Testing issues are becoming more and more important with the quick development of both digital and analog circuit industry. In this paper, we study the utilization of evolutionary algorithms for optimal input vectors ...
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Testing issues are becoming more and more important with the quick development of both digital and analog circuit industry. In this paper, we study the utilization of evolutionary algorithms for optimal input vectors derivation of neural network based analog and mixed signal circuits fault diagnosis approach and compare the results with normal method. We have introduced a new procedure which uses the n-detection test set concept and selects the input samples in a way that for each case of fault injection, there will be at least n sample to activate that fault. This procedure performs the optimization in two ways. The first one called speed method generates samples in a way that acceptable decision strength and lower training phase duration would be achieved. The second one called stamina method generates samples in a way that best decision strength and higher training phase duration would be achieved. Experimental results demonstrate that the obtained input voltages yields fault diagnosis with increased fault coverage and high decision strength. (C) 2008 Elsevier Ltd. All rights reserved.
A phase retrieval technique for multifrequency scattered field acquisition is presented in this paper. The proposed setup is based on indirect holography schemes so that it combines the desired signal with a known ref...
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A phase retrieval technique for multifrequency scattered field acquisition is presented in this paper. The proposed setup is based on indirect holography schemes so that it combines the desired signal with a known reference signal. The setup is used together with a two-dimensional frequency scanning (FS) antenna and, therefore, the positions of scatterers can be retrieved without the need of moving either the antenna or the objects.
The max-cut problem is NP-hard combinatorial optimization problem with many real world applications. In this paper, we propose an integrated method based on particle swarm optimization and estimation of distribution a...
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The max-cut problem is NP-hard combinatorial optimization problem with many real world applications. In this paper, we propose an integrated method based on particle swarm optimization and estimation of distribution algorithm (PSO-EDA) for solving the max-cut problem. The integrated algorithm overcomes the shortcomings of particle swarm optimization and estimation of distribution algorithm. To enhance the performance of the PSO-EDA, a fast local search procedure is applied. In addition, a path relinking procedure is developed to intensify the search. To evaluate the performance of PSO-EDA, extensive experiments were carried out on two sets of benchmark instances with 800 to 20000 vertices from the literature. Computational results and comparisons show that PSO-EDA significantly outperforms the existing PSO-based and EDA-based algorithms for the max-cut problem. Compared with other best performing algorithms, PSO-EDA is able to find very competitive results in terms of solution quality.
Detecting local community structure in complex networks is an appealing problem that has attracted increasing attention in various domains. However, most of the current local community detection algorithms, on one han...
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Detecting local community structure in complex networks is an appealing problem that has attracted increasing attention in various domains. However, most of the current local community detection algorithms, on one hand, are influenced by the state of the source node and, on the other hand, cannot effectively identify the multiple communities linked with the overlapping nodes. We proposed a novel local community detection algorithm based on maximum clique extension called LCD-MC. The proposed method firstly finds the set of all the maximum cliques containing the source node and initializes them as the starting local communities;then, it extends each unclassified local community by greedy optimization until a certain objective is satisfied;finally, the expected local communities will be obtained until all maximum cliques are assigned into a community. An empirical evaluation using both synthetic and real datasets demonstrates that our algorithm has a superior performance to some of the state-of-the-art approaches.
During the recent years several chaotic image encryption algorithms have been proposed, but most of them encountered some drawbacks such as small key space, low speed, lack of robustness and low security. In this pape...
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During the recent years several chaotic image encryption algorithms have been proposed, but most of them encountered some drawbacks such as small key space, low speed, lack of robustness and low security. In this paper, we have proposed an image algorithm based on the combination of a one-dimensional polynomial chaotic map and a piecewise nonlinear chaotic map. Theoretical analysis and computer simulations, both confirm that the new algorithm possesses high security, robust fast encryption speed for practical image encryption and solves the problem of small key space. (C) 2011 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
We present a novel representation and algorithm, ReduceM, for memory efficient ray tracing of large scenes. ReduceM exploits the connectivity between triangles in a mesh and decomposes the model into triangle strips. ...
