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Adolescent idiopathic scoliosis (AIS) is a common disease that, in many cases, can be conservatively treated through bracing. High adherence to brace prescription is fundamental to gaining the maximum benefit from this treatment approach. Wearable sensors are available that objectively monitor the brace-wearing time, but their use, combined with other interventions, is poorly investigated. The aims of the current review are as follows: (i) to summarize the real compliance with bracing reported by studies using sensors; (ii) to find out the real brace wearing rate through objective electronic monitoring; (iii) to verify if interventions made to increase adherence to bracing can be effective according to the published literature. We conducted a systematic review of the literature published on Medline, EMBASE, CINAHL, Scopus, CENTRAL, and Web of Science. We identified 466 articles and included examples articles, which had a low to good methodological quality. We found that compliance a greatly varied between 21.8 and 93.9% (weighted average: 58.8%), real brace wearing time varied between 5.7 and 21 h per day (weighted average 13.3), and specific interventions seemed to improve both outcomes, with compliance increasing from 58.5 to 66% and brace wearing increasing from 11.9 to 15.1 h per day. Two comparative studies showed positive effects of stand-alone counseling and information on the sensors' presence when added to counseling. Sensors proved to be useful tools for objectively and continuously monitoring adherence to therapy in everyday clinical practice. Specific interventions, like the use of sensors, counseling, education, and exercises, could increase compliance. However, further studies using high-quality designs should be conducted in this field. [ABSTRACT FROM AUTHOR]

Cancer is a major cause of mortality and morbidity worldwide. Detection and quantification of cancer biomarkers plays a critical role in cancer early diagnosis, screening, and treatment. Clinicians, particularly in developing countries, deal with high costs and limited resources for diagnostic systems. Using low-cost substrates to develop sensor devices could be very helpful. The interest in paper-based sensors with colorimetric detection increased exponentially in the last decade as they meet the criteria for point-of-care (PoC) devices. Cellulose and different nanomaterials have been used as substrate and colorimetric probes, respectively, for these types of devices in their different designs as spot tests, lateral-flow assays, dipsticks, and microfluidic paper-based devices (μPADs), offering low-cost and disposable devices. However, the main challenge with these devices is their low sensitivity and lack of efficiency in performing quantitative measurements. This review includes an overview of the use of paper for the development of sensing devices focusing on colorimetric detection and their application to cancer biomarkers. We highlight recent works reporting the use of paper in the development of colorimetric sensors for cancer biomarkers, such as proteins, nucleic acids, and others. Finally, we discuss the main advantages of these types of devices and highlight their major pitfalls. [ABSTRACT FROM AUTHOR]

An inkjet-printed relative humidity sensor based on capacitive changes which responds to different humidity levels in the environment is presented in this work. The inkjet-printed silver interdigitated electrodes configuration on the paper substrate allowed for the fabrication of a functional proof-of-concept of the relative humidity sensor, by using the paper itself as a sensing material. The sensor sensitivity in terms of relative humidity changes was calculated to be around 2 pF/RH %. The response time against different temperature steps from 3 to 85°C was fairly constant (about 4-5 min), and it was considered fast for the aimed application, a smart label. [ABSTRACT FROM AUTHOR]

In this work, the authors explored the interaction of a suite of fluorescent zinc complexes with H2S. The authors provide evidence that HS− binds the zinc center of all the complexes under investigation, allowing them to possibly function as sensors by a 'coordinative-based' approach. Naked-eye color changes occur when treating the systems with HS−, so the fluorescence responses are modulated by the presence of HS−, which has been related to a change in the energy level and coupling of excited states through a computational study. The results show the potential of the systems to function as HS−/H2S colorimetric and fluorescent sensors. Paper-strip-based sensing experiments foresee the potential of using this family of complexes as chemosensors of HS− in more complex biological fluids. [ABSTRACT FROM AUTHOR]

Research, design, fabrication and results of various screen printed capacitive humidity sensors is presented in this paper. Two types of capacitive humidity sensors have been designed and fabricated via screen printing on recycled paper and cardboard, obtained from the regional paper and cardboard industry. As printing ink, commercially available silver nanoparticle-based conductive ink was used. A considerable amount of work has been devoted to the humidity measurement methods using paper as a dielectric material. Performances of different structures have been tested in a humidity chamber. Relative humidity in the chamber was varied in the range of 35%-80% relative humidity (RH) at a constant temperature of 23 °C. Parameters of interest were capacitance and conductance of each sensor material, as well as long term behaviour. Process reversibility has also been considered. The results obtained show a mainly logarithmic response of the paper sensors, with the only exception being cardboard-based sensors. Recycled paper-based sensors exhibit a change in value of three orders of magnitude, whereas cardboard-based sensors have a change in value of few 10s over the entire scope of relative humidity range (RH 35%-90%). Two different types of capacitor sensors have been investigated: lateral (comb) type sensors and modified, perforated flat plate type sensors. The objective of the present work was to identify the most important factors affecting the material performances with humidity, and to contribute to the development of a sensor system supported with a Radio Frequency Identification (RFID) chip directly on the material, for use in smart packaging applications. Therefore, the authors built a passive and a battery-supported wireless module based on SL900A smart sensory tag's IC to achieve UHF-RFID functionality with data logging capability [ABSTRACT FROM AUTHOR]

Shortage of labor and increased work of young people are causing problems in terms of care and welfare of a growing proportion of elderly people. This is a looming social problem because people of advanced ages are increasing. Necessary in the fields of care and welfare, pneumatic artificial muscles in actuators of robots are being examined. Pneumatic artificial muscles have a high output per unit of weight, and they are soft, similarly to human muscles. However, in previous research of robots using pneumatic artificial muscles, rigid sensors were often installed at joints and other locations due to the robots' structures. Therefore, we developed a smart actuator that integrates a bending sensor that functions as a human muscle spindle; it can be externally attached to the pneumatic artificial muscle. This paper reports a smart artificial muscle actuator that can sense contraction, which can be applied to developed self-monitoring and robot posture control. [ABSTRACT FROM AUTHOR]

With characters of low cost, portability, easy disposal, and high accuracy, as well as bulky reduced laboratory equipment, paper-based sensors are getting increasing attention for reliable indoor/outdoor onsite detection with nonexpert operation. They have become powerful analysis tools in trace detection with ultra-low detection limits and extremely high accuracy, resulting in their great popularity in medical detection, environmental inspection, and other applications. Herein, we summarize and generalize the recently reported paper-based sensors based on their application for mechanics, biomolecules, food safety, and environmental inspection. Based on the biological, physical, and chemical analytes-sensitive electrical or optical signals, extensive detections of a large number of factors such as humidity, pressure, nucleic acid, protein, sugar, biomarkers, metal ions, and organic/inorganic chemical substances have been reported via paper-based sensors. Challenges faced by the current paper-based sensors from the fundamental problems and practical applications are subsequently analyzed; thus, the future directions of paper-based sensors are specified for their rapid handheld testing. [ABSTRACT FROM AUTHOR]

