A home automation architecture based on LoRa technology and Message Queue Telemetry Transfer protocol

none5noIn recent years, Internet of Things technologies gained momentum in various application areas, including the Smart Home field. In this view, the smart objects available in the house can communicate with each other and with the outside world by adopting solutions already proposed for Internet of Things. In fact, among the challenges to face during the design and implementation of an Internet of Things–based Smart Home infrastructure, battery usage represents a key point for the realization of an efficient solution. In this context, the communication technology chosen plays a fundamental role, since transmission is generally the most energy demanding task, and Internet of Things communication technologies are designed to reduce as much as possible the power consumption. This article describes an Internet of Things-oriented architecture for the Smart Home, based on the long-range and low-power technology LoRa. Moreover, in order to enable the devices to communicate with each other and the outside world, the Message Queue Telemetry Transfer protocol is used as a domotic middleware. We show that LoRa, designed by having in mind the typical requirements of Internet of Things (i.e. low power consumption, sporadic transmission, and robustness to interference), is well-suited to also meet the need of more established home automation systems, specifically the low latency in message delivery. Interoperability among different devices may also be obtained through the Message Queue Telemetry Transfer midlleware.openEnnio Gambi, Laura Montanini, Danny Pigini, Gianluca Ciattaglia, Susanna SpinsanteGambi, Ennio; Montanini, Laura; Pigini, Danny; Ciattaglia, Gianluca; Spinsante, Susann

AUTOMOTIVE RADAR APPLICATION FOR STRUCTURAL HEALTH MONITORING

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Modern techniques to process micro-Doppler signals from mmWave Radars

I sistemi radar mmWave stanno diventando molto comuni sui veicoli e le loro capacità, in termini di portata e velocità, li rendono adatti a un'altra classica applicazione radar classica, quella relativa all'effetto micro-Doppler. Dall'elaborazione dei segnali radar mmWave, l'effetto micro-Doppler può essere sfruttato, rendendo così possibile estrarre informazioni interessanti sui bersagli. Con l'enorme larghezza di banda e il breve tempo di trasmissione del segnale, l effetto micro-Doppler può essere utilizzato per diversi scopi come la vibrazione del bersaglio o la classificazione dei bersagli. Grazie anche al progresso delle tecniche di Machine Learning, la loro combinazione con elaborazione del segnale radar è un campo interessante da esplorare e può essere usato per fornire soluzioni a diversi problemi radar. L'effetto Micro-Doppler ha una lunga storia nei sistemi radar, un sacco di letteratura può essere trovata su questo argomento, ma la maggior parte di loro considera dispositivi non commerciali quindi è abbastanza lontano da un caso pratico. In questa dissertazione, diverse tecniche per elaborare i segnali micro-Doppler provenienti da radar automobilistici sarà presentato, con lo scopo di classificarli ed estrarre informazioni sulle vibrazioni dal bersaglio. Il contributo principale di questo lavoro è la proposta di nuove tecniche che possono essere applicato su un sensore commerciale e li rende adatti per il micro- Doppler.mmWave Radar systems are becoming very common on vehicles and their capabilities, in terms of range and velocity, make them suitable for another classical radar application, the one related to the micro-Doppler effect. From the processing of mmWave radar signals, the micro-Doppler effect can be exploited, making so possible to extract interesting information on the observed targets. With the huge bandwidth and the short signal transmission time, the micro-Doppler effect can be used for different purposes such as target vibration measurements or targets classification. Thanks also to the advance of Machine Learning techniques, their combination with radar signal processing is an interesting field to explore and can be used to provide solutions to different radar problems. The Micro-Doppler effect has a long story in Radar systems, a lot of literature can be found on this topic but most of them consider non-commercial devices so is quite away from a practical case. In this dissertation, different techniques to process the micro-Doppler signals coming from automotive radars will be presented, with the purpose of classifying them and extracting vibration information from the target. The main contribution of this work is the proposal of novel techniques that can be applied on a commercial sensor and makes them suitable for the micro- Doppler application

