Search

Oil-water two-phase flow atoilfield wellheads is a common occurrence in distribution. This paper proposes using a heat transfer method to accurately measure and monitorthe total fluid volume at the wellhead. To achieve this, thermal sensors of types PT1000 and PT20, which are suitable for measuring oil-water mixed flow, are first designed. The feasibility and linearity of the sensors are simulated and calculated. Secondly, the heat transfercoefficient is computed using experimental methods and a functional relationship between the heat transfer coefficient and the flow rate is derived. Finally, the calculated results demonstrate the feasibility of using the heattransfer method to measure oil-water two-phase flow. If the water-liquid ratio (WLR) is known, the flow rate can be calculated through the functional relationship between the heat transfer coefficient and the flow rate. This method can improve the accuracy of estimating the flow rate. [ABSTRACT FROM AUTHOR]

In the context of global warming, rising air temperatures are increasing evapotranspiration ( ET c ) in all agricultural crops, including rice, a staple food worldwide. Simultaneously, the occurrence of droughts is reducing water availability, affecting traditional irrigation methods for rice cultivation (flood irrigation). The objective of this study was to determine ET c (water use) and yield performance in rice crop under different irrigation regimes: treatments with continuous flood irrigation (CF) and irrigations with alternating wetting and drying ( A W D 5 , A W D 10 , and A W D 20 ) in an experimental area in INIA–Vista Florida. Water balance, rice physiological data, and yield were measured in the field, and local weather data and thermal and multispectral images were collected with a meteorological station and a UAV (a total of 13 flights). ET c values obtained by applying the METRICTM (Mapping Evapotranspiration at High Resolution using Internalized Calibration) energy balance model ranged from 2.4 to 8.9 mm d−1 for the AWD and CF irrigation regimes. In addition, ET c was estimated by a water balance using the AquaCrop model, previously parameterized with RGB image data and field weather data, soil, irrigation water, and crops, obtaining values between 4.3 and 7.1 mm d−1 for the AWD and CF irrigation regimes. The results indicated that AWD irrigation allows for water savings of 27 to 28%, although it entails a yield reduction of from 2 to 15%, which translates into an increase in water use efficiency (WUE) of from 18 to 36%, allowing for optimizing water use and improving irrigation management. [ABSTRACT FROM AUTHOR]

In the face of the climate change crisis, the increase in air temperature negatively impacts rice crop productivity due to stress from water scarcity. The objective of this study was to determine the rice crop water stress index (CWSI) and stomatal conductance (Gs) under different irrigation regimes, specifically continuous flood irrigation treatments (CF) and irrigations with alternating wetting and drying (AWD) at water levels of 5 cm, 10 cm, and 20 cm below the soil surface ( AWD 5 ,   AWD 10 , and AWD 20 ) in an experimental area of INIA-Vista Florida and in six commercial areas of the Lambayeque region using thermal images captured with thermal sensors. The results indicated that AWD irrigation generated more water stress, with CWSI values between 0.4 and 1.0. Despite this, the yields were similar in CF and AWD 20.   In the commercial areas, CWSI values between 0.38 and 0.51 were obtained, with Santa Julia having the highest values. Furthermore, a strong Pearson correlation (R) of 0.91 was established between the CWSI and Gs, representing a reference scale based on Gs values for evaluating water stress levels. [ABSTRACT FROM AUTHOR]

Flexible thermal sensors have attracted immense interest as an alternative to conventional rigid sensors in personal healthcare monitoring, biomedical applications, and human‐machine interaction. However, most metal‐based flexible thermal sensors exhibit low sensitivity, primarily resulting from electron scattering due to manufacturing defects. The heat‐intolerant polymer substrate hinders the pathway to improve the sensitivity of metal thin films through high‐temperature annealing. Herein, a recrystallization‐induced laser lift‐off strategy is proposed to overcome the contradiction between the flexible substrate and high‐temperature annealing. Systematically theoretical and experimental studies reveal the mechanism of thermal‐induced recrystallization in increasing the temperature coefficient of resistance (TCR) and the separation of sensors from rigid substrates. Valuable insights are acquired in the modulation of interface adhesion by altering the surface roughness of nickel. A critical temperature is provided to guide the detachment of the sensor. The fabricated thin‐film flexible thermal sensor achieves a TCR of 6.2‰°C−1, approaching the TCR limit of bulk material. This strategy opens a new general route for fabricating high‐temperature annealed sensors on flexible substrates. [ABSTRACT FROM AUTHOR]

A ratiometric fiber optic temperature sensor based on a highly coupled seven-core fiber (SCF) is proposed and experimentally demonstrated. A theoretical analysis of the SCF's sinusoidal spectral response in transmission configuration is presented. The proposed sensor comprises two SCF devices exhibiting anti-phase transmission spectra. Simple fabrication of the devices is shown by just splicing a segment of a 2 cm long SCF between two single-mode fibers (SMFs). The sensor proved to be robust against light source fluctuations, as a standard deviation of 0.2% was registered in the ratiometric measurements when the light source varied by 12%. Its low-cost detection system (two photodetectors) and the range of temperature detection (25 °C to 400 °C) make it a very attractive and promising device for real industrial applications. [ABSTRACT FROM AUTHOR]

Power plants, electric generators, high-frequency controllers, battery storage, and control units are essential in current transportation and energy distribution networks. To improve the performance and guarantee the endurance of such systems, it is critical to control their operational temperature within certain regimes. Under standard working conditions, those elements become heat sources either during their entire operational envelope or during given phases of it. Consequently, in order to maintain a reasonable working temperature, active cooling is required. The refrigeration may consist of the activation of internal cooling systems relying on fluid circulation or air suction and circulation pulled from the environment. However, in both scenarios pulling surrounding air or making use of coolant pumps increases the power demand. The augmented power demand has a direct impact on the power plant or electric generator autonomy, while instigating higher power demand and substandard performance from the power electronics and batteries' compounds. In this manuscript, we present a methodology to efficiently estimate the heat flux load generated by internal heat sources. By accurately and inexpensively computing the heat flux, it is possible to identify the coolant requirements to optimize the use of the available resources. Based on local thermal measurements fed into a Kriging interpolator, we can accurately compute the heat flux minimizing the number of sensors required. Considering the need for effective thermal load description toward efficient cooling scheduling. This manuscript presents a procedure based on temperature distribution reconstruction via a Kriging interpolator to monitor the surface temperature using a minimal number of sensors. The sensors are allocated by means of a global optimization that minimizes the reconstruction error. The surface temperature distribution is then fed into a heat conduction solver that processes the heat flux of the proposed casing, providing an affordable and efficient way of controlling the thermal load. Conjugate URANS simulations are used to simulate the performance of an aluminum casing and demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present the sensor placement strategies that currently determine the thermal monitoring of the phase conductors of high-voltage power lines. In addition to reviewing the international literature, a new sensor placement concept is presented based on a strategy centered on the following question: What are the chances of thermal overload if devices are only placed in certain tension sections? In this new concept, the number and installation location of the sensors are determined in three steps, and a new type of tension-section-ranking constant is introduced that is universal in space and time. The simulations based on this new concept show that the data-sampling frequency and the type of thermal constraint influence the number of sensors. The paper's main finding is that there are cases when only a distributed sensor placement strategy can result in safe and reliable operation. However, due to requiring a large number of sensors, this solution means additional expenses. In the last section, the paper presents different possibilities to reduce costs and introduces the concept of low-cost sensor applications. These devices can result in more flexible network operation and more reliable systems in the future. [ABSTRACT FROM AUTHOR]

