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In this study, by combining the polymers of polyvinylidene fluoride (PVDF) and 10,12-pentacosadinoic acid and adding the zinc oxide (ZnO) nanoparticles to their solution, three component composite nanofibers were electrospun and their thermo-chromic reversibility were investigated. Different amounts of ZnO nanoparticles based on the weight of PVDF were added to the mixture of PVDF/PDA (polydiacetylene) polymers and composite nanofibers were produced by electrospinning method. Adding ZnO to the electrospinning solvent affects nanofibers structural morphology and helps formation of beads in the electrospun nanofibers in comparison with that of PVDF/PDA sample. The crystal structure of the samples and the chemical changes in the structure of the polymers indicate that the addition of ZnO nanoparticles increases the stability of the nanofiber structure against heat and chromic change. Samples containing more than 2% ZnO with different UV irradiation times were reversible up to 140 °C. The best thermo-chromic reversibility of the PVDF/PDA/ZnO composite nanofibers could be obtained by sample containing 3% ZnO nanoparticles where the minimum irreversible temperature with an UV irradiation time of 5 min for this sample is 170 °C. [ABSTRACT FROM AUTHOR]

An automated device is designed to measure the density of a liquid material using hydrostatic pressure method. A low cost pressure sensor is calibrated and used to get highly accurate readings. The calibration is done by measuring the pressure values vs. the generated voltage signal. The calibration has been challenging due to the low accuracy of the sensor but proved to be highly effective in applications. The interface is developed using a microcontroller, motor drives, analog to digital converters and sensors. The device is designed to get several readings automatically by changing the positions of the device/liquid column heights to increase the accuracy. Also the device can be programmed to measure the real time density of a liquid continuously. The readings were analyzed and averaged by a software developed in python language. The instruments accuracy was tested against 3 liquid types, water, coconut oil, kerosene oil, and showed a low error (0.007%, 0.001%, and 0.002% respectively) compared to the readings of a standard Pycnometer. The low error percentages confirm the accuracy of the device and the effectiveness of the sensor calibrations. [ABSTRACT FROM AUTHOR]

Density functional theory (DFT) driven by the quantum ESPRESSO code was used in this study to investigate the structural, opto-electronic, and thermoelectric properties of ternary perovskite chalcogenide compound. This is to examine their possible use in optoelectronics and reducing the dependency on silicon and fossil fuel. The Perovskite compounds crystalize in the cubic phase with a space group Pm-3m. The volume versus energy is fitted by the Birch-Murnaghan equation of state which yielded the equilibrium lattice constant of 8.353, 8.488, 8.629 and 8.806, bulk modulus of 255.8, 242.7, 233.5 and 222.4, for BiTlS3 , BiTlS2 Se1 , BiTlS1 Se2 and BiTlSe3 . Indirect band gaps of 2.6 eV, 2.7 eV, 2.9 eV, and 3.2 eV were obtained for BiTlS3 , BiTlS2 Se1 , BiTlS1 Se2 and BiTlSe3 compounds, respectively. Also, increment in the energy from 0 eV to 10 eV resulted in optical properties fluctuation within 0 cm-1 to 1.5 × 109 cm-1 for absorption coefficient in the compounds. However, a variation from 10 eV to 20 eV moved the absorption coefficient to 4.2 × 109 cm-1 for all the compounds from visible to the UV range. Furthermore, the observed properties show that the value of figure of merit obtained is 0.125 for BiTlS3 , 0.100 for BiTlS2 Se1 , 0.068 for BiTlS1 Se2 and 0.055 for BiTlSe3 at 300 K. By adjusting the chalcogen ratio, BiTlX3 (X = S, Se) possess the tunable band gap in the whole visible light range, which is of great significance for the development of new-type, high-efficient semiconductor material and optoelectronic devices, while the low thermoelectric values predict the compounds use as sensors. The proposed results may pave the way for further investigations into the use of the perovskite compounds for optoelectronic devices.

Optical functional materials such as nanostructured silicates have been studied for photonics applications involving energy conversion. In this scenario, we studied Zn2SiO4:Mn2+ nanostructured powders prepared by combustion synthesis for optical thermometry based on photon downshifting. The structural analysis showed that Zn2SiO4 particles were found embedded in clustered silica nanoparticles. The photoluminescence analysis showed that the samples exhibit intense green emission (centered around 525 nm), corresponding to the electronic transition 4T1 → 6A1 of Mn2+, when exposed to a low power ultraviolet lamp (centered around 255 nm). The temperature sensing performance of this material was evaluated using three different methodologies, i.e. the luminescence decay time constant, the spectral full width at half maximum, and the luminescence peak intensity from the 4T1 → 6A1 radiative transition. The thermometric analysis based on luminescence peak intensity provided a maximum relative sensitivity of ∼4.9x10−3 K−1 at 498 K, while the decay lifetime and the spectral width at half maximum provided maximum relative temperature sensitivities of ∼2.9x10−3 K−1 at 523 K and ∼1.7x10−3 K−1 at 298 K, respectively.

