Seminars and Defenses

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

June 12, 12PM, ENG460
Robust Discriminative Analysis Framework For Gaze And Head-Pose Estimation
Head movements, combined with gaze, play a fundamental role in predicting a person's action and intention. Head-pose and gaze estimation have been studied separately in the literature; previous research shows that satisfactory accuracy in head-pose and gaze estimation can be achieved in constrained settings. However, in non-constrained settings the process is complex, and performance can degrade significantly in the presence of variation in head-pose, gaze position, occlusion and ambient illumination. In this thesis, a framework is therefore proposed to fuse and combine head-pose and gaze information to obtain more robust gaze estimation. The framework is designed to enhance the accuracy of head-pose and gaze estimation, for its application in real life settings.
The proposed framework combines appearance-based and geometrical-based features which include eye location, facial landmarks and head angles. These features incorporate both statistical and geometrical indexing and are in essence calibration free as they do not require any subsequent adjustments.
The results of experiments conducted in this work demonstrate that the proposed framework improves the accuracy of head-pose and gaze estimation, in comparison to the existing state of the art methods in literature. Furthermore, the proposed framework considerably extends its operating range by overcoming the problems introduced by variations of head-pose (beyond the typical frontal emphasis in other research efforts), occlusion (in the presence of glasses, thick eyebrows, eyelids obscuring the iris, and hair occlusions, such as those introduced by the presence of hair bangs, moustaches and beards) and non-uniform illumination. Throughout the narration of this thesis each chapter not only presents its own contribution but builds on the previous as will be demonstrated below.
May. 14, 10AM, ENG460
Towards building a clinically-inspired ultrasound hub: Design, Development and Clinical Validation of Ultrasound probes for Imaging, Therapeutics, Sensing and other applications
Dr. Amir Manbachi • Research Seminar
Ultrasound is a relatively established modality with a number of exciting, yet not fully explored applications, ranging from imaging and image-guided navigation, to tumor ablation, neuro-modulation, piezoelectric surgery, and drug delivery. In this talk, Dr. Manbachi will be discussing some of his ongoing projects aiming to address low-frequency bone sonography, minimally invasive ablation of neuro-oncology and implantable sensors for spinal cord blood flow measurements.

Dr. Manbachi is an assistant research professor at Johns Hopkins School of Medicine. He received his Ph.D., M.A.Sc., B.A.Sc. degrees all from University of Toronto in 2015, 2010, and 2008 respectively. He was a Postdoctoral Scholar at Brigham and Womens Hospital in Harvard-MIT Division of Health Sciences and Technology in 2016.
His research interests include engineering design, development and clinical validation of acoustic probes (ultrasound transducers) and minimally invasive medical devices for clinically inspired diagnoses and treatments.
Dr. Manbachi is an author on 25 peer-reviewed journal articles, 30+ conference proceedings, 10 invention disclosures / patent applications and a book. His interdisciplinary research has been recognized by a number of awards, including University of Toronto's 2015 Inventor of year award, and Ontario Brain Institute 2013 fellowship.
May. 6, 2PM, ENG460
Modeling Hybrid Metaheuristic Optimizer for Convergence Prediction
Noel Jose Thengappurackal Laiju • MENG PROJECT ORAL EXAM
The project aims at the design and development of six hybrid nature inspired algorithms based on Grey Wolf Optimizer with Artificial Bee Colony Optimizer (GWOABC), Moth Flame Optimization Algorithm with Ant Lion Optimizer (MFOALO), Cuckoo Search Optimizer with Fire Fly Optimization Algorithm(CSFFA), Multi-Verse Optimizer with Particle Swarm Optimization Algorithm (MVOPSO), Grey Wolf Optimizer with Whale Optimization Algorithm (GWOWOA), and Binary Bat Optimization Algorithm with Particle Swarm Optimization Algorithm(BATPSO). Hybrid optimizations assume the implementation of two or more algorithms for the same optimization problem. "Hybrid algorithm" does not refer to simply combining multiple algorithms to solve a different problem but rather many algorithms can be considered as combinations of simpler pieces.The hybrid approach combines algorithms that solve the same problem but differs in other characteristics notably performance. A hybrid optimization uses a heuristic to choose the best of these algorithms to apply in a given situation. The proposed hybrid algorithms are benchmarked using a set of 23 classical benchmark functions employed to test different characteristics of hybrid optimizers. The results of the fitness functions prove that the proposed hybrid algorithms are able to produce better or more competitive output with respect to improved exploration, local optima avoidance, exploitation, and convergence. All these hybrid algorithms find superior optimal designs for quintessential engineering problems engaged, showcasing that these algorithms are capable of solving constrained complex problems with diverse search spaces. Optimization results demonstrate that all hybrid algorithms are very competitive compared to the state-of-the-art optimization methods and validated by fitness function. The hybrid algorithms are applied for optimal efficiency determination in various design challenges based on cantilever beam problem.
May 6, 10AM, ENG460
Non-Orthogonal Multiple Access Techniques: Opportunities and Challenges
Dr. Mojtaba Vaezi • Research Seminar
Future radio access technologies are expected to support a massive number of users with a diverse set of requirements in terms of delay, throughput, etc. In view of emerging applications such as the Internet of Things (IoT), and to fulfill the need for massive numbers of connections with diverse requirements in terms of latency and throughput, 5th generation (5G) and beyond cellular networks are experiencing a paradigm shift in design philosophy: shifting from orthogonal to non-orthogonal techniques in waveform, multiple access, and random-access design. A common feature of newly designed multiple access (MA) schemes is the use of non-orthogonal multiple access (NOMA) schemes in lieu of the conventional orthogonal schemes. By allowing multiple users to share the same time/frequency/code/space, NOMA scales up the number of served users, increases the spectral efficiency, and improves user-fairness compared to existing orthogonal MA techniques.

