Proceedings, 2020, ECSA-6 2019

This work gathers and presents three lectin-based impedimetric biosensors for the selective detection of specific aberrant cancer-associated O-glycans, namely STn, Tn and T antigens. These truncated glycans are well-established pan-carcinoma biomarkers that are synthesized by tumour cells during protein glycosylation. Glycoproteins carrying these aberrant glycans are then secreted into the blood stream, where they can be detected as cancer biomarkers. Detection of aberrant O-glycoproteins in serum can be successfully performed by using lectin biosensors, as lectins show high selectivity towards particular glycan structures. Lectins are immobilized on the sensor surface, maintaining intact their binding ability towards the glycans present in the sample. For these three biosensors, Sambucus nigra agglutinin, Vicia villosa agglutinin and Arachis hypogeae agglutinin were used as biorecognition elements, with specificity for STn, Tn and T antigens, respectively. The binding event between each lectin and the corresponding aberrant O-glycan was monitored by electrochemical impedance spectroscopy, measuring the increase in the biosensor’s impedance after incubating the samples. The increase in impedance was related to the lectin-glycan complex formation. The performance of the developed biosensors, prepared on screen printed gold electrodes, was evaluated, namely in what concerns selectivity, sensitivity, limit of detection, reproducibility and robustness. Furthermore, a thorough validation was carried out by analyzing serum samples from cancer patients and from healthy donors. Results showed that the three biosensors could efficiently discriminate between controls and patients. Moreover, by analyzing the same samples with the different biosensors, distinct glycosylation profiles could be observed. Full article

In recent years, the organization of cross-country and popular races where hundreds of people participate has become a significant trend. In these events, runners usually subject the body to extreme situations that can lead to various types of indisposition, and they can also suffer falls. Currently, the electronic systems used in this type of race only monitor when runners pass through checkpoints. However, it is necessary to implement systems that enable the control of the population of runners and the monitoring of their status all the times. For this reason, this paper proposes the design of a low-cost system for monitoring and controlling runners in this type of event. The system is formed by a network architecture in infrastructure mode based on low-power wide-area network (LPWAN) technology. Each runner will carry an electronic device that will allow their position and vital signs to be monitored. Likewise, it will incorporate an S.O.S. button that will allow runners to send a signal to the organization should they require help. All these data will be sent through the network to a database, which will allow the organization and bystanders of the race to check the location and history of vital signs of runners. This paper shows the proposal of a design of our system and the different practical experiments that have been carried out with the devices that have allowed for the proposition of this design. Full article

Over the decades, visual inspection has been adopted as a means to monitor infrastructure health. While visual inspection provides insights on a bridge’s condition, it has been generally agreed that it is insufficient and inefficient. This has called for the creation of autonomous, robust, continuous, and quantitative structural health monitoring (SHM) systems to detect potential deficiencies in an early stage, and monitor future condition. Various methods have been explored that associate changes in condition with changes in the structure’s vibration characteristics. These methods have been mostly tested on laboratory specimens experiencing simulated damage. There is need for extending validation of these SHM methods on in-situ structures experiencing real damage under operational and environmental conditions. This paper summarizes a full-scale experiment exploring bridge damage detection effectiveness under variable traffic loads. Three different types of damage were introduced into a full-scale, bridge deck mock-up. These included crash-induced bridge barrier damage, controlled barrier damage, and damage to the deck slab. At the end of each introduced damage case, the bridge’s response to the multiple passages was recorded using specific vehicles specifications. Data was extracted and analyzed to identify damage using principal component analysis (PCA) and independent component analysis (ICA) as damage-sensitive features. The extracted damage features were thereafter used as input for unsupervised learning (novelty detection). One interesting observation was how PCA revealed possibly significant damage after a crash, which under visual inspection appeared to be minor. Novelty detection using PCA as its damage feature was shown to provide robust damage detection irrespective of load, speed variation, and signal noise levels. Full article

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 in order 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 that are difficult to reach for wiring 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 of integrating all sensor node elements in a single device using advanced interconnect technologies. Full article

Chemical oxygen demand (COD) is a widely used parameter in analyzing and controlling the degree of pollution in water. Methods of analysis based on electrochemical sensors are increasingly being used for COD quantitation because they could be simple, accurate, sensitive and environmentally friendly. Electro-oxidizing the organic contaminants to completely transform them into CO₂ and H₂O is considered the best method for COD estimation using sensors. In this sense, copper electrodes have been reported based on the fact that copper in alkaline media acts as a powerful electrocatalyst for oxidation of aminoacids and carbohydrates, which are believed to be the major culprits of organic pollution. In this work, three kinds of copper/copper oxide electrodes were studied that employed the cyclic voltammetry technique: electrodeposited copper nanoparticle electrode, copper nanoparticle–graphite composite electrode and copper oxide nanoparticle–graphite composite electrode. Actual COD estimations are based on the measurements of oxidation currents of organic compounds. Glucose, potassium hydrogen phthalate and ethylene glycol were chosen to be the standard substances to observe the responses, and to correlate the current intensity vs. the COD values. The performed calibrations showed that glucose and ethylene glycol can be oxidized by these three electrodes, as the current intensity increased along with increasing concentrations. However, only the electrodeposited copper nanoparticle electrode showed the ability to oxidize potassium hydrogen phthalate. Besides, the obtained voltammetric profiles presented different shapes with the tested organic compounds, suggesting this can be used as a potential fingerprint for distinguishing the organic compounds. Ongoing work is focused on optimizing measuring conditions and detecting the COD values of real samples. Full article

Advances in permanent magnet technology has seen more reports of sensor applications of low field magnetic resonance. Whilst most are either in the 10–20 MHz range or in the earth’s field, measurements at below 1 MHz are beginning to become more widespread. This range is below the need for careful radio frequency electronics design but above the audio domain and represents an interesting cross over. Many commercial spectrometers do not include the pulse power amplifier, duplexer and preamplifier as these depend on the frequency range used. In this work we demonstrate that, with the current specifications of the humble operational amplifier, the most simple form of an inverting design using only two resistors and decoupling, can effectively provide this ‘front end’ electronics. The low powers used mean crossed Ge diodes provide an excellent duplexer and it is suitable for battery powered applications. Full article

The microstrip patch antenna sensor is a novel sensor used for structural health monitoring which can measure a metal structure’s crack defects in a wireless manner. However, it is difficult to identify the reflected signal from the signal of an antenna sensor. The radio-frequency identification (RFID) antenna sensor, which combines RFID technology and the microstrip patch antenna sensor, can solve the measurement problems that are difficult to the conventional wireless testing technologies. In this study, a dual-chip RFID antenna sensor was designed. The influence of the wireless testing method on the monitoring results of crack defects was investigated by tests, including the wireless tests of resonant frequency and the crack sensitivity tests. The tests results revealed that the antenna sensor had good wireless testing performance with regard to the metal structure’s crack defects. Additionally, the maximum of wireless identification distance reached 1.96 m. Full article

The results of an investigation of the electrical resistivity of Ga₂O₃ thin films modified with silicon under the influence of oxygen in the range of O₂ from 9 to 100 vol. % and changes in the heating temperature of structures from 25 to 700 °C were presented. Thin films of Ga₂O₃ were obtained by RF magnetron sputtering of Ga₂O₃ targeted with pieces of Si on the target’s surface in oxygen–argon plasma. The possibility of developing selective oxygen sensors based on thin films Ga₂O₃ modified with silicon with a temperature of maximum response 400 °C was shown. Oxygen influence leads to a reversible increase in the samples’ resistance, due to the chemisorption of oxygen on the surface of thin Ga₂O₃ films. An increase in the response of sensors based on the thin polycrystalline films of gallium oxide modified with silicon is caused an increase in the adsorption centers for O⁻, due to an increase in the surface inhomogeneity and the appearance of additional adsorption centers Si⁴⁺. Full article

In this work, a graphite-epoxy electrode with cyclic voltammetry electrodeposited reduced graphene oxide and nickel oxyhydroxide nanoparticles was prepared by decomposition in NaOH alkaline solution of cyclic voltammetry electrodeposited nickel hexacyanoferrate. FE-SEM studies were performed to confirm the NiOOH nanoparticle; the average size of the NiOOH nanoparticles was 61 ± 16 nm and EDX was applied to analyze chemical composition. To verify the performance of the prepared electrode, it was used in the electrooxidation of alcohols in alkaline medium by cyclic voltammetry. By performing different calibration experiments of methanol, ethanol, and glycerol, it was possible to extract some information about the electrode in the presence of alcohols. The LOD for methanol, ethanol, and glycerol were 2.16 mM, 2.73 mM and 0.09 mM, respectively, with sensitivity values of 1.32 µA mM⁻¹, 1.80 µA mM⁻¹ and 24.60 µA mM⁻¹, respectively. Multivariate inspection of the data using Principal Component Analysis (performed with the ClustVis online tool) demonstrated the potential ability to discriminate between the different alcohols, whereas the explained variance with the first two components was as high as 89.7%. Full article

Colloids are metastable suspensions of particles dispersed in a base fluid, with high scientific and industrial importance, but the monitoring of these systems still demands expensive and large instrumentation. In this research, the measurement of concentration gradients in colloidal silica samples using an optical fiber sensor is reported. Silica nanoparticles (measuring 189 nm) were sedimented in test tubes for creating environments with different concentrations. The fiber probe was immersed in the assessed liquid, resulting in an increase in the dispersion of the reflected light intensity, which is caused by the particles Brownian motion. Therefore, the quasi-elastic light scattering phenomenon related to the diffusivity can be analyzed, providing information about the concentration gradients of the nanosystem with a straightforward, in situ, and non-destructive approach. Full article

