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World Congress on intelligent control and Automation (WCICA)

作者： Xuncai Zhang Xuemei Hu Guangzhao Cui Yanfeng Wang Ying Niu College of Electrical and Electronic Engineering Zhengzhou University of Light Industry Zhengzhou China Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China Department of Electrical Engineering Henan Polytechnic Institute Henan Nanyang China

Shuffled frog leaping (SFL) is a population based, cooperative search metaphor inspired by natural memetics. Its ability of adapting to dynamic environment makes SFL become one of the most important memetic algorithms. In order to improve the algorithmpsilas stability and the ability to search the global optimum, a novel dasiacognition componentpsila is introduced to enhance the effectiveness of the SFL, namely frog not only adjust its position according to the best individual within the memeplex or the global best of population but also according to thinking of the frog itself. To validate the improved SFL (ISFL) method, numerous simulations were conducted to compare SFL and ISFL using six benchmark problems for continuous and discrete optimization. According to the simulation results, adding the cognitive behavior to SFL significantly enhances the performance of SFL in solving the optimization problems, and the improvements are more evident with the scale of the problem increasing.

关键词： Optimization Evolution (biology) Cognition Algorithm design and analysis Heuristic algorithms Evolutionary computation Space exploration

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Design of a PSO Based Fuzzy Logic controller for Vessel Mooring Shifting System

Design of a PSO Based Fuzzy Logic Controller for Vessel Moor...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Zhen Huang Yongji Wang Department of Control Science and Engineering HuaZhong University of Science and Technology Wuhan Hubei China School of Automation Wuhan University of Technology Wuhan Hubei China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Department of Control Science andEngineering Huazhong University of Science슠and슠Technology Wuhan Hubei China

A fuzzy logic controller (FLC) is designed to achieve course-keeping for mooring shifting system, which is the main system of non self-propelled vessels. Compared with manual operation, the automatic operation and monitoring system with the FLC can perform higher precision and efficiency. The particle swarm optimization (PSO) algorithm is introduced to optimize the proposed FLCpsilas parameters. A series of simulation studies have been undertaken to compare the performance of a basis FLC and PSO based FLC. The results demonstrate that the latter has the better controlling quality.

关键词： Fuzzy logic control systems Automatic control Particle swarm optimization Fuzzy systems Design engineering Knowledge engineering Design automation Manuals Computerized monitoring

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Computer aided diagnosis of fatty liver ultrasonic images based on support vector machine

Computer aided diagnosis of fatty liver ultrasonic images ba...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Guokuan Li Yu Luo Wei Deng Xiangyang Xu Aihua Liu Enmin Song School of Computer Science and Technology Key Laboratory for Image Processing & Intelligent Control of Ministry of Education Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology China Ultrasound Department Sixth Hospital of Wuhan City China

B-scan ultrasound is the primary means for the diagnosis of fatty liver. However, due to use of various ultrasound equipments, poor quality of ultrasonic images and physical differences of patients, fatty liver diagnosis is mainly qualitative, and often depends on the subjective judgment of technicians and doctors. Therefore, computer-aided feature extraction and quantitative analysis of liver B-scan ultrasonic images will help to improve clinical diagnostic accuracy, repeatability and efficiency, and could provide a measure for severity of hepatic steatosis. This paper proposed a novel method of fatty liver diagnosis based on liver B-mode ultrasonic images using support vector machine (SVM). Fatty liver diagnosis was transformed into a pattern recognition problem of liver ultrasound image features. According to the different characteristics of fatty liver and healthy liver, important image features were extracted and selected to distinguish between the two categories. These features could be represented by near-field light-spot density, near-far-field grayscale ratio, grayscale co-occurrence matrix, and neighborhood gray-tone difference matrix (NGTDM). A SVM classifier was modeled and trained using the clinical ultrasound images of both fatty liver and normal liver. It was then exploited to classify normal and fatty livers, achieving a high recognition rate. The diagnostic results are satisfactorily consistent with those made by doctors. This method could be used for computer-aided diagnosis of fatty liver, and help doctors identify the fatty liver ultrasonic images rapidly, objectively and accurately.

