UMBC Eminent Scholar Program

Smart Distribution Systems

Dr. Karen Butler-Purry
Texas A&M University

11:00-12:00 Thursday, 13 March 2014, ITE 325B

Smart Grid refers to the computerizing of the grid via the addition of monitoring, analysis, control, and communication capabilities to improve its reliability, efficiency, and security. Smart meter devices, that include sensors to gather data and two-way digital communication between the smart meters in the field and the utility’s grid operations center, are associated with the grid. The smart grid can take advantage of new technologies, such as plug-in hybrid electric vehicles, various forms of renewable and conventional distributed generation, lighting management systems, automation technology that lets the utility adjust and control each individual device or millions of devices from a central location, and many more. This presentation will discuss some of the current research projects being investigated by Butler-Purry’s group on smart distributions systems, in grid or island operation. One project investigates the impact of cyber attacks on the operation of smart distribution systems. The second project developed two new approaches to enhance the protection of smart distribution systems. One approach uses smart meters during distribution planning to improve selectivity of protection, and the other approach uses smart meters during operation to improve the sensitivity of protection.

Karen L. Butler-Purry, PhD, PE, is Associate Provost for Graduate and Professional Studies and Professor in the Department of Electrical and Computer Engineering at Texas A&M University where she has served on the faculty since 1994. She received a B.S. in Electrical Engineering in 1985 from Southern University in Baton Rouge, Louisiana. She was awarded a M.S. degree in 1987 from the University of Texas at Austin and a Ph.D. in Electrical Engineering in 1994 from Howard University in Washington, D.C. Her research interests are in the areas of protection and control of distribution systems and isolated power systems such as all electric power systems for ships, mobile grids, and microgrids; cybersecurity protection; and intelligent systems for equipment deterioration and fault diagnosis.

Host: Prof. Gymama Slaughter, Sorry, you need javascript to view this email address.

UMBC Eminent Scholar Program

Smart Distribution Systems

Dr. Karen Butler-Purry
Texas A&M University

11:00-12:00 Thursday, 13 March 2014, ITE 325B

Smart Grid refers to the computerizing of the grid via the addition of monitoring, analysis, control, and communication capabilities to improve its reliability, efficiency, and security. Smart meter devices, that include sensors to gather data and two-way digital communication between the smart meters in the field and the utility’s grid operations center, are associated with the grid. The smart grid can take advantage of new technologies, such as plug-in hybrid electric vehicles, various forms of renewable and conventional distributed generation, lighting management systems, automation technology that lets the utility adjust and control each individual device or millions of devices from a central location, and many more. This presentation will discuss some of the current research projects being investigated by Butler-Purry’s group on smart distributions systems, in grid or island operation. One project investigates the impact of cyber attacks on the operation of smart distribution systems. The second project developed two new approaches to enhance the protection of smart distribution systems. One approach uses smart meters during distribution planning to improve selectivity of protection, and the other approach uses smart meters during operation to improve the sensitivity of protection.

Karen L. Butler-Purry, PhD, PE, is Associate Provost for Graduate and Professional Studies and Professor in the Department of Electrical and Computer Engineering at Texas A&M University where she has served on the faculty since 1994. She received a B.S. in Electrical Engineering in 1985 from Southern University in Baton Rouge, Louisiana. She was awarded a M.S. degree in 1987 from the University of Texas at Austin and a Ph.D. in Electrical Engineering in 1994 from Howard University in Washington, D.C. Her research interests are in the areas of protection and control of distribution systems and isolated power systems such as all electric power systems for ships, mobile grids, and microgrids; cybersecurity protection; and intelligent systems for equipment deterioration and fault diagnosis.

Host: Prof. Gymama Slaughter, Sorry, you need javascript to view this email address.

The UMBC Council of Computing Majors will hold its first hands-on Raspberry Pi workshop from 2:00-4:00 this Friday, March 7, in ITE240.

The Raspberry Pi is a $35 credit-card-sized, single-board computer that runs a version of Unix. Originally developed for teaching computer programming to children, it is now being used in many useful and exciting applications, from near-space weather balloons to baby monitors to media servers. The possibilities are only limited by your imagination.