Nowadays, research works into the dynamic and static human activities on Smart spaces abounds. Artificial Intelligence (AI) and low cost non-privacy invasive ambient sensors have made this ubiquitous. This review presents a state-of-the-art analysis, performance evaluation, and future research direction. One of the aims of activity recognition (especially that of humans) systems using thermal sensors and AI is the safety of persons in Smart spaces. In a Smart home, human activity detection systems are put in place to ensure the safety of persons in such an environment. This system should have the ability to monitor issues like fall detection, a common home-related accident. In this work, a review of trends in thermal sensor deployment, an appraisal of the popular datasets, AI algorithms, testbeds, and critical challenges of the recent works was provided to direct the research focus. In addition, a summary of AI models and their performance under various sensor resolutions was presented.

Unmanned aerial vehicles (UAVs) provide images at decametric spatial resolutions. Their flexibility, efficiency, and low cost make it possible to apply UAV remote sensing to multisensor data acquisition. In this frame, the present study aims at employing RGB UAV images (at a 3 cm resolution) and multispectral images (at a 16 cm resolution) with related vegetation indices (VIs) for mapping surfaces according to their illumination. The aim is to map land cover in order to access temperature distribution and compare NDVI and MTVI2 dynamics as a function of their illuminance. The method, which is based on a linear discriminant analysis, is validated at different periods during the phenological cycle of the crops in place. A model based on a given date is evaluated, as well as the use of a generic model. The method provides a good capacity of separation between four classes: vegetation, no-vegetation, shade, and sun (average kappa of 0.93). The effects of agricultural practices on two adjacent plots of maize respectively submitted to conventional and conservation farming are assessed. The transition from shade to sun increases the brightness temperature by 2.4 °C and reduces the NDVI by 26% for non-vegetated surfaces. The conservation farming plot is found to be 1.9 °C warmer on the 11th of July 2019, with no significant difference between vegetation in the sun or shade. The results also indicate that the NDVI of non-vegetated areas is increased by the presence of crop residues on the conservation agriculture plot and by the effect of shade on the conventional plot which is different for MTVI2. [ABSTRACT FROM AUTHOR]

A thermally driven, micrometer-scale switch technology has been created that utilizes the ErH3/Er2O3 materials system. The technology is comprised of novel thin film switches, interconnects, on-board micro-scale heaters for passive thermal environment sensing, and on-board micro-scale heaters for individualized switch actuation. Switches undergo a thermodynamically stable reduction/oxidation reaction leading to a multi-decade (>11 orders) change in resistance. The resistance contrast remains after cooling to room temperature, making them suitable as thermal fuses. An activation energy of 290 kJ/mol was calculated for the switch reaction, and a thermos-kinetic model was employed to determine switch times of 120 ms at 560 °C with the potential to scale to 1 ms at 680 °C.

This paper details the development and validation of a temperature sensing methodology using an un-trimmed oscillator-based integrated sensor implemented in the 0.18- μ m SOI XFAB process, with a focus on thermal monitoring in system-on-chip (SoC) based DC-DC converters. Our study identifies a quadratic relationship between the oscillator output frequency and temperature, which forms the basis of our proposed calibration mechanism. This mechanism aims at mitigating process variation effects, enabling accurate temperature-to-frequency mapping. Our research proposes and characterizes several trimming-free calibration techniques, covering a spectrum from zero to thirty-one frequency-temperature measurement points. Notably, the Corrected One-Point calibration method, requiring only a single ambient temperature measurement, emerges as a practical solution that removes the need for a temperature chamber. This method, after adjustment, successfully reduces the maximum error to within ± 2.95 °C. Additionally, the Two-Point calibration method demonstrates improved precision with a maximum positive error of +1.56 °C at −15 °C and a maximum negative error of −3.13 °C at +10 °C ( R 2 value of 0.9958). The Three-Point calibration method performed similarly, yielding an R 2 value of 0.9956. The findings of this study indicate that competitive results in temperature sensor calibration can be achieved without circuit trimming, offering a viable alternative or a complementary approach to traditional trimming techniques. [ABSTRACT FROM AUTHOR]

This article proposes and experimentally characterizes two implementations of a novel front-end circuit for three-wire connected resistive sensors with a wire-resistance compensation. The first implementation relies on two twin diodes, whereas the second on a switch; in both cases, those devices are non-remote (i.e., they are placed at the circuit end). The two circuit proposals have a square-wave input excitation so that a constant current with the two polarities is alternatively generated. Then, depending on that polarity, the current goes through either the sensor and the wire parasitic resistances or just the parasitic resistances. This generates a square-wave bipolar output signal whose average value, which is obtained by a low-pass filter, is proportional to the sensor resistance and only depends on the mismatch between two of the three wire resistances involved. Experimental tests applied to resistances related to a Pt100 thermal sensor show a remarkable linearity. For example, the switch-based front-end circuit offers a non-linearity error lower than 0.01% full-scale span, and this is practically insensitive to both the presence and the mismatch between the wire resistances. [ABSTRACT FROM AUTHOR]

