Special Issue "State-of-the-Art Sensors Technology in the UK 2013"

Guest Editor
Prof. Dr. Nicholas Dale
Department of Biological Sciences, The University of Warwick Coventry, CV4 7AL, UK
E-Mail: n.e.dale@warwick.ac.uk
Interests: amperometric biosensors, neuroscience, physiology, chemosensing, purinergic signalling

Dear Colleagues,

The aim of this special issue is to provide a comprehensive view of the state-of-the-art sensors technology in the UK. Research articles and reviews are solicited which will provide a comprehensive insight into the state-of-the-art in the UK on any aspect of novel sensor development and applications.

Prof. Dr. Nicholas Dale
Guest Editor

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs).

Type of Paper: Article
Title: The Placement of Accelerometers for the Detection of Everyday Activities
Authors: Ian Cleland ¹, Basel Kikhia ², Josef Hallberg ², Chris Nugent ¹, Kare Synnes ², Dewar Finlay ¹ and Sally McClean ³
Affiliations: ¹ School of Computing and Mathematics, University of Ulster, Northern, Ireland
² Lulea University of Technology, Sweden
³ School of Computing and Information Engineering, University of Ulster, Northern Ireland; E-Mail: sally@infc.ulst.ac.uk
Abstract: This article describes an investigation to determine the optimal placement of accelerometers for the purpose detecting a range of everyday activities. The paper investigates the effect of combining data from accelerometer data from multiple locations on the accuracy of activity detection. Eight healthy males participated within the study. Data were collected from 6 wireless tri-axial accelerometers placed at the chest, wrist, lower back, hip, thigh and foot. Activities included, walking, running on a motorized treadmill, sitting, lying, standing and walking up and down stairs. The Neural Network provided the most accurate detection of activities from the machine learning algorithms investigated. Data from the hip was the best single location for activity recognition using the Neural Network with a single accelerometer. Combining data from just two accelerometers was shown to produce the same accuracy as combining data from all six location. It was noted however, that the difference in activity detection using single or multiple accelerometers may be more pronounced when trying to detect finer grain activities. Future work shall therefore investigate the effects of accelerometer placement on a larger range of these activities.

Type of Paper: Article
Title: In-Plane Resonant Nano-Electro-Mechanical Sensors: A Comprehensive Study on Design, Fabrication and Characterization’s Challenges
Authors: Faezeh Arab Hassani ¹, Yoshishige Tsuchiya ² and Hiroshi Mizuta ^1,2
Affiliations: ¹ School of Material Science, Japan Advanced Institute of Science and Technology (JAIST), 923-1292, Nomi, Ishikawa, Japan
² School of Electronics and Computer Science, University of Southampton, SO17 1BJ, Southampton, United Kingdom
Abstract: The newly proposed in-plane resonant nano-electro-mechanical (IP R-NEM) sensor, that includes a doubly clamped suspended beam as the important building block and two side electrodes, achieved the mass sensitivity less than zepto g/Hz based on analytical and numerical analyses. The comparison of this sensor with other recent NEM sensors clarifies the unique characteristics of this sensor. The high frequency characterization and numerical/analytical studies of the fabricated sensor show that the high vacuum measurement environment will ease the resonance detection using the capacitance detection technique if only the thermoelsatic damping plays the dominant role for the total quality factor of the sensor. Knowing this fact, the introduction of the monolithically integrated in-plane MOSFET with the suspended beam provides a solution for the resonance frequency detection of the sensor taking advantage of the intrinsic gain of the MOSFET for the amplification of the output signal. The newly proposed challenging fabrication technology for the in-plane resonant suspended gate FET  (IP RSG-FET) sensor leads us to some post processing and simulation steps to fully explore and improve the DC characteristics of the sensor for the consequent high frequency measurement. The results of modeling and characterization in this research provide a realistic guideline for these potential ultra-sensitive NEM sensors.