Recursive Diagonal Torus (RDT) is a class of interconnection network for massively parallel computers with 216 nodes. In this paper, message transfer algorithms on the RDT are proposed and discussed. First, a simple one-to-one message routing algorithm called the vector routing is introduced and its practical extension called the floating vector routing is proposed. In the floating vector routing both the diameter and average distance are improved compared with the fixed vector routing. Next, broadcasting and hypercube emulation algorithm scheme on the RDT are shown. Finally, deadlock-free message routing algorithms on the RDT are discussed. By a simple modification of the e-cube routing and a small numbers of additional virtual channels, both one-to-one message transfer and broadcast can be achieved without deadlock.

Skyline query is very important because it is the basis of many applications, e.g., decision making, user-preference queries. Given an N-dimensional dataset D, a point p is said to dominate another point q if p is better than q in at least one dimension and equal to or better than q in the remaining dimensions. In this paper, we study a generalized problem of skyline query that, users are more interested in the details of the dominant relationship in a dataset, i.e., a point p dominates how many other points and whom they are. We show that the existing framework proposed in can not efficiently solve this problem. We find the interrelated connection between the partial order and the dominant relationship. Based on this discovery, we propose a new data structure, ParCube, which concisely represents the dominant relationship. We propose some effective strategies to construct ParCube. Extensive experiments illustrate the efficiency of our methods.

With the widespread use of indoor positioning technology, the need for high-precision positioning services is rising; nevertheless, there are several challenges, such as the difficulty of simulating the distribution of interior location data and the enormous inaccuracy of probability computation. As a result, this paper proposes three different neural network model comparisons for indoor location based on WiFi fingerprint - indoor location algorithm based on improved back propagation neural network model, RSSI indoor location algorithm based on neural network angle change, and RSSI indoor location algorithm based on depth neural network angle change - to raise accurately predict indoor location coordinates. Changing the action range of the activation function in the standard back-propagation neural network model achieves the goal of accurately predicting location coordinates. The revised back-propagation neural network model has strong stability and enhances indoor positioning accuracy based on experimental comparisons of loss rate (loss), accuracy rate (acc), and cumulative distribution function (CDF).