Background: The current development of sensor technologies towards ever more cost-effective and powerful systems is steadily increasing the application of low-cost sensors in different horticultural sectors. In plant in vitro culture, as a fundamental technique for plant breeding and plant propagation, the majority of evaluation methods to describe the performance of these cultures are based on destructive approaches, limiting data to unique endpoint measurements. Therefore, a non-destructive phenotyping system capable of automated, continuous and objective quantification of in vitro plant traits is desirable. Results: An automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated. Unique hardware and software components were selected to construct a xyz-scanning system with an adequate accuracy for consistent data acquisition. Relevant plant growth predictors, such as projected area of explants and average canopy height were determined employing multi-sensory imaging and various developmental processes could be monitored and documented. The validation of the RGB image segmentation pipeline using a random forest classifier revealed very strong correlation with manual pixel annotation. Depth imaging by a laser distance sensor of plant in vitro cultures enabled the description of the dynamic behavior of the average canopy height, the maximum plant height, but also the culture media height and volume. Projected plant area in depth data by RANSAC (random sample consensus) segmentation approach well matched the projected plant area by RGB image processing pipeline. In addition, a successful proof of concept for in situ spectral fluorescence monitoring was achieved and challenges of thermal imaging were documented. Potential use cases for the digital quantification of key performance parameters in research and commercial application are discussed. Conclusion: The technical realization of "Phenomenon" allows phenotyping of plant in vitro cultures under highly challenging conditions and enables multi-sensory monitoring through closed vessels, ensuring the aseptic status of the cultures. Automated sensor application in plant tissue culture promises great potential for a non-destructive growth analysis enhancing commercial propagation as well as enabling research with novel digital parameters recorded over time. [ABSTRACT FROM AUTHOR]

We demonstrate a novel approach, to the best of our knowledge, to collect and detect thermal infrared signals from relatively hot objects with a focusing conical horn and non-matrix (single element) thermal sensor. The presented technique includes a horn as an alternative to a lens which allows to vary human detection range and field of view. Through theoretical research, we obtained the gain characteristics of the conical horn depending on its design parameters and observation angle when a single reflection on the horn's inner conical surface is considered. To detect humans at long distances (up to 40 m), the use of narrow horns with cone apex angles of 16° ÷ 30° is recommended, while horns with apex angles of 45° ÷ 60° provide considerable detection range and field of view. The developed technique can be applied in indoor and outdoor environments for safety purposes to monitor humans, vehicles and prevent fires. [ABSTRACT FROM AUTHOR]

Sensor Data Fusion (SDT) algorithms and models have been widely used in diverse applications. One of the main challenges of SDT includes how to deal with heterogeneous and complex datasets with different formats. The present work utilised both homogenous and heterogeneous datasets to propose a novel SDT framework. It compares data mining-based fusion software packages such as RapidMiner Studio, Anaconda, Weka, and Orange, and proposes a data fusion framework suitable for in-home applications. A total of 574 privacy-friendly (binary) images and 1722 datasets gleaned from thermal and Radar sensing solutions, respectively, were fused using the software packages on instances of homogeneous and heterogeneous data aggregation. Experimental results indicated that the proposed fusion framework achieved an average Classification Accuracy of 84.7% and 95.7% on homogeneous and heterogeneous datasets, respectively, with the help of data mining and machine learning models such as Naïve Bayes, Decision Tree, Neural Network, Random Forest, Stochastic Gradient Descent, Support Vector Machine, and CN2 Induction. Further evaluation of the Sensor Data Fusion framework based on cross-validation of features indicated average values of 94.4% for Classification Accuracy, 95.7% for Precision, and 96.4% for Recall. The novelty of the proposed framework includes cost and timesaving advantages for data labelling and preparation, and feature extraction. [ABSTRACT FROM AUTHOR]

In this article, a novel front-end circuit for remote two-wire resistive sensors that is insensitive to the wire resistances is proposed and experimentally characterized. The circuit relies on an OpAmp-based current source with a square-wave excitation, two twin diodes in the feedback path, and a low-pass filter at the output. Using such a circuit topology, the output is a DC voltage proportional to the sensor resistance and independent of the wire resistances. A prototype was built measuring resistances that correspond to a Pt100 thermal sensor and with different values of wire resistance. The experimental results show that the output voltage is almost insensitive to both the wire resistances and their mismatch, with a relative error (with respect to the case with null parasitic resistance) in the range of 0.01–0.03% Full-Scale Span (FSS). In addition, the proposed circuit shows a remarkable linearity (around 0.01% FSS), and again this is independent of the presence and also of the mismatch of the wire resistances. [ABSTRACT FROM AUTHOR]