Simple Summary: A total of 251 putative TRPs from saurian are divided into 2 groups, belonging to 6 TRPs subfamilies, excluding the TRPN subfamily. The most conserved proteins of TRP box 1 are located in motif 1, and those of TRP box 2 are located in motif 10. The TRPA and TRPV in saurian tend to be one cluster, as a sister cluster with TRPC, and the TRPM is a root of group I. TRPM, TRPV, and TRPP are clustered into two clades, and TRPP is organized into TRP PKD1-like and PKD2-like. Segmental duplications mainly occur in the TRPM subfamily, and the tandem duplications only occur in the TRPV subfamily. Fifteen sites were under positive selection for TRPA1 and TRPV2 genes. The branch model revealed that positive selection fit the data better than the null model for the genes TRPC5 and TRPV3. The transient receptor potential plays a critical role in the sensory nervous systems of vertebrates in response to various mechanisms and stimuli, such as environmental temperature. We studied the physiological adaptive evolution of the TRP gene in the saurian family and performed a comprehensive analysis to identify the evolution of the thermo-TRPs channels. All 251 putative TRPs were divided into 6 subfamilies, except TRPN, from the 8 saurian genomes. Multiple characteristics of these genes were analyzed. The results showed that the most conserved proteins of TRP box 1 were located in motif 1, and those of TRP box 2 were located in motif 10. The TRPA and TRPV in saurian tend to be one cluster, as a sister cluster with TRPC, and the TRPM is the root of group I. The TRPM, TRPV, and TRPP were clustered into two clades, and TRPP were organized into TRP PKD1-like and PKD2-like. Segmental duplications mainly occurred in the TRPM subfamily, and tandem duplications only occurred in the TRPV subfamily. There were 15 sites to be under positive selection for TRPA1 and TRPV2 genes. In summary, gene structure, chromosomal location, gene duplication, synteny analysis, and selective pressure at the molecular level provided some new evidence for genetic adaptation to the environment. This result provides a basis for identifying and classifying TRP genes and contributes to further elucidating their potential function in thermal sensors. [ABSTRACT FROM AUTHOR]

Depth maps acquired with ToF cameras have a limited accuracy due to the high noise level and to the multi-path interference. Deep networks can be used for refining ToF depth, but their training requires real world acquisitions with ground truth, which is complex and expensive to collect. A possible workaround is to train networks on synthetic data, but the domain shift between the real and synthetic data reduces the performances. In this paper, we propose three approaches to perform unsupervised domain adaptation of a depth denoising network from synthetic to real data. These approaches are respectively acting at the input, at the feature and at the output level of the network. The first approach uses domain translation networks to transform labeled synthetic ToF data into a representation closer to real data, that is then used to train the denoiser. The second approach tries to align the network internal features related to synthetic and real data. The third approach uses an adversarial loss, implemented with a discriminator trained to recognize the ground truth statistic, to train the denoiser on unlabeled real data. Experimental results show that the considered approaches are able to outperform other state-of-the-art techniques and achieve superior denoising performances. [ABSTRACT FROM AUTHOR]

This paper reported the thermal and light sensing properties of graphene oxide (GO) and reduced graphene oxide (RGO) films obtained by spin coating on soda lime glass. GO showed a higher thermal sensing response of 9.0 compared to RGO which had a maximum thermal sensing response of 2.2 at 623 K. The thermal conductivity of GO and RGO increased with temperature although RGO typified better thermal conductivity. For GO, a negative temperature coefficient of resistance (NTC) of 0.0032153 /K was obtained which is higher than values obtained in literatures while RGO had a NTC value of 0.00201/K. For the photo sensing, the peak photo resistance of GO corresponds to an energy band gap of 3.5 eV while the peak photo resistance of RGO corresponds to a decreased energy band gap of 2.76 eV. It was also confirmed that lower bandgap favored improved photosensing function. RGO film showed intrinsic photoconductivity as the photoconductivity increased at longer wavelengths. The photo sensing of GO and RGO were dominated by inter-band optical transitions in the UV to mid infrared region. The obtained NTC values for GO and RGO and the photoconductivity of the materials can promote further utilization in robotics and optical applications. [ABSTRACT FROM AUTHOR]

Interturn short-circuit faults are one of the most (if not the most) harmful electrical machine failures, that if not detected and mitigated at a very incipient stage of development may involve serious consequences, both in terms of repair costs and safety. This article proposes a novel interturn fault diagnostic technique for synchronous reluctance machines based on the Luenberger state observer and current's second-order harmonic. The superiority of the proposed approach over the conventional model-based techniques is the ability to evaluate the severity of the fault as well as the efficiency in detecting faults in early stages and under various operating conditions. The effectiveness of the proposed method is demonstrated and validated through several experimental tests. [ABSTRACT FROM AUTHOR]

Thermography is a useful imaging technique as it works well in poor visibility conditions. High-resolution thermal imaging sensors are usually expensive and this limits the general applicability of such imaging systems. Many thermal cameras are accompanied by a high-resolution visible-range camera, which can be used as a guide to super-resolve the low-resolution thermal images. However, the thermal and visible images form a stereo pair and the difference in their spectral range makes it very challenging to pixel-wise align the two images. The existing guided super-resolution (GSR) methods are based on aligned image pairs and hence are not appropriate for this task. In this paper, we attempt to remove the necessity of pixel-to-pixel alignment for GSR by proposing two models: the first one employs a correlation-based feature-alignment loss to reduce the misalignment in the feature-space itself and the second model includes a misalignment-map estimation block as a part of an end-to-end framework that adequately aligns the input images for performing guided super-resolution. We conduct multiple experiments to compare our methods with existing state-of-the-art single and guided super-resolution techniques and show that our models are better suited for the task of unaligned guided super-resolution from very low-resolution thermal images. [ABSTRACT FROM AUTHOR]

