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Algorithms, Volume 7, Issue 3 (September 2014), Pages 276-491

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Research

Open AccessArticle Group Sparse Reconstruction of Multi-Dimensional Spectroscopic Imaging in Human Brain in vivo
Algorithms 2014, 7(3), 276-294; doi:10.3390/a7030276
Received: 26 February 2014 / Revised: 29 April 2014 / Accepted: 26 May 2014 / Published: 26 June 2014
Cited by 5 | PDF Full-text (11707 KB) | HTML Full-text | XML Full-text
Abstract
Four-dimensional (4D) Magnetic Resonance Spectroscopic Imaging (MRSI) data combining 2 spatial and 2 spectral dimensions provides valuable biochemical information in vivo; however, its 20–40 min acquisition time is too long to be used for a clinical protocol. Data acquisition can be accelerated by
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Four-dimensional (4D) Magnetic Resonance Spectroscopic Imaging (MRSI) data combining 2 spatial and 2 spectral dimensions provides valuable biochemical information in vivo; however, its 20–40 min acquisition time is too long to be used for a clinical protocol. Data acquisition can be accelerated by non-uniformly under-sampling (NUS) the ky t1 plane, but this causes artifacts in the spatial-spectral domain that must be removed by non-linear, iterative reconstruction. Previous work has demonstrated the feasibility of accelerating 4D MRSI data acquisition through NUS and iterative reconstruction using Compressed Sensing (CS), Total Variation (TV), and Maximum Entropy (MaxEnt) reconstruction. Group Sparse (GS) reconstruction is a variant of CS that exploits the structural sparsity of transform coefficients to achieve higher acceleration factors than traditional CS. In this article, we derive a solution to the GS reconstruction problem within the Split Bregman iterative framework that uses arbitrary transform grouping patterns of overlapping or non-overlapping groups. The 4D Echo-Planar Correlated Spectroscopic Imaging (EP-COSI) gray matter brain phantom and in vivo brain data are retrospectively under-sampled 2×, 4×, 6×, 8×, and 10___ and reconstructed using CS, TV, MaxEnt, and GS with overlapping or non-overlapping groups. Results show that GS reconstruction with overlapping groups outperformed the other reconstruction methods at each NUS rate for both phantom and in vivo data. These results can potentially reduce the scan time of a 4D EP-COSI brain scan from 40 min to under 5 min in vivo. Full article
(This article belongs to the Special Issue Data Compression for the Life Sciences)
Open AccessArticle Solving the Examination Timetabling Problem in GPUs
Algorithms 2014, 7(3), 295-327; doi:10.3390/a7030295
Received: 27 March 2014 / Revised: 22 May 2014 / Accepted: 12 June 2014 / Published: 3 July 2014
Cited by 2 | PDF Full-text (503 KB) | HTML Full-text | XML Full-text
Abstract
The examination timetabling problem belongs to the class of combinatorial optimization problems and is of great importance for every University. In this paper, a hybrid evolutionary algorithm running on a GPU is employed to solve the examination timetabling problem. The hybrid evolutionary algorithm
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The examination timetabling problem belongs to the class of combinatorial optimization problems and is of great importance for every University. In this paper, a hybrid evolutionary algorithm running on a GPU is employed to solve the examination timetabling problem. The hybrid evolutionary algorithm proposed has a genetic algorithm component and a greedy steepest descent component. The GPU computational capabilities allow the use of very large population sizes, leading to a more thorough exploration of the problem solution space. The GPU implementation, depending on the size of the problem, is up to twenty six times faster than the identical single-threaded CPU implementation of the algorithm. The algorithm is evaluated with the well known Toronto datasets and compares well with the best results found in the bibliography. Moreover, the selection of the encoding of the chromosomes and the tournament selection size as the population grows are examined and optimized. The compressed sparse row format is used for the conflict matrix and was proven essential to the process, since most of the datasets have a small conflict density, which translates into an extremely sparse matrix. Full article
(This article belongs to the Special Issue Bio-inspired Algorithms for Combinatorial Problems)
Open AccessArticle Economic Dispatch Using Modified Bat Algorithm
Algorithms 2014, 7(3), 328-338; doi:10.3390/a7030328
Received: 17 April 2014 / Revised: 11 June 2014 / Accepted: 25 June 2014 / Published: 3 July 2014
