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Adaptive Quantization with Fuzzy C-mean Clustering for Liver Ultrasound Compression |
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Rattikorn Sombutkaew, Orachat Chitsobuk(King Mongkut’s Institute of Technology Ladkrabang, Thailand) |
| With the massive increment of patients’ medical information and images also limitation in transmission bandwidth, it is a challenging task for developing efficient medical information and image encoding techniques for digital picture archiving and communications (PACS). In order to achieve higher encoding efficiency, this research proposes adaptive quantization via fuzzy classified priority mapping. Image statistical characteristics are used as key features for Fuzzy C-mean clustering. The derived priority map is used to identify levels of importance for each image area. The significant... |
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Dual Intuitionistic Fuzzy Sets and Its Application in Group Decision Making |
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Liang Wu, Ya-ming Zhuang(School of Economics and Management, Southeast University, China) |
| Dual Intuitionistic Fuzzy Sets and Its Application in Group Decision Making
Liang Wu,Ya-ming Zhuang
(Southeast University,China)
In this paper, we set up dual intuitionistic fuzzy sets (DIFS), encompassing all the known fuzzy sets as special cases. Then we investigate the basic operations and properties of DIFS, and propose an extension principle of DIFS. Finally, a practical example about multicriteria group decision making problem is given to verify our results. |
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Leak Detection of Pipeline Using a Hybrid of Neural-Adaptive Tabu Search Algorithm |
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Sunisa Sornmuang, Jittiwut Suwatthikul, Songkord Thirachai(National Electronics and Computer Technology Center, Thailand) |
| This paper presents a new hybrid of Neural-Adaptive Tabu Search (NATS) for leakage detection in pipelines. The proposed cooperative algorithms are formed from Artificial Neural Network (ANN) and Adaptive Tabu Search (ATS). The article shows comparison studies of the ANN and NATS algorithms. The search performance evaluation is performed on the standard benchmark from University of California at Irvine (UCI) Machine Learning Repository. The experiment uses water leakage signals from a field-test yard. The results show that the leaking pipeline can be efficiently detected. |
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Speeded-up Cuckoo Search using Opposition-Based Learning |
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So-Youn Park, Yeoun-Jae Kim, Jeong-Jung Kim, Ju-Jang Lee(Korea Advanced Institute of Science and Technology, Republic of Korea) |
| Recently proposed cuckoo search (CS), one class of swarm intelligence, mimics behaviors of cuckoo. From the previous studies, it has quite a potential, so it could outperform existing algorithms. Regarding to the convergence, however, CS shows slow performance. In this paper, we combine opposition-based learning with CS, so the convergence speed of CS becomes faster, not deteriorating the search ability of the algorithm. Through the simulation, the proposed algorithm outperforms the original algorithm in terms of convergence speed as well as solution accuracy and success rate. |
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Bed-Leaving Behavior Detection and Recognition Based on Time-Series Learning Using Elman-Type Counter Propagation Networks |
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Hirokazu Madokoro, Kantarou Kakuta, Ryo Fujisawa, Nobuhiro Shimoi, Kazuhito Sato, Li Xu(Akita Prefectural University, Japan) |
| This paper presents a bed-leaving detection method using Elman-type Counter Propagation Networks (ECPNs), a novel machine-learning-based method used for time-series signals. In our earlier study, we used CPNs, a form of supervised model of Self-Organizing Maps (SOMs), to produce category maps to learn relations among input and teaching signals. For this study, we inserted a feedback loop as the second Grossberg layer for learning time-series features. Moreover, we developed an original caster-stand sensor using piezoelectric films to measure weight changes of a subject on a bed. |
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Fast Parallel CRC Implementation in Software |
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Dukki Chung, Jonathan R Engdahl(Rockwell Automation, United States) |
| CRC is one of the most commonly used error detecting codes in communication. Before a message is transferred, a transmitter calculates the CRC using the agreed upon polynomial, and attaches the resulting residue to the message. When the message is received, a receiver calculates the CRC using the same polynomial and verifies the message. In this paper, a fast CRC computation is presented using a software based parallelization scheme. In an ARM Cortex-A15 implementation of the proposed methodology, it achieves 2.6 times faster speed compared to a conventional table lookup CRC computation. |
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