Search

Purpose To evaluate the surgical management, outcomes and prognostic factors of full thickness macular holes without residual internal limiting membrane (NO-ILM FTMHs). Methods We performed a multicenter, retrospective study of 116 NO-ILM FTMHs. Human amniotic membrane (hAM) plug, autologous ILM free flap transplantation (AILMT), and autologous retinal graft transplantation (ART) were performed in 58, 48, and 10 patients, respectively. Data were collected before and up to 12 months after surgery. The primary outcomes were hole closure and final best-corrected visual acuity (BCVA). Results The final BCVA (0.78 ± 0.51 logMAR) was significantly better than and correlated with the initial BCVA (p 680 μm had a higher closure rate with hAM than with AILMT (p = 0.02). Conclusions AILMT and hAM were the most frequently performed surgeries with both high closure rate and significant functional improvement. Preoperative BCVA was correlated with final BCVA. The minimum FTMH diameter may guide the treatment choice.

This work is aimed at the development of innovative, easy and cheap methods for the stabilization, inertization and valorisation of the organic fraction of municipal solid waste (OFMSW). For the first time, two original processes for transforming the organic waste into an inert, odorless and sanitized material were here proposed. The first one, called dual step, starts with grinding of the OFMSW, by means of an industrial shredder. After being finely ground, the organic waste was exposed to a sterilization process by means of UV/ozone radiations or thermal treatment (stabilization phase) in order to obtain a complete removal of the OFMSW’s bacterial activity. By means of several microbiological analyses, the best sterilization method was chosen. The incorporation in a thermosetting matrix was, then, carried out through mixing the sterilized and finely ground organic waste into a water soluble urea formaldehyde (UF) based resin, with a formaldehyde content less than 1% wt, followed by a thermal treatment for UF-resin crosslinking (inertization phase). An alternative cheaper and easier process, called one step, was also proposed and investigated, by combining the sterilization with the curing thermal process (at higher temperature) of the thermosetting matrix. The preliminary experimental results reported in this paper suggest that both the proposed methods could be considered suitable for the production of high valorized innovative OFMSW-derived panels or bricks, that could find application in several fields, such as building or constructions materials. Finally a brief description of the prototype machinery is reported properly designed for implementing OFMSW stabilization and valorisation processes developed in this research work.

The use of carbon nanotubes (CNTs) in the combustion and propulsion sector, which is the object of this work, is due to the discovery of photo-ignition properties of such nano-material, when they are exposed to an intense luminous flash [1]. This phenomenon allows obtaining fuels combustion system more efficient and clean (HCCI engine) [2]. Most of the literature studies involve a Xe-lamp to ignite the CNTs mixed with metal catalyst; the use of this light source is not without criticism because it requires very high supply voltages, has an intrinsic mechanical instability, and it can’t work at frequencies required by an automotive engine running [3]. A LED-based ignition system can be considered the optimum solution, because LEDs have high luminous efficiency, higher mechanical stability and for the absence of frequency limitations. In this work, a LEDs-based experimental setup used to perform combustion tests of gaseous fuels, by means of photo-ignition of MWCNTs/FeCp2, has been proposed (Fig. 1). The setup uses a multi-LED ignition-system, placed outside the combustion chamber, convoying the light emitted by each LED source into the chamber by a fiber optic. The electronic section drives and controls the LED sources, synchronizing temporally them with the input of the enriched air-fuel mixture. Moreover, it will also handle and monitor all physical / environmental parameters involved in the combustion process, such as temperature and pressure inside the combustion chamber, etc. In order to obtain a light pulse of controlled duration, a driving and control electronic system was realized (Fig. 2). The white power LEDs (Cree XHP70) were driven by proper LED drivers; to generate a single light pulse, a pulsed signal is applied to the enable control input of each LED driver. This last signal is obtained on PC audio channel by proper LabVIEW application and after conditioning by an interface board. A four LEDs source was used to perform ignition tests on the dry mixtures MWCNTs/FeCp2 to obtain energy density comparable to which obtained with the Xe lamp. In the Figs. 3a and b, the setup used to perform ignition tests on dry mixtures MWCNTs/FeCp2, is shown; the driving and control unit is constituted by four LED drivers, the interface board and the PC with LabVIEW application (Fig. 3a), whereas tests area with the four LEDs source and the power/energy meter (Thorlabs PM100D) equipped with pyroelectric sensor (Thorlabs ES145C) are shown in Fig. 3b. The light source is placed at 1cm at least from the pyroelectric sensor and then from CNTs sample (Fig. 3b). Using this experimental setup, the minimum pulse durations needed to ignite the MWCNTs/FeCp2 samples for the different concentrations by weight, are determined. Known the light source intensity, the minimum ignition energy of the MWCNTs/FeCp2 samples for the considered concentrations, are calculated (Fig. 4).

