ALBERT

Description: Heat stress is considered a limiting factor for sheep production. We used information from physiological characteristics linked to heat tolerance to determine whether infrared thermography temperatures were able to separate groups of animals and determine the most important variables in this differentiation. Forty-eight four-month-old male lambs from eight genetic groups were used. Physiological (rectal temperature–RT, heart rate–HR, respiratory rate–RR) and blood traits, infrared thermography temperatures, heat tolerance indices, body measurements, weight and carcass traits were measured. Statistical analyses included variance, correlations, factor, discrimination and regression. Observing the correlations between physiological characteristics (RT, RR and HR) with temperatures measured by infrared thermography, regions for further studies should include the mean temperature of flank, nose and rump. Results show that there are strong relationships between thermograph measurements and RR, RT and HR in lambs, which are suggested to be directly correlated with heat tolerance capacity of the different genetic groups evaluated in this study. The assessment of body surface temperature measured by the thermograph could be used as a noninvasive tool to assess heat tolerance of the animals.

Description: Problems related to quality (and quantity) of water in natural resources or in artificial reservoirs are frequently arising and are at the center of attention of authorities and governments around the world. Many times the monitoring is not performed in an efficient time frame and a precise manner, whereas the adoption of fast and punctual solutions would undoubtedly improve the water quality and consequently enhance the life of people. To minimize or diminish such kinds of problems, we propose an architecture for sensors installed in a robotic platform, an autonomous sail boat, able to acquire raw data relative to water quality, to process and make them available to people that might be interested in such information. The main contributions are the sensors architecture itself, which uses low cost sensors, with practical experimentation done with a prototype. Results show data collected for points in lakes and rivers in the northeast of Brazil. This embedded system is fixed in the sailboat robot with the intention to facilitate the study of water quality for long endurance missions. This robot can help monitoring water bodies in a more consistent manner. Nonetheless the system can also be used with fixed vases or buoys in strategic points.

Description: Dengue fever is the most prevalent vector-borne disease in the world, with nearly 100 million people infected every year. Early diagnosis and identification of the pathogen are crucial steps for the treatment and for prevention of the disease, mainly in areas where the co-circulation of different serotypes is common, increasing the outcome of dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). Due to the lack of fast and inexpensive methods available for the identification of dengue serotypes, herein we report the development of an electrochemical DNA biosensor for the detection of sequences of dengue virus serotype 3 (DENV-3). DENV-3 probe was designed using bioinformatics software and differential pulse voltammetry (DPV) was used for electrochemical analysis. The results showed that a 22-m sequence was the best DNA probe for the identification of DENV-3. The optimum concentration of the DNA probe immobilized onto the electrode surface is 500 nM and a low detection limit of the system (3.09 nM). Moreover, this system allows selective detection of DENV-3 sequences in buffer and human serum solutions. Therefore, the application of DNA biosensors for diagnostics at the molecular level may contribute to future advances in the implementation of specific, effective and rapid detection methods for the diagnosis dengue viruses.

Description: Sensors, Vol. 18, Pages 2895: An Integrated Dead Reckoning with Cooperative Positioning Solution to Assist GPS NLOS Using Vehicular Communications Sensors doi: 10.3390/s18092895 Authors: Pedro Paulo Liborio Lima do Nascimento Bruno Yuji Lino Kimura Daniel Ludovico Guidoni Leandro Aparecido Villas In Intelligent Transportation Systems (ITS), the Vehicular Ad Hoc Networks (VANETs) paradigm based on the WAVE IEEE 802.11p standard is the main alternative for inter-vehicle communications. Recently, many protocols, applications, and services have been developed with a wide range of objectives, ranging from comfort to security. Most of these services rely on location systems and require different levels of accuracy for their full operation. The Global Positioning System (GPS) is an off-the-shelf solution for localization in VANETs and ITS. However, GPS systems present problems regarding inaccuracy and unavailability in dense urban areas, multilevel roads, and tunnels, posing a challenge for protocols, applications, and services that rely on localization. With this motivation, we carried out a characterization of the problems of inaccuracy and unavailability of GPS systems from real datasets, and regions around tunnels were selected. Since the nodes of the vehicular network are endowed with wireless communication, processing and storage capabilities, an integrated Dead Reckoning aided Geometric Dilution of Precision (GDOP)-based Cooperative Positioning solution was developed and evaluated. Leveraging the potential of vehicular sensors, such as odometers, gyroscopes, and digital compasses, vehicles share their positions and kinematics information using vehicular communication to improve their location estimations. With the assistance of a digital map, vehicles adjust the final estimated position using the road geometry. The situations of GPS unavailability characterized in the datasets were reproduced in a simulation environment to validate the proposed localization solution. The simulation results show average gains in Root Mean Square Error (RMSE) between 97% to 98% in comparison with the stand-alone GPS solution, and 83.00% to 88.00% against the GPS and Dead Reckoning (DR) only solution. The average absolute RMSE was reduced to the range of 3 to 5 m by vehicle. In addition, the proposed solution was shown to support 100% of the GPS unavailability zones on the evaluated scenarios.

