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Volume 22
Issue 19
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Sensors, Volume 22, Issue 19 (October-1 2022) – 595 articles

Cover Story (view full-size image): We spend a third of our lives asleep, yet there is a lack of acceptable and reliable tools to monitor our condition at night-time. In particular, respiratory rate is a clinically important predictor of cardio-respiratory deteriorations. The mainstay of clinical measurement comprises manual counting of chest movements, which is unreliable and infeasible in home settings. Emerging solutions are limited by poor adherence and acceptability or are not clinically validated. Albus HomeTM is a contactless, multi-sensor and automated bedside system for nocturnal monitoring that captures multiple symptoms and signs without users needing to do or wear anything. In this study, we validated the high accuracy of respiratory rate monitoring by Albus Home against the clinical gold standard in a diverse population and in real-world home bedroom environments. View this paper
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16 pages, 1181 KiB  
Article
A Plug-and-Play Solution for Smart Transducers in Industrial Applications Based on IEEE 1451 and IEC 61499 Standards
by Diogo Oliveira, João Pinheiro, Luís Neto, Vítor H. Pinto and Gil Gonçalves
Sensors 2022, 22(19), 7694; https://doi.org/10.3390/s22197694 - 10 Oct 2022
Cited by 7 | Viewed by 2789
Abstract
In a cyberphysical production system, the connectivity between the physical entities of a production system with the digital component that controls and monitors that system takes fundamental importance. This connectivity has been increasing from the transducers’ side, through gathering new functionalities and operating [...] Read more.
In a cyberphysical production system, the connectivity between the physical entities of a production system with the digital component that controls and monitors that system takes fundamental importance. This connectivity has been increasing from the transducers’ side, through gathering new functionalities and operating increasingly independently, taking the role of smart transducers, and from the applications’ side, by being developed in a distributed and decentralized paradigm. This work presents a plug-and-play solution capable of integrating smart transducers compliant with the IEEE 1451 standard in industrial applications based on the IEC 61499 standard. For this, we implemented the NCAP module of the smart transducer defined in IEEE 1451, which, when integrated with 4diac IDE and DINASORE (development and execution tools compliant with IEC 61499), enabled a solution that presented automatically the smart sensors and actuators in the IDE application and embedded their functionalities (access to data and processing functions) in the runtime environment. In this way, a complete plug-and-play solution was presented from the connection of the transducer to the network until its integration into the application. Full article
(This article belongs to the Special Issue Intelligent Sensing and Decision-Making in Advanced Manufacturing)
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16 pages, 3899 KiB  
Article
Evaluating the Performance of Airborne and Ground Sensors for Applications in Precision Agriculture: Enhancing the Postprocessing State-of-the-Art Algorithm
by Karel Pavelka, Paulina Raeva and Karel Pavelka, Jr.
Sensors 2022, 22(19), 7693; https://doi.org/10.3390/s22197693 - 10 Oct 2022
Cited by 4 | Viewed by 2554
Abstract
The main goals of the following paper are to evaluate the performance of two multispectral airborne sensors and compare their image data with in situ spectral measurements. Moreover, the authors aim to present an enhanced workflow for processing multitemporal image data using both [...] Read more.
The main goals of the following paper are to evaluate the performance of two multispectral airborne sensors and compare their image data with in situ spectral measurements. Moreover, the authors aim to present an enhanced workflow for processing multitemporal image data using both commercial and open-source solutions. The research was provoked by the need for a relevant comparison between airborne and ground sensors for vegetation analysis and monitoring. The research team used an eBee fixed-wing platform and the multiSPEC 4c and Sequoia sensors. The authors carried out field measurements using a handheld spectrometer by Trimble—GreenSeeker. There were two flight campaigns which took place near the village of Tuhan in the Czech Republic. The results from the first campaign were discouraging, showing less possibility in the correlation between the aerial and field data. The second campaign resulted in a very high percentage of correlation between both types of data. The researchers present the image processing steps and their enhanced photogrammetric workflow for multitemporal data which helps experts and nonprofessionals to reduce their processing time. Full article
(This article belongs to the Special Issue Precision Agriculture and Sensor Systems)
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22 pages, 1093 KiB  
Article
Building Heating and Cooling Load Prediction Using Ensemble Machine Learning Model
by Rajasekhar Chaganti, Furqan Rustam, Talal Daghriri, Isabel de la Torre Díez, Juan Luis Vidal Mazón, Carmen Lili Rodríguez and Imran Ashraf
Sensors 2022, 22(19), 7692; https://doi.org/10.3390/s22197692 - 10 Oct 2022
Cited by 27 | Viewed by 3498
Abstract
Building energy consumption prediction has become an important research problem within the context of sustainable homes and smart cities. Data-driven approaches have been regarded as the most suitable for integration into smart houses. With the wide deployment of IoT sensors, the data generated [...] Read more.
Building energy consumption prediction has become an important research problem within the context of sustainable homes and smart cities. Data-driven approaches have been regarded as the most suitable for integration into smart houses. With the wide deployment of IoT sensors, the data generated from these sensors can be used for modeling and forecasting energy consumption patterns. Existing studies lag in prediction accuracy and various attributes of buildings are not very well studied. This study follows a data-driven approach in this regard. The novelty of the paper lies in the fact that an ensemble model is proposed, which provides higher performance regarding cooling and heating load prediction. Moreover, the influence of different features on heating and cooling load is investigated. Experiments are performed by considering different features such as glazing area, orientation, height, relative compactness, roof area, surface area, and wall area. Results indicate that relative compactness, surface area, and wall area play a significant role in selecting the appropriate cooling and heating load for a building. The proposed model achieves 0.999 R2 for heating load prediction and 0.997 R2 for cooling load prediction, which is superior to existing state-of-the-art models. The precise prediction of heating and cooling load, can help engineers design energy-efficient buildings, especially in the context of future smart homes. Full article
(This article belongs to the Special Issue AI and Big Data for Smart Construction)
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11 pages, 1293 KiB  
Article
Multi-Path Routing Algorithm for Wireless Sensor Network Based on Semi-Supervised Learning
by Yiping Guo, Guyu Hu and Dongsheng Shao
Sensors 2022, 22(19), 7691; https://doi.org/10.3390/s22197691 - 10 Oct 2022
Cited by 2 | Viewed by 1882
Abstract
Multi-path transmission can well solve the data transmission reliability problems and life cycle problems caused by single-path transmission. However, the accuracy of the routing scheme generated by the existing multi-path routing algorithms is difficult to guarantee. In order to improve the accuracy of [...] Read more.
