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Assistive Devices are employed in various applications, ranging from in-home monitoring of medical symptoms to support in reminding activities of daily living. In these devices, crucial characteristics are non-obtrusiveness, low-maintenance, and simplicity for the user, for data extraction as well as for application development. The Set for Intuitive Movement and Proximity Logging for Everyday (SIMPLE) use focuses on the research and development of applications related to the detection and measurement of movement and interaction, i.e., proximity to objects of daily use. It encompasses the sensors themselves, charging stations, as well as devices for online and offline data collection, forwarding, and extraction. It also supports the adjustment of basic recording parameters as well as changing the device's firmware needing only a memory card reader. Basic power saving and movement detection algorithms are already implemented. SIMPLE-use closes a gap, offering both movement and interaction acquisition in one easy to use sensor set. [ABSTRACT FROM PUBLISHER]

Advances in electronics, sensor technology, information science, electrical and computer engineering give rise to emerging technologies, some of which could be applied to the inspection, monitoring, and condition assessment of buried water mains. This paper presents a state of the review of sensor technologies used for monitoring indicators pointing to pipe structural deterioration. The potential for multi-sensor system and sensor data fusion for condition-based maintenance are also discussed. [ABSTRACT FROM PUBLISHER]

In this paper, a new electronic system for gas detection is presented. Particular attention is focused on electronic noses that employ several resistive sensors. New resistive sensors may have high value due to new substances (TiO2) or to low-cost fabrication process and, supposing to use these sensors together with traditional ones (SnO2), a novel instrument to manage high-value resistive sensors varying over a wide range, from kilohms to gigohms is required. The proposed hardware approach is a modular architecture which takes advantage from an improved resistance-to-period converter, where sensors are DC powered. Experimental results show a relative standard deviation below 0.01% and a relative displacement to the reference line less than 1% over six decades if commercial resistors are considered. A prototype has been realized to manage up to eight sensors, detect and estimate substance concentrations, and communicate results to the Internet. [ABSTRACT FROM PUBLISHER]

The field of air-quality monitoring is gaining increasing interest, with regard to both indoor environment and air-pollution control in open space. This work introduces a pattern recognition technique based on adaptive K-nn applied to a multisensor system, optimized for the recognition of some relevant tracers for air pollution in outdoor environment, namely benzene, toluene, and xylene (BTX), NO2, and CO. The pattern-recognition technique employed aims at recognizing the target gases within an air sample of unknown composition and at estimating their concentrations. It is based on PCA and K-nn classification with an adaptive vote technique based on the gas concentrations of the training samples associated to the K-neighbors. The system is tested in a controlled environment composed of synthetic air with a fixed humidity rate (30%) at concentrations in the ppm range for BTX and NO2, in the range of 10 ppm for CO. The pattern recognition technique is experimented on a knowledge base composed of a limited number of samples (130), with the adoption of a leave-one-out procedure in order to estimate the classification probability. In these conditions, the system demonstrates the capability to recognize the presence of the target gases in controlled conditions with a high hit-rate. Moreover, the concentrations of the individual components of the test samples are successfully estimated for BTX and NO2 in more than 80% of the considered cases, while a lower hit-rate (69%) is reached for CO. [ABSTRACT FROM PUBLISHER]