Humidity sensors are indispensable for various electronic systems and instrumentations. To develop a new humidity sensing mechanism is the key for the next generation of sensor technology. In this work, a novel flexible paper-based current humidity sensor is proposed. The developed alternating current electroluminescent devices (ACEL) consist of the electroless plating Ni on filter paper and silver nanowires (AgNWs) as the bottom and upper electrodes, and ZnS:Cu as the phosphor layer, respectively. The proposed humidity sensor is based on ACEL with the paper substrate and the ZnS:Cu phosphor layer as the humidity sensing element. The moisture effect on the optical properties of ACELs has been studied firstly. Then, the processing parameters of the paper-based ACELs such as electroless plated bottom electrode and spin-coated phosphor layer as a function of the humidity-sensitive characteristics are investigated. The sensing mechanism of the proposed sensor has been elucidated based on the Q ~ V analysis. The sensor exhibits an excellent linearity ( R 2 = 0.99965 ) within the humidity range from 20% to 90% relative humidity (RH) and shows excellent flexibility. We also demonstrate its potential application in postharvest preservation where the EL light is used for preservation and the humidity can be monitored simultaneously through the current. [ABSTRACT FROM AUTHOR]

Identifying the structural state without baseline data is an important engineering problem in the field of structural health monitoring, which is crucial for assessing the safety condition of structures. In the context of limited accelerometers available, this paper proposes a correlation-based damage identification method using Variational Autoencoder neural networks. The approach involves initially constructing a Variational Autoencoder network model for bridge damage detection, optimizing parameters such as loss functions and learning rates for the model, and ultimately utilizing response data from limited sensors for model training analysis to determine the structural state. The contribution of this paper lies in the ability to identify structural damage without baseline data using response data from a small number of sensors, reducing sensor costs and enhancing practical applications in engineering. The effectiveness of the proposed method is demonstrated through numerical simulations and experimental structures. The results show that the method can identify the location of damage under different damage conditions, exhibits strong robustness in detecting multiple damages, and further enhances the accuracy of identifying bridge structures. [ABSTRACT FROM AUTHOR]

Toxic and nontoxic volatile organic compound (VOC) gases are emitted into the atmosphere from certain solids and liquids as a consequence of wastage and some common daily activities. Inhalation of toxic VOCs has an adverse effect on human health, so it is necessary to monitor their concentration in the atmosphere. In this work, we report on the fabrication of inorganic nanotube (INT)-tungsten disulfide, paper-based graphene–PEDOT:PSS sheet and WS2 nanotube-modified conductive paper-based chemiresistors for VOC gas sensing. The WS2 nanotubes were fabricated by a two-step reaction, that is oxide reduction and sulfurization, carried out at 900 °C. The synthesized nanotubes were characterized by FE-SEM, EDS, XRD, Raman spectroscopy, and TEM. The synthesized nanotubes were 206–267 nm in diameter. The FE-SEM results show the length of the nanotubes to be 4.5–8 µm. The graphene–PEDOT:PSS hybrid conductive paper sheet was fabricated by a continuous coating process. Then, WS2 nanotubes were drop-cast onto conductive paper for fabrication of the chemiresistors. The feasibility and sensitivity of the WS2 nanotube-modified paper-based chemiresistor were tested in four VOC gases at different concentrations at room temperature (RT). Experimental results show the proposed sensor to be more sensitive to butanol gas when the concentration ranges from 50 to 1000 ppm. The limit of detection (LOD) of this chemiresistor for butanol gas was 44.92 ppm. The WS2 nanotube-modified paper-based chemiresistor exhibits good potential as a VOC sensor with the advantages of flexibility, easy fabrication, and low fabrication cost. [ABSTRACT FROM AUTHOR]

Human access to safe water has become a major problem in many parts of the world as increasing human activities continue to spill contaminants into our water systems. To guarantee the protection of the public as well as the environment, a rapid and sensitive way to detect contaminants is required. In this work, a paper-based microbial fuel cell was developed to act as a portable, single-use, on-site water quality sensor. The sensor was fabricated by combining two layers of paper for a simple, low-cost, and disposable design. To facilitate the use of the sensor for on-site applications, the bacterial cells were pre-inoculated onto the device by air-drying. To eliminate any variations, the voltage generated by the microorganism before and after the air-drying process was measured and calculated as an inhibition ratio. Upon the addition of different formaldehyde concentrations (0%, 0.001%, 0.005%, and 0.02%), the inhibition ratios obtained were 5.9 ± 0.7%, 6.9 ± 0.7%, 8.2 ± 0.6%, and 10.6 ± 0.2%, respectively. The inhibition ratio showed a good linearity with the formaldehyde concentrations at R2 = 0.931. Our new sensor holds great promise in monitoring water quality as a portable, low-cost, and on-site sensor. [ABSTRACT FROM AUTHOR]

The zero-velocity update (ZUPT) algorithm is a pivotal advancement in pedestrian navigation accuracy, utilizing foot-mounted inertial sensors. Its key issue hinges on accurately identifying periods of zero-velocity during human movement. This paper introduces an innovative adaptive sliding window technique, leveraging the Fourier Transform to precisely isolate the pedestrian's gait frequency from spectral data. Building on this, the algorithm adaptively adjusts the zero-velocity detection threshold in accordance with the identified gait frequency. This adaptation significantly refines the accuracy in detecting zero-velocity intervals. Experimental evaluations reveal that this method outperforms traditional fixed-threshold approaches by enhancing precision and minimizing false positives. Experiments on single-step estimation show the adaptability of the algorithm to motion states such as slow, fast, and running. Additionally, the paper demonstrates pedestrian trajectory localization experiments under a variety of walking conditions. These tests confirm that the proposed method substantially improves the performance of the ZUPT algorithm, highlighting its potential for pedestrian navigation systems. [ABSTRACT FROM AUTHOR]

Paper-based sensors fabricated using the pencil-on-paper method are expected to find wide usage in many fields owing to their low cost and high reproducibility. Here, hydrogen (H2) detection was realized by applying palladium (Pd) nanoparticles (NPs) to electronic circuits printed on paper using a metal mask and a pencil. We confirmed that multilayered graphene was produced by the pencil, and then characterized Pd NPs were added to the pencil marks. To evaluate the gas-sensing ability of the sensor, its sensitivities and reaction rates in the presence and absence of H2 were measured. In addition, sensing tests performed over a wide range of H2 concentrations confirmed that the sensor had a detection limit as low as 1 ppm. Furthermore, the sensor reacted within approximately 50 s at all H2 concentrations tested. The recovery time of the sensor was 32 s at 1 ppm and 78 s at 1000 ppm. Sensing tests were also performed using Pd NPs of different sizes to elucidate the relationship between the sensing rate and catalyst size. The experimental results confirmed the possibility of fabricating paper-based gas sensors with a superior sensing capability and response rate. [ABSTRACT FROM AUTHOR]