The prevalence of neck pain, a chronic musculoskeletal disease, has significantly increased due to the uncontrollable use of social media (SM) devices. The use of SM devices by younger generations increased enormously during the COVID-19 pandemic, being—in some cases—the only possibility for maintaining interpersonal, social, and friendship relationships. This study aimed to predict the occurrence of neck pain and its correlation with the intensive use of SM devices. It is based on nine quantitative parameters extracted from the retrospective X-ray images. The three parameters related to angle_1 (i.e., the angle between the global horizontal and the vector pointing from C7 vertebra to the occipito-cervical joint), angle_2 (i.e., the angle between the global horizontal and the vector pointing from C1 vertebra to the occipito-cervical joint), and the area between them were measured from the shape of the neck vertebrae, while the rest of the parameters were extracted from the images using the gray-level co-occurrence matrix (GLCM). In addition, the users’ ages and the duration of the SM usage (H.mean) were also considered. The decision tree (DT) machine-learning algorithm was employed to predict the abnormal cases (painful subjects) against the normal ones (no pain). The results showed that angle_1, area, and the image contrast significantly increased statistically with the time of SM-device usage, precisely in the range of 2 to 9 h. The DT showed a promising result demonstrated by classification accuracy and F1-scores of 94% and 0.95, respectively. Our findings confirmed that the objectively detected parameters, which elucidate the negative impacts of SM-device usage on neck pain, can be predicted by DT machine learning.

A quantitative evaluation of kinetic parameters, the joint’s range of motion, heart rate, and breathing rate, can be employed in sports performance tracking and rehabilitation monitoring following injuries or surgical operations. However, many of the current detection systems are expensive and designed for clinical use, requiring the presence of a physician and medical staff to assist users in the device’s positioning and measurements. The goal of wearable sensors is to overcome the limitations of current devices, enabling the acquisition of a user’s vital signs directly from the body in an accurate and non–invasive way. In sports activities, wearable sensors allow athletes to monitor performance and body movements objectively, going beyond the coach’s subjective evaluation limits. The main goal of this review paper is to provide a comprehensive overview of wearable technologies and sensing systems to detect and monitor the physiological parameters of patients during post–operative rehabilitation and athletes’ training, and to present evidence that supports the efficacy of this technology for healthcare applications. First, a classification of the human physiological parameters acquired from the human body by sensors attached to sensitive skin locations or worn as a part of garments is introduced, carrying important feedback on the user’s health status. Then, a detailed description of the electromechanical transduction mechanisms allows a comparison of the technologies used in wearable applications to monitor sports and rehabilitation activities. This paves the way for an analysis of wearable technologies, providing a comprehensive comparison of the current state of the art of available sensors and systems. Comparative and statistical analyses are provided to point out useful insights for defining the best technologies and solutions for monitoring body movements. Lastly, the presented review is compared with similar ones reported in the literature to highlight its strengths and novelties.

Human–machine interaction (HMI) refers to systems enabling communication between machines and humans. Systems for human–machine interfaces have advanced significantly in terms of materials, device design, and production methods. Energy supply units, logic circuits, sensors, and data storage units must be flexible, stretchable, undetectable, biocompatible, and self-healing to act as human–machine interfaces. This paper discusses the technologies for providing different haptic feedback of different natures. Notably, the physiological mechanisms behind touch perception are reported, along with a classification of the main haptic interfaces. Afterward, a comprehensive overview of wearable haptic interfaces is presented, comparing them in terms of cost, the number of integrated actuators and sensors, their main haptic feedback typology, and their future application. Additionally, a review of sensing systems that use haptic feedback technologies—specifically, smart gloves—is given by going through their fundamental technological specifications and key design requirements. Furthermore, useful insights related to the design of the next-generation HMI devices are reported. Lastly, a novel smart glove based on thin and conformable AlN (aluminum nitride) piezoelectric sensors is demonstrated. Specifically, the device acquires and processes the signal from the piezo sensors to classify performed gestures through an onboard machine learning (ML) algorithm. Then, the design and testing of the electronic conditioning section of AlN-based sensors integrated into the smart glove are shown. Finally, the architecture of a wearable visual-tactile recognition system is presented, combining visual data acquired by a micro-camera mounted on the user’s glass with the haptic ones provided by the piezoelectric sensors.