Cited by 3 | PDF Full-text (527 KB) | HTML Full-text | XML Full-text
Abstract
Economic dispatch is an important non-linear optimization task in power systems. In this process, the total power demand is distributed amongst the generating units such that each unit satisfies its generation limit constraints and the cost of power production is minimized. This paper
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Economic dispatch is an important non-linear optimization task in power systems. In this process, the total power demand is distributed amongst the generating units such that each unit satisfies its generation limit constraints and the cost of power production is minimized. This paper presents an over view of three optimization algorithms namely real coded genetic algorithm, particle swarm optimization and a relatively new optimization technique called bat algorithm. This study will further propose modifications to the original bat. Simulations are carried out for two test cases. First is a six-generator power system with a simplified convex objective function. The second test case is a five-generator system with a non-convex objective function. Finally the results of the modified algorithm are compared with the results of genetic algorithm, particle swarm and the original bat algorithm. The results demonstrate the improvement in the Bat Algorithm. Full article
Open AccessArticle Model Checking Properties on Reduced Trace Systems
Algorithms 2014, 7(3), 339-362; doi:10.3390/a7030339
Received: 7 February 2014 / Revised: 16 May 2014 / Accepted: 24 June 2014 / Published: 8 July 2014
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Abstract
Temporal logic has become a well-established method for specifying the behavior of distributed systems. In this paper, we interpret a temporal logic over a partial order model that is a trace system. The satisfaction of the formulae is directly defined on traces on
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Temporal logic has become a well-established method for specifying the behavior of distributed systems. In this paper, we interpret a temporal logic over a partial order model that is a trace system. The satisfaction of the formulae is directly defined on traces on the basis of rewriting rules; so, the graph representation of the system can be completely avoided; moreover, a method is presented that keeps the trace system finite, also in the presence of infinite computations. To further reduce the complexity of model checking temporal logic formulae, an abstraction technique is applied to trace systems. Full article
Open AccessArticle Pressure Model of Control Valve Based on LS-SVM with the Fruit Fly Algorithm
Algorithms 2014, 7(3), 363-375; doi:10.3390/a7030363
Received: 18 May 2014 / Revised: 1 July 2014 / Accepted: 7 July 2014 / Published: 11 July 2014
Cited by 4 | PDF Full-text (349 KB) | HTML Full-text | XML Full-text
Abstract
Control valve is a kind of essential terminal control component which is hard to model by traditional methodologies because of its complexity and nonlinearity. This paper proposes a new modeling method for the upstream pressure of control valve using the least squares support
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Control valve is a kind of essential terminal control component which is hard to model by traditional methodologies because of its complexity and nonlinearity. This paper proposes a new modeling method for the upstream pressure of control valve using the least squares support vector machine (LS-SVM), which has been successfully used to identify nonlinear system. In order to improve the modeling performance, the fruit fly optimization algorithm (FOA) is used to optimize two critical parameters of LS-SVM. As an example, a set of actual production data from a controlling system of chlorine in a salt chemistry industry is applied. The validity of LS-SVM modeling method using FOA is verified by comparing the predicted results with the actual data with a value of MSE 2.474 × 10−3. Moreover, it is demonstrated that the initial position of FOA does not affect its optimal ability. By comparison, simulation experiments based on PSO algorithm and the grid search method are also carried out. The results show that LS-SVM based on FOA has equal performance in prediction accuracy. However, from the respect of calculation time, FOA has a significant advantage and is more suitable for the online prediction. Full article
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Open AccessArticle A Hybrid Metaheuristic Approach for Minimizing the Total Flow Time in A Flow Shop Sequence Dependent Group Scheduling Problem
