Leading Undergraduate Students to Big Data Generation

People are facing a flood of data today. Data are being collected at unprecedented scale in many areas, such as networking, image processing, virtualization, scientific computation, and algorithms. The huge data nowadays are called Big Data. Big data is an all encompassing term for any collection of data sets so large and complex that it becomes difficult to process them using traditional data processing applications. In this article, the authors present a unique way which uses network simulator and tools of image processing to train students abilities to learn, analyze, manipulate, and apply Big Data. Thus they develop students handson abilities on Big Data and their critical thinking abilities. The authors used novel image based rendering algorithm with user intervention to generate realistic 3D virtual world. The learning outcomes are significant

Conductance modulation in spin field-efect transistors under finite bias voltages

The conductance modulations in spin field-effect transistors under finite bias voltages were studied. It was shown that when a finite bias voltage is applied between two terminals of a spin field-effect transistor, the spin precession states of injected spin-polarized electrons in the semiconductor channel of the device will depend not only the gate-voltage controlled Rashba spin-orbit coupling but also depend on the bias voltage and, hence, the conductance modulation in the device due to Rashba spin-orbit coupling may also depend sensitively on the bias voltage.Comment: 7 pages, 3 figures, to appear in Physical Review B, (April, 2004

Computational Multimedia for Video Self Modeling

Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of oneself. This is the idea behind the psychological theory of self-efficacy - you can learn or model to perform certain tasks because you see yourself doing it, which provides the most ideal form of behavior modeling. The effectiveness of VSM has been demonstrated for many different types of disabilities and behavioral problems ranging from stuttering, inappropriate social behaviors, autism, selective mutism to sports training. However, there is an inherent difficulty associated with the production of VSM material. Prolonged and persistent video recording is required to capture the rare, if not existed at all, snippets that can be used to string together in forming novel video sequences of the target skill. To solve this problem, in this dissertation, we use computational multimedia techniques to facilitate the creation of synthetic visual content for self-modeling that can be used by a learner and his/her therapist with a minimum amount of training data. There are three major technical contributions in my research. First, I developed an Adaptive Video Re-sampling algorithm to synthesize realistic lip-synchronized video with minimal motion jitter. Second, to denoise and complete the depth map captured by structure-light sensing systems, I introduced a layer based probabilistic model to account for various types of uncertainties in the depth measurement. Third, I developed a simple and robust bundle-adjustment based framework for calibrating a network of multiple wide baseline RGB and depth cameras

An Immersive Telepresence System using RGB-D Sensors and Head Mounted Display

We present a tele-immersive system that enables people to interact with each other in a virtual world using body gestures in addition to verbal communication. Beyond the obvious applications, including general online conversations and gaming, we hypothesize that our proposed system would be particularly beneficial to education by offering rich visual contents and interactivity. One distinct feature is the integration of egocentric pose recognition that allows participants to use their gestures to demonstrate and manipulate virtual objects simultaneously. This functionality enables the instructor to ef- fectively and efficiently explain and illustrate complex concepts or sophisticated problems in an intuitive manner. The highly interactive and flexible environment can capture and sustain more student attention than the traditional classroom setting and, thus, delivers a compelling experience to the students. Our main focus here is to investigate possible solutions for the system design and implementation and devise strategies for fast, efficient computation suitable for visual data processing and network transmission. We describe the technique and experiments in details and provide quantitative performance results, demonstrating our system can be run comfortably and reliably for different application scenarios. Our preliminary results are promising and demonstrate the potential for more compelling directions in cyberlearning.Comment: IEEE International Symposium on Multimedia 201

Effects of spin imbalance on the electric-field driven quantum dissipationless spin current in pp-doped Semiconductors

It was proposed recently by Murakami et al. [Science \textbf{301}, 1348(2003)] that in a large class of $p$-doped semiconductors, an applied electric field can drive a quantum dissipationless spin current in the direction perpendicular to the electric field. In this paper we investigate the effects of spin imbalance on this intrinsic $spin$ Hall effect. We show that in a real sample with boundaries, due to the presence of spin imbalance near the edges of the sample, the spin Hall conductivity is not a constant but a sensitively $position$-$dependent$ quantity, and due to this fact, in order to take the effects of spin imbalance properly into account, a microscopic calculation of both the quantum dissipationless spin Hall current and the spin accumulation on an equal footing is thus required. Based on such a microscopic calculation, a detailed discussion of the effects of spin imbalance on the intrinsic spin Hall effect in thin slabs of $p$-doped semiconductors are presented.Comment: 8 pages, 2 figures, An extended version with detailed calculations To appear in Phys. Rev.