A precise sensor fault detection technique using statistical techniques for wireless body area networks

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.     

A survey of quality of service in IEEE 802.11 networks

Developed as a simple and cost-effective wireless technology for best effort services, IEEE 802.11 has gained popularity at an unprecedented rate. However, due to the lack of built-in quality of service support, IEEE 802.11 experiences serious challenges in meeting the demands of multimedia services and applications. This article surveys 802.11 QoS schemes, including service differentiation in the MAC layer, admission control and bandwidth reservation in MAC and higher layers, and link adaptation in the physical layer, designed to meet these challenges by providing the necessary enhancements for the required QoS. Furthermore, the article addresses issues that arise when end-to-end QoS has to be guaranteed in today's pervasive heterogeneous wired-cum-wireless networks. Among these challenges, protocol interoperability, multihop scheduling, full mobility support, and seamless vertical handoff among multiple mobile/wireless interfaces are specifically addressed.     

A method for functional magnetic resonance imaging of olfaction

A method for generating olfactory stimuli for humans within a functional magnetic resonance imaging (fMRI) experimental design is described. The system incorporates a nasal-mask in which the change from odorant to no-odorant conditions occurs in less than 500 ms and is not accompanied by visual, auditory, tactile, or thermal cues. The mask provides an ordorant-free environment following prolonged ordorant presence. Specific imaging parameters that are conducive to the study of the human olfactory system are described. In a pilot study performed using these methods, the specific patterns of activation observed converged with published experimental and clinical findings.     

An application-specific protocol architecture for wireless microsensor networks

Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These networks require robust wireless communication protocols that are energy efficient and provide low latency. We develop and analyze low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality. LEACH includes a new, distributed cluster formation technique that enables self-organization of large numbers of nodes, algorithms for adapting clusters and rotating cluster head positions to evenly distribute the energy load among all the nodes, and techniques to enable distributed signal processing to save communication resources. Our results show that LEACH can improve system lifetime by an order of magnitude compared with general-purpose multihop approaches.     

Synchronization models for multimedia presentation with user participation

This paper addresses the key issue of providingflexible multimedia presentation with user participation and suggests synchronization models that can specify the user participation during the presentation. We study models like the Petrinet-based hypertext model and the object composition Petri nets (OCPN). We suggest adynamic timed Petri nets structure that can model pre-emptions and modifications to the temporal characteristics of the net. This structure can be adopted by the OCPN to facilitate modeling of multimedia synchronization characteristics with dynamic user participation. We show that the suggested enhancements for the dynamic timed Petri nets satisfy all the properties of the Petri net theory. We use the suggested enhancements to model typical scenarios in a multimedia presentation with user inputs.     

Approximate channel identification via δ‐signed correlation

A method of approximate channel identification is proposed that is based on a simplification of the correlation estimator. Despite the numerical simplification (no multiplications or additions are required, only comparisons and an accumulator), the performance of the proposed estimator is not significantly worse than that of the standard correlation estimator. A free (user selectable) parameter moves ‘smoothly’ from a situation with small sum‐squared channel estimation error but hard‐to‐identify channel peaks, to one with a larger sum‐squared estimation error but easy‐to‐identify channel peaks. The proposed estimator is shown to be biased and its behaviour is analysed in a number of situations. Applications of the proposed estimator to sparsity detection, symbol timing recovery and to the initialization of blind equalizers are suggested. Copyright © 2002 John Wiley & Sons, Ltd.     

On-line emission and economic load dispatch using adaptive Hopfield neural network

This paper presents an adaptive Hopfield neural network (AHNN) based methodology, where the slope of the activation function is adjusted, for finding approximate Pareto solutions for the multi-objective optimization problem of emission and economic load dispatch (EELD). We have placed emphasis on finding solutions quickly, rather than the global Pareto solutions, so that our algorithm can be employed in large on-line power systems where variations in load are quite frequent. To enable faster convergence, adaptive learning rates have been developed by using energy function and applied to the slope adjustment method. The proposed algorithm has been tested on selected IEEE bus benchmark systems. The convergence of AHNN is found to be nearly 50% faster than the non-adaptive version.     

Forced convection subcooled boiling of binary mixtures

A mode based on an additive mechanism of heat transfer is proposed for forced convection subcooled boiling of binary mixtures. The contributing modes of heat transfer are: (i) the heat transferred as latent heat by the rising bubbles, (ii) the heat transferred as the heat contained in the superheated thermal layer that is removed from the surface in the wake of the rising bubbles and (iii) the single phase forced convection heat transfer from the heating surface not influenced by the bubbles. Experimental data from the literature on binary systems show good agreement with the model, validating the postulated mechanism.