The leaks in roofs and cracks in walls of buildings are common and need immediate attention. The roof leaks or cracks lead to water seepage which results in structural damage to the ceiling wall. In this work, the roof leaks or cracks are identified using the proposed thermal sensor-based decision support system. Further, the thermal camera is interfaced with a handy single on-board computer. The supervised machine learning algorithm is coded inside the single on-board computer and the thermal images captured using the thermal camera is utilized for the fault identification. Further, the trained network is tested using a new set of thermal images for the identification of faults. Results demonstrate that the proposed system is efficient in locating and the identification of faults. Since the single on-board has an inbuilt Wi-Fi, the decision support can be stored in the cloud server with a specific unique Uniform Resource Locator (URL) address. Also, by accessing the appropriate URL, the decision support system can be accessed from remote locations. Full article

Deep Learning strategies recently emerged as powerful tools for the characterization of heterogeneous materials. In this work, we discuss an approach for the characterization of the mechanical response of polysilicon films that typically constitute the movable structures of micro-electro-mechanical systems (MEMS). A dataset of microstructures is digitally generated and a neural network is trained to provide the appropriate scattering in the values of the overall stiffness (in terms of the Young’s modulus) of the grain aggregate. Since results are framed within a stochastic procedure, the aim of the learning strategy is not to accurately reproduce the microstructure-informed response of the polysilicon film, but instead to provide a fast tool to be used at the device level for Monte Carlo analysis of the relevant performance indices. Accuracy of the proposed approach is assessed for very small samples of the polycrystalline aggregate to check if size effects are correctly captured. Full article

Wearable electrical sensors based on zeolites can be used for breath monitoring. The high silicon content of clinoptilolite makes this type of zeolite very adequate for fabricating sensitive water sensors. In addition to sensitivity, response fastness also represents a sensor characteristic of fundamental importance for breath monitoring. Here, the response fastness of a clinoptilolite-based water sensor has been evaluated by measuring the current intensity behavior upon exposition to a constant humidity atmosphere (75%). In particular, the clinoptilolite surface has been biased with a sinusoidal signal (20 V_pp, 5 kHz), and the true-RMS current intensity value has been recorded during exposition to the constant humidity atmosphere. Since current intensity is proportional to the adsorbed water concentration (only hydrated cations are charge carriers) a kinetic analysis has been possible. The clinoptilolite dehydration kinetics in a dry atmosphere have been evaluated too. According to this kinetic analysis, water adsorption is described by a Lagergren pseudo-first-order model with a rate constant of (58.6 ± 0.9)·10⁻⁴ min⁻¹, while desorption in dry air follows a first-order kinetic model with a specific rate of (202.7 ± 0.3)·10⁻⁴ min⁻¹ at 25 °C. Full article

The purpose of the present study is to monitor tool condition in a grinding operation through the electromechanical impedance (EMI) using wavelet analysis. To achieve this, a dressing experiment was conducted on an industrial aluminum oxide grinding wheel by fixing a stationary single-point diamond tool. The proposed approach was verified experimentally at various dressing tool conditions. The signals obtained from an EMI data acquisition system, composed of a piezoelectric diaphragm transducer attached to the tool holder, were processed using discrete wavelet transform. The approximation and detail coefficients obtained from wavelet decomposition were used to estimate tool condition using the correlation coefficient deviation metric (CCDM). The results show excellent performance in tool condition monitoring by the proposed technique, which effectively contributes to modern machine tool automation. Full article

This paper describes the development of an algorithm robotic plug-in of a charging system for mobile platform. In the first chapter, there is a short overview of possibilities of automatic plug-in system, including proprietary industrial solution. In the main part, there is a description of the system based on UR robot with build-in force torque sensors and Intel RealSense Camera. This camera combines IR depth lens with regular RGB camera and six DOF inertial sensor, which is used in our application too. The conventional solution of this problem is usually based on RGB image processing in various state of the art, from simple pattern matching, neural network, or genetic algorithm to complex AI solution. The quality of the solution mostly depends on robustness of image processing. In our cases, we use simple sensor fusion. Thanks to multiple information and constrain of values, we can assume, if the algorithm is proceeding successfully or not. The system uses the internal parameters of the robotic arm, e.g., end-effector position and orientation and force-torque information in tool center point. The next information is RGB camera image and camera depth image, and the inertial unit build in camera. The other important information is the location of the vehicle inlet on the mobile platform, where the shape of mobile platform is considered as a constrain for image processing. The system is validated only on a physical model with CCS type 2 plug and vehicle inlet, because the mobile platform is under construction by another team. Full article

Abstract: Acetalated dextran (Ac-Dex) is a promising pH-sensitive biocompatible and biodegradable polymer for nanomedicine applications. In this work, Ac-Dex nanoparticles were synthesized by two different solvent evaporation methods, the single nanoemulsion and the double nanoemulsion. The Ac-Dex particles were characterized by scanning electron microscopy and the synthesis of highly homogeneous spherical particles was verified. Then, an optical fiber sensor based on quasi-elastic light scattering and comprised of only single-mode optical fibers and standard telecommunication devices showed sensitivity regarding the nanoparticles concentrations and was used for monitoring their degradation over 12 h under pH and temperature conditions of cancerous tissues. The results revealed a well-controlled degradation pattern, corroborating the suitability of the modified polymer to the release of active compounds in a sustainable manner and also demonstrating the applicability of the sensor for the in situ evaluation of the degradation. Full article

A porous silica gel plate impregnated with a colorimetric reagent, 4-amino-3-penten-2-one (Fluoral-P) has been fabricated for the first time to determinate formaldehyde. The reaction of formaldehyde and Fluoral-P produced a yellow product 3,5-diacetyl-1,4-dihydrolutidine (DDL), which was further photographed by a smartphone. A good linear relationship has been found between the intensity of blue component from the digital image and formaldehyde concentration in the range of 0–50 mg L⁻¹ with low detection limit of 2.2 ± 0.1 mg L⁻¹. A good precision in the range of 0.59–7.75%RSD and an accuracy with the relative error of +3.7% from control samples are also obtained. These results demonstrate that our developed low-cost sensor, together with digital image colorimetry, has potential for sensitively and quickly measuring formaldehyde. Full article

The paper represents a preliminary analysis of the physiological and psychological impact of vibration and acoustic noise on a person examined by a low-field magnetic resonance imaging (MRI) tomograph. First, a methodology for the measurement of different signals of a tested person was found. The main investigation consists of a parallel heart rate and blood pressure measurement using a photoplethysmographic (PPG) optical sensor and standard portable blood pressure monitors. The recorded PPG signal is filtered and processed to obtain a clean waveform used to determine an instantaneous heart rate. Different types of portable blood pressure monitors are tested and compared to choose the best one for further experiments. Full article

Deep learning techniques are being widely applied to Human Activity Recognition (HAR). This paper describes the implementation and evaluation of a HAR system for daily life activities using the accelerometer of an iPhone 6S. This system is based on a deep neural network including convolutional layers for feature extraction from accelerations and fully-connected layers for classification. Different transformations have been applied to the acceleration signals in order to find the appropriate input data to the deep neural network. This study has used acceleration recordings from the MotionSense dataset, where 24 subjects performed 6 activities: walking downstairs, walking upstairs, sitting, standing, walking and jogging. The evaluation has been performed using a subject-wise cross-validation: recordings from the same subject do not appear in training and testing sets at the same time. The proposed system has obtained a 9% improvement in accuracy compared to the baseline system based on Support Vector Machines. The best results have been obtained using raw data as input to a deep neural network composed of two convolutional and two max-pooling layers with decreasing kernel sizes. Results suggest that using the module of the Fourier transform as inputs provides better results when classifying only between dynamic activities. Full article

Hydrophobically modified poly(vinyl alcohol)s of varied copolymer composition were tested as active media for optical sensing of humidity. Copolymer thin films were deposited on silicon substrate using water-methanol solution in a volume ratio of 20:80 and concentration of 1 wt%. Films were subjected to low (60 °C) and moderate (180 °C) temperature annealing in order to study the temperature influence on optical and humidity sensing properties. Refractive index, extinction coefficient along with thickness of the films were determined by non-linear minimization of the goal function comprising measured and calculated reflectance spectra at normal light incidence. The humidity sensing ability of the films was studied through reflectance measurements at different humidity levels in the range 5–95 %RH. The influence of temperature annealing on optical and sensing properties was demonstrated and discussed. Full article

Pattern recognition can be adopted for structural health monitoring (SHM) based on statistical characteristics extracted from raw vibration data. Structural condition assessment is an important step of SHM, since changes in the relevant properties may adversely affect the behavior of any structure. It looks therefore necessary to adopt efficient and robust approaches for the classification of different structural conditions using features extracted from the said raw data. To achieve this goal, it is essential to correctly distinguish the undamaged and damage states of the structure; the aim of this work is to present and compare classification methods using feature selection techniques to classify the structural conditions. All of the utilized classifiers need a training set pertinent to the undamaged/damaged conditions of the structure, as well as relevant class labels to be adopted in a supervised learning strategy. The performance and accuracy of the considered classification methods are assessed through a numerical benchmark concrete beam. Full article