Many scientists have been investigating Land Surface Temperature (LST) because of its relevance in water management science due to its direct influence on the hydrological water cycle. This effect stems from being one of the most significant variables influencing evapotranspiration. One of the most important reasons for the evapotranspiration retrieved from MODIS data's limited suitability for scheduling and planning irrigation schemes is the lack of spatial resolution. As a result, high-resolution LST is required for estimating evapotranspiration. The goal of this study is to improve the resolution of the available LST data, to improve evapotranspiration (ETa) estimation using statistical downscaling with Normalized Difference Vegetation Index (NDVI) as a predictor. The DisTrad (Disaggregation of Radiometric Surface Temperature) method was used for the LST downscaling procedure, which is based on aggregating the NDVI map to the LST map resolution and then calculating the coefficient of variation of the native NDVI map within the aggregated pixel and classifying the aggregated map into three classes: NDVI < 0.2 for the bare soil, 0.2 ≤ NDVI ≤ 0.5 for the partial vegetation, and NDVI > 0.5 for the full vegetation. DisTrad uses 25% of the pixels with the lowest coefficient of variation from each class to calculate the regression coefficients. In this work, adjustments to the DisTrad method were implemented to enhance downscaling LST and to examine the impacts of that alteration on the evapotranspiration estimation. The linear regression model was tested as an alternative to the original second-order polynomial. In using 10% of the pixels instead of the originally proposed 25% with the lowest coefficient of variation values, it is assumed that a group of pixels with a lower coefficient of variation represents a more homogeneous area, thus it gives more accurate values. The downscaled LST map retrieval was validated using Landsat 8 thermal maps (100 m). Applying the modified DisTrad approach to disaggregate Landsat LST to 30 m (NDVI resolution) yielded an R2 of 0.72 for the 10%, 0.74 for the 25% and 0.61 for the second-order polynomial lowest coefficient of variation compared to native LST Landsat, which means that 10% can be used as an alternative. Applying the downscaled LST map to estimate ETa yielded R2 0.84 in both cases, compared to ETa yielded from the native Landsat LST. These results prove that using the robust linear regression provided better results than using polynomial regression. With the downscaled Land Surface Temperature data, it was possible to create detailed ETa maps of the small agricultural fields in the test area. [ABSTRACT FROM AUTHOR]

The thermal covert channels (TCCs) in many-core systems can cause detrimental data breaches. In this article, we present a three-step scheme to detect and fight against such TCC attacks. Specifically, in the detection step, each core calculates the spectrum of its own CPU workload traces that are collected over a few fixed time intervals, and then it applies a frequency scanning method to detect if there exists any TCC attack. In the next positioning step, the logical cores running the transmitter threads are located. In the last step, the physical CPU cores suspiciously engaging in a TCC attack have to undertake dynamic voltage frequency scaling (DVFS) such that any possible TCC trace will be essentially wiped out. Our experiments have confirmed that on average 97% of the TCC attacks can be detected, and with the proposed defense, the packet error rate (PER) of a TCC attack can soar to more than 70%, literally shutting down the attack in practical terms. The performance penalty caused by the inclusion of the proposed DVFS countermeasures is found to be only 3% for an $8\times 8$ many-core system. [ABSTRACT FROM AUTHOR]

This paper describes the parametric analysis of an optical fiber–based gamma thermometer (OFBGT) that is intended to be used to infer the power distribution in the Ohio State University Research Reactor (OSURR). The OFBGT measures the radial temperature difference Δ T between an optical fiber that is within the thermal mass and an optical fiber that is within the capillary tube that is attached to the exterior of the outer sheath of the OFBGT. This gas gap acts as a thermal resistance to volumetric gamma heating q ′ ′ ′ of the OFBGT thermal mass. Of the six that are analyzed, one is deemed most appropriate for operation in the OSURR Central Irradiation Facility. This design produces a maximum Δ T of ~50°C at full reactor power (450 kW). A comparison of the six OFBGT designs generally shows how modifications of the design that increase Δ T suffer from decreased spatial resolution. [ABSTRACT FROM AUTHOR]

Tracking of moving objects in pan-tilt-zoom (PTZ) cameras is a critical feature for automated surveillance applications. The PTZ auto tracking function enables the camera to control its electric motor mechanisms to adjust the course of the optical sensor field of view (FOV) to track moving objects automatically. In this paper, a proposed novel object tracking method based on the fusion of thermal, optical sensor and millimeter-wave data is implemented. The implementation of the proposed method is performed on a pan-tilt setup, which ultimately tracks the object of interest based on thermal data while keeping the optical image resolution the same as the thermal image resolution and provides valuable distance measurements to the target based on the millimeter-wave radar readings. The proposed method offers improvements over conventional optical sensor tracking by adding an extra layer to object tracking through additional thermal and millimeter-wave sensor data, which in turn provides a surplus of information of the object of interest that is being tracked. The proposed method compares favorably with commercially available surveillance systems. [ABSTRACT FROM AUTHOR]

• Thermal array sensors accurately recognize OWTPF flow patterns. • Use AOK-TFR, URP to realize the distinction of flow patterns. • First application of SSA-SVM for flow patterns segmentation. To understand the flow characteristics and kinetic modelling of oil–water two-phase flow(OWTPF), this paper employs thermal array sensors to identify the flow pattern (FP) in OWTPF horizontal pipelines. Firstly, the thermal array sensor installation was constructed. Secondly, the time-varying characteristics of multi-feature signals were qualitatively analyzed using the adaptive optimal kernel time–frequency representation (AOK-TFR) method and unthresholded recurrence plot (URP) method. Finally, the sparrow search algorithm-support vector machine (SSA-SVM) machine learning algorithm is used for quantitative prediction of multi-feature classification, which leads to high prediction accuracy. The experiment proves that the correct rate of the thermal method in recognizing six types of OWTPF is above 91%, the correct rate of five types of flow is above 97.33%. This paper adopts the thermal method to identify the multiphase FP, which is a major innovation in this paper and also lays the foundation for recognizing the flow characteristics of multiphase flow. [ABSTRACT FROM AUTHOR]

