Proceedings, 2019, ECSA-5 2018

This study presents a new scheme for autonomous health monitoring of railroad infrastructure using a continuous stream of structural health monitoring data. The study utilized measured strains from an optimized sensor set deployed on a double track, steel, railway, truss bridge located in central Nebraska. The most common failure mode for the superstructure of this structural system is the stringer-to-floor beam connection failure, which was the focus of this study. However, the proposed methodology could be used to assess the condition of a wide range of structural elements and details. The damage feature adopted in this framework was the variations of Proper Orthogonal Modes (POMs) of the measured structural response. To automatically detect the occurrence, location, and intensity of deficiencies from the POMs, Artificial Neural Networks (ANN) were adopted. POM variations, which are traditionally input (load) dependent, were ultimately utilized as damage indicators. To alleviate the variability of POMs due to non-stationarity of the train loads, a preset windowing of measured output was completed in conjunction with automated peak-picking. Furthermore, input variability necessitated implementing ANNs to help decouple POM changes due to load variations from those caused by deficiencies, changes that would render the proposed framework input independent; a significant advancement. Damage “scenarios” were artificially introduced into select output (strain) datasets recorded while monitoring train passes across the selected bridge. This information, in turn, was used to train ANNs using MATLAB’s Neural Net Toolbox. Trained ANNs were tested against monitored loading events and artificial damage scenarios. Applicability of the proposed, output-only framework was investigated via studies of the bridge under operational conditions. To account for the effects of potential deficiencies at the stringer-to-floor beam connections, measured signal amplitudes were artificially decreased at select locations. Finally, to validate the applicability of the proposed method using low-cost measurement devices, the measured signals were corrupted by high levels of white, Gaussian noises featuring spatial correlations. It was concluded that the proposed framework could successfully identify 20 damage indices, which were artificially imposed on measured signals under operational conditions. Full article

Efficient task co-ordination is an important problem in multirobot systems. The explicit programming of each robot to perform specific tasks (e.g., cleaning) is too cumbersome and inefficient, as the areas to serve in a map may vary with time. Moreover, the number of available robots to serve may also vary, as some of the robots may be charging and not available. Improper task division can cause two or more robots to serve the same areas of the map, which is a waste of computation and resources. Hence, there is a need for a simpler scheme for the autonomous task co-ordination of multiple robots without the need for explicit programming. This paper presents a bioinspired algorithm that uses the attractive and repelling behavior of pheromones for autonomous task co-ordination. The proposed algorithm uses a node representation of navigational paths for autonomous exploration. This repelling mechanism also allows robots to capture areas or subareas of the map so that there is efficient task co-ordination, and robots work without interruption from other robots. We show through experiments that the proposed scheme enables multiple service robots to intelligently perform co-operative tasks without any explicit programming or commands. Full article

The development of new low-cost transducers and systems has been extensively aimed at in both industry and academia to promote a correct failure diagnosis in aerospace, naval, and civil structures. In this context, structural health monitoring (SHM) engineering is focused on promoting human safety and a reduction in the maintenance costs of these components. Traditionally, SHM aims to detect structural damages at the initial stage, before it reaches a critical level of severity. Numerous approaches for damage identification and location have been proposed in the literature. One of the most common damage location techniques is based on acoustic waves triangulation, which stands out as an effective approach. This method uses a piezoelectric transducer as a sensor to capture acoustic waves emitted by cracks or other damage. Basically, the damage location is defined by calculating the difference in the time of arrival (TOA) of the signals. Although it may be simple, the detection of TOA requires complex statistical and signal processing techniques. Based on this issue, this work proposes the evaluation of a low-cost piezoelectric transducer to determine damage location in metallic structures by comparing two methodologies of TOA identification, the Hinkley criterion and the statistical Akaike criterion. The tests were conducted on an aluminum beam in which two piezoelectric transducers were attached at each end. The damage was simulated by pencil lead break (PLB) test applied at four different points of the specimen and the acoustic signals emitted by the damage were acquired and processed by Hinkley and Akaike criteria. The results indicate that, although both signal processing methodologies were able to determine the damage location, Akaike presented higher precision when compared to Hinkley approach. Moreover, the experimental results indicated that the low-cost piezoelectric sensors have a great potential to be applied in the location of structural failures. Full article

The energy consumption in wireless sensor networks is the critical concern of different studies, especially because of the great effort, or even the impossibility, to replace the battery of their motes. Consequently, it is fundamental to investigate and evaluate the energy spent by every individual task executed by the motes in order to provide an efficient use of their batteries. In this work, we employ different metrics to present a thorough study of how the use of multiple transmission power levels affects multihop wireless sensor networks. This work is motivated by the current employment of the multiple transmission power levels, on both academic works and commercial solutions, which is a novel feature of some radio transceivers commonly used in wireless sensor network motes. Aiming for reliable and extensive analysis, this study employs simulations in different scenarios and models of commonly employed electronic components. The contribution of this works is a detailed investigation of the impact caused by the use of different transmission power levels employing different metrics, offering a wide perspective on the subject. In general, the results of this study indicate that the use of multiple power levels grants both positive and negative results, according to the scenario and metrics analyzed. Full article

In this work, we present a prototype of a compact linear array with three elements that is able to reproduce the acoustic signature of Ultra High Energy (UHE) neutrino interaction in water using the parametric acoustic effect. Mimicking this signal is important because it is a very directive bipolar transient signal with cylindrical symmetry. We characterized the prototype by measuring the signal waveform, the attenuation, intensity variation and directivity, with numerical simulations and experimentally in a pool. We also studied different kinds of signals to determine the best application for the array. The results confirmed the utility of this array for the proposed application in marine neutrino telescopes. Full article

Hadrontherapy makes it possible to deliver high doses of energy to cancerous tumors by using the large energy deposition in the Bragg-peak. However, uncertainties in the patient positioning and or in the anatomical parameters can cause distortions in the calculation of the dose distribution. In order to maximize the effectiveness of heavy particle treatments, an accurate monitoring system of the deposited dose depending on the energy, the beam time, and the spot size is necessary. The localized deposition of this energy leads to the generation of a thermoacoustic pulse that can be detected using acoustic technologies. This article presents different experimental and simulation studies of the acoustic localization of thermoacoustic pulses by generating similar signals that have been captured with a set of sensors around the samples. In addition, numerical simulations have been done where thermoacoustic pulses are emitted for the specific case of a proton beam of 100 MeV. Full article

