Two Proposals of a Simple Analog Conditioning Circuit for Remote Resistive Sensors with a Three-Wire Connection.

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.

Calibration of Ring Oscillator-Based Integrated Temperature Sensors for Power Management Systems.

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-µm 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 (R2 value of 0.9958). The Three-Point calibration method performed similarly, yielding an R2 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.

A Front-End Circuit for Two-Wire Connected Resistive Sensors with a Wire-Resistance Compensation.

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.

Data Mining and Fusion Framework for In-Home Monitoring Applications.

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.

"Oxygen Pheriwala:" An innovative model for SARS-CoV-2 screening in resource-limited settings.

The second wave of SARS-CoV-2 infection came as a hypoxic emergency and situation became worse in rural India, where undiagnosed COVID-19 patients died without any diagnosis or intervention. The primary aim of this innovative model was the early diagnosis of suspected SARS-CoV-2 cases, providing empirical treatment and timely referral to appropriate COVID care facilities. Fever was measured with infrared thermometer and oxygen saturation level with pulse oximeter. A total of 8203 people were screened, of which 274 persons were febrile and 69 (25%) were hypoxic too. Sixty-four out of 69 (93%) patients turned COVID-19 positive on reverse transcription-polymerase chain reaction. At the end of 3 weeks, 48/64 (75%) patients were successfully discharged. This model can be easily implemented in resource-limited regions to identify and prioritize the patients not only in this pandemic but also in outbreak of other communicable diseases.

Photophysical Investigation of a Benzimidazole Derivative and Its Applications in Selective Detection of Fe3+, Thermosensing and Logic Gates.

The fluorescence based applications such as chemosensing of Fe3+ ion, temperature sensing, NOR-gate molecular logic operations, and fluorescent ink were achieved using an expired medicine namely Pantoprazole (sensor1) which is a derivative of benzimidazole class. The phenomenon of quenching was a basic principle used in sensing Fe3+. We found that there was a combined effect of dynamic and static processes in quenching of fluorescence of sensor1. To confirm the selectiveness of sensor1, we performed interference experiments with other metal ions. There was no interference between these metal ions and Fe3+. The low LOD value of 1.032 µM suggested that the molecule is highly sensitive towards Fe3+. A high quantum yield of 8.087% increased the possibility of using sensor1 for light applications. There was a 2:1 stoichiometric ratio between sensor1 and Fe3+. The FTIR data analysis confirmed the presence of functional groups S = O, C-O-C, etc. The average particle size found with the DLS method was 130.5 nm. The zeta potential of -12.7 mV indicated that sensor1 is quite stable in solution form. For logic gate application EDTA (Ethylenediaminetetraaceticacid) and Fe3+ were considered as inputs and fluorescence intensity was taken as an output. In thermal sensing application, the high value of activation energy i.e. 908 meV and relative sensitivity of 1.2% 0C-1 affirmed that sensor1 can be used for thermal applications. The sensor1 was also apt for fluorescent ink application. Hence this investigation deduced that sensor1 can be a potential candidate for such applications over other conventional synthesized fluorescent probes.

Tactile and Thermal Sensors Built from Carbon-Polymer Nanocomposites-A Critical Review.

This paper provides a critical review of tactile and thermal sensors which are built from carbon nanomaterial-filled polymer composites (CNPCs). To make the review more comprehensive and systematic, the sensors are viewed as a system, and a general knowledge architecture for a system called function-context-behavior-principle-state-structure (FCBPSS) is employed to classify information as well as knowledge related to CNPC sensors. FCBPSS contains six basic concepts, namely, F: function, C: context, B: behavior, P: principle, and SS: state and structure. As such, the principle that explains why such composites can work as temperature and pressure sensors, various structures of the CNPC sensor, which realize the principle, and the behavior and performance of CNPC sensors are discussed in this review. This review also discusses the fabrication of the CNPC sensor. Based on the critical review and analysis, the future directions of research on the CNPC sensor are discussed; in particular, the need to have a network of CNPC sensors that can be installed on curved bodies such as those of robots is elaborated.