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We present a novel representation and algorithm, ReduceM, for memory efficient ray tracing of large scenes. ReduceM exploits the connectivity between triangles in a mesh and decomposes the model into triangle strips. We also describe a new stripification algorithm, Strip-RT, that can generate long strips with high spatial coherence. Our approach uses a two-level traversal algorithm for ray-primitive intersection. In practice, ReduceM can significantly reduce the storage overhead and ray trace massive models with hundreds of millions of triangles at interactive rates on desktop PCs with 4-8GB of main memory.
In previous work we have proposed a supervised globalized dual heuristic programming (GDHP) controller as a solution to the fault tolerant control (FTC) problem of nonlinear plants subject to abrupt and incipient faul...
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In previous work we have proposed a supervised globalized dual heuristic programming (GDHP) controller as a solution to the fault tolerant control (FTC) problem of nonlinear plants subject to abrupt and incipient faults capable of drastically modifying the system dynamics to maintain stability and performance. The neural network (NN) based adaptive critic controller presented the best choice for the flexibility and power necessary to accomplish the task, however no success guarantees can be made for the online training of neural weights for the unrestricted fault recovery problem. Built on the existing framework, we propose a novel supervisory system capable of detecting controller malfunctions before the stability of the plant is compromised. Furthermore, due to its ability to discern between controller malfunctions and faults within the plant, the proposed supervisor acts in a specific fashion in the event of a controller malfunction to provide new avenues with a greater probability of convergence using information from a dynamic model bank. The classification and distinction of controller malfunctions from the faults in the plant itself is achieved through an advanced decision logic based on three independent quality indexes. Proof-of-the-concept simulations over a nonlinear plant demonstrate the validity of the approach.
Accurately tracking the video object in video sequence is a crucial stage for video object processing which has wide applications in different fields. In this paper, a novel video object tracking algorithm based on th...
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Accurately tracking the video object in video sequence is a crucial stage for video object processing which has wide applications in different fields. In this paper, a novel video object tracking algorithm based on the improved gradient vector flow (GVF) snake model and intra-frame centroids tracking algorithm is proposed. Unlike traditional gradient vector flow snake, the improved gradient vector flow snake adopts anisotropic diffusion and a four directions edge operator to solve the blurry boundary and edge shifting problem. Then the improved gradient vector flow snake is employed to extract the object contour in each frame of the video sequence. To set the initial contour of the gradient vector flow snake automatically, we design an intra-frame centroids tracking algorithm. Splitting the original video sequence into segments, for each segment, the initial contours of first two frames are set by change detection based on t-distribution significance test. Then, utilizing the redundancy between the consecutive frames, the subsequent frames' initial contours are obtained by intra-frame motion vectors. Experimental results with several test video sequences indicate the validity and accuracy of the video object tracking. (C) 2014 Elsevier Ltd. All rights reserved.
This study investigates the design of a field-programmable gate array-based custom computer architecture solution for implementing model predictive control (MPC). The solution employs a primal logarithmic-barrier inte...
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This study investigates the design of a field-programmable gate array-based custom computer architecture solution for implementing model predictive control (MPC). The solution employs a primal logarithmic-barrier interior-point algorithm in order to handle actuator constraints. The solution also incorporates practical aspects of a control algorithm including state observation and data sampling. The resulting circuit is profiled by application to a disturbance rejection control problem of a 14th-order lightly damped flexible beam structure with actuator constraints. This is achieved at 2 kHz sampling frequency and with 16-sample prediction horizon.
Dynamic bandwidth allocation in passive optical networks presents a key issue for providing efficient and fair utilization of the PON upstream bandwidth while supporting the QoS requirements of different traffic class...
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Dynamic bandwidth allocation in passive optical networks presents a key issue for providing efficient and fair utilization of the PON upstream bandwidth while supporting the QoS requirements of different traffic classes. In this article we compare the typical characteristics of DBA, such as bandwidth utilization, delay, and jitter at different traffic loads, within the two major standards for PONs, Ethernet PON and gigabit PON. A particular PON standard sets the framework for the operation of DBA and the limitations it faces. We illustrate these differences between EPON and GPON by means of simulations for the two standards. Moreover, we consider the evolution of both standards to their next-generation counterparts with the bit rate of 10 Gb/s and the implications to the DBA. A new simple GPON DBA algorithm is used to illustrate GPON performance. It is shown that the length of the polling cycle plays a crucial but different role for the operation of the DBA within the two standards. Moreover, only minor differences regarding DBA for current and next-generation PONs were found.
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