Abstract Aerial vehicles equipped with infrared thermal sensors facilitate quick density estimates of wildlife, but detection error can arise from the thermal sensor and viewer of the infrared video. We reviewed published research to determine how commonly these sources of error have been assessed in studies using infrared video from aerial platforms to sample wildlife. The number of annual articles pertaining to aerial sampling using infrared thermography has increased drastically since 2018, but past studies inconsistently assessed sources of imperfect detection. We illustrate the importance of accounting for some of these types of error in a case study on white‐tailed deer Odocoileus virginianus in Indiana, USA, using a simple double‐observer approach. In our case study, we found evidence of false negatives associated with the viewer of infrared video. Additionally, we found that concordance between the detections of two viewers increased when using a red‐green‐blue camera paired with the infrared thermal sensor, when altitude decreased and when more stringent criteria were used to classify thermal signatures as deer. We encourage future managers and ecologists recording infrared video from aerial platforms to use double‐observer methods to account for viewer‐induced false negatives when video is manually viewed by humans. We also recommend combining infrared video with red‐green‐blue video to reduce false positives, applying stringent verification standards to detections in infrared and red‐green‐blue video and collecting data at lower altitudes over snow when needed.

Son yıllarda IoT temelli uygulamaların artması ile teknolojik gelişmeler ve mikroelektroniksel alandaki başarımlar, sensör tabanlı sistemlerin gelişmesine yol açmıştır. Temelde sağlık hizmetlerinin maliyetlerini düşürmek ve insanlara sağlık sektörü üzerinde olumlu bir etki gösterecek olan bu sistemlerin yardımı ile yüksek bir derecede doğru sonuçlar elde edilmektedir. Bununla birlikte, dünya çapında Covid-19 salgınının yayılmasıyla da düşük maliyetli olarak sıcaklık ölçümü önem kazanmıştır. Bu makalede mobil platform üzerinde termal sensör kullanılarak canlı varlık tespiti yapılmasına yönelik bir çalışma gerçekleştirilmiştir. Temelde tespitin yapılabilmesi için termal sensöre bir çeşit termal kamera özelliği kazandırılması planlanmıştır. Sensörün görüş alanındaki cisimler yaydıkları sıcaklık nedeniyle fark edilebildiğinden dolayı cisimlerin hareketleri kolayca takip edilebilecektir. Termopil sensörün mobil platform bağlantısı I2C protokolüyle sağlanmıştır. Çalışmada, mobil platform üzerinde bir sunucu oluşturulmuş ve Python programlama dilinde TCP soket programlama teknolojisi ile haberleşme gerçekleştirilmiştir. TCP istemcisi sensörden gerçek zamanlı veri alınması işlemini gerçekleştirmektedir. Bunlara ek olarak, verilerin kullanıcıların anlayabileceği şekilde görüntülenebilmesi için bir masaüstü uygulaması tasarlanmıştır. Uygulamada veriler alınan 64 adet float veri dizisi ile işlenerek ısı haritası görüntüsü elde edilmiştir. Bununla beraber, çeşitli kişilerden farklı ortam ve sıcaklıklar altında denemeler yapılarak çalışmanın doğruluğu test edilmiştir. Son olarak, yapılan çalışmada termal görüntü ile kamera görüntüsü eşleştirilerek insan ve canlı nesnelerin ısı haritası gösterilmiştir.

In this study, europium doped titania nanophosphors are synthetized and characterize in order to understand the relationship between structural changes induced by heat treatments and luminescence properties. After heat treatments, phase changes are observed between 600 °C and 700 °C and characterized using X-ray diffraction and Raman spectroscopy. These phase changes include the anatase – rutile transition and the apparition of secondary phases such as europium oxide and pyrochlore phase (Eu2Ti2O7). Along with the phase changes and crystallite size evolution, the luminescence intensity of the 5D0→ 7F2 transition and the lifetime decay are measured after green laser excitation (532 nm). The europium concentration is a key parameter to obtain a monotone and bijective relationship between the temperature and the luminescence behavior.

Aerial vehicles equipped with infrared thermal sensors facilitate quick density estimates of wildlife, but detection error can arise from the thermal sensor and viewer of the infrared video. We reviewed published research to determine how commonly these sources of error have been assessed in studies using infrared video from aerial platforms to sample wildlife. The number of annual articles pertaining to aerial sampling using infrared thermography has increased drastically since 2018, but past studies inconsistently assessed sources of imperfect detection. We illustrate the importance of accounting for some of these types of error in a case study on white‐tailed deer Odocoileus virginianus in Indiana, USA, using a simple double‐observer approach. In our case study, we found evidence of false negatives associated with the viewer of infrared video. Additionally, we found that concordance between the detections of two viewers increased when using a red‐green‐blue camera paired with the infrared thermal sensor, when altitude decreased and when more stringent criteria were used to classify thermal signatures as deer. We encourage future managers and ecologists recording infrared video from aerial platforms to use double‐observer methods to account for viewer‐induced false negatives when video is manually viewed by humans. We also recommend combining infrared video with red‐green‐blue video to reduce false positives, applying stringent verification standards to detections in infrared and red‐green‐blue video and collecting data at lower altitudes over snow when needed. [ABSTRACT FROM AUTHOR]

