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.     

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.     

Deposition of thin film silicon using the pulsed PECVD and HWCVD techniques

We report on the use of pulsed plasma-enhanced chemical vapor deposition (P-PECVD) technique and show that “state-of-the-art” amorphous silicon (a-Si:H) materials and solar cells can be produced at a deposition rate of up to 15 Å/s using a modulation frequency in the range 1–100 kHz. The approach has also been developed to deposit materials and devices onto large area, 30 cm×40 cm, substrates with thickness uniformity (<5%), and gas utilization rate (>25%). We have developed a new “hot wire” chemical vapor deposition (HWCVD) method and report that our new filament material, graphite, has so far shown no appreciable degradation even after deposition of 500 μm of amorphous silicon. We report that this technique can produce “state-of-the-art” a-Si:H and that a solar cell of p/i/n configuration exhibited an initial efficiency approaching 9%. The use of microcrystalline silicon (μc-Si) materials to produce low-cost stable solar cells is gaining considerable attention. We show that both of these techniques can produce thin film μc-Si, dependent on process conditions, with 1 1 1 and/or 2 2 0 orientations and with a grain size of approx. 500 A. Inclusion of these types of materials into a solar cell configuration will be discussed.     

Resource contention in shared-memory multiprocessors: A parameterized performance degradation model

A standard metric conventionally employed to compare the performance of different multiprocessor systems is speedup. Although providing a measure of the improvement in execution speed achievable on a system, this metric does not yield any insight into the factors responsible for limiting the potential improvement in speed. This paper studies the performance degradation in shared-memory multiprocessors as a result of contention for shared-memory resources. A replicate workload framework with a flexible mechanism for workload specification is proposed for measuring performance. Two normalized performance metrics—efficiency and overhead factor—are introduced to quantify the factors limiting performance and facilitate comparison across architectures. Finally, the proposed model is employed to measure and compare the performance of three contemporary shared-memory systems, with special emphasis on the newly released BBN Butterfly-II (TC2000), currently undergoing Beta test.     

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.