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.
In many cases, multiple-fault diagnosis of plant-wide systems based on steady-state data is impossible. To solve this problem, a new diagnosis strategy based on neural networks has been proposed. In the suggested fram...
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In many cases, multiple-fault diagnosis of plant-wide systems based on steady-state data is impossible. To solve this problem, a new diagnosis strategy based on neural networks has been proposed. In the suggested framework, the neural network is used as the diagnoser trained by a hybrid set of steady and dynamic characteristic data of the system. The dynamic characteristic data include overshoot and undershoot values of measured variables and their corresponding occurrence times. To evaluate its performance, the proposed scheme was used in the diagnosis of the concurrent faults of the Tennessee Eastman (TE) process. Various combinations of concurrent faults were considered in this assessment. The results indicate the generality, flexibility, and accuracy of the proposed algorithm such that it is capable of diagnosing various combinations (from single to sextuple) of simultaneous faults, whereas the other diagnosing methods used for the TE process are capable of distinguishing at most three simultaneous faults.
We propose a method and an algorithm for recovery of the nonmonotonic altitude profile of the plasma frequency using the model data on oblique sounding of a spherically layered isotropic ionosphere in the piecewise-qu...
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We propose a method and an algorithm for recovery of the nonmonotonic altitude profile of the plasma frequency using the model data on oblique sounding of a spherically layered isotropic ionosphere in the piecewise-quasiparabolic approximation of the altitude profile of the electron number density. The algorithm has been tested with a fairly complex ionosphere model allowing for the E, F-1, and F-2 layers and the E-F-1 interlayer valley. This method was used to recover the effective altitude profile of the plasma frequency at the midpoints of the Khabarovsk-Tory, Magadan-Tory, Norilsk-Tory, and Usolie-Tory paths from the experimental ionograms having a gap in the single-hop mode traces.
Although type reconstruction for dependently typed languages is common in practical systems, it is still ill-understood. Detailed descriptions of the issues around it are hard to find and formal descriptions together ...
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Although type reconstruction for dependently typed languages is common in practical systems, it is still ill-understood. Detailed descriptions of the issues around it are hard to find and formal descriptions together with correctness proofs are non-existing. In this paper, we discuss a one-pass type reconstruction for objects in the logical framework LF, describe formally the type reconstruction process using the framework of contextual modal types, and prove correctness of type reconstruction. Since type reconstruction will find most general types and may leave free variables, we in addition describe abstraction which will return a closed object where all free variables are bound at the outside. We also implemented our algorithms as part of the Beluga language, and the performance of our type reconstruction algorithm is comparable to type reconstruction in existing systems such as the logical framework Twelf.
This paper presents a rigorous formulation of the multi-alternative routing problem. The memetic algorithm yielding a global transportation plan is developed. We propose an algorithm constructing the optimal path amon...
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This paper presents a rigorous formulation of the multi-alternative routing problem. The memetic algorithm yielding a global transportation plan is developed. We propose an algorithm constructing the optimal path among nodes of a road graph. Finally, the modular structure of the routing subsystem is designed and implemented.
The interval temporal logic (ITL) model checking (MC) technique enhances the power of intrusion detection systems (IDSs) to detect concurrent attacks due to the strong expressive power of ITL. However, an ITL formula ...
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The interval temporal logic (ITL) model checking (MC) technique enhances the power of intrusion detection systems (IDSs) to detect concurrent attacks due to the strong expressive power of ITL. However, an ITL formula suffers from difficulty in the description of the time constraints between different actions in the same attack. To address this problem, we formalize a novel real-time interval temporal logic-real-time attack signature logic (RASL). Based on such a new logic, we put forward a RASL model checking algorithm. Furthermore, we use RASL formulas to describe attack signatures and employ discrete timed automata to create an audit log. As a result, RASL model checking algorithm can be used to automatically verify whether the automata satisfy the formulas, that is, whether the audit log coincides with the attack signatures. The simulation experiments show that the new approach effectively enhances the detection power of the MC-based intrusion detection methods for a number of telnet attacks, p-trace attacks, and the other sixteen types of attacks. And these experiments indicate that the new algorithm can find several types of real-time attacks, whereas the existing MC-based intrusion detection approaches cannot do that.
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