Wind energy is one of the most promising forms of renewable energy. For the constant evolution of power generation technology, the use of sensors is fundamental to the development of wind turbine emulators. A wind turbine emulator allows tests and evaluations of a wind power system, regardless of weather conditions. Therefore, to further improve this technology, this work focuses on the application of a torque transducer and a rotary encoder for the implementation of a closed-loop wind turbine emulator. The sensors provide the torque and speed feedback signals to the computational model so that the model could plot the power curves and produce the setpoint voltage used by a variable-frequency drive (VFD) to control a three-phase induction motor (TIM). The emulator was implemented using a control algorithm designed on Labview with a NI 6211 for data acquisition. Finally, the system emulates the behavior of a wind turbine, considering the variations in wind speed, aerodynamic phenomena, load effects, and pitch angle. Experimental results demonstrated the effectiveness of using the TIM-VFD assembly for emulating a wind turbine since the wind turbine emulator behaved like a wind turbine in real-time.

Epilepsy affects approximately 67 million people worldwide with up to 75% from developing countries. Diagnosing epilepsy using electroencephalogram (EEG) is complicated due to its poor signal-to-noise ratio, high sensitivity to various forms of artifacts, and low spatial resolution. Laplacian EEG signal via novel and noninvasive tripolar concentric ring electrodes (tEEG) is superior to EEG via conventional disc electrodes due to its unique capabilities which allow automatic attenuation of common movement and muscle artifacts. In this work, we apply exponentially embedded family (EEF) to show feasibility of automatic detection of gamma band high-frequency oscillations (HFOs) in tEEG data from two human patients with epilepsy as a step toward the ultimate goal of using the automatically detected HFOs as auxiliary features for seizure onset detection to improve diagnostic yield of tEEG for epilepsy. Obtained preliminary results suggest the potential of the approach and feasibility of detecting HFOs in tEEG data using the EEF based detector with high accuracy. Further investigation on a larger dataset is needed for a conclusive proof.

The current technological trends associated with Industry 4.0 and the Internet of Things (IoT) require an interconnected network of sensor nodes providing distributed information on the environment to enable intelligent action to be taken by control systems. Typical examples are the condition monitoring of machines or industrial equipment, or the detection of hazardous environmental conditions (e.g. in chemical plants). Such sensors need to be distributed in areas difficult to reach for wirings or to exchange batteries, and thus need to be self-powered and energy independent.

In this work we provide an overview of possible strategies to realise a positive energy balance in autonomous sensor nodes without the use of batteries, focussing on gas sensors for air quality monitoring as a use case. We will first present ways to reduce the power budget of sensing elements using self-heating nanowires made of CMOS-compatible metal oxides. We will then concentrate on energy harvesting and storage, showing state-of-the-art possibilities in both cases: broadband piezoelectric harvesters, perovskite-based photovoltaic elements, and high-energy density ceramic capacitors.

Finally, we will discuss the possibility to integrate all sensor node elements in a single device using advanced interconnect technologies.

The human-robot collaboration is combining the extended capabilities of human and robot to create a more inclusive and human-centered production system in the future. However, human safety is the primary concern for manufacturing industries. Therefore, safe human-robot collaboration may require real-time motion tracking and prediction of a robot, a human and an object inside the configured working space. In this work, our task is to investigate a concept for unified real-time motion tracking for human-robot collaboration. A low cost and game-based motion tracking systems are integrated into digital human model using Unity3D environment. In this context, we applied a biomechanical model to realize the joint position and orientation of the digital human model. A unified robot description format describes kinematics of the robot. It is the joint controller that prescribes joint sensor motions. Finally, an assembly operation involving snap joining is applied to analyze the performance of the system in real-time process. The analysis considered the distribution of joint variables in spatial-space and time-space. The results suggest that low cost motion capturing methods are useful to derive a concept, particularly in lab environment. Therefore, by predicting non-naturalistic human movement towards robot joints, human safety can be improved. However, system performance needs further investigation regarding jitter, latency, and occlusion.

Road traffic noise (RTN) is one of the main pollutants in urban and suburban areas, negatively affecting the quality of life of their inhabitants. In the context of the European LIFE DYNAMAP project, a noise mapping system has been developed to determine the acoustic impact of road infrastructures in real-time. The project has deployed two Wireless Acoustic Sensor Networks (WASN): one in the District 9 of Milan (urban area), and another in the A90 motorway of Rome (suburban area). The system should be able to identify and remove those anomalous noise events (ANE) unrelated to regular road traffic present in both areas (e.g., sirens, horns, speech, doors, etc.) since its goal is to monitor only RTN, following the European Noise Directive 2002/49/EC. To do so, an Anomalous Noise Event Detector (ANED) has been included in the dynamic noise monitoring system running in real-time in the low-cost acoustic sensors to avoid biasing the computation of the equivalent traffic noise levels. After deploying the WASN in both pilot areas, two acoustic datasets have been built to adapt the previous version of the ANED algorithm to run in real-operation conditions using the data collected through each of the 24 low-cost acoustic sensor networks. In this work, we describe the preliminary results of the analysis of the 154h WASN-based urban acoustic dataset from Milan in terms of the characteristics of both RTN and ANEs (e.g., number of occurrences, signal-to-noise ratio, duration, etc.), considering the origin of the data according to the location of the sensors of the network. The results show that both the number and variability of ANEs are greater in the dataset of the urban area compared to those collected in the Rome suburban area, which underlines the importance of a specific training of the ANED algorithm for each environment.

