Jyh-Perng Fang

A GPU-Based Simulation of Tsunami Propagation and Inundation

Tsunami simulation consists of fluid dynamics, numerical computations, and visualization techniques. Nonlinear shallow water equations are often used to model the tsunami propagation. By adding the friction slope to the conservation of momentum, it also can model the tsunami inundation. To solve these equations, we use the second order finite difference MacCormack method. Since it is a finite difference method, it brings the possibility to be parallelized. We use the parallelism provided by GPU to speed up the computations. By loading data as textures in GPU memory, the computation processes can be written as shader programs and the operations will be done by GPU in parallel. The results show that with the help of GPU, the simulation can get a significant improvement in the execution time for each of the computation steps.

Simulated annealing band selection approach for hyperspectral imagery

In hyperspectral imagery, greedy modular eigenspace (GME) was developed by clustering highly correlated bands into a smaller subset based on the greedy algorithm. Unfortunately, GME is hard to find the optimal set by greedy scheme except by exhaustive iteration. The long execution time has been the major drawback in practice. Accordingly, finding the optimal (or near-optimal) solution is very expensive. Instead of adopting the band-subset-selection paradigm underlying this approach, we introduce a simulated annealing band selection (SABS) approach, which takes sets of non-correlated bands for high-dimensional remote sensing images based on a heuristic optimization algorithm, to overcome this disadvantage. It utilizes the inherent separability of different classes embedded in high-dimensional data sets to reduce dimensionality and formulate the optimal or near-optimal GME feature. Our proposed SABS scheme has a number of merits. Unlike traditional principal component analysis, it avoids the bias problems that arise from transforming the information into linear combinations of bands. SABS can not only speed up the procedure to simultaneously select the most significant features according to the simulated annealing optimization scheme to find GME sets, but also further extend the convergence abilities in the solution space based on simulated annealing method to reach the global optimal or near-optimal solution and escape from local minima. The effectiveness of the proposed SABS is evaluated by NASA MODIS/ASTER (MASTER) airborne simulator data sets and airborne synthetic aperture radar images for land cover classification during the Pacrim II campaign. The performance of our proposed SABS is validated by supervised k-nearest neighbor classifier. The experimental results show that SABS is an effective technique of band subset selection and can be used as an alternative to the existing dimensionality reduction method.

Tile-graph-based power planning

In this paper, we introduce a tile-graph-based approach to power planning. For a given floorplan solution, the power inputs are modeled into a tile graph, the minimum capacity of each power input and the maximum power need of each module in a floorplan are accumulated in an associated tile. An efficient cost evaluation algorithm is adopted to calculate the cost of power planning. As its computation time is quite short, it is reasonable to integrate such an algorithm into an iterative floorplanning environment.

An Enhanced BSA for Floorplanning

In the floorplan design of System-on-Chip (SOC), Buffer Site Approach (BSA) has been used to relax the buffer congestion problem. However, for a floorplan with dominant wide bus, BSA may instead worsen the congestion. Our proposed Enhanced Buffer Site Approach (EBSA) extends existing BSA in a way that buffers of dominant wide bus can be distributed more evenly while reserving the same fast operation speed as BSA does. Experiments have been performed to integrate our model into an iterative floorplanning algorithm, and the results reveal that buffer congestion in a floorplan with dominant wide bus can be much abated.

A simulated annealing band selection approach for hyperspectral imagery

For hyperspectral imagery, greedy modular eigenspaces (GME) has been developed by clustering highly correlated hyperspectral bands into a smaller subset of band modules based on greedy algorithm. Instead of greedy paradigm as adopted in GME approach, this paper introduces a simulated annealing band selection (SABS) approach for hyperspectral imagery. SABS selects sets of non-correlated hyperspectral bands for hyperspectral images based on simulated annealing (SA) algorithm while utilizing the inherent separability of different classes in hyperspectral images to reduce dimensionality and further to effectively generate a unique simulated annealing module eigenspace (SAME) feature. The proposed SABS features: (1) avoiding the bias problems of transforming the information into linear combinations of bands as does the traditional principal components analysis (PCA); (2) selecting each band by a simple logical operation, call SAME feature scale uniformity transformation (SAME/FSUT), to include different classes into the most common feature clustered subset of bands; (3) providing a fast procedure to simultaneously select the most significant features according to SA scheme. The experimental results show that our proposed SABS approach is effective and can be used as an alternative to the existing band selection algorithms.

