Transformers are essential equipment in electrical energy systems and their failure may lead to loss of 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 the loss of the insulating properties of the material until, finally, complete electric asset failure occurs. 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 measurements were produced under three different voltage levels using a copper electrode. The low cost piezoelectric sensor was coupled to the transformer housing and 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 great applicability in the diagnosis of the partial discharges evolution, and, thus, can assist in the planning of electrical maintenance.

Due the high level of control and automation networks in modern industries, the sensor-based systems play a key role in the industrial scenario. Actual predictive techniques commonly use sensors to anticipate machinery malfunctions, this kind of intervention has a high operational value once it can avoid corrective maintenance stops, i. e. before the failure reaches a high level of severity and compromise the machine. In consequence of that, the development of sensors applied to non-destructive techniques (NDT) for failure monitoring in electrical machines have become a recurrent theme in recent studies. In this paper, it was employed the vibration analysis technique, which is a NDT that already proved to be efficient for detection of many structural anomalies in induction motors. Besides that, it was shown an alternative approach for the costly commercial sensors using the piezoelectric low-cost sensor, the feasible market prices can turn this NDT more likely to industrial applications. In order to verify the capability of piezoelectric sensor as a transducer for vibration analysis, its frequency response was performed using the pencil lead break (PLB) test. After this validation, the RMS value from the voltage samples obtained in the test bench was used as a signal processing method. The comparison between the results for different levels of mechanical load attached to the machine shaft indicates not only a 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.

Cardiopulmonary Resuscitation – CPR is a recurring practice in medical urgency and emergency. CPR is characterizes by a set of maneuvers performed in an attempt to reanimate the victim of cardiac and / or respiratory arrest and is intended to cause the heart and lung to return to their normal functions while maintaining oxygenation of the brain. This scenario represents an extreme medical emergency, which can lead to irreversible brain injury and even death if appropriate measures to restore blood flow and breathing are not performed properly. Thus, students and professionals in the area must acquire skills that enable them to act quickly and efficiently during patient care. This work proposes to adapt an existing sensor and embed on mannequins used in 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 expirometric techniques in the elaboration of its model, improving the realism of the dummies. The projected sensor presented an agreement with its theoretical model and with the expirometric model, besides advantages over the sensors that are used for this purpose. It was suitable for applications with an accuracy of ±17 mL, and resolution of 50 mL and 26 mL for initial and final measurements, respectively, ranging from 30 to 1800 mL.

The development of new low-cost transducers and systems has been extensively aimed in both industry and academics in order to promote a correct failure diagnosis in several types of aerospace, naval and civil structures. In this context, structural healthy monitoring (SHM) engineering field is focused on promoting human safety and reduction of 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 literature. One of the most common damage location technique is based on acoustic waves triangulation, which stands out to be an effective approach. This method uses a piezoelectric transducer as a sensor to capture acoustic waves emitted by a crack or 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 require complex statistical and signal processing techniques. Based on this issue, this work proposes the evaluation of a low cost piezoelectric transducer to 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 aluminium bar which two piezoelectric transducers were attached at each end. The damage was simulated by mass variation applied in four different spots of the component and the acoustic signals emitted by the damage were acquired and processed by Hinkley and Akaike criterion. The results indicate that, although both signal processing methodologies were able to perform 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 damage structures.

Geomagnetic navigation has become popular for autonomous navigation method with features such as autonomous, all-weather, no time-accumulated error and so on. Its accuracy mainly depends on the accuracy of geomagnetic matching algorithms. Pedestrian Dead Reckoning technology is a positioning technology that calculates the relative position of pedestrians based on sensor information but can only obtain relative position information. According to the advantages and disadvantages of the two technologies, this paper proposes a high precision indoor positioning method which uses a smartphone as a hardware platform to build a magnetometer sensor model. An improved particle filter algorithm is used to solve the problem of geomagnetic fingerprint's fuzzy solution. The mean square error criterion establishes a matching trajectory and iterative calculations achieve real-time correction of PDR cumulative error. Finally, simulation experiments are performed. The experimental results show that the fusion location algorithm proposed in this paper is 42% higher than the PDR algorithm. Compared to a single geomagnetic fingerprint matching algorithm, positioning accuracy increased by 57%.

In most of autonomous underwater vehicles (AUVs), the navigation system is based on an inertial navigation system (INS) aided by a Doppler velocity log (DVL). If acoustic localization measurements (like long baseline systems) are also available, DVL measurements are also used between two successive position updates.

In several INSs only the velocity vector, provided by the DVL, can be used as input and thereby the integration approach is limited only to a loosely coupled one. There, in situations of partial DVL measurements (such as failure to maintain bottom lock) the DVL cannot provide the velocity vector and as a result the navigation solution will rely 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 research, we elaborate the ELC approach by taking into account the covariance matrix of the correlated process (INS) and measurement (Partial DVL) noises. This covariance matrix is evaluated based on the specific assumptions used in the ELC approach and then implemented in the EKF algorithm. Using 6DOF AUV simulation, results show that the proposed methodology improves the performance of the ELC integration approach.

Sensors applied in the food industry are important tools to quality control. Current analyses checking adulteration in milk are expensive and time consuming, because the sample need to be evaluated in laboratory environment. Thus, is important to develop methodologies and sensors to monitoring milk production. A common type of fraud is performed adding substances such as sodium hydroxide in order to increasing milk shelf life. In this study, we propose to use low-cost piezoelectric diaphragms transducers to implement a methodology to identify milk adulteration by 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 was acquitted the response signal of the second sensor installed in the opposite side since the actuator with a rate of 250 kHz. After acquire the data, these were processed using the chromatic technique, which extract 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 differ raw and contaminated milk with 1% of sodium hydroxide. Therefore, the results reported in this study indicate that low-cost piezoelectric diaphragms are promissory to liquids quality control.

The uncontainablefuture development of Smart regions, as a set of Smart cities’ networks assembled, is directly associated with a growing demand of full interactive and connected ubiquitous smart environments. To achieve this global connection goal, 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 the devices, networks or systems, they use. Adequate, efficient and effective radio wave propagation tools, methodologies and analyses in complex environments (indoor and outdoor) are crucially required to prevent communication limitations such as coverage, capacity or 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 2.4GHZ and 5GHz Wireless Sensor Networks (WSNs), has been assessed. The selected scenario is an auditorium placed in a city free open area surrounded by inhomogeneous vegetation. User density within the scenario, in terms of inherent transceivers density, poses challenges in 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.

An open-air magnetic resonance imaging (MRI) tomograph is a huge intelligent sensor used for non-invasive scanning of various parts of a human body without being a burden to it as in the case of X-ray equipment. MRI is successfully used for health monitoring of therapy progress after vocal fold cancer surgery or monitoring of cartilage recovery in legs or arms after their implantation, etc. For selection of 3-D coordinates of a tested object, the MRI device contains a gradient system producing a significant mechanical vibration causing image blurring and an acoustic noise significantly degrading the simultaneously recorded speech signal during MR scanning of the human vocal tract. There is also a negative effect on a person’s psychical state depending on the intensity and time duration of the exposition. Vibration and noise energy relationships in the MRI scanning area must be mapped to minimize these factors. The paper analyzes how different setting of MR scan sequence parameters (echo time, repetition time, orientation of scan slices, sequence type, tested object mass) affects the energy of the produced noise and vibration. Measured sound pressure levels together with recorded noise and vibration signals were stored in a database and then processed using similar methods as in 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, statistically analyzed, and the obtained results were visually and numerically compared.

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 in a real recreation area located within a dense oak 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, which takes into account the previously mentioned path loss and multipath propagation phenomena.