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In this paper, the processing of the data of a 3D light detection and distance measurement (LiDAR) sensor mounted on a mobile robot is shown, introducing an innovative methodology to manage the data and extracting useful information. The LiDAR sensor is placed on a mobile robot with a modular design that permits the easy change of the number of wheels, which was designed to travel through several environments, saving energy by preliminarily changing the number and arrangement of the wheels in the environment. Furthermore, the robot can recognise landmark in a structured environment by using a classification technique on each frame acquired by the LiDAR. Furthermore, considering the experimental tests, a new simple algorithm based on the LiDAR data processing together with the inertial data (IMU sensor) through a Kalman filter is proposed to characterise the robot’s pose by surrounding environment with fixed landmarks. Finally, the limits of the proposed algorithm have been analysed, highlighting new improvements in the future perspective development for permitting autonomous navigation and environment perception with a simple, modular, and low-cost device.

One of the main tasks of a cadastral surveyor is to accurately determine property boundaries by measuring control points and calculating their coordinates. This paper proposes the development of a remotely-controlled tracking system to perform cadastral measurements. A Bluetooth-controlled rover was developed, including a Raspberry Pi Zero W module that acquires position data from a VBOX 3iSR global navigation satellite system (GNSS) receiver, equipped with a specific modem to download real-time kinematic (RTK) corrections from the internet. Besides, the Raspberry board measures the rover speed with a hall sensor mounted on a track, adjusting the acquisition rate to collect data at a fixed distance. Position and inertial data are shared with a cloud platform, enabling their remote monitoring and storing. Besides, the power supply section was designed to power the different components included in the acquisition section, ensuring 2 hours of energy autonomy. Finally, a mobile application was developed to drive the rover and real-time monitor the travelled path. The tests indicated a good agreement between rover measurements and those obtained by a Trimble R10 GNSS receiver (+0.25% mean error) and proved the superiority of the presented system over a traditional metric wheel.

Este articulo presenta un novedoso sistema vestible destinado a asistir la movilidad de personas invidentes y debiles visuales en entornos urbanos mediante el simple uso de un telefono inteligente y retroalimentacion tactil. El sistema utiliza los datos de posicionamiento provistos por el sensor GPS del telefono inteligente para localizar en tiempo real al usuario en el espacio y determinar las direcciones hacia un destino. Las instrucciones de navegacion resultantes se codifican como patrones de vibracion y se despliegan al usuario a traves de una interfaz tactil integrada al zapato. Para validar la pertinencia del sistema propuesto, se realizaron dos experimentos con usuarios. El primero involucro la participacion de un grupo de 20 sujetos voluntarios visualmente sanos a los que se les pidio reconocer las instrucciones de navegacion desplegadas mediante el dispositivo de estimulacion podotactil. Los resultados muestran altas tasas de reconocimiento. El segundo experimento consistio en conducir a dos sujetos voluntarios invidentes a lo largo de espacios publicos urbanos hacia destinos previamente establecidos. Los resultados fueron exitosos y sugieren que el sistema incrementa la navegacion independiente y de forma segura de personas con discapacidad visual. A su vez, se muestra el potencial de los telefonos inteligentes y los dispositivos podotactiles en las tecnologias de asistencia. Palabras clave: tecnologias de asistencia, localizacion GPS, movilidad de personas invidentes, estimulacion podotactil, interfaz vibrotactil, sistema vestible.

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.

Assisted technology combined with sensor networks is an emerging technology for better homecare of people requiring specific support. In this work, we focus our attention on monitoring vital signs and accident prevention of older adults in residential areas without invasive and uncomfortable medical devices. We prototyped two vital signal monitoring devices integrated in structures deployed in common areas such as an arch and a bench. In addition, an alarm system was implemented to monitor residents in common areas and prevent vehicle accidents. The system was tested using Zigbee and LoRa wireless technologies and the network’s overall performance was compared. In addition, the proposal was compared to commercially available medical equipment and results show a 96% similarity in vital signs monitoring. The system offers a silent noninvasive operation together with a friendly user interaction. It allows caregivers and family members to monitor vital signs of the residents remotely without the need of wearable specialized devices.

This work proposes the development of two BLE-connected chest bands for monitoring breathing activity. The first relies on a custom strain sensor based on a piezoresistive fabric and a low-power conditioning and acquisition section. This last process the gathered information jointly to inertial data provided by an integrated IMU for extracting the respiration rate (RR). Also, the second band uses an inertial-based detection method through a dual-accelerometer approach for deriving the respiratory signal. In detail, two IMUs are placed on the user’s chest and back for extracting the differential inertial signal, containing information related to the breathing activity regardless of the body movements. Both chest bands integrate a BLE module to remotely transmit the acquired information to a host device. Several tests were carried out to determine the performances of the two respiratory monitoring systems. The performance comparison demonstrates the effectiveness of the dual-inertial solution, obtaining a -0.4 BrPM mean difference (MD) and +1.21 and -2.01 BrPM limits of agreement (LoA).

