44674200
Decomposition of binary morphological structuring elements based on genetic algorithms
Most image processing architectures adapted to morphological operations use structuring elements of a limited size. Various algorithms have been developed for decomposing a large sized structuring element into dilations of small structuring components. However, these decompositions often come with certain restricted conditions. In this paper, we present an improved technique using genetic algorithms to decompose arbitrarily shaped binary structuring elements. The specific initial population, fitness functions, dynamic threshold adaptation, and the recursive size reduction strategy are our features to enhance the performance of decomposition. It can generate the solution in less computational costs, and is suited for parallel implementation.
34177630
Genetic algorithm based methodology for breaking the steganalytic systems
Steganalytic techniques are used to detect whether an image contains a hidden message. By analyzing various image features between stego-images (the images containing hidden messages) and cover-images (the images containing no hidden messages), a steganalytic system is able to detect stego-images. In this paper, we present a new concept of developing a robust steganographic system by artificially counterfeiting statistic features instead of the traditional strategy by avoiding the change of statistic features. We apply genetic algorithm based methodology by adjusting gray values of a cover-image while creating the desired statistic features to generate the stego-images that can break the inspection of steganalytic systems. Experimental results show that our algorithm can not only pass the detection of current steganalytic systems, but also increase the capacity of the embedded message and enhance the peak signal-to-noise ratio of stego-images.
62726183
Further improve circuit partitioning using GBAW logic perturbation techniques
Efficient circuit partitioning is gaining more importance with the increasing size of modern circuits. Conventionally, circuit partitioning is solved by modeling a circuit as a hypergraph for the ease of applying graph algorithms. However there exists room for further improvement on even optimum hypergraph partitioning results, if logic information can be applied for perturbation. In this paper we present a multi-way partitioning framework which can couple any excellent hypergraph partitioner and a noval logic perturbation based technique (GBAW) for further improvement over very excellent partitioning results. Our approach can integrate with any graph partitioner. We performed experiments on 2-, 3-, 4-, and 5-way partitionings for various circuits of different sizes from MCNC benchmarks. We have chosen the state-of-the-art hMetis-Kway to obtain high quality initial solutions for the experiments. Our experiments showed that this partitioning approach can achieve a further 15% reduction in cut size for 2-way partitioning with an area penalty of only 0.33%. The good results demonstrated the effectiveness of this new partitioning technique.
5502752
Natural Textures for Weather Data Visualization
In this paper we present a novel method to visualize weather data with multi-layer controllable texture synthesis. Texture possesses multiple principal perceptual channels, which makes it good at encoding multiple data attributes contained in weather data. The natural textures existed in the real world especially provide plenty of choices to encode the data with visually pleasing images. A controllable texture synthesis method is developed to generate a large amount of textures which change the appearances of their individual perceptual dimensions according to the underlying distribution of data attributes. In order to encode more data attributes we further propose multi-layer texture synthesis. The background and foreground textures are separately synthesized and then combined together for display. In the end, we apply our method to some real-world weather data and demonstrate its effectiveness with a user study
15215532
Estimating Answer Sizes for XML Queries
Estimating the sizes of query results, and intermediate results, is crucial to many aspects of query processing. In particular, it is necessary for effective query optimization. Even at the user level, predictions of the total result size can be valuable in "next-step" decisions, such as query refinement. This paper proposes a technique to obtain query result size estimates effectively in an XML database.Queries in XML frequently specify structural patterns, requiring specific relationships between selected elements. Whereas traditional techniques can estimate the number of nodes (XML elements) that will satisfy a node-specific predicate in the query pattern, such estimates cannot easily be combined to provide estimates for the entire query pattern, since element occurrences are expected to have high correlation.We propose a solution based on a novel histogram encoding of element occurrence position. With such position histograms, we are able to obtain estimates of sizes for complex pattern queries, as well as for simpler intermediate patterns that may be evaluated in alternative query plans, by means of a position histogram join (pH-join) algorithm that we introduce. We extend our technique to exploit schema information regarding allowable structure (the no-overlap property) through the use of a coverage histogram.We present an extensive experimental evaluation using several XML data sets, both real and synthetic, with a variety of queries. Our results demonstrate that accurate and robust estimates can be achieved, with limited space, and at a miniscule computational cost. These techniques have been implemented in the context of the TIMBER native XML database [22] at the University of Michigan.
15918446
Gesture Recognition Using Quadratic Curves
This paper presents a novel method for human gesture recognition based on quadratic curves. Firstly, face and hands in the images are extracted by skin color and their central points are kept tracked by a modified Greedy Exchange algorithm. Then in each trajectory, the central points are fitted into a quadratic curve and 6 invariants from this quadratic curve are computed. Following these computations, a gesture feature vector composed of 6n such invariants is constructed, where n is the number of the trajectories in this gesture. Lastly, the gesture models are learnt from the feature vectors of gesture samples and an input gesture is recognized by comparing its feature vector with those of gesture models. In this gesture recognition method, the computational cost is low because the gesture duration does not need to be considered and only simple curvilinear integral and matrix computation are involved. Experiments on hip-hop dance show that our method can achieve a recognition rate as high as 97.65% on a database of 16 different gestures, each performed by 8 different people for 8 different times.
10558586
A dual-mode built-in self-test technique for capacitive MEMS devices
A dual-mode built-in self-test (BIST) scheme which partitions the fixed (instead of movable) capacitance plates of a capacitive microelectromechanical system (MEMS) device is proposed. The BIST technique divides the fixed capacitance plate(s) at each side of the movable microstructure into three portions: one for electrostatic activation and the other two equal portions for capacitance sensing. Due to such a partitioning method, the BIST technique can be applied to surface- and bulk-micromachined MEMS devices and other technologies. Further, the sensitivity and symmetry dual BIST modes based on this partitioning can also be developed. The combination of both BIST modes covers a larger defect set, so a more robust testing result for the device can be expected. The BIST technique is verified by three typical capacitive MEMS devices. Simulation results show that the proposed technique is an effective BIST solution for various capacitive MEMS devices.