The present study intends to solve the problem of low-temperature sensitivity of most conventional period materials. A solid/solid one-dimensional phononic crystal design is proposed as a pragmatic thermal sensor. Here, each unit cell of the proposed phononic crystal sensor is designed from epoxy and concrete. In this structure, irregularity in layers' arrangement results in the generation of resonant peaks at the transmission spectrum. The irregularity is introduced to the system by stacking two mirror phononic crystals to form a whole symmetric phononic crystal design. Thermal variations lead to significant changes in Young's modulus of concrete and epoxy; consequently, transmission spectra and local resonant peaks are shifted as well. Firstly, the band gap changes, and sensor performance were investigated under different conditions such as the incident angle and the number of unit cells to get the optimum parameters. Meanwhile, promising results have been obtained, with increasing the incident angle, the sensitivity increases exponentially till it reaches 1.5 × 103 Hz/°C at an incident angle = 18°. Also, with increasing the temperature from 25 to 100 °C, the sensitivity increases dramatically from 188.8 to 362.4 Hz/°C, respectively. In addition, the largest value of Q-factor was investigated at periodicity number = 4. Moreover, in this regard, the Q-factor has the value of 3708 at 25 °C and it reaches to the value of 896.6 at 100 °C. That is to say, the obtained results may be useful in designing thermal sensors with a high sensitivity value. [ABSTRACT FROM AUTHOR]

Security is a sensitive area that concerns all authorities around the world due to the emerging terrorism phenomenon. Contactless biometric technologies such as face recognition have grown in interest for their capacity to identify probe subjects without any human interaction. Since traditional face recognition systems use visible spectrum sensors, their performances decrease rapidly when some visible imaging phenomena occur, mainly illumination changes. Unlike the visible spectrum, Infrared spectra are invariant to light changes, which makes them an alternative solution for face recognition. However, in infrared, the textural information is lost. We aim, in this paper, to benefit from visible and thermal spectra by proposing a new heterogeneous face recognition approach. This approach includes four scientific contributions. The first one is the annotation of a thermal face database, which has been shared via Github with all the scientific community. The second is the proposition of a multi-sensors face detector model based on the last YOLO v3 architecture, able to detect simultaneously faces captured in visible and thermal images. The third contribution takes up the challenge of modality gap reduction between visible and thermal spectra, by applying a new structure of CycleGAN, called TV-CycleGAN, which aims to synthesize visible-like face images from thermal face images. This new thermal-visible synthesis method includes all extreme poses and facial expressions in color space. To show the efficacy and the robustness of the proposed TV-CycleGAN, experiments have been applied on three challenging benchmark databases, including different real-world scenarios: TUFTS and its aligned version, NVIE and PUJ. The qualitative evaluation shows that our method generates more realistic faces. The quantitative one demonstrates that the proposed TV -CycleGAN gives the best improvement on face recognition rates. Therefore, instead of applying a direct matching from thermal to visible images which allows a recognition rate of 47,06% for TUFTS Database, a proposed TV-CycleGAN ensures accuracy of 57,56% for the same database. It contributes to a rate enhancement of 29,16%, and 15,71% for NVIE and PUJ databases, respectively. It reaches an accuracy enhancement of 18,5% for the aligned TUFTS database. It also outperforms some recent state of the art methods in terms of F1-Score, AUC/EER and other evaluation metrics. Furthermore, it should be mentioned that the obtained visible synthesized face images using TV-CycleGAN method are very promising for thermal facial landmark detection as a fourth contribution of this paper.

BACKGROUND: Ventriculoperitoneal shunts (VPSs) have been the mainstay of treating hydrocephalus since the 1950s. However, shunts have a reported complication rate reaching nearly 50%. Devices have been developed that utilize noninvasive thermal transcutaneous diffusion technology. These shunt evaluation devices measure temperature gradients to detect shunt cerebrospinal fluid flow. We assessed the utility using a thermal diffusion technique to work up shunt failure in the emergency room (ER). METHODS: This was a retrospective case series at a single medical center ER. We included consecutive patients with possible VPS malfunction who were evaluated with a thermal sensor. The time period of data collection included September 2015-April 2018. RESULTS: Eight patients were reviewed, including four males and four females. The average age of reviewed patients was 35.1 (+/- ; 16.5). In our case series, three patients had adequate shunt flow as assessed by the shunt evaluation device, and the decision was made to discharge the patient and follow-up in clinic. In two patients, the flow was diminished, but due to other reassuring signs, the patients were still discharged with follow-up. Two patients were noted to have poor flow and were admitted for shunt revision. CONCLUSION: It is important to determine whether a malfunction is present and whether an intervention is necessary for patients who present to the emergency department with possible symptoms of shunt failure. A thermal sensor is a fast and noninvasive tool in the evaluation of shunt flow and helps determine whether it is safe to send a patient home or intervene appropriately.