This study describes an innovative complete car control system against children abandonment in unattended vehicles. Even if systems of this kind are already available in the automotive market, other solutions are currently object of intense researches by several important Research and Development centres or under patent procedure. In this study, a description of the technologies already adopted in the currently used car control systems and in those that are in research and testing phase too is provided. Then, it will be introduced to the proposed control system, which enables to improve all the described systems by providing innovative technologies and functionalities. The proposed system implements a microwave motion sensor, two infrared sensors, a carbon dioxide sensor, a temperature/humidity sensor, a vocal detector and a micro-camera. These acquisition devices are connected to a microcontroller board that is able to combine all acquired data for detecting the alarm scenario and consequently acting some warning procedures: alarm short message service transmission with vehicle Global Positioning System position, vehicle's horn activation and/or car windows lowering. Thanks to such a wide set of sensors, the system can correlate the acquired information in order to ensure a good detection capability in every condition in which it can operate.

This work describes the design of innovative IoT-oriented farm management system that employs low-cost WSN for detecting soil, environmental and crops parameters that properly processed, in combination with weather forecasts, are used to determine future farming activities based on agronomic models implemented in the software platform. Goals of carried out research activity are to guarantee production both qualitatively and quantitatively of cultivated crops, to increase company income and reduce environmental impacts; achieving these objectives is a prerequisite for implementing of sustainable agriculture. Connection between traditional cropping systems and innovative technologies is achieved through the utilization of low-cost wireless electronic modules for acquiring data from connected sensors. The fertigation system operation is supported by solar-powered low-cost WSN capable of constantly monitoring soil and environmental parameters; thanks to sensor nodes properly positioned in cultivated land or greenhouse, interest parameters of both air and soil are detected and transmitted to on-cloud database, to allow monitoring and remote control from enabled users (farmer or agronomist). For this aim, an application specifically realized and tested during the different fertigation system’s functional tests, allows users to visualize and eventually remotely intervene by using a smart-phone or tablet.

Aim of the present manuscript is to provide an overview of all possible methods and light source typologies used by the different research groups for obtaining the energetic nano-materials’ photo-ignition, showing the latest progress related to such phenomenon employing, also, alternative radiation sources to the common Xe lamp. In fact, the employment of a different source typology can open new usage prospects respect to those enabled by the Xe lamp, mainly due to its technological limitations. Therefore, several studies are faced to test light sources, such as lasers and LEDs, for igniting the nano-energetic materials (as CNTs mixed with metallic catalyzers, Al / CuO nano-particles, etc); these nano-materials are usefully employed for starting, in volumetric and controlled way, the combustion of air-fuel mixtures inside internal combustion engines, leading to significant benefits to the combustion process also in terms of efficiency, reliability, and emissions of pollutants. Several research works are presented in literature concerning the ignition of liquid / gaseous fuels, without nano-particles, employing laser sources (i.e laser-based plugs in place of the common spark plugs); therefore, an innovative solution is proposed that employs multi-point laser-plugs for inducing the ignition of nano-materials dispersed into the air-fuel mixture inside the cylinder, so further improving the combustion of the fuel in an internal combustion engine.

This paper presents a smart garment to monitor the environmental parameters and user’s vital signs; the wearable application is meant to monitor workers’ conditions in harsh workplaces. The smart jacket integrates a multisource harvesting section for gathering energy from sources associated with the human body (light, heat, movements), guaranteeing the energy autonomy of the sensing unit. This last relies on a custom microcontroller board to acquire and process biophysical and environmental data from several sensors distributed on the garment. Also, it wirelessly sends the acquired data towards the IBM Cloud platform, allowing remote monitoring of worker’s conditions, real-time anomalies detection, and decentralized storing of acquired data. The tests indicated that the harvesting section could provide up to 217 mW mean power, fully covering the energy requirements of the sensing unit in all tested scenarios. However, the 380 mAh LiPo battery, charged by the harvesting system, allows the device about 16 days lifetime when no further energy contribution is available.

