Special Issue "Advanced Data Processing Algorithms in Engineering"

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A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 September 2014)

Special Issue Editors

Guest Editor
Prof. Dr. Chen-Chung Liu

Department of Electronic Engineering, National Chin-Yi University of Technology, No.57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung 41170, Taiwan
E-Mail
Fax: +886 4 23926610
Interests: computer graphics, pattern recognition, image analysis, and digital signal processing
Guest Editor
Prof. Dr. Wen-Yuan Chen

Department of Electronic Engineering, National Chin-Yi University of Technology, Taichung, Taiwan
Website | E-Mail
Interests: image processing; multi-media compressed technique; multi-media communication; computer programming; and computer game design
Guest Editor
Dr. Ruey-Maw Chen

Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, No.57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung 41170, Taiwan
E-Mail
Phone: +886 4 23924505
Fax: +886 4 23917426
Interests: meta-heuristics, scheduling optimization (with applications), and computer networks

Special Issue Information

Dear Colleagues,

Diverse data processing has become an important component in a variety of research. Meanwhile, algorithms in engineering have become increasingly crucial in developing high-end applications. This Special Issue, “Advanced Data Processing Algorithms in Engineering”, is dedicated to algorithms on data analysis (including decision analysis, decision making, etc.), optimization (including scheduling, project planning, vehicle routing, network routing, task assigning on computational grids, sensor distribution, etc.), image processing (including recognition, segmentation, enhancement, etc.), parallel computation, etc. This Special Issue is not limited to any specific algorithm or method (such as evolutionary algorithms, or meta-heuristic and neural network theories); the issue will encompass the hybridization of two or more existing methods and will account for all fields of engineering. Novel algorithms and potential ideas concerning the fundamental impact of methods used for engineering are welcomed. Papers discussing real-world applications (based on existing algorithms) are also acceptable.

Prof. Chen-Chung Liu
Prof. Wen-Yuan Chen
Dr. Ruey-Maw Chen
Guest Editors

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed Open Access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 300 CHF (Swiss Francs). English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.

Published Papers (10 papers)