Contactless Walking Recognition based on mmWave RADAR

Analysis of a person's movement provides important information about his or her health status. This analysis can be performed with wearable devices or with contactless technologies. These latter in particular are of some interest, since the subject is free to move and the analysis of the movement is realistic. Despite being designed for other purposes, automotive mmWaves radars represent a powerful low-cost technology for detecting people's movements without contact which finds interesting applications as a support for home monitoring of health conditions. In this paper it is shown how to exploit commercial radars to distinguish with high precision the way of walking of a subject and the position of his hands during the activity carried out. The application of Principal Component Analysis (PCA) for feature extraction from raw data is considered, together with supervised machine learning algorithms for the actual classification of the various activities carried out during the experiments

People Walking Classification using Automotive Radar

none4Automotive radars are able to guarantee high performances at the expenses of a relatively low cost, and recently their application has been extended to several fields in addition to the original one. In this paper we consider the use of this kind of radars to discriminate different types of people’s movements in a real context. To this end, we exploit two different maps obtained from radar, that is, a spectrogram and a range-Doppler map. Through the application of dimensionality reduction methods, such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) algorithm, and the use of machine learning techniques we prove that is possible to classify with a very good precision people’s way of walking even employing commercial devices specifically designed for other purposes.noneSenigagliesi, Linda; Ciattaglia, Gianluca; De Santis, Adelmo; Gambi, EnnioSenigagliesi, Linda; Ciattaglia, Gianluca; De Santis, Adelmo; Gambi, Enni

UAV Propeller Rotational Speed Measurement through FMCW Radars

The growing number of civil applications in which Unmanned Aerial Vehicles (UAVs) are involved can create many concerns for airspace security and surveillance. Gathering as much information as possible about a drone can be crucial to apply proper countermeasures if a potentially dangerous situation is detected. Of course, the presence of a UAV can be detected by radar, but it is possible to extend the system capabilities to obtain additional information. For example, in the case in which the UAV is equipped with propellers, the radar-measured rotational speed could be important information to classify the type of UAV or to reveal if it is carrying some possibly harmful payload. In addition, the rotational speed measured through radar could be used for different purposes, such as to detect a drone manumission, to estimate its maximum payload, or for predictive maintenance of the drone. Measuring the propellers’ rotational speed with radar systems is a critical task, as the Doppler generated by the rotation can be very high, and it is very difficult to find commercial radar systems in the market able to handle such a high Doppler. Another problem is caused by the typically very small Radar Cross-Section (RCS) of the propellers, which makes their detection even more difficult. In the literature, common detection techniques are based on the measurement of the Doppler effect produced by the propellers to derive their rotational speed, but due to the very limited capabilities of commercial sensors, this approach can be applied only at very low values of the rotational speed. In this work, a different approach based on a Frequency-Modulated Continuous Wave (FMCW) radar is proposed, which exploits the vibration of the UAV generated by the rotation of the propellers. The phenomenon and how the sensor can detect it will be presented, which is joined with a performance analysis comparing different estimation techniques for the indirect measurement of the propellers’ speed to evaluate the potential benefits of the proposed approach

Performance Evaluation of Vibrational Measurements through mmWave Automotive Radars

Thanks to the availability of a significant amount of inexpensive commercial Frequency Modulated Continuous Wave Radar sensors, designed primarily for the automotive domain, it is interesting to understand if they can be used in alternative applications. It is well known that with a radar system it is possible to identify the micro-Doppler feature of a target, to detect the nature of the target itself (what the target is) or how it is vibrating. In fact, thanks to their high transmission frequency, large bandwidth and very short chirp signals, radars designed for automotive applications are able to provide sub-millimeter resolution and a large detection bandwidth, to the point that it is here proposed to exploit them in the vibrational analysis of a target. The aim is to evaluate what information on the vibrations can be extracted, and what are the performance obtainable. In the present work, the use of a commercial Frequency Modulated Continuous Wave radar is described, and the performances achieved in terms of displacement and vibration frequency measurement of the target are compared with the measurement results obtained through a laser vibrometer, considered as the reference instrument. The attained experimental results show that the radar under test and the reference laser vibrometer achieve comparable outcomes, even in a cluttered scenario

A Technological Approach to Support the Care Process of Older in Residential Facilities

Faced with an increasing number of elderly housed in residential facilities, there is a request for greater transparency regarding the state of health of the guests and the level of assistance that these guests are offered. The OPENCARE project described in this article aims to respond to this need to promote communication between residential structures and guest families, by introducing a technological platform able to meet this requirement without the need to increase the workload of the operators. Therefore, this article describes the solution adopted, which are based both on data acquired from sensors and on those entered by the operators through a suitably designed interface