This research paper presents a case study on the application of Metal Oxide Semiconductor (MOX)-based VOC/TVOC sensors for indoor air quality (IAQ) monitoring. This study focuses on the ease of use and the practical benefits of these sensors, drawing insights from measurements conducted in a university laboratory setting. The investigation showcases the straightforward integration of MOX-based sensors into existing IAQ monitoring systems, highlighting their user-friendly features and the ability to provide precise and real-time information on volatile organic compound concentrations. Emphasizing ease of installation, minimal maintenance, and immediate data accessibility, this paper demonstrates the practicality of incorporating MOX-based sensors for efficient IAQ management. The findings contribute to the broader understanding of MOX sensor capabilities, providing valuable insights for those seeking straightforward and effective solutions for indoor air quality monitoring. This case study outlines the feasibility and benefits of utilizing MOX-based sensors in various environments, offering a promising avenue for the widespread adoption of user-friendly technologies in IAQ management. [ABSTRACT FROM AUTHOR]

Algorithms for QRS detection are fundamental in the ECG interpretive processing chain. They must meet several challenges, such as high reliability, high temporal accuracy, high immunity to noise, and low computational complexity. Unfortunately, the accuracy expressed by missed or redundant events statistics is often the only parameter used to evaluate the detector's performance. In this paper, we first notice that statistics of true positive detections rely on researchers' arbitrary selection of time tolerance between QRS detector output and the database reference. Next, we propose a multidimensional algorithm evaluation method and present its use on four example QRS detectors. The dimensions are (a) influence of detection temporal tolerance, tested for values between 8.33 and 164 ms; (b) noise immunity, tested with an ECG signal with an added muscular noise pattern and signal-to-noise ratio to the effect of "no added noise", 15, 7, 3 dB; and (c) influence of QRS morphology, tested on the six most frequently represented morphology types in the MIT-BIH Arrhythmia Database. The multidimensional evaluation, as proposed in this paper, allows an in-depth comparison of QRS detection algorithms removing the limitations of existing one-dimensional methods. The method enables the assessment of the QRS detection algorithms according to the medical device application area and corresponding requirements of temporal accuracy, immunity to noise, and QRS morphology types. The analysis shows also that, for some algorithms, adding muscular noise to the ECG signal improves algorithm accuracy results. [ABSTRACT FROM AUTHOR]

This document discusses the use of machine learning and deep learning in data processing for various sensors. It includes research papers on topics such as toxicity detection in online discussions, evaluation of infrastructure for connected and automated vehicles, an IoT-based system for managing classroom activities during COVID-19, text classification in different languages, multi-delay identification methods, attention heat maps in benchmarks, video quality assessment with packet loss, optimization of 3DCNN models for human activity recognition, an IoT-based connected university system, usability evaluation of Apple MacBook Pro laptops for machine learning research, data augmentation for student behavior identification, defect image classification in ductile cast iron pipe images, supervised classification models for image classification, and sample selection methods for training classification models. The authors provide detailed explanations of each research topic and present their findings and conclusions. [Extracted from the article]

This paper proposes a novel short-term photovoltaic voltage (PV) prediction scheme using IoT sensor data with the two-stage neural network model. It is efficient to use environmental data provided by the meteorological agency to predict future PV generation. However, such environmental data represent the average value of the wide area, and there is a limitation in detecting environmental changes in the specific area where the solar panel is installed. In order to solve such issues, it is essential to establish IoT sensor data to detect environmental changes in the specific area. However, most conventional research focuses only on the efficiency of IoT sensor data without taking into account the timing of data acquisition from the sensors. In real-world scenarios, IoT sensor data is not available precisely when needed for predictions. Therefore, it is necessary to predict the IoT data first and then use it to forecast PV generation. In this paper, we propose a two-stage model to achieve high-accuracy prediction results. In the first stage, we use predicted environmental data to access IoT sensor data in the desired future time point. In the second stage, the predicted IoT sensors and environmental data are used to predict PV generation. Here, we determine the appropriate prediction scheme at each stage by analyzing the model characteristics to increase prediction accuracy. In addition, we show that the proposed prediction scheme could increase prediction accuracy by more than 12% compared to the baseline scheme that only uses a meteorological agency to predict PV generation. [ABSTRACT FROM AUTHOR]

Immunoassay has the advantages of high sensitivity, high specificity, and simple operation, and has been widely used in the detection of mycotoxins. For several years, time-resolved fluorescence immunochromatography (TRFIA) paper-based sensors have attracted much attention as a simple and low-cost field detection technology. However, a traditional TRFIA paper-based sensor is based on antibody labeling, which cannot easily meet the current detection requirements. A second antibody labeling method was used to amplify the fluorescence signal and improve the detection sensitivity. Polystyrene fluorescent microspheres were combined with sheep anti-mouse IgG to prepare fluorescent probes (Eu-IgGs). After the probe fully reacted with the antibody (Eu-IgGs-Abs) in the sample cell, it was deployed on the paper-based sensor using chromatography. Eu-IgGs-Abs that were not bound to the target were captured on the T-line, while those that were bound were captured on the C-line. The paper-based sensor reflected the corresponding fluorescence intensity change. Because a single molecule of the deoxynivalenol antibody could bind to multiple Eu-IgGs, this method could amplify the fluorescence signal intensity on the unit antibody and improve the detection sensitivity. The working standard curve of the sensor was established under the optimum working conditions. It showed the lower limit of detection and higher recovery rate when it was applied to actual samples and compared with other methods. This sensor has the advantages of high sensitivity, good accuracy, and good specificity, saving the amount of antibody consumed and being suitable for rapid field detection of deoxynivalenol. [ABSTRACT FROM AUTHOR]

Technological advancements are enabling new applications within biomedical engineering. As a connection point between the outer environment and the human system, the oral cavity offers unique opportunities for sensing technologies. This paper systematically reviews the performance of measurement systems tested in the human oral cavity. Performance was defined by metrics related to accuracy and agreement estimation. A comprehensive search identifying human studies that reported on the accuracy or agreement of intraoral sensors found 85 research papers. Most of the literature (62%) was in dentistry, followed by neurology (21%), and physical medicine and rehabilitation (12%). The remaining papers were on internal medicine, obstetrics, and aerospace medicine. Most of the studies applied force or pressure sensors (32%), while optical and image sensors were applied most widely across fields. The main challenges for future adoption include the lack of large human trials, the maturity of emerging technologies (e.g., biochemical sensors), and the absence of standardization of evaluation in specific fields. New research should aim to employ robust performance metrics to evaluate their systems and incorporate real-world evidence as part of the evaluation process. Oral cavity sensors offer the potential for applications in healthcare and wellbeing, but for many technologies, more research is needed. [ABSTRACT FROM AUTHOR]

