Seminars and Defenses

All are welcome and encouraged to attend the seminars and defenses.

Aug. 23, 11AM
Mohammed Baljon • PhD internal thesis defense
Resource allocation for energy harvesting communication systems over fading channels under delay constraint
Energy Harvesting (EH) is an emerging communication paradigm to defeat the limitation of network longevity by recharging the nodes by harvesting energy from the environment. The Energy Harvesting Network (EHN) requires a stable and efficient power control scheme like other conventional communication systems. It is more complicated than that in conventional communication networks, in that it should not only consider the quality of service requirements of the network but also adapt the randomness of the energy arrival. In this thesis, several optimal offline and online resource allocation strategies for a point-to-point and two-hop EH communication networks over wireless fading channels are investigated.
As a first step, we introduce RGWF (Recursive Geometric Water-filling) algorithm that provides an optimal offline transmission policy for a point-to-point EH communication system. Next, we consider a network comprising of a source, a relay, and a destination, where the source is an EH node. Joint time scheduling and power allocation problems are formulated to maximize the network throughput by considering conventional and buffer–aided link adaptive relaying protocols. Based on the modified RGWF algorithm, we decouple the joint power allocation and transmission time scheduling problem and propose optimal offline and suboptimal online schemes. In the second part, we aim to obtain the optimal transmission policy that maximizes the average total throughput of a point-to-point EH communication system network in an online manner. The optimal solution is obtained using dynamic programming by casting the optimization problem as a semi-Markov decision process (SMDP). In a delay-tolerant approach, we consider a cross-layer adaptation, where the proposed policy chooses modulation constellation for EH networks dynamically depending on battery state, data buffer state in addition to channel state. The proposed SMDP-based dynamic programming approach has been approved in dynamically adaptive the change of the channel and/or buffers’ states that optimally satisfy the BER requirements at the physical layer, and the overflow requirements at the data-link layer.
Aug. 22, 3PM
Ghassan Mohammed Halawani • MEng project
Convolutional Neural Network for Image Classification Based on Transfer Learning Technique
The main purpose of this project is to design a convolutional neural network for image classification, based on deep learning framework. A transfer learning technique is used by MATLAB tool kit to modify and train the pre-trained network with a new dataset in order perform classifications of thousand images.
Firstly, the general common architecture of most neural network and its benefits are presented. The mathematical models and the role of each part in the neural network are explained in detail. Secondly, different neural networks are studied in term of architecture, application, and the working method to highlight the strengths and weaknesses of each of neural network. The last part conducts a detail study on one of the most powerful deep learning networks in image classification, convolutional neural network, and how it can be modified to suit different classification tasks by using transfer learning technique in MATLAB.
Aug. 22, 1PM
Adnan Arapovic • MEng oral exam
Fault Analysis Considering Fault Current Contributions from Distributed Energy Resources
For over a century, electric power systems used synchronous machines for the generation of electricity. However, with emerging concerns over climate change and the need for reduced greenhouse gas emissions, together with the growing awareness of the importance of the natural environment and the depletion of the earth’s non-renewable energy resources, the generation of electricity from distributed renewable energy resources such as solar photovoltaic (PV) and wind energy has begun to expand at a rapid pace. Proliferation of converter-based distributed energy resources at the transmission and distribution systems has introduced new challenges in determining the maximum possible fault currents that a power system must be able to withstand without being destroyed. As part of a connection impact assessment for a new generation facility, utilities require detailed information on fault characteristics of the generation sources. However, there is limited knowledge or contradictory conclusions regarding the behaviour of converter-based distributed energy resources (e.g., solar PV and wind power generation stations) during faults on transmission or distribution systems. Currently, concern is widespread throughout Ontario regarding the constraints limiting solar PV and wind power generation stations from connecting to the electrical grid for projects equal to or less than 500kW. Many solar PV and wind industry participants have had connection applications rejected or are experiencing delays regarding the connection of their projects. Therefore, it is imperative to develop the mathematical and software simulation models that approximate the response of converter-based distributed energy resources during a fault on the transmission or distribution system in order to determine the fault current contributions to the electrical grid that a transmission or distribution utility needs to reflect in their connection impact assessments. As part of this project, theoretical models that have been presented in various literary works and publications on the topic are implemented using a program developed in MATLAB for performing fault current analysis of a power system using conventional fault analysis techniques. The MATLAB fault current analysis program is used to perform fault analysis of a power system with converter-based distributed energy resources in order to demonstrate how to implement these models to approximate the fault response of converter-based distributed energy resources within the framework of conventional fault analysis techniques and accurately simulate the fault current contributions to the electrical grid that a transmission or distribution utility needs to reflect in their connection impact assessments.
