The reflection signal in the inverse synthetic aperture radar is measured in the polar coordinate defined by the object rotation angle and the frequency. The reconstruction of fixed scene images requires the coordinate transformation of the polar format data into the rectangular spatial frequency domain, which is then processed by the inverse Fourier transform. In this paper a fast and flexible method of coordinate transformation based on the nearest neighbor interpolation utilizing the Delauney triangulation is at first presented. Then, the induced errors in the transformed rectangular spatial frequency data and the resultant fixed scene images are investigated by simulation under the uniform plane wave transmit-receive mode over the swept frequency 120-160 GHz, and the results which demonstrate the validity of the current coordinate transformation are presented.

In this paper, two efficient codebook searching algorithms for vector quantization (VQ) are presented. The first fast search algorithm utilizes the compactness property of signal energy of orthogonal transformation. On the transformed domain, the algorithm uses geometrical relations between the input vector and codeword to discard many unlikely codewords. The second algorithm, which transforms principal components only, is proposed to alleviate some calculation overhead and the amount of storage. The relation between the principal components and the input vector is utilized in the second algorithm. Since both of the proposed algorithms reject those codewords that are impossible to be the nearest codeword, they produce the same output as conventional full search algorithm. Simulation results confirm the effectiveness of the proposed algorithms.

In this paper, we present two parallel algorithms for computing the all nearest neighbors of an n n binary image on the Bulk-Synchronous Parallel(BSP) model. The BSP model is an asynchronous parallel computing model, where its communication features are abstracted by two parameters L and g: L denotes synchronization periodicity and g denotes a reciprocal of communication bandwidth. We propose two parallel algorithms for the all nearest neighbor problems based on two distance metrics. The first algorithm is for Lp distance, and the second algorithm is for weighted distance. Both two algorithms run in O(n2/p + L) computation time and in O(g(n/p) + L) communication time using p (1 p n) processors and in O(n2/p + (d+L)(log(p/n)/log(d+1))) computation time and in O(g(n/p) + (gd+L)(log(p/n)/log(d+1))) communication time using p (n< p n2) processors on the BSP model, for any integer d(1 dp/n).

A learning algorithm is presented for nearest neighbor pattern classifiers for the cases where mixed supervised and unsupervised training data are given. The classification rule includes rejection of outlier patterns and fuzzy classification. This partially supervised learning problem is formulated as a multiobjective program which reduces to purely super-vised case when all training data are supervised or to the other extreme of fully unsupervised one when all data are unsupervised. The learning, i. e. the solution process of this program is performed with a gradient method for searching a saddle point of the Lagrange function of the program.