Multi-path transmission can well solve the data transmission reliability problems and life cycle problems caused by single-path transmission. However, the accuracy of the routing scheme generated by the existing multi-path routing algorithms is difficult to guarantee. In order to improve the accuracy of the multi-path routing scheme, this paper innovatively proposes a multi-path routing algorithm for a wireless sensor network (WSN) based on the evaluation. First, we design and implement the real-time evaluation algorithm based on semi-supervised learning (RESL). We prove that RESL is better in evaluation time and evaluation accuracy through comparative experiments. Then, we combine RESL to design and implement the multi-path routing algorithm for wireless sensor networks based on semi-supervised learning (MRSSL). Then, we prove that MRSSL has advantages in improving the accuracy of the multi-path routing scheme through comparative experiments. Full article
(This article belongs to the Section Intelligent Sensors)
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16 pages, 3241 KiB  
Article
Trunk Posture from Randomly Oriented Accelerometers
by Aidan R. W. Friederich, Musa L. Audu and Ronald J. Triolo
Sensors 2022, 22(19), 7690; https://doi.org/10.3390/s22197690 - 10 Oct 2022
Cited by 1 | Viewed by 2010
Abstract
Feedback control of functional neuromuscular stimulation has the potential to improve daily function for individuals with spinal cord injuries (SCIs) by enhancing seated stability. Our fully implanted networked neuroprosthesis (NNP) can provide real-time feedback signals for controlling the trunk through accelerometers embedded in [...] Read more.
Feedback control of functional neuromuscular stimulation has the potential to improve daily function for individuals with spinal cord injuries (SCIs) by enhancing seated stability. Our fully implanted networked neuroprosthesis (NNP) can provide real-time feedback signals for controlling the trunk through accelerometers embedded in modules distributed throughout the trunk. Typically, inertial sensors are aligned with the relevant body segment. However, NNP implanted modules are placed according to surgical constraints and their precise locations and orientations are generally unknown. We have developed a method for calibrating multiple randomly oriented accelerometers and fusing their signals into a measure of trunk orientation. Six accelerometers were externally attached in random orientations to the trunks of six individuals with SCI. Calibration with an optical motion capture system resulted in RMSE below 5° and correlation coefficients above 0.97. Calibration with a handheld goniometer resulted in RMSE of 7° and correlation coefficients above 0.93. Our method can obtain trunk orientation from a network of sensors without a priori knowledge of their relationships to the body anatomical axes. The results of this study will be invaluable in the design of feedback control systems for stabilizing the trunk of individuals with SCI in combination with the NNP implanted technology. Full article
(This article belongs to the Special Issue Biomedical Sensing for Human Motion Monitoring)
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21 pages, 2333 KiB  
Article
A Video Summarization Model Based on Deep Reinforcement Learning with Long-Term Dependency
by Xu Wang, Yujie Li, Haoyu Wang, Longzhao Huang and Shuxue Ding
Sensors 2022, 22(19), 7689; https://doi.org/10.3390/s22197689 - 10 Oct 2022
Cited by 5 | Viewed by 4557
Abstract
Deep summarization models have succeeded in the video summarization field based on the development of gated recursive unit (GRU) and long and short-term memory (LSTM) technology. However, for some long videos, GRU and LSTM cannot effectively capture long-term dependencies. This paper proposes a [...] Read more.
Deep summarization models have succeeded in the video summarization field based on the development of gated recursive unit (GRU) and long and short-term memory (LSTM) technology. However, for some long videos, GRU and LSTM cannot effectively capture long-term dependencies. This paper proposes a deep summarization network with auxiliary summarization losses to address this problem. We introduce an unsupervised auxiliary summarization loss module with LSTM and a swish activation function to capture the long-term dependencies for video summarization, which can be easily integrated with various networks. The proposed model is an unsupervised framework for deep reinforcement learning that does not depend on any labels or user interactions. Additionally, we implement a reward function (R(S)) that jointly considers the consistency, diversity, and representativeness of generated summaries. Furthermore, the proposed model is lightweight and can be successfully deployed on mobile devices and enhance the experience of mobile users and reduce pressure on server operations. We conducted experiments on two benchmark datasets and the results demonstrate that our proposed unsupervised approach can obtain better summaries than existing video summarization methods. Furthermore, the proposed algorithm can generate higher F scores with a nearly 6.3% increase on the SumMe dataset and a 2.2% increase on the TVSum dataset compared to the DR-DSN model. Full article
(This article belongs to the Topic Lightweight Deep Neural Networks for Video Analytics)
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16 pages, 1251 KiB  
Article
Development of Low Hysteresis, Linear Weft-Knitted Strain Sensors for Smart Textile Applications
by Beyza Bozali, Sepideh Ghodrat, Linda Plaude, Joris J. F. van Dam and Kaspar M. B. Jansen
Sensors 2022, 22(19), 7688; https://doi.org/10.3390/s22197688 - 10 Oct 2022
Cited by 10 | Viewed by 2640
Abstract
In recent years, knitted strain sensors have been developed that aim to achieve reliable sensing and high wearability, but they are associated with difficulties due to high hysteresis and low gauge factor (GF) values. This study investigated the electromechanical performance of the weft-knitted [...] Read more.
In recent years, knitted strain sensors have been developed that aim to achieve reliable sensing and high wearability, but they are associated with difficulties due to high hysteresis and low gauge factor (GF) values. This study investigated the electromechanical performance of the weft-knitted strain sensors with a systematic approach to achieve reliable knitted sensors. For two elastic yarn types, six conductive yarns with different resistivities, the knitting density as well as the number of conductive courses were considered as variables in the study. We focused on the 1 × 1 rib structure and in the sensing areas co-knit the conductive and elastic yarns and observed that positioning the conductive yarns at the inside was crucial for obtaining sensors with low hysteresis values. We show that using this technique and varying the knitting density, linear sensors with a working range up to 40% with low hysteresis can be obtained. In addition, using this technique and varying the knitting density, linear sensors with a working range up to 40% strain, hysteresis values as low as 0.03, and GFs varying between 0 and 1.19 can be achieved. Full article
(This article belongs to the Section Sensors Development)
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22 pages, 10795 KiB  
Article
Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data
by Afzal Ahmed Soomro, Ainul Akmar Mokhtar, Waleligne Molla Salilew, Zainal Ambri Abdul Karim, Aijaz Abbasi, Najeebullah Lashari and Syed Muslim Jameel
Sensors 2022, 22(19), 7687; https://doi.org/10.3390/s22197687 - 10 Oct 2022
Cited by 7 | Viewed by 3417
Abstract
In the energy management of district cooling plants, the thermal energy storage tank is critical. As a result, it is essential to keep track of TES results. The performance of the TES has been measured using a variety of methodologies, both numerical and [...] Read more.
