Electrical & Computer Engineering Department
room: SL 165 (723 West Michigan street)
Edison da Silva, PhD/Federal University of Campina Grande
There are different structures of multilevel converters that can be utilized for machine drive and for renewable energy systems. This talk addresses some of these possibilities. In addition, a fault-tolerant system is presented based in the adequate reconfiguration of one type of multilevel inverter with the objective of continuous operation, even under fault, until a programmed stop.
Edison Roberto Cabral da Silva received the M.S.E.E. degree from the University of Rio de Janeiro, Rio de Janeiro, Brazil, in 1968, and the Dr. Eng. degree from the Université Paul Sabatier, Toulouse, France, in 1972. He is currently Professor Emeritus at the Federal University of Campina Grande, Brazil and Visiting Professor at the Federal University of Paraíba, Brazil. His current research interests include power electronic structures and motor drives.
ROOM: SL 165 (723 WEST MICHIGAN STREET)
Nabil Abdennadher, Ph.D.
Professor, Department of Computer Science and Engineering
University of Applied Sciences, Western Switzerland
The cloud computing ecosystem comprises hundreds of providers, offering diverse computing services, incompatible APIs, and significantly different pricing models. Cloud application management platforms hide the heterogeneity of the services and APIs, allowing, to varying degrees, portability between providers. These tools remove technical barriers to switching providers, but they do not provide a mechanism for evaluating the cost effectiveness of switching. This talk presents a decision support system, working within cloud application management platforms, that evaluates the costs of a customer’s applications using resources from different cloud service providers. The system (1) generalizes and normalizes multiple cloud pricing models and (2) gathers pricing data from cloud providers. These, in conjunction with the application resource consumption model, allow the cloud pricing module to estimate a price for the application for each cloud provider. To demonstrate this, the cloud pricing module has been integrated with the SlipStream multi-cloud application management platform, allowing its users to optimize their choice of provider(s).
Nabil Abdennadher received the Diploma in Engineering (Computer science) from Ecole Nationale des Sciences de l'Informatique (ENSI, Tunisia), and the Ph.D. degrees in Computer Science from University of Valenciennes in 1988 and 1991, respectively. He was an assistant professor at the University of Tunis II from 1992 to 1998 and a research assistant at the Computer Science Department of the Swiss Federal Institute of Technology (EPFL) from 1999 to 2000. In 2001, he joined the Department of Computer Engineering at the University of Applied Sciences, Western Switzerland (HESSO, hepia) as an assistant professor. In 2008, he became an associate professor and in 2017 he was promoted to full professor. He is currently head of both the inIT Research Institute and the ‘Large Scale Distributed Systems research group’. His major research interests include high performance computing, Internet of Things and urban computing. Nabil Abdennadher is currently leading several projects funded by the Swiss confederation (CTI and SNF). During the last years, he has participated in two FP7 European projects. He is also the founder of two companies in Tunisia and Switzerland.
To contact ECE seminars email firstname.lastname@example.org with ECE SEMINARS in the title
The tunnel field-effect transistor (TFET) is one of the candidates for beyond CMOS devices. The TFET devices are aimed at supply voltages less than 0.5 V which is enabled by the sub 60 mV sub-threshold swing. To design TFET circuits an analytical device model is required that accurately estimates terminal currents and charges versus terminal voltages. The Notre Dame TFET (NDTFET) model has been developed and coded in Verilog-A. The Verilog-A code has been made publicly available for the global design community at nanohub.org. The talk starts with a discussion of the electrical characteristics of TFETs followed by a short description of the most important features of the NDTFET model. Afterwards, several examples are presented that use the NDTEFT model to design analog and mixed-signal integrated circuits, such as, for example, amplifiers, analog-to-digital converters, ultra-low voltage digital circuits, ultra-low voltage oscillators and charge pumps.
Trond Ytterdal received his M.Sc. and Ph.D. degrees in electrical engineering from the Norwegian Institute of Technology in 1990 and 1995, respectively. He was employed as a research associate at the University of Virginia (1995-1996) and as a research scientist at Rensselaer Polytechnic Institute in Troy, New York (1996-1997). From 1997 to 2001 he worked as an ASIC designer at Nordic Semiconductor in Trondheim, Norway. Since 2001 he has been on the faculty of the Norwegian University of Science and Technology (NTNU), where he is a Professor at the Department of Electronic Systems. He has authored or co-authored more than 200 papers and is a co-developer of the circuit simulator AIM-Spice. His current research interests include design of analog integrated circuits, behavioral modeling and simulation of mixed-signal systems, modeling of nanoscale transistors and novel device structures. Prof. Ytterdal is a member of The Norwegian Academy of Technological Sciences and a Senior Member of IEEE.
