Evaluation of facial temperature distribution changes during meditation using infrared thermal imaging:An experimental,cross-over study

Objective:To investigate the differences between meditation and resting states using infrared thermal imaging(IRTI)to determine facial temperature distribution features during meditation and annotate the patterns of facial temperature changes during meditation from the perspective of traditional Chinese medicine facial diagnosis.Methods:Each participant performed 10 min meditation and 10 min resting but in different sequences.A concentration test was set as the task load,followed by a meditation/resting or resting/meditation session,during which the participants'facial temperatures were observed using IRTI.Participants were scored on the Big Five Inventory(BFI)and Mindful Attention Awareness Scale(MAAS).Results:Forehead temperatures decreased more during meditation than during the resting state.The chin temperature increased only during meditation(P<.0001).For the subjects with meditation experience,there were significant differences in the temperatures of the left forehead(P<.01),right forehead(P<.01)and chin(P<.05)between the meditation and resting state at the 10~(th)min.In the nontask state,the BFI-Extraversion showed a negative correlation with the temperature of the left forehead(R=-0.41,P=.03).In the post-task state,the temperature of the left forehead was negatively correlated with scores on the MAAS(R=-0.42,P=.02).Conclusion:Using IRTI to study meditation offers a practical solution to the challenges in meditation research.The results indicate that an increase in chin temperature may be a representative feature of a meditation state,and forehead temperature is also a potential indicator.

Design of Uncooled Thermal Imaging Systems Operating at Multiple Temperatures

The reasons why thermal imaging systems consume power are analyzed,and a low power consumption design scheme is presented for the thermal imaging systems operating at multiple temperatures. The relation between the response performance of α-Si microbolometer detector and its operating temperature is studied by means of formulas of microbolometer detector's noise equivalent temperature difference(NETD) and detectivity. Numerical analysis based on true parameters demonstrates that the detectivity decreases slightly and NETD increases slightly when operating temperature rises,which indicates that α-Si microbolometer detector has approximately uniform response in a wide operating temperature range. According to these analyses,a thermal imaging system operating at multiple temperatures is designed. The power of thermoelectric stabilizer(TEC) is less than 350 mW and NETD is less than 120 mK in the ambient temperature range of-40 ℃-60 ℃,which shows that this system not only outputs high-quality images but consumes low power.

Recognition of big mammal species in airborne thermal imaging based on YOLO V5 algorithm

Unmanned aerial vehicle(UAV)technology,artificial intelligence,and the relevant hardware can be used for monitoring wild animals.However,existing methods have several limitations.Therefore,this study explored the monitoring and protection of Amur tigers and their main prey species using images from UAVs by optimizing the algorithm models with respect to accuracy,model size,recognition speed,and elimination of environmental inter-ference.Thermal imaging data were collected from 2000 pictures with a thermal imaging lens on a DJI M300RTK UAV at the Hanma National Nature Reserve in the Greater Khingan Mountains in Inner Mongolia,Wangqing National Nature Reserve in Jilin Province,and Siberian Tiger Park in Heilongjiang Province.The YOLO V5s al-gorithm was applied to recognize the animals in the pictures.The accuracy rate was 94.1%,and the size of the model weight(total weight of each model layer trained with the training set)was 14.8 MB.The authors improved the structures and parameters of the YOLO V5s algorithm.As a result,the recognition accuracy rate became 96%,and the model weight was 9.3 MB.The accuracy rate increased by 1.9%,the model weight decreased by 37.2%from 14.8 MB to 9.3 MB,and the recognition time of a single picture was shortened by 34.4%from 0.032 to 0.021 s.This not only increases the recognition accuracy but also effectively lowers the hardware requirements that the algorithm relies on,which provides a lightweight fast recognition method for UAV-based edge computing and online investigation of wild animals.

Real Time Thermal Image Based Machine Learning Approach for Early Collision Avoidance System of Snowplows

In an effort to reduce vehicle collisions with snowplows in poor weather conditions, this paper details the development of a real time thermal image based machine learning approach to an early collision avoidance system for snowplows, which intends to detect and estimate the distance of trailing vehicles. Due to the operational conditions of snowplows, which include heavy-blowing snow, traditional optical sensors like LiDAR and visible spectrum cameras have reduced effectiveness in detecting objects in such environments. Thus, we propose using a thermal infrared camera as the primary sensor along with machine learning algorithms. First, we curate a large dataset of thermal images of vehicles in heavy snow conditions. Using the curated dataset, two machine-learning models based on the modified ResNet architectures were trained to detect and estimate the trailing vehicle distance using real-time thermal images. The trained detection network was capable of detecting trailing vehicles 99.0% of the time at 1500.0 ft distance from the snowplow. The trained trailing distance network was capable of estimating distance with an average estimation error of 10.70 ft. The inference performance of the trained models is discussed, along with the interpretation of the performance.

Influence of UAV flight speed on droplet deposition characteristics with the application of infrared thermal imaging

A plant protection unmanned aerial vehicle(UAV)applied for spraying pesticide has the advantages of low cost,high efficiency and environmental protection.However,the complex and changeable farmland environment is not conductive to perform spray test effectively.It is therefore necessary to carry out spray test under controlled conditions.The current study aimed to illuminate the variation law of droplet deposition characteristics under different UAV flight speeds,and to verify the feasibility for applying infrared thermal imaging in detection of droplet deposition effects.A UAV simulation platform with an airborne spray system was established and an analysis program Droplet Analysis for dealing with water-sensitive paper was developed.The results showed that,when the flight speed was set at 0.3 m/s,0.5 m/s,0.7 m/s,0.9 m/s and 1 m/s,respectively,the droplet deposition density,droplet deposition coverage and arithmetic mean of droplet size D0 decreased as the UAV flight speed increased.On the contrary,the droplet diameter variation coefficient CV increased with the increase of UAV flight speed,resulting in the worse uniformity of sprayed droplet distribution as well.The results can provide a theoretical support for optimizing the spraying parameters of plant protection UAV,and demonstrate the practicability of infrared thermal imaging in evaluating the droplet deposition in the field of aerial spraying.