In the past decade, remarkable progress has been made in the domain of augmented reality/virtual reality (AR/VR). The need for realistic and immersive augmentation has propelled the development of haptics interfaces‐enabled AR/VR. The haptics interfaces facilitate direct interaction and manipulation with both real and virtual objects, thus augmenting the perception and experiences of the users. The level of augmentation can be significantly improved by thermal stimulation or sensing, which facilitates a higher degree of object identification and discrimination. This review discusses the thermal technology‐enabled augmented reality and summarizes the recent progress in the development of different thermal technology such as thermal haptics including thermo‐resistive heater and Peltier devices, thermal sensors including resistive, pyroelectric, and thermoelectric sensors, which can be utilized to improve the realism of augmentation. The fundamental mechanism, design strategies, and the rational guidelines for the adoption of these technologies in AR/VR is explicitly discussed. The conclusion provides an outlook on the existing challenges and outlines the future roadmap for the realization of next‐generation thermo‐haptics enabled augmented reality. [ABSTRACT FROM AUTHOR]

We propose a bidirectional flowmeter based on specialty fiber interferometry by controlled mode excitation for measuring the frequency of the vortices induced by a bluff body. The proposed arrangement of the cylindrical bluff body with a slit and the interferometric system based on modal excitation allows the bidirectional flowmeter to determine the flow velocities in either direction precisely and effectively. We have encapsulated the specialty fiber interferometer in a protective metal casing, which not only allows the interferometer to respond to the vortex frequency but also provides mechanical stability to the whole flowmeter so at to operate in a harsh environment. Experiments are conducted by introducing the modal interferometer in the slit of the cylindrical bluff body at various flow rates ranging from Reynolds number (Re) = 7200 to 56 600. A linear response is observed between the flow velocity and the vortex frequency from both directions. Thus, this work provides a reconfigurable bidirectional flowmeter, which is sensitive even at low flow rates and demonstrates a large operating range. [ABSTRACT FROM AUTHOR]

Thermal inertia has been applied to map soil water content exploiting remote sensing data in the short and long wave regions of the electromagnetic spectrum. Over the last years, optical and thermal cameras were sufficiently miniaturized to be loaded onboard of unmanned aerial systems (UASs), which provide unprecedented potentials to derive hyperspatial resolution thermal inertia for soil water content mapping. In this study, we apply a simplification of thermal inertia, the apparent thermal inertia (ATI), over pixels where underlying thermal inertia hypotheses are fulfilled (unshaded bare soil). Then, a kriging algorithm is used to spatialize the ATI to get a soil water content map. The proposed method was applied to an experimental area of the Alento River catchment, in southern Italy. Daytime radiometric optical multispectral and day and nighttime radiometric thermal images were acquired via a UAS, while in situ soil water content was measured through the thermo-gravimetric and time domain reflectometry (TDR) methods. The determination coefficient between ATI and soil water content measured over unshaded bare soil was 0.67 for the gravimetric method and 0.73 for the TDR. After interpolation, the correlation slightly decreased due to the introduction of measurements on vegetated or shadowed positions (r2 = 0.59 for gravimetric method; r2 = 0.65 for TDR). The proposed method shows promising results to map the soil water content even over vegetated or shadowed areas by exploiting hyperspatial resolution data and geostatistical analysis. [ABSTRACT FROM AUTHOR]

In this article, by using 0.35-μm two-polysilicon four-metal CMOS MEMS technology, a thermoresistive micro calorimetric flow (TMCF) sensor with two packaging designs is systematically investigated: Open-space (O-type) and probe-with-channel (P-type) designs. In comparison with the O-type design, the P-type sensor shows an 80% enhanced sensitivity with a relieved tilt angle effect on the mainstream airflow detection. Accordingly, the P-type packaged flow sensor for HVAC gains a prominent normalized sensitivity (S*) of 228 μV/(m/s)/mW, while also having a low power consumption (P) of less than 4.2 mW. Moreover, within the linear range of –2 to 2 m/s, an accuracy of ±0.05 m/s is achieved, thus enabling this robust flow sensor (repeatability <0.2%) for indoor airflow measurements even in a variable ambient environment. Furthermore, the proposed P-type TMCF sensor and its wireless multisensor module are applied for the development of personalized ventilation and the monitoring of predicted mean vote, both of which confirm the functionality of the developed sensor for indoor airflow measurements in the HVAC system. [ABSTRACT FROM AUTHOR]

Electronic skins (e‐skins) that mimic the thermosensation and mechanosensation functionalities of natural skin are highly desired for the emerging fields of prosthetics and robotics. Advances in the materials and architecture of e‐skins have been made; nevertheless, sensing mechanism innovations are rarely explored. Here, inspired by the skin sensory behaviors, a single potentiometric sensing scheme for both thermosensation and mechanosensation functionalities are presented. Through careful materials selection, component optimization, and structure configuration, the coupling effect between thermosensation and mechanosensation can be significantly minimized. Such a potentiometric sensing scheme enables one to create a new class of energy‐efficient e‐skin with distinctive characteristics that are highly analogous to those of natural human skin. The e‐skin reported here features ultralow power consumption (at nanowatt level), greatly simplified operation (only voltage output), ultrahigh sensitivity (non‐contact sensing capability), all‐solution‐processing fabrication, and, more importantly, good capability for simultaneous monitoring/mapping of both thermal and mechanical stimulations. In addition to proposing a new sensory mechanism, integration of the dual‐functional e‐skin with a soft robotic gripper for object manipulation is demonstrated. The presented concise yet efficient sensing scheme for both thermosensation and mechanosensation opens up previously unexplored avenues for the future design of skin prosthetics, humanoid robotics, and wearable electronics. [ABSTRACT FROM AUTHOR]