This paper presents the design of a sensing device for monitoring and storing the atmospheric and noise pollution data, using technology with zero impact on the environment. The system consists of an eco-friendly sensor pitch equipped with a multi-sources harvesting system used to scavenge energy from renewable sources (wind and solar light), making it suitable to be accumulated into a storage device. The harvested energy is used to feed a sensing section, which samples the environmental parameters (i.e. PM2.5, PM10, NH3, CO2, NO2, CO) at regular intervals. The hardware and firmware development of the sensor node is discussed; this last is based on the Arduino Pro Micro microcontroller board, which acquires, processes and stores the data from sensors and manages their transmissions towards the cloud platform. Finally, the test results of the developed sensor pitch are presented, demonstrating the correct operation of all system sections.

As explainability seems to be the driver for a wiser adoption of Artificial Intelligence in healthcare and in critical applications, in general, a comprehensive study of this field is far from being completed. On one hand, a final definition and theoretical measurements of explainability have not been assessed, yet, on the other hand, some tools and frameworks for the practical evaluation of this feature are now present. This paper aims to present a concrete experience in using some of these explainability-related techniques in the problem of predicting the size of amino acids in real-world protein structures. In particular, the feature importance calculation embedded in Random Forest (RF) training is compared with the results of the Eli-5 tool applied to the Neural Network (NN) model. Both the predictors are trained on the same dataset, which is extracted from Protein Data Bank (PDB), considering 446 myoglobins structures and process it with several tools to implement a geometrical model and perform analyses on it. The comparison between the two models draws different conclusions about the residues’ geometry and their biological properties.

The demand for wearable devices to measure respiratory activity is constantly growing, finding applications in a wide range of scenarios (e.g., clinical environments and workplaces, outdoors for monitoring sports activities, etc.). Particularly, the respiration rate (RR) is a vital parameter since it indicates serious illness (e.g., pneumonia, emphysema, pulmonary embolism, etc.). Therefore, several solutions have been presented in the scientific literature and on the market to make RR monitoring simple, accurate, reliable and noninvasive. Among the different transduction methods, the piezoresistive and inertial ones satisfactorily meet the requirements for smart wearable devices since unobtrusive, lightweight and easy to integrate. Hence, this review paper focuses on innovative wearable devices, detection strategies and algorithms that exploit piezoresistive or inertial sensors to monitor the breathing parameters. At first, this paper presents a comprehensive overview of innovative piezoresistive wearable devices for measuring user’s respiratory variables. Later, a survey of novel piezoresistive textiles to develop wearable devices for detecting breathing movements is reported. Afterwards, the state-of-art about wearable devices to monitor the respiratory parameters, based on inertial sensors (i.e., accelerometers and gyroscopes), is presented for detecting dysfunctions or pathologies in a non-invasive and accurate way. In this field, several processing tools are employed to extract the respiratory parameters from inertial data; therefore, an overview of algorithms and methods to determine the respiratory rate from acceleration data is provided. Finally, comparative analysis for all the covered topics are reported, providing useful insights to develop the next generation of wearable sensors for monitoring respiratory parameters.

The field programmable gate array (FPGA) devices are ideal solutions for high-speed processing applications, given their flexibility, parallel processing capability, and power efficiency. In this review paper, at first, an overview of the key applications of FPGA-based platforms in 5G networks/systems is presented, exploiting the improved performances offered by such devices. FPGA-based implementations of cloud radio access network (C-RAN) accelerators, network function virtualization (NFV)-based network slicers, cognitive radio systems, and multiple input multiple output (MIMO) channel characterizers are the main considered applications that can benefit from the high processing rate, power efficiency and flexibility of FPGAs. Furthermore, the implementations of encryption/decryption algorithms by employing the Xilinx Zynq Ultrascale+MPSoC ZCU102 FPGA platform are discussed, and then we introduce our high-speed and lightweight implementation of the wellknown AES-128 algorithm, developed on the same FPGA platform, and comparing it with similar solutions already published in the literature. The comparison results indicate that our AES-128 implementation enables efficient hardware usage for a given data-rate (up to 28.16 Gbit/s), resulting in higher efficiency (8.64 Mbps/slice) than other considered solutions. Finally, the applications of the ZCU102 platform for high-speed processing are explored, such as image and signal processing, visual recognition, and hardware resource management.

