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Measuring the actual junction temperature ${T}_{J}$ of power light emitting diodes used in high-end luminaires is important for implementing measures to reduce the stress and therefore extend the lifetime of these fundamental light sources. The direct measurement of this key parameter can be performed by imposing a known and precise forward current ${I}_{{probe}}$ through the LED, and tracking the bias voltage appearing at its terminals. The technique proposed in this paper is based on the same approach, but does not require a stable current source, relying instead on the exponential fitting of random I-V measurements and on the estimation of the bias voltage at the proper ${I}_{{probe}}$ through a mathematical interpolation. The technique is implemented on a custom-designed circuit, and experimental data are obtained on commercial power LEDs, allowing to assess the impact of ${I}_{{probe}}$ and sampling current range on the measurement error.

All technology instruments use electrical and electronic systems that, before their production, need to be verified via simulation software. A new simulation software called Python for Analog and Mixed Signals (PyAMS) has been programmed. As presented in this paper, the main objective of this software is to simplify the modeling of analog elements and circuits by using the python language to describe design schematics involving libraries, packages, and symbols. PyAMS would be a free software (GNU license). The circuit simulation in PyAMS allows a detailed frequency-domain analysis, DC analysis, and time-domain analysis. The output signals are acquired in different operating points and they are displayed by means of a dedicated waveform editor. The behavioral modeling of analog elements and the simulations results of different test circuits are reported in the text.

Indoor positioning of objects and people is becoming of great importance in the Internet of Things (IoT), in-home automation, and navigation in malls, airports, or very large buildings. Positioning is determined by multiple distance measurements between reference points and sensors. Distance measurement uses the time of flight of an ultrasonic signal traveling from an emitter to receiving sensors. This requires close synchronization between the emitter and the sensors and a sharp time resolution of the time of arrival (TOA) of the ultrasonic signal. Usually, TOA is detected using cross-correlation processing requiring significant computational resources at the sensors level. In this work, the synchronization is done using the RFID standard protocol features. The TOA detection is performed firstly by training off-line a Machine Learning model using as input the peaks indexes of the ultrasonic signal received and the output of a cross-correlation-based positioning system, as ground-truth. In a second phase, the positioning is evaluated and tested on-board using the previously trained model on a microcontroller. The system architecture is presented and experimental results on the positioning accuracy are shown accordingly. Results show a mean positioning error below 25 cm in 95% of the positionings in a typical room.

Indoor positioning of objects and people has becoming of great importance in the Internet of Things (IoT), in-home automation, and navigation in mall, airports or very large buildings. Position can be geometrically calculated from multiple measurements of the distance between reference points and moving sensors. Distance measurement can be done by measuring the time of flight of an ultrasonic signal traveling from an emitter to receiving sensors. This requires close synchronization between the emitter and the sensors. In this work this synchronization is done using the RFID standard protocol features. RFID tags included in the emitter and sensor circuitry are synchronized each other via the RFID reader interrogation pulse. The system architecture is explained and experimental results on the synchronization accuracy are presented. Moreover, the synchronization uncertainty influence on the positioning performances is discussed. Calculations ensure a mean positioning error below 5 cm in the volume of a typical room.

Connected cars and automotive car driving are emerging topics and many researchers are looking to provide new solutions and technologies that could revolutionize the driving experience. However, human car-driving is an activity that will certainly interact, in the near future, with autonomous driven vehicles. Hence, the need to provide an information layer about driving behavior and style that could benefit a traffic management system. Both insurance and car rental providers could also benefit of the knowledge of the driving behavior to custom fit insurance and rental rate. In this work, a machine learning model was deployed on a tiny hardware for performing the classification of car driving style based on inertial measurement data, augmented with in-vehicle diagnostic. The results show that an accurate (98%) model could be successfully deployed on an ultra-low-power STM32L4 Series MCU (MicroContoller Units) based on Arm® Cortex®-M4 core micro controller, capable to infer the proper classification among 4 classes in less than 5 ms after acquiring data from sensors. The system is also able to detect start and stop occurring during normal traffic driving.