The continuously increasing number of mobile devices actively being used in the world amounted to approximately 6.8 billion by 2022. Consequently, this implies a substantial increase in the amount of personal data collected, transported, processed, and stored. The authors of this paper designed and implemented an integrated personal health data management system, which considers data-driven software and hardware sensors, comprehensive data privacy techniques, and machine-learning-based algorithmic models. It was determined that there are very few relevant and complete surveys concerning this specific problem. Therefore, the current scientific research was considered, and this paper comprehensively analyzes the importance of deep learning techniques that are applied to the overall management of data collected by data-driven soft sensors. This survey considers aspects that are related to demographics, health and body parameters, and human activity and behaviour pattern detection. Additionally, the relatively complex problem of designing and implementing data privacy mechanisms, while ensuring efficient data access, is also discussed, and the relevant metrics are presented. The paper concludes by presenting the most important open research questions and challenges. The paper provides a comprehensive and thorough scientific literature survey, which is useful for any researcher or practitioner in the scope of data-driven soft sensors and privacy techniques, in relation to the relevant machine-learning-based models. [ABSTRACT FROM AUTHOR]

Rice is a staple food that feeds nearly half of the world's population. With the population of our planet expected to keep growing, it is crucial to carry out accurate mapping, monitoring, and assessments since these could significantly impact food security, climate change, spatial planning, and land management. Using the PRISMA systematic review protocol, this article identified and selected 122 scientific articles (journals papers and conference proceedings) addressing different remote sensing-based methodologies to map paddy croplands, published between 2010 and October 2022. This analysis includes full coverage of the mapping of rice paddies and their various stages of crop maturity. This review paper classifies the methods based on the data source: (a) multispectral (62%), (b) multisource (20%), and (c) radar (18%). Furthermore, it analyses the impact of machine learning on those methodologies and the most common algorithms used. We found that MODIS (28%), Sentinel-2 (18%), Sentinel-1 (15%), and Landsat-8 (11%) were the most used sensors. The impact of Sentinel-1 on multisource solutions is also increasing due to the potential of backscatter information to determine textures in different stages and decrease cloud cover constraints. The preferred solutions include phenology algorithms via the use of vegetation indices, setting thresholds, or applying machine learning algorithms to classify images. In terms of machine learning algorithms, random forest is the most used (17 times), followed by support vector machine (12 times) and isodata (7 times). With the continuous development of technology and computing, it is expected that solutions such as multisource solutions will emerge more frequently and cover larger areas in different locations and at a higher resolution. In addition, the continuous improvement of cloud detection algorithms will positively impact multispectral solutions. [ABSTRACT FROM AUTHOR]

To meet the real demand for broadband full-band high-gain antenna sensors in the process of partial discharge (PD) Ultra-High frequency (UHF) detection test and online monitoring of power equipment, this paper builds a resonant cavity monopole UHF antenna sensor based on Fabry–Perot resonant cavity antenna technology, conducts the sensor Voltage Standing Wave Ratio (VSWR) optimization study using curved flow technology, conducts the sensor gain optimization study using slot dual resonant structure, and, finally, tests the sensor performance using the built PD detection test platform. The resonant cavity monopole antenna exhibits outstanding VSWR performance in the frequency range of 0.37 GHz–3 GHz, according to simulation and test data: the average gain in the frequency range of 0.3 GHz–3 GHz is 4.92 dBi, and the highest gain at the primary resonant frequency of 1.0 GHz is 7.16 dBi, with good radiation performance over the whole frequency spectrum. The electromagnetic pulse signal sensed by the UHF sensor developed in this paper can demonstrate the energy spectrum distribution characteristics of PD radiation electromagnetic wave signal more comprehensively, laying a firm technical foundation for thoroughly understanding the electromagnetic wave radiation characteristics of various types of PD insulation defects of various power equipment and the selection of a specific direction for its supporting optimization. [ABSTRACT FROM AUTHOR]

Magnetoelectric (ME) sensors cannot effectively detect broadband magnetic field signals due to their narrow bandwidth, and existing readout circuits are unable to vary the bandwidth of the sensors. To expand the bandwidth, this paper introduces a negative-feedback readout circuit, fabricated by introducing a negative-feedback compensation circuit based on the direct readout circuit of the ME sensor. The negative-feedback compensation circuit contains a current amplifier, a feedback resistor, and a feedback coil. For this purpose, a Metglas/PVDF/Metglas ME sensor was prepared. Experimental measurements show that there is a six-fold difference between the maximum and minimum values of the ME voltage coefficients in the 6–39 kHz frequency band for the ME sensor without the negative-feedback compensation circuit when the sensor operates at the optimal bias magnetic field. However, the ME voltage coefficient in this band remains stable, at 900 V/T, after the charge amplification of the direct-reading circuit and the negative-feedback circuit. In addition, experimental results show that this negative-feedback readout circuit does not increase the equivalent magnetic noise of the sensor, with a noise level of 240 pT/√Hz in the frequency band lower than 25 kHz, 63 pT/√Hz around the resonance frequency of 30 kHz, and 620 pT/√Hz at 39 kHz. This paper proposes a negative-feedback readout circuit based on the direct readout circuit, which greatly increases the bandwidth of ME sensors and promotes the widespread application of ME sensors in the fields of broadband weak magnetic signal detection and DBS electrode positioning. [ABSTRACT FROM AUTHOR]

Hand gesture recognition, which is one of the fields of human–computer interaction (HCI) research, extracts the user's pattern using sensors. Radio detection and ranging (RADAR) sensors are robust under severe environments and convenient to use for hand gestures. The existing studies mostly adopted continuous-wave (CW) radar, which only shows a good performance at a fixed distance, which is due to its limitation of not seeing the distance. This paper proposes a hand gesture recognition system that utilizes frequency-shift keying (FSK) radar, allowing for a recognition method that can work at the various distances between a radar sensor and a user. The proposed system adopts a convolutional neural network (CNN) model for the recognition. From the experimental results, the proposed recognition system covers the range from 30 cm to 180 cm and shows an accuracy of 93.67% over the entire range. [ABSTRACT FROM AUTHOR]

Colorimetric sensors are widely used for various sensing applications due to their specificity, sensitivity, cost-effectiveness, and ease of use. They have been developed to detect a range of substances including metal and non-metal ions, proteins, small molecules, gases, viruses and bacteria, DNA/RNA, acidity/base, and biomarkers in clinical diagnostics. Recent advancements in nanomaterials have played a significant role in the development of colorimetric sensors. This article provides an overview of the field, discussing the design and fabrication of sensors for various applications such as alkaline vapors, dopamine detection, uric acid detection, organic thiol analysis, H2O2 and glucose detection, HPV detection, metal ion detection, protein identification, synthetic food dye detection, and phenolic compound detection. These sensors offer promising applications in different fields, providing rapid, low-cost, and portable solutions for detection. The article also highlights the development of multifunctional sensors and the integration of colorimetric sensors with smartphones, color-recording devices, and optical sensing arrays, which have improved the portability, sensitivity, and specificity of detection. The article concludes that with further advancements in materials and analytical chemistry, colorimetric sensors will continue to contribute to the fields of chemical and biological sensing. [Extracted from the article]

