Prof. Dr. Guo received B. E., M.E., and Dr. E degrees in electronic engineering from Toyohashi University of Technology in 1990, 1992, and 1996, respectively. He is currently a Professor of Department of Electrical and Electronic Engineering, Saga University as well as Director of Saga University Synchrotron Light Application Center. His research interests include epitaxial growth and characterization of semiconductor materials. Prof. Guo has published more than 300 papers in scientific journals including Nature Communications, Advanced Materials, Physical Review B, and Applied Physics Letters with more than 7200 citations (h-index: 43).

Dr. Jun Chen is currently an assistant professor in the Department of Bioengineering, University of California, Los Angeles. His current research focuses on nanotechnology and bioelectronics for energy, sensing, environment and therapy applications in the form of smart textiles, wearables, and body area sensor networks.

Recent real disastrous crowd incidents have shown that crowded places can be exposed to significant safety dangers and that the presence of many pedestrians can potentially result in injuries and fatalities at large scales if not planned and managed reasonably. This fact has resulted in significant challenges for managing the safety of large volumes of pedestrians in dense areas. In retrospect, many such real crowd disasters could have been avoided with better crowd management. Better tools and methodologies to predict crowd behavior during planning for potential emergencies would enable authorities to plan and prepare for improved public safety in crowded environments. Better still, real-time management of crowds might avert disasters if live event data could be used to make rapid predictions of crowd dynamics over the immediate future, allowing management to be optimized as an event unfolds. Such tools do not yet exist, and the technical demands of creating them are not trivial; they will require innovative approaches to both empirical research and modeling.

Dr. Ping Chen now works as a full Professor in the Institute of Semiconductors, Chinese Academy of Sciences (Beijing China). He received his bachelor’s degree of Physics from the University of Science and Technology of China (USTC) in 2003, and doctor’s degree of Microelectronics and Solid State Electronics from the Graduate School in University of Chinese Academy of Sciences in 2008. He worked in Georgia Institute of Technology (Atlanta, GA) as a Visiting Scholar from 2017 to 2019.

Dr. Naresh Chand is a Life Fellow of IEEE, Associate Vice President, Chapter Relations of the IEEE Photonics Society, and the Chair, Photonics Society, North Jersey Chapter. Dr. Naresh Chand was previously with US R&D Center of Huawei Technologies in NJ in 2011-2019 where he was working on developing low-cost advanced technologies for Ultra Broadband Optical Access Networks. Prior to this, he worked for BAE Systems (2003-11), Agere Systems and AT&T/Lucent Bell Laboratories (1986-2003), and Dept of Electronics, Government of India (1974-79).

Dr. Rajendra Singh is presently a Professor at the Department of Physics, IIT Delhi. He did M.Sc. (Physics) from D.B.S. College, Dehra Dun (affiliated to H.N.B. Garhwal University) in 1995. After that he joined Inter University Accelerator Centre (formerly Nuclear Science Centre), New Delhi for Ph.D. His Ph.D. work was related to the study of the effect of swift heavy ion irradiation on electrical properties of Si and GaAs. He completed his Ph.D. in 2001 with degree from Jawaharlal Nehru University, New Delhi. He then joined Walter Schottky Institute (WSI), Technical University of Munich (TUM), Germany as a post doctoral fellow. There he worked on the design, fabrication and characterization of InP-based heterojunction bipolar transistors (HBTs). He extensively used Class 100 Cleanroom facilities at WSI working on various processing tools such as photolithography, wet etching, reactive ion etching, UHV metallization and rapid thermal annealing. In January 2004 he joined the Max Planck Institute of Microstructure Physics, Halle, Germany as a post doctoral fellow. There he worked in the area of direct wafer bonding and layer splitting of semiconductors for the fabrication of silicon-on-insulator (SOI) and strained silicon-on-insulator (sSOI). He worked in a Class 10 Cleanroom facility at MPI Halle using processing tools such as wet benches, wafer bonding system, plasma enhanced chemical vapour deposition (PECVD) and annealing furnaces.

