According to the image reconstruction accuracy influenced by the "soft field" nature and ill-conditioned problems in electrical capacitance tomography, a superresolution image reconstruction algorithm based ...
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According to the image reconstruction accuracy influenced by the "soft field" nature and ill-conditioned problems in electrical capacitance tomography, a superresolution image reconstruction algorithm based on Landweber is proposed in the paper, which is based on the working principle of the electrical capacitance tomography system. The method uses the algorithm which is derived by regularization of solutions derived and derives closed solution by fast Fourier transform of the convolution kernel. So, it ensures the certainty of the solution and improves the stability and quality of image reconstruction results. Simulation results show that the imaging precision and real-time imaging of the algorithm are better than Landweber algorithm, and this algorithm proposes a new method for the electrical capacitance tomography image reconstruction algorithm.
Urban facades regularly contain interesting variations due to allowed deformations of repeated elements (e.g., windows in different open or close positions) posing challenges to state-of-the-art facade analysis algori...
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Urban facades regularly contain interesting variations due to allowed deformations of repeated elements (e.g., windows in different open or close positions) posing challenges to state-of-the-art facade analysis algorithms. We propose a semi-automatic framework to recover both repetition patterns of the elements and their individual deformation parameters to produce a factored facade representation. Such a representation enables a range of applications including interactive facade images, improved multi-view stereo reconstruction, facade-level change detection, and novel image editing possibilities.
Using local invariant features has been proven by published literature to be powerful for image processing and pattern recognition tasks. However, in energy aware environments, these invariant features would not scale...
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Using local invariant features has been proven by published literature to be powerful for image processing and pattern recognition tasks. However, in energy aware environments, these invariant features would not scale easily because of their computational requirements. Motivated to find an efficient building recognition algorithm based on scale invariant feature transform (SIFT) keypoints, we present in this paper uSee, a supervised learning framework which exploits the symmetrical and repetitive structural patterns in buildings to identify subsets of relevant clusters formed by these keypoints. Once an image is captured by a smart phone, uSee preprocesses it using variations in gradient angle-and entropy-based measures before extracting the building signature and comparing its representative SIFT keypoints against a repository of building images. Experimental results on 2 different databases confirm the effectiveness of uSee in delivering, at a greatly reduced computational cost, the high matching scores for building recognition that local descriptors can achieve. With only 14.3% of image SIFT keypoints, uSee exceeded prior literature results by achieving an accuracy of 99.1% on the Zurich Building Database with no manual rotation;thus saving significantly on the computational requirements of the task at hand.
The dynamic water quality assessment is a challenging and critical issue in water resource management systems. To deal with this complex problem, a dynamic water assessment model based on multiagent technology is prop...
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The dynamic water quality assessment is a challenging and critical issue in water resource management systems. To deal with this complex problem, a dynamic water assessment model based on multiagent technology is proposed, and an improved Q-learning algorithm is used in this paper. In the proposed Q-learning algorithm, a fuzzy membership function and a punishment mechanism are introduced to improve the learning speed of Q-learning algorithm. The dynamic water quality assessment for different regions and the prewarning of water pollution are achieved by using an interaction factor in the proposed approach. The proposed approach can deal with various situations, such as static and dynamic water quality assessment. The experimental results show that the water quality assessment based on the proposed approach is more accurate and efficient than the general methods.
Parallel jobs submitted to processors should be efficiently scheduled to achieve high performance. Early scheduling strategies for parallel jobs make use of either space-sharing approach or time-sharing approach. The ...
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Parallel jobs submitted to processors should be efficiently scheduled to achieve high performance. Early scheduling strategies for parallel jobs make use of either space-sharing approach or time-sharing approach. The scheduling strategy proposed in this work, makes use of both the policies for parallel jobs while scheduling under clusters. Static and dynamic scheduling algorithms were developed for communication intensive jobs. The algorithms are used to handle different types of jobs such as serial, parallel and mixed jobs. For performance evaluation, the workload from Grid5000 platform is considered. The main objective is to achieve performance and power improvement. The dynamic scheduling algorithm with communication aware policy gives better performance when compared to static scheduling algorithm that is tested under the given workload. (C) 2013 Elsevier Ltd. All rights reserved.
A new algorithm for iterative blind image restoration is presented in this paper. The method extends blind equalization found in the signal case to the image. A neural network blind equalization algorithm is derived a...