In this article, we present a novel post-silicon approach to locating the dominant heat sources on commercial multicore processors using heatmaps measured via an infrared (IR) thermal imaging setup. To locate the heat sources, 2-D spatial Laplacian transformation is performed on the heatmaps followed by K-means clustering to find the dominant power/heat-source clusters. This is an exclusively post-silicon approach that does not require any knowledge of the underlying design of the commercial chips other than the information that is publicly available. Since the identified clusters are the thermally vulnerable areas on the die, we then propose a machine-learning-based framework to deriving a thermal model capable of estimating their temperatures during online use. Our approach involves collecting transient temperature data of the aforementioned heat sources and synchronized high-level performance metrics from the chip, and training a long-short-term-memory (LSTM) neural network (NN) that uses the performance metrics as inputs to estimate the temperatures of the identified heat sources in real time. Since the model is meant for real-time use, we explore methods of reducing the performance overhead and inference time of the model. This includes a novel power correlation-based approach to identifying the thermally irrelevant performance metrics and eliminating them in order to reduce the input dimensionality of the model, and an analysis on network sizing to determine the ideal NN configuration for the problem at hand. The model is trained and tested exclusively using measured thermal data from commercial multicore processors. The experimental results from two Intel multicore processors (i5-3337U and i7-8650U) show that the proposed approach achieves very high accuracy (root-mean-square error: 0.55 °C–0.93 °C) in estimating the temperatures of all the identified heat sources on the chip. [ABSTRACT FROM AUTHOR]

Thermal-actuation and piezoresistive-detection effects have been employed to pump the effective quality factor of MEMS resonators, targeting better mass sensing performance in air. In this paper, frequency resolution (bias instability) of a thermal-piezoresistive resonator operating in air at room temperature is experimentally investigated. It is found that the dynamic range decreases when increasing the bias direct current whereas the effective quality factor rises. The measurement results indicate a maximum effective quality factor of 169k with a dynamic range of 47.8 dB for a bias current of 6.25 mA, and a minimum effective quality factor of 11.3k with a dynamic range of 70.1 dB for a bias current is 5.8 mA. Our work also shows that the frequency and amplitude bias instabilities are significantly lower due to the dynamic range decrease for a high bias current. [2021-0250] [ABSTRACT FROM AUTHOR]

To establish the RF power traceability up to 50 GHz in Mexico by means of a primary standard for microwave power and millimeter-wave power, a new coaxial microcalorimeter system in 2.4-mm line size along with thermoelectric-type transfer standards (TSs) were developed and their metrological characterization was performed. Coaxial microcalorimeters allow both broadband operation and power substitution through its feeding line; however, characterization and fabrication of millimeter-wave coaxial lines are technologically difficult. Thus, the design of the microcalorimeter is described, as well as the RF performance of its adiabatic lines. To overcome the lack of coaxial TSs up to 50 GHz, commercially available 2.4-mm thermoelectric power sensors are investigated to use them as low-frequency (LF) power substitution detectors. The effective efficiency is measured in the microcalorimeter by means of calorimetric measurements and the substitution of LF power; the developed measurement procedure is presented as well as the measurement results and the uncertainty analysis. The theoretical basis for the procedure employed to determine the microcalorimeter correction factor, based on the use of specially constructed open and short standards, is described with the measurement results and uncertainties are shown as well. To avoid the drift of the millimeter-wave power during the calorimetric measurements, a novel software leveling loop method for thermoelectric power sensors was devised. This method allows to keep the RF power level variations less than 100 ppm and being a straightforward solution to recent complex hardware realizations. [ABSTRACT FROM AUTHOR]

An approach for gas flow velocity measurement at two different closely located points using a simple device incorporating only one thermal sensing element, is introduced and the feasibility to determine different velocities is experimentally demonstrated. An electrostatically actuated initially curved bistable microbeam heated by an electric current and convectively cooled by airflow is switched between two stable locations through the snap-through (ST) and snap-back (SB) buckling mechanisms. The velocities of an air flow at these positions are obtained by measuring the critical ST and SB values of the actuation voltage. In the experiments, our $500~\mu \text{m}$ long, $2~\mu \text{m}$ wide single-crystal Si beam with $\approx \, 2.5\,\mu \text{m}$ nominal initial elevation demonstrated sensitivity of $S_{\text {ST}} \approx 0.25$ V/(m/s) and $S_{\text {SB}} \approx 0.84$ V/(m/s) at the ST and SB points, respectively. In the present device, the distance between the two measurement points is $\approx 10~\mu \text{m}$. Our experimental results indicate that the suggested approach can be used for the velocity gradient measurements. The sensing principle relying on a single bistable sensing element opens new opportunities for measurement of gas flow velocity and velocity gradients at scales significantly smaller than the state-of-the-art multi hot-wire sensors. [ABSTRACT FROM AUTHOR]

Power and thermal management are critical components of high-performance-computing (HPC) systems, due to their high-power density and large total power consumption. The assessment of thermal dissipation by means of compact models directly from the thermal response of the final device enables more robust and precise thermal control strategies as well as automated diagnosis. However, when dealing with large-scale systems “in production,” the accuracy of learned thermal models depends on the dynamics of the power excitation, which depends also on the executed workload, and measurement nonidealities such as quantization. In this article we show that, using an advanced system identification algorithm, we are able to generate very accurate thermal models (average error lower than our sensors quantization step of 1 °C) for a large-scale HPC system on real workloads for very long time periods. However, we also show that: 1) not all real workloads allow for the identification of a good model and 2) starting from the theory of system identification it is very difficult to evaluate if a trace of data leads to a good estimated model. We then propose and validate a set of techniques based on machine learning and deep learning algorithms for the choice of data traces to be used for model identification. We also show that deep learning techniques are absolutely necessary to correctly choose such traces up to 96% of the times. [ABSTRACT FROM AUTHOR]

The multilayer structure of 3-D network-on-chips (3-D NoC) leads to the unequal thermal conductance between different layers and, as a result, an unbalanced thermal distribution in the chip. This issue leads to the low reliability and performance degradation of 3-D NoCs. To ensure thermal safety, 3-D NoCs require effective cooling methods. Several thermal-aware routing algorithms have been proposed to route packets in a way which helps mitigation of imbalance temperature distribution. To have a thermal-aware path selection and safety thermal distribution, we propose a new thermal-aware routing method based on the Q-learning algorithm (Q-Thermal). In our method, thermal information propagates over the network using ordinary network packets. Thanks to the Q-learning algorithm that keeps always the thermal information of routers up-to-date that causes routers steer packets toward the cooler paths (laterally or vertically). Based on the experimental results, Q-Thermal reduces the heat and temperature of different layers of 3-D NoCs significantly in comparison with the previous state-of-the-art techniques. Q-Thermal leads to a balanced thermal distribution in the stacked layers of the chip and it improves the network performance considerably. [ABSTRACT FROM AUTHOR]