Algorithms 2014, 7(3), 376-396; doi:10.3390/a7030376
Received: 15 March 2014 / Revised: 7 May 2014 / Accepted: 4 July 2014 / Published: 14 July 2014
Cited by 2 | PDF Full-text (276 KB) | HTML Full-text | XML Full-text
Abstract
Production processes in Cellular Manufacturing Systems (CMS) often involve groups of parts sharing the same technological requirements in terms of tooling and setup. The issue of scheduling such parts through a flow-shop production layout is known as the Flow-Shop Group Scheduling (FSGS) problem
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Production processes in Cellular Manufacturing Systems (CMS) often involve groups of parts sharing the same technological requirements in terms of tooling and setup. The issue of scheduling such parts through a flow-shop production layout is known as the Flow-Shop Group Scheduling (FSGS) problem or, whether setup times are sequence-dependent, the Flow-Shop Sequence-Dependent Group Scheduling (FSDGS) problem. This paper addresses the FSDGS issue, proposing a hybrid metaheuristic procedure integrating features from Genetic Algorithms (GAs) and Biased Random Sampling (BRS) search techniques with the aim of minimizing the total flow time, i.e., the sum of completion times of all jobs. A well-known benchmark of test cases, entailing problems with two, three, and six machines, is employed for both tuning the relevant parameters of the developed procedure and assessing its performances against two metaheuristic algorithms recently presented by literature. The obtained results and a properly arranged ANOVA analysis highlight the superiority of the proposed approach in tackling the scheduling problem under investigation. Full article
(This article belongs to the Special Issue Bio-inspired Algorithms for Combinatorial Problems)
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Open AccessArticle Algorithm Based on Heuristic Strategy to Infer Lossy Links in Wireless Sensor Networks
Algorithms 2014, 7(3), 397-404; doi:10.3390/a7030397
Received: 7 May 2014 / Revised: 8 July 2014 / Accepted: 11 July 2014 / Published: 29 July 2014
Cited by 1 | PDF Full-text (271 KB) | HTML Full-text | XML Full-text
Abstract
With the maturing of the actual application of wireless sensor networks, network fault management is eagerly demanded. Severe link packet loss affects the performance of wireless sensor networks, so it must be found and repaired. Subject to the constraints on limited resources, lossy
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With the maturing of the actual application of wireless sensor networks, network fault management is eagerly demanded. Severe link packet loss affects the performance of wireless sensor networks, so it must be found and repaired. Subject to the constraints on limited resources, lossy link is inferred using end to end measurement and network tomography. The algorithm based on heuristic strategy is proposed. This maps the problem of lossy links inferences to minimal set-cover problems. The performance of inference algorithms is evaluated by simulation, and the simulation results indicate feasibility and efficiency of the method. Full article
(This article belongs to the Special Issue Algorithms for Wireless Sensor Networks)
Open AccessArticle Seminal Quality Prediction Using Clustering-Based Decision Forests
Algorithms 2014, 7(3), 405-417; doi:10.3390/a7030405
Received: 14 May 2014 / Revised: 23 June 2014 / Accepted: 29 July 2014 / Published: 11 August 2014
Cited by 2 | PDF Full-text (441 KB) | HTML Full-text | XML Full-text
Abstract
Prediction of seminal quality with statistical learning tools is an emerging methodology in decision support systems in biomedical engineering and is very useful in early diagnosis of seminal patients and selection of semen donors candidates. However, as is common in medical diagnosis, seminal
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Prediction of seminal quality with statistical learning tools is an emerging methodology in decision support systems in biomedical engineering and is very useful in early diagnosis of seminal patients and selection of semen donors candidates. However, as is common in medical diagnosis, seminal quality prediction faces the class imbalance problem. In this paper, we propose a novel supervised ensemble learning approach, namely Clustering-Based Decision Forests, to tackle unbalanced class learning problem in seminal quality prediction. Experiment results on real fertility diagnosis dataset have shown that Clustering-Based Decision Forests outperforms decision tree, Support Vector Machines, random forests, multilayer perceptron neural networks and logistic regression by a noticeable margin. Clustering-Based Decision Forests can also be used to evaluate variables’ importance and the top five important factors that may affect semen concentration obtained in this study are age, serious trauma, sitting time, the season when the semen sample is produced, and high fevers in the last year. The findings could be helpful in explaining seminal concentration problems in infertile males or pre-screening semen donor candidates. Full article