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Grégory Ben-Sadoun,1,2 Emeline Michel,3,4 Cédric Annweiler,1,5– 7 Guillaume Sacco3,5,8 1Department of Geriatric Medicine and Memory Clinic, Research Center on Autonomy and Longevity, University Hospital of Angers, Angers, France; 2Normandie Université, UNICAEN, INSERM, COMETE, CYCERON, CHU Caen, Caen, 14000, France; 3Université Côte d’Azur, Centre Hospitalier Universitaire de Nice, Clinique Gériatrique du Cerveau et du Mouvement, Nice, France; 4Université Côte d’Azur, LAMHESS, Nice, France; 5Laboratoire de Psychologie des Pays de la Loire, Univ Angers, Université de Nantes, EA 4638 LPPL, SFR CONFLUENCES, Angers, F-49000, France; 6School of Medicine, Health Faculty, University of Angers, Angers, France; 7Robarts Research Institute, Department of Medical Biophysics, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, Canada; 8Université Côte d’Azur, CoBTek, Nice, FranceCorrespondence: Grégory Ben-SadounDepartment of Geriatric Medicine and Memory Clinic, Research Center on Autonomy and Longevity, University Hospital of Angers, Angers, FranceEmail bensadoun.gregory@hotmail.fr; gregory.bensadoun@chu-angers.fr; gregory.ben-sadoun@unicaen.frAbstract: Systems using passive infrared sensors with a low resolution were recently proposed to answer the dilemma effectiveness–ethical considerations for human fall detection by Information and Communication Technologies (ICTs) in older adults. How effective is this type of system? We performed a systematic review to identify studies that investigated the metrological qualities of passive infrared sensors with a maximum resolution of 16× 16 pixels to identify falls. The search was conducted on PubMed, ScienceDirect, SpringerLink, IEEE Xplore Digital Library, and MDPI until November 26– 28, 2020. We focused on studies testing only these types of sensor. Thirteen articles were “conference papers”, five were “original articles” and one was a found in arXiv.org (an open access repository of scientific research). Since four authors “duplicated” their study in two different journals, our review finally analyzed 15 studies. The studies were very heterogeneous with regard to experimental procedures and detection methods, which made it difficult to draw formal conclusions. All studies tested their systems in controlled conditions, mostly in empty rooms. Except for two studies, the overall performance reported for the detection of falls exceeded 85– 90% of accuracy, precision, sensitivity or specificity. Systems using two or more sensors and particular detection methods (eg, 3D CNN, CNN with 10-fold cross-validation, LSTM with CNN, LSTM and Voting algorithms) seemed to give the highest levels of performance (> 90%). Future studies should test more this type of system in real-life conditions.Keywords: fall detection, older adults, passive infrared sensor, thermal sensor, thermopile

According to research, it is discovered that amongst the Covid19 preventive measures, social distance is easily neglected especially in a public setting such as markets, trading centers, social and political gatherings. Furthermore, according to World Health Organization (WHO), not observing social distance is one the major ways that the corona virus is being transmitted. Hence, working on a vest that can help to remind individuals and alert them in cases where they are not observing social distance. The Social Distance Vest for Covid19 Prevention, is based on Arduino Uno microcontroller board, D6T-44L-06 thermal sensor which detects the presence of a person, HC-SR04 Ultrasonic sensor that calculates the distance from where the person is standing and an alert/warning system that is composed of a Light Emitting Diode and a buzzer. Finally, the whole system is mounted on a reflective vest. The prototype vest works perfectly, in that it is able to detect a person which was not possible in the previous covid 19 distance vests which had only messages, and it is able to calculate the distance from where humans are standing and finally, triggers an alarm in a case where the person is standing at a distance of less than 1 m. The varying temperature ranges were in an array form and from 35 to 38° Celsius it detected the obstacle to be a human and had some ranges of distance 0.334 m measured by the ultrasonic sensor. Key applications of the prototypes are in crowded places like stadiums hospitals and schools.