Monitoring the quality of drinking water is a crucial responsibility for all water infrastructure networks, as it guarantees access to clean water for the communities they serve. With water infrastructure deteriorating due to age and neglect, drinking water violations are on the rise in the US, underscoring the need for improved monitoring capabilities. Among the different sensor technologies, graphene-based chemiresistors have emerged as a promising technology for water quality monitoring due to advantages such as simple design, sensitivity, and selectivity. This review paper provides an overview of recent advances in the development of graphene-based chemiresistors for water quality monitoring, including principles of chemiresistive sensing, sensor design and functionalization, and performance of devices reported in the literature. The paper also discusses challenges and opportunities in the field and highlights future research directions. The development of graphene-based chemiresistors has the potential to revolutionize water quality monitoring by providing highly sensitive and cost-effective sensors that can be integrated into existing infrastructure for real-time monitoring. [ABSTRACT FROM AUTHOR]

This paper presents an occupant localization technique that determines the location of individuals in indoor environments by analyzing the structural vibrations of the floor caused by their footsteps. Structural vibration waves are difficult to measure as they are influenced by various factors, including the complex nature of wave propagation in heterogeneous and dispersive media (such as the floor) as well as the inherent noise characteristics of sensors observing the vibration wavefronts. The proposed vibration-based occupant localization technique minimizes the errors that occur during the signal acquisition time. In this process, the likelihood function of each sensor—representing where the occupant likely resides in the environment—is fused to obtain a consensual localization result in a collective manner. In this work, it becomes evident that the above sources of uncertainties can render certain sensors deceptive, commonly referred to as "Byzantines." Because the ratio of Byzantines among the set sensors defines the success of the collective localization results, this paper introduces a Byzantine sensor elimination (BSE) algorithm to prevent the unreliable information of Byzantine sensors from affecting the location estimations. This algorithm identifies and eliminates sensors that generate erroneous estimates, preventing the influence of these sensors on the overall consensus. To validate and benchmark the proposed technique, a set of previously conducted controlled experiments was employed. The empirical results demonstrate the proposed technique's significant improvement ( 3 ~ 0%) over the baseline approach in terms of both accuracy and precision. [ABSTRACT FROM AUTHOR]

Deep learning algorithms for object detection used in autonomous vehicles require a huge amount of labeled data. Data collecting and labeling is time consuming and, most importantly, in most cases useful only for a single specific sensor application. Therefore, in the course of the research which is presented in this paper, the LiDAR pedestrian detection algorithm was trained on synthetically generated data and mixed (real and synthetic) datasets. The road environment was simulated with the application of the 3D rendering Carla engine, while the data for analysis were obtained from the LiDAR sensor model. In the proposed approach, the data generated by the simulator are automatically labeled, reshaped into range images and used as training data for a deep learning algorithm. Real data from Waymo open dataset are used to validate the performance of detectors trained on synthetic, real and mixed datasets. YOLOv4 neural network architecture is used for pedestrian detection from the LiDAR data. The goal of this paper is to verify if the synthetically generated data can improve the detector's performance. Presented results prove that the YOLOv4 model trained on a custom mixed dataset achieved an increase in precision and recall of a few percent, giving an F1-score of 0.84. [ABSTRACT FROM AUTHOR]

Localization is a keystone for a robot to work within its environment and with other robots. There have been many methods used to solve this problem. This paper deals with the use of beacon-based localization to answer the research question: Can ultra-wideband technology be used to effectively localize a robot with sensor fusion? This paper has developed an innovative solution for creating a sensor fusion platform that uses ultra-wideband communication as a localization method to allow an environment to be perceived and inspected in three dimensions from multiple perspectives simultaneously. A series of contributions have been presented, supported by an in-depth literature review regarding topics in this field of knowledge. The proposed method was then designed, built, and tested successfully in two different environments exceeding its required tolerances. The result of the testing and the ideas formulated throughout the paper were discussed and future work outlined on how to build upon this work in potential academic papers and projects. [ABSTRACT FROM AUTHOR]

The development of the transportation industry has led to an increasing number of overloaded vehicles, which reduces the service life of asphalt pavements. Currently, the traditional vehicle weighing method not only involves heavy equipment but also has a low weighing efficiency. To deal with the defects in the existing vehicle weighing system, this paper developed a road-embedded piezoresistive sensor based on self-sensing nanocomposites. The sensor developed in this paper adopts an integrated casting and encapsulation technology, in which an epoxy resin/MWCNT nanocomposite is used for the functional phase, and an epoxy resin/anhydride curing system is used for the high-temperature resistant encapsulation phase. The compressive stress-resistance response characteristics of the sensor were investigated by calibration experiments with an indoor universal testing machine. In addition, the sensors were embedded in the compacted asphalt concrete to validate the applicability to the harsh environment and back-calculate the dynamic vehicle loads on the rutting slab. The results show that the response relationship between the sensor resistance signal and the load is in accordance with the GaussAmp formula. The developed sensor not only survives effectively in asphalt concrete but also enables dynamic weighing of the vehicle loads. Consequently, this study provides a new pathway to develop high-performance weigh-in-motion pavement sensors. [ABSTRACT FROM AUTHOR]

In many countries, water quality monitoring is limited due to the high cost of logistics and professional equipment such as multiparametric probes. However, low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks, providing valuable water quality information to the public. To facilitate the widespread adoption of these sensors, it is crucial to identify which sensors can accurately measure key water quality parameters, their manufacturers, and their reliability in different environments. Although there is an increasing body of work utilizing low-cost water quality sensors, many questions remain unanswered. To address this issue, a systematic literature review was conducted to determine which low-cost sensors are being used for remote water quality monitoring. The results show that there are three primary vendors for the sensors used in the selected papers. Most sensors range in price from US$6.9 to US$169.00 but can cost up to US$500.00. While many papers suggest that low-cost sensors are suitable for water quality monitoring, few compare low-cost sensors to reference devices. Therefore, further research is necessary to determine the reliability and accuracy of low-cost sensors compared to professional devices. [ABSTRACT FROM AUTHOR]

This paper presents a method for simplifying and quantizing a deep neural network (DNN)-based object detector to embed it into a real-time edge device. For network simplification, this paper compares five methods for applying channel pruning to a residual block because special care must be taken regarding the number of channels when summing two feature maps. Based on the comparison in terms of detection performance, parameter number, computational complexity, and processing time, this paper discovers the most satisfying method on the edge device. For network quantization, this paper compares post-training quantization (PTQ) and quantization-aware training (QAT) using two datasets with different detection difficulties. This comparison shows that both approaches are recommended in the case of the easy-to-detect dataset, but QAT is preferable in the case of the difficult-to-detect dataset. Through experiments, this paper shows that the proposed method can effectively embed the DNN-based object detector into an edge device equipped with Qualcomm's QCS605 System-on-Chip (SoC), while achieving a real-time operation with more than 10 frames per second. [ABSTRACT FROM AUTHOR]