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A new algorithm for iterative blind image restoration is presented in this paper. The method extends blind equalization found in the signal case to the image. A neural network blind equalization algorithm is derived and used in conjunction with Zigzag coding to restore the original image. As a result, the effect of PSF can be removed by using the proposed algorithm, which contributes to eliminate intersymbol interference (ISI). In order to obtain the estimation of the original image, what is proposed in this method is to optimize constant modulus blind equalization cost function applied to grayscale CT image by using conjugate gradient method. Analysis of convergence performance of the algorithm verifies the feasibility of this method theoretically;meanwhile, simulation results and performance evaluations of recent image quality metrics are provided to assess the effectiveness of the proposed method.
We present an approximate method of performing the Fourier transform of the data sampled in nonequidistant readouts. It is shown that the data can be recalculated as equidistant readouts by using a nonuniform convolut...
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We present an approximate method of performing the Fourier transform of the data sampled in nonequidistant readouts. It is shown that the data can be recalculated as equidistant readouts by using a nonuniform convolution, i.e., convolution of a certain function whose form depends on the calculated element and the character of nonequidistance. Thus, this recalculation does not require calculation of the values of the initial data in intermediate readouts (unlike the linear approximation, spline, or other recalculations). Since the size of the kernel of this nonuniform convolution is about 9, the proposed method can be the basis for an efficient computational algorithm. Applicability of the proposed approach to spectral optical coherence tomography is demonstrated.
This paper presents an assembling unsupervised learning framework that adopts the information coming from the supervised learning process and gives the corresponding implementation algorithm. The algorithm consists of...
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This paper presents an assembling unsupervised learning framework that adopts the information coming from the supervised learning process and gives the corresponding implementation algorithm. The algorithm consists of two phases: extracting and clustering data representatives (DRs) firstly to obtain labeled training data and then classifying non-DRs based on labeled DRs. The implementation algorithm is called SDSN since it employs the tuning-scaled Support vector domain description to collect DRs, uses spectrum-based method to cluster DRs, and adopts the nearest neighbor classifier to label non-DRs. The validation of the clustering procedure of the first-phase is analyzed theoretically. A new metric is defined data dependently in the second phase to allow the nearest neighbor classifier to work with the informed information. A fast training approach for DRs' extraction is provided to bring more efficiency. Experimental results on synthetic and real datasets verify that the proposed idea is of correctness and performance and SDSN exhibits higher popularity in practice over the traditional pure clustering procedure.
This paper presents an algorithm for estimating the performance of high-power station systems connected in series, parallel, and mixed series-parallel with collective factor failures caused by any part of the system e...
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This paper presents an algorithm for estimating the performance of high-power station systems connected in series, parallel, and mixed series-parallel with collective factor failures caused by any part of the system equipment. Failures that occur frequently can induce a selective effect, which means that the failures generated from different equipment parts can cause failures in various subsets of the system elements. The objectives of this study are to increase the lifetime of the station and reduce sudden station failures. The case study data was collected from an electricity distribution company in Baghdad, Iraq. Data analysis was performed using the most valid distribution of the Weibull distribution with scale parameter alpha = 1.3137 and shape parameter beta = 94.618. Our analysis revealed that the reliability value decreased by 2.82% in 30 days. The highest critical value was obtained for components T-1, CBF5, CBF7, CBF8, CBF9, and CBF10 and must be changed by a new item as soon as possible. We believe that the results of this research can be used for the maintenance of power systems models and preventive maintenance models for power systems.
In this research we address the problem of discriminant subband selection for texture classification. A novel Effective Information based Subband Selection (EISS) algorithm is proposed which utilizes the intro-class a...
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In this research we address the problem of discriminant subband selection for texture classification. A novel Effective Information based Subband Selection (EISS) algorithm is proposed which utilizes the intro-class and inter-class distributions. Essentially these distributions are used to calculate the class-based entropy for a given subband. This class-based information is incorporated in the total information content of the training images to develop a robust Effective Information (El) criterion. Only the subbands with the top El criteria are allowed to participate in the classification process. The proposed EISS algorithm is evaluated on Brodatz texture database and has shown to outperform the most relevant method based on mutual information criterion. (C) 2012 Elsevier Ltd. All rights reserved.
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