This talk investigates the potentials of the emerging NOMA techniques for 5G and beyond mobile networks. We will first briefly review the MA methods of 1G to 4G cellular networks. After motivating the need for NOMA in 5G and beyond networks, the theoretical basis of NOMA will be discussed, and potential MA methods will be outlined. Details of power allocation and user pairing will be described, and the possibility of combining NOMA with multiple-input multiple-output (MIMO) technologies both in the single-cell and multi-cell setting will be explored. Practical implementation issues will also be discussed. Lastly, challenges and future research directions, including combining NOMA with massive MIMO and millimeter wave communications, security concern, and potential benefit of NOMA from machine learning and deep learning algorithms will be explained.

Mojtaba Vaezi received his Ph.D. in Electrical Engineering from McGill University in 2014 and held several research positions at Princeton University from 2015 to 2018. He is currently an Assistant Professor of ECE at Villanova University, PA, USA. His research interests include the broad areas of wireless communications, signal processing, and information theory, with an emphasis on fifth generation (5G) radio access technologies, physical layer security, Internet of things (IoT), and machine learning for communications. Among his publications in these areas is the book Multiple Access Techniques for 5G Wireless Networks and Beyond (Springer, 2019). Dr. Vaezi has served as the president of McGill IEEE Student Branch and the head of Mobile Radio Network Design and Optimization Group at Ericsson. He is/was an Editor of IEEE Communications Letters and IEEE Communications Magazine and the lead co-organizer of five NOMA workshops at IEEE VTC 2017-Spring, Globecom’17, ICC’18, Globecom’18, and ICC’19. Dr. Vaezi is a recipient of several academic, leadership, and research awards, including the McGill Engineering Doctoral Award, the IEEE Larry K. Wilson Regional Student Activities Award in 2013, the NSERC Postdoctoral Fellowship in 2014, and the Ministry of Science and ICT of Korea’s best paper award in 2017.
May 3, 10AM, ENG460
Electrical Micro and Nano Devices and Sensors
Dr. Arezoo Emadi • Research Seminar
The development of highly sensitive diagnostic and monitoring tools that extends well beyond human senses and perception is an essential strategy for more cost-effective and practical health and environmental maintenance. Today, micromachined sensor systems and wearable electronics are being successfully adapted and adopted to bring leading-edge technologies that transfer significant benefits of micromachining and integration to the fields of medicine and environmental monitoring. The acceleration in micromachined sensors’ implementation is primarily due to their potential for integration, device miniaturization, low power consumption, better performance, lower cost and higher reliability. This seminar introduces ongoing research efforts to introduce emerging smart sensor systems, a new generation of BioMEMS and a highperformance ultrasonic imaging system as practical, non-invasive and sensitive diagnostic tools for medical application. The potential of these technologies for routine treatment efficiency monitoring are further explained with the aim to facilitate cost-effective and more accessible secondary preventive strategies.

