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Upcoming Events

  • Antew Dejene PhD Thesis Examination, Educaiton
    1:00 PM - 4:00 PM
    August 31, 2015
    No Description
  • Ali Shagerdmootaab, PhD Thesis Defense, Mechatronic Systems Engineering
    10:00 AM - 12:30 PM
    September 1, 2015
    PhD Thesis Defense - Ali Shagerdmootaab When: Tuesday, September 1, 2015 @ 10am Where: SFU Surrey campus, Rm. 5380 Examining Committee: Dr. Mehrdad Moallem (Senior Supervisor), Dr. Ahmad Rad (Supervisor), Dr. Behraad Bahreyni (Supervisor), Dr. Jason Wang (Internal Examiner), Dr. John Lam (External Examiner, York University), Dr. Siamak Arzanpour (Chair) Title: Design and Implementation of Power Driver Controllers for LED Strings Abstract: In this thesis, design and implementation of power drives for light-emitting diode (LED) strings is investigated. We particularly focus on design methods for minimizing the size of output filter capacitor in flyback LED drivers. To this end, a novel constant power drive technique was developed to achieve better LED light regulation compared with the constant current technique. We present a filter capacitor minimization algorithm and applying it to an integrated buck-boost/flyback LED driver to achieve a long lasting LED driver. Minimization of the filter capacitor in an ac-dc flyback converter is investigated by utilizing a descent algorithm. The algorithm was proposed using a relationship between input current harmonics and LED electrical and photometric characteristics. The performance of the proposed algorithm in terms of filter capacitor minimization was experimentally verified to achieve input power factor correction along with meeting light flicker requirements. Furthermore, a primary-side constant power drive technique is proposed by utilizing a novel LED power estimation technique and an inner-outer-loop control structure. The proposed technique was implemented on the ac-dc flyback converter to attain simultaneous input power factor correction and LED light regulation. The enhanced performance of LED light regulation for the proposed technique is experimentally verified for different ambient temperatures and compared with the constant current drive method. The above filter capacitor minimization algorithm was utilized and experimentally tested in an ac-dc integrated buck-boost/flyback converter. Utilizing this algorithm, the size of the required filter capacitors can be significantly reduced.
  • Seyed Hossein Hajimirsadeghi, PhD Defence, Computing Science
    1:00 PM - 3:00 PM
    September 8, 2015
    PhD Thesis Defence SEYED HOSSEIN HAJIMIRSADEGHI M.Sc., University of Tehran, Iran, 2010 B.Sc., University of Tehran, Iran, 2008 Tuesday, September 8 th , 2015 1:00 p.m. TASC1 9204 West MULTIPLE INSTANCE LEARNING FOR VISUAL RECOGNITION: LEARNING LATENT PROBABILISTIC MODELS Many visual recognition tasks can be represented as multiple instance problems. Two examples are image categorization and video classification, where the instances are the image segments and video frames, respectively. In this regard, detecting and counting the instances of interest can help to improve recognition in a variety of applications. For example, classifying the collective activity of a group of people can be directed by counting the actions of individual people. Further, encoding the cardinality-based relationships can reduce sensitivity to clutter or ambiguity, in the form of individuals not involved in a group activity or irrelevant segments/frames in an image/video. Multiple instance learning (MIL) aims to use these counting relations in order to recognize patterns from weakly supervised data. Contrary to standard supervised learning, where each training instance is labeled, in the MIL paradigm a bag of instances share a label, and the instance labels are hidden. This weak supervision significantly reduces the cost of full annotation in many recognition tasks. However, it makes learning and recognition more challenging. In a general MIL problem, three major issues usually emerge: how to infer instance labels without full supervision; how the cardinality relations between instance labels contribute to predict the bag label; how the the bag as a whole entity which integrates the instances is labeled. In this thesis we try to address all these challenges. To this end, first we propose a boosting framework for MIL, which can model a wide range of soft and linguistic cardinality relations. Next, a probabilistic graphical model is proposed to capture the interactions and interrelations between instances, instance labels, and the whole bag. This is a general and flexible model, which can encode any cardinality-based relations. For training this model, we introduce novel algorithms based on latent max-margin classification, kernel learning, and gradient boosting. Thus, very rich and high-capacity models are obtained for bag classification. We evaluate our proposed methods in various applications such as image classification, human group activity recognition, human action recognition, video recognition, unconstrained video event detection, and video summarization. Keywords: Multiple instance learning; probabilistic graphical models; latent structured models Ph.D. Examining Committee: Dr. Greg Mori, Senior Supervisor Dr. Anoop Sarkar, Supervisor Dr. Ping Tan, Internal Examiner Dr. Ming-Hsuan Yang, External Examiner Dr. Ze-Nian Li, Chair
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Free Movie Screenings
Please join us for free screenings of the PhD Comics movies this September and October at all three of our SFU campuses. All welcome!