Compact Measurement of the Optical Power in High-Power LED Using a Light-Absorbent Thermal Sensor.

LED (Light-Emitting Diode) presents advantages such as luminescence, reliability, durability compared with conventional lighting. It has been widely applied for life, healthcare, smart farm, industry, and lighting from indoor to the automotive headlamp. However, the LED is vulnerable to thermal damage originated from the high junction temperature, especially in high power applications. Hence, it requires precise qualification on the optical power and the junction temperature from the pilot line to secure reliability. In this study, the photo-thermal sensor is proposed by employing a sheet-type thermocouple composed of photo-absorbent metal film and thermocouple. This sensor aims low-cost qualification in pilot line for high-power luminous devices and optical monitoring of costly luminaire such as automobile LED headlamp. The sensor is designed to detect the increased temperature response of LED hot spots from the transferred thermal power and absorbed optical power. The temperature response of each sheet-type thermocouple is utilized as a signal output of the absorbed optical power and hot spot temperature based on the introduced sensor equation. The proposed thermal sensor is evaluated by comparing the experiment with the measured reference value from the integrating sphere and the attached thermocouple at a junction. The experiment result reveals 3% of the maximum error for the optical power of 645 mW.

Peak-Load-Regulation Nuclear Power Unit Fault Diagnosis Using Thermal Sensors Combined with Improved ICA-RF Algorithm.

Owing to the fact that large-scale peak-load-regulation nuclear power turbine units' thermal signal is greatly influenced by background noise and has non-stationary and nonlinear characteristics, this paper proposes a new fault diagnosis method for thermal sensors based on an improved independent component analysis (Improved-ICA) algorithm and random forest (RF) algorithm. This method is based on independent component analysis (ICA), which is not capable of extracting components independently. Therefore, we propose the use of the maximum approximate information negative entropy optimization model in order to improve the ICA algorithm's independent principal component extraction ability and obtain better non-Gaussian physical source signal separation results. The improved ICA algorithm is used for the blind source separation of the thermal parameters of peak-load-regulation nuclear power units. A series of stationary physical source functions and a series of non-stationary noise signals are obtained. Then, according to the specific signal format and data volume of the nuclear power parameter signal, the network parameters of the random forest algorithm are determined, giving rise to the fault diagnosis model. Finally, the real-time operation data of an 1121 MW nuclear power unit are used to complete the training and fault diagnosis of the random forest network and analyze the diagnosis results. The results indicate that the model can effectively mine the abnormal sample points of thermal parameters and classify the fault type of the thermal sensor during peak load operation of the nuclear power unit. The accuracy rate is found to be at the threshold of 99%.

BPF-Based Thermal Sensor Circuit for On-Chip Testing of RF Circuits.

A new sensor topology meant to extract figures of merit of radio-frequency analog integrated circuits (RF-ICs) was experimentally validated. Implemented in a standard 0.35 µm complementary metal-oxide-semiconductor (CMOS) technology, it comprised two blocks: a single metal-oxide-semiconductor (MOS) transistor acting as temperature transducer, which was placed near the circuit to monitor, and an active band-pass filter amplifier. For validation purposes, the temperature sensor was integrated with a tuned radio-frequency power amplifier (420 MHz) and MOS transistors acting as controllable dissipating devices. First, using the MOS dissipating devices, the performance and limitations of the different blocks that constitute the temperature sensor were characterized. Second, by using the heterodyne technique (applying two nearby tones) to the power amplifier (PA) and connecting the sensor output voltage to a low-cost AC voltmeter, the PA's output power and its central frequency were monitored. As a result, this topology resulted in a low-cost approach, with high linearity and sensitivity, for RF-IC testing and variability monitoring.

Infrared thermography reveals surface body temperature changes during proestrus and estrus reproductive phases in Gyr heifers (Bos taurus indicus).