Dr. Arezoo Emadi is an Assistant Professor in the Department of Electrical and Computer Engineering, University of Windsor. She joined the University of Windsor in July 2017. Dr. Emadi received her Ph.D. degree from the Department of Electrical and Computer Engineering at the University of Manitoba. Her ongoing research efforts are in the area of Micro Electro Mechanical Systems (MEMS), medical MEMS sensors and transducers, bio and chemical sensors, advanced diagnosis sensor technologies, micro and nano electronic devices and fabrication and medical imaging systems. She has led academic and industry crossfunctional projects to introduce and implement next generation micromachined smart sensor systems in a wide range of fields that make abundant use of sensors and transducers and to deliver the benefits of these technologies to a wider segment of the world’s population. She is the principal author/co-author of over 50 journal and conference papers as well as patents and book chapter in this field. Dr. Emadi also holds an Adjunct Professor position at the Department of Electrical and Computer Engineering, University of Manitoba. She is a Senior Member of IEEE and a Professional Engineer, PEng, and an active member of the Windsor Cancer Research Group, WCRG.
May. 2, 3PM, ENG471
Performance Evaluation Of A Big Data Application On Apache Spark
Apache Spark enables a big data application—one that takes massive data as input and may produce massive data along its execution—to run in parallel on multiple nodes. Hence, for a big data application, performance is a vital issue. This project analyzes a WordCount application using Apache Spark, where the impact on the execution time and average utilization is assessed. To facilitate this assessment, the number of executor cores and the size of executor memory are varied across different sizes of data that the application has to process, and the different number of nodes in the cluster that the application runs on. It is concluded that all the four factors— number of executor cores, size of executor memory, number of nodes in the cluster, and the size of input data—impact the performance of the application.
May 2, 10AM, ENG460
Adaptive Electromagnetic Structures for Wireless Communications and IoT Applications
Dr. Marco A. Antoniades • Research Seminar
The recent emergence of a new class of engineered materials that have electromagnetic properties not typically found in nature, known as metamaterials, has led to the creation of novel electromagnetic structures that exhibit new phenomena and demonstrate superior qualities compared to their conventional counterparts. Notable among these are metasurfaces that allow the arbitrary manipulation of fields passing through them. In this presentation, an overview of recent work conducted in the area of electromagnetic metamaterial structures will be presented. The basic operation of transmission-line based metamaterials as these relate to guided-wave and antenna applications will be presented. This will be followed by various metamaterial-based antennas and RF/microwave devices for use in IoT and biomedical applications such as implantable/wearable devices and microwave imaging. Subsequently, compact, multi-band and highly efficient designs will be presented for wireless communication applications. Finally, ongoing and future work on adaptable metasurfaces for wireless communication applications will be discussed.

Marco A. Antoniades (IEEE S’99, M’09, SM’17) received the B.A.Sc. degree in electrical engineering from the University of Waterloo, ON, Canada, in 2001, and the M.A.Sc. and Ph.D. degrees in electrical engineering from the University of Toronto, ON, Canada, in 2003 and 2009, respectively. He is the Director of the Microwaves and Antennas Laboratory, a Founding Member of the EMPHASIS Research Centre, and an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Cyprus. He is also an Adjunct Fellow in the School of Information Technology and Electrical Engineering at The University of Queensland, Australia. His research interests include passive and active antenna design, RF/microwave circuits and metamaterials/metasurfaces for use in broadband wireless communications, biomedical devices and imaging, space/satellite applications, radio-frequency identification, and wireless power transfer. He is an Associate Editor of IEEE Antennas and Wireless Propagation Letters (AWPL), and a member of the IEEE Antennas and Propagation Society (AP-S) Education Committee.
Apr. 25, 10AM, ENG460
This thesis focuses on resource management both in communication and computing sides of the cloud radio access networks (C-RANs). Communication and computing resources are bandwidth, power, baseband unit servers, and virtual machines, which become major resource allocation elements of C-RANs. If they are not properly handled, they create congestion and overload problems in radio access network and core network part of the backbone cellular network. We study two general problems of C-RAN networks, referred to as communication and computing resource allocation problem along with user association, base band unit (BBU) and remote radio heads (RRH) mapping problems to improve energy efficiency, sum data rate and to minimize delay performance of C-RAN networks.