In this paper, three different structures of surface plasmon resonance (SPR) sensors based on the Kretschmann configuration: Au/SiO2 thin film structure, Au/SiO2 nanospheres and Au/SiO2 nanorods are designed by adding three different forms of SiO2 materials behind the gold film of conventional Au-based SPR sensors. The effects of SiO2 shapes on the SPR sensor are investigated through modeling and simulation with the refractive index of the media to be measured ranging from 1.330 to 1.365. The results show that the sensitivity of Au/SiO2 nanospheres could be as high as 2875.4 nm/RIU, which is 25.96% higher than that of the sensor with a gold array. More interestingly, the increase in sensor sensitivity is attributed to the change in SiO2 material morphology. Therefore, this paper mainly explores the influence of the shape of the sensor-sensitizing material on the performance of the sensor. [ABSTRACT FROM AUTHOR]

Ground contact time (GCT) is one of the most relevant factors when assessing running performance in sports practice. In recent years, inertial measurement units (IMUs) have been widely used to automatically evaluate GCT, since they can be used in field conditions and are friendly and easy to wear devices. In this paper we describe the results of a systematic search, using the Web of Science, to assess what reliable options are available to GCT estimation using inertial sensors. Our analysis reveals that estimation of GCT from the upper body (upper back and upper arm) has rarely been addressed. Proper estimation of GCT from these locations could permit an extension of the analysis of running performance to the public, where users, especially vocational runners, usually wear pockets that are ideal to hold sensing devices fitted with inertial sensors (or even using their own cell phones for that purpose). Therefore, in the second part of the paper, an experimental study is described. Six subjects, both amateur and semi-elite runners, were recruited for the experiments, and ran on a treadmill at different paces to estimate GCT from inertial sensors placed at the foot (for validation purposes), the upper arm, and upper back. Initial and final foot contact events were identified in these signals to estimate the GCT per step, and compared to times estimated from an optical MOCAP (Optitrack), used as the ground truth. We found an average error in GCT estimation of 0.01 s in absolute value using the foot and the upper back IMU, and of 0.05 s using the upper arm IMU. Limits of agreement (LoA, 1.96 times the standard deviation) were [−0.01 s, 0.04 s], [−0.04 s, 0.02 s], and [0.0 s, 0.1 s] using the sensors on the foot, the upper back, and the upper arm, respectively. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an innovative technology with billions of sensors in various IoT applications. Important elements used in the IoT are sensors that collect data for desired analyses. The IoT and sensors are very important in smart cities, smart agriculture, smart education, healthcare systems, and other applications. The healthcare system uses the IoT to meet global health challenges, and the newest example is COVID-19. Demand has increased during COVID-19 for healthcare to reach patients remotely and digitally at their homes. The IoT properly monitors patients using an interconnected network to overcome the issues of healthcare services. The aim of this paper is to discuss different applications, technologies, and challenges related to the healthcare system. Different databases were searched using keywords in Google Scholar, Elsevier, PubMed, ACM, ResearchGate, Scopus, Springer, etc. This paper discusses, highlights, and identifies the applications of IoT healthcare systems to provide research directions to healthcare, academia, and researchers to overcome healthcare system challenges. Hence, the IoT can be beneficial by providing better treatments using the healthcare system efficiently. In this paper, the integration of the IoT with smart technologies not only improves computation, but will also allow the IoT to be pervasive, profitable, and available anytime and anywhere. Finally, some future directions and challenges are discussed, along with useful suggestions that can assist the IoT healthcare system during COVID-19 and in a severe pandemic. [ABSTRACT FROM AUTHOR]

In view of the insufficient signal detection sensitivity of Gas Insulated Switchgear (GIS), partial discharge (PD), ultra-high frequency (UHF), and failure to conform with GIS surface structure when the existing rigid stereo structure UHF sensor is built in, this paper, using rectangular patch antenna equivalent technique, trapezoidal ground plane technique, and coplanar waveguide (CPW) feed line index asymptotic linearization technique, conducts research on a flexible built-in high-sensitivity elliptic monopole antenna. The flexible antenna, with a thickness of only 0.28 mm, can be kept at a voltage standing wave ratio (VSWR) of less than three in the 300 MHz to 3 GHz band under the curvature radius of 0, 100, 300, and 500 mm, and at less than two in the 650 MHz to 3 GHz band. Through the true 220 kV-GIS partial discharge experimental platform built to analyze the high frequency electromagnetic wave detection performance of the built-in flexible antenna, it is shown that the flexible built-in high-sensitivity elliptical monopole antenna designed in this paper can effectively detect the characteristic signals of high-frequency electromagnetic waves emitted by partial discharges with an average discharge amount below 10 pC. [ABSTRACT FROM AUTHOR]

It is possible to construct cost-efficient three-dimensional (3D) or four-dimensional (4D) scanning systems using multiple affordable off-the-shelf RGB-D sensors to produce high-quality reconstructions of 3D objects. However, the quality of these systems' reconstructions is sensitive to a number of factors in reconstruction pipelines, such as multi-view calibration, depth estimation, 3D reconstruction, and color mapping accuracy, because the successive pipelines to reconstruct 3D meshes from multiple active stereo sensors are strongly correlated with each other. This paper categorizes the pipelines into sub-procedures and analyze various factors that can significantly affect reconstruction quality. Thus, this paper provides analytical and practical guidelines for high-quality 3D reconstructions with off-the-shelf sensors. For each sub-procedure, this paper shows comparisons and evaluations of several methods using data captured by 18 RGB-D sensors and provide analyses and discussions towards robust 3D reconstruction. Through various experiments, it has been demonstrated that significantly more accurate 3D scans can be obtained with the considerations along the pipelines. We believe our analyses, benchmarks, and guidelines will help anyone build their own studio and their further research for 3D reconstruction. [ABSTRACT FROM AUTHOR]

The field of organic-borne biomarkers has been gaining relevance due to its suitability for diagnosing pathologies and health conditions in a rapid, accurate, non-invasive, painless and low-cost way. Due to the lack of analytical techniques with features capable of analysing such a complex matrix as the human breath, the academic community has focused on developing electronic noses based on arrays of gas sensors. These sensors are assembled considering the excitability, sensitivity and sensing capacities of a specific nanocomposite, graphene. In this way, graphene-based sensors can be employed for a vast range of applications that vary from environmental to medical applications. This review work aims to gather the most relevant published papers under the scope of "Graphene sensors" and "Biomarkers" in order to assess the state of the art in the field of graphene sensors for the purposes of biomarker identification. During the bibliographic search, a total of six pathologies were identified as the focus of the work. They were lung cancer, gastric cancer, chronic kidney diseases, respiratory diseases that involve inflammatory processes of the airways, like asthma and chronic obstructive pulmonary disease, sleep apnoea and diabetes. The achieved results, current development of the sensing sensors, and main limitations or challenges of the field of graphene sensors are discussed throughout the paper, as well as the features of the experiments addressed. [ABSTRACT FROM AUTHOR]

