Nowadays, monitoring of people and events is a common matter in the street, in the industry or at home, and acoustic event detection is commonly used. This increases the knowledge of what is happening in the soundscape, and this information encourages any monitoring system to take decisions depending on the measured events. Our research in this field includes, on one hand, smart city applications, which aim is to develop a low cost sensor network for real time noise mapping in the cities, and on the other hand, ambient assisted living applications through audio event recognition at home. This requires acoustic signal processing for event recognition, which is a challenging problem applying feature extraction techniques and machine learning methods. Furthermore, when the techniques come closer to implementation, a complete study of the most suitable platform is needed, taking into account computational complexity of the algorithms and commercial platforms price. In this work, the comparative study of several platforms serving to implement this sensing application is detailed. An FPGA platform is chosen as the optimum proposal considering the application requirements and taking into account time restrictions of the signal processing algorithms. Furthermore, we describe the first approach to the real-time implementation of the feature extraction algorithm on the chosen platform.

In this work, the performance of Bluetooth-based Wireless Sensor Networks (WSN) deployed within hospital environments is analyzed. Due to the complexity that this kind of scenarios exhibit in terms of radio propagation and coexistence with other wireless communication systems and radiant elements such as magnetic resonances and other electric appliances, the deployment of WSNs becomes a complex task which requires an in-depth radio planning analysis. For that purpose, measurements within real scenarios as well as simulation results obtained by the aim of an in-house developed 3D Ray Launching method are presented. The analyzed scenarios are located at the Hospital Complex of Navarre (HCN), in the city of Pamplona. As hospitals have a wide variety of scenarios, the analysis has been carried out in different zones such as the Emergency room of the HCN, where the interferences are expected to have a low impact since it is a kind of waiting room, and operating rooms area, where the patient security is a major issue and it can be a harsh environment in terms of interference and coexistence with other wireless equipment. The obtained simulation results have been validated with the measurements, and they show how Bluetooth-based WSNs behave within hospital environments in terms of coverage, Signal to Noise Ratio and capacity. Therefore, the presented analysis can aid in obtaining the optimal network configuration and performance of the Bluetooth-based WSNs, making them attractive for the developing of applications for hospital environments.

Extracting tiny amounts of energy from non-conventional sources using Peltier cells, piezoelectrics, antennas or inductive probes has become very popular in recent years to power low-consuming sensors in IoT applications and smart grids. These energy harvesting methods rely on the continuous generation of small quantities of electrical energy scavenged from heat, vibration or electromagnetic emissions. This energy is stored in batteries or capacitors reaching low-voltage levels that cannot be used directly to power any device. In general, the voltage is boosted to more appropriate levels with a converter. Using inductive sensors to harvest energy from electrical power lines is common knowledge. Obtaining this energy from high-power low-frequency signals is currently possible and, in some cases, reliable and profitable. The aim of this paper is to evaluate the possibility of harvesting energy from extremely low-power and high-frequency events that occur in electrical assets when the insulation is damaged. These events, called partial discharges, are used in electrical maintenance to detect possible defects in the insulation. Evaluating partial discharge activity is a common protocol in all utilities that requires the use of expensive sensors and acquisition systems, and in most occasions, decommissioning the asset to connect the measuring system. The energy from these phenomena is stored in capacitors and the use of a high-frequency voltage multiplier allows to reach voltages close to 1 V. This voltage is proportional to the number of partial discharges in a certain time span. Therefore, if the number of partial discharges per time-unit has increased noticeably, the insulation has deteriorated and the asset should be decommissioned to evaluate the damages. The paper tests the possibility of using this method as an early-warning system in the maintenance of electrical assets.

The detection of structure-borne sound can be used to monitor the structural health of solid structures and machine parts. One way to achieve such an implementation is to place vibroacoustic sensors in contact with the structure. The sensors will typically generate an electric signal in response to the acoustic emissions caused by specific events, such as fractures in the structure.

In this paper, vibroacoustic sensors were used to detect structure-borne sound during static tensile testing of metallic samples until complete fracture. The samples used were sections of longitudinal beams made out S700 MC steel. Two different types of piezoelectric sensors were used: PVDF film sensors glued to the sample, and ceramic sensors attached to the sample with a magnet adapter. The bandwidth of the signals was expected from previous studies to be of up to 2 MHz. Simultaneously, force and displacement were measured at the testing machine.

An algorithm was written to process the data acquired from the piezo elements and automatically detect relevant events via a simple comparison with a pre-defined voltage threshold to detect signals above the background noise level. The comparison of the detected events with the force measurements from the tensile test showed a very strong correlation between actual fractures (both the initial fracture and its posterior propagation) and the automatic classification carried out by the algorithm. Thus, the vibroacoustic sensor could with little calibration substitute the other standard measurement systems.

Wearable sensor technologies are a key component in the design of applications for human activity recognition, in areas like healthcare, sports and safety.  We present an iterative learning framework to classify human locomotion activities (e.g. walk, stand, lie and sit) extracted from the Opportunity dataset by implementing a data-driven architecture. Data collected by 12 3D acceleration sensors and 7 inertial measurement units are de-noised using a wavelet filter, prior to the extraction of features such as roll, pitch, yaw, single magnitude vector and the principal components (PCA-2D). Our intention is to combine these features pairwise, in order to extract the best candidates for building the training dataset. This iterative process is based on the Euclidean distances between each class member and the centroid of the corresponding cluster. The resulting dataset is used to identify the best learning parameters for a SVM multi-classifier that produces the lowest prediction error.  The methodology presented in this paper produced a model accuracy of over 86% for activity classification, exceeding the values reported in other studies, while using a much lower number of training samples and being more robust to variations in the quality of input data.

