Brain computer interface (BCI) system allows users to direct interact with the surrounded environment just a blink of their thought. In doing this, the most relevant informative from the electroencelography (EEG) signals need to be extracted from the electrodes of the scalp. Neurophysiology studies have proved that the power density from corresponding electrodes and frequency could identify imagination of the left or right hand movement. They also proved that these feature vary strongly from one subject to another. These spatial-time-frequency components are the keys to unlock the optimal features from the large space of these power density features. In this paper, we proposed the optimal feature extracting method from the basic power density of EEG signal. At first, the all EEG signal from the electrodes were filter using both spatial and temporal filters to enhance the signal to noise ratio of EEG. Then, the time-frequency features were extracted using short-time Fourier transform (STFT) and average power in sub-window band. Genetic algorithm was applied to search for the optimal features. In our simulation, we used the dataset from BCI competition III, IV and the data experimented in our laboratory. To ensure the improvement of our proposed feature extraction method, we applied the extracted feature into the support vector machine.

Classification of limbs motion intention based on electroencephalogram (EEG) is one of the major subjects of brain-computer interface (BCI). EEG signals contained not only noise artefacts, but also variations within and across sessions. To reduce this unnecessary information in EEG signal processing, we applied an extension version of common spatial pattern (CSP). The original CSP has been widely used to extract relevant spatial features from EEG signals. However, one of the CSP limitations is occurred when it concentrated on artifacts and variances sources, which exceeded the variance of endogenous component of the brain, instead of extracting the sources that provide subject's intention. Therefore, we proposed to add more parameter for separating invariance and variance in EEGs. To reduce the variant parts of signal in EEGs, we applied invariant common spatial pattern (iCSP) to maximize one class in the same time minimize other class and invariant sources. Based on neurophysiological knowledge, we made some strong weights to special electrodes related to our experimental paradigm. The raw EEG signals were recorded by auditory stimuli with 3 different frequencies cued for real hand movement and ignored noise. The right or left actual hand movements-based EEG signal states were classified by using linear discriminant analysis (LDA).

For our present life anytime, anywhere access to the network can communicate with the ubiquitous computing, it is essential to human life. We should be able to agree that communication will be enabled. For our present life, anytime, anywhere access to the network can communicate with the ubiquitous computing. Such as the ubiquitous era approached, interaction between the user and the computer has become an important issue. In this paper we use EEG signals to extract the user's intention recognition data, which the Mirror Neuron System Based on HMMs and GMMs to model the convergence of the hybrid model is proposed. This is based on a kind of biological signals using EEG signals to the user's intention recognition techniques have been studied. In addition, EEG signals is generated based on the model, using the user intention recognition method have been studied. The proposed model will be applied in the field of neuro robotics.

In this paper, novel methods for facial emotion recognition in facial image sequences are presented. Our facial emotional feature detection and extracting based on Active Appearance Models (AAM) with Ekman's Facial Action Coding System (FACS). Our approach to facial emotion recognition lies in the dynamic and probabilistic framework based on Dynamic Bayesian Network (DBN) with Kalman Filter for modeling and understanding the temporal phases of facial expressions in image sequences. By combining AAM and DBN, the proposed method can achieve a higher recognition performance level compare with other facial expression recognition methods. The result on the BioID dataset show a recognition accuracy of more than 90% for facial emotion reasoning using the proposed method.

In this paper, we used particle filter to motion estimation algorithm on real-time for mobile surveillance robot. Particle filter based on the Monte Carlo's sampling method, be used Bayesian conditional probability model which having prior distribution probability and posterior distribution probability. By using particle filter, it can be possible to tracking and estimating robustly for object's motion and movement. Also most of the initial probability density was set to define or random manually. Proposed method in this paper, however, using the sum of absolute differences (SAD) is to take the initial probability density. Therefore, by using a particle filter to the object tracking system, it can be configured more efficient.