The paper explores the application of Steiner's most-frequent-value (MFV) statistical method in sensor data analysis. The MFV is introduced as a powerful tool to identify the most-common value in a dataset, even when data points are scattered, unlike traditional mode calculations. Furthermore, the paper underscores the MFV method's versatility in estimating environmental gamma background blue (the natural level of gamma radiation present in the environment, typically originating from natural sources such as rocks, soil, and cosmic rays), making it useful in scenarios where traditional statistical methods are challenging. It presents the MFV approach as a reliable technique for characterizing ambient radiation levels around large-scale experiments, such as the DEAP-3600 dark matter detector. Using the MFV alongside passive sensors such as thermoluminescent detectors and employing a bootstrapping approach, this study showcases its effectiveness in evaluating background radiation and its aptness for estimating confidence intervals. In summary, this paper underscores the importance of the MFV and bootstrapping as valuable statistical tools in various scientific fields that involve the analysis of sensor data. These tools help in estimating the most-common values and make data analysis easier, especially in complex situations, where we need to be reasonably confident about our estimated ranges. Our calculations based on MFV statistics and bootstrapping indicate that the ambient radiation level in Cube Hall at SNOLAB is 35.19 μ Gy for 1342 h of exposure, with an uncertainty range of + 3.41 to − 3.59 μ Gy, corresponding to a 68.27% confidence level. In the vicinity of the DEAP-3600 water shielding, the ambient radiation level is approximately 34.80 μ Gy, with an uncertainty range of + 3.58 to − 3.48 μ Gy, also at a 68.27% confidence level. These findings offer crucial guidance for experimental design at SNOLAB, especially in the context of dark matter research. [ABSTRACT FROM AUTHOR]

MXenes are a new family of two-dimensional (2D) nanomaterials. They are inorganic compounds of metal carbides/nitrides/carbonitrides. Titanium carbide MXene (Ti 3 C 2 -MXene) was the first 2D nanomaterial reported in the MXene family in 2011. Owing to the good physical properties of Ti 3 C 2 -MXenes (e.g., conductivity, hydrophilicity, film-forming ability, elasticity) various applications in wearable sensors, energy harvesters, supercapacitors, electronic devices, etc., have been demonstrated. This paper presents the development of a piezoresistive Ti 3 C 2 -MXene sensor followed by experimental investigations of its dynamic response behavior when subjected to structural impacts. For the experimental investigations, an inclined ball impact test setup is constructed. Stainless steel balls of different masses and radii are used to apply repeatable impacts on a vertical cantilever plate. The Ti 3 C 2 -MXene sensor is attached to this cantilever plate along with a commercial piezoceramic sensor, and their responses for the structural impacts are compared. It is observed from the experiments that the average response times of the Ti 3 C 2 -MXene sensor and piezoceramic sensor are 1.28 ± 0.24 μ s and 31.19 ± 24.61 μ s, respectively. The fast response time of the Ti 3 C 2 -MXene sensor makes it a promising candidate for monitoring structural impacts. [ABSTRACT FROM AUTHOR]

LoRaWAN is a communication protocol designed especially for Internet of Things (IoT) applications that offers benefits such as long-distance connection and low power consumption. Due to the characteristics of LoRaWAN, this technology has gained great popularity in various IoT applications, such as environmental monitoring, smart agriculture, and applications in the areas of health and mobility, among others. Given this situation, the objective of this work is to provide an in-depth overview of LoRaWAN technology in terms of its applications, as well as the devices that have been used for the development of such applications. Additionally, this work reviews what other areas of LoRaWAN have been covered in different scientific articles, i.e., performance improvement and security. Among the main results of this study though analyzing previous works, we can say that most of them have been developed in the area of environmental monitoring and have used low-cost devices such as Arduinos, Raspberry Pis, and relatively low-cost commercial products such as those of the Semtech and STMicroelectronics brands. The analysis of the present work shows objectively and formally that LoRaWAN technology can be applied in various applications and that there are many studies that try to optimize its performance and security. This paper seeks to identify and describe the most relevant applications of LoRaWAN in different sectors, such as agriculture, health, and environmental monitoring, among others, and the challenges and solutions found in each area. This literature review will provide a valuable reference to understand the potential and opportunities offered by LoRaWAN technology. [ABSTRACT FROM AUTHOR]

The paper deals with a brief overview of magnetoelastic sensors and magnetoelastic sensors used in general for sensing bending forces, either directly or sensing bent structures, and defines the current state of the art. Bulk magnetoelastic force sensors are usually manufactured from transformer sheets or amorphous alloys. In praxis, usually, a compressive force is sensed by bulk magnetoelastic sensors; however, in this paper, the sensor is used for the measurement of bending forces, one reason being that the effect of such forces is easily experimentally tested, whereas compressive forces acting on a single sheet make buckling prevention a challenge. The measurement of the material characteristics that served as inputs into a FEM simulation model of the sensor is presented and described. The used material was considered to be mechanically and magnetically isotropic and magnetically nonlinear, even though the real sheet showed anisotropic behavior to some degree. A sinusoidal magnetizing current waveform was used in the experimental part of this paper, which was created by a current source. The effects of various frequencies, amplitudes, and sensor geometries were tested. The experimental part of this paper studies the sensors' RMS voltage changes to different loadings that bend the sheet out of its plane. The output voltage was the induced voltage in the secondary coil and was further analyzed to compute the linearity and sensitivity of the sensor at the specific current characteristic. It was found that for the given material, the most favorable operating conditions are obtained with higher frequency signals and higher excitation current amplitudes. The linearity of the sensor can be improved by placing the holes of the windings at different angles than 90° and by placing them further apart along the sheet's length. The current source was created by a simple op-amp voltage-to-current source controlled by a signal generator, which created a stable waveform. It was found that transformer sheet bending sensors with the dimensions described in this paper are suitable for the measurement of small forces in the range of up to 2 N for the shorter sensors and approximately 0.2 N for the longer sensors. [ABSTRACT FROM AUTHOR]