Description: Sensors, Vol. 17, Pages 2119: PIG’s Speed Estimated with Pressure Transducers and Hall Effect Sensor: An Industrial Application of Sensors to Validate a Testing Laboratory Sensors doi: 10.3390/s17092119 Authors: Gustavo Lima Victor Freitas Renan Araújo André Maitelli Andrés Salazar The pipeline inspection using a device called Pipeline Inspection Gauge (PIG) is safe and reliable when the PIG is at low speeds during inspection. We built a Testing Laboratory, containing a testing loop and supervisory system to study speed control techniques for PIGs. The objective of this work is to present and validate the Testing Laboratory, which will allow development of a speed controller for PIGs and solve an existing problem in the oil industry. The experimental methodology used throughout the project is also presented. We installed pressure transducers on pipeline outer walls to detect the PIG’s movement and, with data from supervisory, calculated an average speed of 0.43 m/s. At the same time, the electronic board inside the PIG received data from odometer and calculated an average speed of 0.45 m/s. We found an error of 4.44%, which is experimentally acceptable. The results showed that it is possible to successfully build a Testing Laboratory to detect the PIG’s passage and estimate its speed. The validation of the Testing Laboratory using data from the odometer and its auxiliary electronic was very successful. Lastly, we hope to develop more research in the oil industry area using this Testing Laboratory.

Description: Sensors, Vol. 17, Pages 2751: Knee Impedance Modulation to Control an Active Orthosis Using Insole Sensors Sensors doi: 10.3390/s17122751 Authors: Ana Villa-Parra Denis Delisle-Rodriguez Jessica Souza Lima Anselmo Frizera-Neto Teodiano Bastos Robotic devices for rehabilitation and gait assistance have greatly advanced with the objective of improving both the mobility and quality of life of people with motion impairments. To encourage active participation of the user, the use of admittance control strategy is one of the most appropriate approaches, which requires methods for online adjustment of impedance components. Such approach is cited by the literature as a challenge to guaranteeing a suitable dynamic performance. This work proposes a method for online knee impedance modulation, which generates variable gains through the gait cycle according to the users’ anthropometric data and gait sub-phases recognized with footswitch signals. This approach was evaluated in an active knee orthosis with three variable gain patterns to obtain a suitable condition to implement a stance controller: two different gain patterns to support the knee in stance phase, and a third pattern for gait without knee support. The knee angle and torque were measured during the experimental protocol to compare both temporospatial parameters and kinematics data with other studies of gait with knee exoskeletons. The users rated scores related to their satisfaction with both the device and controller through QUEST questionnaires. Experimental results showed that the admittance controller proposed here offered knee support in 50% of the gait cycle, and the walking speed was not significantly different between the three gain patterns (p = 0.067). A positive effect of the controller on users regarding safety during gait was found with a score of 4 in a scale of 5. Therefore, the approach demonstrates good performance to adjust impedance components providing knee support in stance phase.