The year 2020 will remain in the history for the diffusion of the COVID-19 virus, originating a pandemic on a world scale with over a million deaths. From the onset of the pandemic, the scientific community has made numerous efforts to design systems to detect the infected subjects in ever-faster times, allowing both to intervene on them, to avoid dangerous complications, and to contain the pandemic spreading. In this paper, we present an overview of different innovative technologies and devices fielded against the SARS-CoV-2 virus. The various technologies applicable to the rapid and reliable detection of the COVID-19 virus have been explored. Specifically, several magnetic, electrochemical, and plasmonic biosensors have been proposed in the scientific literature, as an alternative to nucleic acid-based real-time reverse transcription Polymerase Chain Reaction (PCR) (RT-qPCR) assays, overcoming the limitations featuring this typology of tests (the need for expensive instruments and reagents, as well as of specialized staff, and their reliability). Furthermore, we investigated the IoT solutions and devices, reported on the market and in the scientific literature, to contain the pandemic spreading, by avoiding the contagion, acquiring the parameters of suspected users, and monitoring them during the quarantine period.

Solar energy utilization has been triggered by advances in new technology to reduce the cost of photovoltaic (PV) panels with an increase of efficiency. To improve the energy production quality, it is necessary to undergo the PV panels to characterization both in the indoor and outdoor scenarios; these latter characterizations generally require all seasons-based measurements. Therefore, it is essential to find models for characterizing PV panels in terms of energy production but also production and operating mode tolerance. The paper illustrates the findings of global research dedicated to PV panels ageing and their impact on energy production in the years. At first, an in-depth analysis of the ageing mechanisms affecting II and III generations' PV panels has been presented when exposed to atmospheric agents. Afterwards, the PV panels' characterization, conducted in a short time (i.e. a total of seven days), has been reported, performing outdoor measurements in conjunction with an electronic calibrator able to measure currents and voltages. The MPPT (Maximum Power Point Tracker) device is the core instrumentation of the employed measurement system. Obtained results are convincing since they have been compared with simultaneous measurements of PV panels located in the same place.

Mobile robots, especially, mini-robots and mini-rovers are playing an important role in different industrial applications. Under certain constraints, they are mostly suitable for harsh environments where hard conditions take place such as to be unfit for the protracted human activity, or even where this latter is not allowed. In many circumstances, the energy issues for autonomous and wheeled mobile robots need to be optimized taking into account the mechanical and electrical consumptions. The paper illustrates the design of a semi-custom wheeled mobile robot with integrated a photovoltaic (PV) system on the roof, namely a high-efficiency mono- or poly-crystalline PV panel, for supporting the Li-Ion batteries during particular tasks (rough terrain, obstacles or paths with steep slopes), in order to extend the robot autonomy. An electronic control has been designed and data acquisitions, related to power consumption, have been performed by means of a specific experimental setup. The robot was used for detecting different parameters such as temperature, humidity, concentrations of toxic gas species, and presence of flames, therefore particularly suitable for contaminated environments or industrial plants. For this aim, the mobile robot has been equipped with a wide range of commercial sensors and a Global Positioning System (GPS) receiver for keeping track of the robotic vehicle position. In addition, the robot is equipped with a HC-06 Bluetooth transceiver for sending the acquired data to the developed IoTool smartphone application where they are displayed to be analyzed by the user, and for receiving commands and instructions.

There are many car tests regulated by European and international standards and carried out on tracks to assess vehicle performance. The test preparation phase usually consists of placing road cones on the track with a specific configuration defined by the considered standard; this phase is performed by human operators using imprecise and slow methods, mainly due to the large required distances. In this paper, a new geolocation stake-out system based on GNSS RTK technology was realized and tested, supported by a Matlab-based software application to allow the user to quickly and precisely locate the on-track points on which to position the road cones. The realized stake-out system, innovative and very simple to use, produced negligible average errors (i.e., 2.4–2.9 cm) on the distance between the staked-out points according to the reference standards (distance percentage error 0.29–0.47%). Furthermore, the measured average angular error was also found to be very low, in the range 0.04–0.18°. Finally, ISO 3888-1 and ISO 3888-2 test configurations were reproduced on the proving ground of the Porsche Technical Center by utilizing the realized stake-out system to perform a double lane-change maneuver on car prototypes.