Transillumination is a very well-known non-invasive optical technique, that relies on the use of non-ionizing radiation to obtain information about the internal morphology of biological tissues. In a previous work [1], we implemented a laser-based illuminator operating at a wavelength of 850 nm that, combined with a CMOS digital camera and narrow-band optical detection, showed a great potential for in vivo imaging. A great advantage is the use of low-cost semiconductor lasers, driven by a very low current (about 11 mA), that are spatially distributed as a 6 by 6 matrix covering a 25 cm² area. Thanks to the strong absorption of haemoglobin at this wavelength, we have collected raw data that can be further processed to achieve better quality images. In particular, here we show that a higher contrast can be attained by expansion of grey level histograms to exploit the full range 0-255. This elaboration can be exploited for the recognition of vascular structures with better resolution. Examples are reported about hand dorsal vein pattern and alive chick embryo blood vessels. Analyses can be successfully performed without applying any thermal or mechanical stress to the human tissue under test and without damaging or puncturing any parts of the eggshells.

[1] Merlo, S.; Bello, V.; Bodo, E.; Pizzurro, S. A VCSEL-Based NIR Transillumination System for Morpho-Functional Imaging, Sensors, 2019, 19, 1-13

Health monitoring of civil and industrial structures has been gaining importance since the collapse of the bridge in Genoa (Italy). It is vital for the creation and maintenance of reliable infrastructure. Traditional manual inspections for this task are crucial but time consuming.

We present a novel approach for combining Unmanned Aerial Vehicles (UAVs) and artificial intelligence to tackle the above-mentioned challenges. Modern architectures in Convolutional Neural Networks (CNNs) were adapted to the special characteristics of data streams gathered from UAV visual sensors. The approach allows for automated detection and localisation of various damages to steel structures, coatings and fasteners, e.g. cracks or corrosion, under uncertain and real-life environments.

The proposed model is based on a multi-stage cascaded classifier to account for the variety of detail level from the optical sensor captured during an UAV flight. This allows for reconciliation of the characteristics of gathered image data and crucial aspects from a steel engineer's point of view. To improve performance of the system and minimize manual data annotation, we use transfer learning based on the well-known COCO-dataset combined with field inspection images. This approach provides a solid data basis for object localisation and classification with state-of-the-art CNN architectures.

Data collection plays an important role in pavement health monitoring, which is usually performed using costly devices, including point-based lasers and laser scanners. The main aim of this study is measuring pavement characteristics using an RGB-D sensor. By recording the depth and color images simultaneously, the sensor benefits the data fusion. By mounting the sensor on a moving cart, and fixing the vertical distance from the ground, data was collected along 100 m of the asphalt pavement using MATLAB. At each stop point, multiple frames were captured and the central region of interests was stored followed by applying low pass filters. To create a 3D dimensional surface of the pavement, sensor calibration was performed to map the RGB and depth infrared images. The SURF (Speeded-up Robust Features) and MSAC (M-estimator Sample Consensus) algorithm were used to match the stitched images along the longitudinal profile. A case study of measuring roughness and rutting is applied to test the validity of the method. The result confirms that the proposed system is capable of measuring such indices with acceptable accuracy.

Within the possibilities of non-linear acoustics, the parametric effect offers a range of acoustic applications that are currently being exploited in different areas. In underwater acoustics, environmental monitoring and security are applications that can benefit from these technologies, allowing the transmission of information in a directivity controlled and efficient manner. An essential aspect for the optimal functioning of these technologies is the choice of the modulation that best suits the needs of communication. In the present work, different modulation techniques are explained, through their non-linear propagation, that allows generating the signals to be propagated. Among the modulations presented in this work, we have AM, CPFSK and LFM modulations normally used in communications. These modulations are performed with a modulating signal (sine and sine-sweeps type) whose non-linear demodulation determines the shape of the 1 and 0 bits, through the transmission of a bit string. With all this, comparisons are made between each technique, to obtain a more precise detection and discrimination of the bits.

This work shows the possibility of using arrays of ZnO nanorods grown on a glass substrate as a sensitive element of a gas flow velocity sensor.

It has been theoretically shown that when a ZnO nanorod is blown with a gas stream, the temperature of its free end can decrease by 20–40 ° C, which should lead to a change in the resistance of the nanorod material.

Arrays of ZnO nanorods were synthesized on a silicon substrate by the hydrothermal method in an aqueous solution of zinc nitrate and hexamethylenetetramine (C₆H₁₂N₄) in the temperature range of about 90 ° C for 1-3 h. The formed ZnO nanorods were predominantly vertical with a height of 590-660 nm and had an average transverse size of about 30-40 nm Then, contact metallization of V-Cu-Ni with a thickness of 0.2-0.3 μm was applied over them. The resistance of the obtained samples of sensitive elements ranged from hundreds of ohms to several megohms.

To measure the dependence of the resistance of the ZnO array of nanorods on the gas supply rate, the sample was located in a measuring chamber. The heating temperature of the sample was controlled by a thermocouple and amounted to (200 ° C). Gas was supplied at a speed of 1 to 100 cm / s. The resistance of the sensitive elements, in this case, changed according to a dependence close to linear and increased by 20%. Thus, based on zinc oxide nanorods, a gas flow rate sensor can be built for various applications