In the energy management of district cooling plants, the thermal energy storage tank is critical. As a result, it is essential to keep track of TES results. The performance of the TES has been measured using a variety of methodologies, both numerical and analytical. In this study, the performance of the TES tank in terms of thermocline thickness is predicted using an artificial neural network, support vector machine, and k-nearest neighbor, which has remained unexplored. One year of data was collected from a district cooling plant. Fourteen sensors were used to measure the temperature at different points. With engineering judgement, 263 rows of data were selected and used to develop the prediction models. A total of 70% of the data were used for training, whereas 30% were used for testing. K-fold cross-validation were used. Sensor temperature data was used as the model input, whereas thermocline thickness was used as the model output. The data were normalized, and in addition to this, moving average filter and median filter data smoothing techniques were applied while developing KNN and SVM prediction models to carry out a comparison. The hyperparameters for the three machine learning models were chosen at optimal condition, and the trial-and-error method was used to select the best hyperparameter value: based on this, the optimum architecture of ANN was 14-10-1, which gives the maximum R-Squared value, i.e., 0.9, and minimum mean square error. Finally, the prediction accuracy of three different techniques and results were compared, and the accuracy of ANN is 0.92%, SVM is 89%, and KNN is 96.3%, concluding that KNN has better performance than others. Full article
(This article belongs to the Topic Applied Computing and Machine Intelligence (ACMI))
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54 pages, 7489 KiB  
Review
Opto-Mechanical Eye Models, a Review on Human Vision Applications and Perspectives for Use in Industry
by André Rino Amorim, Boris Bret and José M. González-Méijome
Sensors 2022, 22(19), 7686; https://doi.org/10.3390/s22197686 - 10 Oct 2022
Cited by 4 | Viewed by 5766
Abstract
The purpose of this review is to aggregate technical information on existent optomechanical eye models (OME) described in the literature, for image quality assessment in different applications. Several physical eye models have been reviewed from peer-reviewed papers and patent applications. A typical eye [...] Read more.
The purpose of this review is to aggregate technical information on existent optomechanical eye models (OME) described in the literature, for image quality assessment in different applications. Several physical eye models have been reviewed from peer-reviewed papers and patent applications. A typical eye model includes an artificial cornea, an intraocular lens or other lens to simulate the crystalline lens, an aperture as the pupil, and a posterior retinal surface, which may be connected to a light sensor. The interior of the eye model may be filled with a fluid to better emulate physiological conditions. The main focus of this review is the materials and physical characteristics used and the dimensional aspects of the main components including lenses, apertures, chambers, imaging sensors and filling medium. Various devices are described with their applications and technical details, which are systematically tabulated highlighting their main characteristics and applications. The models presented are detailed and discussed individually, and the features of different models are compared when applicable, highlighting strengths and limitations. In the end there is a brief discussion about the potential use of artificial eye models for industrial applications. Full article
(This article belongs to the Topic Optical and Optoelectronic Materials and Applications)
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28 pages, 7204 KiB  
Article
Investigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications
by Mukesh Mishra, Gourab Sen Gupta and Xiang Gui
Sensors 2022, 22(19), 7685; https://doi.org/10.3390/s22197685 - 10 Oct 2022
Cited by 8 | Viewed by 2552
Abstract
The exponential growth in remote sensing, coupled with advancements in integrated circuits (IC) design and fabrication technology for communication, has prompted the progress of Wireless Sensor Networks (WSN). WSN comprises of sensor nodes and hubs fit for detecting, processing, and communicating remotely. Sensor [...] Read more.
The exponential growth in remote sensing, coupled with advancements in integrated circuits (IC) design and fabrication technology for communication, has prompted the progress of Wireless Sensor Networks (WSN). WSN comprises of sensor nodes and hubs fit for detecting, processing, and communicating remotely. Sensor nodes have limited resources such as memory, energy and computation capabilities restricting their ability to process large volume of data that is generated. Compressing the data before transmission will help alleviate the problem. Many data compression methods have been proposed but mainly for image processing and a vast majority of them are not pertinent on sensor nodes because of memory impediment, energy utilization and handling speed. To overcome this issue, authors in this research have chosen Run Length Encoding (RLE) and Adaptive Huffman Encoding (AHE) data compression techniques as they can be executed on sensor nodes. Both RLE and AHE are capable of balancing compression ratio and energy utilization. In this paper, a hybrid method comprising RLE and AHE, named as H-RLEAHE, is proposed and further investigated for sensor nodes. In order to verify the efficacy of the data compression algorithms, simulations were run, and the results compared with the compression techniques employing RLE, AHE, H-RLEAHE, and without the use of any compression approach for five distinct scenarios. The results demonstrate the RLE’s efficiency, as it surpasses alternative data compression methods in terms of energy efficiency, network speed, packet delivery rate, and residual energy throughout all iterations. Full article
(This article belongs to the Topic Wireless Sensor Networks)
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24 pages, 4163 KiB  
Article
Deep Learning and 5G and Beyond for Child Drowning Prevention in Swimming Pools
by Juan Carlos Cepeda-Pacheco and Mari Carmen Domingo
Sensors 2022, 22(19), 7684; https://doi.org/10.3390/s22197684 - 10 Oct 2022
Cited by 3 | Viewed by 3559
Abstract
Drowning is a major health issue worldwide. The World Health Organization’s global report on drowning states that the highest rates of drowning deaths occur among children aged 1–4 years, followed by children aged 5–9 years. Young children can drown silently in as little [...] Read more.