Open AccessArticle Target Channel Visiting Order Design Using Particle Swarm Optimization for Spectrum Handoff in Cognitive Radio Networks
Algorithms 2014, 7(3), 418-428; doi:10.3390/a7030418
Received: 29 March 2014 / Revised: 29 July 2014 / Accepted: 8 August 2014 / Published: 18 August 2014
PDF Full-text (224 KB) | HTML Full-text | XML Full-text
Abstract
In a dynamic spectrum access network, when a primary user (licensed user) reappears on the current channel, cognitive radios (CRs) need to vacate the channel and reestablish a communications link on some other channel to avoid interference to primary users, resulting in spectrum
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In a dynamic spectrum access network, when a primary user (licensed user) reappears on the current channel, cognitive radios (CRs) need to vacate the channel and reestablish a communications link on some other channel to avoid interference to primary users, resulting in spectrum handoff. This paper studies the problem of designing target channel visiting order for spectrum handoff to minimize expected spectrum handoff delay. A particle swarm optimization (PSO) based algorithm is proposed to solve the problem. Simulation results show that the proposed algorithm performs far better than random target channel visiting scheme. The solutions obtained by PSO are very close to the optimal solution which further validates the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Bio-inspired Algorithms for Combinatorial Problems)
Open AccessArticle 1 Major Component Detection and Analysis (ℓ1 MCDA) in Three and Higher Dimensional Spaces
Algorithms 2014, 7(3), 429-443; doi:10.3390/a7030429
Received: 21 May 2014 / Revised: 21 July 2014 / Accepted: 23 July 2014 / Published: 19 August 2014
PDF Full-text (1519 KB) | HTML Full-text | XML Full-text
Abstract
Based on the recent development of two dimensional ℓ1 major component detection and analysis (ℓ1 MCDA), we develop a scalable ℓ1 MCDA in the n-dimensional space to identify the major directions of star-shaped heavy-tailed statistical distributions with irregularly positioned “spokes”
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Based on the recent development of two dimensional ℓ1 major component detection and analysis (ℓ1 MCDA), we develop a scalable ℓ1 MCDA in the n-dimensional space to identify the major directions of star-shaped heavy-tailed statistical distributions with irregularly positioned “spokes” and “clutters”. In order to achieve robustness and efficiency, the proposed ℓ1 MCDA in n-dimensional space adopts a two-level median fit process in a local neighbor of a given direction in each iteration. Computational results indicate that in terms of accuracy ℓ1 MCDA is competitive with two well-known PCAs when there is only one major direction in the data, and ℓ1 MCDA can further determine multiple major directions of the n-dimensional data from superimposed Gaussians or heavy-tailed distributions without and with patterned artificial outliers. With the ability to recover complex spoke structures with heavy-tailed noise and clutter in the data, ℓ1 MCDA has potential to generate better semantics than other methods. Full article
Open AccessArticle A Novel Contrast Enhancement Technique on Palm Bone Images
Algorithms 2014, 7(3), 444-455; doi:10.3390/a7030444
Received: 1 July 2014 / Revised: 17 August 2014 / Accepted: 22 August 2014 / Published: 5 September 2014
PDF Full-text (3533 KB) | HTML Full-text | XML Full-text
Abstract
Contrast enhancement plays a fundamental role in image processing. Many histogram-based techniques are widely used for contrast enhancement of given images, due to their simple function and effectiveness. However, the conventional histogram equalization (HE) methods result in excessive contrast enhancement, which causes natural