Our aim was to evaluate the application of infrared thermography (IRT) to detect body surface temperature variation of body regions during the proestrus and estrus phases of the reproductive cycle of Gyr heifers and investigate environmental factors that could affect these measurements. Fifty-seven heifers were submitted to an ovulation synchronization protocol. This was followed by monitoring the heifers every 12 h over 60 h. Heifers were monitored for rectal and vaginal temperature using a digital thermometer. The surface temperature of the eye, vulva, and muzzle regions were monitored by IRT. Meteorological data was recorded for temperature and humidity. Observation of sexual behavior was performed to monitor estrus onset. Transrectal ultrasonography was used to identify the dominant follicle and confirm ovulation of all heifers. We observed a decrease in temperature of the rectum and vagina, as well as in the eye and vulva regions within the first 12 h after the completion of the synchronization. This period coincides with the expected proestrus phase of the estrous cycle. A progressive increase in all temperatures was noticed in the following 36 h, which coincides with the estrus phase of the reproductive cycle. The regions evaluated around the vulva and eye exhibited the highest temperature and experienced less environmental distortion than the muzzle area thermographs. Environmental factors, such as rainfall and temperature-humidity index, influenced the IRT readings altering the radiation patterns detected. In conclusion, IRT is an effective method to detect temperature variation during the proestrus and estrus phases in Gyr heifers. Furthermore, biological and environmental effects should be considered when collecting and interpreting IRT data in livestock.

eHomeSeniors Dataset: An Infrared Thermal Sensor Dataset for Automatic Fall Detection Research.

Automatic fall detection is a very active research area, which has grown explosively since the 2010s, especially focused on elderly care. Rapid detection of falls favors early awareness from the injured person, reducing a series of negative consequences in the health of the elderly. Currently, there are several fall detection systems (FDSs), mostly based on predictive and machine-learning approaches. These algorithms are based on different data sources, such as wearable devices, ambient-based sensors, or vision/camera-based approaches. While wearable devices like inertial measurement units (IMUs) and smartphones entail a dependence on their use, most image-based devices like Kinect sensors generate video recordings, which may affect the privacy of the user. Regardless of the device used, most of these FDSs have been tested only in controlled laboratory environments, and there are still no mass commercial FDS. The latter is partly due to the impossibility of counting, for ethical reasons, with datasets generated by falls of real older adults. All public datasets generated in laboratory are performed by young people, without considering the differences in acceleration and falling features of older adults. Given the above, this article presents the eHomeSeniors dataset, a new public dataset which is innovative in at least three aspects: first, it collects data from two different privacy-friendly infrared thermal sensors; second, it is constructed by two types of volunteers: normal young people (as usual) and performing artists, with the latter group assisted by a physiotherapist to emulate the real fall conditions of older adults; and third, the types of falls selected are the result of a thorough literature review.

Intelligent Lecturer Tracking and Capturing System Based on Face Detection and Wireless Sensing Technology.

In this paper, we propose an intelligent lecturer tracking and capturing (ILTC) system to automatically record course videos. Real-time and stable lecturer localization is realized by combining face detection with infrared (IR) thermal sensors, preventing detection failure caused by abrupt and rapid movements in face detection and solving the non-real-time sensing problem for IR thermal sensors. Further, the camera is panned automatically by a servo motor controlled with a microcontroller to keep the lecturer in the center of the screen. Experiments were conducted in a classroom and a laboratory. Experimental results demonstrated that the accuracy of the proposed system is much higher than that of the system without IR thermal sensors. The survey of 32 teachers from two universities showed that the proposed system is a more practical utility and meets the demand of increasing online courses.

An Integrated Thermopile-Based Sensor with a Chopper-Stabilized Interface Circuit for Presence Detection.

