The problem of spyware is incredibly serious and exceeds anyone's imagination. Combining static and dynamic analyses, we propose an integrated architecture to defend against surveillance spyware in this paper. Features extracted from both static and dynamic analyses are ranked according to their information gains. Then using top significant features we construct a Support Vector Machine (SVM) classifier for each client. In order to keep the classifier update-to-date, there is a machine playing as server to collect reports from all clients, retrain, and redistribute the new classifier to each client. Our surveillance spyware detection system (SSDS) has an overall accuracy rate up to 97.9% for known surveillance spywares and 96.4% for unknown ones.

We present two O(1)-time algorithms for solving the 2D all nearest neighbor (2D/spl I.bar/ANN) problem, the 2D closest pair (2D/spl I.bar/CP) problem, the 3D all nearest neighbor (3D/spl I.bar/ANN) problem and the 3D-closest pair (3D/spl I.bar/CP) problem of n points on the linear array with a reconfigurable pipelined bus system (LARPBS) from the computational geometry perspective. The first O(1) time algorithm, which invokes the ANN properties (introduced in this paper) only once, can solve the 2D/spl I.bar/ANN and 2D/spl I.bar/CP problems of n points on an LARPBS of size 1/2n/sup 5/3+c/, and the 3D/spl I.bar/ANN and 3D/spl I.bar/CP problems pf n points on an LARPBS of size 1/2n/sup 7/4+c/, where 0 < /spl epsi/ = 1/2/sup c+1/-1 /spl Lt/ 1, c is a constant and positive integer. The second O(1) time algorithm, which recursively invokes the ANN properties k times, can solve the kD/spl I.bar/ANN, and kD/spl I.bar/CP problems of n points on an LARPBS of size 1/2n/sup 3/2+c/, where k = 2 or 3, 0 < /spl epsi/ = 1/2/sup n+1/-1 /spl Lt/ 1, and c is a constant and positive integer. To the best of our knowledge, all results derived above are the best O(1) time ANN algorithms known.

The main contribution of this paper is the design of several efficient algorithms for modified run-length chain coding and for computing a shape's moments on arrays with reconfigurable optical buses. The proposed algorithms are based on the boundary representation of an object. Instead of using chain code, the boundary can be represented by a modified run-length chain code, where each entity represents a line segment (two adjacent corner pixels). The sequential nature of the chain code makes it difficult to be parallelized. We first propose two constant time algorithms for boundary extraction and run-length chain coding. To the authors' knowledge, these are the most time efficient algorithms yet published. Based on the modified run-length chain coding, and the advantages of both optical transmission and electronic computation, a constant time parallel algorithm for computing a shape's moments using N x N processors is proposed. Additionally, instead of using N x N processors, a scalable moment algorithm using r x r processors is also derived, where r < N. Based on the product of time and the number of processors used, both proposed parallel algorithms are time and cost optimal.

The main contributions of this paper are in designing fast and scalable parallel algorithms for selection and median filtering. Based on the radix-/spl omega/ representation of data and the prune-and-search approach, we first design a fast and scalable selection algorithm on the arrays with reconfigurable optical buses (AROB). To the authors' knowledge, this is the most time efficient algorithm yet published, especially compared to the algorithms proposed by Han et al (2002) and Pan (1994). Then, given an N /spl times/ N image and a W /spl times/ W window, based on the proposed selection algorithm, several scalable median filtering algorithms are developed on the AROB model with a various number of processors. In the sense of the product of time and the number of processors used, most of the proposed algorithms are time or cost optimal.

The main contribution of this paper is the design of several efficient algorithms for modified run-length chain coding and for computing shape's moments on arrays with reconfigurable optical buses. We first propose two constant time algorithms for boundary extraction and run-length chain coding. Based on the modified run-length chain coding, and the advantages of both optical transmission and electronic computation, a constant time parallel algorithm for computing shape's moments using N/spl times/N processors is proposed. Based on the product of time and the number of processors used, the proposed parallel algorithms are time and cost optimal.

In spite of their good filtering characteristics for vector-valued image processing, the usability of vector median filters is limited by their high computational complexity. Given an N /spl times/ N image and a W /spl times/ W window, the computational complexity of vector median filter is O(W/sup 4/N/sup 2/). In this paper, we design three fast and efficient parallel algorithms for vector median filtering based on the 2-norm (L/sub 2/) on the arrays with reconfigurable optical buses (AROB). For 1 /spl les/ p /spl les/ W /spl les/ q /spl les/ N, our algorithms run in O(W/sup 4/ log W/p/sup 4/), O(W/sup 2/N/sup 2//p/sup 4/q/sup 2/ log W) and O(1) times using p/sup 4/N/sup 2/ / log W, p/sup 4/q/sup 2/ / log W, and W/sup 4/N/sup 2/ log N processors, respectively. In the sense of the product of time and the number of processors used, the first two results are cost optimal and the last one is time optimal.

Computing the moments of a two-dimensional (2-D) image involves a significant amount of multiplications and additions in a direct method. In this paper, we use the suffix sums to compute the 2-D moments instead of using a direct method. This method can reduce the number of multiplications tremendously. By integrating the advantages of both optical transmission and electronic computation, the 2-D moments can be computed in constant time on a 2-D arrays with reconfigurable optical buses (AROB). This result achieves optimal speed-up.