Gesture recognition is a recently emerged technology that aims to redesign the interaction between humans and machines. Existing solutions usually recognize the gestures based on camera vision, wearable sensors or specialized signals (e.g., WiFi, acoustic and visible light), but they cope with the drawbacks of high energy consumption or reduced SNR with respect to the surrounding environment, which prevents them from accurately sensing finger movements. In this work, we propose a device-free gesture recognition system that use machine learning techniques on RSSI and phase values from the backscattered signals of an array of tags placed on a plastic plate to recognize different hand gestures. The system has been tested with RFC and SVM algorithms, providing excellent results both in case of stationary hand that in case of moving hand.

Indoor positioning is an emerging technology with wide applications. Augmented and mixed reality need accurate and real-time positioning of user’s limbs or direction of sight in real time, and reference points within the real environment. Positioning is also required for mall navigation, elderly people movements monitoring, and many others contexts. Indoor positioning of mobile units (MUs) can be provided by multilateration techniques that compute the location of an MU starting from distance measurements between the MU and a set of beacons. In this paper, a nonlinear closed-form solution for the trilateration problem is employed to avoid heavy numerical iterative algorithms. The closed-form solution is allowed by a particular arrangement of the beacons, placed at the vertex of a square. A positioning system is presented where MUs, exploiting the light algorithm of the closed-form positioning, are able to autonomously and privately calculate their own positions. A prototype of the positioning system has been designed, realized, and characterized for an average $4 \times 4 \times 3\,\,\text {m}^{3}$ home or office room. It includes four beacons that emit a sequence of ultrasonic chirp signals, several MUs, and a master unit that provides time synchronization via ANT transceivers onboard each MU and the master unit. Thanks to the adopted closed form solution, each tiny battery-operated MU is able to carry out all the computations onboard, including analog and digital signal processing, beacon-MU distance estimation, and finally MU positioning at a rate of 2 Hz.

The number of older people needing healthcare is a growing global phenomenon. The assistance in long-term care comprises a complex of medical, nursing, rehabilitation, and social assistance services. The cost is substantial, but technology can help reduce spending by ensuring efficient health services and improving the quality of life. Advances in artificial intelligence, wireless communication systems, and nanotechnology allow the creation of intelligent home care systems avoiding hospitalization with evident cost containment. They are capable of ensuring functions of recognition of activities, monitoring of vital functions, and tracking. However, it is essential to also have information on location in order to be able to promptly intervene in case of unforeseen events or assist people in carrying out activities in order to avoid incorrect behavior. In addition, the automatic detection of physical activities performed by human subjects is identified as human activity recognition (HAR). This work presents an overview of the positioning system as part of an integrated HAR system. Lastly, this study contains each technology’s concepts, features, accuracy, advantages, and limitations. With this work, we want to highlight the relationship between HAR and the indoor positioning system (IPS), which is poorly documented in the literature.

Measuring the distance between two points has multiple uses. Position can be geometrically calculated from multiple measurements of the distance between reference points and moving sensors. Distance measurement can be done by measuring the time of flight of an ultrasonic signal traveling from an emitter to receiving sensors. However, this requires close synchronization between the emitter and the sensors. This synchronization is usually done using a radio or optical channel, which requires additional hardware and power to operate. On the other hand, for many applications of great interest, low-cost, small, and lightweight sensors with very small batteries are required. Here, an innovative technique to measure the distance between emitter and receiver by using ultrasonic signals in air is proposed. In fact, the amount of the signal attenuation in air depends on the frequency content of the signal itself. The attenuation level that the signal undergoes at different frequencies provides information on the distance between emitter and receiver without the need for any synchronization between them. A mathematical relationship here proposed allows for estimating the distance between emitter and receiver starting from the measurement of the frequency dependent attenuation along the traveled path. The level of attenuation in the air is measured online along the operation of the proposed technique. The simulations showed that the range accuracy increases with the decrease of the ultrasonic transducer diameter. In particular, with a diameter of 0.5 mm, an error of less than ± 2.7 cm (average value 1.1 cm) is reached along two plane sections of the typical room of the office considered (4 × 4 × 3 m3).

