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IEEE Conference on Decision and control

作者： Faiz Rasool Dan Huang Sing Kiong Nguang Department of Electrical and Computer Engineering University of Auckland Auckland New Zealand Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiaotong University Shanghai China

Quantization effects are inevitable in networked control systems (NCSs). These quantization effects can be reduced by increasing the number of quantization levels. However, increasing the number of quantization levels may lead to network congestion, (i.e., the network needs to transfer more information than its capacity). In this paper, we investigate the problem of designing a robust ℋ ∞ output feedback controller for discrete-time networked systems with an adaptive quantization density or limited information. More precisely, the quantization density is designed to be a function of the network load condition which is modeled by a Markov process. A stability criterion is developed by using Lyapunov-Krasovskii functional and sufficient conditions for the existence of a dynamic quantized output feedback controller are given in terms of Bilinear Matrix Inequalities(BMIs). An iterative algorithm is suggested to obtain quasi-convex Linear Matrix Inequalities (LMIs) from BMIs. An example is presented to illustrate the effectiveness of the proposed design.

关键词： Quantization Delay Linear matrix inequalities Markov processes Closed loop systems Stability analysis Output feedback

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Securing JPEG architecture based on enhanced chaotic hill cipher algorithm

Securing JPEG architecture based on enhanced chaotic hill ci...

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International Conference on computer engineering and systems

作者： Ghada Hamissa Amany Sarhan Hatem Abdelkader Mahmoud Fahmy Electrical Engineering Dept. Faculty of Engineering KFS University Kafr-Elsheikh Egypt Information System Department Faculty ofComputer & Information Menoufia University Menoufia Egypt Computer & Automatic Control Department Faculty of Engineering Tanta University Tanta Egypt

In the last two decades, Chaos theory has received a great deal of attention from the cryptographic community. This paper presents two ideas. First idea is using chaotic functions to overcome the weaknesses of the classical Hill cipher. The second idea is proposing a new encoder-decoder architecture, called ChaoEncoDeco, for securing JPEG images. An extra stage of encryption is embedded within the traditional JPEG codec to improve security level of such system. This security stage uses one of the chaotic functions called Logistic Map. This map is used to enhance the Hill cipher and achieve more secure encryption key. The properties of both chaotic system and of the Hill cipher encryption key are all utilized to obtain ultimate secure systems. The proposed encryption algorithm is crypto-analyzed and compared to the standard Hill cipher algorithm. Complete evaluation for the proposed architecture is also performed which indicates the effectiveness of the proposed system.

关键词： Cryptography Image coding Chaos PSNR Transform coding Time measurement Size measurement

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Evaluation of functional connectivity in control and ADHD subjects by generalized Canonical Correlation and Probabilistic Independent Component Analysis

Evaluation of functional connectivity in control and ADHD su...

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Iranian Conference of Biomedical engineering (ICBME)

作者： AmirEhsan Lashkari Hamid Soltanian-Zadeh GholamAli Hosseinzadeh Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA

Functional connectivity can be evaluated by temporal correlation between spatial neurophysiologic events or correlation between neural activities of brain regions. Unlike anatomical connectivity which represents physical connections between two brain regions, functional connectivity investigates interactions between brain regions. In this study, we implemented and evaluated Probabilistic Independent Component Analysis (PICA) and generalized Canonical Correlation Analysis (gCCA) on resting state data of healthy subjects and subjects with ADHD disorders from NITRC database. This was done to detect brain functional connectivity and compare the results in normal and diseased subjects. In addition, speed of the methods was compared. Our study shows that relative to PICA, gCCA has higher precision and accuracy and lower computational complexity and run time in addition to capability of extracting more reproducible statistical maps in both control and ADHD subjects.

关键词： Correlation Magnetic resonance imaging Independent component analysis Educational institutions Biomedical engineering Probabilistic logic Basal ganglia

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Novel emotion dynamic express for robot

Novel emotion dynamic express for robot

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Dong-Hwa Kim Péter Baranyi Department of Instrumentation and Control Engineering Hanbat National University Daejeon South Korea Head of Cognitive Informatics research group involved in the System and Control Laboratory Computer and Automation Research Institute (MTA-SZTAKI) Hungarian Academy of Sciences Hungary

This paper proposes the novel emotion dynamic equation for emotion implementation like human's emotion. Almost general method use artificial approach such as neutral networks to classify emotion by using speech and face image data for human's emotion recognition. But high-dimension and large size of this data cause low-speed learning in robot system. The main idea of the proposed dynamic equation method is to dynamically express emotion by multi-agent function.