The initial workshop will cover the Raspberry Pi, its Raspbian Unix OS, and how to program it using Python for real-world applications. There will be 20 Pi computers for participants to use. The workshop is designed so freshman and non-computer science majors can attend and participate. If you know anyone who would be interested in attending, please send them the link and information!

Space is limited, so sign up to reserve a seat.  Intermediate and advanced workshops will follow later in the semester. See the Pi FAQ for general information on the Pi and Raspbian for information on its operating system.

For more information, contact CCM president Austin Murdock (Sorry, you need javascript to view this email address. ).

The UMBC Council of Computing Majors will hold its first hands-on Raspberry Pi workshop from 2:00-4:00 this Friday, March 7, in ITE240.

The Raspberry Pi is a $35 credit-card-sized, single-board computer that runs a version of Unix. Originally developed for teaching computer programming to children, it is now being used in many useful and exciting applications, from near-space weather balloons to baby monitors to media servers. The possibilities are only limited by your imagination.

The initial workshop will cover the Raspberry Pi, its Raspbian Unix OS, and how to program it using Python for real-world applications. There will be 20 Pi computers for participants to use. The workshop is designed so freshman and non-computer science majors can attend and participate. If you know anyone who would be interested in attending, please send them the link and information!

Space is limited, so sign up to reserve a seat.  Intermediate and advanced workshops will follow later in the semester. See the Pi FAQ for general information on the Pi and Raspbian for information on its operating system.

For more information, contact CCM president Austin Murdock (Sorry, you need javascript to view this email address. ).

Learning and Optimization for Complex Dynamic Networks: The
Cases of Future Power Systems and Cognitive Wireless Networks

Dr. Seung-Jun Kim, University of Minnesota

11:45-12:45 Tuesday, 11 March 2014, ITE325b, UMBC

With enormous growth in sensing and communication capabilities as well as processing power to analyze collected data, we are witnessing exciting opportunities in diverse disciplines to study complex interactions of networked entities. The overarching theme is to explore cutting-edge computational intelligence tools from signal processing, machine learning, optimization, and control to make sense of amassed data and exploit complex interactions to make significant real-world impacts. In this talk, I will make cases for two prime examples, namely, future power systems and cognitive wireless networks. The role of contemporary tools including online learning, sparse and low-dimensional models, distributed and robust algorithms, will be emphasized.

Seung-Jun Kim received his B.S. and M.S. degrees from Seoul National University in Seoul, Korea in 1996 and 1998, respectively, and his Ph.D. from the University of California at Santa Barbara in 2005, all in electrical engineering. From 2005 to 2008, he worked for NEC Laboratories America in Princeton, New Jersey, as a Research Staff Member. He is currently with the Department of Electrical and Computer Engineering and the Digital Technology Center at the University of Minnesota, where he is a Research Associate Professor and a Research Associate. His research interests lie in applying signal processing, optimization, and machine learning techniques to various application domains including wireless communication and networking and smart power grids.

Host: Tinoosh Mohsenin, Sorry, you need javascript to view this email address.

Learning and Optimization for Complex Dynamic Networks: The
Cases of Future Power Systems and Cognitive Wireless Networks

Dr. Seung-Jun Kim, University of Minnesota

11:45-12:45 Tuesday, 11 March 2014, ITE325b, UMBC

With enormous growth in sensing and communication capabilities as well as processing power to analyze collected data, we are witnessing exciting opportunities in diverse disciplines to study complex interactions of networked entities. The overarching theme is to explore cutting-edge computational intelligence tools from signal processing, machine learning, optimization, and control to make sense of amassed data and exploit complex interactions to make significant real-world impacts. In this talk, I will make cases for two prime examples, namely, future power systems and cognitive wireless networks. The role of contemporary tools including online learning, sparse and low-dimensional models, distributed and robust algorithms, will be emphasized.

Seung-Jun Kim received his B.S. and M.S. degrees from Seoul National University in Seoul, Korea in 1996 and 1998, respectively, and his Ph.D. from the University of California at Santa Barbara in 2005, all in electrical engineering. From 2005 to 2008, he worked for NEC Laboratories America in Princeton, New Jersey, as a Research Staff Member. He is currently with the Department of Electrical and Computer Engineering and the Digital Technology Center at the University of Minnesota, where he is a Research Associate Professor and a Research Associate. His research interests lie in applying signal processing, optimization, and machine learning techniques to various application domains including wireless communication and networking and smart power grids.

