فهرس المقالات CUDA

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  1 - Parallelization of Rich Models for Steganalysis of Digital Images using a CUDA-based Approach
  Mahmoud Kazemi Meysam Mirzaee Reza Isfahani
  There are several different methods to make an efficient strategy for steganalysis of digital images. A very powerful method in this area is rich model consisting of a large number of diverse sub-models in both spatial and transform domain that should be utilized. Howev أکثر

  There are several different methods to make an efficient strategy for steganalysis of digital images. A very powerful method in this area is rich model consisting of a large number of diverse sub-models in both spatial and transform domain that should be utilized. However, the extraction of a various types of features from an image is so time consuming in some steps, especially for training phase with a large number of high resolution images that consist of two steps: train and test. Multithread programming is a near solution to decreasing the required time but it’s limited and it ‘snot so scalable too. In this paper, we present a CUDA based approach for data-parallelization and optimization of sub-model extraction process. Also, construction of the rich model is analyzed in detailed, presenting more efficient solution. Further, some optimization techniques are employed to reduce the total number of GPU memory accesses. Compared to single-thread and multi-threaded CPU processing, 10x-12x and 3x-4x speedups are achieved with implementing our CUDA-based parallel program on GT 540M and it can be scaled with several CUDA cards to achieve better speedups. تفاصيل المقالة
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  2 - High Performance Implementation of Fuzzy C-Means and Watershed Algorithms for MRI Segmentation
  Farnaz Hoseini Ghader Mortezaie Dekahi
  Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, fin أکثر

  Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep light variations of medical images. Due to the inherently parallel nature of image segmentation algorithms, they suit well for implementation on a Graphics Processing Unit (GPU). The main goal of this paper is to improve the performance of fuzzy c-means clustering through the parallel implementation of this algorithm. Although fuzzy c-means clustering is an important iterative clustering algorithm, it is computationally intensive and uses the same data between the iterations. The center of the clusters changes in each iteration, which requires a considerable amount of time for large data sets. The parallel fuzzy c-means clustering is implemented by applying pipeline parallelism on GPU. The experimental results show that the performance is improved up to 23.35x. Next, the watershed algorithm is applied to the final segmentation. In this paper using parallel fuzzy c-means clustering and computations we have attained competing results with other papers. The implementation results on the BRATS2015 show that the accuracy of diagnosis in Dice Similarity Coefficient metric 97/33% is obtained. This improvement is achieved using enhancing edges and reducing noises in images. تفاصيل المقالة
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  3 - Speeding up the 0-1 Knapsack Problem Using Shuffled Frog Leaping Algorithm
  Farnaz Hoseini
  The knapsack problem is known as a NP-hard problem. The knapsack or rucksack problem consists of determining, given a set of items, each of which has a cost and a value, the number of items included in a collection such that the total cost is less than a given cost and أکثر

  The knapsack problem is known as a NP-hard problem. The knapsack or rucksack problem consists of determining, given a set of items, each of which has a cost and a value, the number of items included in a collection such that the total cost is less than a given cost and the total value is as large as possible. There is a dynamic programming solution for this problem called the 0-1 knapsack. The 0-1 knapsack problem restricts the number of individual items to zero or one. The shuffled frog-leaping algorithm (SFLA) has long been considered a meta-heuristic algorithm that derives from how frog groups search for food. SFLA can improve computing performance by letting all frogs participate in memetic evolution and access an excellent ability for global search by adding the self-variation behavior to the frog. This study represents an efficient solution for the 0-1 knapsack problem using SFLA. Regarding the parallel nature of most meta-heuristic algorithms, they can be successfully used for speedup. Since it is time-consuming to test all the cases when the problems become larger, Compute Unified Device Architecture (CUDA) is used to implement the solution in parallel. The results of simulating the 0-1 knapsack problem using SFLA on the CUDA platform show that the execution time for a parallel solution decreases as the population of frogs increases. For the 0-1 Knapsack problem, it is 252 times faster than the sequential solution. تفاصيل المقالة
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  4 - اندازه گیری و مقایسه فصلی فلزات سنگین Zn، Cu، Pb و Cd در بافت عضله و کبد ماهی کوتر Sphyraena sp. در آب های بندرعباس (1392 – 1391)
  سینا آقایی آریا اشجع اردلان نرگس مورکی کاظم درویش بسطامی ندا ملا محی الدین
  این مطالعه جهت تعیین میزان فلزات سنگین روی، مس، سرب و کادمیوم در دو بافت عضله و کبد ماهی کوتر Sphyraena sp. متعلق به خانوادهSphyraenidaeدر آب های بندر عباس طی زمستان سال 1391 و بهار، تابستان و پاییز سال 1392 انجام گرفت. جهت انجام این کار در هر فصل 20 نمونه از ماهی مذکور أکثر

  این مطالعه جهت تعیین میزان فلزات سنگین روی، مس، سرب و کادمیوم در دو بافت عضله و کبد ماهی کوتر Sphyraena sp. متعلق به خانوادهSphyraenidaeدر آب های بندر عباس طی زمستان سال 1391 و بهار، تابستان و پاییز سال 1392 انجام گرفت. جهت انجام این کار در هر فصل 20 نمونه از ماهی مذکور با طول متوسط 45 سانتی متر صید گردید. ماهیان مورد آزمایش پس از صید منجمد و بسته بندی شده و جهت تعیین میزان تجمع میزان فلزات سنگین در بافت عضله و کبد به آزمایشگاه ارسال شدند. جهت استخراج فلزات از بافت عضله و کبد ماهیان مورد مطالعه از روش استاندارد هضم با اسید (ASTM)استفاده شد و تعیین غلظت بوسیله دستگاه نشر اتمی ICP صورت گرفت. میانگین نتایج حاصل مقادیر 800/0±675/2، 658/0±385/0، 169/0±325/0 و 012/0±087/0 برای بافت عضله و 943/3±850/11، 632/1±957/4، 2406/0±485/0 و 2402/0±480/0 برای بافت کبد بر حسب میکرو گرم در گرم وزن تر به ترتیب برای فلزات روی، مس، سرب و کادمیوم را نشان داد. به کمک آزمون t و استانداردهای جهانی نظیر: سازمان بهداشت جهانی، وزارت کشاورزی، شیلات و غذایی انگلستان مقایسه و پایین تر بودن غلظت تمام فلزات در بافت عضله و کبد به جز میزان کادمیوم در بافت کبد از استانداردهای فوق نتیجه گیری گردید. بنابر نتایج حاصله، دربافت عضله اختلاف معنی داری بین فلزات سرب، روی و مس در طول سال وجود نداشت (05/0P≥)، ولی در کادمیوم اختلاف معنی داری در طول سال مشاهده گردید (05/0P˂). در بافت کبد اختلاف معنی دار بین میزان هیچ یک از فلزات در طول سال مشاهده نشد (05/0P≥). تفاصيل المقالة