In this paper, a new approach to adaptive direction-of-arrival (DOA) estimation based upon a database retrieval technique is proposed. In this method, angles and signal powers are quantized, and a set of true correlation vectors of the array antenna input vectors for various combinations of the quantized angles and signal powers is stored in a database. The k-d tree is then selected as the data structure to facilitate range searching. Estimated a correlation vector, range searching is performed to retrieve several correlation vectors close to it from the k-d tree. The DOA and the signal power are estimated by taking the weighted average of angles and powers associated with the retrieved correlation vectors. Unlike the other high-resolution methods, this method requires no eigenvalue computation, thus allowing a fast computation. It is shown through simulation results that the processing speed of the proposed method is much faster than that of the root-MUSIC that requires the eigenvalue decomposition.

This paper introduces an adaptive distributed routing algorithm for the faulty star graph. The algorithm is based on that the n-star graph has uniform node degree n-1 and is n-1-connected. By giving two routing rules based on the properties of nodes, an optimal routing function for the fault-free star graph is presented. For a given destination in the n-star graph, n-1 node-disjoint and edge-disjoint subgraphs, which are derived from n-1 adjacent edges of the destination, can be constructed by this routing function and the concept of Breadth First Search. When faults are encountered, according to that there are n-1 node-disjoint paths between two arbitrary nodes, the algorithm can route messages to the destination by finding a fault-free subgraphs based on the local failure information (the status of all its incident edges). As long as the number f of faults (node faults and/or edge faults) is less than the degree n-1 of the n-star graph, the algorithm can adaptively find a path of length at most d+4f to route messages successfully from a source to a destination, where d is the distance between source and destination.

This paper is concerned with the continuous relation between models of the plant and the predicted performances of the system designed based on the models. To state the problem more precisely, let P be the transfer matrix of a plant model, and let A be the transfer matrix of interest of the designed system, which is regarded as a performance measure for evaluating the designed responses. A depends upon P and is written as A=A(P). From the practical point of view, it is necessary that the function A(P) should be continuous with respect to P. In this paper we consider the linear quadratic optimal servosystem with integrators (LQI) scheme as the design methodology, and prove that A(P) depends continuously on the plant transfer matrix P if the topology of the family of plants models is the graph topology. A numerical example is given for illustrating the result.

In this paper we propose two methods, named the time smoothing and the scale smoothing respectively, to reduce the Gibbs overshoot in continuous wavelet transform. In is shown that for a large kind of wavelets the scale smoothing cannot remove the Gibbs overshoot completely as in the case of Fourier analysis, but it is possible to reduce the overshoot for any wavelets by choosing the smoothing window functions properly. The frequency behavior of scale smoothing is similar to that of the time smoothing. According to its frequency behavior we give the empirical conditions for selecting the smoothing window functions. Numerical examples are given for illustrations.