Fourier-Transform Infrared microspectroscopy (μFT-IR) has been usefully applied in the analysis of the complex biological processes occurring during X-ray radiation-cell interaction. Different experimental approaches are available for FT-IR spectra collection (transmission, attenuated total reflection (ATR), and transflection modes) from cells samples. Recently, some problems have been raised about the role of transmitted and reflected components of the infrared beam in transflection mode. For this reason, we investigated two different transflection approaches for collecting spectra from cells exposed to X-ray. In the former approach, cells were grown on MirrIR slides, and for the second approach, cell pellets were prepared. In both cases, SH-SY5Y neuroblastoma cells were used. X-ray exposure was performed at doses of 2 and 4 Gy. Spectra were obtained by using both the approaches in the 600–4000 cm⁻¹ spectral range from exposed and not-exposed samples. The main contributions from proteins, lipids, carbohydrates, and DNA were clearly evidenced in spectra obtained with the two different acquisition approaches. A comparison among them has been also reported. Full article

Fourier-Transform Infrared micro-spectroscopy (µFT-IR) was used for an in vitro investigation on spermatozoa (SPZ) samples separately collected from caput and cauda of mouse epididymis. SPZ are characterized by deep biochemical changes during the transit along the epididymis and they can constitute ideal candidates for a µFT-IR investigation, thanks to the ability of this technique in analyzing cells at a molecular level. Appreciable differences were reported in the infrared spectra from caput and cauda SPZ, and biochemical changes in protein, nucleic acid, lipid, and carbohydrate content of cells were evidenced. The present investigation indicates that µFT-IR can constitute a valuable tool for monitoring, in an easy and fast way, the changes suffered by SPZ during the transit along the epididymis. Full article

Transmission and distribution play a vital role in delivering electricity. The presence of any fault in these systems may stop the delivery of electricity, which may create a huge problem in today’s world. Hence, fault detection has become essential for delivering uninterrupted power supply. In this work, a portable and intelligent system is designed, and the fault detection on underground transmission lines is done using a developed hardware system. Also, the proposed system has a thermal camera which is an 8 × 8 array of infrared thermal sensors interfaced with a system-on-chip device, which collects the real-time thermal images when connected to the device. Further, the thermal camera returns an array of 64 individual infrared temperature readings of the transmission line and locates the point of damage that might occur due to the aging of conductor insulation, physical force, etc. Also, 200 images with thermal information from the different instances and directions are utilized to train the adapted machine learning algorithm. The python software is utilized to code the machine learning algorithm inside the system-on-chip device. The convolutional neural network-based machine learning algorithm is adopted and validated using various performance metrics such as accuracy, sensitivity, specificity, precision, negative predicted value, and F1_score. Results demonstrate that the proposed hardware is highly capable of locating faults in underground transmission lines. Full article

This study aims to show the influences of the sensor installation interface in the industrial environment. This contribution is focused on analyzing the response behavior of piezoelectric transducers subjected to successive installations, using digital signal processing and non-destructive structural health monitoring (SHM) techniques. Tests were performed to simulate the installation conditions of a piezoelectric sensor, which was coupled to a holder carrying a steel body and submitted to successive reinstallations. Different signals were obtained for each installation, and the results can bring initial elucidations on the subject and pave the way for future studies. Full article

A novel microwave high-resolution near-field non-destructive testing technique is proposed and experimentally evaluated in reflectometry imaging scenarios involving planar metal surfaces. Traditionally, microwave reflectometry does not provide high dynamic contrast between the defect and background material in the case of metal structures due to intrinsically high reflection magnitude from the metal surfaces masking defect a microwave signature. A high-Q resonant sensor based on the loaded aperture is designed to interact very strongly even with small defects on the metal surface providing very high two-dimensional spatial resolution of approximately one tenth of a wavelength, λ, at λ/20–λ/10 standoff distance. Experimental results demonstrate a defect-to-background contrast greater than 5 dB amplitude and 50° phase in raw microwave data. To further enhance the spatial resolution and defect contrast, a phase-modulated near field imaging technique is proposed and experimentally evaluated in the case of a defected metal plate. This technique is based on fast variation of the reflection phase in the narrow frequency band around the resonance, which essentially enables elimination of background a microwave signature from the reflected signal. The proposed imaging technique should find applications in non-destructive surface testing and evaluation of metal and alloy structures. Full article

The design and utility of mobile handheld devices have developed tremendously, from being initially intended for audio calls only to the recent incorporation of augmented reality in smartphones. Recent smartphone functions are power-intensive, and cause excessive heating in phone parts, primarily batteries and processors. Left unmanaged, phone temperatures would exceed the threshold temperature of discomfort, negatively affecting user experience. The use of phone casings has simultaneously become common in recent years. They form an additional barrier to heat dissipation from mobile devices, which has not been considered in existing studies. In this work, the thermal profiles of two identical smartphones were assessed during common tasks, including music playing, voice calling, video streaming and 3D online gaming. One of the phones (the test case) was operated while enclosed in a plastic phone casing, while the other (the control case) was bare. Transient surface temperature distributions were obtained with infrared imaging and thermocouple sensors, while processor and battery temperatures were obtained from inbuilt sensors. Test results showed that casings generally redirect the dissipation of the heat generated within the phone. For tasks involving contact with users’ hands, this will protect the user from high phone surface temperatures. However, the processor and battery temperatures are increased as a result. This user protection was not achieved in the online gaming task, for which the heat generated exceeded the insulating capacity of the plastic casing. The range of protection offered to phone users could be extended by using phone casings which incorporate phase change materials. Full article

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, we implemented a laser-based illuminator operating at a wavelength of 850 nm, combined with a CMOS digital camera and narrow-band optical detection that showed 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, spatially distributed as a 6-by-6 matrix covering a 25 cm² area). Thanks to the strong absorption of hemoglobin at this wavelength, we have collected raw data of vascular structures that have been further processed to achieve images with much better quality. In particular, here we show that a higher contrast can be attained by the expansion of gray level histograms to exploit the full range, 0–255. This elaboration can be, for instance, exploited for the recognition of vascular structures with better resolution. Examples are reported relative to hand dorsal vein patterns and live chick embryos’ 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 eggshell. Full article

In general, water quality mapping is done by interpolation of in situ measurement samples. Often, these parameters change with time. Due to geographic variability and the lack of budget in Nepal, such measurements are done less often. Remote sensors that collect spectral information continually can be very useful in the regular monitoring of water quality parameters. Landsat Operational Land Imager (OLI) bands have been used to estimate water quality parameters. In this work, we model two water quality parameters: chlorophyll-a (Chl-a) and dissolved oxygen (DO) using sequential minimal optimization regression (SMOreg), which implements a support vector machine (SVM) algorithm and recursive partitioning tree (REPTree) regressions. A total of 19 measurements were taken from Phewa Lake, Nepal and various secondary bands were derived from using Landsat 8 Operational Land Imager (OLI) bands. These bands underwent feature selection, and regression models were created based on selected bands and sample data. The results showed satisfactory modelling of water quality parameters using Landsat 8 OLI bands in Phewa Lake. Due to a limited number of data, cross-validation was done with 10 folds. The SVM showed a better result than the REPTree regression. For future studies, the performance can be further evaluated in large lakes with larger sample numbers and other water quality parameters. Full article

The 3D printing process deals with the production of three-dimensional objects with defined geometries. However, this manufacturing process has a crucial point established at the beginning of the object manufacturing, where anomalies can occur and compromise the entire object produced. The piezoelectric diaphragm has been studied as an alternative to the conventional acoustic emission (AE) sensor concerning the monitoring of structures and processes. It has in its assembling a ceramic element with piezoelectric properties, which makes its response sensitive to temperature variations. The Pencil Lead Break (PLB) method is widely used due to its efficiency in the characterization of AE sensors. The present work aims to study the influence of temperature on the piezoelectric diaphragm response for the monitoring of the 3D printing process. PLB tests were performed on the glass surface of a 3D printer at three different temperatures, and the raw signal was collected at 5 MHz sample rate. The signal was investigated in the time and frequency domain. The results demonstrate that the frequency response of the sensor is directly influenced by the temperature variations. In addition, the signal amplitude variations occur differently along the entire spectrum, and frequency bands with small and large amplitude variations can be selected for a comparison study. Furthermore, two frequency bands were carefully selected, and the mean error was obtained regarding the reference temperatures of 25 and 45 °C. It can be inferred that the piezoelectric transducer has low sensitivity to temperature variation if a proper frequency band is selected, where an acceptable error of 16.9% was obtained. Full article

Sensor networks are formed by fixed or mobile sensor nodes and their functions are to capture the events that occur within a certain area and then relay to a central node. Normally, sensor nodes are not able to transmit or receive information over long distances due to the need to use less energy and thus extend their useful life. Therefore, the number of sensor nodes in a given area directly influences the coverage of this area and the ability of information to be relayed by several sensors to the central node. If there are many missed messages, the application will have its performance compromised. In this paper, we use a statistical method based on Monte Carlo approach to estimate the probability of message loss and area coverage. The position and proper motion of the sensors are randomly chosen and from that we estimate how many nodes can communicate with the central node directly or through another sensor working as relay. The free variables in our analysis are node density, node displacement velocity, and sensor quantity. The results obtained are compared analytically with simple cases in order to validate the results obtained by the simulations performed. Full article