Internet of Things (IoT) applications for smart environments demand challenging requirements for wireless networks in terms of security, coverage, availability, power consumption, and scalability. The technologies employed so far to cope with IoT scenarios are not yet able to manage simultaneously all these demanding requirements, but recent solutions like Low-Power Wide Area Networks (LPWANs) have emerged as a promising alternative to provide low-cost and low-power consumption connectivity to nodes spread throughout a wide area. Specifically, the Long-Range Wide Area Network (LoRaWAN) standard is one of the most recent developments, receiving attention from both industry and academia. This work presents a comprehensive case study on the use of LoRaWAN under a realistic scenario within a smart city: a smart campus. Such a medium-scale scenario has been implemented through an in-house-developed 3D ray launching radio planning simulator that takes into consideration traffic lights, vehicles, people, buildings, urban fixtures, and vegetation. The developed tool is able to provide accurate radio propagation estimations within the smart campus scenario in terms of coverage, capacity, and energy efficiency of the network. These results are compared with an empirical validation in order to assess the operating conditions and the system accuracy. Moreover, the presented results provide some guidelines for IoT vendors, network operators, and city planners to investigate further deployments of LoRaWAN for other medium-scale smart city applications. Full article

Alzheimer’s disease (AD) is the most common cause of dementia. Several haemodynamic risk factors for AD have been identified, including ageing, increased arterial stiffness, high systolic blood pressure (BP) and brain hypoperfusion. We propose a novel approach for assessing haemodynamic risk factors by analysing arterial pulse waves (PWs). The aim of this feasibility study was to determine whether features extracted from PWs measured by wearable sensors might have utility for stratifying patients at risk of AD. A numerical model of PW propagation was used to simulate PWs for virtual subjects of each age decade from 25 to 75 years (16 subjects in total), with subjects at each age exhibiting normal variation in arterial stiffness. Several PW features were extracted, and their relationships with AD risk factors were investigated. PWs at the wrist were found to exhibit changes with age and arterial stiffness, indicating that it may be possible to identify changes in risk factors from smart wearables. Several candidate PW features were identified which changed significantly with age for future testing. This study demonstrates the potential feasibility of assessing haemodynamic risk factors for AD from non-invasive PWs. These factors could be assessed from the PPG PW, which can be acquired by smart watches and phones. If the findings are replicated in clinical studies, then this may provide opportunities for patients to assess their own risk and make lifestyle changes accordingly. Full article

Grinding is one of the most commonly used finishing processes in the manufacture of precision components that also needs to be monitored. Monitoring of the workpiece surface quality is considered highly complex due to particularities of the cutting tool and material removal mechanism. In this context, the monitoring of the grinding process is very important for the metalworking industry and a topic of great interest for machining researchers. Many studies on grinding process monitoring have been developed and most of them focus on process automation. The objective of this work is to monitor the workpiece material removal during grinding by using piezoelectric transducers in the emitter and receiver modes along with digital signal-processing techniques. Tests were performed on a peripheral surface grinding machine equipped with an aluminum oxide grinding wheel. The SAE 4340 steel grade was used as workpiece material. The transducer signals were sampled at a sampling frequency of 2 MHz. The digital signal processing was performed through spectrum analysis and the application of techniques such as root mean square. The mass of the workpieces was measured by means of a digital scale prior to and after grinding tests. The number of grinding passes was varied in order to increase the material removal. The results show that the monitoring technique proposed in this work is sensitive to the material removal in the grinding process. The appropriate selection of frequency bands allows for the best diagnosis in relation to the events that occur during the grinding process. Full article

Rogowski coils are inductive sensors based on Faraday’s and Ampère’s Laws to measure currents through conductors without galvanic contact. The main advantage of Rogowski coils when compared with current transformers is the fact that the core is air so they never saturate and the upper cut-off current can be higher. These characteristics make Rogowski coils ideal candidates to measure high-amplitude pulsed currents. However, there are two main drawbacks. On the one hand, the output voltage is the derivative of the primary current so it has to be integrated to measure the original signal; on the other hand, the transfer function is resonant as a result of the capacitance and the self-inductance of the coil. The solution is the use of a passive integration with a terminating resistor at the output of the sensor that splits the two complex poles and gives a constant transfer function for a determined bandwidth. The downside is a loss of sensitivity. Since it is possible to calculate the electrical parameters of the coil based on its geometrical dimensions, the geometry can be adapted to design sensors for different applications depending on the time characteristics of the input current. This paper proposes the design of Rogowski coils based on their geometric characteristics maximizing the gain-bandwidth product using particle swarm optimization and adapting the coils to the specific requirements of the application. Full article

The improvement of the quality of life in the framework of the smart city paradigm cannot be limited to a set of objective measures carried out over several critical parameters (e.g., noise, air pollution). The citizen’s perception of the problem to be solved, as well as the perception of the improvement achieved with the policies defined for this purpose are more important than the objectivity and the measurement of the change achieved. A first auralization approach for the evaluation of the acoustic perception of street noise is presented in this work. The wireless acoustic sensor network can pick up street noise and can even record specific sounds that reach a higher equivalent level for study, but the most important thing for administration is whether the neighbor has noticed an improvement in the quality of life. This work is a first approximation to an estimation of the real perception of citizens of the street urban noises collected by a low-cost wireless acoustic sensor network. Full article

The rate at which an individual recovers from exercise is known to be indicative of cardiovascular risk. It has been widely shown that the reduction in heart rate immediately after exercise is predictive of mortality. However, little research has been conducted into whether the time taken for the blood vessels to return to normal is also indicative of risk. In this study, we present a novel approach to assess vascular recovery rate (VRR) using the photoplethysmogram (PPG) signal, which is monitored by smart wearables. The VORTAL dataset (http://peterhcharlton.github.io/RRest/) was used for this study, containing PPG signals from 39 healthy subjects before (baseline) and after exercise. 31 VRR indices were extracted from the PPG pulse wave shape, as well as heart rate for comparison. The rate at which indices returned to baseline after exercise was quantified, and the consistency of changes between subjects was assessed statistically. Many VRR indices exhibited changes after exercise which were consistent between subjects. Indices derived from the timings and second derivative of pulse waves were identified as candidates for future work. The rate at which the indices returned to baseline differed between indices and subjects, indicating that they may provide additional information beyond that of heart rate, and that they may be useful for stratifying subjects. This study demonstrated the feasibility of assessing VRR after exercise from the PPG. Future studies should investigate whether VRR indices are associated with cardiovascular fitness, and the potential utility of incorporating the indices into wearable sensors. Full article

