Bandwidth reservation has been increasingly used to provide QoS for various network applications. To accommodate a high-priority bandwidth reservation request (BRR), the bandwidth scheduler sometimes needs to preempt existing bandwidth reservations that have been made for BRRs with a lower priority, which is traditionally known as connection preemption. When such preemption is unavoidable, one primary goal of bandwidth scheduling is to minimize the disruption to existing reservations. In this paper, we study the problem of bandwidth reservation preemption for two types of BRRs, bandwidth- and data transfer- oriented, respectively, on one given link of the scheduling network with two different objectives: (i) minimize the number and then the total bandwidth of existing bandwidth reservations to be preempted, and (ii) minimize the total bandwidth and then the number of existing bandwidth reservations to be preempted. We prove these four problems to be NP-complete and propose a heuristic algorithm for each. We also design baseline heuristic algorithms for performance comparison. Extensive simulation results show that the proposed heuristic algorithms outperform those in comparison.

In this paper, we propose the Interest Meter (IM), a system making the computer conscious of user's reactions to measure user's interest and thus use it to conduct video summarization. The IM takes account of users' spontaneous reactions when they view videos. To estimate user's viewing interest, quantitative interest measures are devised based on the perspectives of attention and emotion. For estimating attention states, variations of user's eye movement, blink, and head motion are considered. For estimating emotion states, facial expression is recognized as positive or neural emotion. By combining characteristics of attention and emotion by a fuzzy fusion scheme, we transform users' viewing behaviors into quantitative interest scores, determine interesting parts of videos, and finally concatenate them as video summaries. Experimental results show that the proposed concept “editing by viewing” works well and may provide a promising direction to consider the human factor in video summarization.

In this paper, we propose the Interest Meter (IM), a system making computer conscious of user's reactions, to measure user's interest in real time. The Interest Meter takes account of users' spontaneous reactions when users interact with computers. In this work, we analyze variations of user's eye movement, blink, head motion, and facial expression. Furthermore, we propose an algorithm to combine those signals into interest score and determine important parts of video shots when people watch raw home videos. Experimental result shows that this new type of editing mechanism can effectively generate home video summaries.

This paper presents i-m-Space, an interactive multimedia rehabilitation space that helps the post-surgery recovery of breast cancer patients. Our goal is to improve patients' physical therapy and psychological relaxation experience through careful applications of multimedia technology. i-m-Space consists of three types of breathing-based relaxation and three types for interactive exercise-based rehabilitation.