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Research

Open AccessArticle An Efficient SAR Image Segmentation Framework Using Transformed Nonlocal Mean and Multi-Objective Clustering in Kernel Space
Algorithms 2015, 8(1), 32-45; doi:10.3390/a8010032
Received: 28 November 2014 / Revised: 13 January 2015 / Accepted: 30 January 2015 / Published: 9 February 2015
Cited by 1 | PDF Full-text (1019 KB) | HTML Full-text | XML Full-text
Abstract
Synthetic aperture radar (SAR) image segmentation usually involves two crucial issues: suitable speckle noise removing technique and effective image segmentation methodology. Here, an efficient SAR image segmentation method considering both of the two aspects is presented. As for the first issue, the famous
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Synthetic aperture radar (SAR) image segmentation usually involves two crucial issues: suitable speckle noise removing technique and effective image segmentation methodology. Here, an efficient SAR image segmentation method considering both of the two aspects is presented. As for the first issue, the famous nonlocal mean (NLM) filter is introduced in this study to suppress the multiplicative speckle noise in SAR image. Furthermore, to achieve a higher denoising accuracy, the local neighboring pixels in the searching window are projected into a lower dimensional subspace by principal component analysis (PCA). Thus, the nonlocal mean filter is implemented in the subspace. Afterwards, a multi-objective clustering algorithm is proposed using the principals of artificial immune system (AIS) and kernel-induced distance measures. The multi-objective clustering has been shown to discover the data distribution with different characteristics and the kernel methods can improve its robustness to noise and outliers. Experiments demonstrate that the proposed method is able to partition the SAR image robustly and accurately than the conventional approaches. Full article
(This article belongs to the Special Issue Advanced Data Processing Algorithms in Engineering)
Open AccessArticle An Improved Shuffled Frog-Leaping Algorithm for Flexible Job Shop Scheduling Problem
Algorithms 2015, 8(1), 19-31; doi:10.3390/a8010019
Received: 24 October 2014 / Revised: 26 December 2014 / Accepted: 15 January 2015 / Published: 4 February 2015
Cited by 2 | PDF Full-text (343 KB) | HTML Full-text | XML Full-text
Abstract
The flexible job shop scheduling problem is a well-known combinatorial optimization problem. This paper proposes an improved shuffled frog-leaping algorithm to solve the flexible job shop scheduling problem. The algorithm possesses an adjustment sequence to design the strategy of local searching and an
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The flexible job shop scheduling problem is a well-known combinatorial optimization problem. This paper proposes an improved shuffled frog-leaping algorithm to solve the flexible job shop scheduling problem. The algorithm possesses an adjustment sequence to design the strategy of local searching and an extremal optimization in information exchange. The computational result shows that the proposed algorithm has a powerful search capability in solving the flexible job shop scheduling problem compared with other heuristic algorithms, such as the genetic algorithm, tabu search and ant colony optimization. Moreover, the results also show that the improved strategies could improve the performance of the algorithm effectively. Full article
(This article belongs to the Special Issue Advanced Data Processing Algorithms in Engineering)
Open AccessArticle The Lobe Fissure Tracking by the Modified Ant Colony Optimization Framework in CT Images
Algorithms 2014, 7(4), 635-649; doi:10.3390/a7040635
Received: 16 June 2014 / Revised: 8 November 2014 / Accepted: 17 November 2014 / Published: 24 November 2014
PDF Full-text (3874 KB) | HTML Full-text | XML Full-text
Abstract
Chest computed tomography (CT) is the most commonly used technique for the inspection of lung lesions. However, the lobe fissures in lung CT is still difficult to observe owing to its imaging structure. Therefore, in this paper, we aimed to develop an efficient
[...] Read more.
Chest computed tomography (CT) is the most commonly used technique for the inspection of lung lesions. However, the lobe fissures in lung CT is still difficult to observe owing to its imaging structure. Therefore, in this paper, we aimed to develop an efficient tracking framework to extract the lobe fissures by the proposed modified ant colony optimization (ACO) algorithm. We used the method of increasing the consistency of pheromone on lobe fissure to improve the accuracy of path tracking. In order to validate the proposed system, we had tested our method in a database from 15 lung patients. In the experiment, the quantitative assessment shows that the proposed ACO method achieved the average F-measures of 80.9% and 82.84% in left and right lungs, respectively. The experiments indicate our method results more satisfied performance, and can help investigators detect lung lesion for further examination. Full article
(This article belongs to the Special Issue Advanced Data Processing Algorithms in Engineering)
Open AccessArticle Eight-Scale Image Contrast Enhancement Based on Adaptive Inverse Hyperbolic Tangent Algorithm
Algorithms 2014, 7(4), 597-607; doi:10.3390/a7040597
Received: 11 June 2014 / Revised: 17 October 2014 / Accepted: 20 October 2014 / Published: 28 October 2014
PDF Full-text (2608 KB) | HTML Full-text | XML Full-text
Abstract
The Eight-Scale parameter adjustment is a natural extension of Adaptive Inverse Hyperbolic Tangent (AIHT) algorithm. It has long been known that the Human Vision System (HVS) heavily depends on detail and edge in the understanding and perception of scenes. The main goal of
[...] Read more.