Aug. 29, 11AM, ENG460
Abdullah Siddiqui • MASc thesis defense
Performance and Energy Optimization of Heterogeneous CPU-GPU Systems for Embedded Applications
One of the most critical steps of embedded system design is Hardware-Software partitioning. It is characterized by distributing the tasks or components of an embedded application between hardware and software such that the system-designer’s functional and non-functional constraints are satisfied. Heterogeneous computing platforms consisting of CPUs and GPUs have tremendous potential for enhancing the performance of embedded applications. The challenges of application partitioning for CPU-GPU mapping and scheduling are greater for such platforms due to their unique and diverse characteristics. In this thesis, an optimization methodology is devised and presented for partitioning and mapping computational tasks on CPU-GPU platforms while keeping a check on the power consumption. Our methodology also uses parallelism in applications and their tasks by utilizing the architectural capabilities of GPU. The optimizing algorithm is tested for a MJPEG decoder application as well as several benchmarks and synthetic task graphs.
Aug. 10, 11AM, ENG460
Hasan Farahneh • PhD final thesis defense
Investigation of Vehicle to Vehicle Communication System Using Visible Light Technology
Visible Light Communication (VLC) has emerged as an attractive alternative to radio frequency (RF) communication, due to cost-effectiveness and being license free. It has also proved its applicability in automotive applications, as an alternative and/or a complement to the traditional RF based communications. We investigate the suitability of VLC in Intelligent Transportation Systems (ITS) and discuss its advantages in terms of safety enhancement and improved efficiency of the ITS. In this thesis, a VLC-based Vehicle-to-Vehicle (V2V) system in practical environments, considering both Line-of-Sight (LOS) and Non-Line of Sight (NLOS) paths is presented. The thesis investigates robust communication between a Light Emitting Diodes (LEDs) based VLC emitter and Photodiodes (PDs) based VLC receiver. For consideration of a V2V communication system, we consider transmitter on vehicle headlights and receivers on taillight making (2x2) Multiple-Input-Multiple-Output (MIMO) communication link. A closed form expression of the Channel Impulse Response (CIR) is derived and the effect of various channel parameters is analyzed. Optical-Orthogonal Frequency Division Multiplexing (O-OFDM) with adaptive modulation schemes is proposed for system improvement. Its performance is evaluated in terms of Inter Symbol Interference (ISI) mitigation, and data rate improvement. Moreover, the effect of sunlight on V2V-VLC system is investigated with appropriate denoising schemes. Two denoising schemes are proposed and evaluated as a solution to combat the effect of the solar irradiance on VLC signal. Firstly, we use a differential receiver for denoising purposes followed by K-Nearest Neighbor (KNN) based adaptive filtering algorithm, which is a supervised Machine Learning (MLE) technique. The shadowing effect is also studied. Moreover, an application of VLC in Foglet based ITS is described. The simulation validation of the VLC-based V2V system is performed under various environmental conditions and scenarios. Obtained results emphasize the suitability of VLC technology for automobile applications.
