Logical workow nets (LWNs) provide a good tool for modeling business systems with passing value indeterminacy and batch processing function. However, complicated business processes modeling is a hard work. And thus, t...
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In this paper, we study capacity scaling laws of the deterministic dissemination (DD) in random wireless networks under the generalized physical model (GphyM). This is truly not a new topic. Our motivation to readdres...
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In this paper, we study capacity scaling laws of the deterministic dissemination (DD) in random wireless networks under the generalized physical model (GphyM). This is truly not a new topic. Our motivation to readdress this issue is two-fold: Firstly, we aim to propose a more general result to unify the network capacity for general homogeneous random models by investigating the impacts of different parameters of the system on the network capacity. Secondly, we target to close the open gaps between the upper and the lower bounds on the network capacity in the literature. We derive the general upper bounds on the capacity for the arbitrary case of (λ, nd, ns) by introducing the Poisson Boolean model of continuum percolation, where λ, nd, and ns are the general node density, the number of destinations for each session, and the number of sessions, respectively. We prove that the derived upper bounds are tight according to the existing general lower bounds constructed in the literature.
In semiconductor manufacturing, wafer residency time constraints make the scheduling problem of cluster tools complicated. A process module (PM) in cluster tools is prone to failure. It is crucial to deal with any suc...
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Analyzing human activities in surveillance videos a challenging task due to the high volume of data that needs to be processed in a timely manner. This paper presents a GPU based Gaussian Mixture Model (GMM) to detect...
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Analyzing human activities in surveillance videos a challenging task due to the high volume of data that needs to be processed in a timely manner. This paper presents a GPU based Gaussian Mixture Model (GMM) to detect abnormal activities in crowded scenes. GMM is a fully unsupervised method that predicts abnormal crowd behaviors based on the processing of normal crowd behaviors. Specifically, we use crowd distribution and GMM to estimate the speed and to predict the behaviors of the crowd. The performance of the parallel GMM is evaluated from the aspects of computation efficiency and accuracy in terms of area under the curve.
Virtualizing Hadoop clusters provides many benefits, including rapid deployment, on-demand elasticity and secure multi-tenancy. However, a simple migration of Hadoop to a virtualized environment does not fully exploit...
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Virtualizing Hadoop clusters provides many benefits, including rapid deployment, on-demand elasticity and secure multi-tenancy. However, a simple migration of Hadoop to a virtualized environment does not fully exploit these benefits. The dual role of a Hadoop worker, acting as both a compute node and a data node, makes it difficult to achieve efficient IO processing, maintain data locality, and exploit resource elasticity in the cloud. We find that decoupling per-node storage from its computation opens up opportunities for IO acceleration, locality improvement, and on-the-fly cluster resizing. To fully exploit these opportunities, we propose StoreApp, a shared storage appliance for virtual Hadoop worker nodes co-located on the same physical host. To completely separate storage from computation and prioritize IO processing, StoreApp pro-actively pushes intermediate data generated by map tasks to the storage node. StoreApp also implements late-binding task creation to take the advantage of prefetched data due to mis-aligned records. Experimental results show that StoreApp achieves up to 61% performance improvement compared to stock Hadoop and resizes the cluster to the (near) optimal degree of parallelism.
Reachability is an important dynamic property of Petri nets. Its determination given an unbounded net and initial marking has remained an open problem since 1960s. Due to its extreme difficulty, a great deal of resear...
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ISBN:
(纸本)9781479969241
Reachability is an important dynamic property of Petri nets. Its determination given an unbounded net and initial marking has remained an open problem since 1960s. Due to its extreme difficulty, a great deal of research has been focused on some subclasses of unbounded nets. For arbitrary ones, this work intends to propose a new reachability tree (NRT), which consists of only but all reachable markings from its initial marking. Furthermore, an NRT-based method to decide deadlocks of unbounded nets is presented. An example is provided to show the new results.
In order to solve current realistic problems such as low-efficiency in resource allocation, difficulty in headquarters integrated management, high-risk in finance, and inaccuracy and untimeliness of information sharin...
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Compared to the traditional SAR imaging algorithm, Back Projection(BP) algorithm is an accurate point-by-point imaging radar algorithm based on time-domain, with simple principle and without any approximation error in...
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ISBN:
(纸本)9781510822023
Compared to the traditional SAR imaging algorithm, Back Projection(BP) algorithm is an accurate point-by-point imaging radar algorithm based on time-domain, with simple principle and without any approximation error in the imaging process. However, because of intensive computation and low efficiency, it's a new challenge to storage to capacity, throughput and processing ability of DSPs, a single DSP is not enough to meet these demands. So a parallel implementation method of BP algorithm based on TMS320C6678 DSP is proposed in this *** put forward a large point FFT multi-core parallel processing method on 2/4/8 cores what is frequently used in BP algorithm, and a multi-core synchronization method based on distributed memory. Finally using the measured data, we verify the parallel method can greatly enhance the multi-core parallelism, and the real-time performance of BP algorithm has been significantly improved.
Learning automata (LA) represent important leaning mechanisms with applications in automated system design, biological system modeling, computer vision, and transportation. They play the critical roles in modeling a p...
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ISBN:
(纸本)9781467381840
Learning automata (LA) represent important leaning mechanisms with applications in automated system design, biological system modeling, computer vision, and transportation. They play the critical roles in modeling a process as well as generating the appropriate signal to control it. They update their action probabilities in accordance with the inputs received from the environment and can improve their own performance during operations. The action probability vector in LA takes charge of two functions: 1) The cost of convergence, i.e., the size of sampling budget;2) The allocation of sampling budget among actions to identify the optimal one. These two intertwined functions lead to a problem: The sampling budget mostly goes to the currently estimated optimal action due to its high action probability regardless whether it can help identify the real optimal action or not. This work proposes a new class of LA that separates the allocation of sampling budget from the action probability vector. It uses the action probability vector to determine the size of sampling budget and then uses Optimal computing Budget Allocation (OCBA) to accomplish the allocation of sampling budget in a way that maximizes the probability of identifying the true optimal action. Simulation results verify its significant speedup ranging from 10.93% to 65.94% over the best existing LA algorithms.
Attention Deficit Hyperactivity Disorder (ADHD) is one of the common diseases of brain and has brought the growth of teenagers and even the adult indelible damage. It is very different to classify the ADHD symptoms an...
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ISBN:
(纸本)9781479966226
Attention Deficit Hyperactivity Disorder (ADHD) is one of the common diseases of brain and has brought the growth of teenagers and even the adult indelible damage. It is very different to classify the ADHD symptoms and normal by the existing research. In this paper, the contributions are as two aspects: one is that the attributes of brain network of the resting state fMRI data have been calculated to discriminate three categories ADHD from the controls. And the average accuracies of various categories are 42.49% and 63.46% on the ADHD-200 datasets of NYU and KKI respectively, which is better than the average best imaging-based diagnostic performance of 35.19% and 61.90% achieved in the ADHD-200 global competition. The other one is that we put forward a new method named G-algorithm to construct the whole brain network, which based on certain rules. The same or even better classification results have been achieved by this method, which also verifies its feasibility and effectiveness.
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