   The titanium nitride–aluminum oxide–hafnium oxide–silicon oxide–silicon device with aluminum oxide as charge-blocking layer (hereafter TAHOS) could be a candidate for nonvolatile total ionization dose (TID) radiation sensor. In this paper, gamma radiation induces a significant decrease in the threshold voltage V_T of TAHOS and the radiation-induced V_T decrease on TAHOS is nearly 1.3 times of that on a standard titanium nitride–silicon oxide–hafnium oxide–silicon oxide–silicon (hereafter TOHOS) device after 5 Mrad TID gamma irradiation. The change in V_T of TAHOS after gamma irradiation also has a strong correlation to TID up to 5 Mrad gamma irradiation. The V_T retention characteristics of TAHOS devices can be improved before and after gamma irradiation. Moreover, the V_T retention characteristic  of TAHOS device can be markedly improved and is nearly 13% better than that of a standard TOHOS device after 5 Mrad gamma irradiation. Therefore, the TAHOS device in this study has demonstrated the possibility using TAHOS for high TID response and good TID data retention for non-volatile TID radiation sensing.

Due to the increasingly growth in population, it is important to better use natural resources for food production and efficiency, driving the use of sensors each time more to monitor several aspects of the soil and of the crops in the field. However, it is known that the harsh conditions of the field environment demands more robust and energy efficient sensor devices. One example is soil water monitoring for irrigation: Brazil, for example, consumes 69% of its freshwater only for irrigation purposes, which shows the need of using adequate water moisture sensors. Based on that, this work proposes a modular architecture that integrates several sensor technologies, including battery-less sensors and low power sensors for soil moisture measurements, but not limited to them. The proposed system relies on a mobile robot that can locate each deployed sensor autonomously, collect its data and make it available on-line using cloud services. As proof of concept, a low-cost mobile robot is built using a centimeter level accuracy location system, that allows the robot to travel to each sensor and collect their data. The robot is equipped with an UHF antenna to provide power to RF powered battery-less sensors, a Bluetooth low energy data collector and a Zigbee data collector. An experimental evaluation compares reading distance and successful rate of sensor location and reading.

This paper presents a system to estimate soil moisture through the reading of standard Ultra High Frequency (UHF) passive RFID tags that can be buried in the soil, allowing wireless moisture measurement without the need of using batteries in the field for long periods. In the proposed system, one or more passive EPC/GEN2 tags acts as sensors buried in the soil. The system dispenses external cables and antennas and may be composed of a single RFID tag buried in a specific soil depth or by several RFID tags buried at different depths. An antenna coupled to a RFID reader can be pointed to the place of installation of these tags, and by measuring the received signal strength indicator (RSSI) and other variables, direct estimation of the water content can be done. In addition to its simplified installation procedure, the system allows manual and automatic robotic reading through irrigation systems or other systems for irrigation scheduling.

The article describes the result of the open source Smart lamp project. The first version of this “smart” object, built following a DIY approach using a microcontroller, an integrated temperature and relative humidity sensor and techniques of Additive Manufacturing, allowed to adjust the Indoor Climate Quality (ICQ), by interacting directly with the air conditioner. The Smart Lamp, placed in an office, showed how this approach effectively reduced the energy consumption, optimizing the thermal comfort of the workers. The holistic concept  of Indoor Environmental Quality (IEQ), in addition to providing thermal comfort, includes the Indoor Air Quality (IAQ) and the Indoor Lighting Quality (ILQ). The upgrade of the Smart Lamp bridges this gap regarding the possibility to interact with the air exchange unit and lighting system, in order to get an overview of the potential of a nearable device in the management of the IEQ. The upgraded version was tested in an office equipped with a mechanical ventilation and air conditioning system and occupied by 4 workers. The experiment was compared with a baseline scenario and the results showed how the application of the nearable effectively optimizes both the IAQ and ILQ. 

Electrical insulation can have imperfections due to manufacturing or ageing. When the insulation is electrically stressed, discharges may happen in these inhomogeneous imperfect locations resulting in partial discharge (PD) which have very fast rise times and short time durations. Since charges are accelerated within PD activity, radiated electromagnetic energy across a wide bandwidth of frequencies can occur. The measurement of the radiated PD energy is widely employed to identify defective insulation within high voltage equipment. Based on assessment of the strength and nature of the emitted PD signals, determination is made to carry out predictive maintenance in order to prevent equipment breakdown. The location of emitted radiated PD signals may be determined using multi-lateration techniques using an array of at least 4 antennas. Depending on the relative position between the antennas and the PD source, the radiated emissions from the PD source arrive at each antenna at different times. The relative time differences of arrivals (TDOA) together with the antennas position are variables used to locate the PD source in 3D space. The effect on the location error of a PD source using TDOA calculations based on acquisition sample time errors is a topic which has previously been studied (see bibliography). This paper now investigates the accuracy on PD location as a consequence of error on the measured positions of the antennas. This paper evaluates the influence of positional antenna error on the possible accuracy of the localization of the PD source. This error is analyzed for 3 different antenna array layouts and for different vector directions from the arrays. Additionally, the least sensitive layout with regard to positioning errors is proposed to assist in improving the location accuracy of PD sources.