July 30, 10AM, ENG460
Marcos Aguirre • MASC thesis defense
A Single-Phase DC-AC Dual-Active-Bridge Based Resonant Converter for Grid-Connected Photovoltaic Solar Applications
In the wake of the global energy crisis, the integration of renewable energy resources, energy storage devices, and electric vehicles into the electric grid has been of great interest towards replacing conventional, fossil-fuel-dependent energy resources. This thesis presents the circuit topology and a control strategy for a 250-W single-phase grid-connected dc-ac converter for photovoltaic (PV) solar applications. The converter is based on the dual active bridge (DAB) kernel employing a series-resonant link and a high-frequency isolation stage. For interfacing the 60-Hz ac grid with the 78-kHz resonant circuit, the converter utilizes a four-quadrant switch array that functions as an ac-ac stage. Therefore, a bipolar low-frequency voltage source, that is the grid voltage, is used to synthesize a symmetrical high-frequency voltage pulse-train for the resonant circuit. Thus, soft switching and the use of a compact ferrite-core transformer have been possible. Then, a fast current-control loop ensures that the converter injects a sinusoidal current in phase with the grid voltage, while a relatively slower feedback loop regulates the converter dc-side voltage, that is, the PV array voltage, at a desired value. To simulate the converter and to design the controllers, the thesis also presents nonlinear large-signal and linearized small-signal state-space averaged models. The performance of the converter is assessed through simulation studies conducted using the aforementioned averaged models, a detailed topological model in the PLECS software environment, and a prototype.
July 6, 3:30PM, ENG460
Sulaiman Aljeddani • MASC thesis defense
Runtime thermal management based on task migration techniques in 3D chip multiprocessors
The industry trend of Chip Multiprocessors (CMPs) architecture is to move from 2D CMPs to 3D CMPs architecture which obtains higher performance, more reliability, and reduced memory access latency. However, one key challenge in designing the 3D CMPs is the thermal issue as a result of maximizing the throughput. Therefore, applying Runtime Thermal Management (RTM) has become crucial for controlling thermal hotspots. In this thesis, two methods of run-time task migration are presented to balance the temperature and reduce the number of hotspots in the 3D CMPs. The proposed techniques consider hotspots both in the core and the memory layers simultaneously to make the optimum run-time task migration decisions. The first proposed approach is divided into two algorithms working in parallel, which aim at maximizing the throughput on the 3D CMPs while satisfying the peak temperature constraints. Experimental results show that the proposed architecture yields up to 60% reduction in overall chip energy. The proposed architecture improves the IPC for canneal and fluidanimate applications by 16% and 14%, respectively. In the second method, the proposed technique migrates the hottest core with the optimal coldest core instead of the coldest core in the core layer. The optimal coldest core is selected by considering hotspots DRAM banks in the memory layer. The simulation results indicate up to 33℃ (on average 13℃) reduction in the cores' temperature of the target 3D CMPs. Finally, the proposed techniques are efficiency clarified in the simulation results that the maximum temperature of cores in the core and memory layers are both less than the maximum temperature limit, 80℃.
Aug 20, 1PM, ENG460
Shima Mohammadali Pishnamaz • MASC thesis defense
Retinal Fundus Image Processing and Ensemble Learning: Optic Disc and Optic Cup Segmentation
Ophthalmologists have widely used retinal fundus imaging systems to examine the health of the optic nerve, vitreous, macula, retina and its blood vessels. Many critical diseases can be diagnosed by analyzing retinal fundus images. Retinal image-based glaucoma detection is a comprehensive diagnostic approach that examines the head cup-to-disc ratio (CDR) as an important indicator for detecting the presence and the extent of glaucoma in a patient. The accurate segmentations of the optic disc and optic cup are critical for the calculation of CDR. Therefore, machine learning based algorithms are very helpful to efficiently exploit the vast amounts of retinal fundus data.
In this thesis project, the main goal is to develop image processing and machine learning algorithms to automatically detect the optic disc and optic cup from fundus images. This goal has been achieved by developing and applying several image enhancement techniques. First, an algorithm is proposed and tested on several fundus images to detect the optic disc. The proposed algorithm is based on a combination of contrast limited adaptive histogram equalization, alternative sequential filters, thresholding, and circular Hough transform methods. It has been shown in the results section that the proposed algorithm is highly efficient in segmentation of optic disc from other parts of the fundus image. Several classification and modeling methods are studied in order to classify detected optic disc into optic cup and non-optic cup regions. In this thesis project three main ensemble modeling algorithms are studied to segment optic cup. The studied ensemble models are Random Forest (RF), Gradient Boosting Machines (GBM), and Extreme Gradient Boosting Machines (XGBoost). The comparison between these models shows that they have more accurate results than conventional classification methods such as Linear Regression (LR) or Support Vector Machines (SVM). This study shows that XGBoost is the fastest and most accurate approach to segment optic cup within the optic disc region.