Drowning is a major health issue worldwide. The World Health Organization’s global report on drowning states that the highest rates of drowning deaths occur among children aged 1–4 years, followed by children aged 5–9 years. Young children can drown silently in as little as 25 s, even in the shallow end or in a baby pool. The report also identifies that the main risk factor for children drowning is the lack of or inadequate supervision. Therefore, in this paper, we propose a novel 5G and beyond child drowning prevention system based on deep learning that detects and classifies distractions of inattentive parents or caregivers and alerts them to focus on active child supervision in swimming pools. In this proposal, we have generated our own dataset, which consists of images of parents/caregivers watching the children or being distracted. The proposed model can successfully perform a seven-class classification with very high accuracies (98%, 94%, and 90% for each model, respectively). ResNet-50, compared with the other models, performs better classifications for most classes. Full article
(This article belongs to the Special Issue Artificial Neural Networks for IoT-Enabled Smart Applications)
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15 pages, 1365 KiB  
Article
A Narrow Optical Pulse Emitter Based on LED: NOPELED
by Diego Real, David Calvo, Antonio Díaz, Francisco Salesa Greus and Agustín Sánchez Losa
Sensors 2022, 22(19), 7683; https://doi.org/10.3390/s22197683 - 10 Oct 2022
Cited by 1 | Viewed by 2046
Abstract
Light sources emitting short pulses are needed in many particle physics experiments using optical sensors as they can replicate the light produced by the particles being detected and are also an important calibration and test element. This work presents NOPELED, a light source [...] Read more.
Light sources emitting short pulses are needed in many particle physics experiments using optical sensors as they can replicate the light produced by the particles being detected and are also an important calibration and test element. This work presents NOPELED, a light source based on LEDs emitting short optical pulses with typical rise times of less than 3 ns and Full Width at Half Maximum lower than 7 ns. The emission wavelength depends on the model of LED used. Several LED models have been characterized in the range from 405 to 532 nm, although NOPELED can work with LED emitting wavelengths outside of that region. While the wavelength is fixed for a given LED model, the intensity and the frequency of the optical pulse can be controlled. NOPELED, which also has low cost and simple operation, can be operated remotely, making it appropriate for either different physics experiments needing in-place light sources such as astrophysical neutrino detectors using photo-multipliers or positron emission tomography devices using scintillation counters, or, beyond physics, applications needing short pulses of light such as protein fluorescence or chemodetection of heavy metals. Full article
(This article belongs to the Section Electronic Sensors)
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21 pages, 4558 KiB  
Review
A Comprehensive Review on Lane Marking Detection Using Deep Neural Networks
by Abdullah Al Mamun, Em Poh Ping, Jakir Hossen, Anik Tahabilder and Busrat Jahan
Sensors 2022, 22(19), 7682; https://doi.org/10.3390/s22197682 - 10 Oct 2022
Cited by 12 | Viewed by 5863
Abstract
Lane marking recognition is one of the most crucial features for automotive vehicles as it is one of the most fundamental requirements of all the autonomy features of Advanced Driver Assistance Systems (ADAS). Researchers have recently made promising improvements in the application of [...] Read more.
Lane marking recognition is one of the most crucial features for automotive vehicles as it is one of the most fundamental requirements of all the autonomy features of Advanced Driver Assistance Systems (ADAS). Researchers have recently made promising improvements in the application of Lane Marking Detection (LMD). This research article has taken the initiative to review lane marking detection, mainly using deep learning techniques. This paper initially discusses the introduction of lane marking detection approaches using deep neural networks and conventional techniques. Lane marking detection frameworks can be categorized into single-stage and two-stage architectures. This paper elaborates on the network’s architecture and the loss function for improving the performance based on the categories. The network’s architecture is divided into object detection, classification, and segmentation, and each is discussed, including their contributions and limitations. There is also a brief indication of the simplification and optimization of the network for simplifying the architecture. Additionally, comparative performance results with a visualization of the final output of five existing techniques is elaborated. Finally, this review is concluded by pointing to particular challenges in lane marking detection, such as generalization problems and computational complexity. There is also a brief future direction for solving the issues, for instance, efficient neural network, Meta, and unsupervised learning. Full article
(This article belongs to the Section Vehicular Sensing)
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18 pages, 8088 KiB  
Article
An Assessment of Waveform Processing for a Single-Beam Bathymetric LiDAR System (SBLS-1)
by Yifu Chen, Yuan Le, Lin Wu, Shuai Li and Lizhe Wang
Sensors 2022, 22(19), 7681; https://doi.org/10.3390/s22197681 - 10 Oct 2022
Cited by 3 | Viewed by 2229
Abstract
The single-beam bathymetric light detection and ranging (LiDAR) system 1 (SBLS-1), which is equipped with a 532-nm-band laser projector and two concentric-circle receivers for shallow- and deep-water echo signals, is a lightweight and convenient prototype instrument with low energy consumption. In this study, [...] Read more.
The single-beam bathymetric light detection and ranging (LiDAR) system 1 (SBLS-1), which is equipped with a 532-nm-band laser projector and two concentric-circle receivers for shallow- and deep-water echo signals, is a lightweight and convenient prototype instrument with low energy consumption. In this study, a novel LiDAR bathymetric method is utilized to achieve single-beam and dual-channel bathymetric characteristics, and an adaptive extraction method is proposed based on the cumulative standard deviation of the peak and trough, which is mainly used to extract the signal segment and eliminate system and random noise. To adapt the dual-channel bathymetric mechanism, an automatic channel-selection method was used at various water depths. A minimum half-wavelength Gaussian iterative decomposition is proposed to improve the detection accuracy of the surface- and bottom-water waveform components and ensure bathymetric accuracy and reliability. Based on a comparison between the experimental results and in situ data, it was found that the SBLS-1 obtained a bathymetric accuracy and RMSE of 0.27 m and 0.23 m at the Weifang and Qingdao test fields. This indicates that the SBLS-1 was bathymetrically capable of acquiring a reliable, high-efficiency waveform dataset. Hence, the novel LiDAR bathymetric method can effectively achieve high-accuracy near-shore bathymetry. Full article
(This article belongs to the Section Optical Sensors)
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14 pages, 3495 KiB  
Article
Learning Assurance Analysis for Further Certification Process of Machine Learning Techniques: Case-Study Air Traffic Conflict Detection Predictor
by Javier A. Pérez-Castán, Luis Pérez Sanz, Marta Fernández-Castellano, Tomislav Radišić, Kristina Samardžić and Ivan Tukarić
Sensors 2022, 22(19), 7680; https://doi.org/10.3390/s22197680 - 10 Oct 2022
Cited by 5 | Viewed by 2650
Abstract
Designing and developing artificial intelligence (AI)-based systems that can be trusted justifiably is one of the main issues aviation must face in the coming years. European Union Aviation Safety Agency (EASA) has developed a user guide that could be potentially transformed as means [...] Read more.
