Characterization of a novel antibody immobilization combining protein G with parylene-H for surface plasmon resonance immunosensors

Here we present a method for selectively and efficiently immobilizing antibodies to enhance the detection performance of surface plasmon resonance immune-sensors (SPRIs) for diagnostic applications. To improve the performance of antibody arrays, protein G was used as antibody-selective linkage layer with aldehyde functionalized poly-(para-xylylene) film. To estimate the efficiency of antibody immobilization, immunoglobulin G (IgG) was measured using the anti-IgG immobilized SPRIs. To demonstrate the proof-of-concept validation, the signal detected from the IgG using parylene-H film was compared with that of a combination of parylene-H and protein G in SPRIs. The results showed that the detection of IgG on the immobilized anti-IgG layer using the combination of parylene-H and protein G has a larger change of signal than that of using parylene-H layer. These results also imply that the anti-IgG was densely and efficiently immobilized on the modified surface with the linkage layer in a combination with parylene-H and protein G. Therefore, we believe that this combinatorial approach could selectively immobilize the antibodies, and also be applied for detection and diagnosis of immune diseases in the field of many SPRIs applications.     

Development of emotion recognition interface using complex EEG/ECG bio-signal for interactive contents

With the changes in the interface paradigm, a user may not be satisfied using only a behavior-based interface such as a mouse and keyboard. In this paper, we propose a real-time user interface with emotion recognition that depends on the need for skill development to support a change in the interface paradigm to one that is more human centered. The proposed emotion recognition technology may provide services to meet the need to recognize emotions when using contents. Until now, most studies on an emotion recognition interface have used a single signal, which was difficult to apply because of low accuracy. In this study, we developed a complex biological signal emotion recognition system that blends the ratio of an ECG for the autonomic nervous system and the relative power value of an EEG (theta, alpha, beta, and gamma) to improve the low accuracy. The system creates a data map that stores user-specific probabilities to recognize six kinds of feelings (amusement, fear, sadness, joy, anger, and disgust). It updates the weights to improve the accuracy of the emotion corresponding to the brain waves of each channel. In addition, we compared the results of the complex biological signal data set and EEG data set to verify the accuracy of the complex biological signal, and found that the accuracy had increased by 35.78%. The proposed system will be utilized as an interface for controlling a game and smart space for a user with high accuracy.     

Slow eye movement as a possible predictor of reaction delays to auditory warning alarms in a drowsy state

In recently developed intelligent vehicles, warning alarms are often used to prompt avoidance behaviours from drivers facing imminent hazardous situations. However, when critical reaction delays to auditory stimulation are anticipated, the alarm should be activated earlier to compensate for such delays. It was found that reaction times to an auditory stimulus significantly increased in the presence of slow eye movement (SEM), which is known to occur frequently during the wake-sleep transition. The reaction delay could not be attributed to temporal effects such as fatigue and was invariant regardless of response effectors (finger or foot). Moreover, it was found that applied pedal force decreased immediately after an auditory stimulus interrupted SEM. Consequently, it was concluded that SEM can be a good predictor of reaction delays to auditory warning alarms when drivers are in a drowsy state. STATEMENT OF RELEVANCE: The present study demonstrated that simple auditory reaction time significantly increased when SEM emerged. In the design of vehicle safety systems using warning alarms to prompt avoidance behaviours from drivers, such reaction delays during SEM must be taken into account.     

A global structural optimization technique using an interval method

This paper deals with a global optimization scheme for structural systems that require finite element analysis to evaluate the constraints or the objective function. The paper proposes a strategy for finding the global optimum using an interval method in conjunction with a multipoint function approximation. The highly nonlinear and nonconvex objective and constraint functions are first represented in the design space using linear and adaptive local approximations and these approximations are blended globally with the use of proper weighting functions. The interval method is then employed to trace the global optimum in the approximated function space. The procedure is tested with several examples with known global solutions and it is successfully applied to optimize the fiber-orientation angles of laminated composite plates for minimum deflections. Received December 22, 2000     

Reconfigurable Service Composition and Categorization for Power-Aware Mobile Computing

The requirement of agile adaptation to varying resource constraints in mobile systems motivates the use of a service-oriented architecture (SOA), which can support the composition of two or more services to form a complex service. In this paper, we propose SOA-based middleware to support QoS control of mobile applications and to configure an energy-efficient service composition graph. We categorize services into two layers:functionality-centric services, which are connected to create a complex service to meet the user's intentions, and resource-centric services, which undertake distributed functionality-centric services in a way that increases the success rate of service composition while reducing contention at specific service nodes. We also present a service routing algorithm to balance the resource consumption of service providers on a service-overlay network. Through simulation of power-aware service composition using a realistic model based on ns-2 and traced data, we demonstrate that our approach can help both the mobile devices and the servers in a service-overlay network to reduce energy consumption without an increase in response time.     

Learning type PID control system using input dependence reinforcement scheme

PID control has widely used in the field of process control and a lot of methods have been used to design PID parameters. When the characteristic values of a controlled object are changed due to a change over the years or disturbance, the skilled operators observe the feature of the controlled responses and adjust the PID parameters using their knowledge and know-how, and a lot of labors are required to do it. In this research, we design a learning type PID control system using the stochastic automaton with learning function, namely learning automaton, which can autonomously adjust the control parameters updating the state transition probability using relative amount of controlled error. We show the effectiveness of the proposed learning type PID control system by simulations. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Adaptive robust fuzzy control for path tracking of a wheeled mobile robot

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Interactive classification for pre-integrated volume rendering of high-precision volume data

The pre-integrated volume rendering technique is widely used for creating high quality images. It produces good images even though the transfer function is nonlinear. Because the size of the pre-integration lookup table is proportional to the square of data precision, the required storage and computation load steeply increase for rendering of high-precision volume data. In this paper, we propose a method that approximates the pre-integration function proportional to the data precision. Using the arithmetic mean instead of the geometric mean and storing opacity instead of extinction density, this technique reduces the size and the update time of the pre-integration lookup table so that it classifies high-precision volume data interactively. We demonstrate performance gains for typical renderings of volume datasets.     

Suboptimal filter for continuous‐time linear systems with unknown parameters

The filtering problem for continuous‐time linear systems with unknown parameters is considered. A new suboptimal filter is herein proposed. It is based on the optimal mean‐square linear combination of the local Kalman filters. In contrast to the optimal weights, the suboptimal weights do not depend on current observations; thus, the proposed filter can easily be implemented in real‐time. Examples demonstrate high accuracy and efficiency of the suboptimal filter. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society