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Diderot is a parallel domain-specific language forthe analysis and visualization of multidimensional scientific images, such as those produced by CT and MRI scanners. Diderot is designed to support algorithms that are based on differential tensor calculus and produces a higher-order mathematical model which allows direct manipulation of tensor fields. One of the main challenges of the Diderot implementation...
Ordinary Fluid Simulation focuses on the effects of the fluid surface, like seas, rivers and lakes, which is massive and unshakable. Few researches have devoted to simulate the phenomenon of fluid in containers, especially the behaviors of contained fluid being moved or tilted. This paper concentrates on dynamic simulation of fluid in containers. Based on Unity3D and Smoothed Particle Hydrodynamics...
High performance computing (HPC) systems frequently suffer errors and failures from hardware components that negatively impact the performance of jobs run on these systems. We analyzed system logs from two HPC systems at Purdue University and created statistical models for memory and hard disk errors. We created a small-scale error injection testbed—using a customized QEMU build, libvirt, and Python—that...
A kernel-based nonparametric approach to identification of linear systems in the presence of bounded noise affecting both input and output measurements is proposed in this paper. The problem to be solved is firstly formulated in terms of robust optimization. The solution to such a problem is then obtained by proving that the originally formulated robust optimization problem is equivalent to a standard...
Classification is at the very center of the supervised learning. In this work, we propose a novel algorithm to classify the test data set with the aid of a vector field, emanating from the training data set. In particular, the vector field is constructed such that the location of each training data point becomes a local minimum of the potential. The test data points are allowed to evolve under the...
In the process of large-scale chemical engineering, more useful industrial process information can be obtained by increasing measuring variables, defined as system features. However, the increase in amount of features will lead to the high computation cost and reduce the efficiency of the process monitoring system. To solve this issue, those features that are redundant or bring an incorrect result...
Background Subtraction is the major important step in many image processing applications which can be applied in much of video surveillances. The major result of this method is accuracy as well as processing time. So we mainly focused on these two challenges. We parallelized the Two Layered CodeBook Model on Graphical Processing Unit (GPU) for increasing the processing speed and the accuracy of the...
Today the technology advancement in communication technology permits a malware author to introduce code obfuscation technique, for example, Application Programming Interface (API) hook, to make detecting the footprints of their code more difficult. A signature-based model such as Antivirus software is not effective against such attacks. In this paper, an API graph-based model is proposed with the...
Presilicon simulation is one of the key toolsets for computer architects to evaluate and optimize their future designs. As Graphics Processing Units (GPUs) have become the platform of choice in many computing communities due to their impressive processing capabilities, computer architecture researchers need a simulation framework that allows them to quantitatively consider design tradeoffs. In this...
While the task of Optical Character Recognition is deemed to be a solved problem in many languages, it still requires certain improvements in some languages with more complex script structures such as Farsi. Furthermore, Deep Convolution Neural Networks have reached excellent results in various computer vision tasks, including character recognition. Although, these networks require a great amount...
DNNs (Deep Neural Networks) have demonstrated great success in numerous applications such as image classification, speech recognition, video analysis, etc. However, DNNs are much more computation-intensive and memory-intensive than previous shallow models. Thus, it is challenging to deploy DNNs in both large-scale data centers and real-time embedded systems. Considering performance, flexibility, and...
Convolutional neural networks (CNNs) have recently broken many performance records in image recognition and object detection problems. The success of CNNs, to a great extent, is enabled by the fast scaling-up of the networks that learn from a huge volume of data. The deployment of big CNN models can be both computation-intensive and memory-intensive, leaving severe challenges to hardware implementations...
Heterogeneous CPU-GPU systems have recently emerged as an energy-efficient computing platform. A robust integrated CPU-GPU simulator is essential to facilitate researches in this direction. While few integrated CPU-GPU simulators are available, similar tools that support OpenCL 2.0, a widely used new standard with promising heterogeneous computing features, are currently missing. In this paper, we...
Image procession algorithms for compensation of scattered radiation influence in X-ray imaging were proposed, studied and optimized by numerical simulations. The algorithms include scattering estimation by convolution (superposition) technique, estimation of kernel functions by Monte-Carlo (MC) simulations, determination the optimal number and shape of kernel functions and images segmentation. Determination...
Sparsity in the weights of deep convolutional networks presents a tremendous opportunity to reduce computational requirements. In order to optimize flow of traffic systems, any viable solution must be able to operate at real-time. Existing computation frameworks do not yet realize the full potential speedup afforded by sparse neural networks. Meanwhile, the power consumption for a GPU is too great...
This paper highlights on-going research to effectively utilize a commercially available spatially reconfigurable platform and the OpenCL framework to improve the run-time performance and reduce the overall energy consumption of an existing 2.5D Electrostatic Particle-in-Cell type plasma simulation. This problem is constrained by the finite internal FPGA resources and the performance mandate that all...
In the last years, the integration of specialized hardware accelerators in Multiprocessor System-on-Chip (MpSoC) led to a new kind of architectures combining both software (SW) and hardware (HW) computational resources. For these new Heterogeneous MpSoC (HMpSoC) architectures, performance and energy consumption depend on a large set of parameters such as the HW/SW partitioning, the type of HW implementation...
MRI parameter quantification has diverse applications, but likelihood-based methods typically require nonconvex optimization due to nonlinear signal models. To avoid expensive grid searches used in prior works, we propose to learn a nonlinear estimator from simulated training examples and (approximate) kernel ridge regression. As proof of concept, we apply kernel-based estimation to quantify six parameters...
In this communication, a general Volterra model for complex-valued systems is employed to analyze the model structure for a power amplifier (PA) and for an I/Q modulator in different situations. Emphasis is placed on showing the differences in the present regressors when the PA is driven by a signal generator with impairments in contrast to the case in which the modulator impairments are previously...
This paper utilizes the deep learning algorithm to classify the Street View images. We did some research to find the appropriate convolutional neural network model that suits the classification of the street view images. We firstly collected our own dataset. Based on the convolutional neural network model AlexNet and according to the characteristics the dataset mentioned above to adjust the model...
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