EE3731C Signal Processing Methods

Fall 2014, Fall 2013

Prof. Thomas Yeo and Prof. Qi Zhao

Synopsis: This course provides an introduction to signal processing methods. It aims at preparing students for high-level technical electives and graduate courses in signal processing and new media. Topics include digital filtering, multirate digital signal processing, introduction to wavelet transform, probability and random signals, Wiener filter, AMAR model, linear prediction, singular value decomposition, principle component analysis and multimedia applications.

EE5731R Visual Computing

Fall 2015

Prof. Robby Tan and Prof. Qi Zhao

Synopsis: This is an introductory course to visual computing. It will cover fundamentals, important tools, and applications of computer vision. As one of the most fast growing and exciting field in artificial intelligence, computer vision has drawn tremendous attention from both academia and industry. This course is designed to open doors to students interested in this area, to introduce key techniques and the cutting edge knowledge in the field, and to prepare them for real world tasks.

EE6903 Advanced Models of Biological Perception

Fall 2013, Fall 2011

Prof. Loong Fah Cheong, Prof. Shih-Cheng Yen, and Prof. Qi Zhao

Synopsis: Computational models of biological perception are used increasingly in multimedia, computer vision, robotics, computer-human interaction, and biological signal processing. Understanding the biological processes governing perception is useful in building well-founded models. This course discusses selected papers on current research in this area, with topics covering neuronal and computational models of perception, and perceptually driven applications in computer vision and AI.

GEK*1501 Information Technology And Us

Spring 2016, Spring 2015, Spring 2014, Spring 2013

Prof. Qi Zhao

Synopsis: This course discusses information technology for campus-wide non-EECS students. Its objective is to provide students with the background to survive and thrive in this increasingly technological world. Topics include introductions to information technologies (e.g., computer hardware, software, Internet, mobile devices, social media, etc.) as well as social impact of information technology on both public and private sectors (e.g., IT in business, education, management, law, and government, privacy and security issues, etc.).

(*General Education program at NUS)

GS*6004 Vision and Perception

Spring 2015, Spring 2012

Prof. Dale Purves, Prof. Brown Hsieh, Prof. Thorsten Wohland, Prof. Shih-Cheng Yen, and Prof. Qi Zhao

Synopsis: The human brain is the most complex structure in the known universe, with large areas responsible for processing visual information. In this course, we adopt an interdisciplinary approach to studying the human visual system as a model for understanding the functional organization of the brain. We begin with how photons are converted into neural synaptic potentials. Next, we explore the anatomical and physiological organization of the cortical visual areas, as well as computational models of their function. We then review the psychophysical studies of object recognition and visual attention. We conclude by studying mathematical models used in artificial vision systems.

(*Graduate School for Integrative Sciences and Engineering)

GS6506 Computational Neuroscience and Neuroengineering

Spring 2013

Prof. John-John Cabibihan, Prof. Xiaoping Li, Prof. Kwok Fook Kay Kenneth, Prof. Shih-Cheng Yen, and Prof. Qi Zhao

Synopsis: This interdisciplinary course covers four parts: 1) quantitative methods to study information encoding and decoding in the responses of single neurons and of populations of neurons in the visual system; 2) non-invasive recording methods in humans including EEG and fMRI; 3) neural network methods, including back-propagation, radial basis functions, and Hebbian plasticity; and 4) principles of biological tactile sensing and technologies capable of simulating touch in artificial systems. They will expose students to computational techniques used to characterize different kinds of neuronal responses, providing deeper insights into how neurons can be integrated to create functional systems. Students will also learn techniques for creating neuroengineering devices that can be used to augment the capabilities of human users.