Advanced Wireless LAN

The past two decades have witnessed starling advances in wireless LAN technologies that were stimulated by its increasing popularity in the home due to ease of installation, and in commercial complexes offering wireless access to their customers. This book presents some of the latest development status of wireless LAN, covering the topics on physical layer, MAC layer, QoS and systems. It provides an opportunity for both practitioners and researchers to explore the problems that arise in the rapidly developed technologies in wireless LAN

Approaches to Generating Selectivity in Microcantilever Sensors

Microcantilever (MC) sensors have emerged as sensing transducers that offer greater sensitivity than comparable sensors due in large part to their very small dimensions. MCs have been utilized in many chemical sensing applications. Not only do MCs demonstrate greater sensitivity, but they also are relatively low in cost, they can be used in an array format, and they can be integrated into on-chip electronic circuitry. While MC sensors demonstrate great sensitivity, an area of weakness that MC sensors must overcome is that of selectivity. The response of a MC sensor to analyte is mechanical; these mechanical responses lack the information rich spectral features like those found in vibrational spectroscopic techniques. Thus the underlying goal of this research is to develop approaches to enhancing selectivity in MC sensors. The initial research focused simply on demonstrating that MC sensors could be functionalized with thiolated self-assembled monolayers (SAMs) and then used to detect metal ions in the liquid phase. The initial research not only demonstrated the moderate selectivity of SAMs to metal ions, but also the good sensitivity at which these metal ions could be detected. The second phase of the research represented the first time that microcantilever array sensors (MCAs) were functionalized with SAMs having different ligand functionalities on one sensor chip. The MCA was exposed to different metal ions and the response signatures used in conjunction with pattern recognition algorithms to identify and quantitate the metal ion injected. In an extension of the metal ion array research, the SAM MCA was coupled to an ion-exchange chromatography (IEC) column for the separation and detection of metal ions. The second major division of research presented in this work involves improving the selectivity of detection of analytes in the gas phase. MCAs differentially coated with polymeric RPs by way of PVD were made. Experimental parameters were adjusted to determine if the parameters would impact the selectivity of the MCA. The final project involved taking the former gas phase project a step further by invoking the use of gas chromatography (GC) to impart selectivity to the system

Signalling Design in Sensor-Assisted mmWave Communications for Cooperative Driving

Millimeter-Wave (mmWave) Vehicle-To-Vehicle (V2V) communications are a key enabler for connected and automated vehicles, as they support the low-latency exchange of control signals and high-resolution imaging data for maneuvering coordination. The employment of mmWave V2V communications calls for Beam Alignment and Tracking (BAT) procedures to ensure that the antenna beams are properly steered during motion. The conventional beam sweeping approach is known to be unsuited for the high vehicular mobility and its large overhead reduces transmission efficiency. A promising solution to reduce BAT signalling foresees the integration of V2V communication systems with on-board vehicle sensors. We focus on a cooperative sensor-assisted architecture for mmWave V2V communications in line of sight, where vehicles exchange the estimate of antenna position and its uncertainty to compute the optimal beam direction and dimension. We analyze and compare different signalling strategies for sharing the information on antenna estimate, evaluating the tradeoff between signalling overhead and performance loss for different position and uncertainty encoding strategies. Main attention is given to differential quantization on both the antenna position and uncertainty. Analyses over realistic urban mobility trajectories suggest that differential approaches introduce a negligible performance loss while significantly reducing the BAT signalling communication overhead

Data-Reserved Periodic Diffusion LMS With Low Communication Cost Over Networks

In this paper, we analyze diffusion strategies in which all nodes attempt to estimate a common vector parameter for achieving distributed estimation in adaptive networks. Under diffusion strategies, each node essentially needs to share processed data with predefined neighbors. Although the use of internode communication has contributed significantly to improving convergence performance based on diffusion, such communications consume a huge quantity of power in data transmission. In developing low-power consumption diffusion strategies, it is very important to reduce the communication cost without significant degradation of convergence performance. For that purpose, we propose a data-reserved periodic diffusion least-mean-squares (LMS) algorithm in which each node updates and transmits an estimate periodically while reserving its measurement data even during non-update time. By applying these reserved data in an adaptation step at update time, the proposed algorithm mitigates the decline in convergence speed incurred by most conventional periodic schemes. For a period p, the total cost of communication is reduced to a factor of 1/p relative to the conventional adapt-then-combine (ATC) diffusion LMS algorithm. The loss of combination steps in this process leads naturally to a slight increase in the steady-state error as the period p increases, as is theoretically confirmed through mathematical analysis. We also prove an interesting property of the proposed algorithm, namely, that it suffers less degradation of the steady-state error than the conventional diffusion in a noisy communication environment. Experimental results show that the proposed algorithm outperforms related conventional algorithms and, in particular, outperforms ATC diffusion LMS over a network with noisy links.11Ysciescopu

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications