Long-distance Wi-Fi technology has shown promise in several network applications that cannot utilize conventional technologies effectively. Although the network performance of Wi-Fi technology is affected by a number of environmental factors, there is a dearth of long-term, continuous, and systematic studies on the correlations between those factors and the technology's performance. In this study, we deployed a long-distance Wi-Fi testbed on our campus and conducted a one-year experiment. Comprehensive data analysis of the measurement results shows that rainfall is the major weather attribute that affects the network performance of long-distance Wi-Fi links. In addition, the performance is highly correlated to human activities in the immediate vicinity. The results also demonstrate it is possible to infer people's daily routines on campus by exploiting the long-term measurement data.

In this paper, we pay attention to the inter-task dynamic voltage scaling (DVS) algorithms for periodic real-time task systems. We propose a fast dynamic reclaiming scheme for power-aware hard real-time systems and discuss their performances and time complexities against other inter-task DVS algorithms. The time complexity of our off-line and on-line algorithms are O(nlogn) and O(n), respectively, where n denotes the number of task.

In this paper, we pay attention to the real-time multiprocessor scheduling for Distance-Constrained Tasks. By using Early-release-fair (ERfair) model, we propose two efficient algorithms to probe whether the distance constraints of every task can be guaranteed. Moreover, they gather the unfeasible tasks and inflate them with a reweighting function. The proposed algorithms are linear-time and increase significantly the ratio of schedulable task sets.

Most existing recommendation systems may not be very effective due to the lack of the adequate knowledge of user's behavior or interests. Some systems are not] efficient enough because of the huge on-line computational demand. In this paper, we propose a personal documents recommendation system that effectively filters the on-line news on WWW for each individual user. The proposed system recommends useful news by employing the profiling techniques of modified content- and collaborative-based filtering. In order to reduce the on-line computation and improve the recommendation quality, we design a tree-based data mining algorithm that treats users' behavior and interest as input and filter the news documents efficiently.

In this paper, we propose and analyze a new interconnection network called bubblesort star graph, which is the merger of the bubblesort graph and the star graph. We present the deadlock-free wormhole routing algorithm for the proposed network. We also develop the method to embed a mesh into a bubblesort star graph with dilation two and expansion one. Besides, we use the recursive scheme to embed the multiple disjoint copies of the hypercube into a bubblesort star graph with all faults recovery capacity as well as constant expansion and dilation one or two. This reflects the fact that the embeddability of the bubblesort star graph is much better than that of the star graph.

This paper proposes an approach for embedding a complete binary tree with height k/spl times/(n-2/sup k/+1)+(k-2)/spl times/2/sup k/+1, where k=[log n], into an n-dimensional complete transposition graph (CT/sub n/), star graph (ST/sub n/), and bubblesort graph (BS/sub n/) with dilations 1,3, and 2n-3 respectively. Furthermore, a fault-tolerant scheme is developed to recover multiple faults, and the dilations after recovery become at most 3,5, and 2n-1 for the CT/sub n/, ST/sub n/, and BS/sub n/ respectively.

The problem of assigning a partitioned task to a distributed computing system is studied. Considering communication overhead and idle time, it is possible to develop a mathematical model to describe the cost function, which is defined to evaluate the task turnaround time, under a general model of distributed computing systems. Task assignment is formulated as a DU-mapping, which maps a directed acyclic task graph onto an undirected system graph. The search of optimal DU-mapping is NP-complete and is transformed into a state-space search problem. An approach called critical sink underestimate is developed to attain an optimal DU-mapping. This approach allows the most nodes in the state-space tree to be pruned. Experimental results reveal that this method performs very well due to its close evaluation to the real cost.>