One of the current focuses in precision medicine study is to fully characterize the phenotype associated heterogeneous characteristics shared by certain but unknown subsets of samples. We recently developed a probabilistic model based bi-clustering approach to comprehensively capture the heterogeneous characteristics shared by subset of samples in large scale omics data. With this computational capability, we developed computational frameworks for single cell data analysis and study of drug resistance mechanisms. Based on this capability, we also developed a method to predict the gain or loss of functions led by specific individual or collective effect of mutations by integrating cancer genomics and transcriptomics data.
Dr. Chi Zhang received his B.S degree in Mathematics from Peking University in 2010 and Ph.D. in Bioinformatics from the University of Georgia in 2015. Dr. Chi Zhang joined IU-School of Medicine in the August of 2016. His research focus including computational modeling of cancer micro-environment by using large scale omics data and developing novel computation methods to integrate multiple tissue level and single cell omics data types to understand the mechanism of cancer initiation, progression, metastasis and cancer tumor tissue’s resistance to certain therapies.
ROOM: SL 165 (723 WEST MICHIGAN STREET)
Russell Eberhart, Ph.D.
Professor Emeritus, Electrical & Computer Engineering, Purdue School of Engineering & Technology, IUPUI
This presentation discusses possible approaches to evolving intelligence in which blended intelligence and extended analog computing play roles. Deep learning and universal learning are briefly summarized. A definition of blended intelligence is proposed, followed by an introduction to extended analog computing. Implementing extended analog computing to achieve intelligence without algorithms is discussed. A revised definition of blended intelligence that includes algorithm-less computing is proposed.
Dr. Russell C. Eberhart is an independent consultant. He is also Professor Emeritus of Electrical and Computer Engineering at the Purdue School of Engineering and Technology, Indiana University Purdue University Indianapolis (IUPUI). He was formerly CTO of Phoenix Data Corporation, Indianapolis, Indiana. Prior to that, he served as Vice President and CTO of Computelligence, LLC. He received his Ph.D. from Kansas State University in electrical engineering. He is co-editor of a book on neural networks (1991), and co-author of Computational Intelligence PC Tools, published in 1996 by Academic Press. He is co-author of a book with Jim Kennedy and Yuhui Shi entitled Swarm Intelligence: Concepts to Implementations, published in August 2007 by Morgan Kaufmann/Elsevier. He was awarded the IEEE Third Millenium Medal. In January 2001, he became a Fellow of the IEEE. He was elected a Fellow of the American Institute for Medical and Biological Engineering in 2002. In 2011, he and Dr. James Kennedy were awarded the IEEE Pioneer Award, the highest award an IEEE society can bestow upon an individual for their development of particle swarm optimization. He has been granted six U.S. Patents, and has done ground-breaking work in applying swarm intelligence to human tremor analysis, sleep disorders medicine, evolutionary analog computing, logistics, spectrum warfare, and optimization of resource allocation. His current research is in blended intelligence, the melding of
DATE: November 11, 2016, noon
ROOM: SL 165 (723 WEST MICHIGAN STREET)
Department of Mechanical Engineering,
Indiana University-Purdue University Indianapolis
For more than a decade, there have been various attempts to improve motor vehicle safety by designing crashworthy structures using topology optimization methods. For mildly nonlinear problems, investigators have relied on simplifications of the dynamic multi-body interaction and the structure’s nonlinear behavior to approximate sensitivity coefficients. That approach has been shown to be of limited use in large-scale, industrial applications. This research proposes a new design algorithm inspired in the distributed control mechanisms that govern biological functional adaptation—or the means by which biological structures become better suited to their environment. In this numerical approach, sensor and actuator are distributed throughout a prescribed design domain using cellular automata (CAs). A desired global structural response is achieved by design rules that locally modify the material distribution around each CA. The results obtained by the CA control-based design algorithm show a dramatic improvement with respect to traditional topology optimization. Further improvement is also achieved through the introduction of a design and analysis of computer experiments (DACE) method. Results are demonstrated in the design of various lightweight, energy-absorbing vehicle including progressively folding thin-walled structures.