In this study, a biocompatible and non-toxic pH-responsive composite film was prepared for food packaging application. The films are composed from polyvinyl alcohol as the main polymeric matrix, nanoclay as a reinforcing component, and red cabbage extract as a non-toxic indicator. The prepared films showed lower water uptake values when the amount of nanoclay was increased up to 25%. It was observed that the films become brittle at high loading of nanoclay (40%). The prepared films exhibited color change in alkaline and acidic medium due to the presence of red cabbage extract, which turned pinkish in acidic medium and greenish in an alkaline environment. The prepared films were characterized by FTIR and visible spectroscopy. The maximum absorption in acidic medium was (λ_max = 527 nm), while a red-shift occurred in the alkaline medium (λ_max = 614 nm). Future work will focus on the crosslinking of the prepared films to improve their mechanical properties. Full article

Greenhouse detection is important with respect to urban and rural planning, yield estimation and crop planning, sustainable development, natural resource management, and risk analysis and damage assessment. The aim of this study is to detect greenhouse areas by using color and infrared orthophoto (RGB-NIR), topographic map, and Digital Surface Model (DSM) approaches. The study was implemented in the Kumluca district of Antalya, Turkey, which includes intensive greenhouse areas. In this study, color and infrared orthophotos, a normalized Digital Surface Model (nDSM), Normalized Difference Vegetation Index (NDVI), and Visible Red-Based Built-Up Index (VrNIR-BI) were used, and the greenhouse areas were detected using an Object-Based Image Analysis (OBIA). In this process, the optimum scale parameter was determined automatically by the Estimation of Scale Parameter2 (ESP2) tool and Multi Resolution Segmentation (MRS) was used as the segmentation algorithm. In the classification stage, K-Nearest Neighbor (K-NN), Random Forest (RF), and Support Vector Machine (SVM) classification techniques were used, and the accuracies of the classification results were compared. The obtained results demonstrated that greenhouse areas can be determined from color and infrared orthophoto and DSM data successfully by using the OBIA. The highest overall accuracy was obtained when the SVM classifier was used, with 94.80%. Full article

Auditoriums and theaters are buildings in which concerts, shows, and conferences are held, offering a diverse and dynamic cultural program to citizens. Unfortunately, people with impairments usually have difficulties in fully experiencing all the provided cultural activities, since such environments are not totally adapted to their necessities. For example, in an auditorium, visually impaired users have to be accompanied to their seats by staff, as well as when the person wants to leave the event in the middle of the show (e.g., to go to the toilet), or when he/she wants to move around during breaks. This work is aimed at improving the autonomy of disabled people within the mentioned kinds of environments, as well as enhancing their show experiences by deploying wireless sensor networks and wireless body area networks connected to an augmented reality device (Microsoft HoloLens smart glasses). For that purpose, intensive measurements have been taken in a real scenario (the Baluarte Congress Center and Auditorium of Navarre) located in the city of Pamplona. The results show that this kind of environment presents high wireless interference at different frequency bands, due to the existing wireless systems deployed within them, such as multiple WiFi access points, wireless microphones, or wireless communication systems used by the show staff. Therefore, radio channel simulations have been also performed with the aim of assessing the potential deployment of the proposed solution. The presented work can lead to the deployment of augmented reality systems within auditoriums and theaters, boosting the development of new applications. Full article

A novel method for electromagnetic (EM) characterization of engineered artificial materials such as biomaterials, nanomaterials, and composite materials is proposed and experimentally evaluated in this paper. The method is based on resonance transmission properties of capacitively loaded apertures in conductive screens. The advantage of this new method over the existing techniques (free space, loaded waveguide, microstrip and coplanar waveguide resonators, coaxial probe, etc.) is three-fold: (i) resonance EM field enhancement inside the loaded aperture leads to very high sensitivity and therefore accuracy of EM parameters de-embedding, (ii) only small thin samples of material under test are required (with a sample area substantially smaller than squared wavelength of radiation, ~0.01 λ²), (iii) the method is easily scalable over the frequency and wavelength and based on relatively simple permittivity and permeability de-embedding procedure. The experimental setup in the microwave S-band (2–3 GHz) is based on two dipole antennas, capacitive aperture in the conductive screen, unloaded and loaded with material under test, and vector network analyzer (VNA) for signal generation and data acquisition. Analytical de-embedding procedure is developed and applied to the characterization of carbon nanotube (CNT) material microwave absorption. It is demonstrated that the method offers very high accuracy in material characterization based on minimal material samples. Full article

The KM3NeT deep-sea neutrino telescope will use thousands of Digital Optical Modules (DOMs) forming a 3D array to detect the Cherenkov’s light produced by the particles generated after a neutrino interaction in the medium. The DOMs are arranged in Detection Units (DUs), structures anchored and maintained vertical by buoyancy each one containing 18 DOMs at different height. The DOMs are, thus, subject to movements due to sea currents. For a correct reconstruction of events detected by the telescope, it is necessary to monitor the position of each DOM with 10 cm accuracy. For this, an Acoustic Positioning System (APS) with a piezo-ceramic transducer installed in each DOM and a long baseline of acoustic transmitters and receivers on the seabed is used. Besides, there is a system of compass/accelerometers in the DOMs to determine their orientation. Then, a mechanical model is used to reconstruct the shape of the DU taking as input the information from the positioning sensors and using the sea current velocity as free parameter of the DU Line Fit method. The mechanical equations consider the buoyancy and the drag force of any item in the DU line. This work describes the data process of the different sensors and systems to obtain the fit shape of DUs, the situation for the first DUs installed as an example and to study the viability and define the full process to apply in KM3NeT. Full article

Humanity’s growing long-term energy demand will be the opportunity for new energy generation sources. In this scenario, the use of hydrogen as an energy source has become an interesting alternative to energy production, as the use of fossil fuels can lead to harmful consequences, such as the emission of greenhouse gases. This paper presents the development of a low-cost instrumentation system for monitoring the temperature, current, voltage, and gas flow rate of a dry electrolytic cell. Through the electrolysis process, the cell generates a hydrogen-rich gas which is used as an additive in an internal combustion engine to reduce pollutant gas emissions and primary fuel consumption. The measured variables are presented as a function of the time to analyze the behavior of the electrolyzer. The main advance reported in this work is related to the use of a low-cost sensor for a hydrogen-rich gas flow measurement, in which calibration was performed indirectly using a rotameter as a reference. The calibration curve adjusted to the experimental data by linear regression presented a coefficient of determination of 0.9957. Thus, the use of the low-cost sensor is a feasible alternative for measuring the electrolysis gas generated by the cell. Full article

This research paper presents the design and implementation of an internet of things-based (IoT) smart framework for human heartbeat rate monitoring and control system. A comprehensive study of various techniques and technologies that are used in controlling the heartbeat rate is explored. The proposed system was designed and implemented on a breadboard with the various system components that are assembled, connected and tasted. Experimental results obtained from the implemented prototype were found to be accurate, as the system was able to sense and read the heartbeat rate of its user and transmit the sensed data through the internet. The system components were soldered on a breadboard, and cased inside a plastic container with the heart pulse sensor stretched, so as to be clipped on the fingertip of the system’s user. Experimental results demonstrate that the resting heartbeat rate of children below the age of 17 is between 65 to 115 beats per minute (bpm) and the resting heartbeat rate of an adult between the ages of 17 to 60 is 60 to 100 bpm. In addition, the resting heartbeat rate of old people who are 60 years old and above, their heartbeat rate is between 65 to 120 bpm. These findings are in agreement with the state-of-the-art in the medical field. Furthermore, this research paper presents an approach that is flexible, reliable, and confidential for heartbeat rate monitoring and control system using sensor network and IoT technology which can be deployed to the medical field to assist the medical practitioners in doing their work easily. Full article

Remote sensing technologies are important for radiation safety and environmental security applications. A dual-mode Cs₂LiYCl₆:Ce³⁺ (CLYC) sensor was developed for simultaneous neutron measurements and gamma-ray spectroscopy. To keep users away from hazardous areas, an unmanned aerial system was used as a mobile sensor platform. The sensor was integrated into a multicopter platform as a ‘plug and fly’ component allowing deployment in the field conditions. The photon energy resolution of the CLYC sensor was measured as less than 5% at 662 keV. The detection of neutrons was achieved via ⁶Li(n,α)t reaction. The sensor’s signal communication and data fusion were programmed using robot operating system framework, as well as on-board signal analysis functions including the neutron-photon pulse shape discrimination and the identification of photo peaks in the gamma spectrum. These data with added real-time kinematic GPS and time stamps were reported to the user enabling real time awareness of the monitored area, further analysis in temporal and spatial domains, and radiation mapping and source search tasks. Full article

Microgrids are one of the main drivers in achieving sustainable energy management in the context of smart cities and smart regions. In this way, multiple energy sources are employed and overall system performance is given by adequate information handling in terms of energy consumption requirements as well as user behavior profiles. This paper introduces a framework for wireless mesh communication, monitoring, and distributed energy management for domestic microgrids. A communication scheme based on a combination of sensors which describe energy consumption profiles (i.e., current probes, power consumption level at different loads), environmental factors (temperature, humidity and illumination level) and user behavior profiles (presence sensor detectors) is employed in order to provide an interactive scenario in terms of the management of multiple energy sources. Practical tests have been performed by using an XBee ZigBee network in a meshed configuration connected to an experimental microgrid implemented at the Public University of Navarre (UPNA). The system has been implemented in order to provide cloud-enabled data gathering, sending the required information via web services to a private cloud. These initial results are being scaled with the aim of providing a multi-microgrid communication and control scheme. Full article