Designing and developing artificial intelligence (AI)-based systems that can be trusted justifiably is one of the main issues aviation must face in the coming years. European Union Aviation Safety Agency (EASA) has developed a user guide that could be potentially transformed as means of compliance for future AI-based regulation. Designers and developers must understand how the learning assurance process of any machine learning (ML) model impacts trust. ML is a narrow branch of AI that uses statistical models to perform predictions. This work deals with the learning assurance process for ML-based systems in the field of air traffic control. A conflict detection tool has been developed to identify separation infringements among aircraft pairs, and the ML algorithm used for classification and regression was extreme gradient boosting. This paper analyses the validity and adaptability of EASA W-shaped methodology for ML-based systems. The results have identified the lack of the EASA W-shaped methodology in time-dependent analysis, by showing how time can impact ML algorithms designed in the case where no time requirements are considered. Another meaningful conclusion is, for systems that depend highly on when the prediction is made, classification and regression metrics cannot be one-size-fits-all because they vary over time. Full article
(This article belongs to the Special Issue Sensors for Navigation and Control Systems)
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20 pages, 7839 KiB  
Article
Water IoT Monitoring System for Aquaponics Health and Fishery Applications
by Mohammad Alselek, Jose M. Alcaraz-Calero, Jaume Segura-Garcia and Qi Wang
Sensors 2022, 22(19), 7679; https://doi.org/10.3390/s22197679 - 10 Oct 2022
Cited by 10 | Viewed by 8739
Abstract
Aquaponic health is a very important in the food industry field, as currently there is a huge amount of fishing farms, and the demands are growing in the whole world. This work examines the process of developing an innovative aquaponics health monitoring system [...] Read more.
Aquaponic health is a very important in the food industry field, as currently there is a huge amount of fishing farms, and the demands are growing in the whole world. This work examines the process of developing an innovative aquaponics health monitoring system that incorporates high-tech back-end innovation sensors to examine fish and crop health and a data analytics framework with a low-tech front-end approach to feedback actions to farmers. The developed system improves the state-of-the-art in terms of aquaponics life cycle monitoring metrics and communication technologies, and the energy consumption has been reduced to make a sustainable system. Full article
(This article belongs to the Special Issue Use Wireless Sensor Networks for Environmental Applications)
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16 pages, 6663 KiB  
Article
State of Charge Estimation of Li-Ion Battery Based on Adaptive Sliding Mode Observer
by Qi Wang, Jiayi Jiang, Tian Gao and Shurui Ren
Sensors 2022, 22(19), 7678; https://doi.org/10.3390/s22197678 - 10 Oct 2022
Cited by 8 | Viewed by 2306
Abstract
As the main power source of new energy electric vehicles, the accurate estimation of State of Charge (SOC) of Li-ion batteries is of great significance for accurately estimating the vehicle’s driving range, prolonging the battery life, and ensuring the maximum efficiency of the [...] Read more.
As the main power source of new energy electric vehicles, the accurate estimation of State of Charge (SOC) of Li-ion batteries is of great significance for accurately estimating the vehicle’s driving range, prolonging the battery life, and ensuring the maximum efficiency of the whole battery pack. In this paper, the ternary Li-ion battery is taken as the research object, and the Dual Polarization (DP) equivalent circuit model with temperature-varying parameters is established. The parameters of the Li-ion battery model at ambient temperature are identified by the forgetting factor least square method. Based on the state space equation of power battery SOC, an adaptive Sliding Mode Observer is used to study the estimation of the State of Charge of the power battery. The SOC estimation results are fully verified at low temperature (0 °C), normal temperature (25 °C), and high temperature (50 °C). The simulation results of the Urban Dynamometer Driving Schedule (UDDS) show that the SOC error estimated at low temperature and high temperature is within 2%, and the SOC error estimated at normal temperature is less than 1%, The algorithm has the advantages of accurate estimation, fast convergence, and strong robustness. Full article
(This article belongs to the Section Electronic Sensors)
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16 pages, 5376 KiB  
Article
Backers Beware: Characteristics and Detection of Fraudulent Crowdfunding Campaigns
by SeungHun Lee, Wafa Shafqat and Hyun-chul Kim
Sensors 2022, 22(19), 7677; https://doi.org/10.3390/s22197677 - 10 Oct 2022
Cited by 4 | Viewed by 2754
Abstract
Crowdfunding has seen an enormous rise, becoming a new alternative funding source for emerging companies or new startups in recent years. As crowdfunding prevails, it is also under substantial risk of the occurrence of fraud. Though a growing number of articles indicate that [...] Read more.
Crowdfunding has seen an enormous rise, becoming a new alternative funding source for emerging companies or new startups in recent years. As crowdfunding prevails, it is also under substantial risk of the occurrence of fraud. Though a growing number of articles indicate that crowdfunding scams are a new imminent threat to investors, little is known about them primarily due to the lack of measurement data collected from real scam cases. This paper fills the gap by collecting, labeling, and analyzing publicly available data of a hundred fraudulent campaigns on a crowdfunding platform. In order to find and understand distinguishing characteristics of crowdfunding scams, we propose to use a broad range of traits including project-based traits, project creator-based ones, and content-based ones such as linguistic cues and Named Entity Recognition features, etc. We then propose to use the feature selection method called Forward Stepwise Logistic Regression, through which 17 key discriminating features (including six original and hitherto unused ones) of scam campaigns are discovered. Based on the selected 17 key features, we present and discuss our findings and insights on distinguishing characteristics of crowdfunding scams, and build our scam detection model with 87.3% accuracy. We also explore the feasibility of early scam detection, building a model with 70.2% of classification accuracy right at the time of project launch. We discuss what features from which sections are more helpful for early scam detection on day 0 and thereafter. Full article
(This article belongs to the Special Issue Data-Driven Social Intelligence and Its Applications)
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20 pages, 8757 KiB  
Article
Impact of a Turbulent Ocean Surface on Laser Beam Propagation
by Omar Alharbi, Tim Kane and Diane Henderson
Sensors 2022, 22(19), 7676; https://doi.org/10.3390/s22197676 - 10 Oct 2022
Cited by 4 | Viewed by 3293
Abstract
The roughness of the ocean surface significantly impacts air-to-sea imaging, oceanographic monitoring, and optical communication. Most current and previous methods for addressing this roughness and its impact on optical propagation are either entirely statistical or theoretical, or are ‘mixed methods’ based on a [...] Read more.