Chemical pressure storage tanks are containers designed to store fluids at high pressures, i.e., their internal pressure is higher than the atmospheric pressure. They can come in various shapes and sizes, and may be fabricated from a variety of materials. As aggressive chemical agents stored under elevated pressures can cause significant damage to both people and the environment, it is essential to develop systems for the early damage detection and the monitoring of structural integrity of such vessels. The development of early damage detection and condition monitoring systems could also help to reduce the maintenance costs associated with periodic inspections of the structure and unforeseen operational breaks due to unmonitored damage development. It could also reduce the related environmental burden. In this paper, we consider a hybrid material composed of glass-fiber-reinforced polymers (GFRPs) and a polyethylene (PE) layer that is suitable for pressurized chemical storage tank manufacturing. GFRPs are used for the outer layer of the tank structure and provides the dominant part of the construction stiffness, while the PE layer is used for protection against the stored chemical medium. The considered damage scenarios include simulated cracks and an erosion of the inner PE layer, as these can be early signs of structural damage leading to the leakage of hazardous liquids, which could compromise safety and, possibly, harm the environment. For damage detection, PZT sensors were selected due to their widely recognized applicability for the purpose of structural health monitoring. For sensor installation, it was assumed that only the outer GFRP layer was available as otherwise sensors could be affected by the stored chemical agent. The main focus of this paper is to verify whether elastic waves excited by PZT sensors, which are installed on the outer GFRP layer, can penetrate the GFRP and PE interface and can be used to detect damage occurring in the inner PE layer. The efficiency of different signal characteristics used for structure evaluation is compared for various frequencies and durations of the excitation signal as well as feasibility of PZT sensor application for passive acquisition of acoustic emission signals is verified. [ABSTRACT FROM AUTHOR]

This paper reports on the design, modeling, analysis, and evaluation of a micro-electromechanical systems acoustic sensor and the novel design of an acoustic vector sensor array (AVS) which utilized this acoustic sensor. This research builds upon previous work conducted to develop a small, lightweight, portable system for the detection and location of quiet or distant acoustic sources of interest. This study also reports on the underwater operation of this sensor and AVS. Studies were conducted in the lab and in the field utilizing multiple acoustic sources (e.g., generated tones, gun shots, drones). The sensor operates at resonance, providing for high acoustic sensitivity and a high signal-to-noise ratio (SNR). The sensor demonstrated a maximum SNR of 88 dB with an associated sensitivity of −84.6 dB re 1 V/μPa (59 V/Pa). The sensor design can be adjusted to set a specified resonant frequency to align with a known acoustic signature of interest. The AVS demonstrated an unambiguous, 360-degree, in-plane, azimuthal coverage and was able to provide an acoustic direction of arrival to an average error of within 3.5° during field experiments. The results of this research demonstrate the potential usefulness of this sensor and AVS design for specific applications. [ABSTRACT FROM AUTHOR]

Over the past twenty years, the use of electronic mobile sensors by children and youngsters has played a significant role in environmental education projects in Portugal. This paper describes a research synthesis of a set of case studies (environmental education projects) on the use of sensors as epistemic mediators, evidencing the technological, environmental, social, and didactical dimensions of environmental education projects over the last two decades in Portugal. The triggers of the identified changes include: (i) the evolution of sensors, information and communication platforms, and mobile devices; (ii) the increasing relevance of environmental citizenship and participation; (iii) the recognition of the role of multisensory situated information and quantitative information in environmental citizenship; (iv) the cause–effect relation between didactical strategies and environmental education goals; (v) the potential of sensory and epistemic learners' practices in the environment to produce learning outcomes and new knowledge. To support the use of senses and sensors in environmental education projects, the SEAM model was created based on the developed research synthesis. [ABSTRACT FROM AUTHOR]

Dashcams are considered video sensors, and the number of dashcams installed in vehicles is increasing. Native dashcam video players can be used to view evidence during investigations, but these players are not accepted in court and cannot be used to extract metadata. Digital forensic tools, such as FTK, Autopsy and Encase, are specifically designed for functions and scripts and do not perform well in extracting metadata. Therefore, this paper proposes a dashcam forensics framework for extracting evidential text including time, date, speed, GPS coordinates and speed units using accurate optical character recognition methods. The framework also transcribes evidential speech related to lane departure and collision warning for enabling automatic analysis. The proposed framework associates the spatial and temporal evidential data with a map, enabling investigators to review the evidence along the vehicle's trip. The framework was evaluated using real-life videos, and different optical character recognition (OCR) methods and speech-to-text conversion methods were tested. This paper identifies that Tesseract is the most accurate OCR method that can be used to extract text from dashcam videos. Also, the Google speech-to-text API is the most accurate, while Mozilla's DeepSpeech is more acceptable because it works offline. The framework was compared with other digital forensic tools, such as Belkasoft, and the framework was found to be more effective as it allows automatic analysis of dashcam evidence and generates digital forensic reports associated with a map displaying the evidence along the trip. [ABSTRACT FROM AUTHOR]

Several sensor families are included in this group, such as sensors for assistance systems, sensors for vehicle dynamics, sensors for capturing information from the vehicle's surroundings, sensors for capturing data from the vehicle interior, sensors for driver supervision, etc. New vehicles include several systems that improve their safety, comfort, and performance. Ref. [[14]] focuses on the development of a full-stack autonomous vehicle using a limited amount of sensors to avoid creating a very expensive autonomous vehicle with limited application. A key part of these systems is the use of several sensors around the vehicle, capturing information from the vehicle and its surroundings. [Extracted from the article]

In this paper, we present a laser triangulation sensor to measure the distance between the sensor and an object without contact using a diffraction slit rather than a traditional lens. We show that by replacing the lens with a slit, we can exploit the resulting diffraction pattern to have finer and yet simpler image analysis, yielding better estimation of the distance to the object. To test our hypothesis, we build a precision position table and a laser triangulation sensor, generate large data sets to test different estimation algorithms on various materials, and compare data acquisition using a traditional lens versus using a slit. We show that position estimation when using a slit is both more precise and more accurate than comparable methods using a lens. [ABSTRACT FROM AUTHOR]

As graphene-related technology advances, the benefits of graphene metamaterials become more apparent. In this study, a surface-isolated exciton-based absorber is built by running relevant simulations on graphene, which can achieve more than 98% perfect absorption at multiple frequencies in the MWIR (MediumWavelength Infra-Red (MWIR) band as compared to the typical absorber. The absorber consists of three layers: the bottom layer is gold, the middle layer is dielectric, and the top layer is patterned with graphene. Tunability was achieved by electrically altering graphene's Fermi energy, hence the position of the absorption peak. The influence of graphene's relaxation time on the sensor is discussed. Due to the symmetry of its structure, different angles of light source incidence have little effect on the absorption rate, leading to polarization insensitivity, especially for TE waves, and this absorber has polarization insensitivity at ultra-wide-angle degrees. The sensor is characterized by its tunability, polarisation insensitivity, and high sensitivity, with a sensitivity of up to 21.60 THz/refractive index unit (RIU). This paper demonstrates the feasibility of the multi-frequency sensor and provides a theoretical basis for the realization of the multi-frequency sensor. This makes it possible to apply it to high-sensitivity sensors. [ABSTRACT FROM AUTHOR]