In this thesis, we propose, implement, and evaluate several solution strategies, namely posterior probability based user association and power allocation method, double-sided auction based distributed resource allocation method, the energy efficient joint workload scheduling and BBU allocation and iterative resource allocation method to deal with the resource management problems in both orthogonal and non-orthogonal multiple access supported C-RAN networks. In the posterior probability based user association and power allocation method, we apply Bayes theory to solve the multi-cell association problem in the coordinated multi-point supported C-RANs. We also use queueing and auction theory to solve the joint communication and computing resource optimization problem. As the joint optimization problem, we investigate the delay and sum data rate performance of C-RANs. To improve the energy efficiency of C-RANs, we employ Dinkelbach theorem and propose an iterative resource allocation method. The non-orthogonal multiple access supported C-RANs, we consider and implement the sparse code multiple access (SCMA) scheme to jointly optimize the codebook and power allocation. To solve the NP-hard joint optimization problem, we decompose the original problem into two sub-problems: codebook allocation and power allocation. Using the graph theory, we propose the throughput aware sparse code multiple access based codebook selection method, which generates a stable codebook allocation solution within a finite number of steps. For the power allocation solution, we propose the iterative level-based power allocation method, which incorporates different power allocation approaches (e.g., weighted and successive interference cancellation) into different levels to satisfy the maximum power requirement. Simulation results show that the sum data rate and energy efficiency performance of non-orthogonal multiple access supported C-RANs significantly increases with the number of users when the successive interference cancellation aware geometric water-filling based power allocation is used.
Apr. 15, 1PM, ENG460
Consensus Cooperative-based Clustering and its Applications
Dr. Rasha Kashef • Research Seminar
This talk summarizes my current research in the field of consensus unsupervised machine learning and its applications to support better decision-making in Big Data practice. Data clustering methods can transform raw data into building blocks necessary for configuring automated behaviors. Moreover, it supports in extracting the inherent grouping structure of large-scale data with the absence of labeled decision variables. There are potential shortcomings for existing clustering techniques. Consensus clustering reconciles clustering information about the same data set coming from different sources or different runs of the same algorithm. My research in big data clustering emphasizes on using cooperative-based consensus learning that increases the homogeneity of objects within clusters through finding the multifaceted agreement. Cooperative clustering is consistent, reusable, and scalable concerning the number of clustering approaches adopted and the size of the data.

Dr. Rasha Kashef received her Ph.D. from the University of Waterloo, Department of Electrical and Computer Engineering in 2008. She worked as Assistant Professor at the school of computing at the AAST institute in 2009-2011. She also worked as a Research Associate at Microsoft Corp. In 2010 she succeeded with her Machine learning research, and she received the Early Researcher Award. Her research interests span the use of machine learning in big data analysis in different applications including healthcare, revenue management, and software engineering. A particular focus is to use consensus learning to achieve better decision-making process. She worked as a post-doctoral fellow at the department of applied mathematics at the University of Waterloo from 2011 until 2013. She also joined the department of management science at the University of Waterloo from 2013-2016. She is also a professional engineer in Ontario. Currently, she is hired as an assistant professor at the IVEY business School in Management science group with a focus on Data Analytics.
Apr. 9, 1PM, ENG460
Semantics-driven Information Retrieval
Dr. Faezeh Ensan • Research Seminar
Knowledge Graphs (KGs), e.g., DBPedia (derived from Wikipedia), Yago, and Freebase, provide points of references for allowing machines to make semantic interpretation of content. As a consequence, knowledge graphs have played an important role in enabling semantic search over large-scale interrelated Web data by providing accurate interpretation of content in context. Given the growing role of KGs, e.g., Google's KG holds over 70 billion facts, I will first discuss how scalable reasoning over KGs can be facilitated through knowledge base modularization. In the talk, I will show that modularization of knowledge graphs can have both theoretical and practical benefits for performing knowledge reasoning tasks. I will then explore how KGs can be exploited for developing semantics-enabled information retrieval systems. I will thoroughly describe three semantics-driven information retrieval techniques that I have developed based on probabilistic graphical models and discuss their performance and characteristics.

