Neural network decoupling technique and its application to a powered wheelchair system

© 2015 IEEE. This paper proposes a neural network decoupling technique for an uncertain multivariable system. Based on a linear diagonalization technique, a reference model is designed using nominal parameters to provide training signals for a neural network decoupler. A neural network model is designed to learn the dynamics of the uncertain multivariable system in order to avoid required calculations of the plant Jacobian. To avoid overfitting problem, both neural networks are trained by the Lavenberg-Marquardt with Bayesian regulation algorithm that uses a real-time recurrent learning algorithm to obtain gradient information. Three experimental results in the powered wheelchair control application confirm that the proposed technique effectively minimises the coupling effects caused by input-output interactions even under the condition of system uncertainties

Looking into the way the Vietnamese Media discussed the September Terrorist Attacks in America

This study examines the way the Vietnamese mainstream and unpublished media used the Vietnam War experiences to discuss the Sept. 11, 2001 terrorist attacks in America. Examination of 30 issues of The People newspaper showed that the mainstream media did not directly use the Vietnam War experiences to discuss the September terrorist attacks. Few articles directly mentioned or referred to the Vietnam War experiences. Examination of 80 threads of discussion from two Vietnamese websites showed that the Vietnamese unpublished media used the Vietnam War experiences more directly to discuss the September attacks. Three specific ways were recognized: (1) to compare the attacks and the new war of America with the Vietnam War and justify the new war using experiences about the involvement of the U.S. in the Vietnam War (2) to make fun of America using experiences of the Vietnam War and the American culture, and (3) to implicitly refer to the Vietnam War while discussing the attacks and the new war

Real-time video streaming with multi-camera for a telepresence wheelchair

© 2016 IEEE. This paper presents a new approach for telepresence wheelchairs equipped with multiple cameras. The aim of this system is to provide effective assistance for the elderly and people with disabilities. The work explores the integration of the Internet of Things, such as multimedia, wireless Internet communication, and automation control techniques into a powered wheelchair system. In particular, multiple videos are streamed in real-time from an array of cameras mounted on the wheelchair, allowing wide visualization surrounding the wheelchair. By using video communication and interaction, remote users can assist to navigate a wheelchair via the Internet through wireless connections in a distant location. The experimental results show that video streaming can achieve high-quality video with the streaming rate up to 30 frames per second (fps) in real-time. The average round-trip time is under 27 milliseconds (ms). The results confirmed the effectiveness of the proposed system for tele-monitoring and remote control to achieve safer navigation tasks for wheelchair users

Neural network based diagonal decoupling control of powered wheelchair systems

This paper proposes an advanced diagonal decoupling control method for powered wheelchair systems. This control method is based on a combination of the systematic diagonalization technique and the neural network control design. As such, this control method reduces coupling effects on a multivariable system, leading to independent control design procedures. Using an obtained dynamic model, the problem of the plant's Jacobian calculation is eliminated in a neural network control design. The effectiveness of the proposed control method is verified in a real-time implementation on a powered wheelchair system. The obtained results confirm that robustness and desired performance of the overall system are guaranteed, even under parameter uncertainty effects. © 2013 IEEE

Real-time transmission of panoramic images for a telepresence wheelchair

© 2015 IEEE. This paper proposes an approach to transmit panoramic images in real-time for a telepresence wheelchair. The system can provide remote monitoring and assistive assistance for people with disabilities. This study exploits technological advancement in image processing, wireless communication networks, and healthcare systems. High resolution panoramic images are extracted from the camera which is mounted on the wheelchair. The panoramic images are streamed in real-time via a wireless network. The experimental results show that streaming speed is up to 250 KBps. The subjective quality assessments show that the received images are smooth during the streaming period. In addition, in terms of the objective image quality evaluation the average peak signal-to-noise ratio of the reconstructed images is measured to be 39.19 dB which reveals high quality of images

An electro-mechanical contact formulation for DRY/WET electrode-scalp interfaces in an EEG headset

The process of generating an initial prototype for a new dry electrode wearable EEG headset system design can be time and resource intensive. The ability to predict the mechanical and electrical characteristics of this recording device could lead to major cost savings in this process. Since the skin surface roughness has a deep impact on the decrease of brain electric contact conductance (or the increase of the contact impedance) when electrode with bristles contact scalp skin, the estimation of electric conductance across rough dry and wet boundaries is a challenging task in the designing optimization of the wearable EEG headset system. In this contribution, the contact mechanism to predict the electrical impedance of scalp skin pressed against the electrode is considered as the electrical connection by the mechanical contact. With this, we have extended the Pohrt and Popov model by including the effects of conductive gel. An experiment is developed and carried-out to validate the interfacial contact impedance model

A novel target following solution for the electric powered hospital bed

© 2015 IEEE. The paper proposes a novel target following solution for an electric powered hospital bed. First, an improved real-time decoupling multivariable control strategy is introduced to stabilize the overall system during its operation. Environment laser-based data are then collected and pre-processed before engaging a neural network classifier for target detection. Finally, a high-level control algorithm is implemented to guarantee safety condition while the hospital bed tracks its target. The proposed solution is successfully validated through real-time experiments

Development of an assistive patient mobile system for hospital environments

This paper presents an assistive patient mobile system for hospital environments, which focuses on transferring the patient without nursing help. The system is a combination of an advanced hospital bed and an autonomous navigating robot. This intelligent bed can track the robot and routinely navigates and communicates with the bed. The work centralizes in building a structure, hardware design and robot detection and tracking algorithms by using laser range finder. The assistive patient mobile system has been tested and the real experiments are shown with a high performance of reliability and practicality. The accuracy of the method proposed in this paper is 91% for the targeted testing object with the error rate of classification by 6%. Additionally, a comparison between our method and a related one is also described including the comparison of results. © 2013 IEEE

Robust online adaptive neural network control for the regulation of treadmill exercises

The paper proposes a robust online adaptive neural network control scheme for an automated treadmill system. The proposed control scheme is based on Feedback-Error Learning Approach (FELA), by using which the plant Jacobian calculation problem is avoided. Modification of the learning algorithm is proposed to solve the overtraining issue, guaranteeing to system stability and system convergence. As an adaptive neural network controller can adapt itself to deal with system uncertainties and external disturbances, this scheme is very suitable for treadmill exercise regulation when the model of the exerciser is unknown or inaccurate. In this study, exercise intensity (measured by heart rate) is regulated by simultaneously manipulating both treadmill speed and gradient in order to achieve fast tracking for which a single input multi output (SIMO) adaptive neural network controller has been designed. Real-time experiment result confirms that robust performance for nonlinear multivariable system under model uncertainties and unknown external disturbances can indeed be achieved. © 2011 IEEE

Analysis of chaotic mixing in plugs moving in meandering microchannels

Droplets moving in meandering microchannels can serve as a passive and robust strategy to produce chaotic mixing of species in droplet-based microfluidics. In this paper, a simplified theoretical model is proposed for plug-shaped droplets moving in meandering microchannels at Stokes flow. With this model to provide the velocity field, particle tracking, which requires a large computation time, is performed directly and easily without interpolation. With this convenience, a broad survey of the parameter space is carried out to investigate chaotic mixing in plugs, including the channel curvature, the Peclet number, the viscosity ratio, and the plug length. The results show that in order to achieve rapid mixing in plugs in meandering microchannels, a large curvature, a small Peclet number, a moderate viscosity ratio, and a moderate plug length are preferred. © 2011 American Physical Society