As end-consumers of electricity become more proactive and as many countries around the world push for a deeper penetration of renewable resources into the power grid, critical issues and challenges arise to the design and operation of deregulated electricity markets. In this presentation, we show how one can exploit tools from game theory to address some of these critical issues. Firstly, wholesale and retail markets are becoming more integrated due to the increasing adoption of distributed energy resources, creating a large gap in the current understanding of the impact of such small-scale energy resources on the larger power system operation and electricity market outcomes. This motivates us to develop a metric, called the Price of Aggregation, which quantifies the impact of integrating distributed energy resources in the retail-level on wholesale market efficiency.  Secondly, evidence from real markets indicate that large-scale adoption of wind energy in the transmission system leads to significantly higher price volatility in wholesale markets. To mitigate the effects of price volatility, we propose an add-on centralized clearing mechanism that is applicable to any wholesale market, with the aim of allowing any market participant to hedge against profit volatilities, without changing the existing market operations. Finally, we develop a multiperiod-multicompany demand response framework in retail markets, which captures the behavior of competing companies and their price-responsive end-consumers. Using real-life data, we demonstrate potential savings that can exceed 30% for end-consumers, in addition to revealing desirable mathematical properties and deep insights.

Prof. Hieu Nguyen received the B.S. degree in Physics from Vietnam National University in Ho Chi Minh City, Vietnam (2005), the M.S. degree in Electronics Engineering from Ajou University, South Korea (2009), and the PhD. degree in Electrical Engineering from McGill University, Canada (2012). In September 2014, he joined the Electrical and Computer Engineering Department, New Jersey Institute of Technology. He has authored/co-authored 1 book chapter, 1 patent, 48 journal articles, and more than 70 conference presentations.

One of the main goals in computer vision is to achieve a human-like understanding of images. This understanding has been recently represented in various forms, including image classification, object detection, semantic segmentation, among many others. Nevertheless, image understanding has been mainly studied in the 2D image frame, so more information is needed to relate them to the 3D world. With the emergence of 3D sensors (e.g. the Microsoft Kinect), which provide depth along with color information, the task of propagating 2D knowledge into 3D becomes more attainable and enables interaction between a machine (e.g. robot) and its environment. This dissertation focuses on three aspects of indoor 3D scene understanding: (1) 2D-driven 3D object detection for single frame scenes with inherent 2D information, (2) 3D object instance segmentation for 3D reconstructed scenes, and (3) using room and floor orientation for automatic labeling of indoor scenes that could be used for self-supervised object segmentation. These methods allow capturing of physical extents of 3D objects, such as their sizes and actual locations within a scene.

No summary is available.

Dr. Jing Zhang is currently the Kate Gleason Endowed Assistant Professor in the Department of Electrical and Microelectronic Engineering at Rochester Institute of Technology. She obtained B.S. degree in Electronic Science and Technology from Huazhong University of Science and Technology (2009), and Ph.D. degree in Electrical Engineering from Lehigh University (2013). Dr. Zhang’s research focuses on developing highly efficient III-Nitride and GaO semiconductor based photonic, optoelectronic, and electronic devices. Her research group is working on the development of novel quantum well active regions and substrates for enabling high-performance ultraviolet and visible LEDs/ lasers, as well as engineering of advanced device concepts for nanoelectronics.

This talk aims to 1) provide an envisioned picture of 6G, 2) serve as a research guideline in the beyond 5G era, and 3) go over the recently proposed solutions to provide high-speed connectivity in under-covered areas to serve and contribute to the development of far-flung regions. The role of Internet and Communication Technology (ICT) in bringing about a revolution in almost all aspects of human life needs no introduction. It is indeed a well-known fact that the transmission of the information at a rapid pace has transformed all spheres of human life such as economy, education, and health to name a few. In this context, and as the standardization of the fifth generation (5G) of wireless communication systems (WCSs) has been completed, and 5G networks are in their early stage of deployment, the research visioning and planning of the sixth generation (6G) of WCSs are being initiated. 6G is expected to be the next focus in wireless communication and networking and aim to provide new superior communication services to meet the future hyper-connectivity demands in the 2030s.

Brief Biography

Prof. Jae-Hyun Ryou is an Associate Professor of Mechanical Engineering

In many problems in statistical signal processing, regularization is employed to deal with uncertainty, ill-posedness, and insufficiency of training data. It is possible to tune these regularizers optimally asymptotically, i.e. when the dimension of the problem becomes very large, by using tools from random matrix theory and Gauss Process Theory. In this talk, we demonstrate the optimal turning of regularization for three problems : i) Regularized least squares for solving ill-posed and/or uncertain linear systems, 2) Regularized least squares for signal detection in multiple antenna communication systems and 3) Regularized linear and quadratic discriminant binary classifiers.