Host: Tinoosh Mohsenin, Sorry, you need javascript to view this email address.

From Terabyte-Sized Stem Cell Images to Knowledge

Peter Bajcsy, PhD
Information technology Laboratory
National Institute of Standards and Technology

10:00am Monday, 10 March 2014, ITE 346, UMBC

This talk will present the computational challenges and approaches to knowledge discovery from terabyte-sized images. The motivation comes from experimental systems for imaging and analyzing human pluripotent stem cell cultures at the spatial and temporal coverage of colonies that lead to terabyte-sized image data. The objective of such an unprecedented cell study is to characterize pluripotency of stem cell colonies over time at high statistical significance in order to understand the stem cell culture quality parameters and guide a repeatable growth of high quality stem cell colonies. The terabyte- sized images represented a stem cell line that was engineered to produce green fluorescent protein (GFP) under the influence of Oct4 promoter and then imaged in a mosaic of contiguous frames covering approximately 180 square millimeters, over five days under both phase contrast and GFP channels.

We overview multiple computer and computational science problems related to correcting (flat-field, dark current and background), stitching, segmenting, tracking, re-projecting and then representing large images for interactive visualization and sampling in a web browser. We researched extensions to Amdahl’s law for Map-Reduce computations, established benchmarks for image processing on a Hadoop platform, and introduced cluster node utilization coefficients for modeling memory demanding computations running on a computer cluster/cloud. The theoretical aspects of algorithmic complexity and cluster utilization at terabyte scale are extended to the experimental aspects of efficient image representation and client-server workload distribution in the context of visualization interactivity and image sampling. We report such experimental results for the NIST extensions to the Deep Zoom paradigm. The presentation will conclude with illustrations of enabled stem cell discoveries and collaboration opportunities to create a reference resource not only for cell biologists but also for computer scientists focusing on terabyte scale image analyses.

Peter Bajcsy received his Ph.D. in Electrical and Computer Engineering in 1997 from the University of Illinois at Urbana-Champaign and a M.S. in Electrical and Computer Engineering in 1994 from the University of Pennsylvania. He worked for machine vision, government contracting, and research and educational institutions before joining the National Institute of Standards and Technology (NIST) in 2011. At NIST, he has been leading a project focusing on the application of computational science in biological metrology, and specifically stem cell characterization at very large scales. Peter’s area of research is large-scale image-based analyses and syntheses using mathematical, statistical and computational models while leveraging computer science fields such as image processing, machine learning, computer vision, and pattern recognition. He has co-authored more than more than 24 journal papers and eight books or book chapters, and close to 100 conference papers.

Host: Yelena Yesha (Sorry, you need javascript to view this email address. )

From Terabyte-Sized Stem Cell Images to Knowledge

Peter Bajcsy, PhD
Information technology Laboratory
National Institute of Standards and Technology

10:00am Monday, 10 March 2014, ITE 346, UMBC

This talk will present the computational challenges and approaches to knowledge discovery from terabyte-sized images. The motivation comes from experimental systems for imaging and analyzing human pluripotent stem cell cultures at the spatial and temporal coverage of colonies that lead to terabyte-sized image data. The objective of such an unprecedented cell study is to characterize pluripotency of stem cell colonies over time at high statistical significance in order to understand the stem cell culture quality parameters and guide a repeatable growth of high quality stem cell colonies. The terabyte- sized images represented a stem cell line that was engineered to produce green fluorescent protein (GFP) under the influence of Oct4 promoter and then imaged in a mosaic of contiguous frames covering approximately 180 square millimeters, over five days under both phase contrast and GFP channels.

We overview multiple computer and computational science problems related to correcting (flat-field, dark current and background), stitching, segmenting, tracking, re-projecting and then representing large images for interactive visualization and sampling in a web browser. We researched extensions to Amdahl’s law for Map-Reduce computations, established benchmarks for image processing on a Hadoop platform, and introduced cluster node utilization coefficients for modeling memory demanding computations running on a computer cluster/cloud. The theoretical aspects of algorithmic complexity and cluster utilization at terabyte scale are extended to the experimental aspects of efficient image representation and client-server workload distribution in the context of visualization interactivity and image sampling. We report such experimental results for the NIST extensions to the Deep Zoom paradigm. The presentation will conclude with illustrations of enabled stem cell discoveries and collaboration opportunities to create a reference resource not only for cell biologists but also for computer scientists focusing on terabyte scale image analyses.