Temperature is one of crucial and integral parts of chemical experiments. Since the properties or shape of the material may change depending on the temperature, the device that measures it plays a vital role in an experiment. Such devices commonly used in experiments to this day are unable to perform temperature measurement and data processing simultaneously. Here, we present a low‐price, non‐contact convenient platform capable of solving both problems at the same time, with automatically set different measurement time intervals for each experiment, and shows processed data anywhere real‐time on the Internet. This device can operate within temperature range of −20 and 80 °C with a respectable error range of ±2.5 °C. [ABSTRACT FROM AUTHOR]

This paper presents a detailed analysis of a micromachined thermopile detector featuring high responsivity and a versatile mosaic structure, based on 128 60 µm × 60 µm pixels connected in series and/or in parallel. The mosaic structure is based on the one employed for the thermal sensor known as TMOS, which consists of a CMOS-SOI transistor embedded in a suspended and thermally isolated absorbing membrane, released through microelectro mechanical system (MEMS) post-processing. Two versions of the thermopile detector, featuring different series/parallel connections, are presented and were experimentally characterized. The most performant of the two achieved 2.7 × 10 4 V/W responsivity. The thermopile sensors' performances are compared to that of the TMOS sensor, adopting different configurations, and their application as proximity detectors was verified through measurements. [ABSTRACT FROM AUTHOR]

The temperature of a river is a fundamental aspect of its water quality and has a bearing on its ecosystem to a greater extent. Therefore, in systematic planning for optimal stream monitoring programs involving the determination of location at monitoring stations, understanding this crucial thermal parameter is much desired. This would help to give an integrated scenario regarding physical, chemical, and biological processes occurring in the river ecosystem. Water quality parameters of the river such as dissolved oxygen, pH, salinity get affected due to change in river thermal patterns. In this study, the Landsat-8 TIR sensor was used to study spatial and temporal variations of river temperature. Thermal bands of 23 cloud-free Landsat-8 images from June 2013 to November 2020 were processed to prepare thermal maps of a stretch of river Ganges at Varanasi, India. The work has been validated by in-situ temperature measurement with a portable thermal sensor having high accuracy (± 0.1 °C). A good correlation (R2 = 0.927 and RMSE = 0.956) was observed between the sensor's estimated temperature and the in-situ temperature. The results exemplify that water surface temperature at confluence points was relatively higher due to the incoming effluents than the mid-river temperature. The 'confluence point 3' has the least relative temperature variation. The relative temperature variation has been more prominent for the month of February in comparison to June and November. Owing to the time series data availability and worldwide coverage of the Landsat-8 satellite, the present work provides a promising strategy for studying the thermal patterns in other rivers. [ABSTRACT FROM AUTHOR]

Security is a sensitive area that concerns all authorities around the world due to the emerging terrorism phenomenon. Contactless biometric technologies such as face recognition have grown in interest for their capacity to identify probe subjects without any human interaction. Since traditional face recognition systems use visible spectrum sensors, their performances decrease rapidly when some visible imaging phenomena occur, mainly illumination changes. Unlike the visible spectrum, Infrared spectra are invariant to light changes, which makes them an alternative solution for face recognition. However, in infrared, the textural information is lost. We aim, in this paper, to benefit from visible and thermal spectra by proposing a new heterogeneous face recognition approach. This approach includes four scientific contributions. The first one is the annotation of a thermal face database, which has been shared via Github with all the scientific community. The second is the proposition of a multi-sensors face detector model based on the last YOLO v3 architecture, able to detect simultaneously faces captured in visible and thermal images. The third contribution takes up the challenge of modality gap reduction between visible and thermal spectra, by applying a new structure of CycleGAN, called TV-CycleGAN, which aims to synthesize visible-like face images from thermal face images. This new thermal-visible synthesis method includes all extreme poses and facial expressions in color space. To show the efficacy and the robustness of the proposed TV-CycleGAN, experiments have been applied on three challenging benchmark databases, including different real-world scenarios: TUFTS and its aligned version, NVIE and PUJ. The qualitative evaluation shows that our method generates more realistic faces. The quantitative one demonstrates that the proposed TV-CycleGAN gives the best improvement on face recognition rates. Therefore, instead of applying a direct matching from thermal to visible images which allows a recognition rate of 47,06% for TUFTS Database, a proposed TV-CycleGAN ensures accuracy of 57,56% for the same database. It contributes to a rate enhancement of 29,16%, and 15,71% for NVIE and PUJ databases, respectively. It reaches an accuracy enhancement of 18,5% for the aligned TUFTS database. It also outperforms some recent state of the art methods in terms of F1-Score, AUC/EER and other evaluation metrics. Furthermore, it should be mentioned that the obtained visible synthesized face images using TV-CycleGAN method are very promising for thermal facial landmark detection as a fourth contribution of this paper. [ABSTRACT FROM AUTHOR]

Despite the use of transition metal dichalcogenides being widespread in various applications, the knowledge and applications of MoxW1−xS2 compounds are relatively limited. In this study, we deposited a MoW alloy on a Si substrate using a sputter system. Consequently, we successfully utilized a furnace to sulfurize the MoW alloy from 800 to 950 °C, which transferred the alloy into a MoxW1−xS2 ternary compound. The Raman spectra of the MoxW1−xS2 samples indicated an additional hybridized Raman peak at 375 cm−1 not present in typical MoS2 and WS2. With increasing sulfurization temperature, the scanning electron microscopy images revealed the surface morphology of the MoxW1−xS2 gradually becoming a sheet-like structure. The X-ray diffraction results showed that the crystal structure of the MoxW1−xS2 tended toward a preferable (002) crystal orientation. The I–V results showed that the resistance of MoxW1−xS2 increased when the samples were sulfurized at a higher temperature due to the more porous structures generated within the thin film. Furthermore, a high-temperature coefficient of resistance for the MoxW1−xS2 thin film sulfurized at 950 °C was about −1.633%/K−1. This coefficient of resistance in a MoxW1−xS2 thin film indicates its suitability for use in thermal sensors. [ABSTRACT FROM AUTHOR]