This talk presents an overview of challenges, state-of the-art, and applications for distributed robotic systems. In distributed robotic systems, there is a group of robots that seek to achieve a collective task. Applications include environmental monitoring, search and rescue, and programmable self-assembly. Settings can range from a small team of cooperative robots to a swarm of many interacting agents. An essential feature of such systems is that individual robots make decisions based on available local information as well as limited communications with other robots. The challenge is to design local protocols that result in desired global outcomes. In contrast to a traditional centralized paradigm, both measurements and decisions are distributed among multiple actors.

In this talk, I will present the potential of nano-islands grown by the atomic layer deposition (ALD) in addition to novel material combinations in scaled memory devices. Moreover, in order to relax the scalability requirement, I will introduce an integrated potential single device solution by infusing memory device which can monitor and remember our actions and the surrounding environment. Such a device performs the roles of both a MEMory and a senSOR at once and is referred to as a MEMSOR. The MEMSOR is directly programmed by external physical stimuli rather than an applied electric potential as in conventional devices. As a result, the MEMSOR leads to faster data analysis, lower footprint area, energy consumption, and ideally, the cost of the system. Finally, the MEMSOR device will be based on a MOSFET architecture with a mature manufacturing process, and thus it can be integrated with conventional Flash devices on the same silicon wafer with minimal additional process steps.

Recently, wireless power transfer has been used in mobile phones, electric vehicles, medical implants, wireless sensor networks, unmanned aerial vehicles, and so on. In this seminar, we will walk through circuit theory and simulations that illustrate the nature and constraints of the technology. We will also address contemporary topics related to application of wireless power transfer in electric vehicle applications.

The first part of this talk provides a brief introduction of the EMPC (motivation, challenges, and solutions). The second part of this talk proposes a regret-based robust EMPC paradigm for nonlinear systems subject to unknown but bounded disturbance. The main motivation of the proposed work is the possible improvement of the economic performance when one considers the regret function as the objective function for the robust EMPC algorithm instead of the worst cost. The third part of this talk introduces an integrated framework that combines a Neural Network (NN) algorithm with an MPC scheme that can guarantee closed-loop stability in the presence of deception cyberattacks (e.g., min-max cyberattack). Both discrete-time and continuous-time nonlinear systems will be utilized throughout the talk to demonstrate the applicability and effectiveness of the proposed control methods. Finally, future research directions will be presented at the end of the talk.

The goal of our research is to understand the physical origin of these behaviors and trasform them into sustainable technologies that tackle contemporary problem of global interest, ranging from energy harvesting to clean water production, design of smart materials, biomedical applications, information security, artificial intelligence, global warming, and so on. Creating technologies from complex natural systems is a modern interdisciplinary research field that permeates many different scientific areas, ranging from physics to mathematics, to engineering and the theory of linguistics. This is a very challenging, yet very promising research. It involves the understanding of what we consider complex, which translates as something “involved, intricate, complicated, not easily understood or analyzed”. This sets the challenge of being able to understand the mechanisms of these systems and cross many different disciplines in order to constructively harness their properties into reproducible applications.

In this seminar, the science of MOCVD, the material science of InGaN, and the new-born InGaN-based red LED performance will be discussed. The three primary colors in light are RGB. Green and blue LEDs have been realized by using InGaN active region. The current red LEDs are based on AlGaAs or InGaP as the active region. If we can realize red LEDs by InGaN, it is possible to integrate RGB LEDs in a wafer. Such RGB integration is a breakthrough to develop the next displays, so-called, micro-LED displays that are the next after the OLED displays, and functional LED lightings.

This dissertation is devoted to the fabrication and electrical and optical characterization of a new class of III-nitride light-emitter known as superluminescent diode (SLD). SLD works in an amplified spontaneous emission (ASE) regime, and it combines several advantages from both LD and LED, such as droop-free, speckle-free, low-spatial coherence, broader emission, high-optical power, and directional beam. Here, SLDs were fabricated by a focused ion beam by tilting the front facet of the waveguide to suppress the lasing mode. They showed a high-power of 474 mW on c-plane GaN-substrate with a large spectral bandwidth of 6.5 nm at an optical power of 105 mW. To generate SLD-based white light, a YAG-phosphor-plate was integrated, and a CRI of 85.1 and CCT of 3392 K were measured. For the VLC link, SLD showed record high-data rates of 1.45 Gbps and 3.4 Gbps by OOK and DMT modulation schemes, respectively. Additionally, a widely single- and dual-wavelength tunability were designed using SLD-based external cavity (SLD-EC) configuration for a tunable blue laser source.