The roughness of the ocean surface significantly impacts air-to-sea imaging, oceanographic monitoring, and optical communication. Most current and previous methods for addressing this roughness and its impact on optical propagation are either entirely statistical or theoretical, or are ‘mixed methods’ based on a combination of statistical models and parametric-based physical models. In this paper, we performed experiments in a 50-foot-wave tank on wind-generated waves, in which we varied the wind speed to measure how the surface waves affect the laser beam propagation and develop a geometrical optical model to measure and analyze the refraction angle and slope angle of the laser beam under various environmental conditions. The study results show that the laser beam deviations/distortions and laser beam footprint size are strongly related to wind speed and laser beam incidence angle. Full article
(This article belongs to the Section Environmental Sensing)
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21 pages, 2055 KiB  
Article
Leveraging IoT-Aware Technologies and AI Techniques for Real-Time Critical Healthcare Applications
by Angela-Tafadzwa Shumba, Teodoro Montanaro, Ilaria Sergi, Luca Fachechi, Massimo De Vittorio and Luigi Patrono
Sensors 2022, 22(19), 7675; https://doi.org/10.3390/s22197675 - 10 Oct 2022
Cited by 41 | Viewed by 5699
Abstract
Personalised healthcare has seen significant improvements due to the introduction of health monitoring technologies that allow wearable devices to unintrusively monitor physiological parameters such as heart health, blood pressure, sleep patterns, and blood glucose levels, among others. Additionally, utilising advanced sensing technologies based [...] Read more.
Personalised healthcare has seen significant improvements due to the introduction of health monitoring technologies that allow wearable devices to unintrusively monitor physiological parameters such as heart health, blood pressure, sleep patterns, and blood glucose levels, among others. Additionally, utilising advanced sensing technologies based on flexible and innovative biocompatible materials in wearable devices allows high accuracy and precision measurement of biological signals. Furthermore, applying real-time Machine Learning algorithms to highly accurate physiological parameters allows precise identification of unusual patterns in the data to provide health event predictions and warnings for timely intervention. However, in the predominantly adopted architectures, health event predictions based on Machine Learning are typically obtained by leveraging Cloud infrastructures characterised by shortcomings such as delayed response times and privacy issues. Fortunately, recent works highlight that a new paradigm based on Edge Computing technologies and on-device Artificial Intelligence significantly improve the latency and privacy issues. Applying this new paradigm to personalised healthcare architectures can significantly improve their efficiency and efficacy. Therefore, this paper reviews existing IoT healthcare architectures that utilise wearable devices and subsequently presents a scalable and modular system architecture to leverage emerging technologies to solve identified shortcomings. The defined architecture includes ultrathin, skin-compatible, flexible, high precision piezoelectric sensors, low-cost communication technologies, on-device intelligence, Edge Intelligence, and Edge Computing technologies. To provide development guidelines and define a consistent reference architecture for improved scalable wearable IoT-based critical healthcare architectures, this manuscript outlines the essential functional and non-functional requirements based on deductions from existing architectures and emerging technology trends. The presented system architecture can be applied to many scenarios, including ambient assisted living, where continuous surveillance and issuance of timely warnings can afford independence to the elderly and chronically ill. We conclude that the distribution and modularity of architecture layers, local AI-based elaboration, and data packaging consistency are the more essential functional requirements for critical healthcare application use cases. We also identify fast response time, utility, comfort, and low cost as the essential non-functional requirements for the defined system architecture. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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18 pages, 17210 KiB  
Article
Fast and Efficient Image Novelty Detection Based on Mean-Shifts
by Matthias Hermann, Georg Umlauf, Bastian Goldlücke and Matthias O. Franz
Sensors 2022, 22(19), 7674; https://doi.org/10.3390/s22197674 - 10 Oct 2022
Cited by 6 | Viewed by 2570
Abstract
Image novelty detection is a repeating task in computer vision and describes the detection of anomalous images based on a training dataset consisting solely of normal reference data. It has been found that, in particular, neural networks are well-suited for the task. Our [...] Read more.
Image novelty detection is a repeating task in computer vision and describes the detection of anomalous images based on a training dataset consisting solely of normal reference data. It has been found that, in particular, neural networks are well-suited for the task. Our approach first transforms the training and test images into ensembles of patches, which enables the assessment of mean-shifts between normal data and outliers. As mean-shifts are only detectable when the outlier ensemble and inlier distribution are spatially separate from each other, a rich feature space, such as a pre-trained neural network, needs to be chosen to represent the extracted patches. For mean-shift estimation, the Hotelling T2 test is used. The size of the patches turned out to be a crucial hyperparameter that needs additional domain knowledge about the spatial size of the expected anomalies (local vs. global). This also affects model selection and the chosen feature space, as commonly used Convolutional Neural Networks or Vision Image Transformers have very different receptive field sizes. To showcase the state-of-the-art capabilities of our approach, we compare results with classical and deep learning methods on the popular dataset CIFAR-10, and demonstrate its real-world applicability in a large-scale industrial inspection scenario using the MVTec dataset. Because of the inexpensive design, our method can be implemented by a single additional 2D-convolution and pooling layer and allows particularly fast prediction times while being very data-efficient. Full article
(This article belongs to the Special Issue Unusual Behavior Detection Based on Machine Learning)
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4 pages, 179 KiB  
Editorial
Micro-/Nano-Fiber Sensors and Optical Integration Devices
by Jin Li
Sensors 2022, 22(19), 7673; https://doi.org/10.3390/s22197673 - 10 Oct 2022
Cited by 4 | Viewed by 1891
Abstract
Because of their strong surface evanescent field, micro-/nanofibers have been used to develop optical sensors and modulation devices with a high performance and integration [...] Full article
(This article belongs to the Special Issue Micro-/Nano-Fiber Sensors and Optical Integration Devices)
8 pages, 1225 KiB  
Article
Towards an Automated Approach for Monitoring Tree Phenology Using Vehicle Dashcams in Urban Environments
by Doreen S. Boyd, Sally Crudge and Giles Foody
Sensors 2022, 22(19), 7672; https://doi.org/10.3390/s22197672 - 10 Oct 2022
Cited by 4 | Viewed by 2074
Abstract
Trees in urban environments hold significant value in providing ecosystem services, which will become increasingly important as urban populations grow. Tree phenology is highly sensitive to climatic variation, and resultant phenological shifts have significant impact on ecosystem function. Data on urban tree phenology [...] Read more.