In this paper, we report a two-dimensional (2D) Thermoresistive Micro Calorimetric Flow (TMCF) sensor by using a 0.35μm 2P4M CMOS MEMS technology. For the airflow, the fabricated and the open-space packaged 2D flow sensor system achieved the highest normalized sensitivity of 32 mV/W/(m/s) with respect to input heating power and gain. To overcome the sensor output drifting issue due to the locally redistributed fluid flow near the bonding wires, a digital signal conditioning algorithm using the inverse distance weighting (IDW) method, instead of the conventional fitting method with sine and cosine function, was proposed. Accordingly, the calibrated 2D flow sensor showed an improved velocity accuracy of ±4% and the angle accuracy of ±3° for an input airflow from 0 to 20 m/s. Therefore, the developed highly sensitive 2D TMCF sensor will be a promising device for the airflow measurement in the smart buildings and the meteorological monitoring system. [2019-0259] [ABSTRACT FROM AUTHOR]

Accurate modeling of a thermal field is one of the fundamental requirements in engineering thermal management in numerous industries. Existing studies have shown that using differential equations to model a thermal field delivers good performance when the parameters are predetermined through physical or experimental analysis. However, due to variations of the inner medium affected by certain latent factors, the parameters in differential equation models may not be treated as constants while the thermal field is estimated, and this fact poses a new challenge to field estimation by directly solving the differential equation models. In this study, a novel approach to thermal field modeling is developed by considering the parameters as functional variables that vary temporally in partial differential equations (PDEs). This approach provides a new perspective to model the dynamic thermal field by fully using the collected sensor data from the thermal system. Specifically, time-varying parameters can be constructed through a combination of basis functions whose coefficients can be efficiently estimated through the sensor data. A two-level iterative parameter estimation algorithm is also tailored to obtain the parameters in the PDE model. Both simulation and real case studies show that our proposed approach provides satisfactory estimation performance compared with the benchmark method that uses the constant parameter estimation. Note to Practitioners—The proposed method aims to model a thermal field using PDEs with time-varying parameters. To better implement this method in practice, three things are noteworthy: first, the proposed method models a thermal field by fully considering physics-specific engineering knowledge using PDEs and the collected sensor data from thermal systems. Second, because time-varying parameters in PDEs cannot be estimated directly, the proposed model represents the time-varying parameters by a combination of B-spline basis functions in terms of time. Estimating time-varying parameters is converted into estimating the constant coefficients of the basis functions. Because the derivatives of a thermal field might not have an analytical expression, the proposed model represents the thermal field by a combination of B-spline basis functions. Taking the derivatives of the thermal field is converted into taking the derivatives of the corresponding basis functions. Third, the proposed method can not only model a thermal field but can also be applied in other physics-specific engineering cases. [ABSTRACT FROM AUTHOR]

As the speed gap of the modern processor and the off-chip main memory enlarges, on-chip cache capacity increases to sustain the performance scaling. As a result, the cache power occupies a large portion of the total power budget. Spin transfer torque magnetic memory (STT-MRAM) is proposed as a promising solution for the low power cache design due to its high integration density and ultralow leakage power. Nevertheless, the high write power and latency of STT-MRAM become new barriers for the commercialization of this emerging technology. In this paper, we investigate the thermal effect on the access performance of STT-MRAM, and observe that the temperature can affect the write delay and energy significantly. Then, we explore the nonuniform cache access (NUCA) design of the chip-multiprocessors with STT-MRAM-based last level cache (LLC). A thermal aware data migration policy, called “Thermosiphon,” which takes advantage of the thermal property of STT-MRAM, is proposed to reduce the LLC write energy. This policy splits the LLC into different regions dynamically based on the thermal distribution monitored by thermal sensors available on-chip, and adaptively migrates write intensive data among different thermal regions considering the thermal gradient. Compared to the conventional NUCA design, our proposed design can save 41.2% write energy at most and 13.01% on average with negligible hardware overhead. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cells (PEMFCs) are considered as one of the most promising power sources for transportation in the near future. Proper and robust thermal management is a key issue in fuel cell applications. This paper mainly studies the sensor fault detection and isolation (FDI) and fault tolerant control (FTC) for the thermal management of PEMFC systems. The thermal model of fuel cell is established and analyzed by structure analysis, and the residual generator of sensor faults are designed by Dulmage–Mendelsohn decomposition to rearrange the biadjacency matrix of the thermal model. A sliding-mode-based active FTC (AFTC) strategy is proposed for the thermal management of fuel cell systems. The effectivenesses of the proposed FDI method and AFTC strategy are verified on a fuel cell test bench. The experimental results show that the temperature of the PEMFC stack can be maintained at the reference value with high accuracy even when sensor fails. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate the use of regenerated chirped grating (RCG)-Michelson interferometer for the measurement of laser beam intensity profile. When the grating is exposed to CO2 laser irradiation, heat is generated in the fiber due to the absorption of the laser by the glass fiber. The RCG is manufactured from an 18-mm long seed chirped grating through a thermal annealing treatment, to enhance its thermal resistance and durability against the high temperature induced by the CO2 laser irradiation. The intensity profile of the incident laser on the grating can be determined from the phase shift derivative function calculated from the output spectra of the interferometer. By scanning the grating across the laser beam, a 2-D intensity profile of the CO2 laser can be attained. [ABSTRACT FROM AUTHOR]

The optical schemes and characteristics of atomic absorption Hg0 analyzers based on the Zeeman effect and a low-pressure capillary mercury lamp, as well as on 190 fm linewidth UV dye laser systems have been studied in detail. The method of evaluating the limit of detection (LOD) of atomic absorption analyzers is proposed. The characteristics of modern ultra-narrowband sensors and sources of UV radiation are analyzed. The efficiency of using new thermal ultrasonic sensors as optical power detectors in the atomic absorption analyzer is discussed. Technical characteristics and reliability of such sensors are compared with all known thermal sensors. The main physical and technical limitations that have a negative effect on the sensitivity and miniaturization of thermal ultrasound sensors are presented. The study shows that such thermal sensors have low sensitivity and accuracy, which makes their use in portable express analyzers of background mercury impractical. The evaluation results prove that the analyzers which use a transverse magnetic field for splitting the lamp emission line also cannot register background mercury concentration. This fact is explained by the low signal-to-noise ratio (SNR) of the discharge low-pressure UV lamp operating in a continuous mode and by the absorption of forbidden oxygen lines by the reference optical signal. The estimation results shown that the portable Hg0 analyzers based on UV dye laser systems can reach the LOD < 0.1 ng/ $\text{m}^{3}$. Thus, the main stage in the design of the portable express background mercury analyzers is a measurement of the SNR of UV sources and sensors. [ABSTRACT FROM AUTHOR]

