• Journal of Korea Multimedia Society
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• v.17 no.9
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• pp.1041-1051
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• 2014

In this paper, we propose a new image alignment technique based on CUDA SURF in order to solve the initial image alignment problem that frequently occurs in machine vision applications. Machine vision systems using multi-spectral images have recently become more common for solving various decision problems that cannot be performed by the human vision system. These machine vision systems mostly use markers for the initial image alignment. However, there are some applications where the markers cannot be used and the alignment techniques have to be changed whenever their markers are changed. In order to solve these problems, we propose a new image alignment method for multi-spectral machine vision applications based on SURF extracting image features without depending on markers. In this paper, we propose an image alignment method that obtains a sufficient number of feature points from multi-spectral images using SURF and removes outlier iteratively based on a least squares method. We further propose an effective preliminary scheme for removing mismatched feature point pairs that may affect the overall performance of the alignment. In addition, we reduce the execution time by implementing the proposed method using CUDA based on GPGPU in order to guarantee real-time operation. Simulation results show that the proposed method is able to align images effectively in applications where markers cannot be used.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.5
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• pp.267-275
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• 2017

Time of difference of arrival (TDOA) method using passive sonar sensor array has normally been used to estimate the location of a concealed moving target in underwater environment. Particle filter has been introduced for effective target estimation for non-Gaussian and nonlinear systems. In this paper, we propose a GPU-based acceleration of target position estimation using particle filter and propose efficient embedded system and software architecture. For the TDOA measurement from the passive sonar sensor, we use the generalized cross correlation phase transform (GCC-PHAT) method to obtain the correlation coefficient of the signal using FFT and we try to accelerate the calculation of GCC-PHAT based TDOA measurements using FFT with GPU CUDA. We also propose parallelization method of the target position estimation algorithm using the GPU CUDA to update the state of each particle for the target position estimation using the measured values. The target estimation algorithm was verified using Matlab and implemented using GPU CUDA. Then, we realized the proposed signal processing acceleration system using NVIDIA Jetson TX1 as the target board to analyze in terms of the execution time. The execution time of the algorithm is reduced by 55% to the CPU standalone-operation on the target board. Experiment results show that the proposed architecture is a feasible solution in terms of high-performance and area-efficient architecture.

• Journal of IKEEE
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• v.22 no.2
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• pp.281-288
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• 2018

In this paper, we propose an intelligent face recognition and tracking system that distributes GPU resources using CUDA. The proposed system consists of five steps such as GPU allocation algorithm that distributes GPU resources in optimal state, face area detection and face recognition using deep learning, real time face tracking, and PTZ camera control. The GPU allocation algorithm that distributes multi-GPU resources optimally distributes the GPU resources flexibly according to the activation level of the GPU, unlike the method of allocating the GPU to the thread fixedly. Thus, there is a feature that enables stable and efficient use of multiple GPUs. In order to evaluate the performance of the proposed system, we compared the proposed system with the non - distributed system. As a result, the system which did not allocate the resource showed unstable operation, but the proposed system showed stable resource utilization because it was operated stably. Thus, the utility of the proposed system has been demonstrated.

• Journal of Broadcast Engineering
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• v.20 no.3
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• pp.421-429
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• 2015

This paper is about an approach for pedestrian detection and tracking with infrared imagery. We used the CUDA(Computer Unified Device Architecture) that is a parallel processing language in order to improve the speed of video-based pedestrian detection and tracking. The detection phase is performed by Adaboost algorithm based on Haar-like features. Adaboost classifier is trained with datasets generated from infrared images. After detecting the pedestrian with the Adaboost classifier, we proposed a particle filter tracking strategies on HSV histogram feature that exploit adaptively at the same time. The proposed approach is implemented on an NVIDIA Jetson TK1 developer board that is full-featured device ideal for software development within the Linux environment. In this paper, we presented the results of parallel processing with the NVIDIA GPU on the CUDA development environment for detection and tracking of pedestrians. We compared the object detection and tracking processing time for night-time images on both GPU and CPU. The result showed that the detection and tracking speed of the pedestrian with GPU is approximately 6 times faster than that for CPU.