Andres Tovar, Ph.D. is an Assistant Professor of Mechanical Engineering and Adjunct Assistant Professor of Biomedical Engineering at IUPUI (2011-Present). He served as a Research Assistant Professor of Aerospace and Mechanical Engineering at the University of Notre Dame (2008-2011) and as an Associate Professor of Mechanical and Mechatronic Engineering at the National University of Colombia. Dr. Tovar received his B.S. in Mechanical Engineering and M.S. in Industrial Automation from the National University in 1995 and 2000, respectively. He earned his M.S. and Ph.D. in Mechanical Engineering from the University of Notre Dame in 2004 and 2005, respectively. Currently, Dr. Tovar is the director of the Engineering Design Research Lab and the Center for Additive Manufacturing Research at IUPUI. His main research areas include simulation-based design methodologies for large-scale, nonlinear applications in materials and mechanical components.
DATE: SEPTEMBER 23, 2016 11:00-12:00AM
ROOM: SL 165 (723 WEST MICHIGAN STREET)
Computer Information and Graphic Technology
Indiana University Purdue University Indianapolis
With rapid development of electronic devices, mobile devices and computer software and applications, huge volume of data is being generated every second. Big data has hit business, government, healthcare and scientific sectors. There is no double that value, competitiveness and efficiency are driven by data in all these sectors. In this talk, I will present three projects in the area of data mining and analytics that I have worked on. These projects landed in the text mining, email marketing effectiveness prediction and health data analytics. Challenges and successes in data extraction, data representation, data mining and data analytics methodologies will be discussed. In this talk, I will also talk about the future research projects in the health informatics domain.
Dr. Xiao Luo is currently an assistant professor at the department of Computer Information and graphic technology of IUPUI. Before joining IUPUI, she is a senior data engineer at Nova Scotia Health and Wellness in Canada. She obtained her PhD of Computer Science from Dalhousie University in 2009. She previously worked on multiple Natural Sciences and Engineering Research Council of Canada (NSERC) funded projects as a Postdoctoral Fellow at Dalhousie University and Saint Mary’s University. She also worked as a research officer for National Research Council of Canada and as a research computing consultant for Atlantic Research Data Center. Dr. Xiao Luo’s research interests include intelligent and cross disciplines data analytics, applied data mining and machine learning, big data representation and integration, and automated data extraction. She has published academic papers in international conferences and industrial white papers with different organizations. She served as a PC member or reviewer for different international conferences and journals, such as Journal of Data Mining and Knowledge Discovery, Journal of Discovery Science, Recent Advances on Computational Intelligence in Defense and Security, and so on.
Archived Seminars 2016-2017
Room: SL 108 (723 West Michigan street)
Xia Ning, Ph.D.
Computer & Information Science, Indiana University – Purdue University Indianapolis
We are in the era of Big Data, where the sheer volume, high velocity, data heterogeneity and complexity have introduced unprecedented challenges to data mining and machine learning research and their applications in real life. Effectively mining, learning and eventually creating values from Big Data become critical for many high-impact application domains.
In this talk, I will address some Big-Data issues for two specific application domains, i.e., Recommender Systems and Chemical Informatics. For Recommender Systems, I will introduce SLIM, a sparse linear method, that can achieve high prediction accuracy and has low computational requirements for Top-N recommendation from Big Data. I will also present a unified frame based on SLIM that enables effective exploration and incorporation of additional meta-data for better recommendation performance. For Chemical Informatics, I will present my work iSAR on predicting compound activities, where iSAR provides a guided-search approach over the entire chemical space (~10100 compounds) by leveraging available information from related protein targets. I will also briefly talk about my work on Bioinformatics and on-going projects on drug discovery problems.