In the facial image, emotions are most widely represented with eye and mouth expressions. If we want to recognize the human's emotion via the facial image, we need to extract features of the facial image. Active Shape Model (ASM) is one of the most popular methods for facial feature extraction. Regarding the traditional ASM depends on the setting of the initial parameters of the model, in this paper we propose a facial emotion recognizing method based on ASM and Bayesian Network. Firstly, we obtain the reconstructive parameters of the new gray-scale image by sample-based learning and use them to reconstruct the shape of the new image and calculate the initial parameters of the ASM by the reconstructed facial shape. Then reduce the distance error between the model and the target contour by adjusting the parameters of the model. Finally get the model which is matched with the facial feature outline after several iterations and use them to recognize the facial emotion by using Bayesian Network.

Even though the genetic algorithm is known to be a very effective method to solve the global minimization problem, it needs much time (a large population size and a large number of generations) for a reliable answer and thus it seems to be inadequate for on-line performance. We propose a population feedback GA scheme. we show the effectiveness of our scheme by finding an observer for the discrete-time nonlinear autonomous systems with simulations.

This paper presents evolvable neural networks based on a developmental model for navigation control of autonomous mobile robots in dynamic operating environments. Bio-inspired mechanisms have been applied to autonomous design of artificial neural networks for solving practical problems. The proposed neural network architecture is grown from an initial developmental model by a set of production rules of the L-system that are represented by the DNA coding. The L-system is based on parallel rewriting mechanism motivated by the growth models of plants. DNA coding gives an effective method of expressing general production rules. Experiments show that the evolvable neural network designed by the production rules of the L-system develops into a controller for mobile robot navigation to avoid collisions with the obstacles.

The function of the robot is being complex and various. The robot with various inputs generates various outputs. A proper controller is needed to control such a complex robot. We introduce a 2-layer fuzzy controller (2LFC). It can control robustly and has the small number of rules in many input variables. It can not only deal with various inputs but also generate various outputs. The main problem in the fuzzy system is how to design the fuzzy knowledge base. For this specific problem, we optimize the fuzzy controller using the schema co-evolutionary algorithm (SCEA) to find a global optimal solution. Through the design of the optimal fuzzy controller using the SCEA and the simulation of the mobile robot, we verify the efficacy of the 2-layer fuzzy controller and the schema co-evolutionary algorithm.

Researches on the design of the optimal fuzzy controller have been carried out for many years. Various approaches to fuzzy modeling have been proposed. In this paper, we introduce a novel algorithm, schema coevolutionary algorithm, for fuzzy modeling. We demonstrate the schema coevolutionary algorithm and compare it with other similar coevolutionary algorithms: virus-evolutionary genetic algorithm and coevolution of Handa. Then, we apply it to design the optimal fuzzy controller. The fuzzy controller is used to control the mobile robot and optimized by the schema coevolutionary algorithm. We verify the efficacy of this algorithm through the experiment and comparison with other algorithms.

Recently, robot technique has been developed remarkably. But, they cannot do emotional tasks and the work they can is limited. In this view, it is important for machine to understand human's emotion. Also, emotion recognition is necessary to make an intimate robot. This paper shows simulation results which classify emotions by learning 4 pitch patterns and results from some analyses. The pitch contour includes emotion information. This is why the pitch has been widely used for emotion recognition. However, because the pitch contour is not sufficient for recognizing emotion, we should add other elements. Thus, several analyses are done and the analyzed elements are called acoustic elements for convenience. These elements are the fundamental for more accurate recognition. In addition to this, we analyze the relation between emotion and acoustic elements. The brain is high-dimensional nonlinear dynamical system. So, it is essential to utilize a system that is capable of storing internal states and utilize a system that is capable of storing internal states and implementing complex dynamics. DRNN fits such a system. The simulator is composed of the DRNN (dynamic recurrent neural network), feature extraction.