Hospital patients recovering from major cardiac surgery are at risk of paroxysmal atrial fibrillation (AF), an arrhythmia which can be life-threatening. Wearable sensors are routinely used for electrocardiogram (ECG) monitoring in patients at risk of AF, providing real-time AF detection. However, wearable sensors could have greater impact if used to identify the subtle changes in P-wave morphology which precede AF. This would allow prophylactic treatment to be administered, potentially preventing AF. However, ECG signals acquired by wearable sensors are susceptible to artefact, making it difficult to distinguish between physiological changes in P-wave morphology, and changes due to noise. The aim of this study was to design and assess the performance of a novel automated P-wave quality assessment tool to identify high-quality P-waves, for AF prediction. We designed a two-stage algorithm which uses P-wave template-matching to assess quality. Its performance was assessed using the AFPDB, a database of wearable sensor ECG signals acquired from both healthy subjects and patients susceptible to AF. The algorithm’s quality assessments of 97,989 P-waves were compared to manual annotations. The algorithm identified high-quality P-waves with high sensitivity (93%) and good specificity (82%), indicating that it may have utility for identifying high-quality P-waves in wearable sensor data. Measurements of P-wave morphology derived from high-quality P-waves could be used to predict AF, improving patient outcomes, and reducing healthcare costs. Further studies assessing the clinical utility of the presented tool are warranted for validation. Full article

Tuning the pore diameter of porous silicon (PS) is essential for some applications such as biosensing, where the pore size can filter the entrance of some analytes or increase its sensitivity. However, macropore (>50 nm) formation on p-type silicon is still poorly known due to the strong dependence on resistivity. Electrochemically etching heavily doped p-type silicon usually forms micropores (<5 nm), but it has been found that bigger sizes can be achieved by adding an organic solvent to the electrolyte. In this work, we present the results of using dimethylformamide (DMF), dimethylsulfoxide (DMSO), potassium hydroxide (KOH) and sodium hydroxide (NaOH) for macropore formation in p-type silicon with a resistivity between 0.001 and 0.02 Ω∙cm, achieving pore sizes from 5 to 100 nm. Full article

A label-free biosensor based on silicon-on-insulator (SOI) photonic bandgap (PBG) structures is performed for specific protein detection. First, the SOI sensing surface is functionalized using triethoxyvinylsilane (TEVS) organosilane. Then, a UV light photocatalyzed immobilization of polyclonal half anti-bovine serum albumin (haBSA) antibodies is performed. Finally, a direct detection of target BSA antigen is carried out. Both the immobilization and the detection steps are monitored by making a continuous tracking of the PBG edge shift. In order to confirm the recognition of the antigen by the immobilized antibody, a fluorophore-labelled secondary antibody was flowed at the end of the experiment in order to perform a confirmation fluorescence test after the photonic detection. Full article

The grinding process is usually one of the last stages in the manufacturing process chain since it can provide superior surface finish and closer dimensional tolerances. However, due to peculiarities of the grinding process, a workpiece material is susceptible to many problems, and demands a reliable real-time monitoring system. Some grinding monitoring systems have been proposed by means of sensors. However, the literature is still scarce in terms of employing time–frequency analysis techniques during the grinding of ceramics. Thus, this paper proposes an application of a low-cost piezoelectric transducer (PZT) in the analysis of the surface quality of ceramic workpieces during the grinding process by means of the frequency–time domain technique along with the ratio of power (ROP) parameter. An integrated, high-cost, commonly-used acoustic emission (AE) sensor was employed in order to compare the results with the low-cost PZT transducer. Tests were performed using a surface grinding machine. Three depth of cut values were selected in order to represent slight, moderate, and severe grinding conditions. Signals were collected at 2 MHz. The short-time Fourier transform (STFT) was studied in order to obtain the frequency variations over time. An analysis of the ROP values was performed in order to establish a correlation with the surface roughness. The ROP values are highly desirable for setting a threshold to detect the workpiece surface quality and for implementing it into a monitoring system. The results using the PZT transducer showed a great similarity to those obtained using the AE sensor. Full article

A self-standing porous silicon (PS) multilayer structure detached from the substrate by the lift-off method and integrated with a microfluidic cell is presented. Experiments of refractive index changes sensing flow through open-ended pores are reported. We continuously recorded the reflectance spectra of the PS membrane each 30 s and measured the shift as different substances flowed through the structure. The experimental sensitivity values are in good agreement with the theoretical simulations performed. Full article

A preliminary investigation on human tears by Surface Enhanced Raman Spectroscopy (SERS) is reported. This study is aimed to test the feasibility of SERS for sensing tear composition and potential changes in their composition due to pathologies, eye diseases and/or inflammatory processes. Home-made gold nano-particles have been used for preparing glass substrates that are able to efficiently induce a SERS effect in fluid samples excited by HeNe laser (λ = 633 nm). The system has been preliminarily tested on Rhodamine 6G aqueous solutions at different concentrations, proving the possibility to sense substance concentration as low as a few μM, i.e., of the order of the main tear organic component levels. Tears have been obtained by informed healthy patients by means of non-invasive methods based on a direct collection of the tear fluid by means of a suitable micro-capillary tube. A clear SERS response has also been obtained for human tear samples, allowing a preliminary insight on tear composition. Full article

The attenuation due to vegetation can limit drastically the performance of Wireless Sensor Networks (WSN) and the Internet of Things (IoT) communication systems. Even more for the envisaged high data rates expected for the upcoming 5G mobile wireless communications. In this context, radio planning tasks become necessary in order to assess the validity of future WSN and IoT systems operating in vegetation environments. For that purpose, path loss models for scenarios with vegetation play a key role since they provide RF power estimations that allow an optimized design and performance of the wireless network. Although different propagation models for vegetation obstacles can be found in the literature, a model combining path loss and multipath propagation is rarely considered. In this contribution, we present the characterization of the radio channel for IoT and 5G systems working at 2.4 GHz, focusing on the radio links blocked by oak and pine trees modelled from specimens found in a real recreation area located within a dense forest environment. This specific forest, composed of thick in-leaf trees, is called Orgi Forest and it is situated in Navarre, Spain. In order to fit and validate a radio channel model for this type of scenarios, both measurements and simulations by means of an in-house developed 3D Ray Launching algorithm have been performed, offers as outcomes the path loss and multipath information of the scenario under study. A geometrical and dielectric model of the trees were created and introduced in the simulation software. The path loss was then estimated as dependent of the radio link range for two species of trees at 2.4 GHz. We concluded that the scattering produced by the tree can be divided into two zones with different dominant propagation mechanisms: a free-space zone far from the tree and a diffraction zone around the edge of the tree. 2D planes of delay spread value are also presented which similarly reflects the proposed two-zone model. Full article