Wireless networks are technologies with a growing interest in the area of telecommunications, such as the case of MANETs. Despite its advantages, MANETs present several challenges in the transmission of information due to the limited bandwidth, high error rate, energy consumption restriction, and variable topologies. The transmission power can significantly influence some of the aforementioned issues. This paper proposes Density Power Control Mechanism (DPCM), which employs a cross-layering approach between AODV (Ad-hoc On-Demand Distance Vector) routing protocol and the physical layer to adapt power transmission. DPCM aims to reduce collisions, maintain or improve the performance of AODV as well as to save power in the nodes. Our results indicate that our proposal can improve the performance of the network and save power at the same time. Moreover, it is especially useful for low- and medium-density scenarios. Full article

Crack initiation and propagation vary the mechanical properties of the asphalt pavement and further alter its designate function. As such, this paper describes a numerical study of a multi-layered strain sensor for the structural health monitoring (SHM) of asphalt pavement. The core of the sensor is an H-shaped Araldite GY-6010 epoxy-based structure with a set of polyvinylidene difluoride (PVDF) piezoelectric transducers in its center beam, which serve as a sensing unit, and a polyurethane foam layer at its external surface which serves as a thermal insulation layer. Sensors are coated with a thin layer of urethane casting resin to prevent the sensor from being corroded by moisture. As a proof-of-concept study, a numerical model is created in COMSOL Multiphysics to simulate the sensor-pavement interaction, in order to design the strain sensor for SHM of asphalt pavement. The results reveal that the optimum thickness of the middle polyurethane foam is 11 mm, with a ratio of the center beam/wing length of 3.2. The simulated results not only validate the feasibility of using the strain sensor for SHM (traffic load monitoring and damage detection), but also to optimize design geometry to increase the sensor sensitivity. Full article

This work shows the possibility of using arrays of ZnO nanorods grown on a glass substrate as a sensitive element for measuring air flow velocity. Since oxide semiconductors have a temperature dependence of resistance, a theoretical and experimental assessment was made of the influence of air velocity on the increase in resistance of a sensitive element. It has been theoretically shown that when air is blown through, the temperature of the free end of the ZnO nanorod can decrease by several degrees. An experimental evaluation showed that when gas is blown at a speed of 12.5 cm/s, the resistance of the sensing element increases by about 20%, which is equivalent to a temperature increase of about 4 degrees. In addition, it was found that the dependence of the increase in the resistance of the sensitive element when exposed to an air flow from 0 to 12.5 cm / s is close to linear. Full article

Rail inspections are required and used to ensure safety and preserve the availability of railway infrastructure. According to the statistics published by railroad administrations worldwide, the transverse fissure appearing in railhead is the principal cause of rail accidents. These particular defects are initiated inside the railhead. Detection of these cracks has always been challenging because a defect signature remains mostly small until the defect size reaches a significant value. The present work deals with the theoretical analysis of an integrated contact-less system for rail diagnosis, which is based on ultrasounds. The generation of these waves was performed through non-ablative laser sources. Rotational laser vibrometry was used to achieve the reception of the echoes. Detection of flaws in the rail was monitored by considering special ultrasound wave signal based indicators. Finite element modeling of the rail system was performed, and transverse defect detection of the rail was analyzed. Full article

Advanced ceramics are widely used in industry due to their unique properties. However, the machining of ceramic components by conventional methods is difficult due to their high level of hardness and brittleness. In this sense, laser beam machining (LBM) is presented as an alternative to conventional methods, enabling the machining of workpieces through more accurate and less invasive techniques. Despite the advantages of laser machining, the process still needs to be studied in detail, as advanced ceramic machining is considered a stochastic process. Thus, real-time monitoring systems are required in order to optimize the ceramic laser machining. Therefore, this paper proposes a novel method for monitoring the cutting kerf in the laser cutting process of ceramic components using a low-cost piezoelectric transducer (PZT) and digital signal processing. Tests were performed on the surface of an alumina ceramic workpiece under different machining conditions. The cutting kerf was measured by a digital microscope and the raw signals from the PZT transducer were collected at a sampling rate of 2 MHz. Time domain and frequency domain analyses were performed in order to find a frequency band that best correlates with the process conditions. Finally, a linear regression was calculated in order to correlate the PZT signal and the measured kerf. The results showed that the piezoelectric transducer was sensitive to the acoustic activity generated during the process, allowing the real-time monitoring of the cutting kerf. Thus, the approach proposed in this paper can be used efficiently in the monitoring of the laser cutting process. Full article

In recent years, cleaning robots like Roomba have gained popularity. These cleaning robots have limited battery power, and therefore, efficient cleaning is important. Efforts are being undertaken to improve the efficiency of cleaning robots. Most of the previous works have used on-robot cameras, developed dirt detection sensors which are mounted on the cleaning robot, or built a map of the environment to clean periodically. However, a critical limitation of all the previous works is that robots cannot know if the floor is clean or not unless they actually visit that place. Hence, timely information is not available if the room needs to be cleaned. To overcome such limitations, we propose a novel approach that uses external cameras, which can communicate with the robots. The external cameras are fixed in the room and detect if the floor is untidy or not through image processing. The external camera detects if the floor is untidy, along with the exact areas, and coordinates of the portions of the floor that must be cleaned. This information is communicated to the cleaning robot through a wireless network. Thus, cleaning robots have access to a `bird’s-eye view’ of the environment for efficient cleaning. In this paper, we demonstrate the dirt detection using external camera and communication with robot in actual scenarios. Full article

In Nepal, most of the farmers depend upon traditional agricultural practices. Adapting modern agricultural technology plays an important role in improving overall efficiency as well as the productivity of their yields. In modern agriculture, the Internet of Things (IoT) connects farmers to their farm via sensors so that they can easily monitor the real-time conditions of their farm from anywhere. The White Button Mushroom is a widely cultivated crop among Nepalese farmers. Although being the most consumed and cultivated crop, it is still overshadowed by the traditional cultivation approach, which is resulting in low productivity, high manpower efficiency, and more effort and cost. This work aims to develop a monitoring system to monitor the environmental conditions of a mushroom farm. It enables a user to monitor crucial factors such as temperature, humidity, moisture, and light intensity on a mushroom farm through the end devices. White Button Mushroom requires an optimum temperature ranging from 22 to 25 °C and humidity from 70% to 90%. Sensors are placed at fixed locations and spots of the farm. Then, the sensors measure the status of parameters, which are transmitted to the remote monitoring station via a low power Node MCU (micro-controller unit). Thus, obtained data are stored in a cloud platform. The codes for the controller are written in the Arduino programming language, debugged, compiled, and burnt into the microcontroller using the Arduino integrated development environment. The result shows successful monitoring of environmental conditions accessing the Internet from anywhere. It minimizes human efforts and automates production, which could be beneficial to Nepalese farmers. Full article

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 measures 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 were collected along 100 m of the asphalt pavement using MATLAB. At each stop point, multiple frames were collected, the central region of interests was stored, and a low pass filter was subsequently applied to the data. To create a 3D 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) algorithms 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. Full article

Human-robot collaboration combines the extended capabilities of humans and robots to create a more inclusive and human-centered production system in the future. However, human safety is the primary concern for manufacturing industries. Therefore, real-time motion tracking is necessary to identify if the human worker body parts enter the restricted working space solely dedicated to the robot. Tracking these motions using decentralized and different tracking systems requires a generic model controller and consistent motion exchanging formats. In this work, our task is to investigate a concept for a unified real-time motion tracking for human-robot collaboration. In this regard, a low cost and game-based motion tracking system, e.g., HTC Vive, is utilized to capture human motion by mapping into a digital human model in the Unity3D environment. In this context, the human model is described using a biomechanical model that comprises joint segments defined by position and orientation. Concerning robot motion tracking, a unified robot description format is used to describe the kinematic trees. Finally, a concept of assembly operation that involves snap joining is simulated to analyze the performance of the system in real-time capability. The distribution of joint variables in spatial-space and time-space is analyzed. The results suggest that real-time tracking in human-robot collaborative assembly environments can be considered to maximize the safety of the human worker. However, the accuracy and reliability of the system regarding system disturbances need to be justified. Full article

This paper presents the design of a portable electrocardiograph (ECG) device using the AD8232 microchip as the analog front-end (AFE). Starting with the manufacturer’s evaluation board of the AFE chip for testing circuit configurations, open-source hardware and software components were integrated into a breadboard prototype. Ultimately, a custom printed circuit board (PCB) was produced. The prototype required to accommodate the microchip on a SMD-to-DIP adapter for testing with the breadboard-friendly Arduino microcontroller alongside a data logger and a Bluetooth breakout board. The analog ECG signal from the AFE output was digitized using one channel of the 10-bit analog-to-digital Converter (ADC) of the ATmega328 microcontroller contained in the Arduino Nano board. The digitized ECG signal can be transmitted not only by serial cable using the Arduino functions, but also via Bluetooth to a PC or to an Android smartphone system when the HC-06 shield is used. The data logging shield provides gigabytes of storage, and the signal is recorded to a micro SD card adapter along with the date and time stamp data of the sample capture (real-time clock provided). In addition to hardware and software development, a simulation was used in the analog circuit design with SPICE Multisim software and the related macromodel library to assess system stability. Besides the analog filters in the AFE stage, digital filtering by means of simple difference equations was investigated. A menu was incorporated to choose from the several modes of operation of the device. The ECG test signals were obtained from a patient simulator (SimCube) and real patients. A portable ECG system for monitoring applications that complies with electrical safety regulations and medical equipment design was realized. Full article