It enables small antibodies to detect numerous pathogens which the natural immune system maintains the variety of B cells and T cells. The combination of genes being located in the V-region, D-region and J-region of B cell's chromosome can generate tens of thousands of B cells. Also T cell receptor in the T cell has four kinds of receptor protein molecules-a, /spl beta/, ?, d chain-which maintain the variety of T cells using the combination of them. After the T cells being recombined are passed through the strict verification procedure in the thymus, only T cells which survive can detect the intrusion of external pathogens. In this paper, we introduce the maintenance mechanism for the variety of T cells and propose the algorithm which detects the intrusion using detectors composed of those which are maintained after being passed through the negative selection procedures in the thymus. Finally, we prove the efficiency of the algorithm using computer simulation.

We propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on the immune system in distributed autonomous robotic system (DARS). The immune system is a living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in a dynamically changing environment. For applying the immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy and its behavior strategy is stimulated and suppressed by other robots using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and the idiotopic network hypothesis. It is used for decision making of optimal swarm strategy. By T-cell modeling, adaptation ability of robot is enhanced in dynamic environments.

The authors propose a method of constructing neural networks using bio-inspired emergent and evolutionary concepts. This method is an algorithm that is based on the characteristics of biological DNA and the growth of plants. The authors propose a construction method to make a DNA coding method for production rule of L-system. The L-system is based on the so-called parallel rewriting mechanism. The DNA coding method has no limitation in expressing the production rule of L-system. Evolutionary algorithms motivated by Darwinian natural selection are population based search methods, the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it to one step ahead prediction of Mackey-Glass time series and Sun spot data.

With the rising innovative antigens (such as intruders and viruses) through the Internet, reliable security mechanisms are required to perceptively detect and put them down. However, defense techniques of the current host system over the Internet may not properly analyze Internet antigens, because trends of attacks are unexpectedly shifted. We introduce an Antibody Layer that mediates proper security services based on the biological mechanism to rapidly disclose and remove innovative antigens. The proposed Antibody Layer also employs a new topology called antibody cooperation protocol to support real time security QoS for one host as well as host alliance.

This paper presents a genetic programming based evolutionary strategy for on-line adaptive learnable evolvable hardware. Genetic programming can be a useful control method for evolvable hardware for its unique tree structured chromosome. However it is difficult to represent the tree structured chromosome in hardware, and it is difficult to use the crossover operator in hardware. Therefore, genetic programming is not as popular as genetic algorithms in the evolvable hardware community in spite of its possible strengths. We propose a chromosome representation method and a hardware implementation method that can be helpful for this situation. Our method uses a context switchable identical block structure to implement a genetic tree in evolvable hardware. We compose an evolutionary strategy for evolvable hardware by combining the proposed method with other research results. The proposed method is applied to the autonomous mobile robots cooperation problem to verify its usefulness.

We present a new type of neural architecture consisting of chaotic neurons and apply it to the prediction of chaotic time series signals. To evolve chaotic neural systems, we use cellular automata whose production rules are evolved based on a DNA coding method. The structure of networks are appropriate for learning nonlinear, chaotic, and nonstationary systems. In order to verify their effectiveness, we apply the evolutionary chaotic neural systems to one-step ahead prediction of Mackey-Glass time series data.

The fuzzy classifier system (FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. The classifier system can evaluate the usefulness of rules represented by classifiers with repeated learning It is the FCS that applies this ability of the machine learning to the concept of fuzzy controller. It is that the antecedent and consequent of classifier is same as a fuzzy rule of the rule base. In this paper, the FCS is the Michigan style and fuzzifies the input values to create the messages. The system stores those messages in the message list and uses the implicit bucket brigade algorithms. Also the FCS employs the genetic algorithms (GAs) to make new rules and modify rules when performance of the system needs to be improved.

An adaptive neural-fuzzy control scheme for intelligent control is proposed. The control system consists of a fuzzy-neural controller (FNC) and model neural network (MNN). In the FNC, the antecedence and consequence of the fuzzy rule are constructed by a clustering method and a multilayer neural network. In the MNN, a multilayer neural network is utilized to identify an unknown controlled plant. The error backpropagation algorithm has been adopted as a learning technique. The effectiveness of the scheme is demonstrated by computer simulations of a cart-pole and a two-d.o.f. robot manipulator.