Positioning can be calculated from measurement of the distances between multiple reference points and moving sensors. A new technique to measure the distance between emitter and receiver by using ultrasonic signals in air is proposed, without the use of the time of flight measurement based on expensive time synchronization between emitter and receiver. The attenuation level that the ultrasonic signal suffers at different frequencies provides information on the emitter-receiver distance. A mathematical relationship between emitter and receiver distance and the attenuation level experienced along the traveled path by two ultrasonic signals is provided. The actual level of attenuation in the air for the employed frequencies is measured online during the ranging operations. The simulations show the effectiveness of the technique under feasible operational conditions. In particular, an error of less than ±2.5 cm is reached along two significant paths of the vertical section of a typical office room.

A circuit is presented for the on-line measurement of the junction temperature (T J ) of power LEDs. The circuit is integrated with, and makes use of, an off-the-shelf LED driver, so that the proposed architecture can be easily made compatible with existing high-end luminaire platforms. The measurement technique, based on the linear junction voltage vs. temperature relationship, shows good precision and repeatability among devices with the same part number, with an absolute error below 3 °C.

Catastrophic and accidental natural events (e.g., earthquakes, sudden floods, fires, etc.) and deficiencies in appropriate management activities require proper plans for disaster management and real-time definition of safe paths and escape routes. Emergencies can affect the structural health status of structures and infrastructures and, consequently, the safety in highly populated areas (e.g., urban contexts, occasional assembly points for crowds, theme parks, etc.) is compromised. Consequently, the use of a decision platform for the management of structures and infrastructures, which is able to gather real-time data from sensors and provide alerts and augmented information about safe paths, paves the way to the adoption of a proactive form of risk management. In this work, objectives were confined into the validation of a Near-Field Communication (NFC) system. Data coming from user smartphones using the NFC technology and sensor data deriving from the decision platform were used as input in a Machine Learning (ML) algorithm. Results show that the ML algorithm mentioned above is able to refine the safe path recognition strategy defined from the platform. In addition, the bidirectional and touch less NFC technology allows the delivery of alerts and disaster plan dissemination, also in the case of connectivity shutdown.

Expected and unexpected events affect road infrastructure leading to several issues that require maintenance interventions or prompt actions. In order to face the issues mentioned above, specially designed platforms can be used. Despite the high number of solutions presented in the last decades, it is difficult to find real applications of systems deputed to the emergency management of the abovementioned infrastructures. This paper aims at describing an innovative platform and the results of its first application. The platform consists of a system and algorithms that allow environmental and structural monitoring and emergency management. The proposed system consists of a network of wireless sensor nodes that are self-powered by mean of photovoltaic energy harvesters. Two different types of algorithms were designed to monitor the environmental and structural condition (i.e., the vibro-acoustic signature) of different types of structures and infrastructures, and to display, thanks to a dashboard, several predefined alarms, escape routes, safe place, and gathering points. The results presented in the paper refers to the first application of the platform, here tailored for an Italian theme park in the framework of the project "SICURVIA" funded by Regione Calabria, Italy. These results, presented for the first time, show that the platform is able to extract crucial information about the conditions of the thematic park’s assets, and, at the same time, this information are used to recognize an emergency, trigger the related alarm, and provide a plan to manage the extraordinary event happened.