The latest Augmented Reality (AR) and Mixed Reality (MR) systems are able to provide innovative methods for user interaction, but their full potential can only be achieved when they are able to exchange bidirectional information with the physical world that surround them, including the objects that belong to the Internet of Things (IoT). The problem is that elements like AR display devices or IoT sensors/actuators often use heterogeneous technologies that make it difficult to intercommunicate them in an easy way, thus requiring a high degree of specialization to carry out such a task. This paper presents an open-source framework that eases the integration of AR and IoT devices as well as the transfer of information among them, both in real time and in a dynamic way. The proposed framework makes use of widely used standard protocols and open-source tools like MQTT, HTTPS or Node-RED. In order to illustrate the operation of the framework, this paper presents the implementation of a practical home automation example: an AR/MR application for energy consumption monitoring that allows for using a pair of Microsoft HoloLens smart glasses to interact with smart power outlets. Full article

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 one of the 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 Amplitude Modulation (AM), Continuous Phase Frequency Shift Keying (CPFSK), and Linear Frequency Modulation (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. Full article

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. Full article

The evolution of wireless communications has led to the adoption of a wide range of applications and utilities not only by the general public but also by administrative authorities. Consequently, the huge growth of new city services requires in some specific cases the construction of underground tunnels in order to reduce visual impact within the city center, as well as enabling the maintenance and operation works of utilities. One of the main challenges is that, inherently, underground service tunnels lack coverage from exterior wireless systems, such as mobile networks or municipal WLAN networks, which can be potentially dangerous for maintenance personnel working within the tunnels. In this work, wireless channel characterization for urban tunnel scenarios will be analyzed based on the assessment of LoRaWAN and ZigBee technologies operating at 868 MHz. For this purpose, a real urban utility tunnel has been modeled and simulated by means of an in-house 3D ray launching code and compared with experimental measurements, showing good agreement. The singularity and complexity of the limited tunnel dimensions and the inclusion of additional elements such as service trays, user pathways, and handrails have been considered. Results provide an adequate radio planning approach for the deployment of wireless systems in urban utility scenarios, with optimal coverage and enhanced quality of service. Besides, in order to have access to the data obtained by the potential WSN deployed within the tunnels, a solution to store such information in a cloud is included in the study. Full article

Recently, Lidl had to set a recall action due to dangerous pieces of plastic found in the cheese products. The plastic shards, if swallowed, can cut the oral cavity or obstruct breathing. Current inspection techniques in the cheese industry are for the detection of metals using X-ray that does not offer a complete solution, as many foreign bodies can go undetected. This paper demonstrates the use of a portable real-time microwave sensing technique for the nondestructive detection of plastic in cheese. The electromagnetic (EM) patch antenna was designed and tested on five Cheddar cheese samples. Different sizes of plastic shards, 1 × 10 mm, 2 × 15 mm and 5 × 20 mm, were inserted into the samples, and measurements were taken with and without foreign objects. The initial results demonstrated that the patch antenna at 4GHz was able to detect and classify Polyvinyl Chloride (PVC) shards with an R² = 0.95. The initial results are promising, and further investigation will be undertaken to detect different shapes and types of foreign objects in food products. Full article

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 set point 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 an NI 6211 for the data acquisition. Finally, the system emulates the behaviour 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. Full article

Machine learning (ML) techniques are widely used in structural health monitoring (SHM) and non-destructive testing (NDT), but the learning process, the learned models, and the prediction consistency are poorly understood. This work investigates and compares a wide range of ML models and algorithms for the detection of hidden damage in materials monitored using low-cost strain sensors. The investigation is performed by means of a multi-domain simulator imposing a tight coupling of physical and sensor network simulation in the real-time scale. The device under test is approximated by using a mass-spring network and a multi-body physics solver. Full article

This paper presents a new methodology for the estimation of the mass and size of a common Vietnamese fruit, the cavendish-type banana. We only used top-view images. Most previous works focused on volume estimation using a plurality of cameras to infer the three-dimensional information. In this work, we only used a single camera mounted on top of the fruit. We have found that our proposal leads to a relatively small estimation error (approximately 6%) compared to the results obtained from the measurements using a water-displacement method and a static digital scale. The results indicate that our system shows a great potential to be used in a real industrial setting. Future work will aim to investigate other features such as ripeness and bruises to increase the effectiveness and practicality of the system. Full article

A Linear Fresnel Reflector (LFR) is a recent technology with good potential in small-scale solar power applications. It is composed of many long rows of mirrors that focus the sunlight onto a fixed elevated tubular receiver. Mirror segments are aligned horizontally and track the sun such that the receiver does not need to be moved. The efficiency with which the LFR can convert solar to thermal energy depends on the accuracy of the sun tracking system. To maximize the degree of sunlight capture, precise solar tracking is needed so that incident solar rays can be adequately focused to the focal point given by the location of the tubular receiver. The tilt angles of each row are relevant for the tracking controller to achieve correct positioning. Encoders are generally employed in closed-loop tracking systems as feedback signals used to inform the controller with the actual position of collector mirrors. Recently, inclinometers have begun to replace encoders as the most viable and cost-effective sensor technology solution; they offer simpler and more precise feedback, as they measure the angle of tilt with respect to gravity and provide the ability to adjust the system to the optimal angle for maximum output. This paper presents the research results on the development of remote measurements for the precise control of an LFR tracking system, by using distributed angle measurements. The applied methodology enables precision measurement LFR inclination angles through the fusion of data from multiple accelerometers, supported by low-cost wireless transceivers in a wireless sensor network, capable of exchanging information in a cloud infrastructure. Full article

This proceeding shows the results of the investigation of the techniques of the integration, management, and visualization of massive data from the digitalization of environmental and procedural parameters of facilities that operate in the marine environment. The work focuses on three main lines: (1) research on the development of a cloud-based system for big data, which allows the hosting of the data generated by different devices to be monitored (GPS, sounds, vibrations, video, temperature, emissions, consumption, power, etc.); (2) the implementation of a first layer of analysis and visualization of information; and (3) big data analytics research for the post-processing of information. The studies will be applied to underwater noise monitoring. With this, progress has been made in another of the pillars of Web 4.0—the use of context information—as the application is in charge of intelligently processing the data of the different variables together although they are not, in principle, directly related. Full article

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, two Wireless Acoustic Sensor Networks (WASN) have been deployed to monitor RTN: one in District 9 of Milan, and another along the A90 motorway of Rome. Since the dynamic mapping system should be able to identify and remove those Anomalous Noise Events (ANEs) unrelated to regular road traffic (e.g., sirens, horns, speech, and doors), an Anomalous Noise Event Detector (ANED) has been included in the dynamic noise mapping pipeline to avoid biasing the computation of the equivalent RTN levels. After deploying the 24 low-cost acoustic sensor networks in both pilot areas, WASN-based acoustic datasets were built to adapt the previous version of the ANED algorithm to run in real-operation conditions. In this work, we describe the preliminary results of the analysis of the 154 h WASN-based urban acoustic dataset obtained from the Milan city in terms of the main characteristics of ANEs. The results confirm the unbalanced nature of the problem (83.7% of the data corresponds to RTN), showing the urban WASN-based dataset a larger number of ANEs with higher local predominance than what was observed in the previous expert-based recording campaign, which underlines the importance of the accurate modeling of the urban acoustic environment to train the ANED properly. Full article

For several years, the requirements on miniaturization of electronic implants with application in functional electrostimulation have been increasing, while functionality and reliability should not be impaired. One solution concept is to use neither active electronic components nor sensors or batteries. Instead, the functionalities are ensured by the use of intrinsic nonlinear properties of the already used components and energy is transferred by inductive coupling. In this paper, ceramic capacitors are investigated as a first step towards exploiting the nonlinear characteristics of ferroelectric materials. The ceramic capacitors are characterized by simulation and measurements. The modeling is carried out in Mathcad Prime 3.1 and ANSYS 2019 R2 Simplorer and different solvers are compared for exemplary calculations. Finally, a measurement setup is realized to validate the models. Calculations show that the trapezoid method with a number of 500 k points in the given solution interval is best suited for ANSYS. In Mathcad, the Adams, Bulirsch–Stoer, Backward Differentiation Formula, Radau5, and fourth order Runge–Kutta methods with an adaptive step width and a resolution of 50 k points are the most suitable. The nonlinear properties of ferroelectric materials in ceramic capacitors modeled with these methods using ANSYS and Mathcad show small and equal deviation from the measurements. Full article