May 4, 11AM, ENG471
Prathap Siddavaatam • Final PHD Thesis Defense
Today IoT is a major disruption that will mark an epoch in communication technology such that every physical object can be connected to the Internet. With the advent of 5G communications, IoT is in urgent need of optimized architectures that can efficiently support wide ranging heterogeneous multi-objective requirements of communication, hardware and security aspects. The optimization challenges are rooted in the technology and how the information is acquired and manipulated by this technology. Our research in this thesis provides a description of compelling challenges faced by IoT and how to mitigate these challenges by designing resource-aware communication protocols, resource-constrained device hardware with low computing power and low-powered computational security enhancements. This thesis lays the foundation for optimizing these challenging IoT paradigms by introducing a novel Delta-Diagram based synthesizing model. The Delta-Diagram provides a road-map linking the behavioral and structural domains of a given IoT paradigm to generate respective optimizer domain parameters, which can be utilized by any optimizer framework. The fundamental part of the communication synthesizer is a mathematical model, developed to obtain the best possible routing paths and communication parameters among things. The ultimate aim of the entire synthesis process is to devise a design automation tool for IoT, which exploits the interrelations between different layer functionalities. This thesis also proposes a novel cross-layer Grey wolf optimizer for IoT, which outperforms some of the contemporary optimizer algorithms such as Particle Swarm, Genetic Algorithm, Differential Evolution optimizers in solving unimodal, multi-modal and composition benchmark problems. The purpose of this optimizer is to accurately capture both the high heterogeneity of the IoT and the impact of the Internet as part of delta diagram synthesis enabled network architecture. In addition, the Grey wolf optimizer for IoT plays a crucial role in design exploration of system on chip architecture for IoT device hardware. The results generated by the optimizer yielded the most optimum feasible solutions in the design space exploration process of the IoT.
May 4, 11AM, ENG460
Yuguang "Michael" Fang • Seminar
Beef Up the Edge: How to Build a More Powerful IoT System
Connected things in various cyber-physical systems (CPSs), namely IoTs, enable us to sense physical environments, extract intelligent information, and better regulate physical systems we heavily depend on in our daily life. Unfortunately, how to design effective and efficient systems to meet specific applications with diverse quality of service requirements is of paramount importance but highly challenging due to the spatial and temporal variations of user traffic, network spectrum resource, computing capability, storage, and device types. One holistic design approach from the end-to-end perspective seems to be in dire need.
In this talk, the speaker will discuss various related problems and challenges in a connected world and then present a novel collaborative network architecture to enabling connected things to effectively harvest in-network capability (spectrum, energy, storage, and computing power) in a cognitive fashion and intelligently manage the spectrum efficiency, energy efficiency, and yes, security! By pushing in-network capability in communications, computing, and storage to the edge, this network architecture provides an effective and robust approach to IoT.

Dr. Yuguang "Michael" Fang received MS degree from Qufu Normal University, Shandong, China in 1987, PhD degree from Case Western Reserve University in 1994 and PhD degree from Boston University in 1997. He was an assistant professor in Department of Electrical and Computer Engineering at New Jersey Institute of Technology from 1998 to 2000. He then joined the Department of Electrical and Computer Engineering at University of Florida in 2000 and has been a full professor since 2005. He held a University of Florida Research Foundation (UFRF) Professorship (2006-2009, 2017-2020), a University of Florida Term Professorship (2017-2019) and Changjiang Scholar Chair Professorship awarded by the Ministry of Education of China (is currently affiliated with Dalian Maritime University).
Dr. Fang received the US National Science Foundation Career Award in 2001 and the Office of Naval Research Young Investigator Award in 2002, 2015 IEEE Communications Society CISTC Technical Recognition Award, 2014 IEEE Communications Society WTC Recognition Award, and multiple Best Paper Awards from IEEE Globecom (2015, 2011 and 2002) and IEEE ICNP (2006). He has also received 2010-2011 UF Doctoral Dissertation Advisor/Mentoring Award, 2011 Florida Blue Key/UF Homecoming Distinguished Faculty Award, and the 2009 UF College of Engineering Faculty Mentoring Award. He was the Editor-in-Chief of IEEE Transactions on Vehicular Technology (2013-2017), the Editor-in-Chief of IEEE Wireless Communications (2009-2012), and serves/served on several editorial boards of journals including IEEE Transactions on Mobile Computing (2003-2008, 2011-2016), IEEE Transactions on Communications (2000-2011), and IEEE Transactions on Wireless Communications (2002-2009). He has been actively participating in conference organizations such as serving as the Technical Program Co-Chair for IEEE INFOCOM’2014 and the Technical Program Vice-Chair for IEEE INFOCOM'2005. He is a fellow of the IEEE (2008) and a fellow of the American Association for the Advancement of Science (AAAS) (2015).
