOUD
Creator: Gupte, Naval
Date: 2016, 2016-05
Description: Reliability and performance of modern ICs is becoming increasingly susceptible to supply voltage variations. Increased demand for low voltage...
Show moreReliability and performance of modern ICs is becoming increasingly susceptible to supply voltage variations. Increased demand for low voltage integrated circuits has made power grid analysis extremely critical and indispensable in modern design flows. Efficient validation of on-chip power distribution network is computationally demanding because of increasing grid sizes. Power grid simulation is critical for analysis and verification of power supply noises for robust and reliable IC designs. Computational demands to simulate power grids for ICs with increasing complexity is never-ending. Cloud computing platforms can be leveraged to mitigate costs associated with making these resources available. However, since simulation data usually contains sensitive design information, simulating on third-party platforms lead to major security concerns. In this study, we propose a framework for secure power grid simulation on Cloud. A transformation algorithm to hide current excitations is presented, while still allowing a majority of computations to be completed on Cloud. We employ multiple compression strategies to significantly reduce communication and storage overheads. Experiments show that our framework can achieve similar turn-around time as an insecure simulator on Cloud, while securing current excitations and output voltage vectors with reasonable communication and computational overheads. Vectorless technique to grid verification estimates worst-case voltage noises without detailed enumeration of load current excitations. We study voltage noise assessment in RLC models of VDD and GND networks in integrated power grids. Abstract grid model is utilized to abbreviate runtime, while transient constraints capture transitory circuit behaviour. Heuristics are employed to extract constraints that restrict power consumption profiles to realistic scenarios. Multiple linear programming problems are formulated to evaluate bounds on voltage overshoots and undershoots. We propose ways to mitigate storage and computational requirements on processing resources, enabling users to deploy computations on economical Cloud Computing platforms. Recommended solution is parallelizable, thereby reducing the overall verification time. Data compression is applied to fully exploit the compute capabilities of contemporary processors for higher throughputs. Experimental results suggest that the proposed technique is practical and scalable for industrial grids.
Ph.D. in Electrical Engineering, May 2016
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