In this article, we propose a computational framework to estimate the physical properties that govern the thermal response of laser‐irradiated tissue. We focus in particular on two quantities, the absorption and scattering coefficients, which describe the optical absorption of light in the tissue and whose knowledge is vital to correctly plan medical laser treatments. To perform the estimation, we utilize an implementation of the ensemble Kalman filter (EnKF), a type of Bayesian filtering algorithm for data assimilation. Unlike prior approaches, in this work, we estimate the tissue optical properties based on observations of the tissue thermal response to laser irradiation. This method has the potential for straightforward implementation in a clinical setup, as it would only require a simple thermal sensor, for example, a miniaturized infrared camera. Because the optical properties of tissue can undergo shifts during laser exposure, we employ a variant of EnKF capable of tracking time‐varying parameters. Through simulated experimental studies, we demonstrate the ability of the proposed technique to identify the tissue optical properties and track their dynamic changes during laser exposure, while simultaneously tracking changes in the tissue temperature at locations beneath the surface. We further demonstrate the framework's capability in estimating additional unknown tissue properties (i.e., the volumetric heat capacity and thermal conductivity) along with the optical properties of interest. [ABSTRACT FROM AUTHOR]

This paper shows some practical experiences of using unmanned aerial vehicles-based platform for remote sensing in supporting precision agriculture mapping. There have been studies on unmanned aerial vehicles used to calculate plant water stress; however, the scientific reports of drone images that are used to predict best time and height are rare. The trial was conducted during 2020, in a five-year-old Anji tea plant experimental field, where drone captures images in a different time series of 27 flights during experimental days. This work aims to (1) investigate the appropriate thermography timing and altitude based on unmanned aerial vehicles remote sensing, (2) conduct a quantitative and qualitative study of various thermal orthomosaics and photographs, (3) establish workflow for high-resolution remote sensing application. All flights were operated at 3 m/s flying speed. Flights were performed during the testing day at about 09:00 h, 11:00 h, and 13:00 h. The drone images were taken at relative flying heights of 25 m, 40 m, and 60 m each day. The relationship between canopy temperature and plant-based variables was also established. The results reported that flights at 11:00 h and 60-m altitude orthomosaic could provide the best relation and accurate canopy temperature. On the other hand, the high relationship between stomatal conductance and canopy temperature was R2 0.98 at 11:00 h. The selection of optimal timing and altitude can provide rapid and reliable canopy temperature information. Overall, high resolution with low-altitude unmanned aerial vehicles images proved good relationship in order to assess the canopy temperature. [ABSTRACT FROM AUTHOR]

This case study investigates the magnitude and nature of the spatial effect generated by the anti-COVID measures on land surface temperature (LST) in the city of Târgu MureȘ (Marosvásárhely), Romania. The measures were taken by the Romanian government during the state of emergency (March 16 -- May 14, 2020) due to the SARSCoV- 2 coronavirus pandemic. The study shows that -- contrary to previous studies carried out on cities in China and India -- in most of the urban areas of Marosvásárhely LST has increased in the period of health emergency in 2020 concerning the large average of the years 2000-2019. Remote sensing data from the MODIS and the Landsat satellites show, that MODIS data, having a moderate spatial (approximately 1 km) but good temporal resolution (daily measurements), show a temperature increase of +0.78 °C, while Landsat data, having better spatial (30 m) but lower temporal resolution, show an even greater increase, +2.36 °C in the built-up areas. The difference in temperature increase is mainly due to the spatial resolution difference between the two TIR band sensors. The LST anomaly analysis performed with MODIS data also shows a positive anomaly increase of 1 °C. However, despite this increase, with the help of the hotspot-coldspot analysis of the Getis-Ord Gi* statistic we were able to identify 46 significant coldspots that showed a 1-- 2 °C decrease of LST in April 2020 compared to the average of the previous years in April. Most of these coldspots correspond to factory areas, public transport epicenters, shopping centers, industrial polygons, and non-residential areas. This shows that anti-COVID measures in the medium-sized city of Marosvásárhely had many effects on LST in particular areas that have links to the local economy, trade, and transport. Paired sample t-test for areas identified with LST decrease shows that there is a statistically significant difference in the average LST observed before and after anti-COVID measures were applied. MODIS-based LST is satisfactory for recognizing patterns and trends at large or moderate geographical scales. However, for a hotspot-coldspot analysis of the urban heat islands, it is more suitable to use Landsat data. [ABSTRACT FROM AUTHOR]