This paper presents a sensor-readout circuit system suitable for presence detection. The sensor consists of a miniaturized polysilicon thermopile, realized employing MEMS micromachining by STMicroelectronics, featuring a responsivity value equal to 180 V/W, with 13 ms response time. The readout circuit is implemented in a standard 130-nm CMOS process. As the sensor output signal behaves substantially as a DC, the interface circuit employs the chopper technique in order to minimize offset and noise contributions at low frequency, achieving a measured input referred offset standard deviation equal to 1.36 µ V. Measurements show that the presented system allows successfully detecting the presence of a person in a room standing at 5.5 m from the sensor. Furthermore, the correct operation of the system with moving targets, considering people either walking or running, was also demonstrated.

On-Line Temperature Estimation for Noisy Thermal Sensors Using a Smoothing Filter-Based Kalman Predictor.

Dynamic thermal management (DTM) mechanisms utilize embedded thermal sensors to collect fine-grained temperature information for monitoring the real-time thermal behavior of multi-core processors. However, embedded thermal sensors are very susceptible to a variety of sources of noise, including environmental uncertainty and process variation. This causes the discrepancies between actual temperatures and those observed by on-chip thermal sensors, which seriously affect the efficiency of DTM. In this paper, a smoothing filter-based Kalman prediction technique is proposed to accurately estimate the temperatures from noisy sensor readings. For the multi-sensor estimation scenario, the spatial correlations among different sensor locations are exploited. On this basis, a multi-sensor synergistic calibration algorithm (known as MSSCA) is proposed to improve the simultaneous prediction accuracy of multiple sensors. Moreover, an infrared imaging-based temperature measurement technique is also proposed to capture the thermal traces of an advanced micro devices (AMD) quad-core processor in real time. The acquired real temperature data are used to evaluate our prediction performance. Simulation shows that the proposed synergistic calibration scheme can reduce the root-mean-square error (RMSE) by 1.2 ∘ C and increase the signal-to-noise ratio (SNR) by 15.8 dB (with a very small average runtime overhead) compared with assuming the thermal sensor readings to be ideal. Additionally, the average false alarm rate (FAR) of the corrected sensor temperature readings can be reduced by 28.6%. These results clearly demonstrate that if our approach is used to perform temperature estimation, the response mechanisms of DTM can be triggered to adjust the voltages, frequencies, and cooling fan speeds at more appropriate times.

Evolution of Heat Sensors Drove Shifts in Thermosensation between Xenopus Species Adapted to Different Thermal Niches.

Temperature is one of the most critical environmental factors affecting survival, and thus species that inhabit different thermal niches have evolved thermal sensitivities suitable for their respective habitats. During the process of shifting thermal niches, various types of genes expressed in diverse tissues, including those of the peripheral to central nervous systems, are potentially involved in the evolutionary changes in thermosensation. To elucidate the molecular mechanisms behind the evolution of thermosensation, thermal responses were compared between two species of clawed frogs (Xenopus laevis and Xenopus tropicalis) adapted to different thermal environments. X. laevis was much more sensitive to heat stimulation than X. tropicalis at the behavioral and neural levels. The activity and sensitivity of the heat-sensing TRPA1 channel were higher in X. laevis compared with those of X. tropicalis The thermal responses of another heat-sensing channel, TRPV1, also differed between the two Xenopus species. The species differences in Xenopus TRPV1 heat responses were largely determined by three amino acid substitutions located in the first three ankyrin repeat domains, known to be involved in the regulation of rat TRPV1 activity. In addition, Xenopus TRPV1 exhibited drastic species differences in sensitivity to capsaicin, contained in chili peppers, between the two Xenopus species. Another single amino acid substitution within Xenopus TRPV1 is responsible for this species difference, which likely alters the neural and behavioral responses to capsaicin. These combined subtle amino acid substitutions in peripheral thermal sensors potentially serve as a driving force for the evolution of thermal and chemical sensation.

In Situ Electroluminescence Color Tuning by Thermal Deprotonation Suitable for Thermal Sensors and Anti-fraud Labels.