Increasing efforts toward the development of positioning techniques testify the growing interest for indoor position-based applications and services. Many applications require accurate indoor positioning or tracking of people and assets, and some market sectors are starting a rapid growth of products based on these technologies. Ultrasonic systems have already been demonstrating their effectiveness and to possess the desired positioning accuracy and refresh rates. In this work, it is shown that a typical signal used in ultrasonic positioning systems to estimate the range between the target and reference points&mdash, namely, the linear chirp&mdash, due to the effects of acoustic diffraction, in some cases, undergoes a shape aberration, depending on the shape and size of the transducer and on the angle under which the transducer is seen by the receiver. In the presence of such signal shape aberrations, even one of the most robust ranging techniques, which is based on cross-correlation, provides results affected by a much greater error than expected. Numerical simulations are carried out for a typical ultrasonic chirp, ultrasonic emitter, and range technique based on cross-correlation and for a typical office room, obtained using the academic acoustic simulation software Field II. Spatial distributions of the ranging error are provided, clearly showing the favorable low error regions. The work demonstrates that particular attention must be paid to the design of the acoustic section of the ultrasonic positioning systems, considering both the shape and size of the ultrasonic emitters and the shape of the acoustic signal used.

Indoor localization and tracking of persons and assets with centimeter-level accuracy for inventory, security, medical monitoring, and training, as well as gesture interfaces for domotics, is highly desirable in the framework of the emerging IoT paradigm. Low cost, tiny, battery, or batteryless operated position sensors are required. 3-D localization can be computed by combining three or more distance measurements between sensor and reference points. Our aim is to give the capability of measuring the distance from a reference point to radio frequency identification (RFID) tags. The main challenge is in the estimation of the distances with millimeter accuracy in presence of both size and power supply strict constraints, and thus with very limited computational power. An accurate ranging technique using cross-correlation and small RFID-based sensors is proposed. Its originality resides in moving the main computational efforts from the sensor to an external processing unit with sufficient computational and supply power, thus overcoming the sensor limits. The system is composed of a beacon that emits ultrasound chirps and RF sync signals, a RFID-based distance sensor, a commercial RFID reader, and a processing unit. Main advantages are the high miniaturization and low power consumption of the remote sensor, and its compatibility with existing RFID standards.

The conversion efficiency of an RF energy harvester is studied at 868 MHz as a function of temperature on a printed circuit board charge-pump, based on off-the-shelf diodes and capacitors. Extensive experimental measurements are presented in the temperature range from 25°C to 85°C, showing that the harvested power may significantly degrade with T, in particular at the lower incident power regimes. It is shown that the rectifiers quality, and precisely the temperature dependent rectification ratio of silicon Schottky diodes commonly used for this application, has the strongest impact of the harvester performances.

An experimental study is carried out to assess the effect of temperature on the conversion efficiency of ultrahigh-frequency energy harvesters based on diode-capacitor Dickson charge pumps, frequently used in self-energizing circuits, such as in radio frequency identification tags or in wireless sensor nodes. Using off-the-shelf Schottky diodes often adopted for this application, it is shown that the harvester conversion efficiency at 868 MHz is temperature dependent due to the changing rectification ratio, namely the ratio between the forward and the reverse current flowing through the low barrier height Schottky diodes, which both show a positive derivative with T. The experimental study, supported by SPICE simulations, has shown that a temperature variation might be particularly harmful at the lowest incident power regimes, when even a minimal drop in the conversion efficiency might determine the out-of-servicing of a wirelessly energized circuit.

Indoor localization and tracking of persons and assets for gaming, augmented reality, medical monitoring, training, security, and inventory is an emerging technology. Localization can be computed from multiple distance measurements between reference beacons and sensors. Low cost, small, light, tiny-battery, or even batteryless operated sensors are required. Ranging can be carried out by measuring the time of flight of an ultrasonic chirp traveling from an emitting beacon to receiving sensors. That requires a tight synchronization between the beacon and the sensors. In this article, we present a ranging system based on miniature commercial modules featuring ANT wireless personal area network communication plus onboard microprocessor. A protocol is developed for microseconds synchronization and accurate onboard ranging computations are carried out through ultrasonic signal cross correlation. The sensor privately computes its own range. The ranging system is composed of a synchronizing unit, a beacon emitting ultrasonic chirps, and a battery-operated distance sensor. The sensor shows low average power consumption (less than 22 mW) and a ranging accuracy of about 1.8 mm for distances up to 2.5 m, under stable environmental conditions.