Faezeh Ensan is a Postdoctoral Fellow at the Faculty of Computer Science, University of New Brunswick. Prior to this, she was affiliated with University of British Columbia's Sauder School of Business. She has successfully applied for and being granted funds by agencies such as Mitacs and NSERC. Her peer-reviewed publications have appeared in reputable journals such as Information Processing & Management, Information Systems, and Knowledge & Information Systems and highly selective conferences such as AAAI, ISWC, CIKM and WSDM.
Apr. 26, 10AM, ENG471
Performance-Oriented VM Provisioning in Clouds
Managing applications on the cloud requires extensive decision making on the part of the Application Provider (AP). Applications are designed to be scalable such that they handle their fluctuating workload by increasing and decreasing the number of services. These services run on Virtual Machine (VM) or container instances. APs decide on how the applications are scaled through VM provisioning and the placement of the services on those VMs. Various drivers guide this decision making. Application performance and cost are two such drivers. This thesis answers the question of how APs can meet the performance constraints of their applications while minimizing the cost of the running VMs. Two versions of the problem are presented. The first version expects to meet mean response time constraints given a deployment configuration through the replication of VMs and addition of virtual processors. The presented solution is based on layered bottlenecks. A case study shows the solution meets response time constraints and uses fewer resources in comparison to a simple utilization based approach. The second version adds the minimization of cost as an objective, where VM-types having different cost rates are used. This problem does not require a deployment configuration and provides a complete solution, where resources can be added and removed. A novel solution based on the layered bottleneck strength value with genetic algorithm has been presented. For the case study, a decision maker is implemented for a web application. The solution is compared with three algorithms, where it is shown that this solution provides shorter runtime than the exhaustive search, and is able to meet response time constraints with near optimal minimization of cost. The bottleneck and genetic algorithm based solution results in better cost than a plain genetic algorithm solution and random search, at the expense of slightly longer runtime.
Apr. 2, 1PM, ENG460
Automatically Mitigating and Fixing Software Vulnerabilities
Dr. Zhen Huang • Research Seminar
With the rise of smart phones and IoTs, computer systems have become an indispensable part of our lives. Our reliance on computer systems make software security extremely important. However, software security is continuously threatened by software vulnerabilities, because exploiting software vulnerabilities can compromise computer systems and drastically increase the scale and speed of security attacks. While it is ideal to fix software vulnerabilities, creating a correct fix takes time and leaves a window for adversaries to exploit them. In this talk, I will demonstrate the need for automatic solutions to address software vulnerabilities with a study on the life cycle and complexity of real-world security patches, and present tools that I have built to mitigate and fix real-world software vulnerabilities. These tools leverage novel program analysis techniques to address two main challenges: 1) mitigating a large number of software vulnerabilities rapidly and safely, and 2) generating correct security patches for complex software vulnerabilities. I will conclude this talk with future directions of my research.

Zhen Huang is a postdoc scholar in the Department of Computer Science and Engineering at Pennsylvania State University. He earned his Ph.D from the Department of Electrical & Computer Engineering at University of Toronto in 2018. His research focuses on automatically mitigating and fixing software vulnerabilities.
Apr. 1, 1PM, ENG460
Data and Problems: Mapping Books to Time
Dr. Aminul Islam • Research Seminar
Many of the natural language processing problems can be solved by using only text data. In this talk, I will discuss an approach that predicts the year a book or document was written based on Google Books N-gram corpus. Predicting the time when a book or document was written is useful for a variety of tasks and applications, ranging from authorship and plagiarism detection to identification of literary movements. Two different datasets (English and French) are used to evaluate the approach.

Aminul Islam joined the School of Computing and Informatics at the University of Louisiana at Lafayette as an assistant professor in August 2016. He received his PhD in Computer Science in 2011 from the University of Ottawa. Before coming to UL Lafayette, Dr. Islam was a Research Associate and Adjunct Faculty member (2014-2016) and Postdoctoral Research Fellow (2011-2013) in the Faculty of Computer Science at Dalhousie University. He received NSERC IRDF Fellowship and NSERC PGS D. Dr. Islam's research interests are in the areas of Natural Language Processing (NLP), Text and Data Analytics. He has authored more than 40 peer-reviewed articles published in premier journals and conferences in his research areas. His research ideas are well-adopted and cited (more than 1,000 times) by the research community.