A novel and versatile approach to tune photoluminescence and electroluminescence by in situ controlled thermal deprotonation is presented. This methodology, based on a single organic π-conjugated material (pyrene derivative), allows the manufacturing of organic light-emitting diode (OLED) prototypes by solution methods with controlled tunable emission ranging from the orange (protonated form of the dye) to the blue (pristine or deprotonated form). While several protonation/deprotonation cycles can be performed on thin films, for the devices only one cycle is possible so that their use as anti-fraud labels can be envisaged. OLEDs exhibit daylight visible brightness of 150 cd m-2 and device lifetime exceeding 30 hours of continuous operation. Thanks to the simplicity of both material design and device fabrication our approach opens new perspectives in the wide field of thermal sensors for customer care or risk perception.

Tracking and Classification of In-Air Hand Gesture Based on Thermal Guided Joint Filter.

The research on hand gestures has attracted many image processing-related studies, as it intuitively conveys the intention of a human as it pertains to motional meaning. Various sensors have been used to exploit the advantages of different modalities for the extraction of important information conveyed by the hand gesture of a user. Although many works have focused on learning the benefits of thermal information from thermal cameras, most have focused on face recognition or human body detection, rather than hand gesture recognition. Additionally, the majority of the works that take advantage of multiple modalities (e.g., the combination of a thermal sensor and a visual sensor), usually adopting simple fusion approaches between the two modalities. As both thermal sensors and visual sensors have their own shortcomings and strengths, we propose a novel joint filter-based hand gesture recognition method to simultaneously exploit the strengths and compensate the shortcomings of each. Our study is motivated by the investigation of the mutual supplementation between thermal and visual information in low feature level for the consistent representation of a hand in the presence of varying lighting conditions. Accordingly, our proposed method leverages the thermal sensor's stability against luminance and the visual sensors textural detail, while complementing the low resolution and halo effect of thermal sensors and the weakness against illumination of visual sensors. A conventional region tracking method and a deep convolutional neural network have been leveraged to track the trajectory of a hand gesture and to recognize the hand gesture, respectively. Our experimental results show stability in recognizing a hand gesture against varying lighting conditions based on the contribution of the joint kernels of spatial adjacency and thermal range similarity.

A Novel Thermal Sensor for the Sensitive Measurement of Chemical Oxygen Demand.

A novel rapid methodology for determining the chemical oxygen demand (COD) based on a thermal sensor with a flow injection analysis system was proposed and experimentally validated. The ability of this sensor to detect and monitor COD was based on the degree of enthalpy increase when sodium hypochlorite reacted with the organic content in water samples. The measurement results were correlated with COD and were compared against the conventional method using potassium dichromate. The assay required only 5-7 min rather than the 2 h required for evaluation by potassium dichromate. The linear range was 5-1000 mg/L COD, and the limit of detection was very low, 0.74 mg/L COD. Moreover, this method exhibited high tolerance to chloride ions; 0.015 mol/L chloride ions had no influence on the response. Finally, the sensor was used to detect the COD of different water samples; the results were verified by the standard dichromate method.

Wireless Low-Power Integrated Basal-Body-Temperature Detection Systems Using Teeth Antennas in the MedRadio Band.

This study proposes using wireless low power thermal sensors for basal-body-temperature detection using frequency modulated telemetry devices. A long-term monitoring sensor requires low-power circuits including a sampling circuit and oscillator. Moreover, temperature compensated technologies are necessary because the modulated frequency might have additional frequency deviations caused by the varying temperature. The temperature compensated oscillator is composed of a ring oscillator and a controlled-steering current source with temperature compensation, so the output frequency of the oscillator does not drift with temperature variations. The chip is fabricated in a standard Taiwan Semiconductor Manufacturing Company (TSMC) 0.18-µm complementary metal oxide semiconductor (CMOS) process, and the chip area is 0.9 mm². The power consumption of the sampling amplifier is 128 µW. The power consumption of the voltage controlled oscillator (VCO) core is less than 40 µW, and the output is -3.04 dBm with a buffer stage. The output voltage of the bandgap reference circuit is 1 V. For temperature measurements, the maximum error is 0.18 °C with a standard deviation of ±0.061 °C, which is superior to the required specification of 0.1 °C.