Trees in urban environments hold significant value in providing ecosystem services, which will become increasingly important as urban populations grow. Tree phenology is highly sensitive to climatic variation, and resultant phenological shifts have significant impact on ecosystem function. Data on urban tree phenology is important to collect. Typical remote methods to monitor tree phenological transitions, such as satellite remote sensing and fixed digital camera networks, are limited by financial costs and coarse resolutions, both spatially and temporally and thus there exists a data gap in urban settings. Here, we report on a pilot study to evaluate the potential to estimate phenological metrics from imagery acquired with a conventional dashcam fitted to a car. Dashcam images were acquired daily in spring 2020, March to May, for a 2000 m stretch of road in Melksham, UK. This pilot study indicates that time series imagery of urban trees, from which meaningful phenological data can be extracted, is obtainable from a car-mounted dashcam. The method based on the YOLOv3 deep learning algorithm demonstrated suitability for automating stages of processing towards deriving a greenness metric from which the date of tree green-up was calculated. These dates of green-up are similar to those obtained by visual analyses, with a maximum of a 4-day difference; and differences in green-up between trees (species-dependent) were evident. Further work is required to fully automate such an approach for other remote sensing capture methods, and to scale-up through authoritative and citizen science agencies. Full article
(This article belongs to the Special Issue Sensors and Their Application in Phenological Studies)
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7 pages, 1368 KiB  
Article
A Red-Emitting Fluorescence Sensor for Detecting Boronic Acid-Containing Agents in Cells
by Naoya Kondo, Erika Aoki, Shinya Takada and Takashi Temma
Sensors 2022, 22(19), 7671; https://doi.org/10.3390/s22197671 - 10 Oct 2022
Cited by 6 | Viewed by 2528
Abstract
The amount and localization of boron-10 atoms delivered into tumor cells determines the therapeutic effect of boron neutron capture therapy (BNCT) and, consequently, efforts have been directed to develop fluorescence sensors to detect intracellular boronic acid compounds. Currently, these sensors are blue-emitting and [...] Read more.
The amount and localization of boron-10 atoms delivered into tumor cells determines the therapeutic effect of boron neutron capture therapy (BNCT) and, consequently, efforts have been directed to develop fluorescence sensors to detect intracellular boronic acid compounds. Currently, these sensors are blue-emitting and hence are impracticable for co-staining with nucleus staining reagents, such as DAPI and Hoechst 33342. Here, we designed and synthesized a novel fluorescence boron sensor, BS-631, that emits fluorescence with a maximum emission wavelength of 631 nm after reaction with the clinically available boronic acid agent, 4-borono-l-phenylalanine (BPA). BS-631 quantitatively detected BPA with sufficiently high sensitivity (detection limit = 19.6 µM) for evaluating BNCT agents. Furthermore, BS-631 did not emit fluorescence after incubation with metal cations. Notably, red-emitting BS-631 could easily and clearly visualize the localization of BPA within cells with nuclei co-stained using Hoechst 33342. This study highlights the promising properties of BS-631 as a versatile boron sensor for evaluating and analyzing boronic acid agents in cancer therapy. Full article
(This article belongs to the Special Issue Advances in Optical, Fluorescent and Luminescent Biosensors)
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45 pages, 6468 KiB  
Review
A Comprehensive Review of the Recent Developments in Wearable Sweat-Sensing Devices
by Nur Fatin Adini Ibrahim, Norhayati Sabani, Shazlina Johari, Asrulnizam Abd Manaf, Asnida Abdul Wahab, Zulkarnay Zakaria and Anas Mohd Noor
Sensors 2022, 22(19), 7670; https://doi.org/10.3390/s22197670 - 10 Oct 2022
Cited by 16 | Viewed by 9037
Abstract
Sweat analysis offers non-invasive real-time on-body measurement for wearable sensors. However, there are still gaps in current developed sweat-sensing devices (SSDs) regarding the concerns of mixing fresh and old sweat and real-time measurement, which are the requirements to ensure accurate the measurement of [...] Read more.
Sweat analysis offers non-invasive real-time on-body measurement for wearable sensors. However, there are still gaps in current developed sweat-sensing devices (SSDs) regarding the concerns of mixing fresh and old sweat and real-time measurement, which are the requirements to ensure accurate the measurement of wearable devices. This review paper discusses these limitations by aiding model designs, features, performance, and the device operation for exploring the SSDs used in different sweat collection tools, focusing on continuous and non-continuous flow sweat analysis. In addition, the paper also comprehensively presents various sweat biomarkers that have been explored by earlier works in order to broaden the use of non-invasive sweat samples in healthcare and related applications. This work also discusses the target analyte’s response mechanism for different sweat compositions, categories of sweat collection devices, and recent advances in SSDs regarding optimal design, functionality, and performance. Full article
(This article belongs to the Special Issue Advanced Wearable Sensors Technologies for Healthcare Monitoring)
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17 pages, 3348 KiB  
Article
A Reconfigurable Visual–Inertial Odometry Accelerated Core with High Area and Energy Efficiency for Autonomous Mobile Robots
by Yonghao Tan, Mengying Sun, Huanshihong Deng, Haihan Wu, Minghao Zhou, Yifei Chen, Zhuo Yu, Qinghan Zeng, Ping Li, Lei Chen and Fengwei An
Sensors 2022, 22(19), 7669; https://doi.org/10.3390/s22197669 - 9 Oct 2022
Cited by 1 | Viewed by 2395
Abstract
With the wide application of autonomous mobile robots (AMRs), the visual inertial odometer (VIO) system that realizes the positioning function through the integration of a camera and inertial measurement unit (IMU) has developed rapidly, but it is still limited by the high complexity [...] Read more.
With the wide application of autonomous mobile robots (AMRs), the visual inertial odometer (VIO) system that realizes the positioning function through the integration of a camera and inertial measurement unit (IMU) has developed rapidly, but it is still limited by the high complexity of the algorithm, the long development cycle of the dedicated accelerator, and the low power supply capacity of AMRs. This work designs a reconfigurable accelerated core that supports different VIO algorithms and has high area and energy efficiency, precision, and speed processing characteristics. Experimental results show that the loss of accuracy of the proposed accelerator is negligible on the most authoritative dataset. The on-chip memory usage of 70 KB is at least 10× smaller than the state-of-the-art works. Thus, the FPGA implementation’s hardware-resource consumption, power dissipation, and synthesis in the 28 nm CMOS outperform the previous works with the same platform. Full article
(This article belongs to the Special Issue Computer Vision and Sensor Technology)
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19 pages, 3255 KiB  
Article
COVID-19 Contagion Risk Estimation Model for Indoor Environments
by Sandra Costanzo and Alexandra Flores
Sensors 2022, 22(19), 7668; https://doi.org/10.3390/s22197668 - 9 Oct 2022
Cited by 4 | Viewed by 2481
Abstract
COVID-19 is an infectious disease mainly transmitted through aerosol particles. Physical distancing can significantly reduce airborne transmission at a short range, but it is not a sufficient measure to avoid contagion. In recent months, health authorities have identified indoor spaces as possible sources [...] Read more.