关键词： Robot sensing systems Humans Dynamics Face Equations

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A novel type of trigonometric neural network trained by Extended Kalman Filter

A novel type of trigonometric neural network trained by Exte...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： M. Norouzi M. Mansouri M. Teshnehlab M. Aliyari Shoorehdeli Faculty of Engineering Department of Computer Engineering Science and Research Branch Islamic Azad University Iran Intelligent System Laboratory (ISLAB) Faculty of Electrical Engineering Control Department K.N. Toosi University of Technology Tehran Iran Faculty of Electrical Engineering Department of Mechatronics Engineering Advanced Process Automation &Control Laboratory (APAC) K.N. Toosi University of Technology Tehran Iran

In this study, a new type of trigonometric neural network is presented by adding frequency and phase to trigonometric activation functions. The proposed trigonometric neural network has more flexibility in comparison with conventional trigonometric neural networks and even other types of neural networks. Due to the low convergence rate and high posibility of trapping in a local minimum of backpropagation algorithm, Extended Kalman Filter algorithm is used to train the neural network's parameters which they appear in a nonlinear form. The Simulation of the suggested neural network based on the prediction of Mackey-Glass time series and identification of a nonlinear dynamic ystem reveals the efficiency of the proposed network. To show the efficiency of this method, the results are compared with the results of the others.

关键词： Biological neural networks Kalman filters Equations Neurons Vectors Covariance matrix Mathematical model

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Sport Video Classification Using an Ensemble Classifier

Sport Video Classification Using an Ensemble Classifier

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Iranian Machine Vision and Image Processing (MVIP)

作者： Mohamad Hoseyn Sigari Samaneh Abbasi Sureshjani Hamid Soltanian-Zadeh Control & Intelligent Processing Center of Excellence (CIPCE) School of Electrical & Computer Engineering College of Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI USA

Sport video classification is an application of video analysis which can be useful in video indexing and retrieval. In this article, a new method for sport video classification using ensemble classifier is proposed. The proposed method uses 6 features: 3 dominant colors, dominant gray level, cut rate and motion rate. These features are classified by 4 simple classifiers in an ensemble classifier: Nearest Neighbor (NN), Linear Discriminant Analysis (LDA), Decision Tree (DT) and Probabilistic Neural Network (PNN). To combine the output of simple classifiers and make final decision, weighted majority vote is used while the weight of each simple classifier is equal to corresponding correct classification rate (CCR). Experimental result shows that the CCR of proposed system is 78.8%. In this experiment, 104 clips in 7 different sport classes are used: football, basketball, tennis, swimming, futsal, ski and box.

关键词： Feature extraction Color Image color analysis Classification algorithms Accuracy Artificial neural networks Histograms

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Gearbox Ripple Rejection of Robots Using Observer and Adaptive control Theory

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IFAC Proceedings Volumes 2011年第1期44卷 893-898页

作者： Sebastian Maier Johann Bals Marc Bodson Department of System Dynamics and Control Institute of Robotics and Mechatronics German Aerospace Center (DLR) 82234 Wessling Germany Muenchner Str. 20 Department of Electrical and Computer Engineering University of Utah 50 S Central Campus Dr. Salt Lake City UT 84112 USA

Abstract Robots are often run with permanent magnet synchronous motors (PMSM) with a high ratio gearbox. Both parts can produce parasitic oscillations (ripples), which let the robot shake at tool center point. The gearbox ripple problem is more complicated to be solved with control theory because only motor side sensors should be used. Due to the internal model principle gearbox side information is necessary to solve the problem. The first algorithm uses an observer to get gearbox side information where the second algorithm uses a gearbox side rate sensor. The algorithms are tested with a nonlinear SISO problem and with a nonlinear MIMO system. In both cases the ripples are canceled with an adaptive controller which estimates the phase and magnitude of the ripple. This adaptive controller is designed separately and is added to the existing basis controller. The algorithms are tested in simulation and on a testbed, which is an industrial application.