May 2, 10AM, ENG460
Md Forhad Ebn Anwar • MENG FINAL DEFENCE
Collision of vehicles in highways are very frequent. Because of high speed (more than 100 km/hour), the momentum of collision is too high that leads severe casualty. Automatic Driving Assistance system can assist the vehicle operators to take decision based on realistic practical calculation on safety measures. It is always better to have third eye working parallel with human to avoid road accident. There are several technologies used to develop perfect driving assistance system to achieve higher accuracy in detection, identification and distance measurement of obstacles where vision based system is one of them. Mono-vision system provides cheap and fast solution rather stereo vision. This project work conducted with objective to comprehend computational complexity in implementation of mono-vision camera based object detection where system will generate warning if the detected object has a motion towards target. Processing and analysing of captured video image is the focused mechanism of implementation and used internal image generator module to mimic actual video camera. Appeared size of the shape of object considered for the decision making.
May 2, 10AM, ENG471
George Seif • MASC thesis defense
Large Receptive Field Networks for Accurate High-Scale Image Super-Resolution
This thesis presents a novel convolutional neural network architecture for high-scale image super-resolution. In particular, we introduce two separate modifications that can be made to the convolutional layers in the network: one-dimensional separable kernels and dilated kernels. We show how both of these methods can be used to expand the receptive field and performance of super-resolution networks, without increasing the number of trainable parameters or network depth. We show that these modifications can easily be integrated into any convolutional neural network to improve performance. Our methods are especially effective for the challenging high scale super-resolution due to the expanded network receptive field. We conduct extensive empirical evaluations to demonstrate the effectiveness of our methods, showing strong improvements over the state-of-the-art.
Apr. 25, 2P, ENG460
Ismail Sheikh • MASC thesis defense
The Effect of Parallel Execution on Multi-Site Computation Offloading in Mobile Cloud Computing
As smart mobile devices are becoming an indispensable part of our daily life, the demand for running complex applications on such devices are increasing. However, the limitations of resources (e.g. battery life, computation power, and bandwidth) of these devices are restricting the type of applications that can run on them. The restrictions can be overcome by allowing the devices to offload computation and run parts of an application in the powerful cloud servers. The greatest benefit from computation offloading can be obtained by optimally allocating the parts of an application to different devices (i.e. the mobile device and the cloud servers) that minimizes the total cost. The cost can include the response time of the application, mobile battery usage, or the monetary cost incurred by the user for execution on the cloud servers. Further, different types of devices can have a different number of processing cores. Unlike prior work in computation offloading, this work considers the effect of parallel execution of parts of an application—on different devices (external parallelism) and on the different cores of a single device (internal parallelism)—on offloading allocation. This work models each device as a multi-server queueing station. It uses genetic algorithm to determine the optimal allocation. This work is more advantageous for cases where the workflow of an application has multiple tasks that can be executed in parallel. The results show that considering the effect of parallel execution yields more optimal solution for the allocation problem compared to not accounting for parallelism at all.
Apr. 10, 12P, ENG460
Jinming Wen • Seminar
An Introduction to Integer Signal and Sparse Signal Recovery
In many applications, such as wireless communications, signal processing and GPS, we need to recover an integer parameter vector from an integer linear model. While in some other application domains including computer vision and machine learning, one is frequently required to recover a sparse signal from a few linear measurements. In the first part of this talk, we will theoretically show that some commonly used lattice reductions, which are preprocess tools for recovering integer vectors, can improve the correct recovery probability of some commonly used suboptimal recovery algorithms. In the second part of the talk, we will introduce some algorithms and theory for recovering sparse signals.
