12288623
Discontinuity detection from band-limited signals
Abstract The problem of detection, classification, and measurement of discontinuities arises in many applications in science and technology. It is complex because of the noise corrupting the data. It is difficult to distinguish the discontinuities of the underlying structure from the false discontinuities from the noise. Furthermore, in applications such as edge detection from intensity images, due to the effects of the point spread function of the optic system, the discontinuities of the intensity surface of the underlying scene are not well represented in the intensity image, which is band-limited. This introduces additional complication into the discontinuity detection process. We study discontinuity detection from band-limited signals. We propose a discontinuity detector, which consists of a pair of a pattern and a linear filter. We show that for a discontinuity in the signal there is a scaled pattern in the filter response. The location of the pattern is the location of the discontinuity, and the scaling factor of the pattern is the size of the discontinuity. We derive a necessary and sufficient condition for the one-to-one correspondence between the discontinuities of the signal and the scaled patterns in the filter response. Therefore, the problem of discontinuity detection and measurement is reduced to searching for the (scaled) pattern in the filter response. In the presence of noise, the pattern matching is approximate. We propose a statistical method for the pattern search. We study optimal detectors. We show that for white noise the optimal detectors are natural splines. Some issues related to edge detection are discussed.
38528864
Coping With Discontinuities In Computer Vision: Their Detection, Classification, And Measurement
45853491
Testing Finite-State Machines: State Identification and Verification
We study the complexity of two fundamental problems in the testing of finite-state machines. 1) Distinguishing sequences (state identification). We show that it is PSPACE-complete to determine whether a finite-state machine has a preset distinguishing sequence. There are machines that have distinguishing sequences, but only of exponential length. We give a polynomial time algorithm that determines whether a finite-state machine has an adaptive distinguishing sequence. (The previous classical algorithms take exponential time.) Furthermore, if there is an adaptive distinguishing sequence, then we give an efficient algorithm that constructs such a sequence of length at most n(n/spl minus/1)/2 (which is the best possible), where n is the number of states. 2) Unique input output sequences (state verification). It is PSPACE-complete to determine whether a state of a machine has a unique input output sequence. There are machines whose states have unique input output sequences but only of exponential length. >
206832588
Optimization problems from feature testing of communication protocols
In feature testing of communication protocols, we want to construct a minimal number of tests with a desirable fault coverage. We model the protocols by extended finite state machines and reduce the test generation process to optimization problems on graphs. We study efficient algorithms and their complexity. We report experimental results on real systems, including Personal HandyPhone System, a 5ESS based ISDN wireless system, and 5ESS Intelligent Network Application Protocol.
31110512
Discontinuity Detection and Thresholding-A Stochastic Approach
We study discontinuity detection and thresholding using a stochastic approach. We discuss a general form of detectors, which includes a number of well known detectors as special cases. Thresholding is indispensable to eliminate spurious responses from the detection process. We propose a Weighted Thresholding, which is designed to cope with a variety of anomalies. Our analysis and experimental results on real images show that intelligent thresholding methods can make a significant difference for discontinuity detection.
11856887
Approximation of linear functionals on a banach space with a Gaussian measure
Abstract We study approximation of linear functionals on separable Banach spaces equipped with a Gaussian measure. We study optimal information and optimal algorithms in average case, probabilistic, and asymptotic settings, for a general error criterion. We prove that adaptive information is not more powerful than nonadaptive information and that μ-spline algorithms, which are linear, are optimal in all three settings. Some of these results hold for approximation of linear operators. We specialize our results to the space of functions with continuous r th derivatives, equipped with a Wiener measure. In particular, we show that the natural splines of degree 2 r + I yield the optimal algorithms. We apply the general results to the problem of integration.
1336097
Distributed synchronous control units for dataflow graphs under allocation of telescopic arithmetic units
In order to enjoy the performance improvement effects of variable computation time arithmetic units at a system level, we propose a new synchronous control unit design methodology for dataflow graphs under allocation of a telescopic arithmetic unit, which is one of the well-known synchronous variable computation time arithmetic units. The proposed method generates an independent synchronous controller for each component arithmetic unit, and builds a distributed synchronous control unit through integrating the derived controllers. The distributed structure of the final synchronous control unit maximizes the performance improvement effect of telescopic arithmetic units through a complete preservation of original concurrency among operations.