BIOGRAPHY: Dr. Xia Ning is currently an assistant professor at the Computer & Information Science Department, Indiana University – Purdue University Indianapolis (IUPUI). Before joining IUPUI in 2014, she was a researcher at NEC Labs America. She got her Ph.D. degree from the Department of Computer Science & Engineering, University of Minnesota, Twin Cities, in 2012, under the supervision of Prof. George Karypis. Dr. Ning's primary research interests lie in Big Data analytics, data mining and machine learning, with specific applications in Recommender Systems, Chemical Informatics and Health Informatics. She has published papers on both high-impact journals (e.g., Journal of Chemical Informatics and Models) and top conferences (e.g., KDD, ICDM, SDM, CIKM, Recsys, WWW and AISTATS). She has also served as a PC member for top venues as well as a panelist for NSF. Dr. Ning holds 11 pending/granted patents with NEC Labs and Qualcomm, Inc. Her research is currently supported by NSF.
Date: Monday, June 6, 2016
Title: Optimum Functional Forms of Spoofing Attacks, Optimum Processing for Man-in- the-Middle Attacks and Interesting Implications to Unattacked Quantized Sensor Estimation Systems
Speaker: Rick S. Blum, Robert W. Wieseman Endowed Professor of Electrical Engineering, Electrical and Computer Engineering Dept., Lehigh University
Abstract: Estimation of an unknown deterministic vector from quantized sensor data is considered in the presence of spoofing and man-in-the-middle attacks which alter the data presented to several sensors. First, asymptotically optimum processing, which identifies and categorizes the attacked sensors into different groups according to distinct types of attacks, is outlined in the face of man-in-the-middle attacks. Necessary and sufficient conditions are provided under which utilizing the attacked sensor data will lead to better estimation performance when compared to approaches where the attacked sensors are ignored. Next, necessary and sufficient conditions are provided under which spoofing attacks provide a guaranteed attack performance in terms of the Cramer-Rao Bound (CRB) regardless of the processing the estimation system employs, thus defining a highly desirable attack. Interestingly, these conditions imply that, for any such attack when the attacked sensors can be perfectly identified by the estimation system, either the Fisher Information Matrix (FIM) for jointly estimating the desired and attack parameters is singular or the attacked system is unable to improve the CRB for the desired vector parameter through this joint estimation even though the joint FIM is nonsingular. It is shown that it is always possible to construct such a highly desirable attack by properly employing an attack vector parameter having a sufficiently large dimension relative to the number of quantization levels employed, which was not observed previously. For unattacked quantized estimation systems, a general limitation on the dimension of a vector parameter which can be accurately estimated is uncovered.
Biography: Rick S. Blum received a B.S.E.E from Penn State in 1984 and an M.S./Ph.D. in EE from the University of Pennsylvania in 1987/1991. From 1984 to 1991 he was with GE Aerospace. Since 1991, he has been at Lehigh. His research interests include signal processing for smart grid, communications, sensor networking, radar and sensor processing. He was an AE for IEEE Trans. on Signal Processing and for IEEE Communications Letters. He has edited special issues for IEEE Trans. on Signal Processing, IEEE Journal of Selected Topics in Signal Processing and IEEE Journal on Selected Areas in Communications. He was a member of the SAM Technical Committee (TC) of the IEEE Signal Processing Society. He was a member of the Signal Processing for Communications TC of the IEEE Signal Processing Society and is a member of the Communications Theory TC of the IEEE Communication Society. He was on the awards Committee of the IEEE Communication Society. Dr. Blum is a Fellow of the IEEE, a former IEEE Signal Processing Society Distinguished Lecturer, an IEEE Third Millennium Medal winner, a member of Eta Kappa Nu and Sigma Xi, and holds several patents. He was awarded an ONR Young Investigator Award and an NSF Research Initiation Award.
Location:Science & Engineering Building723 West Michigan St., SL 165Indianapolis, IN 46202
All Seminars are held in SL 165, unless otherwise noted.
ECE Faculty Research Interests
Adjunct Faculty Research Interests
PhD, Indiana University, 2015. Master of Science, Huazhong University of Science and Technology, 2009.Her research has been on exploring brain imaging genetics associations by developing efficient and scalable computational and bioinformatics approaches. Specifically, Yan is interested in the combination of machine learning and network science such that rich biological knowledge can be properly incorporated to guide the learning procedure and help yield more interpretable results.
He received his Ph.D. from University of Georgia in 2016. Research activities include high dimensional matrix low/local-rank representation, multi-omics/biomedical data integration, computational biology and bioinformatics, system biology and statistical learning.