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Events for May 13, 2014
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Integrated Systems Seminar
Tue, May 13, 2014 @ 11:00 AM - 12:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Steven Bowers, California Institute of Technology
Talk Title: Holistic electromagnetics: integrated co-design of microwave, analog, digital, and photonic systems
Abstract: Continued integration of various devices on to a single semiconductor substrate as well as the scaling of those devices to ever smaller feature sizes have opened up a new design space for system level innovations that are no longer constrained by many of the restrictions of discrete system design. This talk will present holistic design methodologies for integrated power generation and radiation at mm-wave frequencies that are enabled by this continued integration of various electronic and electromagnetic (EM) structures onto the same substrate. One benefit of this integration available to mm-wave designers is the vast computational power available on chip. A fully integrated self-healing power amplifier at 28 GHz in 45nm SOI CMOS takes advantage of this processing power to heal the PA against process variation, mismatch, environmental variation and transistor failure.
Continuing with the observation that transistors and their connections to EM radiating structures on an integrated substrate are essentially incrementally free, the concept of multi-port driven (MPD) radiators is introduced, and proof of concept mm-wave radiators using 130nm SiGe BiCMOS and silicon photonics are demonstrated.
These systems showcase the benefits of utilizing a co-design between various fields, such as analog circuit design, digital circuit design and applied electromagnetics. By removing many of the boundaries between these various disciplines, new system architectures can be realized that can further push the limits of achievable performance.
Biography: Steven M. Bowers received his B.S. in Electrical Engineering from the University of California, San Diego and his M.S. and Ph.D. specializing in mm-wave circuits and systems from the California Institute of Technology, where he is currently working as a postdoctoral fellow. His research interests include holistic integration of high-frequency analog circuits, advanced digital circuits, novel electromagnetic structures and integrated silicon photonics to enable the next generation of mm-wave applications, specifically in adaptive and self-healing mm-wave circuits and mm-wave power generation and radiation. He received the Caltech Institute Fellowship in 2007, Analog Devices Outstanding Student Designer Award in 2009, is a member of IEEE, HKN and Tau-Beta-Pi, and was the recipient of the IEEE RFIC Symposium Best Student Paper award in 2012 and the IEEE IMS Best Student Paper award in 2013.
Host: Hossein Hashemi
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Hossein Hashemi
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PhD Defense - Chung-Cheng Chiu
Tue, May 13, 2014 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Generating Gestures from Speech for Virtual Humans Using Machine Learning Approaches
PhD Candidate: Chung-Cheng Chiu
Committee:
Stacy Marsella (Chair)
Jonathan Gratch
Louis-Philippe Morency
Ulrich Neumann
Stephen Read (outside member)
Time: 12pm
Location: EEB 248
There is a growing demand for animated characters capable of simulating face-to-face interaction using the same verbal and nonverbal behavior that people use. For example, research in virtual human technology seeks to create autonomous characters capable of interacting with humans using spoken dialog. Further, as video games have moved beyond first person shooters, there is a tendency for gameplay to comprise more and more social interaction where virtual characters interact with each other and with the player's avatar. Common to these applications, the autonomous characters are expected to exhibit behaviors resembling a real human.
The focus of this work is generating realistic gestures for virtual characters, specifically the coverbal gestures that are performed in close relation to the content and timing of speech. A conventional approach for animating gestures is to construct gesture animations for each utterance the character speaks, by handcrafting animations or using motion capture techniques. The problem with this approach is that it is costly in time and money and is not even feasible for characters designed to generate novel utterances on the fly.
This thesis address using machine learning approaches to learn a data-driven gesture generator from human conversational data that can generate behavior for novel utterances and therefore saves development effort. This work assumes that learning to generates from speech is a feasible task. The framework exploits a classification scheme about gestures to provide domain knowledge about gestures and help the machine learning models to realize the generation of gestures from speech. The framework is composed of two components: one realizes the relation between speech and gesture classes and the other performs gesture generation based on the gesture classes. To facilitate the training process this research has collected a real-world conversation data involving dyadic interviews and a set of motion capture data for human gesturing while speaking. The evaluation experiments assess the effectiveness of each component by comparing with state-of-the-art approaches and evaluate the overall performance by conducting studies involving human subjective evaluations. An alternative machine learning framework has also been proposed to compare with the framework addressed in this thesis. The evaluation experiments have shown the framework outperforms state-of-the-art approaches.
The central contribution of this research is a machine learning framework that capable of learning to generate gestures from the conversation data collected from different individuals while preserving the motion style of specific speakers. In addition, our framework will allow the incorporation of data recorded through other media and thereby significantly enrich the training data. The resulting model provides an automatic approach for deriving a gesture generator which realizes the relation between speech and gestures. A secondary contribution is a novel time-series prediction algorithm that predict gestures from the utterance. This prediction algorithm can address time-series problems with complex input and be applied to other applications for classifying time series data.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
