RFID-based interactive multimedia system for the children

In this paper, we present an interactive edutainment system for the children that leverages multimedia and RFID technologies in a seamless manner. The proposed system allows children to learn about new objects/entities by tapping on physical objects through a specially designed RFID-Bluetooth based Tangible User Interface (TUI) tool. The output of the system is delivered as a set of appropriate multimedia representations related to the objects being tapped. The TUI uses RFID technology for object identification and Bluetooth communication to transmit data to the computer where the system??s software is running. We incorporated our system in three games that allow children of different ages to benefit from the system??s functionalities and encourage them to interact with it.     

Type-2 Fuzzy Logic Control of a Flexible-Joint Manipulator

A type-2 fuzzy logic controller (FLC) is proposed in this article for robot manipulators with joint elasticity and structured and unstructured dynamical uncertainties. The proposed controller is based on a sliding mode control strategy. To enhance its real-time performance, simplified interval fuzzy sets are used. The efficiency of the control scheme is further enhanced by using computationally inexpensive input signals independently of the noisy torque and acceleration signals, and by adopting a trade off strategy between the manipulator’s position and the actuators’ internal stability. The controller is validated through a set of numerical experiments and by comparing it against its type-1 counterpart. It is shown through these experiments the higher performance of the type-2 FLC in compensating for larger magnitudes of uncertainties with severe nonlinearities. This work was partially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canadian Microelectronics Corporation (CMC).     

Robust computationally efficient control of cooperative closed-chain manipulators with uncertain dynamics

This article presents a decentralized control scheme for the complex problem of simultaneous position and internal force control in cooperative multiple manipulator systems. The proposed controller is composed of a sliding mode control term and a force robustifying term to simultaneously control the payload's position/orientation as well as the internal forces induced in the system. This is accomplished independently of the manipulators dynamics. Unlike most controllers that do not require prior knowledge of the manipulators dynamics, the suggested controller does not use fuzzy logic inferencing and is computationally inexpensive. Using a Lyapunov stability approach, the controller is proven to be robust in the face of varying system's dynamics. The payload's position/orientation and the internal force errors are also shown to asymptotically converge to zero under such conditions.     

A context-aware multimedia framework toward personal social network services

People use various Internet-based services including social networks to conduct tasks of diversified categories such as informational, professional, educational, hobbies, health, recreational, academic, and news. Many people not only maintain association with a number of different services but also share information with their social ties in their daily life. However, a person only consumes a subset of services and needs to communicate with a subset of social ties at any given context. In this paper, we present a framework called SenseFace that leverages sensory data coming from one’s body sensor network and multimedia information contained within Internet-based services to recommend context-aware services and community of interest. We present the detailed design and implementation of the framework and share our preliminary test results.     

A Robust Hybrid Intelligent Position/Force Control Scheme for Cooperative Manipulators

We examine in this paper the complex problem of simultaneous position and internal force control in multiple cooperative manipulator systems. This is done in the presence of unwanted parametric and modeling uncertainties as well as external disturbances. A decentralized adaptive hybrid intelligent control scheme is proposed here. The controller makes use of a multi-input multi-output fuzzy logic engine and a systematic online adaptation mechanism. Unlike conventional adaptive controllers, the proposed controller does not require a precise dynamical model of the system's dynamics. As a matter of fact, the controller can achieve its control objectives starting from partial or no a priori knowledge of the system's dynamics. The ability to incorporate the already acquired knowledge about the system's dynamics is among what distinguishes the proposed controller from its predecessor adaptive fuzzy controllers. Using a Lyapunov stability approach, the controller is proven to be robust in the face of varying intensity levels of the aforementioned uncertainties. The position and the internal force errors are also shown to asymptotically converge to zero under such conditions     

Experimental Testing of a Hybrid Power Plant for a Dirigible UAV

Dirigibles have the ability to take off and land vertically, hover and maintain lift without consuming energy, and can be easily deflated for packaging and transportation. As such, dirigibles are well suited for surveillance and surveyance missions such as rescue and aid operations after disasters. This paper reviews hybrid dirigible UAV design considerations, then presents a novel hybrid power plant design. The hybrid power plant design consists of a 2-stroke 4cc glow engine in-line with a brushless DC motor/ generator and variable pitch propeller. The experimental results have shown that the hybrid power plant is capable of producing a power output of 148.4 W.     

Mobile robot trajectory tracking using noisy RSS measurements: An RFID approach

Most RF beacons-based mobile robot navigation techniques rely on approximating line-of-sight (LOS) distances between the beacons and the robot. This is mostly performed using the robot's received signal strength (RSS) measurements from the beacons. However, accurate mapping between the RSS measurements and the LOS distance is almost impossible to achieve in reverberant environments. This paper presents a partially-observed feedback controller for a wheeled mobile robot where the feedback signal is in the form of noisy RSS measurements emitted from radio frequency identification (RFID) tags. The proposed controller requires neither an accurate mapping between the LOS distance and the RSS measurements, nor the linearization of the robot model. The controller performance is demonstrated through numerical simulations and real-time experiments.     

The hierarchical expert tuning of PID controllers using tools ofsoft computing

We present soft computing-based results pertaining to the hierarchical tuning process of PID controllers located within the control loop of a class of nonlinear systems. The results are compared with PID controllers implemented either in a stand alone scheme or as a part of conventional gain scheduling structure. This work is motivated by the increasing need in the industry to design highly reliable and efficient controllers for dealing with regulation and tracking capabilities of complex processes characterized by nonlinearities and possibly time varying parameters. The soft computing-based controllers proposed are hybrid in nature in that they integrate within a well-defined hierarchical structure the benefits of hard algorithmic controllers with those having supervisory capabilities. The controllers proposed also have the distinct features of learning and auto-tuning without the need for tedious and computationally extensive online systems identification schemes.     

Learning-based resource optimization in asynchronous transfer mode (ATM) networks

This paper tackles the issue of bandwidth allocation in asynchronous transfer mode (ATM) networks using recently developed tools of computational intelligence. The efficient bandwidth allocation technique implies effective resources utilization of the network. The fluid flow model has been used effectively among other conventional techniques to estimate the bandwidth for a set of connections. However, such methods have been proven to be inefficient at times in coping with varying and conflicting bandwidth requirements of the different services in ATM networks. This inefficiency is due to the computational complexity of the model. To overcome this difficulty, many approximation-based solutions, such as the fluid flow approximation technique, were introduced. Although such solutions are simple, in terms of computational complexity, they nevertheless suffer from potential inaccuracies in estimating the required bandwidth. Soft computing-based bandwidth controllers, such as neural networks- and neurofuzzy-based controllers, have been shown to effectively solve an indeterminate nonlinear input-output (I-O) relations by learning from examples. Applying these techniques to the bandwidth allocation problem in ATM network yields a flexible control mechanism that offers a fundamental tradeoff for the accuracy-simplicity dilemma.