The growing interest for indoor position-based applications and services, as well as ubiquitous computing and location aware information, have led to increasing efforts toward the development of positioning techniques. Many applications require accurate positioning or tracking of people and assets inside buildings, and some market sectors are waiting for such technologies for starting a fast growth. Ultrasonic systems have already been shown to possess the desired positioning accuracy and refresh rate. However, they still require accurate synchronization between ultrasound emitters and receivers to work properly. Usually, synchronization is carried out through radio frequency (RF) signals, adding system complexity and raising the cost. In this work, this limit is overcome by introducing a novel self-synchronizing indoor positioning technique. Ultrasonic signals travel from emitters placed at fixed reference positions to any number of mobile devices (MD). The travelled distance is computed from the time of flight (TOF), which requires in turn synchronism between emitter and receiver. It is shown that this synchronism can be indirectly estimated from the time difference of arrival (TDOA) of the ultrasonic signals. The obtained positioning information is private, in the sense that the positioning infrastructure is not aware of the number or identity of the MDs that use it. Computer simulations and experimental results obtained in a typical office room are provided.

Positioning objects such as appliances inside rooms has become of fundamental importance in the Internet o Things (IoT) and ooin-home automation, as well as in augmented reality (AR). A new positioning system based on a smartphone and radio-frequency identification (RFID) tags applied to the objects to be localized is presented. The 3D positioning of the smartphone is obtained through an ultrasound system while its orientation in space is obtained with the onboard magnetometers and accelerometers. When a certain RFID tag is read through the near-field communication (NFC) interface of the smartphone, from its distance and from the orientation of the smartphone that reads it, the 3D position of the tagged object is obtained. The system architecture is explained and simulation results are presented. Positioning accuracy of about ten centimeters is achieved

The use of 4H-SiC junction devices as effective temperature sensors is demonstrated by measuring, elaborating and comparing the experimental I- V characteristics of three different devices, namely pin and Schottky diodes and the body diode of a commercial power MOSFET. Due to the strong dependence of the I-V characteristics on temperature, the sensor performs a high sensitivity. It is shown that the correct choice of the probe current during sensing is fundamental to obtain an excellent linearity with T in the sensor response.

The conversion efficiency of a multi-stage RF energy harvester on a printed circuit board charge-pump, based on off-the-shelf diodes and capacitors is studied, in the UHF band, as a function of temperature. The considered temperature range varies from 25°C to 85°C, highlighting that the effects of the temperature may cause a severe degradation on the harvested power, in particular at the lowers incident power regimes. Experimental measurements are presented to show that the rectifiers quality has the biggest impact of the harvester performances. Precisely, the temperature dependent rectification ratio of silicon Schottky diodes commonly used for this application can be considered as a quality factor of the converter.

The use of non-destructive techniques (NDT) for structural health monitoring (SHM) of civil infrastructures is crucial especially for those structures, like buildings and roads, that need efficient and reliable emergency management systems. For this reason, this study aims at designing and validating a new sensor-based NDT platform for SHM, emergency management, and decision support. Different structures of a thematic park have been monitored. The effects of different events (e.g., seismic) and failures (i.e., cracks) have been investigated using a wireless monitoring system consisting in several sensor units (properly placed), wirelessly linked to one controlling unit. The set-up of the software platform has been tuned using the data gathered through in-lab simulations and on-site experiments. Results show that the hardware and software platform paves the way to automatic detection of failures, to identify which infrastructures are still available around the monitored areas, and to generate a proper emergency plan.