In this research work, a fast and lightweight AES-128 cypher based on the Xilinx ZCU102 FPGA board is presented, suitable for 5G communications. In particular, both encryption and decryption algorithms have been developed using a pipelined approach, so enabling the simultaneous processing of the rounds on multiple data packets at each clock cycle. Both the encryption and decryption systems support an operative frequency up to 220 MHz, reaching 28.16 Gbit/s maximum data throughput; besides, the encryption and decryption phases last both only ten clock periods. To guarantee the interoperability of the developed encryption/decryption system with the other sections of the 5G communication apparatus, synchronization and control signals have been integrated. The encryption system uses only 1631 CLBs, whereas the decryption one only 3464 CLBs, ascribable, mainly, to the Inverse Mix Columns step. The developed cypher shows higher efficiency (8.63 Mbps/slice) than similar solutions present in literature.

This paper introduces a novel low-cost solar-powered wearable assistive technology (AT) device, whose aim is to provide continuous, real-time object recognition to ease the finding of the objects for visually impaired (VI) people in daily life. The system consists of three major components: a miniature low-cost camera, a system on module (SoM) computing unit, and an ultrasonic sensor. The first is worn on the user’s eyeglasses and acquires real-time video of the nearby space. The second is worn as a belt and runs deep learning-based methods and spatial algorithms which process the video coming from the camera performing objects’ detection and recognition. The third assists on positioning the objects found in the surrounding space. The developed device provides audible descriptive sentences as feedback to the user involving the objects recognized and their position referenced to the user gaze. After a proper power consumption analysis, a wearable solar harvesting system, integrated with the developed AT device, has been designed and tested to extend the energy autonomy in the different operating modes and scenarios. Experimental results obtained with the developed low-cost AT device have demonstrated an accurate and reliable real-time object identification with an 86% correct recognition rate and 215 ms average time interval (in case of high-speed SoM operating mode) for the image processing. The proposed system is capable of recognizing the 91 objects offered by the Microsoft Common Objects in Context (COCO) dataset plus several custom objects and human faces. In addition, a simple and scalable methodology for using image datasets and training of Convolutional Neural Networks (CNNs) is introduced to add objects to the system and increase its repertory. It is also demonstrated that comprehensive trainings involving 100 images per targeted object achieve 89% recognition rates, while fast trainings with only 12 images achieve acceptable recognition rates of 55%.

Taking the advantages offered by smart high-performance electronic devices, transradial prosthesis for upper-limb amputees was developed and tested. It is equipped with sensing devices and actuators allowing hand movements; myoelectric signals are detected by Myo armband with 8 ElectroMyoGraphic (EMG) electrodes, a 9-axis Inertial Measurement Unit (IMU) and Bluetooth Low Energy (BLE) module. All data are received through HM-11 BLE transceiver by Arduino board which processes them and drives actuators. Raspberry Pi board controls a touchscreen display, providing user a feedback related to prosthesis functioning and sends EMG and IMU data, gathered via the armband, to cloud platform thus allowing orthopedic during rehabilitation period, to monitor users’ improvements in real time. A GUI software integrating a machine learning algorithm was implemented for recognizing flexion/extension/rest gestures of user fingers. The algorithm performances were tested on 9 male subjects (8 able-bodied and 1 subject affected by upper-limb amelia), demonstrating high accuracy and fast responses.

This paper aims to study the limitations and performances of the main energy storage devices commonly used in energy harvesting applications, namely super-capacitors (SC) and lithium polymer (LiPo) batteries. The self-discharge phenomenon is the main limitation to the employment of SCs to store energy for a long time, thus reducing efficiency and autonomy of the energy harvesting system. Therefore, the analysis of self-discharge trends was carried out for three different models of commercial SCs, describing the phenomenon in terms of self-discharge rate and internal resistance. In addition, physical interpretations concerning the self-discharge mechanism based on the experimental data are provided, thus explaining the two super-imposed phenomena featured by distinct time constants. Afterwards, the dependence of self-discharge phenomenon from the charging time duration (namely, SCs charged at 5 V and then kept under charge for one or five hours) was analyzed; by comparing the voltage drop during the self-discharge process, a self-discharge reduction for longer charging durations was obtained and the physical interpretation provided (at best −6.8% after 24 h and −13.4% after 120 h). Finally, self-discharge trends of two commercial 380 mAh LiPo batteries (model LW 752035) were acquired and analyzed; the obtained results show an open circuit voltage reduction of only 0.59% in the first 24 h and just 1.43% after 124 h.