The Eight-Scale parameter adjustment is a natural extension of Adaptive Inverse Hyperbolic Tangent (AIHT) algorithm. It has long been known that the Human Vision System (HVS) heavily depends on detail and edge in the understanding and perception of scenes. The main goal of this study is to produce a contrast enhancement technique to recover an image from blurring and darkness, and at the same time to improve visual quality. Eight-scale coefficient adjustments can provide a further local refinement in detail under the AIHT algorithm. The proposed Eight-Scale Adaptive Inverse Hyperbolic Tangent (8SAIHT) method uses the sub-band to calculate the local mean and local variance before the AIHT algorithm is applied. This study also shows that this approach is convenient and effective in the enhancement processes for various types of images. The 8SAIHT is also capable of adaptively enhancing the local contrast of the original image while simultaneously extruding more on object details. Full article
(This article belongs to the Special Issue Advanced Data Processing Algorithms in Engineering)
Open AccessArticle Processing KNN Queries in Grid-Based Sensor Networks
Algorithms 2014, 7(4), 582-596; doi:10.3390/a7040582
Received: 10 June 2014 / Revised: 3 October 2014 / Accepted: 20 October 2014 / Published: 23 October 2014
Cited by 1 | PDF Full-text (306 KB) | HTML Full-text | XML Full-text
Abstract
Recently, developing efficient processing techniques in spatio-temporal databases has been a much discussed topic. Many applications, such as mobile information systems, traffic control system, and geographical information systems, can benefit from efficient processing of spatio-temporal queries. In this paper, we focus on processing
[...] Read more.
Recently, developing efficient processing techniques in spatio-temporal databases has been a much discussed topic. Many applications, such as mobile information systems, traffic control system, and geographical information systems, can benefit from efficient processing of spatio-temporal queries. In this paper, we focus on processing an important type of spatio-temporal queries, the K-nearest neighbor (KNN) queries. Different from the previous research, the locations of objects are located by the sensors which are deployed in a grid-based manner. As the positioning technique used is not the GPS technique, but the sensor network technique, this results in a greater uncertainty regarding object location. With the uncertain location information of objects, we try to develop an efficient algorithm to process the KNN queries. Moreover, we design a probability model to quantify the possibility of each object being the query result. Finally, extensive experiments are conducted to demonstrate the efficiency of the proposed algorithms. Full article
(This article belongs to the Special Issue Advanced Data Processing Algorithms in Engineering)
Open AccessArticle Multi-Sensor Building Fire Alarm System with Information Fusion Technology Based on D-S Evidence Theory
Algorithms 2014, 7(4), 523-537; doi:10.3390/a7040523
Received: 15 June 2014 / Revised: 18 September 2014 / Accepted: 28 September 2014 / Published: 14 October 2014
Cited by 4 | PDF Full-text (320 KB) | HTML Full-text | XML Full-text
Abstract
Multi-sensor and information fusion technology based on Dempster-Shafer evidence theory is applied in the system of a building fire alarm to realize early detecting and alarming. By using a multi-sensor to monitor the parameters of the fire process, such as light, smoke, temperature,
[...] Read more.
Multi-sensor and information fusion technology based on Dempster-Shafer evidence theory is applied in the system of a building fire alarm to realize early detecting and alarming. By using a multi-sensor to monitor the parameters of the fire process, such as light, smoke, temperature, gas and moisture, the range of fire monitoring in space and time is expanded compared with a single-sensor system. Then, the D-S evidence theory is applied to fuse the information from the multi-sensor with the specific fire model, and the fire alarm is more accurate and timely. The proposed method can avoid the failure of the monitoring data effectively, deal with the conflicting evidence from the multi-sensor robustly and improve the reliability of fire warning significantly. Full article
(This article belongs to the Special Issue Advanced Data Processing Algorithms in Engineering)
Open AccessArticle Power Cable Fault Recognition Based on an Annealed Chaotic Competitive Learning Network
Algorithms 2014, 7(4), 492-509; doi:10.3390/a7040492
Received: 13 June 2014 / Revised: 3 September 2014 / Accepted: 15 September 2014 / Published: 26 September 2014
PDF Full-text (474 KB) | HTML Full-text | XML Full-text
Abstract
In electric power systems, power cable operation under normal conditions is very important. Various cable faults will happen in practical applications. Recognizing the cable faults correctly and in a timely manner is crucial. In this paper we propose a method that an annealed
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In electric power systems, power cable operation under normal conditions is very important. Various cable faults will happen in practical applications. Recognizing the cable faults correctly and in a timely manner is crucial. In this paper we propose a method that an annealed chaotic competitive learning network recognizes power cable types. The result shows a good performance using the support vector machine (SVM) and improved Particle Swarm Optimization (IPSO)-SVM method. The experimental result shows that the fault recognition accuracy reached was 96.2%, using 54 data samples. The network training time is about 0.032 second. The method can achieve cable fault classification effectively. 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,
[...] Read more.
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)
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
[...] Read more.
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 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
[...] Read more.
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)

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