September 17, 2015 at 12:00 p.m. Dr. Min Wu, Professor of Electrical and Computer Engineering and a Distinguished Scholar-Teacher at the University of Maryland, College Park will be presenting "Exploring Power Network Signatures for Information Forensics". Speaker: Dr. Min Wu IEEE Fellow Professor of Electrical and Computer Engineering University of Maryland, College Park Day & Time: Thursday, September 17, 2015 12:00 p.m. - 1:00 p.m. Location: Room ENG105, Ryerson University 245 Church Street, Toronto, Ontario M5B 2K3 Click here to see the Map - Look for ENG Organizer: IEEE Signal Processing Society Electrical and Computer Engineering Graduate Program CASPAL Ryerson Contact: Prof. Xiao-Ping Zhang CASPAL (Communications and Signal Processing Applications Lab.) Department of Electrical and Computer Engineering, Ryerson University Abstract: Osama bin Laden’s video propaganda prompted numerous information forensic questions: given a video under question, when and where was it shot? Was the sound track captured together at the same time/location as the visual, or superimposed later? Similar questions about the time, location, and integrity of multimedia and other sensor recordings are important to provide evidence and trust in crime solving, journalism, infrastructure monitoring, smart grid management, and other informational operations. An emerging line of research toward addressing these questions exploits novel signatures induced by the power network. An example is the small random-like fluctuations of the electricity frequency known as the Electric Network Frequency (ENF), owing to the dynamic control process to match the electricity supplies with the demands in the grid. These environmental signatures reflect the attributes and conditions of the power grid and become naturally “embedded” into various types of sensing signals. They carry time and location information and may facilitate integrity verification of the primary sensing data. This talk will provide an overview of recent information forensics research on ENF carried out by our Media and Security Team (MAST) at University of Maryland, and discuss some on-going and open research issues in and beyond security applications. Biography:Min Wu is a Professor of Electrical and Computer Engineering and a Distinguished Scholar-Teacher at the University of Maryland, College Park. She received her Ph.D. degree in electrical engineering from Princeton University in 2001. At UMD, she leads the Media and Security Team (MAST), with main research interests on information security and forensics and multimedia signal processing. Her research and education have been recognized by a NSF CAREER award, a TR100 Young Innovator Award from the MIT Technology Review Magazine, an ONR Young Investigator Award, a Computer World "40 Under 40" IT Innovator Award, a University of Maryland Invention of the Year Award, an IEEE Mac Van Valkenburg Early Career Early Career Teaching Award, and several paper awards from IEEE SPS, ACM, and EURASIP. She was elected IEEE Fellow for contributions to multimedia security and forensics. Dr. Wu chaired the IEEE Technical Committee on Information Forensics and Security (2012-2013), and has served as Vice President - Finance of the IEEE Signal Processing Society (2010-2012) and Founding Chief Editor of the IEEE SigPort initiative (2013-2014). Currently, she is serving as Editor-in-Chief (2015-2017) of the IEEE Signal Processing Magazine and an IEEE Distinguished Lecturer.

Monday November 23, 2015 at 2:00 p.m. Professor Dinesh Kumar, RMIT University of Melbourne, Australia will be presenting “Intelligent Medical Devices for Affordable Healthcare”. Speaker: Professor Dinesh Kumar RMIT University Melbourne, Australia Day & Time: Monday, November 23, 2015 2:00 p.m. – 3:00 p.m. Location: Room ENG 106 George Vari Engineering and Computing Centre Ryerson University 245 Church Street Toronto Organizer: IEEE Toronto Signal Processing Chapter Contact: Sri Kirshnan, Email:krishnan@ryerson.ca Abstract: Technology is giving us longer and healthier lives. However, this comes at the cost, both, in terms of the research, infrastructure, and the cost of running the devices. Often, this makes many of these technologies only suitable for the wealthy societies. Prof Kumar will share his vision for devices and technologies for affordable healthcare. He will count the real cost of the devices, and suggest methods for making these more affordable without compromising the efficacy in improving the health outcomes. While automatic devices are often considered the demand of the wealthy, Kumar will show that these intelligent devices are the necessity for remote communities. Biography: Dr. Dinesh Kumar is a Professor of Electrical and Computer Engineering at RMIT University in Melbourne, Australia. Dr. Kumar did his B.E (Hons) and PhD in Biomedical Engineering from Indian Institute of Technology (IIT), Chennai and Delhi and has been researching in the field of developing affordable medical devices for 20 years. Dr. Kumar has been working towards developing intelligent devices and techniques that facilitate the user for early detection of disease, perform risk assessment of disease and provide assistive technologies for people who are frail or disabled. He has published over 350 refereed publications and his work has been cited over 5000 times.

Monday March 7th, 2016 at 3:00 p.m. Prof. Hamid Krim, Department of Electrical & Computer Engineering of North Carolina State University, will be presenting a distinguished lecture, “Convexity, Sparsity, Nullity and all that… in Data Analysis”. Speaker: Prof. Hamid Krim Department of Electrical & Computer Engineering North Carolina State University Raleigh, NC, US Day & Time: Monday, March 7th, 2016 3:00 p.m. – 4:00 p.m. Location: Room VIC300, Ryerson University 285 Victoria St, Toronto Map: https://goo.gl/maps/EAvPDLGSqrt Contact: Mehrnaz Shokrollahi Abstract: High dimensional data exhibit distinct properties compared to its low dimensional counterpart; this causes a common performance decrease and a formidable computational cost increase of traditional approaches. Novel methodologies are therefore needed to characterize data in high dimensional spaces. Considering the parsimonious degrees of freedom of high dimensional data compared to its dimensionality, we study the union-of-subspaces (UoS) model, as a generalization of the linear subspace model. The UoS model preserves the simplicity of the linear subspace model, and enjoys the additional stability to address nonlinear data. We show a sufficient condition to use l1 minimization to reveal the underlying UoS structure, and further propose a bi-sparsity model (R0Sure) as an effective algorithm, to recover the given data characterized by the UoS model from errors/corruptions. As an interesting twist on the related problem of Dictionary Learning Problem, we discuss the sparse null space problem (SNS). Based on linear equality constraint, it first appeared in 1986 and has since inspired results, such as sparse basis pursuit, we investigate its relation to the analysis dictionary learning problem, and show that the SNS problem plays a central role, and may naturally be exploited to solve dictionary learning problems. Substantiating examples are provided, and the application and performance of these approaches are demonstrated on a wide range of problems, such as face clustering and video segmentation. Biography: Hamid Krim received his BSc., MSc. and PhD. in Electrical Engineering. He was a member of Technical staff at AT&T Bell Labs, where he has conducted R&D in the areas of telephony and digital communication systems/subsystems. Following an NSF post-doctoral fellowship at Foreign Centers of Excellence, LSS/University of Orsay, Paris, France. He later joined the Laboratory for Information and Decision Systems, MIT, Cambridge, MA as a Research Scientist, where he was performing and supervising research. He is presently Professor of Electrical Engineering in the ECE department, North Carolina State University, Raleigh, leading the Vision, Information and Statistical Signal Theories and Applications group. His research interests are in statistical signal and image analysis, and mathematical modelling, with a keen emphasis on applied problems in classification and recognition using geometric and topological tools. He is currently serving on the IEEE editorial board of SP, and the TCs of SPTM and Big Data Initiative, as well as an AE of the new IEEE Transactions on SP on Information Processing on Networks, and of the IEEE SP Magazine. He is also one of the 2015-2016 Distinguished Lecturers of the IEEE SP Society.