A one-dimensional photonic crystal supporting two modes is presented as an interferometric integrated optical sensor. The sensing is carried out by obtaining the phase difference between both modes propagated through the same nanometric structure and how it changes when a refractive index (RI) variation over the sensor takes place. Due to the slow-light phenomenon, high sensitivities values are reached near the photonic bandgap edge region. As a result, a high performance, compact, and single-channel optical sensing approach is theoretically calculated and demonstrated with a wide range of applications in the biosensing field. Full article

A simple transient effect method for the determination of the spin-lattice relaxation time using continuous wave NMR (TEDSpiL) with a marginal oscillator was recently reported (doi:10.1002/mrc.4594). Such a system measures a parameter called T_x that is related to T₁ and allows T₁ to be determined with the aid of calibration samples. For such a system, the process of making the T_x measurement does not require variable parameters and so is ideal for implementing in microcontroller code. In this article, we demonstrate that TEDSpiL may be automated using two microcontrollers from the Teensy family to make a low power and portable system. Full article

The optimal development of biosensors is a costly and time-consuming task, since an enormous amount of experiments is required. Therefore, the possibility of reusing the biosensors is highly desirable. In this work, a protocol based on the use of formamide for the regeneration of nanophotonic biosensors used for oligonucleotides detection is presented. This protocol was carried out online using the microfluidic system used to drive the target samples to the nanophotonic biosensor, thus allowing the possibility of running several experiments in a row using the same biosensor. Full article

Since the 1970s, Korea has achieved exponential economic growth over a short period of time, with a huge amount of infrastructure built. However, 30 years on, this infrastructure is now deteriorating at a rapid pace due to extensive use and climatic factors, raising safety issues in recent years. The current task force faces limitations in monitoring and maintenance due to various reasons: Insufficient budget, increasing number of infrastructure facilities requiring maintenance, shortage of manpower, and a rapidly increasing number of aging infrastructure facilities. To overcome these limitations, a new approach is required that is different from the manual inspection methods under the existing rules and regulations. In this context, this study aimed to explore the efficiency of bridge inspection for cracks by an Unmanned Aerial Vehicle (UAV) that could observe inaccessible areas, could be conveniently and easily controlled, and could offer high economic benefits. A case study of UAV-based crack detection for Wonjudaegyo Bridge, Korea was performed. The results show more effective detection of cracks in the structure than traditional methods. Full article

Geomagnetism has become a popular technology for indoor positioning, and its accuracy mainly depends on the accuracy of the geomagnetic matching algorithm. Pedestrian dead reckoning technology can calculate the relative position of pedestrians based on sensor information, but only obtain relative position information. According to the advantages and disadvantages of these two techniques, a high-precision GPDR indoor positioning method is proposed, and the improved particle filter algorithm is used to solve the problem of geomagnetic fingerprint fuzzy solution. Finally, a simulation experiment was conducted. The experimental results show that the accuracy of the proposed fusion localization algorithm is 42% higher than that of the PDR algorithm. Compared with a single geomagnetic fingerprint matching algorithm, the positioning accuracy is improved by 57%. Full article

Energy harvesting engineering fields constitutes a promising area to provide electrical power for low-power electric applications obtained from other sources of energy available in the environment such as thermal, electromagnetic, vibrational and acoustic by using transducers. Vibrational sources stand out as a main alternative to be used for generating electric power in sensor nodes in microelectronic devices due to the greater energy conversion efficiency and the use of a simple structure. The cantilever is the main system implemented in studies of obtaining electric energy from vibrations using piezoelectric transducers. Most of piezoelectric transducers in the literature are not yet commercially available and/or are difficult to access for purchase and use. This paper proposes the characterization of low-cost piezoelectric transducers, configured as sensors, for Energy Harvesting applications using three different sizes of circular piezoelectric transducers (PZTs.) with diameters of 3.4 cm, 2.6 cm and 1.5 cm. For all three different PZTs, it was found that the maximum power transfer occurs for a resistive load of 82 kΏ. The maximum power generated in the load for the three PZTs was 40 uW, 14 uW and 1.4 W; with RMS voltages of 2.8 V, 2.10 V and 0.6 V; an acceleration of 1.3 g and a vibration frequency approximate of 7 Hz. Full article

Industry 4.0 has paved the way for a world where smart factories will automate and upgrade many processes through the use of some of the latest emerging technologies. One such technology is Unmanned Aerial Vehicles (UAVs), which have evolved a great deal in the last several years in terms of technology (e.g., control units, sensors, UAV frames) and have reduced significantly their cost. UAVs can help industry in automatable and tedious tasks, like the ones performed on a regular basis for determining the inventory and for preserving the traceability of certain items. Moreover, in such tasks, it is essential to determine whether the collected information is valid or true, especially when it comes from untrusted third-parties. In such a case, blockchain, another Industry 4.0 technology that has become very popular in other fields like finance, has the potential to provide a higher level of transparency, security, trust and efficiency in the supply chain and enable the use of smart contracts. Thus, in this paper, the design and preliminary results are presented of a UAV-based system aimed at automating the inventory and keeping the traceability of industrial items attached to Radio-Frequency IDentification (RFID) tags. Such a system can use a blockchain to receive the inventory data collected by UAVs, validate them, ensure their trustworthiness and make them available to the interested parties. Full article

Refractive index is the main parameter measured by current optical sensors. Among all the photonic structures available for their design, porous materials have become an excellent option, since they provide better sensitivities. In our work, commercially available polycarbonate track-etched membranes were used as porous photonic structures. By means of reflectivity measurements, we demonstrated their capability to detect the presence of ethanol in the medium and showed the possibility of reusing them in several sensing assays. This new material could become an easier-to-obtain and cheaper alternative to current porous materials commonly used in optical sensors for refractive index sensing. Full article