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Feature fusion as a practical solution toward noncooperative iris recognition

Feature fusion as a practical solution toward noncooperative...

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International Conference on Information Fusion

作者： Nima Tajbakhsh Babak N. Araabi Hamid Soltanianzadeh Control & Intelligent Processing Center of Excellence School of Electrical & Computer Engineering University of Tehran Tehran Iran School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

In noncooperative Iris recognition one should deal with uncontrolled behavior of the subject as well as uncontrolled lighting conditions. That means imperfect focus, contrast, brightness, and orientation among the others. To cope with this situation we propose to take iris images at both near infrared (NIR) and visible light (VL) and use them simultaneously for recognition. In this paper, a novel approach for iris recognition is proposed so that extracted features of NIR and VL images are fused to improve the recognition rate. When the images do not have enough quality due to focus, contrast, etc., effects of feature fusion is more pronounced. This is the situation in UTIRIS database, which is used in our experiments. Experimental results show that the proposed approach, especially in small training samples, leads to a remarkable improvement on recognition rate compared with either NIR or VL recognition.

关键词： Iris Feature extraction Discrete cosine transforms image recognition Databases Training Humans

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Constrained optimization of nonparametric entropy-based segmentation of brain structures

Constrained optimization of nonparametric entropy-based segm...

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IEEE International Symposium on Biomedical Imaging

作者： Alireza Akhondi Asl Hamid Soltanian-Zadeh School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

We propose a constrained, three-dimensional, nonparametric, entropy-based, coupled, multi-shape approach to segment subcortical brain structures from magnetic resonance images (MRI). The proposed method uses PCA to develop shape models that capture structural variability. It integrates geometrical relationship between different structures into the algorithm by coupling them (limiting their independent deformations). On the other hand, to allow variations among coupled structures, it registers each structure separately when building the shape models. It defines an entropy-based energy function, which is minimized using quasi-Newton algorithm. To this end, probability density functions (pdf) are estimated iteratively using nonparametric Parzen window method. In the optimization algorithm, constraints are used to improve segmentation quality. These constraints are extracted from training data. Sample results are given for the segmentation of caudate, hippocampus, and putamen, illustrating highly superior performance of the proposed method compared to the most similar methods in the literature.

关键词： Constraint optimization Brain Iterative algorithms image segmentation Shape Couplings Magnetic resonance Magnetic resonance imaging Principal component analysis Buildings

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A wearable device for repetitive hand therapy

A wearable device for repetitive hand therapy

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Kexin Xing Qi Xu Jiping He Yongji Wang Zhongwei Liu Xiaolin Huang Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Technology Huazhong University of Science and Technology Wuhan China Harrington Department of Bioengineering and Center for Neural Interface Design at the Biodesign Institute Arizona State University Tempe AZ USA Department of Rehabilitation Medicine Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China

Intensive task-oriented repetitive physical therapies provided by individualized interaction between the patient and a rehabilitation specialist can improve hand motor performance in patients survived from stroke and traumatic brain injury. However, the therapy process is long and expensive and difficult to evaluate quantitatively and objectively. The goal of this research is to develop a novel wearable device for robotic assisted hand repetitive therapy. We designed a pneumatic muscle (PM) driven therapeutic device that is wearable and provides assistive forces required for grasping and release movements. The robot has two distinct degrees of freedom at the thumb and the fingers. The embedded sensors feedback position and force information for robot control and quantitative evaluation of task performance. It has the potential of providing supplemental at-home therapy in addition to in the clinic treatment.