In some parts of the world, climate change has led to periods of drought that require managing efficiently the scarce water and energy resources. This paper proposes an IoT smart irrigation system specifically designed for urban areas where remote IoT devices have no direct access to the Internet or to the electrical grid, and where wireless communications are difficult due to the existence of long distances and multiple obstacles. To tackle such issues, this paper proposes a LoRaWAN-based architecture that provides long distance and communications with reduced power consumption. Specifically, the proposed system consists of IoT nodes that collect sensor data and send them to local fog computing nodes or to a remote cloud, which determine an irrigation schedule that considers factors such as the weather forecast or the moist detected by nearby nodes. It is essential to deploy the IoT nodes in locations within the provided coverage range and that guarantee good speed rates and reduced energy consumption. Due to this reason, this paper describes the use of an in-house 3D-ray launching radio-planning tool to determine the best locations for IoT nodes on a real medium-scale scenario (a university campus) that was modeled with precision, including obstacles such as buildings, vegetation, or vehicles. The obtained simulation results were compared with empirical measurements to assess the operating conditions and the radio planning tool accuracy. Thus, it is possible to optimize the wireless network topology and the overall performance of the network in terms of coverage, cost, and energy consumption. Full article

With the growing demand of vehicle-mounted sensors over the last years, the amount of critical data communications has increased significantly. Developing applications such as autonomous vehicles, drones or real-time high-definition entertainment requires high data-rates in the order of multiple Gbps. In the next generation of vehicle-to-everything (V2X) networks, a wider bandwidth will be needed, as well as more precise localization capabilities and lower transmission latencies than current vehicular communication systems due to safety application requirements; 5G millimeter wave (mmWave) technology is envisioned to be the key factor in the development of this next generation of vehicular communications. However, the implementation of mmWave links arises with difficulties due to blocking effects between mmWave transceivers, as well as different channel impairments for these high frequency bands. In this work, the mmWave channel propagation characterization for V2X communications has been performed by means of a deterministic in-house 3D ray launching simulation technique. A complex heterogeneous urban scenario has been modeled to analyze the different propagation phenomena of multiple mmWave V2X links. Results for large and small-scale propagation effects are obtained for line-of-sight (LOS) and non-LOS (NLOS) trajectories, enabling inter-data vehicular comparison. These analyzed results and the proposed methodology can aid in an adequate design and implementation of next generation vehicular networks. Full article

Smartphone mode classification is essential to many applications, such as daily life monitoring, healthcare, and indoor positioning. In the latter, it was shown that knowledge of the smartphone location on pedestrians can improve the positioning accuracy. Most of the research conducted in this field is focused on pedestrian motion in a horizontal plane. In this research, we use supervised machine learning techniques to recognize and classify the smartphone mode (text, talk, pocket and swing) while accounting for the movement up and downstairs. We distinguish between the going up and the down motion, each with four different smartphone modes, making eight states in total. This classification is based on the use of an optimal set of sensors that varies according to battery life and the energy consumption of each sensor. The classifier was trained and tested on a dataset constructed from multiple user measurements (total of 94 min) to achieve robustness. This provided an accuracy of more than 90% in the cross validation method and 91.5% if the texting mode is excluded. When considering only stairs motion, regardless of the direction, the accuracy improves to 97%. These results may assist many algorithms, mainly in pedestrian dead reckoning, in improving a variety of challenges such as speed and step length estimation and cumulative error reduction. Full article

In recent years, the advancement of the microelectronics industry has allowed for a major expansion in the development of sensor-based equipment and applications, driven primarily by the cost reduction of micro-electro-mechanical systems (MEMS) devices. Currently, using this type of component, it is feasible to develop cost-effective systems aimed at early detection of failures in electrical machines and, in special cases, three-phase induction motors (TIM). These devices, coupled with predictive maintenance records, can prevent unexpected shutdowns due to malfunctions and signal the need for actions to extend the life cycle of the equipment. This is a relevant topic considering that the industrial sector is increasingly seeking for solutions based on non-destructive techniques (NDT) for preventive and predictive fault diagnosis. In this scenario, the objective of this work is to evaluate the application of a low-cost MEMS accelerometer to identify insulation failures in stator windings through vibration analysis. For this purpose, two MEMS accelerometers were coupled on either side of the frame of a TIM. Then, vibration signals were acquired for different types and levels of insulation failures. The data obtained were processed using different metrics such as root mean square (RMS), kurtosis, and skewness. The results allowed us to identify the insulation faults applied to the TIM, confirming the feasibility of applying the low-cost MEMS accelerometer in the vibration analysis for fault diagnosis. Full article

Recent advances in sensor technologies coupled with the development of machine/deep learning strategies are opening new frontiers in Structural Health Monitoring (SHM). Dealing with structural vibrations recorded with pervasive sensor networks, SHM aims at extracting meaningful damage-sensitive features from the data, shaped as multivariate time series, and taking real-time decisions concerning the safety level. Within this context, we discuss an approach able to detect and localize a structural damage avoiding any pre-processing of the acquired data. The method takes advantage of the capability of Deep Learning of Fully Convolutional Networks, trained during an offline SHM phase. As a hybrid model- and data-based solution is looked for, Reduced Order Models are also built in the offline phase to reduce the computational burden of the whole monitoring approach. Through a numerical benchmark test, we show how the proposed method can recognize and localize different damage states. Full article

Robot mapping and exploration is basic to many robotic applications such as search and rescue operations in disaster scenarios, warehouse management, service robotics, patrolling and autonomous driving. With recent advances in robot navigation and sensor compactness, single robot systems can accurately model the environment and perform complex autonomous navigation tasks. On the other hand, multi-robot systems can speed up mapping and exploration tasks in emergency situations, such as rescue missions, by making use of distributed sensors, thereby increasing the range of exploration tasks to an extent that is not possible with a single robot. Each robot explores and maps different areas of the same environment that are finally merged and connected to make a global map. To build a map of an unknown environment, each robot must perform SLAM, or Simultaneous Localization and Mapping. A big challenge with a multi-robot SLAM system is the transfer of shared map information between multiple robots. There is a possibility of transferring individual measurement errors to the global map, resulting in excess computation and memory required to store such maps. To overcome this problem, we propose to use topological feature map representation that can store information into nodes and edges and does not have any large memory requirements. We present a combined metric-topological mapping approach to multi-robot SLAM. This method maintains a topological pose graph with sensor information stored in nodes and edges that can be optimized globally with reduced information sharing. By combining local metric and topological maps built by individual robots, the reduced graph structure can be merged and extended to map large areas effectively. To robustly merge local maps into global one, we used visual features from each robot that are matched in a distributed system. The graph node-edge structure is used for path planning and navigation. At the same time information sharing between robots results in optimized task distribution between multi-robots. Full article

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 localization 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 localization and classification with state-of-the-art CNN architectures. Full article

Development of indoor location systems that use smartphone sensors has been a topic of interest to industry and academia. In this paper, we describe an experiment that was performed to evaluate the feasibility of creating a mobile indoor localization model based on data from participatory sensing. To achieve it, seven smartphone users used their integrated magnetometers to collected magnetic field information on a building. The data collected are utilized to train three machine learning algorithms: The k Nearest Neighbors (KNN), Decision Trees (J48), and Naïve Bayes algorithms. The performance of the algorithms was measured through the accuracy and kappa statistics. Our results indicate that it is possible to create an infrastructure-less indoor localization model at room level using data from participatory sensing. The model with the most significant performance was obtained with the KNN, since it offers an accuracy of 97.12%, while the model with the most reduced performance was Naïve Bayes, since it offers an accuracy of 50.79%. Full article

A full chain simulation of the acoustic hadron therapy monitoring for brain tumors is presented in this work. For the study, a proton beam of 100 MeV was considered. In the first stage, Geant4 was used to simulate the energy deposition and to study the behavior of the Bragg peak. The energy deposition in the medium produced local heating that can be considered instantaneous with respect to the hydrodynamic time scale producing a sound pressure wave. The resulting thermoacoustic signal was subsequently obtained by solving the thermoacoustic equation. The acoustic propagation was simulated by the Finite Element Method (FEM) in the brain and the skull, where a set of piezoelectric sensors were placed. Lastly, the final received signals in the sensors were processed in order to reconstruct the position of the thermal source and, thus, to determine the feasibility and accuracy of acoustic beam monitoring in hadron therapy. Full article

Three-phase induction motors (IMs) are electrical machines used on a large scale in industrial applications because they are versatile, robust and low maintenance devices. However, IMs are significantly affected when fed by unbalanced voltages. Prolonged operation under voltage unbalance (VU) conditions degrades performance and shortens machine life by producing imbalances in stator currents that abnormally raise winding temperature. With the development of new technologies and research on non-destructive techniques (NDT) for fault diagnoses in IMs, it is relevant to obtain economically accessible, efficient and reliable sensors capable of acquiring signals that allow the identification of this type of failure. The objective of this study is to evaluate the application of low-cost piezoelectric sensors in the acquisition of acoustic emission (AE) signals and the identification of VU through the analysis of short-term Fourier transform (STFT) spectrograms. The piezoelectric sensor makes NDT feasible, as it is an affordable and inexpensive component. In addition, STFT allows time-frequency analyses of acoustic emission signals. In this NDT, two sensors were coupled on both sides of an induction motor frame. The AE signals obtained during the IM operation were processed and the resulting spectrograms were analyzed to identify the different VU levels. After comparing the AE signals for faulty conditions with the signals for the IM operating at balanced voltages, it was possible to obtain a desired identification that confirmed the successful application of low-cost piezoelectric sensors for VU condition detection in three-phase induction machines. Full article