The uncontainable future development of smart regions, as a set of smart cities’ assembled networks, is directly associated with a growing demand of full interactive and connected ubiquitous smart environments. To achieve this goal of global connection, a large number of transceivers and multiple wireless systems will be involved to provide user services and applications (i.e., Ambient Assisted Living, emergency situations, e-health monitoring, or Intelligent Transportation Systems) anytime and anyplace, regardless of the devices, networks, or systems used. Adequate, efficient, and effective radio wave propagation tools, methodologies, and analyses in complex environments (indoor and outdoor) are crucial to prevent communication limitations such as coverage, capacity, speed, or channel interferences due to nodes’ density or channel restrictions. In this work, radio wave propagation characterization in an urban indoor and outdoor environment, at ISM 2.4 GHZ and 5 GHz Wireless Sensor Networks (WSNs), has been assessed. The selected scenario is an auditorium placed in a free open area surrounded by inhomogeneous vegetation. User density within the scenario, in terms of inherent transceivers density, poses challenges to the overall system operation, given by multiple node operation which increases overall interference levels. By means of an in-house developed 3D ray launching algorithm, the impact of variable density wireless sensor network operation within this complex scenario is presented. This analysis and the proposed simulation methodology can lead in an adequate interference characterization, considering conventional transceivers as well as wearables, which provide suitable information for the overall network performance in complex crowded indoor and outdoor scenarios. Full article

The paper analyzes the influence of different magnetic resonance (MR) scan sequence parameters (echo time, repetition time, orientation of scan slices, sequence type, mass of a tested object) on the energy of the produced noise and vibration. The measured sound pressure levels, together with the recorded noise and vibration signals, were stored in a database and then processed using similar methods to speech signal analysis because the main frequencies of the acoustic noise and vibration lie in the standard audio frequency range. In the signal processing phase, four types of parameters describing the signal energy were determined and statistically analyzed, and the obtained results were visually and numerically compared. Full article

In this paper, we propose a vision-based recognition approach to control the posture of a robotic arm with three degrees of freedom (DOF) using static and dynamic human hand gestures. Two different methods are investigated to intuitively control a robotic arm posture in real-time using depth data collected by a Kinect sensor. In the first method, the user’s right index fingertip position is mapped to compute the inverse kinematics on the robot. Using the Forward And Backward Reaching Inverse Kinematics (FABRIK) algorithm, the inverse kinematics (IK) solutions are displayed in a graphical interface. Using this interface and his left hand, the user can intuitively browse and select a desired robotic arm posture. In the second method, the user’s left index position and direction are respectively used to determine the end-effector position and an attraction point position. The latter enables the control of the robotic arm posture. The performance of these real-time natural human control approaches is evaluated for precision and speed against static and dynamic obstacles. Full article

A new frontier in clinical disease diagnostics was quietly launched by a series of recent discoveries of dysbiosis-related phenomena. These developments make important connections between the metabolic activities of resident microbes and human diseases. Numerous studies have demonstrated that biochemical mechanisms leading to disease development involve not only pathogenesis, but also interactions between microbiota in the oral cavity, lungs, and gut, as well as the microbial metabolites they produce, and the human immune system. Microbial dysbiosis (MD) or changes in commensal microbiota diversity and composition, often modulate disease development by at least two different mechanisms, including disease-induced dysbiosis and alterations in gut microbiota (GM), caused by abiotic and exogenous factors (diet, drug use, and environment). This paper summarizes recent evidence demonstrating how electronic-nose (e-nose) technologies with multi-sensor arrays and chemical-analysis capabilities could potentially be used for early diagnosis of certain diseases by identifying a new category of VOC-biomarker metabolites, called dysbiosis-related disease biomarkers (DRDBs). DRDBs are produced in specific locations of the body due to dysbiosis associated with specific diseases. Recent advances in e-nose technologies offer new tools for exploiting the common occurrence of MD for noninvasive early disease detection. Full article

Recent advances in computer vision techniques allow to obtain information on the dynamic behaviour of structures using commercial grade video recording devices. The advantage of such schemes lies in the non-invasive nature of video recording and the ability to extract information at a high spatial density utilizing structural features. This creates an advantage over conventional contact sensors since constraints such as cabling and maximum channel availability are alleviated. In this study, two such schemes are explored, namely Particle Tracking Velocimetry (PTV) and the optical flow algorithm. Both are validated against conventional sensors for a lab-scale shear frame and compared. In cases of imperceptible motion, the recently proposed Phase-based Motion Magnification (PBMM) technique is employed to obtain modal information within frequency bands of interest and further used for modal analysis. The optical flow scheme combined with (PBMM) is further tested on a large-scale post-tensioned concrete beam and validated against conventional measurements, as a transition from lab- to outdoor field applications. Full article

In most autonomous underwater vehicles (AUVs), the navigation system is based on an inertial navigation system (INS) aided by a Doppler velocity log (DVL). In several INSs, only the velocity vector, provided by the DVL, can be used as input for assistance, thus limiting the integration approach to a loosely coupled one. In situations of partial DVL measurements (such as failure to maintain bottom lock) the DVL cannot provide the AUV velocity vector, and as a result, the navigation solution is based only on the standalone INS solution and will drift in time. To circumvent that problem, the extended loosely coupled (ELC) approach was recently proposed. ELC combines the partial DVL measurements and additional information, such as the pervious navigation solution, to form a calculated velocity measurement to aid the INS. When doing so, the assumption made in the extended Kalman filter (EKF) derivation of zero correlated process and measurement noise covariance does not hold. In this paper, we elaborate the ELC approach by taking into account the cross-covariance matrix of the correlated process (INS) and measurement (Partial DVL) noises. At first, this covariance matrix is evaluated based on the specific assumption used in the ELC approach and then implemented in the EKF algorithm. Using a 6DOF AUV simulation, results show that the proposed methodology improves the performance of the ELC integration approach. Full article

In this work, through Monte Carlo analyses on statistical volume elements, we show the effect of the grain morphology and orientation on the effective elastic properties of polysilicon beams constituting critical MEMS components. The outcomes of the numerical investigation are summarized through statistical (lognormal) distributions for the elastic properties as functions of grain size and morphology, quantifying therefore not only the relevant expected mean values, but also the scattering around them. Such statistical distributions represent a simple, yet rigorous alternative to cumbersome numerical analyses. Their utility is testified through the analysis of a statically indeterminate MEMS structure, quantifying the possible initial offset away from the designed configuration due to the residual stresses arising from the micro-fabrication process. Full article