The evolution of low power electronics and the availability of new smart materials are opening new frontiers to develop wearable systems for medical applications, lifestyle monitoring, and performance detection. This paper presents the development and realization of a novel smart insole for monitoring the plantar pressure distribution and gait parameters, indeed, it includes a piezoresistive sensing matrix based on a Velostat layer for transducing applied pressure into an electric signal. At first, an accurate and complete characterization of Velostat-based pressure sensors is reported as a function of sizes, support material, and pressure trend. The realization and testing of a low-cost and reliable piezoresistive sensing matrix based on a sandwich structure are discussed. This last is interfaced with a low power conditioning and processing section based on an Arduino Lilypad board and an analog multiplexer for acquiring the pressure data. The insole includes a 3-axis capacitive accelerometer for detecting the gait parameters (swing time and stance phase time) featuring the walking. A Bluetooth Low Energy (BLE) 5.0 module is included for transmitting in real-time the acquired data toward a PC, tablet or smartphone, for displaying and processing them using a custom Processing® application. Moreover, the smart insole is equipped with a piezoelectric harvesting section for scavenging energy from walking. The onfield tests indicate that for a walking speed higher than 1 ms−1, the device’s power requirements (i.e., ) was fulfilled. However, more than 9 days of autonomy are guaranteed by the integrated 380-mAh Lipo battery in the total absence of energy contributions from the harvesting section.

This paper describes the development and characterization of a smart garment for monitoring the environmental and biophysical parameters of the user wearing it; the wearable application is focused on the control to workers’ conditions in dangerous workplaces in order to prevent or reduce the consequences of accidents. The smart jacket includes flexible solar panels, thermoelectric generators and flexible piezoelectric harvesters to scavenge energy from the human body, thus ensuring the energy autonomy of the employed sensors and electronic boards. The hardware and firmware optimization allowed the correct interfacing of the heart rate and SpO2 sensor, accelerometers, temperature and electrochemical gas sensors with a modified Arduino Pro mini board. The latter stores and processes the sensor data and, in the event of abnormal parameters, sends an alarm to a cloud database, allowing company managers to check them via a web app. The characterization of the harvesting subsection has shown that ≈ 265 mW maximum power can be obtained in a real scenario, whereas the power consumption due to the acquisition, processing and BLE data transmission functions determined that a 10 mAh/day charge is required to ensure the device’s proper operation. By charging a 380 mAh Lipo battery in a few hours by means of the harvesting system, an energy autonomy of 23 days was obtained, in the absence of any further energy contribution.

Aim of the present manuscript is to summarize the multi-disciplinary activities related to the research project called POIROT that proposes to design a platform for transforming the organic waste into an inert, odorless and sanitized material, employable for building applications or as thermal acoustic insulator. The platform prototype is equipped with Arduino-based electronic sensing stations for characterizing the material during the different steps of the transformation process. Further innovation introduced by the proposed system is the integration of the platform with a RFID (radio frequency identification)-based user-recognition and products traceability system, allowing both to identify the enabled users and to track the produced panels/bricks along the whole supply chain. Therefore, a proper software on-cloud application was developed for supporting the products’ traceability functionality.

Surgery decision making is almost basically adopted by imaging, and for internal organs, imaging is the only tool for proceeding on. Interested imaging is obtained by as Xrays, CT (Computer Tomography), MRI (Magnetic Resonance Imaging), NMR (Nuclear Magnetic Resonance), ultrasound, scintigraphy, and PET (Positron Emission Tomography). All the above techniques are correlated to specific instrumentation, and their manufacturers include dedicated software to output high quality images. However, despite advances in biomedical instrumentation technology, new issues, regarding insurgence of pathologies so far unknown, require as much techniques to be developed. In this spirit, infrared thermography, for instance, can ride to help us to solve this thorny issue. Infrared images have been used to discriminate interested organs with respect to tissues and adherences that must be taken in consideration for continuing and optimizing the surgery process. Modified linear and circular Hough transform have been used to process captured images. This paper illustrates findings related to quasi-real time acquisition and processing during surgery. Infrared processing has been compared to another technique based on atomic force microscopy-based algorithm. Hough technique is also treated.