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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Yufang Huang Zhen Cheng Jin Xu Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan Hubei China School of Electronics Engineering and Computer Science Peking University Beijing China

DNA tile self-assembly has been proved to enable programmable manipulation of biological systems as a tool of molecular computation. It is mainly based on the property that is the spontaneous self-ordering of substructure into superstructure driven by annealing of Watson-Crick base-pairing DNA sequences. We take full advantage of the superiority of DNA tile self-assembly to construct a molecular computing system that implements a solution for the 0-1 planning problem. This algorithm can independently and simultaneously yield the data pool, containing all possible solutions when all basic operation tiles are designed beforehand. Then we can use some advanced bio-chemistry techniques to select the optimization solutions of the 0-1 planning problem. Our work has shown that it is possible to work with an exponential number of components to solve NP-complete problems. The method proposed here also can reduce the number of laboratory steps required for computation so that it can improve the computation speed.

关键词： Tiles DNA Self-assembly Planning Assembly Radiation detectors Biology

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Algorithm for elliptic curve Diffie-Hellman key exchange based on DNA tile self-assembly

Algorithm for elliptic curve Diffie-Hellman key exchange bas...

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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Zhen Cheng Yufang Huang Jin Xu Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan Hubei China School of Electronics Engineering and Computer Science Peking University Beijing China

DNA tile self-assembly is a promising paradigm for nanotechnology. Recently, many researches show that computation by DNA tile self-assembly maybe scalable. In this paper, we propose the algorithm for elliptic curve Diffie-Hellman key exchange based on DNA tile self-assembly. First we give the DNA tile self-assembly model to compute the scalar multiplication, then we can successfully implement the Diffie-Hellman key exchange over elliptic curve by extracting the result strand of the scalar multiplication.

关键词： Tiles DNA Self-assembly Elliptic curves Assembly Computational modeling Algorithm design and analysis

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Automatic segmentation of brain structures from MRI integrating atlas-based labeling and level set method

Automatic segmentation of brain structures from MRI integrat...

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Matineh Shaker Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence Department of Electrical and Computer University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Hospital Detroit MI USA

The purpose of this study is to segment hippocampus, amygdala and entorhinal cortex in magnetic resonance images (MRI) of temporal lobe epilepsy (TLE) patients. The proposed method consists of two separate parts. First, we use an atlas-based segmentation method to obtain initial segmentation results for desired structures. Using additional preprocessing steps for image registration and gray matter (GM) segmentation is the specification of this stage of the work. Then, all of the GM voxels are labeled using an anatomical atlas. In the next stage, variational level set formulation without re-initialization is applied on the images. We use the boundaries obtained by atlas-based segmentation as the contour for initialization of level set function. Automatic generation of initial contour makes the final segmentation results operator-independent. The proposed approaches are evaluated by comparing automatic and expertpsilas segmentation results and confirming their similarity.

关键词： Brain Magnetic resonance imaging Labeling Level set image segmentation Hippocampus Magnetic resonance Temporal lobe Epilepsy image registration

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Atlas based segmentation of white matter fiber bundles in DTMRI using fractional anisotropy and principal eigen vectors

Atlas based segmentation of white matter fiber bundles in DT...

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IEEE International Symposium on Biomedical Imaging

作者： Esmaeil Davoodi-Bojd Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineeringr University of Tehran Tehran Iran Radiology Department Henry Ford Hospital Image Analysis and Communications Laboratory Detroit MI USA

In this work, we develop an atlas based method for automatic segmentation of white matter fiber bundles. To this end, we propose a new method for registration of diffusion tensor (DT) images using DTI information which is also used in the fiber tracking process, and we also propose a strategy for segmenting the fiber bundles using the new registration method and a probabilistic white matter atlas. We apply the registration method to 13 real DTI data sets and evaluate the results by comparing the level of alignment of all fibers. Then, we use the proposed strategy to segment 10 major fiber bundles in one of the subjects. One of the advantages of such a method is the robustness of the results thanks to using prior knowledge. The segmented results can be used for comparing and evaluating other fiber bundle segmentation methods.

关键词： Anisotropic magnetoresistance image segmentation image registration Tensile stress Diffusion tensor imaging Cost function Robustness Automatic control Radio control Process control

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Temporal pattern recognition using spiking neural networks for cortical neuronal spike train decoding

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IFAC Proceedings Volumes 2008年第2期41卷 5203-5208页

作者： Huijuan Fang Yongji Wang Jiping He Shan Liu Key Laboratory for Image Processing and Intelligent Control of Education Ministry of China Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China (Tel: 86-27-87557284 The Harrington Department of Bioengineering and the Center for Neural Interface Design of The Biodesign Institute Arizona State University Tempe AZ 85287 USA

Most experimental and decoding algorithm studies of brain neural signals assume that neurons transmit information as a rate coding, but recent studies on the fast cortical computations indicate that temporal coding is probably a more biologically plausible scheme used by neurons. We introduce spiking neural networks (SNN) which consist of spiking neurons propagate information by the timing of spikes to analyze the cortical neural spike trains directly without temporal information lost. The SNN based temporal pattern classification is compared with the conventional artificial neural networks (ANN) based firing rate analysis. The results show that the SNN algorithm can achieve higher accuracy, which demonstrates that temporal coding is a viable code for fast neural information processing and the SNN approach is suitable for recognizing the temporal pattern in the cortical neural signals.

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An improved shuffled frog leaping algorithm with cognitive behavior

An improved shuffled frog leaping algorithm with cognitive b...

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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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