Transformers are essential equipment in electrical energy systems and their failure may lead to the loss of a power supply. Both industry and science have sought to develop sensors and low-cost solutions for the correct diagnosis of their failures. Thus, the use of piezoelectric sensors in the diagnosis of partial discharge in power transformers has been growing significantly, in order to ensure the reduction of maintenance costs, as well as the quality of electric power supply, since this type of failure can lead to a significant cost of repair. In many cases, when partial discharge is detected, there is no immediate need to promote transformer maintenance. In this way, it becomes reasonable to study the evolution of this phenomenon, so that the maintenance of the device can be scheduled and performed correctly. In this regard, this article presents a feasibility study of a low-cost piezoelectric transducer for the identification of the evolution level of partial discharges. For this purpose, in a 30 kVA distribution transformer, three corona partial discharges were produced under three different voltage levels, using a copper electrode. The low cost piezoelectric sensor was coupled to the transformer housing. The acoustic emission signals of the three partial discharge levels were captured and analyzed by the use of acoustic signal metrics, such as energy, peak value, and power spectral density. The experimental results indicated that the low cost sensor is able to identify the evolution of the partial discharge intensity, since the values obtained by the metrics are directly related to the partial discharge levels. Therefore, the results reported in this study indicate that the piezoelectric transducer has a great applicability in diagnosing the partial discharges evolution, and, thus, can assist in the planning of electrical maintenance. Full article

Diabetes Mellitus, usually called only Diabetes, is a worldwide chronic metabolic disorder that is characterized by abnormal oscillations in blood sugar levels. Such levels should be monitored by diabetes patients, which traditionally have had to take blood samples by finger-pricking, at least between twice and four times a day. Finger-pricking has a number of drawbacks that can be tackled by Continuous Glucose Monitors (CGMs), which are able to determine blood sugar levels throughout the day and not only at specific time instants. In this paper, the design of an IoT CGM-based system is proposed, whose collected blood sugar sample values can be accessed remotely; thus being able to monitor patients, specifically dependent ones (e.g., children, elders, and pregnant women) and warn them in the case where a dangerous situation is detected. In order to create such a system, a fog computing system, based on distributed mobile smart phones, has been devised to collect data from the CGMs. Moreover, the use of a blockchain is proposed, to receive, validate, and store the collected data with the objective of avoiding untrusted sources and, thus, to provide a transparent and trustworthy data source of a population, which can vary in age, ethnicity, psychology, education, self-care, and/or geographic location, in a rapid, flexible, scalable, and low-cost way. These crowdsourced data can enable novel mHealth applications for diagnosis, patient monitoring, or even public health actions, which can help to advance in the control of the disease and raise global awareness on the increasing prevalence of diabetes. Full article

Sensors applied in the food industry are important tools for quality control. Current analyses checking adulteration in milk are expensive and time consuming, because the samples need to be evaluated in a laboratory environment. Thus, is important to develop methodologies and sensors to monitor milk production. A common type of fraud is performed adding substances such as sodium hydroxide in order to increase the shelf life of milk. In this study, we propose to use low-cost piezoelectric diaphragms transducers to implement a methodology that identifies milk adulteration using the mechanical waves propagation method (vibration and acoustic emission). Two piezoelectric diaphragms were used, the first was excited by a chirp signal with 1 V of amplitude and a frequency band since 0 to 65 kHz with 2 Hz of step, and concomitantly acquired the response signal of the second sensor installed in the opposite side of the actuator with a rate of 250 kHz. After acquiring the data, these were processed using the chromatic technique, which extracts three features: energy, average band and equivalent bandwidth, in order to classify the raw and the contaminated milk through clustering. The experimental results indicated that the methodology can differentiate between raw and contaminated milk with 1% of sodium hydroxide. Therefore, the results reported in this study indicate that low-cost piezoelectric diaphragms are promising for liquids quality control. Full article

This work proposes to adapt an existing sensor and embed it on mannequins used in cardiopulmonary resuscitation (CPR) training to accurately measure the amount of air supplied to the lungs during ventilation. The proposed sensor consists of measuring the airflow using propellers. The method directly measures the variable of interest and makes reference to spirometric techniques in the elaboration of its model, improving the realism of the dummies. Besides advantages over the sensors that are commonly used for this purpose, the projected sensor presented an agreement with its theoretical model and with the spirometric model. It is suitable for applications with a resolution of 17 mL, and precision of 50 mL and 26 mL for initial (< 900 mL) and final ranges, respectively. Full article

The design was developed and investigated, and a laboratory sample of energy harvester based on 1–1.5 μm thick lead zirconate titanate Pb(Zr, Ti)O₃ (PZT) films was assembled. PZT films were formed on oxidized silicon by high-frequency reactive plasma sputtering in the oxygen atmosphere. The laboratory sample of the energy harvester was sensitive to mechanical acceleration and vibration. Testing of sensors on a special electrodynamic stand showed that the sensor has a high sensitivity in the frequency range from 2–5 Hz with a sensitivity of up to 75 pС/g, which corresponds to a sensitivity of 1.2–1.5 V/g. The same design of the energy harvester was sensitive to scattered electromagnetic energy with a sensitivity of 6.8 10⁻⁴ V/(V/cm). The maximum response to the electromagnetic field was observed at a frequency of 100−200 Hz. Full article

A computational study of the interaction between cardiac troponin I and its specific antibody is carried out. The aim of this study is to characterize the binding process by determining the binding sites, number of interactions and energies. Furthermore, a selectivity study of the binding efficiency of the cardiac troponin I antibody with the cardiac troponin I and with its principal interferon, the skeletal troponin I, is also performed to demonstrate that selectivity assays for sensing studies can be carried out computationally. Computational and simulation tools such as FTSite, FTMap, FTDock and pyDock were used to determine the binding sites and molecular docking performance, allowing us to obtain relevant information for a subsequent sensing system development. Full article

In this work we demonstrate that the 805M1 single axis analogue output accelerometer can be used to monitor honey bee activity and requires only a low-cost microcontroller with an audio shield to log the data. We present accelerometer output signals demonstrating the ability to capture individual honey bee ‘whooping’ signals as well as long-term amplitude monitoring, indicating the brood cycle, using this affordable measurement system. Full article