Jinming Wen received his Ph.D degree in Applied Mathematics from McGill University in 2015. He was a postdoctoral fellow at ENS de Lyon and University of Alberta from March 2015 to August 2017.He has been a postdoctoral fellow at department of Electrical and Computer Engineering, University of Toronto since September 2017. His research interests include sparse recovery and lattice reduction with applications in communications, signal processing and cryptography. He has published around 30 research papers in top journals and conferences including IEEE Transactions on Information Theory/Signal Processing. He is an Associated Editor of IEEE Access.
Apr. 12, 10A, ENG471
Krishnanand Balasundaram • PHD FINAL THESIS DEFENSE
Morphologically Constrained Adaptive Signal Decomposition in Studying Ventricular Arrhythmias
Ventricular fibrillation (VF), a lethal form of ventricular arrhythmias (VA), originates from the lower chambers of the heart and is one of the major causes for sudden cardiac deaths (SCD). Since the duration from the onset of VF to SCD is only few minutes, it is difficult to study VF and it is even harder to perform invasive diagnosis or provide treatment within the short window of time. This dissertation proposes methods to extract meaningful information from VF electrograms and formulate associations to underlying structural and physiological properties of the cardiac tissue and clinical events of interest during VF. In other words, by analyzing clues in the electrograms during VF, the method can be used to infer the underlying anatomical and physiological properties of the cardiac tissue and certain clinical events of interest, which is otherwise not available without invasive procedures. The proposed methods will be of great assistance to cardiologists and cardiac electrophysiologist in the diagnosis and treatment planning of cardiac arrhythmias.
The proposed adaptive time-frequency (TF) signal decomposition was separated into two categories based on two purposes: (1) Time-specific event detection and (2) Time-averaged VA characterization. For the time-specific event detection (in this work rotor detection because rotors are believed to be drivers of VF), electrogram signal features related to the rotor event were identified with an adaptive TF decomposition and a modified criterion function. Using the proposed features and a LDA based classifier with LOO cross validation, overall classification accuracies of 80.77% and 79.41% were achieved in detecting rotor events and separating them from similar but not rotor events.
In the time-averaged ventricular arrhythmia characterization, previously established signal features were used to associate electrogram clues to the structural and physiological characteristics of the cardiac tissue. Using LCKSVD dictionary learning process, dictionaries of TF basis functions were generated to capture specific electric structures and physiological characteristics of the underlying cardiac tissue. The association of these characteristics with the extracted electrogram clues were validated using a cross-validation technique. The cross-validated results obtained were 71.09%, 60.30%, 71.74% and 65.73%, respectively, for the 4 characteristics.
Further to this, to automate and build a function-approximation model with non-linear separable capabilities to capture complex association of the heart characteristics and electrogram signal structures, neural network models were generated. The cross-validated accuracy for the models were 80.77%, 75.73%, 85.00%, 67.71%, 76.83% and 76.88% respectively for each of the developed models.
Apr. 4, 11A, ENG460
Resource allocation strategies for cognitive radio networks coexistence with D2D communications
Ever increasing demand for enhanced wireless services requires higher data rates, even up to tens of Gbps and pushes the spectrum to its limits. While there is constant push for new spectrum, current licensed spectrum is significantly underutilized. Cognitive radio (CR) approach will provide efficient spectral usage using intelligent wireless nodes. For the successful implementation of cognitive radio networks (CRNs), efficient resource allocation (RA) schemes are essential.
As a first step, the CR approach in radio access networks is introduced. In the second step, the taxonomy of the RA process in CRNs is provided. For radio resource allocation (RRA), the most crucial task is to associate a user with a particular serving base station, to assign the channel and to allocate the power efficiently. In this thesis, a subcarrier assignment scheme and a power allocation algorithm using geometric water-filling (GWF) is presented for orthogonal frequency division multiplexing (OFDM) based CRNs. This algorithm is proved to maximize the sum rate of secondary users by allocating power more efficiently. Then, the resource allocation problem is studied to jointly employ CR technology and device-to-device (D2D) communication in cellular networks in terms of spectral efficiency and energy efficiency (EE). In first case, in terms of spectral efficiency, a two-stage approach is considered to allocate the radio resource efficiently where a new adaptive subcarrier allocation (ASA) scheme is designed first and then a novel power allocation (PA) scheme is developed utilizing proven GWF approach that can compute exact solution with less computation. In second case, in terms of EE, the power allocation problem of cellular networks that co-exist with D2D communication considering both underlay and overlay CR approaches are investigated. A proven power allocation algorithm based on GWF approach are utilized to solve the EE maximization problem which results in an exact and low complexity solution.