Description: This work proposes a new methodology for the detection of discontinuities in the weld bead applied in Shielded Metal Arc Welding (SMAW) processes. The detection system is based on two sensors—a microphone and piezoelectric—that acquire acoustic emissions generated during the welding. The feature vectors extracted from the sensor dataset are used to construct classifier models. The approaches based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers are able to identify with a high accuracy the three proposed weld bead classes: desirable weld bead, shrinkage cavity and burn through discontinuities. Experimental results illustrate the system’s high accuracy, greater than 90% for each class. A novel Hierarchical Support Vector Machine (HSVM) structure is proposed to make feasible the use of this system in industrial environments. This approach presented 96.6% overall accuracy. Given the simplicity of the equipment involved, this system can be applied in the metal transformation industries.

Description: In this work we proposed a relative humidity (RH) sensor based on a Bragg grating written in an optical fiber, associated with a coating of organo-silica hybrid material prepared by the sol-gel method. The organo-silica-based coating has a strong adhesion to the optical fiber and its expansion is reversibly affected by the change in the RH values (15.0–95.0%) of the surrounding environment, allowing an increased sensitivity (22.2 pm/%RH) and durability due to the presence of a siliceous-based inorganic component. The developed sensor was tested in a real structure health monitoring essay, in which the RH inside two concrete blocks with different porosity values was measured over 1 year. The results demonstrated the potential of the proposed optical sensor in the monitoring of civil engineering structures.

Description: Sensors, Vol. 18, Pages 3072: Pipeline Inspection Gauge’s Velocity Simulation Based on Pressure Differential Using Artificial Neural Networks Sensors doi: 10.3390/s18093072 Authors: Renan Pires de Araújo Victor Carvalho Galvão de Freitas Gustavo Fernandes de Lima Andrés Ortiz Salazar Adrião Duarte Dória Neto André Laurindo Maitelli Industrial pipelines must be inspected to detect typical failures, such as obstructions and deformations, during their lifetime. In the petroleum industry, the most used non-destructive technique to inspect buried pipelines is pigging. This technique consists of launching a Pipeline Inspection Gauge (PIG) inside the pipeline, which is driven by the pressure differential produced by fluid flow. The purpose of this work is to study the application of artificial neural networks to calculate the PIG’s velocity based on the pressure differential. We launch a prototype PIG inside a testing pipeline, where this PIG gathers velocity data from an odometer-based system, while a supervisory system gathers pressure data from the testing pipeline. Then we train a Multilayer Perceptron (MLP) and a Nonlinear Autoregressive Network with eXogenous Inputs (NARX) network with the gathered data to predict velocity. The results suggest it is possible to use a neural network to model the PIG’s velocity from pressure differential measurements. Our method is a new approach to the typical speed measurements based only on odometer, since the odometer is prone to fail and present poor results under some circumstances. Moreover, it can be used to provide redundancy, improving reliability of data obtained during the test.

Description: Sensors, Vol. 18, Pages 840: Integrated Optical Mach-Zehnder Interferometer Based on Organic-Inorganic Hybrids for Photonics-on-a-Chip Biosensing Applications Sensors doi: 10.3390/s18030840 Authors: Ana Bastos Carlos Vicente Rui Oliveira-Silva Nuno Silva Marta Tacão João Costa Mário Lima Paulo André Rute Ferreira The development of portable low-cost integrated optics-based biosensors for photonics-on-a-chip devices for real-time diagnosis are of great interest, offering significant advantages over current analytical methods. We report the fabrication and characterization of an optical sensor based on a Mach-Zehnder interferometer to monitor the growing concentration of bacteria in a liquid medium. The device pattern was imprinted on transparent self-patternable organic-inorganic di-ureasil hybrid films by direct UV-laser, reducing the complexity and cost production compared with lithographic techniques or three-dimensional (3D) patterning using femtosecond lasers. The sensor performance was evaluated using, as an illustrative example, E. coli cell growth in an aqueous medium. The measured sensitivity (2 × 10−4 RIU) and limit of detection (LOD = 2 × 10−4) are among the best values known for low-refractive index contrast sensors. Furthermore, the di-ureasil hybrid used to produce this biosensor has additional advantages, such as mechanical flexibility, thermal stability, and low insertion losses due to fiber-device refractive index mismatch (~1.49). Therefore, the proposed sensor constitutes a direct, compact, fast, and cost-effective solution for monitoring the concentration of lived-cells.