• Journal of Digital Contents Society
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• v.18 no.4
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• pp.739-745
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• 2017

This paper presents and discusses an implementation of the GPU shifted sorting method to find approximate k nearest neighbors which executes within "warp", the minimum execution unit in GPU parallel architecture. Also, this paper presents the comparison results with other two common nearest neighbor searching methods, GPU-based kd-tree and ANN (Approximate Nearest Neighbor) library. The proposed implementation focuses on the cases when k is small, i.e. 2, 4, 8, and 16, which are handled efficiently within warp to consider it is very common for applications to handle small k's. Also, this paper discusses optimization ways to implementation by improving memory management in a loop for the CUB open library and adopting CUDA commands which are supported by GPU hardware. The proposed implementation shows more than 16-fold speed-up against GPU-based other methods in the tests, implying that the improvement would become higher for more larger input data.

• Journal of Convergence for Information Technology
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• v.9 no.7
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• pp.33-40
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• 2019

As aerospace industry has been activated recently, it is required to commercialize composite analysis software. Until now, commercial software has been mainly used for analyzing composites, but it has been difficult to use due to high price and limited functions. In order to solve this problem, automatic analysis software for both in-plane and corner radius strength, which are all made on-line and generalized, has recently been developed. However, these have the disadvantage that they can not be analyzed simultaneously with multiple failure criteria. In this paper, we propose a method to greatly improve the processing speed while simultaneously handling the analysis of multiple failure criteria using a parallel processing platform that only works with a GPU equipped with a CUDA core. We have obtained satisfactory results when the analysis speed is experimented on the vast structure data.

• Journal of computational fluids engineering
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• v.19 no.3
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• pp.52-61
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• 2014

The present paper deals with the efficient computation of higher-order CFD methods for compressible flow using graphics processing units (GPU). The higher-order CFD methods, such as discontinuous Galerkin (DG) methods and correction procedure via reconstruction (CPR) methods, can realize arbitrary higher-order accuracy with compact stencil on unstructured mesh. However, they require much more computational costs compared to the widely used finite volume methods (FVM). Graphics processing unit, consisting of hundreds or thousands small cores, is apt to massive parallel computations of compressible flow based on the higher-order CFD methods and can reduce computational time greatly. Higher-order multi-dimensional limiting process (MLP) is applied for the robust control of numerical oscillations around shock discontinuity and implemented efficiently on GPU. The program is written and optimized in CUDA library offered from NVIDIA. The whole algorithms are implemented to guarantee accurate and efficient computations for parallel programming on shared-memory model of GPU. The extensive numerical experiments validates that the GPU successfully accelerates computing compressible flow using higher-order method.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.411-417
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• 2016

Since the development of Graphic Processing Unit (GPU) in 1999, the development speed of GPUs has become much faster than that of CPUs and currently, the computational power of GPUs exceeds CPUs dozens and hundreds times in terms of decimal calculations and costs much less. Owing to recent technological development of hardwares, general-purpose computing and utilization using GPUs are on the rise. Thus, in this paper, we have identified the elements to be considered for the Smart Grid Security. Focusing on a Performance Improvement of the Basic Algorithm for the Stateful Inspection to Detect DDoS Attacks using CUDA. In the program, we compared the search speeds of GPU against CPU while they search for the suffix trees. For the computation, the system constraints and specifications were made identical during the experiment. We were able to understand from the results of the experiment that the problem-solving capability improves when GPU is used. The other finding was that performance of the system had been enhanced when shared memory was used explicitly instead of a global memory as the volume of data became larger.

• Journal of Broadcast Engineering
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• v.16 no.6
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• pp.1009-1017
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• 2011

Fast generation of digital hologram is of importance for real-time holography broadcasting. In this paper, we propose such a method that parallelizes the Computer-Generated Holography (CGH) algorithm for digital hologram generation and make it faster using Multi Graphic Processing Unit (Multi-GPU) with help of the Compute Unified Device Architecture (CUDA) and the Open Multi-Processing (OpenMP). In addition, we propose optimization methods such as fixation variable, vectorization, and loop unrolling for making the CGH algorithm much faster. Experimental results show that our method is about 9,700 times faster than a CPU-based one.

• Journal of Internet Computing and Services
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• v.12 no.3
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• pp.131-137
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• 2011

GPUs were originally designed for graphic processing, and GPGPUs are general-purpose GPUs for numerical computation with high performance and low electric power. In this paper, we implemented the parallel LU factorization program for GPGPUs. In CUDA, which is computational environment for Nvidia GPGPUs, domains are divided into blocks, and multi-threads compute each sub-blocks Simultaneously. In LU factorization program, computation order should be artificially decided due to the data dependence. To resolve the data dependancy, we suggested a parallel LU program for GPGPUs, and also explained parallel reduction algorithm for partial pivoting of LU factorization. We finally present performance analysis to show efficiency of the parallel LU factorization program based on multi-threads on GPGPUs.