关键词： Medical treatment Robot sensing systems Force feedback Brain injuries Rehabilitation robotics Muscles Grasping Thumb Fingers Wearable sensors

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Multi-objective carrier chaotic evolutionary algorithm for DNA sequences design

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Progress in Natural Science:Materials International 2007年第12期17卷 1515-1520页

作者： Xiao Jianhua, Xu Jin, Geng Xiutang and Pan Linqiang(Key laboratory of image processing and intelligent control, Department of control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China) Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

DNA computing is a new vista of computation, which is of biochemical type. Since each piece of information is encoded in biological sequences, their design is crucial for successful DNA computation. DNA sequence design is involved with a number of design criteria, which is difficult to be solved by the traditional optimization methods. In this paper, the multi-objective carrier chaotic evolution algorithm (MCCEA) is introduced to solve the DNA sequence design problem. By merging the chaotic search base on power function carrier, a set of good DNA sequences are generated. Furthermore, the simulation results show the efficiency of our method.

关键词： DNA computing carrier chaotic search multi-objective evolutionary algorithm DNA sequences design.

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Towards an inclusive computational model of visual cortex

Towards an inclusive computational model of visual cortex

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： M. Gheiratmand H. Soltanian-Zadeh H. Khaloozadeh Electrical Engineering Department K.N. Toosi University of Technology Tehran Iran Radiology Department Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA School of Cognitive Sciences Institute for Studies Theoretical Physics and Mathematics (IPM) Tehran Iran Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran Iran

Understanding the primatespsila visual system has been one of the challenging problems of different groups of scientists for years. Though many studies, from physiology and neuroscience to computer vision, are done on different aspects of visual processing in the cortex, a comprehensive computational model of visual cortex is still missing. We have implemented a computational model of object recognition in ventral visual pathway in our previous work. This hierarchical model covers visual areas V1/V2, V4/PIT, and AIT sending inputs to the Prefrontal Cortex (PFC) for categorization. To extend our model, in this work, we have added a simple model of motion detection in neurons of areas V1 and MT of the dorsal stream to our previous model. This has enabled the model to perform another principal function of the visual cortex, i.e., motion perception.

关键词： Brain modeling Computational modeling Neurons Biological system modeling Object recognition Motion analysis Solid modeling Visual system Computer vision Motion detection

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Global heuristically search algorithm for DNA encoding

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Progress in Natural Science 2007年第6期17卷 745-749页

作者： Zhang, Kai Pan, Linqiang Xu, Jin Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

A new efficient algorithm is developed to design DNA words with equal length for DNA computing. The algorithm uses a global heuristic optimizing search approach and converts constraints to a carry number to accelerate the convergence, which can generate a DNA words set satisfying some thermodynamic and combinatorial constraints. Based on the algorithm, a software for DNA words design is developed.

关键词： DNA

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A novel DNA computing model based on RecA-mediated triple-stranded DNA structure

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Progress in Natural Science:Materials International 2007年第6期17卷 708-711页

作者： Fang Gang1, Zhang Shemin2, Dong Yafei1 and Xu Jin1(1. The Key laboratory of image processing and intelligent control, Department of control Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China 2. Shaanxi University of Technology, Hanzhong 723000, China) [a]The Key Laboratory of Image Processing and Intelligent Control Department of Control Science & Engineering Huazhong University of Science and Technology Wuhan China [b]Shaanxi University of Technology Hanzhong China

The field of DNA computing emerged in 1994 after Adleman’s paper was published. Henceforth,a few scholars solved some noted NP-complete problems in this way. And all these methods of DNA computing are based on conventional Watson-Crick hydrogen bond of doublehelical DNA molecule. In this paper, we show that the triple-stranded DNA structure mediated by RecA protein can be used for solving computational problems. Sequence-specific recognition of double-stranded DNA by oligonucleotide-directed triple helix (triplex) formation is used to carry out the algorithm. We present procedure for the 3-vertex-colorability problems. In our proposed procedure, it is suggested that it is possible to solve more complicated problems with more variables by this model.

关键词： DNA computing 3-vertex-colorability problem.

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