For massive MIMO transceivers with subspace decomposition, we propose a novel and practical solution leveraging randomized numerical linear algebra. The loss in covariance matrix estimation and subspace decomposition can be minimized while reducing complexity and memory requirements, by adjusting the random embedding levels based on multivariate statistical analysis. An interference-resilient null beamforming structure with random embedding is proposed for both DMRSbased beamforming with/without equalizers (i.e., DMRS-BFEQ and DMRS-BF-NEQ) supported in uplink performance improvement (ULPI). Complexity in the proposed interference null space projection (INSP) based beamformer can be significantly reduced compared to full dimensional MMSE-IRC by removing received signal whitening and exploiting an effective rank of the interference subspace for INSP. It is shown that the proposed INSP based beamformer provides noticeable performance gain against the conventional low complexity receivers, and equivalent or even enhanced performance than full dimensional MMSE-IRC with practical sample size in both interference-free and highly interference-limited environments through link level simulation.

In this poster, we propose an emotion analysis method using context information gathered from sensor data. We observe that there are certain situations in which a specific emotion is strongly felt, i.e. dominant. We develop a "scenario-based" approach that focuses on creating such situations with dominant emotion, using a set of context information from sensor data. These "scenarios" undergo validation process through a preliminary survey. Finally, an application prototype detects these scenarios to return the analyzed emotion to the user.

Today's smartphone application (hereinafter 'app') markets miss a key piece of information, power consumption of apps. This causes a severe problem for continuous sensing apps as they consume significant power without users' awareness. Users have no choice but to repeatedly install one app after another and experience their power use. To break such an exhaustive cycle, we propose PowerForecaster, a system that provides users with power use of sensing apps at pre-installation time. Such advanced power estimation is extremely challenging since the power cost of a sensing app largely varies with users' physical activities and phone use patterns. We observe that the time for active sensing and processing of an app can vary up to three times with 27 people's sensor traces collected over three weeks. PowerForecaster adopts a novel power emulator that emulates the power use of a sensing app while reproducing users' physical activities and phone use patterns, achieving accurate, personalized power estimation. Our experiments with three commercial apps and two research prototypes show that PowerForecaster achieves 93.4% accuracy under 20 use cases. Also, we optimize the system to accelerate emulation speed and reduce overheads, and show the effectiveness of such optimization techniques.

Recently, a number of continuous sensing applications have been actively proposed in research communities and commercially released in the market. However, due to their unique power characteristics, user behavior-dependent battery drain, they bring new challenges for users' power management on these applications. In this demonstration, we present a comprehensive approach to support users' power management for continuous sensing applications. First, at pre-installation time, we provide an instant, personalized power estimation of a continuous sensing application. Without exhaustive trial and error, users can decide judiciously to install a certain application or not. Second, at runtime, we provide mobility-aware battery information. With this information, users can better estimate the phone's remaining battery life based on their imminent mobility conditions and take necessary actions in advance such as carrying an additional battery or minimizing the use of applications.

We explored the possibility of touch-screen-like interaction with a remote control in the TV-viewing environment. A shadow representing the user's thumb touches the screen, presses a button, flicks a cover-flow list, and draws a simple stroke, while the thumb stays and moves on and above the touchpad. In order to implement the concept we developed an optical touchpad for tracking the thumb hovering over its surface, and designed a TV application to demonstrate possible new interaction styles. Throughout two iterations of prototyping, we corrected some of our false expectations, and also verified its potential as a viable option for a TV remote control. This paper presents technical issues and requirements for the hover-tracking touchpad and a complete report of our user studies to explore touch-screen-like interaction for the TV.

Six-degrees-of-freedom (6-DOF) trackers, which were mainly for professional computer applications, are now in demand by everyday consumer applications. With the requirements of consumer electronics in mind, we designed an optical 6-DOF tracker where a few photo-sensors can track the position and orientation of an LED cluster. The operating principle of the tracker is basically source localization by solving an inverse problem. We implemented a prototype system for a TV viewing environment, verified the feasibility of the operating principle, and evaluated the basic performance of the prototype system in terms of accuracy and speed. We also examined its application possibility to different environments, such as a tabletop computer, a tablet computer, and a mobile spatial interaction environment.