Peter Bajcsy received his Ph.D. in Electrical and Computer Engineering in 1997 from the University of Illinois at Urbana-Champaign and a M.S. in Electrical and Computer Engineering in 1994 from the University of Pennsylvania. He worked for machine vision, government contracting, and research and educational institutions before joining the National Institute of Standards and Technology (NIST) in 2011. At NIST, he has been leading a project focusing on the application of computational science in biological metrology, and specifically stem cell characterization at very large scales. Peter’s area of research is large-scale image-based analyses and syntheses using mathematical, statistical and computational models while leveraging computer science fields such as image processing, machine learning, computer vision, and pattern recognition. He has co-authored more than more than 24 journal papers and eight books or book chapters, and close to 100 conference papers.

Host: Yelena Yesha (Sorry, you need javascript to view this email address. )

CSEE Professor Tülay Adali and Professor Kelly Westlake from the University of Maryland School of Medicine received an award from the joint UMBC-UMB Research and Innovation Partnership Seed Grant Program for a project  that ultimately will improve the recovery of stroke victims. The new joint UMBC-UMB Seed Grant Program pairs primary investigators from UMBC and the University of Maryland, Baltimore to conduct research as a team. Successful partners are offered research funding of up to $75,000 over twelve months to pursue their collaboration.

Nearly 800,000 U.S. citizens have a stroke each year, making it the leading cause of long-term disability. Treatment for stroke victims is mainly targeted towards residual functional deficits, especially regaining hand functions. Their project (“Independent Vector Analysis to Investigate Cognitive Neural Networks after Stroke: A Comparison between Two Rehabilitation Interventions”) will have a direct impact on stroke rehabilitation through objective evaluation of the two main treatment paradigms currently in use: unimanual (involving one hand) and bimanual (involving both hands) training. The evaluation will use the new class of medical image analysis techniques, independent vector analysis (IVA) algorithms, developed by Dr. Adali and her research group. The new class of IVA algorithms successfully captures subject variability and perform significantly better than the approaches traditionally used for the problem.

The initial results of the project demonstrate the advantages using IVA for the problem and will be presented this month at the 48th Annual Conference on Information Sciences and Systems in Princeton, NJ. The PIs are preparing two journal submissions, one based on the methods developed for the task and a second one emphasizing clinical significance of the results. These results will also provide the preliminary data for the proposal that Professors Adali and Westlake plan to submit to the NIH later this year.

CSEE Professor Tülay Adali and Professor Kelly Westlake from the University of Maryland School of Medicine received an award from the joint UMBC-UMB Research and Innovation Partnership Seed Grant Program for a project  that ultimately will improve the recovery of stroke victims. The new joint UMBC-UMB Seed Grant Program pairs primary investigators from UMBC and the University of Maryland, Baltimore to conduct research as a team. Successful partners are offered research funding of up to $75,000 over twelve months to pursue their collaboration.

Nearly 800,000 U.S. citizens have a stroke each year, making it the leading cause of long-term disability. Treatment for stroke victims is mainly targeted towards residual functional deficits, especially regaining hand functions. Their project (“Independent Vector Analysis to Investigate Cognitive Neural Networks after Stroke: A Comparison between Two Rehabilitation Interventions”) will have a direct impact on stroke rehabilitation through objective evaluation of the two main treatment paradigms currently in use: unimanual (involving one hand) and bimanual (involving both hands) training. The evaluation will use the new class of medical image analysis techniques, independent vector analysis (IVA) algorithms, developed by Dr. Adali and her research group. The new class of IVA algorithms successfully captures subject variability and perform significantly better than the approaches traditionally used for the problem.

The initial results of the project demonstrate the advantages using IVA for the problem and will be presented this month at the 48th Annual Conference on Information Sciences and Systems in Princeton, NJ. The PIs are preparing two journal submissions, one based on the methods developed for the task and a second one emphasizing clinical significance of the results. These results will also provide the preliminary data for the proposal that Professors Adali and Westlake plan to submit to the NIH later this year.