Hadrontherapy has been constantly evolving in leaps and bounds since the 1950s, when the use of heavy particles was proposed as an alternative treatment to radiotherapy with gamma rays or electrons. The main objective of this treatment is to maximize the dose applied to the tumour, avoiding damage to the surrounding tissue. One of the keys to the success of hadrontherapy is to achieve instantaneous monitoring of the energy deposition in the environment. Since energy deposition leads to the generation of a thermoacoustic pulse, acoustic technologies have been tested with successful results. However, for this purpose, it is essential to increase the sensitivity of the sensors for the acoustical signal and, therefore, to optimize their geometry as a function of the beam that would be used. We have studied a PTZ material in volumetric and surface volumes through experimental measures and FEM methods. In this text, we start with numerical studies which determine the dependence of the thermoacoustic signal frequency with the energy and duration of the hadron beam. Full article

An inertial measurement unit (IMU) typically has three accelerometers and three gyroscopes. The output of those inertial sensors is used by an inertial navigation system to calculate the navigation solution–position, velocity and attitude. Since the sensor measurements contain noise, the navigation solution drifts over time. When considering low cost sensors, multiple IMUs can be used to improve the performance of a single unit. In this paper, we describe our designed 32 multi-IMU (MIMU) architecture and present experimental results using this system. To analyze the sensory data, a dedicated software tool, capable of addressing MIMUs inputs, was developed. Using the MIMU hardware and software tool we examined and evaluated the MIMUs for: (1) navigation solution accuracy (2) sensor outlier rejection (3) stationary calibration performance (4) coarse alignment accuracy and (5) the effect of different MIMUs locations in the architecture. Our experimental results show that 32 IMUs obtained better performance than a single IMU for all testcases examined. In addition, we show that performance was improved gradually as the number of IMUs was increased in the architecture. Full article

Multiphase gravity separators are widely used in the petroleum industry to separate the produced stream of oil, water and natural gas into pure (single-phase) streams. These equipment work based on the density differences of each fluid which tend to settle into layers when dwelled for some time in the separator. It is fundamental to monitor the levels of these phases inside the vessel, so that this information can be used in control strategies in order to increase the efficiency and safety of the process. In this work, we present a novel multiphase level sensor based on capacitance and inductance measurements of planar multi-electrodes and multi-coils. The sensor is low-cost, fast and does not apply ionizing radiation, being therefore simple to operate. The prototype sensor was constructed in standard printed-circuit board (PCB) technology and highly sensitive capacitance and inductance measurements are acquired with modern integrated circuit devices. Since the capacitive sensor readings depend on the water salinity, we perform simultaneous inductance measurements to compensate for such dependence. We have tested the prototype from two different approaches: varying the water salinity and for different water/oil mixtures. Preliminary results have shown the capability of the sensor to differentiate each one of the produced fluids, i.e., salty water, oil and gas, as well as the interfaces between them. A 16 electrodes capacitive-only sensor prototype was also built and tested. Full article

Currently, the mineral industry makes iron ore beneficiation processes in humid or natural moisture. Excessive moisture in iron ore can affect the beneficiation process, causing loss of productivity and transport issues, as well as reducing the efficiency of dewatering subprocesses and safety. The traditional technic for measuring iron ore moisture is the standard oven method, which is very accurate, but not very representative. Furthermore, it has a high time response: up to 24 h for each mineral sample. Consequently, corrective and preventive actions to the process become inefficient. Alternative technics, such as the microwave method, perform online moisture measurements but with low accuracy. Recently, we developed a high accuracy capacitive sensor for measuring ore moisture but not online (bench device). This paper refers to the development of a capacitive, non-invasive, coplanar-electrode transducer for iron ore moisture measurement, designed for online applications. To achieve this, we constructed a signal conditioning system, based on an 8-bit microcontroller and a driven shield for the sensor element. The system transmits the processed data via radio frequency to a computer. Moreover, it applies a statistical filter to the measurements, based on standard deviation and moving average, as a way for minimizing electromagnetic interference. The statical calibration results reached a coefficient of determination of 98.41%. The coplanar, non-invasive approach of the transducer offers the advantage of preserving the physical integrity of the sensor electrodes as well as a future online application. Full article

The latest advances in the different Industry 4.0 technologies have enabled the automation and optimization of complex tasks of production processes thanks to their ability to monitor and track the state of physical elements like machinery, environmental sensors/actuators or industrial operators. This paper focuses on the latter and presents the design and evaluation of a system for monitoring industrial workers that provides a near real-time decentralized response system aimed at reacting and tracing events that affect operator personal safety and health. Such a monitoring system is based on the information collected from sensors encapsulated in IoT wearables that are used to measure both personal and environmental data. The communications architecture relies on LoRaWAN, an LPWAN (Low-Power Wide-Area Network) technology that offers good reliability in harsh communications environments and that provides relatively long distance communications with low-energy consumption. Specifically, each wearable sends the collected information (e.g., heart rate, altitude, external temperature, gas concentration, location) from the sensors to the nearest LoRaWAN gateway, which is transmitted to a pool of nodes where information is stored in a distributed manner. Such a decentralized system allows for providing information redundancy and guarantees its availability as long as there is an operative node. In addition, the proposed system is able to store and to process the collected data through smart contracts in a blockchain, which eliminate the need for a central backend and ensure the traceability and immutability of such data in order to share them with third parties (e.g., insurance companies or medical services). Full article

In communications, channel models are useful approximations to the performance of a real channel, which most of the time is not available for repeated tests. In this work we present the problem of the real Near Vertical Incidence Skywave (NVIS) ionospheric scenario channel sounding, and evaluate the channel propagation characteristics in terms of frequency and time spread, with the final goal of designing a channel model. An NVIS channel model can be obtained from the evaluated channel parameters; however, on one hand, there is the problem of missing data due to bad channel performance in some frequencies, and, on the other hand, the measured parameters have strong dependencies between them that cannot be evinced directly. In this work, we conduct a first set of analyses of the measured parameters of the soundings to determine the dependencies in terms of quality of the channel propagation but refer mainly to the Doppler spread and the delay spread in the sensor. This classification approach allows us to face the second part of the research focusing on the design of the channel model for the ionospheric communication of remote sensors. Full article

The monitoring of the marine environment results in large amounts of data that must be processed and transmitted effectively for efficient resource management. In particular, given its high sampling rate, underwater noise signal acquisition technologies deserve special attention. In this article, a comparative study of the efficiency of different information processing and compression techniques is carried out, depending on the characteristics that want to be transmitted from the original signal. The applications and experiments carried out are focused on responding to the Marine Strategies, a marine environment planning instrument created under the Marine Strategy Framework Directive 2008/56/EC of 17 June, 2008 (MSFD), and, more specifically, to Descriptor 1, which regards the noise levels (both continuous and impulsive), as well as part of Descriptor 11, which is focused on the detection and abundance of cetaceans. Full article

We present results for a specific filter design for silicon waveguides, which features a transmission wavelength and bandwidth well suited for carbon dioxide sensing. Simulations were performed using Comsol Multiphysics and the design was optimized for a central wavelength of 4.26 µm. Furthermore, we included real-world effects like the discrete resolution of the design grid as well as process-specific fabrication tolerances. The devised structures were based on a photonic waveguide concept, which was developed recently for evanescent-field-based sensing applications. Slab waveguides with gratings on top as well as strip waveguides with sidewall gratings were considered. The concept and design are discussed in detail in order to highlight the underlying ideas. Full article

In this work, a novel organic-inorganic blend made from PEGSil (Poly(dimetylsiloksan)-co-[poli(metylohydrosiloksane)-graft-2-winyl-poly(3-heksylthiophene)]-co-[poly(dimetylsiloksane)-graft- metakrylane ethere metylene poly(etylene glicole)]]) mixed with zinc oxide nanomaterial was studied as the sensitive layer for nitrogen dioxide (NO₂) resistance gas sensor application. Moreover, the PEGSil graft copolymer material was tested in two variants, defined by side-chain length of P3HT: shorter hexane fraction (H) and longer chloroform fraction (CH). Elaborated organic–inorganic blend was deposited on interdigital transducers (Au on Si/SiO₂) by the drop coating method from a chlorobenzene-based mixture. Sensor response characteristics to different concentrations of NO₂ (1–10 ppm) in N₂ carrier gas and synthetic air were measured and compared. Measurements were done at room temperature with UV light charge carriers’ activation. Moreover, measurements for low gas concentrations (50–500 ppb) were made and analyzed. The obtained results show that the sensitivity of fabricated sensors is about 6.8% per 1 ppb for hexane fractions of PEGSil and 9.3% for chloroform fractions in the concentration range from 50 to 200 ppb NO₂ in N₂ carrier gas. These results show that the blend of these materials has wide potential as a sensing layer for NO_x low-concentration sensing. Full article

Farm management practices done by machinery generate a high acoustical impact on animals. The acoustic variations in terms of equivalent level ($L_{eq}$) and the different types of noise can affect the well-being of broilers by means of reducing the food and water ingest. In this work, we create a dataset in which we conduct a preliminary analysis of the acoustical impact generated by the farm management in an intensive broiler poultry farm of 25,000 birds. The project collects acoustic data during the first two weeks of the birds life, focusing the study on the first week. To create the dataset, we randomly select some files from each day of the study and they are analysed and labelled manually using an audio analysis software. The acoustical events defined in collaboration with the farmer and vet are the fan and the food and water supply, and definitions are based on duration, impact, and Signal to Noise Ratio (SNR). The analysis concludes that the main acoustical source in a broilers’ farm is the fan, and that it has a non-negligible acoustical impact. Nevertheless, the most frequent acoustical noise source active is food supply, but with less $L_{eq}$ impact. Full article