The detection of cracks is an important monitoring task in civil engineering infrastructure devoted to ensuring durability, structural safety, and integrity. It has been traditionally performed by visual inspection, and the measurement of crack width has been manually obtained with a crack-width comparator gauge (CWCG). Unfortunately, this technique is time-consuming, suffers from subjective judgement, and is error-prone due to the difficulty of ensuring a correct spatial measurement as the CWCG may not be correctly positioned in accordance with the crack orientation. Although algorithms for automatic crack detection have been developed, most of them have specifically focused on solving the segmentation problem through Deep Learning techniques failing to address the underlying problem: crack width evaluation, which is critical for the assessment of civil structures. This paper proposes a novel automated method for surface cracking width measurement based on digital image processing techniques. Our proposal consists of three stages: anisotropic smoothing, segmentation, and stabilized central points by k-means adjustment and allows the characterization of both crack width and curvature-related orientation. The method is validated by assessing the surface cracking of fiber-reinforced earthen construction materials. The preliminary results show that the proposal is robust, efficient, and highly accurate at estimating crack width in digital images. The method effectively discards false cracks and detects real ones as small as 0.15 mm width regardless of the lighting conditions.

Shaking force balancing of mechanisms usually is achieved by an optimal redistribution of the moving masses. This allows the cancellation or the reduction of the variable dynamic loads on the mechanism’s frame. The procedure generally leads to a considerable increment in the mass. Addition of counterweights and counter-inertias are required, thus the driving torques and the shaking moment are incremented too. Recently the shaking force balancing has been successfully achieved by specifying the acceleration motion of the global Center of Mass (CoM) of the robot. Additional masses are avoided. But this may be difficult to apply in practice. Therefore it has been proposed a combined procedure, based on imposing an acceleration motion planning to the manipulator’s tool, and on the selective mass redistribution. This last concept is resumed here using Natural Coordinates, a direct and easy to automate alternative. The force balancing of a 2RRR Planar Parallel Manipulator (PPM), also known as the five-bar mechanism, is used as an application example. The shaking force and the shaking moment are obtained analytically using the proposed direct approach. Linear relation between the acceleration of the CoM and the acceleration of the tool is shown in a clear and explicit way, making it easy to identify the required mass redistribution. A generic CAD model made in Solidworks is used to show the application of the technique and to validate the balancing’s results, obtained from inverse kinematics simulations, through dynamic simulation.

The IEC 62670-3 standard recommends the open-circuit voltage method to calculate the cell temperature inside high-concentrator photovoltaic (HCPV) modules. This method requires knowledge of the temperature coefficient of open-circuit voltage (β), and the same standard provides a procedure to get this parameter. In this paper, an alternative method for the thermal characterisation of HCPV modules is proposed. As an advantage, it allows obtaining both the β parameter and the internal thermal resistance (ρ) of the device from outdoor measurements. No internal sensor for measuring the cell temperature is required as in the case of the IEC 62670-3 standard. Knowing the ρ parameter allows a more accurate characterisation of the cell temperature. The proposed procedure is applied to a real HCPV module. An outdoor experimental campaign of two months in Jaen (Southern Spain) was carried out. The β value was underestimated in a 0.50% and the ρ value was overestimated in a 4.18%. When applying the estimated parameters for the prediction of the cell temperature, the open-circuit voltage method gave a root mean square error (RMSE) of 1.41 °C, while the internal thermal resistance method gave a RMSE of 0.62 °C.

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.

Gait analysis has many applications, and specifically can improve the control of prosthesis, exoskeletons, or Functional Electrical Stimulation systems. The use of canes is common to complement the assistance in these cases, and the synergy between upper and lower limbs can be exploited to obtain information about the gait. This is interesting especially in the case of unilateral assistance, for instance in the case of one side lower limb exoskeletons. If the cane is instrumented, it can hold sensors that otherwise should be attached to the body of the impaired user. This can ease the use of the assistive system in daily life as well as its acceptance. Moreover, Force Sensing Resistors (FSRs) are common in gait phase detection systems, and force sensors are also common in user intention detection. Therefore, a cane that incorporates FSRs on the handle can take advantage from the direct interface with the human and provide valuable information to implement real-time control. This is done in this paper, and the results confirm that many events are detected from variables derived from the readings of the FSRs that provide rich information about gait. However, a large inter-subject variability points to the need of tailored control systems.

