Multiple string matching has a wide range of applications such as network intrusion detection systems, spam filters, information retrieval systems, and bioinformatics. To accelerate multiple string matching, many hardware approaches are proposed to accelerate string matching. Among the hardware approaches, memory architectures have been widely adopted because of their flexibility and scalability. A conventional memory architecture compiles multiple string patterns into a state machine and performs string matching by traversing the corresponding state transition table. Due to the ever-increasing number of attack patterns, the memory used for storing the state transition table increased tremendously. Therefore, memory reduction has become a crucial issue in optimizing memory architectures. In this paper, we propose two parallel string matching algorithms which adopt perfect hashing to compact a state transition table. Different from most state-of-the-art approaches implemented on specific hardware such as TCAM, FPGA, or ASIC, our proposed approaches are easily implemented on commodity DRAM and extremely suitable to be implemented on GPUs. The proposed algorithms reduce up to 99.5 percent memory requirements for storing the state transition table compared to the traditional two-dimensional memory architecture. By studying existing approaches, our results obtain significant improvements in memory efficiency.

The training of a multilayer perceptron neural network (MLPNN) concerns the selection of its architecture and the connection weights via the minimization of both the training error and a penalty term. Different penalty terms have been proposed to control the smoothness of the MLPNN for better generalization capability. However, controlling its smoothness using, for instance, the norm of weights or the Vapnik-Chervonenkis dimension cannot distinguish individual MLPNNs with the same number of free parameters or the same norm. In this paper, to enhance generalization capabilities, we propose a stochastic sensitivity measure (ST-SM) to realize a new penalty term for MLPNN training. The ST-SM determines the expectation of the squared output differences between the training samples and the unseen samples located within their Q -neighborhoods for a given MLPNN. It provides a direct measurement of the MLPNNs output fluctuations, i.e., smoothness. We adopt a two-phase Pareto-based multiobjective training algorithm for minimizing both the training error and the ST-SM as biobjective functions. Experiments on 20 UCI data sets show that the MLPNNs trained by the proposed algorithm yield better accuracies on testing data than several recent and classical MLPNN training methods.

In advanced IC manufacturing, as the gap increases between lithography optical wavelength and feature size, it becomes challenging to detect problematic layout patterns called lithography hotspot. In this paper, we propose a novel fuzzy matching model which extracts appropriate feature vectors of hotspot and nonhotspot patterns. Our model can dynamically tune appropriate fuzzy regions around known hotspots. Based on this paper, we develop a fast algorithm for lithography hotspot detection with high accuracy of detection and low probability of false-alarm counts. In addition, since higher dimensional size of feature vectors can produce better accuracy but requires longer run time, this paper proposes a grid reduction technique to significantly reduce the CPU run time with very minor impact on the advantages of higher dimensional space. Our results are very encouraging, with average 94.5% accuracy and low false-alarm counts on a set of test benchmarks.

In advanced IC manufacturing, as the gap between lithography optical wavelength and feature size increases, it becomes challenging to detect problematic layout patterns called lithography hotspot. In this paper, we propose a novel fuzzy matching model which can dynamically tune appropriate fuzzy regions around known hotspots. Based on this model, we develop a fast algorithm for lithography hotspot detection with very low chances of false-alarm. Our results are very encouraging with under 0.56 CPU-hrs/mm2 runtime.

A three-dimensional photogrammetric-measurement vision system has been developed which consists of TV cameras, a PC/AT computer, 3-D control points and software for both computer vision and photogrammetry. The bulk of the processing is the determination of the subpixel location of the control point in the stereo images and accurate camera calibration, using direct-linear-transformation (DLT) and error-approximation algorithms. Simulation experiments which apply this system to bridge deformation detection are carried out. The average 3-D measurement error (x, y, z):(dx, dy, dz)=(0.24, 0.17, 0.24) mm, or 1/2000 over the field of view and 1/3500 over the depth. The bridge deformation measuring accuracy is +or-0.323 mm.>

A method is proposed to recognize and locate the 3-D objects in the hidden case. Accurate camera calibration and the subpixel edge location algorithm is performed to get precise 3-D data. The algorithms for recognition and location contain three stages. First, 3-D data is analyzed to see how many objects are in the scene. In the second stage, the rotational and transitional parameters are determined only by using 3-D contour derived from stereo images to match to the object models. Finally, according to the matched object model and the rotational and translational parameters the visible line drawings are generated and used as a guide for searching correspondent line segments in original stereo image.>

Recovering scenes depth is important to robot vision. Stereo vision provides a convenient and direct way to obtain 3-D depth. The key problem in stereo vision is stereo image matching. In this paper a feature-based motion stereo matching algorithm is proposed. The stereo images are obtained by translating camera. The feature used is line sagments and three stereo images are used for line segments matching. The experiments with real images are made which show the effectiveness of our algorithm.