Apr. 3, 12P, ENG460
Model Predictive Control of High Power Current-Source Converter for Medium-Voltage Induction Motor Drive
As a crucial player in medium-voltage (MV) applications, high power current-source converters (CSCs) feature some distinct advantages in contrast to their voltage-source counterparts. However, the traditional control techniques, based on linear proportional-integral (PI) regulators and low band-width modulation, impose several technical issues during low switching frequency operation. In order to meet more and more stringent performance requirements on industrial drives, various high performance finite control-set model predictive control (FCS-MPC) schemes are proposed in this thesis to control CSCs employed in MV induction motor (IM) drives.
The continuous-time and discrete-time dynamic models of high power CSC-fed MV IM drive are developed, which are used to predict the evolution of state variables in the system. Issues related to MPC approach, such as prediction horizon, weighting factor selection, control delay compensation, accurate extrapolation of references, and nature of variable switching frequency are addressed as well.
Model predictive power factor control (MPPFC) is proposed to accurately regulate the line power factor of CSR under various operating conditions. Meanwhile, an active damping function is incorporated into MPPFC to suppress the possible line-side LC resonance. Moreover, an online capacitance estimation method is designed in consideration on the perturbation of the filter parameters of CSR.
In order to keep fixed switching frequency of CSC and improve its dynamic responses, model predictive switching pattern control (MPSPC) and model predictive space vector pattern control (MPSVPC) are proposed, in which MPC technique is combined with selective harmonic elimination (SHE) modulation and space vector modulation (SVM), respectively. In steady state, the PWM waveform of CSC follows the pattern of traditional modulation schemes, whereas during transients CSC is governed by MPC approach for the purpose on dynamic performance improvement.
A common-mode voltage (CMV) reduced model predictive control (RCMV-MPC) is studied, with which the peak value of CMV in high power CSC-fed MV IM drive can be further reduced in comparison with the traditional RCMV modulation schemes. The dynamic responses of the motor drive system are further improved as well.
The simulation on a megawatt motor drive system and experimental results on a low power prototype, validate the effectiveness of the proposed various control schemes.
Jan. 15, 1P, ENG471
Inductive Magnetically Coupled Resonant Wireless Power Transmission System with Planar Coils for the Rezence Efficiency Standard, with a 90% Efficient Class-E Amplifier and Auto-Tuning Circuit Technique for Improved Efficiency
MCR-WPT has helped to increase the WPT distance which the PTU can transmit power efficiently to the PRU. This has enabled their rapid adoption in many applications. In this thesis propose new designs and design methodologies used to meet the Rezence standard for portable electronics, which based on magnetic resonant coupling WPT.
Class E PAs, which achieve 100% theoretical efficiency with simple circuit topology, are difficult to analytically design with equations provided. This thesis presents a HF Class-E PA design methodology that simplifies design. The methodology uses ideal Class-E design equations to generate ideal drain impedances, used to impedance match the power FET output for Class-E operation. A high efficiency Class-E PA was designed using a low cost power MOS by following this design flow.
The small size of these devices makes it difficult to design efficient MCR-WPT resonators. A new multi-layer MCR-WPT PSC design and design methodology is proposed which increases efficiency and decrease SRF of the coils. The design is a modified series stacked spiral inductors without vias, and can be stacked more than 2 layers, and the dielectric substrate changed to a higher permittivity material to improve efficiency and SRF. The design methodology takes advantage of the fast Momentum simulations to give good MCR-WPT system SRF and efficiency predictions.
Efficiency of MCR-WPT Tx and Rx resonators is reduced when the distance between the is less that the CCD due to the frequency splitting phenomenon. This thesis presents an auto-tuning circuit that automatically improve efficiency when the PRU is placed at the optimal region. The tuning is done by a binary counter and discrete capacitor array, and a maximum peak detection technique identifies the optimal tuning capacitance for maximum WPT efficiency.