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Events for August 29, 2011

  • 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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  • Repeating EventOn 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

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    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

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