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Contrast enhancement plays a fundamental role in image processing. Many histogram-based techniques are widely used for contrast enhancement of given images, due to their simple function and effectiveness. However, the conventional histogram equalization (HE) methods result in excessive contrast enhancement, which causes natural looking and satisfactory results for a variety of low contrast images. To solve such problems, a novel multi-histogram equalization technique is proposed to enhance the contrast of the palm bone X-ray radiographs in this paper. For images, the mean-variance analysis method is employed to partition the histogram of the original grey scale image into multiple sub-histograms. These histograms are independently equalized. By using this mean-variance partition method, a proposed multi-histogram equalization technique is employed to achieve the contrast enhancement of the palm bone X-ray radiographs. Experimental results show that the multi-histogram equalization technique achieves a lower average absolute mean brightness error (AMBE) value. The multi-histogram equalization technique simultaneously preserved the mean brightness and enhanced the local contrast of the original image. Full article
(This article belongs to the Special Issue Advanced Data Processing Algorithms in Engineering)
Open AccessArticle A Fovea Localization Scheme Using Vessel Origin-Based Parabolic Model
Algorithms 2014, 7(3), 456-470; doi:10.3390/a7030456
Received: 15 June 2014 / Revised: 28 August 2014 / Accepted: 28 August 2014 / Published: 10 September 2014
Cited by 1 | PDF Full-text (1122 KB) | HTML Full-text | XML Full-text
Abstract
At the center of the macula, fovea plays an important role in computer-aided diagnosis. To locate the fovea, this paper proposes a vessel origin (VO)-based parabolic model, which takes the VO as the vertex of the parabola-like vasculature. Image processing steps are applied
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At the center of the macula, fovea plays an important role in computer-aided diagnosis. To locate the fovea, this paper proposes a vessel origin (VO)-based parabolic model, which takes the VO as the vertex of the parabola-like vasculature. Image processing steps are applied to accurately locate the fovea on retinal images. Firstly, morphological gradient and the circular Hough transform are used to find the optic disc. The structure of the vessel is then segmented with the line detector. Based on the characteristics of the VO, four features of VO are extracted, following the Bayesian classification procedure. Once the VO is identified, the VO-based parabolic model will locate the fovea. To find the fittest parabola and the symmetry axis of the retinal vessel, an Shift and Rotation (SR)-Hough transform that combines the Hough transform with the shift and rotation of coordinates is presented. Two public databases of retinal images, DRIVE and STARE, are used to evaluate the proposed method. The experiment results show that the average Euclidean distances between the located fovea and the fovea marked by experts in two databases are 9.8 pixels and 30.7 pixels, respectively. The results are stronger than other methods and thus provide a better macular detection for further disease discovery. Full article
(This article belongs to the Special Issue Advanced Data Processing Algorithms in Engineering)
Open AccessArticle Applying a Dynamic Resource Supply Model in a Smart Grid
Algorithms 2014, 7(3), 471-491; doi:10.3390/a7030471
Received: 9 June 2014 / Revised: 29 August 2014 / Accepted: 5 September 2014 / Published: 22 September 2014
Cited by 1 | PDF Full-text (880 KB) | HTML Full-text | XML Full-text
Abstract
Dynamic resource supply is a complex issue to resolve in a cyber-physical system (CPS). In our previous work, a resource model called the dynamic resource supply model (DRSM) has been proposed to handle resources specification, management and allocation in CPS. In this paper,
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Dynamic resource supply is a complex issue to resolve in a cyber-physical system (CPS). In our previous work, a resource model called the dynamic resource supply model (DRSM) has been proposed to handle resources specification, management and allocation in CPS. In this paper, we are integrating the DRSM with service-oriented architecture and applying it to a smart grid (SG), one of the most complex CPS examples. We give the detailed design of the SG for electricity charging request and electricity allocation between plug-in hybrid electric vehicles (PHEV) and DRSM through the Android system. In the design, we explain a mechanism for electricity consumption with data collection and re-allocation through ZigBee network. In this design, we verify the correctness of this resource model for expected electricity allocation. Full article
(This article belongs to the Special Issue Advanced Data Processing Algorithms in Engineering)

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