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Phase Noise in LC Oscillators: From Basic Concepts to Advanced Topologies

BA 1240, 40 St George Street, University of Toronto

Wednesday November 23, 2016 at 2:10 p.m. Dr. Carlo Samori, Professor at Politecnico di Milano, Italy, will be presenting “Phase Noise in LC Oscillators: From Basic Concepts to Advanced Topologies”. Speaker: Dr. Carlo Samori Professor, Politecnico di Milano, Italy Day & Time: Wednesday, November 23, 2016 2:10 p.m. – 3:10 p.m. Location: BA 1240 Bahen Centre for Information Technology University of Toronto Contact: Dustin Dunwell Organizer: Solid State Circuit Society Abstract: Despite having been the subject of extensive study in last 20 years for the solid-state IC community, the phase noise in voltage-controlled oscillators (VCOs) is still today an important research subject. The main reason is that phase noise is one of the main issues encountered during the design of a transceiver whose understanding is an essential know-how for an RF designer. A second reason is that the intrinsic time-variant nature of VCOs makes these circuits difficult to analyze, therefore new topologies are often proposed, claiming advantages in term of phase noise and/or dissipation that in several cases are hard both to understand and verify without a direct implementation. This lecture will start from the basics of LC VCOs and of phase noise. The phase noise will be calculated in basic topologies and the fundamental trade-off with power dissipation and tuning range will be highlighted. The lecture then will continue by presenting advance VCO topologies, showing how these circuits typically aim to enhance either the current or the voltage efficiency, in order to improve the phase noise vs. power dissipation trade-off. Biography: Carlo Samori received the Ph.D. in electrical engineering in 1995, at the Politecnico di Milano, Italy, where he is now a professor. His research interests are in the area of RF circuits, in particular of design and analysis of VCOs and high performance frequency synthesizers. He has collaborated with several semiconductor companies. He is a co-author of more than 100 papers and of the book Integrated Frequency Synthesizers for Wireless Systems (Cambridge University Press, 2007). Prof. Samori has been a member of the Technical Program Committee of the IEEE International Solid-State Circuits Conference and he is a member of the European Solid-State Circuits Conference. He has been Guest Editor for the December 2014 issue of the Journal of Solid-State Circuits.

AI-Based Software Defect Predictors: Applications and Benefits and Lessons Learned

KHE 225, Ryerson University, 340 Church Street, Toronto

Monday November 21, 2016 at 11:00 a.m. Dr. Ayse Basar Bener, professor and director of Data Science Laboratory at Ryerson University, will be presenting “AI-Based Software Defect Predictors: Applications and Benefits and Lessons Learned”. Speaker: Dr. Ayse Basar Bener Professor, Director of Data Science Laboratory, Department of Mechanical and Industrial Engineering Director of Big Data, Office of Provost and Vice President Academic Ryerson University Day & Time: Monday, November 21, 2016 11:00 a.m. – 12:00 p.m. Location: KHE 225, Ryerson University, 340 Church Street, Toronto Contact: Maryam Davoudpour Organizer: WIE, Magnetics, Measurement/Instrumentation-Robotics, Computer Science Department of Ryerson University Abstract: Software analytics guide practitioners in decision making throughout the software development process. In this context, prediction models can help managers efficiently organize their resources and identify problems by analyzing patterns on existing project data in an intelligent and meaningful manner. In this talk I will share my experiences building and deploying AI (machine learning) models in software organizations over 15 years. We have encountered similar data analytics patterns in diverse organizations and in different problem cases. I will give examples from deployed projects and discuss these patterns following a “software analytics” framework: problem identification, data collection, descriptive statistics, and decision making. Biography: Dr. Ayse Basar Bener is a professor and the director of Data Science Laboratory (DSL) in the Department of Mechanical and Industrial Engineering, Ryerson University. She is the director of Big Data in the Office of Provost and Vice President Academic at Ryerson University. She is a faculty research fellow of IBM Toronto Labs Centre for Advance Studies, and affiliate research scientist in St. Michael’s Hospital in Toronto. Her current research focus is big data applications to tackle the problem of decision-making under uncertainty by using machine learning methods and graph theory to analyze complex structures in big data to build recommender systems and predictive models. She is a member of AAAI, INFORMS, AIS, and senior member of IEEE.

Operational-Log Analysis for Big Data Systems: Challenges and Solutions

Room: ENG 288, 245 Church Street, Toronto, Ontario M5B 2K3

Friday November 18, 2016 at 12:00 p.m. Dr. Andriy Miranskyy, Assistant Professor at the Department of Computer Science, Ryerson University, will be presenting “Operational-Log Analysis for Big Data Systems: Challenges and Solutions”. Speaker: Dr. Andriy Miranskyy Assistant Professor, Department of Computer Science, Ryerson University Day & Time: Friday, November 18, 2016 12:00 p.m. – 1:00 p.m. Location: George Vari Centre for Computing and Engineering Ryerson University Room: ENG 288 245 Church Street, Toronto, Ontario M5B 2K3 Map – http://www.ryerson.ca/maps – Look for ENG Registration: Registration is free, but space is limited. Please register via this link: http://tinyurl.com/systemsEvent Organizers: IEEE Toronto Systems Chapter, Alexei Botchkarev albot@ieee.org IEEE Toronto WIE, Magnetics, Measurement/Instrumentation-Robotics and Computer Science Department of Ryerson University IEEE Toronto WIE Chair: Maryam Davoudpour maryam.davoudpour@ieee.org Abstract: Big data systems (BDSs) are complex, consisting of multiple interacting hardware software components, such as distributed compute nodes, networking, databases, middleware, business intelligence layer, and high availability infrastructure. Any of these components can fail. Finding the failures’ root causes is extremely laborious. Analysis of BDS-generated logs can speed up this process. The logs can also help improve testing processes, detect security breaches, customize operational profiles, and aid with any other tasks requiring runtime-data analysis. However, practical challenges hamper log analysis tools’ adoption. The logs emitted by a BDS can be thought of as big data themselves. When working with large logs, practitioners face seven main issues: scarce storage, unscalable log analysis, inaccurate capture and replay of logs, inadequate log-processing tools, incorrect log classification, a variety of log formats, and inadequate privacy of sensitive data. This talk describes the challenges and practical solutions faced while building and institutionalizing dynamic analysis tools in the industry. Biography: Andriy Miranskyy is an assistant professor at the Department of Computer Science, Ryerson University. His research interests are in the area of mitigating risk in software engineering, focusing on software quality assurance, program comprehension, software requirements, project risk management, Big Data, and Green IT. Andriy received his Ph.D. in Applied Mathematics at the University of Western Ontario. He has 17 years of software engineering experience in information management and pharmaceutical industries. Prior to joining Ryerson, Andriy worked as a software engineer in the IBM Information Management division at the IBM Toronto Software Laboratory; currently, he is the Faculty Fellow of the IBM Centre for Advanced Studies. He has served as Guest Editor for a special edition of IEEE Software as well as organizer, committee member, and reviewer for several software engineering workshops and conferences.