41830429
An Optimal Algorithm for Roundness Determination on Convex Polygons
Abstract In tolerancing, the Out-Of-Roundness factor determines the relative circularity of planar shapes. The measurement of concern in this work is the Minimum Radial Separation, as recommended by the American National Standards Institute (ANSI). Here we show that the algorithm given in Le and Lee [6] runs in θ ( n 2 ) time even for convex polygons. Furthermore, we present an optimal O ( n ) time algorithm to compute the Minimum Radial Separation of convex polygons, which represents not only a factor n improvement over the previously best known algorithm, but also a factor of log n improvement over Le and Lee's conjectured complexity for the problem.
7800252
Imprecise data computation for high performance asynchronous processors
Instruction level parallelism(ILP) is strictly limited by various dependencies. In particular , data dependency is the major performance bottleneck of data intensive applications. To accelerate the execution of sequential code serialized due to data dependencies, this paper proposes an imprecise computation as a fast data computing technique for a high-performance asynchronous processor. To show the performance benefits of the suggested computing model, simulation results are presented. The imprecise computation can be used effectively in data intensive processing with a microprocessor, a Digital Signal Processor or a multimedia processor.
13517188
Computational geometry
Computational geometry [Edelsbrunner 1987; Preparata and Shamos 1985] evolves from the classical discipline of design and analysis of algorithms. It is concerned with the computational complexity of geometric problems that arise in various disciplines such as pattern recognition, computer graphics, computer vision, robotics, VLSI layout, operations research, statistics, and so on. In contrast with the classical approach to proving mathematical theorems about geometry-related problems, this discipline emphasizes the computational aspect of these problems and attempts to exploit the underlying geometric properties possible, for example, the metric space, to derive efficient algorithmic solutions. An objective of this discipline in the theoretical context is to study the intrinsic difficulty of geometric computation under a certain computation model and to devise provably efficient algorithms for the problems at hand. The measure of intrinsic difficulty of a geometric problem or efficiency of an algorithm is in terms of time and space complexities when the problem size tends to `. This is the so-called asymptotic computational complexity. Traditionally there are two types of complexity analysis of the asymptotic behavior of an algorithm: worst-case analysis and average-case analysis. In the former, the behavior of the algorithm with respect to the worst possible case of the input instance is considered, whereas in the latter, the expected behavior is considered when the input is randomly drawn from a given distribution. Recently the notion of randomized algorithms has emerged as an important area of study within algorithm design and analysis. Similar to average-case analysis, the analysis of randomized algorithms is also probabilistic with the exception that in the latter the input instance is fixed whereas the steps taken by the algorithm are random and governed by the outcomes of some probabilistic event, for example, the toss of a coin. Due to its applications to various science and engineering related disciplines, researchers in computational geometry have begun to address the efficacy of the algorithms; the issues concerning robustness and numerical stability [Fortune 1993] and the actual running times of their implementations have now become centers of attention as well. We concentrate here on the theoretical development of this field in the context of sequential computation. We adopt the real RAM (random-access machine) model of computation in which all arithmetic operations, comparisons, kth-root, exponential, or logarithmic functions take unit time. For more details refer to [Preparata and Shamos 1985]. Those who are interested in the implementations or would like more information about available software may consult the Proceedings of the Annual ACM Symposium on Computational Geometry, which has a video session, or the WWW page on Geometry in Action
15086505
A fast content-based indexing and retrieval technique by the shape information in large image database
Abstract In this paper, we present an efficient content-based image retrieval (CBIR) system which employs the shape information of images to facilitate the retrieval process. For efficient feature extraction, we extract the shape feature of images automatically using edge detection and wavelet transform which is widely used in digital signal processing and image compression. To facilitate speedy retrieval, we also propose the Spherical Pyramid-Technique (SPY-TEC), a new indexing method for similarity search in high-dimensional data space. The SPY-TEC is based on a special space partitioning strategy, which is to divide the d-dimensional data space first into 2d spherical pyramids, and then cut the single spherical pyramid into several spherical slices. This partition provides a transformation of d-dimensional space into 1-dimensional space. Thus, we are able to use a B+-tree to manage the transformed 1-dimensional data. We show that our image indexing method supports faster retrieval than other multi-dimensional indexing methods such as the R*-tree through various experiments. Finally, we show the retrieval effectiveness of our CBIR system using the measure proposed by the QBIC (C. Faloutsos et al., 1994) system.