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Events for August 29, 2011
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Meet USC: Admission Presentation, Campus Tour, & Engineering Talk
Mon, Aug 29, 2011
Viterbi School of Engineering Undergraduate Admission
Receptions & Special Events
This half day program is designed for prospective freshmen and family members. Meet USC includes an information session on the University and the Admission process; a student led walking tour of campus and a meeting with us in the Viterbi School. Meet USC is designed to answer all of your questions about USC, the application process and financial aid. Reservations are required for Meet USC. This program occurs twice, once at 8:30 a.m. and again at 12:30 p.m. Please visit https://esdweb.esd.usc.edu/unresrsvp/MeetUSC.aspx to check availability and make an appointment. Be sure to list an Engineering major as your "intended major" on the webform!
Location: Ronald Tutor Campus Center (TCC) - USC Admission Office
Audiences: Everyone Is Invited
Contact: Viterbi Admission
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On Campus Freshmen Admission Interviews continue...Mon, Aug 29, 2011
Viterbi School of Engineering Undergraduate Admission
Receptions & Special Events
Personal Admission Interviews are available to freshmen applicants throughout the Fall practically every weekday until December 9, 2011. Freshman applicant interviews are not required as part of the admission process, however we would like to meet as many of our applicants as possible. All interview appointments are scheduled online. http://viterbi.usc.edu/admission/freshman/interviews/
Audiences: Freshmen Applicants for Fall 2012
Contact: Viterbi Admission
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Software Safety (SFT)
Mon, Aug 29, 2011 @ 08:00 AM - 04:00 PM
Aviation Safety and Security Program
University Calendar
Software requires special attention in system planning, architecture, design and test. This course presents philosophies and methods of developing and analyzing software and highlights managing a software safety program. Software design principles will be taught to create programs that are fault tolerant and acceptable safe.
Location: Aviation Safety & Security Campus
Audiences: Aviation Professionals
Contact: Harrison Wolf
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Aviation Security Program Management (AVSEC)
Mon, Aug 29, 2011 @ 08:00 AM - 04:00 PM
Aviation Safety and Security Program
University Calendar
This course is designed for individuals responsible for managing and implementing aviation security measures at medium to small size aircraft operators, all airports and Indirect Air Carriers (IAC's). This course demonstrates how to apply the SMS principles in the aviation security environment.
Location: Aviation Safety & Security Campus
Audiences: Aviation Professionals
Contact: Harrison Wolf
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Seminars in Biomedical Engineering
Mon, Aug 29, 2011 @ 12:30 PM - 01:50 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Jason Kutch, Ph.D., Brent Liu, Ph.D., Biokinesiology, BME & Radiology, USC
Talk Title: BME Research
Abstract: Jason Kutch, Ph.D., Talk title: The Applied Mathematical Physiology Lab (AMPL): Unravelling compromised neuromuscular control in chronic pain
Brent Liu, Ph.D., Talk title: Medical Imaging Informatics Research and the IPILab
Host: BME Department
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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Ph.D. Defense
Mon, Aug 29, 2011 @ 03:00 PM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Shuping Liu, Computer Engineering
Talk Title: Intelligent Control and Automatic Anomaly Detection/Prediction in Sensor based Systems
Abstract: Sensors are increasingly used for collecting data from the field for monitoring and detecting anomalous behavior. In this thesis a network of sensors are used for data collection, analysis, and detecting abnormal situations in two domains of patient health monitoring and failures in rod pump systems in an oilfield. In these application domains, there are two challenging problems: intelligent control for wireless sensor operations to make decisions on sampling to optimize life time of a sensor network and accurate anomaly detection and prediction using the collected data.
For the health monitoring application domain, a new policy-based framework of Markov Decision Processes (MDP) is formulated for energy efficient optimization problem. The optimal global policy obtained from MPD formulation can be used by distributed sensors to achieve adaptive sampling for optimal and intelligent control of both energy consumption (system lifetime) and detection accuracy. The size of MDP policy may be large with increasing number of sensors having limited memory and discretization granularity of the problem. A decision tree-based learning algorithm is applied for a compact policy representation. Computational complexity is also exponential to the number of sensors and proportional to the discretization granularity of the problem, which causes the computational scalability problem and limits the application of MDP framework on large state space cases. In order to overcome computational scalability problem, three computationally efficient learning algorithms are developed based on approaches to learn local policies for each sensor: RLAA Learning Algorithm, AMRL Learning Algorithm and COL Learning Algorithm. We successfully applied our approaches to healthcare monitoring system, and compared the performance with other methods. The results show that all three learning algorithms are scalable to sensor networks with large state space.
For the oil field domain, learning-based automatic anomaly detection and prediction algorithms are developed for artificial lift rod pump systems which fail due to various reasons and fixing them can be costly and difficult because most parts are underground. Currently, failures in such systems are detected by field experts, which take time and incur labor costs. Our approach is supervised learning-based anomaly detection techniques from field data and we developed a novel combination of two supervised learning algorithms, AdaBNet and AdaDT for this problem. These techniques are successfully applied to detecting and predicting failures in rod pump systems with real data from oilfields. Our automated anomaly detection and prediction approach can allow automated surveillance of large number of wells in an oil field to reduce cost while monitoring wells remotely.
Biography: Shuping Liu received a M .S. (2004) in Electrical Engineering from Helsinki University of Technology in Finland. He also received a 2nd M. S. (2010) in Computer Science from University of Southern California. He obtained a B.S. degree (1998) from Anhui University of Technology in China. Shuping is currently a Ph.D. candidate in the department of Electrical and Computer Engineering at USC. His research spans from wired and wireless networking, mobile computing and security to machine learning and data mining. His primary focuses are on algorithm design, stochastic optimization theorem, wireless body sensor networks for health monitoring, and anomaly detection and prediction.
Host: Raghu Raghavendra (Chair)
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Janice Thompson