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Training ANFIS system with DE algorithm

Training ANFIS system with DE algorithm

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Allahyar Z. Zangeneh Mohammad Mansouri Mohammad Teshnehlab Ali K. Sedigh Computer Engineering Department Science and Research Branch Islamic Azad university of Tehran Iran Intelligent System laboratory (ISLAB) Control Department K. N. Toosi University of Technology Tehran Iran Advanced Process Automation & Control laboratory (APAC) Control Department K. N. Toosi University of Technology Tehran Iran

In this study, a new type of training the adaptive network-based fuzzy inference system (ANFIS) is presented by applying different types of the Differential Evolution branches. The TSK-type consequent part is a linear model of exogenous inputs. The consequent part parameters are learned by a gradient descent algorithm. The antecedent fuzzy sets are learned by basic differential evolution (DE/rand/1/bin) and then with some modifications in it. This method is applied to identification of the nonlinear dynamic system, prediction of the chaotic signal under both noise-free and noisy conditions and simulation of the two-dimensional function. Instead of DE/rand/1/bin, this paper suggests the complex type (DE/current-to-best/1+1/bin & DE/rand/1/bin) on predicting of Mackey-glass time series and identification of a nonlinear dynamic system revealing the efficiency of proposed structure. Finally, the method is compared with pure ANFIS to show the efficiency of this method.

关键词： Vectors Adaptive systems Input variables Training Mathematical model Equations Firing

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Assimilation of Wireless Sensor Networks and remote microcomputer laboratories

Assimilation of Wireless Sensor Networks and remote microcom...

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Proceedings of the International Convention MIPRO

作者： Darko Fudurić Drago Cmuk Petar Knežević Saša Mrvoš Department of Control and Computer Engineering Faculty of Engineering and Computing University of Zagreb Zagreb Croatia Končar-Electronics and Informatics Inc. Zagreb Croatia Department of Fundamentals of Electrical Engineering and Measurements Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Electronic System and Information Processing Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

Emergence of Web 2.0 technologies became a trigger for a revolution that goes far beyond the Web. For an average student Web was transformed from a steady, accessible source of information into a place of action and viable exchange of knowledge - a kind of a virtual classroom. Students started to look for experience and a touch to reality online, and this is where remote laboratories come in place. Such laboratories became an inevitable need in electrical and computer engineering courses dealing with microcomputers. This paper presents an overview of available architectures of microcomputer laboratories used today and addresses their most important limitations. Moreover, an important extension of such architecture is proposed allowing support for remote Wireless Sensor Network (WSN) laboratory. Different aspects of compatibility between microcomputer laboratories and WSNs have been examined to allow creation of an general extension model. With additional effort and adaptation, WSN nodes may serve remote clients as an economic and accessible microcomputer laboratory providing invaluable hands-on experience and a motivation for a course using it.

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Universal Steganalysis Based on Local Prediction Error in Wavelet Domain

Universal Steganalysis Based on Local Prediction Error in Wa...

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International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP)

作者： Anahita Shojaei-Hashemi Mostafa Mehdipour-Ghazi Shahrokh Ghaemmaghami Hamid Soltanian-Zadeh School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Electronics Research Institute Sharif University of Technology Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI USA Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

A passive universal image steganalysis method is proposed that is shown to be of higher detection accuracy than existing truly blind steganalysis methods including Farid's and the WAM. This is achieved by improving some weaknesses of Farid's steganalysis scheme in feature extraction, that is, instead of deriving an over-determined equation system for each sub band of the wavelet decomposition, the sub bands are divided into overlapping blocks and an over-determined equation system is constructed for each block. To guarantee the existence of finite answers, the over-determined equation systems are solved in a way different from Farid's by using Moore-Penrose pseudo-inverse concept. Further improvement to the performance is achieved by adding diagonal directions and increasing the number of moments. The comparative evaluations confirm the superiority of the proposed method over prevalent blind steganalysis schemes.

关键词： Equations Mathematical model Payloads Transform coding Correlation Educational institutions Reliability

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