In this thesis, efficient solutions are sought out to fundamental problems in Electromagnetic (EM) imaging that determines the shape, location, and material properties of an (unknown) object of interest in an investigation domain from the scattered field measured away from it. The solution of an EM inverse scattering problem inherently poses two main challenges: (i) non-linearity, since the scattered field is a non-linear function of the material properties and (ii) ill-posedness, since the integral operator has a smoothing effect and the number of measurements is finite in dimension and they are contaminated with noise. The non-linearity is tackled incorporating a multitude of techniques (ranging from Born approximation (linear), inexact Newton (linearized) to complete non-linear iterative Landweber schemes) that can account for weak to strong scattering problems. The ill-posedness of the EM inverse scattering problem is circumvented by formulating the above methods into a minimization problem with a sparsity constraint, which assumes that the dimension of the unknown object relative to the investigation domain is much smaller. Numerical experiments, which are carried out using synthetically generated measurements, show that the images recovered by these sparsity-regularized methods are sharper and more accurate than those produced by existing methods. The methods developed in this work have potential application areas ranging from oil/gas reservoir engineering to biological imaging where sparse domains naturally exist.

Underwater wireless optical communication (UWOC) has attracted increasing interest for data transfer in various underwater activities, due to its order-of-magnitude higher bandwidth compared to conventional acoustic and radio-frequency (RF) technologies. Our studies pave the way for eventual applications of UWOC by relieving the strict requirements on PAT using UV-based NLOS. Such modality is much sought-after for implementing robust, secure, and high-speed UWOC links in harsh oceanic environments. This work was first started with the investigation of proper NLOS configurations. Path loss (PL) was chosen as a figure-of-merit for link performance. The effects of NLOS geometries, water turbidity, and transmission wavelength are evaluated by measuring the corresponding PL. The experimental results suggest that NLOS UWOC links are favorable for smaller azimuth angles, stronger water turbidity, and shorter transmission wavelength, as exemplified by the use of 375-nm wavelength. With the understanding of favorable NLOS UWOC configurations, we established a NLOS link consisting of an ultraviolet (UV) laser as the transmitter for enhanced light scattering and high sensitivity photomultiplier tube (PMT) as the receiver. A high data rate of 85 Mbit/s using on-off keying (OOK) in a 30-cm emulated highly turbid harbor water is demonstrated. Besides the underwater communication links, UV-based NLOS is also appealing to be the signal carrier for direct communication across wavy water-air interface. The trial results indicate link stability, which alleviates the issues brought about by the misalignment and mobility in harsh environments, paving the way towards real applications.

In this dissertation, we aim at theoretically studying and analyzing deep learning models. Since deep models substantially vary in their shapes and sizes, in this dissertation, we restrict our work to a single fundamental block of layers that is common in almost all architectures. The block of layers of interest is the composition of an affine layer followed by a nonlinear activation function and then lastly followed by another affine layer. We study this block of layers from three different perspectives. (i) An Optimization Perspective. We try addressing the following question: Is it possible that the output of the forward pass through the block of layers highlighted above is an optimal solution to a certain convex optimization problem? As a result, we show an equivalency between the forward pass through this block of layers and a single iteration of certain types of deterministic and stochastic algorithms solving a particular class of tensor formulated convex optimization problems.

Traditional backstepping approaches may struggle to asymptotically stabilize systems in pure feedback form, due to its inherent implicit equations. Approximation based designs only have a limited domain of validity and turn out sensitive to model uncertainty and disturbances. We propose a new design that circumvents the necessity of solving implicit algebraic equations by introducing new state variables. Additional augmentations to the backstepping  Lyapunov design lead to explicit expressions for the associated differential equations. The result is a dynamic state feedback, capable of asymptotically stabilizing the origin of a general class of nonlinear systems, based on just standard assumptions.