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A resolution‐reconfigurable, bandwidth‐scalable analogue‐to‐digital converter (ADC) with programmable‐gain (PG) functionality for a multi‐sensor system, which encompasses various signals such as bio‐signals and battery‐level, is presented. In PG and low‐power mode, a PG first‐order noise‐shaping (NS) successive‐approximation register (SAR) ADC achieves 8.4‐to‐10.2‐bit operating up to 16 kHz while providing a gain of 1/2/4. The PG NS SAR ADC can be reconfigured as an adder and a quantiser in a delta‐sigma (ΔΣ) modulator enhancing the order of the modulator for high‐resolution. The third‐order ΔΣ modulator achieves 16.1 bits in a bandwidth of 1 kHz. The work is implemented in a 0.18 μm CMOS process with a 1.8 V power supply. [ABSTRACT FROM AUTHOR]

In fires, release of volatiles usually happens before generation of smoke/airborne particles. Therefore, fire detection systems based on chemical gas sensors may respond faster than systems based on smoke detectors, which currently cope the market. However, fire detectors based exclusively on gas sensors are prone to false alarms. A key element in those systems is the associated signal and data processing since the detector should discriminate fires form other volatile sources (nuisances). Here, we present a gas sensor array composed of different sensing technologies and a customized data processing algorithm for early and robust fire detection. The gas sensor array was placed in a measurement chamber along with commercially available smoke-based detectors. To test the prototype different types of fire and nuisances were performed in the chamber. Results confirmed the benefits of the gas sensing approach since nuisances were rejected and, for some types of fire, gas-based system triggered the fire alarm faster than the smoke-based detectors. [ABSTRACT FROM AUTHOR]

In this study, an effective wavelet packet decomposition (WPD)‐based robust transcale decentralised estimation fusion algorithm is proposed for multisensor systems. The standard state‐space models of wavelet packet coefficients at the coarsest scale decomposition layer are established by Haar WPD. Based on the above models, the decentralised H∞ filtering fusion formulae is employed to obtain the fusion estimates at the coarsest scale decomposition layer, and these estimates are then used to reconstruct the estimates at the finest scale by an inverse WPD transform. At last, the feasibility analysis of the proposed algorithm is given. Two simulation examples are presented to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

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]

This paper concerns the sequential estimation of a hidden state vector from noisy observations delivered by several sensors. Different from the standard framework, we assume here that the sensors may switch autonomously between different sensor states, that is, between different observation models. This includes sensor failure or sensor functioning conditions change. In our model, sensor states are represented by discrete latent variables, whose prior probabilities are Markovian. We propose a family of efficient particle filters, for both synchronous and asynchronous sensor observations as well as for important special cases. Moreover, we discuss connections with previous works. Lastly, we study thoroughly a wheel land vehicle positioning problem where the GPS information may be unreliable because of multipath/masking effects [ABSTRACT FROM PUBLISHER]