RF-powered UHF-RFID analog sensors platform

An RF powered UHF-RFID passive sensors platform was realized using discrete components and printed antennas designed to resonate at 868 MHz, used both for energy harvesting and data transmission. The tests demonstrate the possibility for the system to operate autonomously within the reading range of a standard RFID reader, that acts both as the RF power source and the receiver of the data stored in the tag user memory. The microcontroller can be interfaced on the same substrate with a sensor made of polymeric materials, sensible to physical parameters or chemical agents. RF-powered UHF-RFID analog sensors platform (PDF Download Available). Available from: http://www.researchgate.net/publication/279193365_RF-powered_UHF-RFID_analog_sensors_platform [accessed Sep 14, 2015]

temperature sensing characteristics and long term stability of power leds used for voltage vs junction temperature measurements and related procedure

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Advanced Sensors and Systems Technologies for Indoor Positioning

There is an increasing interest about indoor positioning, which is an emerging technology with a wide range of applications [...

Edge Machine Learning for AI-Enabled IoT Devices: A Review

In a few years, the world will be populated by billions of connected devices that will be placed in our homes, cities, vehicles, and industries. Devices with limited resources will interact with the surrounding environment and users. Many of these devices will be based on machine learning models to decode meaning and behavior behind sensors’ data, to implement accurate predictions and make decisions. The bottleneck will be the high level of connected things that could congest the network. Hence, the need to incorporate intelligence on end devices using machine learning algorithms. Deploying machine learning on such edge devices improves the network congestion by allowing computations to be performed close to the data sources. The aim of this work is to provide a review of the main techniques that guarantee the execution of machine learning models on hardware with low performances in the Internet of Things paradigm, paving the way to the Internet of Conscious Things. In this work, a detailed review on models, architecture, and requirements on solutions that implement edge machine learning on Internet of Things devices is presented, with the main goal to define the state of the art and envisioning development requirements. Furthermore, an example of edge machine learning implementation on a microcontroller will be provided, commonly regarded as the machine learning “Hello World”

CMOS RF Transmitters with On-Chip Antenna for Passive RFID and IoT Nodes

The performances of two RF transmitters, monolithically integrated with their antennas on a single CMOS microchip fabricated in a standard 0.35 µm process, are presented. The usage of these architectures in the Internet of Things (IoT) paradigm is envisioned, as part of a custom conceived data transmission system. The implemented circuits use two different directly on–off keying (OOK) modulated oscillator topologies whose outputs are employed to feed two loop antennas. The powering of both transmitters is duty-cycled for reducing the average power consumption to a few tenths of a microwatt, allowing the usage as low-power transmitters for IoT nodes. The integrated loop antennas radiate sufficient power for a few meters’ communication range. The OOK transmitted signal can be easily detected using a commercial receiver

Simple and Low-Cost Photovoltaic Module Emulator

The design and testing phase of photovoltaic (PV) power systems requires time-consuming and expensive field-testing activities for the proper operational evaluation of maximum power point trackers (MPPT), battery chargers, DC/AC inverters. Instead, the use of a PV source emulator that accurately reproduces the electrical characteristic of a PV panel or array is highly desirable for in-lab testing and rapid prototyping. In this paper, we present the development of a low-cost microcontroller-based PV source emulator, which allows testing the static and dynamic performance of PV systems considering different PV module types and variable operating and environmental conditions. The novelty of the simple design adopted resides in using a low-cost current generator and a single MOSFET converter to reproduce, from a fixed current source, the exact amount of current predicted by the PV model for the actual load conditions. The I–V characteristic is calculated in real-time using a single diode exponential model under variable and user-selectable operating conditions. The proposed method has the advantage of reducing noise from high-frequency switching, reducing or eliminating ripple and the demand for output filters, and it does not require expensive DC Power source, providing high accuracy results. The fast response of the system allows the testing of very fast MPPTs algorithms, thus overcoming the main limitations of state-of-art PV source emulators that are unable to respond to the quick variation of the load. Experimental results carried on a hardware prototype of the proposed PV source emulator are reported to validate the concept. As a whole result, an average error of ±1% in the reproduction of PV module I–V characteristics have been obtained and reported

Online Black-Box Modeling for the IoT Digital Twins Through Machine Learning

Many applications involving physical systems, such as system control or fault detection, call for a behavioral, black-box, or digital twin of the real system. By observing input-output pairs, a nonlinear system’s black-box twinning model can be built, thus enabling real-time accurate estimation of the system’s health and status. We propose a modeling approach that can be implemented with little hardware resources and predicts system output with acceptable accuracy for a wide range of applications in the IoT and Industry 4.0 application domains, such as cloud and distributed predictive control, maintenance, fault detection, and model drift avoidance. This approach consists of building a compact numerical model, based on the concept of sum-decomposability, with reduced computational complexity and memory requirements, well suited for microcontroller-based IoT applications. The black-box modeling theory, the sizing process, and the learning method are reported. The outputs of two examples of non-linear systems are replicated in real-time using a pioneer experimental setup built around a microcontroller. According to experimental results, online learning and prediction are performed at 1 kS/s with a prediction error comparable to the resolution of the digitalized input-output data. The reduced size of the obtained model calls for real-time sharing and update with cloud and edge-based simulation ecosystems enabling a near real-time digital twinning of field systems

Effects of the Temperature on the Efficiency Degradation in Multi-stage RF Energy Harvesters

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

Mobile Synchronization Recovery for Ultrasonic Indoor Positioning

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