Algorithms

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1019 KiB

Open AccessArticle

An Efficient SAR Image Segmentation Framework Using Transformed Nonlocal Mean and Multi-Objective Clustering in Kernel Space

by Dongdong Yang, Hui Yang and Rong Fei

Algorithms 2015, 8(1), 32-45; https://doi.org/10.3390/a8010032 - 9 Feb 2015

Cited by 2 | Viewed by 6224

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 [...] Read more.

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)

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343 KiB

Open AccessArticle

An Improved Shuffled Frog-Leaping Algorithm for Flexible Job Shop Scheduling Problem

by Kong Lu, Li Ting, Wang Keming, Zhu Hanbing, Takano Makoto and Yu Bin

Algorithms 2015, 8(1), 19-31; https://doi.org/10.3390/a8010019 - 4 Feb 2015

Cited by 25 | Viewed by 7860

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 [...] Read more.

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)

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3874 KiB

Open AccessArticle

The Lobe Fissure Tracking by the Modified Ant Colony Optimization Framework in CT Images

by Chii-Jen Chen, You-Wei Wang, Wei-Chih Shen, Chih-Yi Chen and Wen-Pinn Fang

Algorithms 2014, 7(4), 635-649; https://doi.org/10.3390/a7040635 - 24 Nov 2014

Cited by 2 | Viewed by 7848

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)

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2608 KiB

Open AccessArticle

Eight-Scale Image Contrast Enhancement Based on Adaptive Inverse Hyperbolic Tangent Algorithm

by Cheng-Yi Yu, Chi-Yuan Lin, Sheng-Chih Yang and Hsueh-Yi Lin

Algorithms 2014, 7(4), 597-607; https://doi.org/10.3390/a7040597 - 28 Oct 2014

Cited by 6 | Viewed by 6289

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)

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306 KiB

Open AccessArticle

Processing KNN Queries in Grid-Based Sensor Networks

by Yuan-Ko Huang

Algorithms 2014, 7(4), 582-596; https://doi.org/10.3390/a7040582 - 23 Oct 2014

Cited by 5 | Viewed by 5760

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)

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320 KiB

Open AccessArticle

Multi-Sensor Building Fire Alarm System with Information Fusion Technology Based on D-S Evidence Theory

by Qian Ding, Zhenghong Peng, Tianzhen Liu and Qiaohui Tong

Algorithms 2014, 7(4), 523-537; https://doi.org/10.3390/a7040523 - 14 Oct 2014

Cited by 43 | Viewed by 12173

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)

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474 KiB

Open AccessArticle

Power Cable Fault Recognition Based on an Annealed Chaotic Competitive Learning Network

by Xuebin Qin, Mei Wang, Jzau-Sheng Lin and Xiaowei Li

Algorithms 2014, 7(4), 492-509; https://doi.org/10.3390/a7040492 - 26 Sep 2014

Cited by 4 | Viewed by 6320

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 [...] Read more.

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)

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880 KiB

Open AccessArticle

Applying a Dynamic Resource Supply Model in a Smart Grid

by Kaiyu Wan, Yuji Dong, Qian Chang and Tengfei Qian

Algorithms 2014, 7(3), 471-491; https://doi.org/10.3390/a7030471 - 22 Sep 2014

Cited by 3 | Viewed by 6142

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)

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1122 KiB

Open AccessArticle

A Fovea Localization Scheme Using Vessel Origin-Based Parabolic Model

by Chun-Yuan Yu, Chen-Chung Liu and Shyr-Shen Yu

Algorithms 2014, 7(3), 456-470; https://doi.org/10.3390/a7030456 - 10 Sep 2014

Cited by 5 | Viewed by 5072

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)

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3533 KiB

Open AccessArticle

A Novel Contrast Enhancement Technique on Palm Bone Images

by Yung-Tsang Chang, Jen-Tse Wang and Wang-Hsai Yang

Algorithms 2014, 7(3), 444-455; https://doi.org/10.3390/a7030444 - 5 Sep 2014

Cited by 2 | Viewed by 5199

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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