Band selection for hyperspectral images based on impurity function

Band selection for hyperspectral images is an effective technique to mitigate the curse of dimensionality. A variety of band selection methods have been suggested in the past. This paper presents a novel band prioritization based on impurity function (IF) for the band selection of hyperspectral images. The proposed IF band selection (IFBS) is incorporated with particle swarm optimization (PSO) band selection which has been developed to effectively group highly correlated bands of hyperspectral images into high corrected modules. It uses a particle swarm optimization scheme, which is a well-known method to solve the optimization problems, to develop an effective feature extraction algorithm for hyperspectral imagery. After PSO method is applied to the band reduction of hyperspectral images, the proposed IFBS is applied to enhance the efficiency of band selection. The propose method is evaluated by MODIS/ASTER airborne simulator (MASTER) for land cover classification during the Pacrim II campaign. The performance of IFBS is validated by the supervised k-nearest neighbor (KNN) classifier. Experimental results demonstrate that the proposed IFBS approach is an effective method for dimensionality reduction and feature extraction. Compared to other band selection methods, IFBS can effectively select the most significant bands for the image classification of hyperspectral images.

A Parallel Simulated Annealing Approach to Band Selection for Hyperspectral Imagery

In this paper we present a parallel band selection approach, referred to as parallel simulated annealing band selection (PSABS), for hyperspectral imagery. The approach is based on the simulated annealing band selection (SABS) scheme. The SABS algorithm is originally designed to group highly correlated hyperspectral bands into a smaller subset of band modules regardless of the original order in terms of wavelengths. SABS selects sets of non-correlated hyperspectral bands based on simulated annealing (SA) algorithm and utilizes the inherent separability of different classes in hyperspectral images to reduce dimensionality. In order to be effective, the proposed PSABS is introduced to improve the computational speed by using parallel computing techniques. It allows multiple Markov chains (MMC) to be traced simultaneously and fully utilizes the significant parallelism embedded in SABS to create a set of PSABS modules on each parallel node implemented by the message passing interface (MPI) cluster-based library and the open multi-processing (OpenMP) multicore-based application programming interface. The effectiveness of the proposed PSABS is evaluated by MODIS/ASTER airborne simulator (MASTER) hyperspectral images for hyperspectral band selection during the PACRIM II campaign. The experimental results demonstrated that PSABS can significantly improve the computational loads and provide a more reliable quality of solution compared to the original SABS method.

A Simulated Annealing Feature Extraction approach for hyperspectral images

In this paper, a novel study is proposed for the feature extraction of high volumes of remote sensing images by using a simulated annealing feature extraction (SAFE) approach. For hyperspectral imagery, complete modular eigenspace (CME) has been developed by clustering highly correlated hyperspectral bands into a smaller subset of band modular based on greedy algorithm. Instead of greedy paradigm as adopted in CME approach, this paper introduces a simulated annealing (SA) approach for hyperspectral imagery. It presents a framework which consists of three algorithms, referred to as SAFE, CME and the feature scale uniformity transformation (FSUT). SAFE selects the sets of non-correlated hyperspectral bands based on SA algorithm while utilizing the inherent separability of different classes in hyperspectral images to reduce dimensionality and further to effectively generate a unique CME feature. The proposed SA features avoids the bias problems of transforming the information into linear combinations of bands as does the traditional principal components analysis and provides a fast procedure to simultaneously select the most significant features according to a scheme of SA. The experimental results show that the SAFE approach is effective and can be used as an alternative to the existing feature extraction algorithms.

Parallel positive Boolean function approach to classification of remote sensing images

We present a parallel image classification approach, referred to as the parallel positive Boolean function (PPBF), to multisource remote sensing images. PPBF is originally from the positive Boolean function (PBF) classifier scheme. The PBF multiclassifier is developed from a stack filter to classify specific classes of land covers. In order to enhance the efficiency of PBF, we propose PPBF to reduce the execution time using parallel computing techniques. PPBF fully utilizes the significant parallelism embedded in PBF to create a set of PBF stack filters on each parallel node based on different classes of land uses. It is implemented by combining the message-passing interface library and the open multiprocessing (OpenMP) application programing interface in a hybrid mode. The experimental results demonstrate that PPBF significantly reduces the computational loads of PBF classification.

A Parallel Simulated Annealing Approach for Floorplanning in VLSI

One of the critical issues in floorplanning is to minimize area and/or wire length of a given design with millions of transistors while considering other factors which may influence the success of design flow or even manufacturing. To deal with the floorplan design with enormous amount of interconnections and design blocks, we adopt a parallel computing environment to increase the throughput of solution space searching. Also, we include the fractional factorial analysis to further reduce the time needed to search the acceptable solution. The experimental results indicate that our approach can obtain better space utility rate and it takes less time than the traditional method and parallel method do.