Inductor-capacitor (LC) passive wireless sensors are widely used for remote sensing. These devices are limited in applications where multiparameter sensing is required, because of the mutual coupling between neighboring sensors. This article presents two effective decoupling techniques for multiparameter sensing, based on partially overlapped sensors and decoupling coils, which, when combined, reduce the mutual coupling between sensors to near zero. A multiparameter LC sensor prototype with these two decoupling mechanisms has been designed, simulated, and measured. This prototype is capable of simultaneously measuring four parameters. The measurements demonstrate that the changes in capacitance in one individual sensor do not affect the measurements of the other sensors. This principle has been applied to simultaneous wear sensing using four identical wear sensors.

The objective of this research is to select a reduced group of surface electromyographic (sEMG) channels and signal-features that is able to provide an accurate classification rate in a myoelectric control system for any user. To that end, the location of 32 sEMG electrodes placed around-along the forearm and 86 signal-features are evaluated simultaneously in a static-hand gesture classification task (14 different gestures). A novel multivariate variable selection filter method named mRMR-FCO is presented as part of the selection process. This process finds the most informative and least redundant combination of sEMG channels and signal-features among all the possible ones. The performance of the selected set of channels and signal-features is evaluated with a Support Vector Machine classifier. (C) 2014 Elsevier Ltd. All rights reserved.

This paper presents an analysis to determine the effect of the stress enhancement factor due to the presence of protrusions in the semiconductor shields of HVDC cables. The theoretical analysis of the electric field containing protrusions is based on a harmonic solution for the electric field for spherical and spheroidal protrusions. HVDC analysis takes into account the additional space charge accumulation due to the presence of protrusions.

Power consumption is one of the most critical issues when designing low-cost electronic devices, such as sensing nodes in wireless sensor networks. To support their operation, such systems usually contain a battery; however, when the battery has consumed all its energy, the node (e.g. the sensor) must be retrieved and the battery replaced. If the node is located in a remote and non-accessible placement, battery replacement can become an expensive (and even impossible) task. This way, energy harvesting has emerged as a suitable alternative to supply low-power electronic systems, by converting ambient energy into electric power. Scavenged energy can be used to directly supply the circuits, or stored to be used when needed. This paper summarises the power needs of a general wireless sensor node and describes the main principles of most representative energy harvesting technologies. Copyright (c) 2012 John Wiley & Sons, Ltd.

The performance of a MEMS (Micro Electro-Mechanical Systems) Sensor in a RFID system has been calculated, simulated and analyzed. It documents the viability from the power consumption point of view- of integrating a MEMS sensor in a passive tag maintaining its long range. The wide variety of sensors let us specify as many applications as the imagination is able to create. The sensor tag works without battery, and it is remotely powered through a commercial reader accomplishing the EPC standard Class 1 Gen 2. The key point is the integration in the tag of a very low power consumption pressure MEMS sensor. The power consumption of the sensor is 12.5 mu W. The specifically developed RFID CMOS passive module, with an integrated temperature sensor, is able to communicate up to 2.4 meters. Adding the pressure MEMS sensor - an input capacity, a maximum range of 2 meters can be achieved between the RFID sensor tag and a commercial reader (typical reported range for passive pressure sensors are in the range of a few centimeters). The RFID module has been fabricated with a CMOS process compatible with a bulk micromachining MEMS process. So, the feasibility of a single chip is presented.