RGB-thermal (RGB-T) object tracking, which has attracted much recent attention, uses thermal infrared information to assist object tracking with visible light information. However, it still faces many challenging problems, especially the background inclusion in the target bounding box which easily results in model drifting. To handle this problem, we propose a novel and general approach to learn a local-global multi-graph descriptor to suppress background effects for RGB-T tracking. Our approach relies on a novel graph learning algorithm. First, the object is represented with multiple graphs, with a set of multi-modal image patches as nodes, for the robustness to prevent deformation and partial occlusion. Second, we dynamically learn a joint graph over time with both local and global considerations using spatial smoothness and low-rank representation. In particular, we design a single unified alternating direction method of multipliers-based optimization framework to learn graph structure, edge weights, and node weights simultaneously. Third, we combine multi-graph information with corresponding graph node weights to form a robust object descriptor, and tracking is finally carried out by adopting the structured support vector machine. Extensive experiments conducted on the tracking benchmark data sets demonstrate the effectiveness of the proposed approach against the state-of-the-art RGB-T trackers. [ABSTRACT FROM AUTHOR]

Recently, the Sentinel-2 (S2) satellite constellation was deployed for mapping and monitoring the Earth environment. Images acquired by the sensors mounted on the S2 platforms have three levels of spatial resolution: 10, 20, and 60 m. In many remote sensing applications, the availability of images at the highest spatial resolution (i.e., 10 m for S2) is often desirable. This can be achieved by generating a synthetic high-resolution image through data fusion. To this end, researchers have proposed techniques exploiting the spectral/spatial correlation inherent in multispectral data to sharpen the lower resolution S2 bands to 10 m. In this paper, we propose a novel method that formulates the sharpening process as a solution to an inverse problem. We develop a cyclic descent algorithm called S2Sharp and an associated tuning parameter selection algorithm based on generalized cross validation and Bayesian optimization. The tuning parameter selection method is evaluated on a simulated data set. The effectiveness of S2Sharp is assessed experimentally by comparisons to state-of-the-art methods using both simulated and real data sets. [ABSTRACT FROM AUTHOR]

The viability of using off-chip single-shot imaging techniques for local thermal testing in integrated radio frequency (RF) power amplifiers (PAs) is analyzed. With this approach, the frequency response of the output power and power gain of a Class A RF PA is measured, also deriving information about the intrinsic operation of its transistors. To carry out this paper, the PA is heterodynally driven, and its electrical behavior is down converted into a lower frequency thermal field acquirable with an InfraRed lock-in thermography (IR-LIT) system. After discussing the theory, the feasibility of the proposed approach is demonstrated and assessed with thermal sensors monolithically integrated in the PA. As crucial advantages to RF-testing, this local approach is noninvasive and demands less complex instrumentation than the mainstream commercially available solutions. [ABSTRACT FROM AUTHOR]

Industrial requirements are continuously pushing the sensor limits toward higher signal-to-noise ratio under harsh environments, without compromising production costs by additional electronics. Non-full resistive Wheatstone bridge architectures arise as a compact and cheaper alternative to full-bridge measurement schemes, despite a lower output, linearity, and immunity to thermal drifts promoted by the replacement of the out-of-phase sensing elements by passive elements. However, the combination of state-of-the-art magnetoresistive (MR) sensors with thermally compatible materials used as tunable thin-film resistor at wafer level can lead to competitive prototypes for applications with high constraints. Therefore, the resistivity and the temperature coefficient of resistance (TCR) of Cr, Ti, and TiW thin films were evaluated as potential candidates for a thermal and electrical stable non-full MR bridge. Within a TCR around 1059 ± 12 ppm/°C for a spin-valve sensor lying between the values observed for a Ti thickness range of 300 Å up to 500 Å (571.9 ± 8.3 and 1347 ± 69 ppm/°C, respectively), a sin/cos magnetic encoder was prototyped in a non-full-bridge architecture with a thermal offset drift of 51.48 ± 0.25 nV/Vcc/°C and an offset voltage of 16 mV/Vcc. [ABSTRACT FROM AUTHOR]

The rising field of spin caloritronics focuses on the interactions between spin and heat currents in a magnetic material; the observation of the spin Seebeck effect opened the route to this branch of research. This paper reports the results of a round robin test performed by five partners on a single device highlighting the reproducibility problems related to the measurements of the spin Seebeck coefficient, the quantity that describes the strength of the spin Seebeck effect. This paper stimulated the search for more reproducible measurement methods through the analysis of the systematic effects. [ABSTRACT FROM AUTHOR]

Thermal infrared imaging is an emerging modality that has gained increasing interest in recent years, mostly due to technical advances resulting in the availability of affordable microbolometer-based IR imaging sensors. However, while sensors are widely available, algorithms for thermal image processing still lack robustness and accuracy when compared to their RGB counterparts. Current methods developed for RGB data make use of machine learning algorithms that require large amounts of labeled images which are currently not available for the thermal domain. In this paper, we address the question whether providing a large number of labeled images would allow the application of current image processing methods on the example of solving challenging face analysis tasks. We introduce a high-resolution thermal facial image database with extensive manual annotations and explore how it can be used to adapt methods from the visual domain for infrared images. In addition, we extend existing approaches for infrared landmark detection with a head pose estimation for improved robustness and analyze the performance of a deep learning method on this task. An evaluation of algorithm performance shows that learning algorithms either outperform available solutions or allow completely new applications that could previously not be addressed. As a conclusion, we prove that investing the effort into acquiring appropriate training data and adapting competitive algorithms is not only a viable approach in analysing thermal infrared images but can also allow outperforming specific task-designed solutions. The database is freely available for academic use at https://github.com/marcinkopaczka/thermalfaceproject. [ABSTRACT FROM AUTHOR]

This article, titled "Recrystallization-Induced Laser Lift-Off Strategy for Flexible Thermal Sensors with Near-Limit Sensitivity," discusses the development of metal-based thermal sensors using a recrystallization-induced laser lift-off strategy. These sensors, prepared through magnetron sputtering, often have defects that can be repaired through this strategy. By transferring the high-temperature annealed metal to a flexible organic substrate, the sensors gain both high sensitivity and flexibility. The research presented in this article provides a promising approach for enhancing the performance of flexible sensors. [Extracted from the article]

