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In large indoor environments, accurate positioning and tracking of people and autonomous equipment have become essential requirements. The application of increasingly automated moving transportation units in large indoor spaces demands a precise knowledge of their positions, for both efficiency and safety reasons. Moreover, satellite-based Global Positioning System (GPS) signals are likely to be unusable in deep indoor spaces, and technologies like WiFi and Bluetooth are susceptible to signal noise and fading effects. For these reasons, a hybrid approach that employs at least two different signal typologies proved to be more effective, resilient, robust, and accurate in determining localization in indoor environments. This paper proposes an improved hybrid technique that implements fingerprinting-based indoor positioning using Received Signal Strength (RSS) information from available Wireless Local Area Network (WLAN) access points and Wireless Sensor Network (WSN) technology. Six signals were recorded on a regular grid of anchor points covering the research surface. For optimization purposes, appropriate raw signal weighing was applied in accordance with previous research on the same data. The novel approach in this work consisted of performing a virtual tessellation of the considered indoor surface with a regular set of tiles encompassing the whole area. The optimization process was focused on varying the size of the tiles as well as their relative position concerning the signal acquisition grid, with the goal of minimizing the average distance error based on tile identification accuracy. The optimization process was conducted using a standard Quantum Particle Swarm Optimization (QPSO), while the position error estimate for each tile configuration was performed using a 3-layer Multilayer Perceptron (MLP) neural network. These experimental results showed a 16% reduction in the positioning error when a suitable tile configuration was calculated in the optimization process. Our final achieved value of 0.611 m of location incertitude shows a sensible improvement compared to our previous results. [ABSTRACT FROM AUTHOR]

This paper is concerned with the investigation of a possible working principle of a microwave sensor for the measurement of water levels in plastic pipes. According to the research, most current sensors for water level measurement require an unobstructed path from the sensor in the water and therefore, the integrity of the pipes, in which water flows, will be compromised. The proposed sensor will work at microwave frequencies to measure the flow in the pipes from the outside. This would be non-invasive and non-intrusive. A beam of microwave energy from an antenna is directed towards a water surface within a pipe and the reflection due to the surface of the water will be isolated and knowledge of the pipe dimensions and properties will allow the depth of water to be calculated. Knowledge of the pipe drop may also allow flow to be estimated. It is observed that at microwave frequencies the reflection from the surface of the water is many times greater than the reflection from the surface of a typical plastic pipe. [ABSTRACT FROM AUTHOR]

Silver (Ag) nanoparticles (NPs) are synthesized and characterized by a low-cost chemical reduction method. Silver nanoparticles (Ag NPs) have pre-occupied the consideration of the scientific community due to their wide range of functions, utility and industrial applications, particularly in the fields of sensing technologies and medicine (particularly their efficiency against microbes, the ability of healing the wound and anti-inflammatory properties). Ag NPs are synthesized by a low-cost fabrication method. X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray diffraction (EDX) and photometry techniques are used in this work to identify their nature and potentiality for diverse applications in sensing technologies. [ABSTRACT FROM AUTHOR]