COVID-19 is an infectious disease mainly transmitted through aerosol particles. Physical distancing can significantly reduce airborne transmission at a short range, but it is not a sufficient measure to avoid contagion. In recent months, health authorities have identified indoor spaces as possible sources of infection, mainly due to poor ventilation, making it necessary to take measures to improve indoor air quality. In this work, an accurate model for COVID-19 contagion risk estimation based on the Wells–Riley probabilistic approach for indoor environments is proposed and implemented as an Android mobile App. The implemented algorithm takes into account all relevant parameters, such as environmental conditions, age, kind of activities, and ventilation conditions, influencing the risk of contagion to provide the real-time probability of contagion with respect to the permanence time, the maximum allowed number of people for the specified area, the expected number of COVID-19 cases, and the required number of Air Changes per Hour. Alerts are provided to the user in the case of a high probability of contagion and CO2 concentration. Additionally, the app exploits a Bluetooth signal to estimate the distance to other devices, allowing the regulation of social distance between people. The results from the application of the model are provided and discussed for different scenarios, such as offices, restaurants, classrooms, and libraries, thus proving the effectiveness of the proposed tool, helping to reduce the spread of the virus still affecting the world population. Full article
(This article belongs to the Special Issue Feature Papers in Physical Sensors 2022)
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15 pages, 5630 KiB  
Article
Development and Application of Dual-Polarization Antenna for Dielectric Logging Sensor
by Chen Li, Shaogui Deng, Zhiqiang Li, Jian Wu, Baoyin Lu and Jutao Yang
Sensors 2022, 22(19), 7667; https://doi.org/10.3390/s22197667 - 9 Oct 2022
Cited by 3 | Viewed by 1792
Abstract
Because the dielectric constant of water is greater than that of oil and gas, dielectric logging sensors can effectively distinguish oil and gas reservoirs from water layers by measuring the dielectric parameters of formations. Under the special working conditions during the logging of [...] Read more.
Because the dielectric constant of water is greater than that of oil and gas, dielectric logging sensors can effectively distinguish oil and gas reservoirs from water layers by measuring the dielectric parameters of formations. Under the special working conditions during the logging of boreholes drilled for oil and gas exploration, such as high temperature and pressure and a narrow working space, the endurance and effectiveness of the antenna in the dielectric logging sensor are crucial. This paper presents a design method for a dual-polarization slot antenna for such working conditions. We theoretically analyzed the working principle of this antenna, established the antenna model, and evaluated its radiation characteristics through simulation. Subsequently, we developed and tested the proposed antenna. The antenna could withstand a high temperature and pressure of 175 °C and 140 MPa, respectively. A dielectric logging sensor using the proposed antenna was successfully applied in oilfield logging. Full article
(This article belongs to the Section Electronic Sensors)
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12 pages, 4052 KiB  
Article
Research on Penetration Loss of D-Band Millimeter Wave for Typical Materials
by Xinyi Wang, Weiping Li, Mingxu Wang, Chengzhen Bian, Yi Wei and Wen Zhou
Sensors 2022, 22(19), 7666; https://doi.org/10.3390/s22197666 - 9 Oct 2022
Cited by 8 | Viewed by 3168
Abstract
The millimeter-wave frequency band provides abundant frequency resources for the development of beyond 5th generation mobile network (B5G) mobile communication, and its relative bandwidth of 1% can provide a gigabit-level communication bandwidth. In particular, the D-band (110–170 GHz) has received much attention, due [...] Read more.
The millimeter-wave frequency band provides abundant frequency resources for the development of beyond 5th generation mobile network (B5G) mobile communication, and its relative bandwidth of 1% can provide a gigabit-level communication bandwidth. In particular, the D-band (110–170 GHz) has received much attention, due to its large available bandwidth. However, certain bands in the D-band are easily blocked by obstacles and lack penetration. In this paper, D-band millimeter-wave penetration losses of typical materials, such as vegetation, planks, glass, and slate, are investigated theoretically and experimentally. The comparative analysis between our experimental results and theoretical predictions shows that D-band waves find it difficult to penetrate thick materials, making it difficult for 5G millimeter waves to cover indoors from outdoor macro stations. The future B5G mobile communication also requires significant measurement work on different frequencies and different scenarios. Full article
(This article belongs to the Special Issue Future Trends in Millimeter Wave Communication)
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10 pages, 6204 KiB  
Article
High Performance of Metallic Thin Films for Resistance Temperature Devices with Antimicrobial Properties
by Arthur L. R. Souza, Marcio A. Correa, Felipe Bohn, Helder Castro, Margarida M. Fernandes, Filipe Vaz and Armando Ferreira
Sensors 2022, 22(19), 7665; https://doi.org/10.3390/s22197665 - 9 Oct 2022
Cited by 2 | Viewed by 2140
Abstract
Titanium-copper alloy films with stoichiometry given by Ti1−xCux were produced by magnetron co-sputtering technique and analyzed in order to explore the suitability of the films to be applied as resistive temperature sensors with antimicrobial properties. For that, the [...] Read more.
Titanium-copper alloy films with stoichiometry given by Ti1−xCux were produced by magnetron co-sputtering technique and analyzed in order to explore the suitability of the films to be applied as resistive temperature sensors with antimicrobial properties. For that, the copper (Cu) amount in the films was varied by applying different DC currents to the source during the deposition in order to change the Cu concentration. As a result, the samples showed excellent thermoresistivity linearity and stability for temperatures in the range between room temperature to 110 °C. The sample concentration of Ti0.70Cu0.30 has better characteristics to act as RTD, especially the αTCR of 1990 ×10−6°C−1. The antimicrobial properties of the Ti1−xCux films were analyzed by exposing the films to the bacterias S. aureus and E. coli, and comparing them with bare Ti and Cu films that underwent the same protocol. The Ti1−xCux thin films showed bactericidal effects, by log10 reduction for both bacteria, irrespective of the Cu concentrations. As a test of concept, the selected sample was subjected to 160 h reacting to variations in ambient temperature, presenting results similar to a commercial temperature sensor. Therefore, these Ti1−xCux thin films become excellent antimicrobial candidates to act as temperature sensors in advanced coating systems. Full article
(This article belongs to the Special Issue Sensors – a Weapon in the Fight against Antimicrobial Resistance)
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