This paper reports on the progress of a wearable assistive technology (AT) device designed to enhance the independent, safe, and efficient mobility of blind and visually impaired pedestrians in outdoor environments. Such device exploits the smartphone’s positioning and computing capabilities to locate and guide users along urban settings. The necessary navigation instructions to reach a destination are encoded as vibrating patterns which are conveyed to the user via a foot-placed tactile interface. To determine the performance of the proposed AT device, two user experiments were conducted. The first one requested a group of 20 voluntary normally sighted subjects to recognize the feedback provided by the tactile-foot interface. The results showed recognition rates over 93%. The second experiment involved two blind voluntary subjects which were assisted to find target destinations along public urban pathways. Results show that the subjects successfully accomplished the task and suggest that blind and visually impaired pedestrians might find the AT device and its concept approach useful, friendly, fast to master, and easy to use.

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.

This article presents the design optimization of the four-bar linkage for the reduction of the Shaking Force and the Shaking Moment. It is followed a novel procedure based on fully Cartesian Coordinates (Natural Coordinates) and the use of counterweights. These counterweights with as little restrictive constraints on their geometric parameters as possible. No particular counterweight shape in advance is assumed. The analytical equations for the linear momentum and for the angular momentum are obtained, resulting in equations expressed in terms on the mass and the coordinates of the center of mass of the counterweights. These expressions are then used to obtain the Shaking Force and the Shaking Moment of the system in a very direct way. To solve the inverse dynamics problem the inversion of the transpose of the Jacobian matrix, associated to the kinematic constraints, is not required. The links’ masses are restricted only by imposing positive mass values. As a novelty, the most influencing optimization variables are identified using a global sensitivity analysis method, leading to a reduction on the number of optimization variables. The minimization is done using Evolutionary Computation. The results obtained are validated by simulations, and compared to those presented in previous representative works.

The problem of vibrations in civil structures is common; nevertheless, its negative effects can be significantly reduced using structural control methods with intention of maintaining structural welfare as much as possible. This work deals with the study of structural vibration control in a model of a civil-like structure, which consists of three-level building with a tuned mass damper implemented as a passive vibration absorber, mounted on the top of the structure, to attenuate the harmonic vibrations provided by an electromagnetic actuator connected at the base of the primary system. The action of the tuned mass damper is evaluated from an energy approach. The dissipation of energy in the overall system is conducted in an experimental way, where the passive control technique is designed to minimize the undesirable forced dynamic response of the main structure via the tuned mass damper. Experimental results are provided to show the effective performance of the proposed passive vibration absorption scheme to suppress resonant frequency harmonic excitations disturbing the primary system, evaluating the performance energy and contribution of the dissipative device for the energy release in the overall system.

This paper presents the design, implementation, and characterization of a miniature crab-like walking robot. The first prototype developed is a four-legged servomotor actuated machine that exhibits compact dimensions, low mass, and is capable of overcoming obstacles and moving on irregular terrain and confined spaces. Its bio-inspired design ensures the compliance of its locomotion mechanism even in the presence of external disturbances. The mechanical design, the implementation of the prototype, and its electronic control approach are first discussed. Next, a kinematic analysis characterizing its motion is presented. The aim of this device is to serve as an educational supporting platform for understanding robot kinematics and legged locomotion.

In this paper, the development and preliminary results of a new low-cost electronic nose (e-nose) instrument based on an array of six electrochemical sensors is presented. The aim of such instrument is to detect volatile compounds that might be hazardous for humans such as methane, combustible gas, carbon monoxide, hydrogen, and smoke, among others. In particular, this work focuses on sensor characterization; the sensors’ sensitivity, behavior face to temperature, and step response were experimentally analyzed. Preliminary results with the e-nose prototype show the behavior of the set of sensors. Further signal classification possibilities are presented and discussed.

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.

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.

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.

Point matching in multiple images is an open problem in computer vision because of the numerous geometric transformations and photometric conditions that a pixel or point might exhibit in the set of images. Over the last two decades, different techniques have been proposed to address this problem. The most relevant are those that explore the analysis of invariant features. Nonetheless, their main limitation is that invariant analysis all alone cannot reduce false alarms. This paper introduces an efficient point-matching method for two and three views, based on the combined use of two techniques: (1) the correspondence analysis extracted from the similarity of invariant features and (2) the integration of multiple partial solutions obtained from 2D and 3D geometry. The main strength and novelty of this method is the determination of the point-to-point geometric correspondence through the intersection of multiple geometrical hypotheses weighted by the maximum likelihood estimation sample consensus (MLESAC) algorithm. The proposal not only extends the methods based on invariant descriptors but also generalizes the correspondence problem to a perspective projection model in multiple views. The developed method has been evaluated on three types of image sequences: outdoor, indoor, and industrial. Our developed strategy discards most of the wrong matches and achieves remarkable F-scores of 97%, 87%, and 97% for the outdoor, indoor, and industrial sequences, respectively.