With over 6000 rivers and 5358 lakes, surface water is one of the most important resources in Nepal. However, the quantity and quality of Nepal’s rivers and lakes are decreasing due to human activities and climate change. Therefore, the monitoring and estimation of surface water is an essential task. In Nepal, surface water has different characteristics such as varying temperature, turbidity, depth, and vegetation cover, for which remote sensing technology plays a vital role. Single or multiple water index methods have been applied in the classification of surface water with satisfactory results. In recent years, machine learning methods with training datasets, have been outperforming different traditional methods. In this study, we tried to use satellite images from Landsat 8 to classify surface water in Nepal. Input of Landsat bands and ground truth from high resolution images available form Google Earth is used, and their performance is evaluated based on overall accuracy. The study will be will helpful to select optimum machine learning methods for surface water classification and therefore, monitoring and management of surface water in Nepal. Full article

Mapping and exploration are important tasks of mobile robots for various applications such as search and rescue, inspection, and surveillance. Unmanned aerial vehicles (UAVs) are more suited for such tasks because they have a large field of view compared to ground robots. Autonomous operation of UAVs is desirable for exploration in unknown environments. In such environments, the UAV must make a map of the environment and simultaneously localize itself in it which is commonly known as the SLAM (simultaneous localization and mapping) problem. This is also required to safely navigate between open spaces, and make informed decisions about the exploration targets. UAVs have physical constraints including limited payload, and are generally equipped with low-spec embedded computational devices and sensors. Therefore, it is often challenging to achieve robust SLAM on UAVs which also affects exploration. In this paper, we present an autonomous exploration of UAVs in completely unknown environments using low cost sensors such as LIDAR and an RGBD camera. A sensor fusion method is proposed to build a dense 3D map of the environment. Multiple images from the scene are geometrically aligned as the UAV explores the environment, and then a frontier exploration technique is used to search for the next target in the mapped area to explore the maximum area possible. The results show that the proposed algorithm can build precise maps even with low-cost sensors, and explore the environment efficiently. Full article

The Al doped silicon–silicon oxide–hafnium oxide–silicon oxide–silicon capacitor device (Al-SOHOS) could be a candidate for blue light (BL) radiation total dose (TD) nonvolatile sensor. The BL radiation induces a significant increase in the threshold voltage V_T of the Al-SOHOS capacitor, with the change in V_T of the Al-SOHOS capacitor also having a correlation to BL TD. The experimental results indicate that the BL radiation-induced increase in V_T of the Al-SOHOS capacitor under 10-V gate positive bias stress (PVS) is nearly 2 V after a BL TD of 100 mW·s/cm² irradiation. Moreover, the V_T retention loss of the nonvolatile Al-SOHOS capacitor device after 10 years of retention is below 15%. The BL TD data can be permanently stored and accumulated in the non-volatile Al-SOHOS capacitor device. The Al-SOHOS capacitor device in this study has demonstrated the feasibility of non-volatile BL TD radiation sensing. Full article

Due the complexity of control and automation networks in modern industries, sensor-based systems stand out as effective approaches for failure detection in electrical and mechanical machines. This kind of intervention has a high operational value in industrial scenarios, once it can avoid corrective maintenance stops, i.e., before the failure reaches a high level of severity and compromises the machine. Consequently, the development of sensors applied to non-destructive techniques (NDT) for failure monitoring in electrical machines has become a recurrent theme in recent studies. In this context, this paper investigates the application of low-cost piezoelectric sensors for vibration analysis, which is an NDT that has already proved to be efficient for the detection of many structural anomalies in induction motors. Further, the proposed work presents a low-cost alternative approach for expensive commercial sensors, which will make this NDT more attractive for industrial applications. To describe the piezoelectric sensor frequency response, a pencil lead break (PLB) test was performed. After this validation, the Root Mean Square (RMS) value from the voltage samples obtained in the test bench was used as a signal processing method. A comparison between the results for different levels of mechanical load attached to the machine shaft indicated not only the successful performance of the low-cost sensors for load estimation purposes, but also showed that oversized motors may present higher vibration levels in some components that could cause mechanical wearing. Full article

This paper addresses the issue of robotic haptic exploration of 3D objects using an enhanced model of visual attention, where the latter is applied to obtain a sequence of eye fixations on the surface of objects guiding the haptic exploratory procedure. According to psychological studies, somatosensory data resulting as a response to surface changes sensed by human skin are used in combination with kinesthetic cues from muscles and tendons to recognize objects. Drawing inspiration from these findings, a series of five sequential tactile images are obtained by adaptively changing the size of the sensor surface according to the object geometry for each object, from various viewpoints, during an exploration process. We take advantage of the contourlet transform to extract several features from each tactile image. In addition to these somatosensory features, other kinesthetic inputs including the probing locations and the angle of the sensor surface with respect to the object in consecutive contacts are added as features. The dimensionality of the large feature vector is then reduced using a self-organizing map. Overall, 12 features from each sequence are concatenated and used for classification. The proposed framework is applied to a set of four virtual objects and a virtual force sensing resistor array (FSR) is used to capture tactile (haptic) imprints. Trained classifiers are tested to recognize data from new objects belonging to the same categories. Support vector machines yield the highest accuracy of 93.45%. Full article

An individual’s cardiovascular state is a crucial aspect of a healthy life. However, it is not routinely assessed outside the clinical setting. Smart wearables use photoplethysmography (PPG) to monitor the arterial pulse wave (PW) and estimate heart rate. The PPG PW is strongly influenced by the ejection of blood from the heart, providing an opportunity to monitor cardiac parameters using smart wearables. The aim of this study was to investigate the feasibility of monitoring left ventricular ejection time (LVET) and left ventricular contractility (LVC) from the PPG PW at the wrist. PPG PWs were simulated under a range of cardiovascular conditions using a numerical model of PW propagation. LVET and LVC were estimated from the first and second derivatives of the PPG PWs and compared to reference values extracted from the blood pressure PW at the aortic root. There was strong agreement between the estimated and reference values of LVET, indicating that it may be feasible to assess LVET from PPG signals, including those acquired by smart watches. The correlations between the estimated and reference values of LVC were less strong, indicating that further work is required to assess contractility robustly using smart wearables. This study demonstrated the feasibility of assessing LVET using smart wearables that could allow individuals to monitor their cardiovascular state on a daily basis. Full article