Thermal sigma–delta modulator is a closed-loop measurement approach, where the sensing element performs part of the $\Sigma \Delta$ modulation in the thermal domain. A new architecture is proposed in this paper for measuring the thermal incident radiation using thermoresistive sensors, where the modulator input range is automatically adjusted to fit the complete thermal radiation. The proposed architecture is compared with a similar one, which uses the same transducer interface circuit but without range adjustment, and is validated experimentally with a reference pyranometer. The proposed architecture has the main advantage of presenting a signal-to-noise ratio and sensitivity that is independent of the ambient temperature range definition. Experimental results for an ambient temperature range of 45 °C show a gain of 13 dB over the similar one. [ABSTRACT FROM AUTHOR]

Despite the use of state-of-the-art thermal modeling tools in the design stage, the measured thermal behavior of prototype electrical machines can differ significantly from the modeled ones. This paper shows how a thermal model, based on the finite-element method, of an electric machine can be improved using inverse modeling techniques. In a thorough study, a forward high fidelity finite-element thermal model of a 4-kW axial flux permanent magnet (PM) machine is introduced and improved using inverse modeling techniques via noncollocated thermal sensors. Parametric model order reduction of the high fidelity finite-element thermal model based on the moment matching method is performed to make the recovery of the actual thermal parameters characterizing the thermal behavior of the axial flux PM machine tractable. Furthermore, the same reduced order model is used to identify the different power loss components in the machine. Experimental results confirm that the presented two-stage approach is capable of identifying the thermal parameters and losses with high accuracy. [ABSTRACT FROM AUTHOR]

The great availability of commercial drones has raised growing interest among people, since remotely piloted vehicles can be employed in numerous applications. The pervasive use of these devices has created many privacy and safety concerns that need to be addressed by means of proper surveillance systems able to cope with such threats. In this paper, we propose a WiFi statistical fingerprint-based drone detection approach, which is capable of identifying nearby drone threats, even in the presence of malicious attacks. We present a performance analysis carried out through experimental tests, where our solution is able to achieve very good results in terms of correct recognitions in many real-life scenarios, with a peak true positive rate of ${\text{96}}$ %. [ABSTRACT FROM AUTHOR]

In this brief, a low-cost and high-yield bulk under thin-film (BUT) MEMS technique for the volume production of flow sensors is proposed and developed. The IC-foundry-compatible process is conducted only from the front side of (111) silicon wafers, without double-sided alignment exposure, wafer bonding, cavity silicon on insulator, and double-sided polished wafers needed. With the single-wafer-based BUT structure, the thermopile consisting of 21 pairs of single-crystalline Si/Al thermocouples that show significantly higher Seebeck coefficient and lower noise compared to the traditional poly-Si/Al thermocouples is employed to construct the gas flow sensor, and the fabricated sensor chip size is as small as ${0.65}\,\,\text {mm} \times {0.65}$ mm that facilitates low-cost high-throughput IC-foundry batch fabrication. The fabricated sensor shows a high normalized sensitivity of $199~\mu \text{V}$ /(SLM)/mW for nitrogen gas flow, which is 1 order of magnitude higher than that of the reported thermopile-based gas flow sensor, and a short response time of 3.5 ms could be achieved. With the new technique, the proposed sensors are promising in automotive and process control system applications. [ABSTRACT FROM AUTHOR]

Detection of moving objects in videos is a crucial step toward successful surveillance and monitoring applications. A key component for such tasks is called background subtraction and tries to extract regions of interest from the image background for further processing or action. For this reason, its accuracy and real-time performance are of great significance. Although effective background subtraction methods have been proposed, only a few of them take into consideration the special characteristics of thermal imagery. In this paper, we propose a background subtraction scheme, which models the thermal responses of each pixel as a mixture of Gaussians with unknown number of components. Following a Bayesian approach, our method automatically estimates the mixture structure, while simultaneously it avoids over-/underfitting. The pixel density estimate is followed by an efficient and highly accurate updating mechanism, which permits our system to be automatically adapted to dynamically changing operation conditions. We propose a reference implementation of our method in reconfigurable hardware achieving both adequate performance and low-power consumption. Adopting a high-level synthesis design and demanding floating point arithmetic operations are mapped in reconfigurable hardware, demonstrating fast prototyping and on-field customization at the same time. [ABSTRACT FROM AUTHOR]

Monitoring the composition, blood flow properties, and hydration status of human skin can be important in diagnosing disease and tracking overall health. Current methods are largely limited to clinical environments, and they primarily measure properties of superficial layers of the skin, such as the stratum corneum (10-40 μm). This work introduces soft, skin-like thermal depth sensors (e-TDS) in designs that seamlessly couple with human skin and measure its thermal properties with depth sensitivity that can extend up to 6 mm beneath the surface. Guidelines for tailoring devices to enable measurements through different effective depths follow from a systematic set of experiments, supported by theoretical modeling. On-body testing validates the physiological relevance of measurements using the e-TDS platform, with potential to aid the diagnosis of deep cutaneous and systemic diseases. Specific demonstrations include measurements that capture responses ranging from superficial changes in skin properties that result from application of a moisturizer, to changes in microvascular flow at intermediate depths induced by heating/cooling, to detection of inflammation in the deep dermis and subcutaneous fat in an incidence of a local bacterial infection, cellulitis. [ABSTRACT FROM AUTHOR]

A novel microfabricated, all-electrical measurement platform is presented for a direct, accurate and rapid determination of the thermal conductivity and diffusivity of liquid and solid materials. The measurement approach is based on the bidirectional 3-omega method. The platform is composed of glass substrates on which sensor structures and a very thin dielectric nanolaminate passivation layer are fabricated. Using through-glass vias for contacting the sensors from the chip back side leaves the top side of the platform free for deposition, manipulation and optical inspection of the sample during 3-omega measurements. The thin passivation layer, which is deposited by atomic layer deposition on the platform surface, provides superior chemical resistance and allows for the measurement of electrically conductive samples, while maintaining the conditions for a simple thermal analysis. We demonstrate the measurement of thermal conductivities of borosilicate glass, pure water, glycerol, 2-propanol, PDMS, cured epoxy, and heat-sink compounds. The results compare well with both literature values and values obtained with the steady-state divided bar method. Small sample volumes (∼0.02 mm³) suffice for accurate measurements using the platform, allowing rapid temperature-dependent measurements of thermal properties, which can be useful for the development, optimization and quality testing of many materials, such as liquids, gels, pastes and solids. [ABSTRACT FROM AUTHOR]