Tuesday November 21, 2018 @ 1:00pm

Armstrong Hall 154

Abstract

The research talk presents a systematic study on optimizing the efficiency of ZnO/ZnTe solar cells. Solar Cell Capacitance Simulator was used to perform the analysis. The cell structure under study consists of ZnO/CdS/ZnTe deposited on Indium Tin Oxide covered glass. Three main parameters were optimized: (a) the layer thickness of the absorbent film (ZnTe) and the window layer (ZnO) (b) the lattice mismatch between the metal contact and the window layer (ZnO) (c) adding a buffer layer between the absorbent and the window layers. It was concluded that the highest efficiency can be obtained with an absorbent layer greater than 15 μm, with using Aluminum as the metal contact since it has the lowest lattice mismatch with ZnO, and with adding a CdS buffer layer between ZnO and ZnTe. All the above results were verified using SCAPS software. The highest efficiency obtained was 17.25 % with a short current density (Jsc) 9.8 mA/cm2, open circuit voltage (Voc), 1.89 V and Fill Factor of 92.74 %.
The entrepreneurship talk presents a backpack that was developed by Dr. Kabalan and her team of undergraduate students. The backpack is equipped with a solar panel and a battery that charges by day and allows students to do schoolwork in the safety of their homes at night. She will present her team’s efforts on prototyping the product, testing it and starting a fundraising campaign to send more of the backpacks to refugee camps in Lebanon.

About the Speaker

Dr. Kabalan studied properties of semiconducting materials for photovoltaics applications at Harvard University. She completed her dissertation at Villanova University where she worked on the application of superlattice structures in solar cells. Her research focuses on integrating nanotechnology structures in electronic devices. Currently she is an assistant professor in the electrical engineering where she is teaching electrical engineering courses and working on improving the efficiency of ZnTe/ZnO solar cells. She is also interested in humanitarian technology. She is working on developing solar backpacks for students who lack access to electric power around the world. Outside the lab and the classroom, Dr. Kabalan loves to travel and to immerse herself in different cultural experiences.

This will count for ENG09x Technical Seminar credit.

Friday November 17, 2018 @ 1:00pm

Armstrong Hall 154

Abstract

A major research theme in electronics is to reduce the amount of energy/heat dissipated in a binary operation since that will allow continued downscaling of electronic devices in accordance with Moore’s law. Unlike charge-based CMOS technology, spin-based nanomagnetic technology, that envisages switching the bistable magnetization of a shape anisotropic nanomagnet for a binary operation, has the potential to achieve ultralow energy dissipation because switching does not involve moving electrical charge. However, this has never been realized because most magnet-switching schemes involve generating a current to produce either a magnetic field, or spin-transfer torque, or domain wall motion. Current-induced switching invariably dissipates an exorbitant amount of energy in the switching circuit that nullifies any energy advantage that a magnet may have over a transistor. In this work, Dr. Biswas proposes an extremely energy-efficient non-volatile memory technology where bits are written into cells with voltage generated stress. Dr. Biswas has developed a universal NAND logic device that satisfies the essential characteristics of a Boolean logic gate and a bit comparator, which happens to be all straintronic, yet reconfigurable. This work has wide application in straintrionic spin neuron for image processing, neural computing and hardware-based signal processing and in energy-efficient MRAM technology in a properly scaled architecture.

About the Speaker

Dr. Ayan Kumar Biswas is currently a postdoctoral associate in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), Pittsburgh. He received his Ph.D. from Virginia Commonwealth University (VCU), Richmond, Virginia in 2016 and a Bachelor’s degree from Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh in 2011, both in in Electrical and Electronic Engineering. His research concentrates on the development of energy-efficient nanomagnetic/spintronic technologies beyond complementary metal-oxide-semiconductor (CMOS) technologies for future generation computing. His expertise in the field encompasses both the theory and the experiment of spintronic devices and their integration in existing CMOS circuits.
Dr. Biswas has authored and co-authored 11 journal papers, 8 peer-reviewed conference papers, one book chapter and holds a US-patent. He was awarded VCU school of engineering graduate research award, VCU phi kappa phi scholarship, ECE graduate research award, APS GMAG travel award. He is a reviewer for 5 IEEE journals/conferences.

This will count for ENG09x Technical Seminar credit.

Monday November 20, 2018 @ 1:00pm

Armstrong Hall 154

Abstract

Multimedia data is characterized by its massive volume and high-dimensionality, which dominates the data deluge in today’s Big Data era. While providing detailed information for target services, they also impose significant challenges in that current capability for processing such massive data volume, such as acquisition, transmission, reconstruction, and feature extraction, is far less than the power of data generation. In this talk, I will present my efforts in developing theoretical frameworks to efficiently process these “Multimedia Big Data”, by exploring their intrinsic low-dimensional structure such as sparsity and low-rankness. First, I will present the reconstruction of compressed-sensed multiview videos for pervasive 3D and free viewpoint video applications. The proposed method successfully exploits the spatiotemporal sparsity in the 4D video data via motion- and disparity-compensated total-variation minimization. In the second part of the talk, my focus shifts to compressed-sensed- domain L1-norm principal component analysis (L1-PCA) for outlier-resilient video surveillance. This work posed a new theoretical basis for compressed-sensed- domain robust low-rank subspace learning and opened up a new application area for L1-PCA theory. Finally, I will briefly discuss the potential of adapting the developed tools to address new problems in cybersecurity, cloud-based multimedia services, and wireless communications.

About the Speaker

Ying Liu is a Research Scientist and a Lecturer in the Department of Electrical Engineering, The State University of New York, University at Buffalo (SUNY at Buffalo). Her research lies at the intersection of signal processing, machine learning, and optimization, with applications in Multimedia Big Data. She obtained her Ph.D. and M.S. degree in Electrical Engineering from SUNY at Buffalo in Sept. 2012 and June 2008, respectively, and a B.S. degree in Telecommunications Engineering from Beijing University of Posts and Telecommunications (BUPT), Beijing, China in 2006. She worked in ARCON Corporation, Waltham, MA in 2013 as a Staff Engineer, and in the Multimedia Communications Lab at Illinois Institute of Technology (IIT), Chicago, IL as a Senior Research Associate from July 2013 to Oct. 2014. Dr. Liu has 19 publications in prestigious journals and flagship conferences such as IEEE Trans. Circuits and Systems for Video Technology (TCSVT), IEEE Trans. Multimedia (TMM), SPIE Journal of Electronic Imaging (JEI), ICASSP, ICIP, SPIE Conf. DSS, and Asilomar Conference. She frequently serves as a reviewer for top-ranked journals such as TCSVT, TMM, IEEE Access, Neurocomputing, Ad Hoc Networks, Elsevier Journal of Visual Communication and Image Representation (JVCI), and JEI. She also served as the TPC member of multiple international conferences.

This will count for ENG09x Technical Seminar credit.