Augmented reality is well known for extending the real world by adding computer-generated perceptual information and overlaid sensory information. In contrast, simulation worlds are commonly closed and rely on artificial sensory information generated by the simulator program or using data collected off-line. In this work, a new simulation paradigm is introduced, providing augmented virtuality by integrating crowd sensing and social data mining in simulation worlds by using mobile agents. The simulation world interacts with real world environments, humans, machines, and other virtual worlds in real-time. Mobile agents are closely related to bots that can interact with humans via chat blogs. Among the mining of physical sensors (temperature, motion, position, light, …), mobile agents can perform Crowd Sensing by participating in question–answer dialogs via a chat blog provided by a WEB App that can be used by the masses. Additionally, mobile agents can act as virtual sensors (offering data exchanged with other agents). Virtual sensors are sensor aggregators performing sensor fusion in a spatially region. Full article

Inertial navigation systems (INSs) require an initial attitude before its operation. To that end, the coarse alignment process is applied using inertial sensors readings. For low-cost inertial sensors, only the accelerometers readings are processed to yield the initial roll and pitch angles. The accuracy of the coarse alignment procedure is vitally important for the navigation solution accuracy due to the navigation solution drift accumulating over time. In this paper, we propose using machine learning (ML) approaches, instead of traditional approaches, to conduct the coarse alignment procedure. To that end, a new methodology for the alignment process is proposed, based on state-of-the-art ML algorithms such as random forest (RF) and the more advanced boosting method of gradient tree XGBoost. Results from a simulated alignment of stationary INS scenarios are presented accompanied by a feasibility study. ML results are compared with the traditional coarse alignment methods in terms of time to convergence and accuracy performance. When using the proposed approach, with the examined scenarios, results show a significant improvement of the accuracy and time required for the alignment process. Full article

The DYNAMAP project is aimed at implementing a dynamic noise mapping system able to determine the acoustic impact of road infrastructures in real time. It was established as a result of the European Noise Directive 2002/49/EC. A Wireless Acoustic Sensor Network (WASN) is used to collect the measurements in two pilot areas: in the city of Milan (urban) and on the A90 motorway around Rome (suburban). For a proper evaluation of the noise level of the road infrastructures, the anomalous noise events (ANE) unrelated to traffic noise (e.g., sirens, horns, speech, doors, etc.) should be removed before updating the noise maps. For this purpose, an anomalous noise events detector (ANED) was initially designed and trained using data from a real-life recording campaign. In this work, we continue adapting the ANED algorithm to conform to the requirements of the final 25-node hybrid WASN deployed in the suburban environment. Specifically, the study focuses on the analysis of the spectro-temporal characteristics of the acoustic data in real-operation conditions and their differences between the 19 high-capacity nodes in the Rome pilot area in order to adapt the ANED to run on the entire WASN appropriately. Full article

The impulse response of a piezoelectric transducer can be calculated using the electrical equivalent circuit model with the Manson method for bandwidth transducers. Nevertheless, these approaches are not sufficiently precise because the importance of the homogeneous structure medium where the transducer emits the signal in part determines the bandwidth in which it acts due to the medium interactions with the environment. This paper describes preliminary research results on piezoelectric impulse response measurements in a small space, making use of the procedure presented by Angelo Farina for transducers emitting in reverberant spaces. Combining the basics of the exponential sine sweep (ESS) method, techniques of arrival detection, and signal processing it is possible to obtain the impulse response in a piezoelectric transducer emitting in a homogeneous medium. Full article

The purpose of this project was to design and implement an autonomous system based on Arduino to monitor environmental parameters that intervene in the perception of human comfort such as temperature, humidity, and solar radiation, and use them to analyze factors related to climate control and energy efficiency in buildings. The system was tested in laboratory conditions as well as by in situ measurements of building elements and living spaces. Some of the experiments carried out were contrasted with numerical simulations that allowed us to understand the implemented system. Full article

City noise and sound are measured and processed with the purpose of drawing appropriate government legislation and regulations, ultimately aimed at contributing to a healthier environment for humans. The primary use of urban noise analysis is carried out with the main purpose of reporting or denouncing, to the appropriate authorities, a misconduct or correct a misuse of council resources. We believe that urban sounds carry more information than what it is extracted to date. In this paper we present a cloud-based urban sound analysis system for the capturing, processing and trading of urban sound-based information. By leveraging modern artificial intelligence algorithms running on a FOG computing city infrastructure, we will show how the presented solution can offer a valuable solution for exploiting urban sound information. A specific focus is given to the hardware implementation of the sound sensor and its multimicrophone architecture. We discuss how the presented architecture is designed to allow the trading of sound information between independent parties, transparently, using cloud-based sound processing APIs running on an inexpensive consumer-grade microphone. Full article

The paper focuses on real-time damage detection based on vibration studies carried out by the Central Building Research Institute (CBRI) structural health monitoring team. The experiment was performed on a 1:3 scaled model of a six- story reinforced concrete (RC) frame with masonry infill in the building dynamics laboratory of CBRI. The forward problem is attended by inducing step-by-step damage in infill to investigate the changes in dynamic response as a result of changes in the physical properties of the structure. Recorded time histories are processed for frequency response spectra (FRS) with fast fourier transform (FFT) and mode shapes are obtained. Changes in natural frequency and modal curvature for each of the five damage cases are analysed for damage detection and location in the structure. An algorithm for damage identification viz. curvature damage factor (CDF) approach is presented. Full article

The data recording of underwater noise is a key aspect for the prevention and improvement of management systems of maritime spaces. Thus, due to the presence of activities potentially generating impulsive noise, ports deserve special attention. This article describes and shows the results of the spatial monitoring of both the basal noise and impulsive noise sources present in port facilities. A vessel was equipped with a smart digital hydrophone with a working frequency range between 10 and 200 kHz and an RVR (Received Voltage Response) of, approximately, −170 dB @ 1 V/µPa. Coupling the acoustic data together with the GPS coordinates of the vessel, a GIS map with spatiotemporal distribution of the basal sound pressure levels was made, identifying the sources of impulsive noise of interest and its temporal characteristics. This is a preliminary step for the development of future studies on underwater noise pollution and its potential impact on biodiversity in the environment of port facilities, with the maximum guarantees possible. Full article