Infrared Thermal Imaging-Based Research on the Intermediate Structures of the Lung and Large Intestine Exterior-Interior Relationship in Asthma Patients

Objective: By observing body surface temperature variation of the intermediate structures of the Lung(Fei) and Large Intestine(Dachang) exterior-interior relationship in asthmatic patients, to investigate the pathological response on the pathway of channels and to substantiate the objective existence of the intermediary structures. Methods: The study included 60 subjects meeting the bronchial asthma inclusion criteria(experimental group) and 60 healthy subjects(normal control group). ATIR-M301 infrared thermal imaging device was used for detecting body surface temperature of the subjects and collecting the infrared thermal images. The temperature values of the intermediate structures of Lung and Large Intestine exterior-interior relationship [throat, Quepen, elbow, nose, Lieque(LU 7), Pianli(LI 6)], control areas(0.2 cm lateral to the above structures) and Yintang(EX-HN 3) were measured on the infrared thermal image by infrared imaging system. Then, the above temperature values were compared and analyzed within and between two groups. Results: There were insignificant differences between the temperature on the left and right sides of the intermediate structures(Quepen, elbow, LU 7, LI 6) in normal control group(P>0.05). Except for that of Quepen, there were insignificant differences between the temperature of the intermediate structures and their corresponding control areas in normal control group(P>0.05). In the experimental group, the temperature on the left and right sides of the intermediate structures(Quepen, elbow, LU 7, LI 6) showed statistically significant differences(P<0.05 or P<0.01); the temperature difference between intermediate structure(throat, Quepen, elbow, nose, LI 6) and their respective control areas were also significant(P<0.05 or P<0.01). The temperature of the intermediate structures(throat, Quepen, elbow, LU7, LI 6) between the experimental group and normal control group showed significant differences(P<0.05 or P<0.01). Conclusions: This study is an initial step to validate the objective existence of Lung and Large Intestine exterior-interior relationship intermediate structures, as described in the Chinese classical medical literatures, through the functional imaging angle. The intermediate structures are the pathological reaction areas of the bronchial asthmatic patients.

THERMAL IMAGING STUDY OF SCALAR TRANSPORT IN SHALLOW WAKES

The thermal imaging technique relies on the usage of infrared signal to detect the temperature field.Using temperature as a flow tracer,thermography is used to investigate the scalar transport in the shallow-water wake generated by an emergent circular cylinder.Thermal imaging is demonstrated to be a good quantitative flow visualization technique for studying turbulent mixing phenomena in shallow waters.A key advantage of the thermal imaging method over other scalar measurement techniques,such as the Laser Induced Fluorescence（LIF）and Planar Concentration Analysis（PCA）methods,is that it involves a very simple experimental setup.The dispersion characteristics captured with this technique are found to be similar to past studies with traditional measurement techniques.

Photovoltaic Cell Panels Soiling Inspection Using Principal Component Thermal Image Processing

Intended for good productivity and perfect operation of the solar power grid a failure-free system is required.Therefore,thermal image processing with the thermal camera is the latest non-invasive(without manual contact)type fault identification technique which may give good precision in all aspects.The soiling issue,which is major productivity affecting factor may import from several reasons such as dust on the wind,bird mucks,etc.The efficient power production sufferers due to accumulated soil deposits reaching from 1%–7%in the county,such as India,to more than 25%in middle-east countries country,such as Dubai,Kuwait,etc.This research offers a solar panel soiling detection system built on thermal imaging which powers the inspection method and mitigates the requirement for physical panel inspection in a large solar production place.Hence,in this method,solar panels can be verified by working without disturbing production operation and it will save time and price of recognition.India ranks 3rd worldwide in the usage use age of Photovoltaic(PV)panels now and it is supported about 8.6%of the Nation’s electricity need in the year 2020.In the meantime,the installed PV production areas in India are aged 4–5 years old.Hence the need for inspection and maintenance of installed PV is growing fast day by day.As a result,this research focuses on finding the soiling hotspot exactly of the working solar panels with the help of Principal Components Thermal Analysis(PCTA)on MATLAB Environment.

Optical micro-scanning location calibration of thermal microscope imaging system

A method of micro-scanning location adaptive calibration was proposed, which was real- ized by the digital image micro-displacement estimation. With geometric calculation, this calibration method used the displacement estimation of two thermal microscope images to get the size and direc- tion of each scanning location calibration angle. And each location calibration process was repeated according to the offset given by the system beforehand. The comparison experiments of sequence oversampling reconstruction before and after the micro-scanning location calibration were done. The results showed that the calibration method effectively improved the thermal microscope imaging qual- ity.

Dynamic infrared thermography and smartphone thermal imaging as an adjunct for preoperative, intraoperative, and postoperative perforator free flap monitoring

Aim:The versatile application of perforator free flaps for coverage of any extremity has been well proven.Often,a"freestyle"-like approach is used to design these flaps,as conventional imaging techniques for perforator identification may be too expensive or unavailable.As will be demonstrated,the recent application of a thermal imaging camera using a smartphone is a cheaper and therefore more universal means to better identify the requisite perforators upon which a free flap can be designed and then monitored.Methods:Smartphone thermography can be used on any patient preoperatively to identify preferable perforators or vascular network"hot spots"within the desired donor site territory.Intraoperative management of the choice of perforators and subsequent flap dissection can be similarly facilitated.Intermittent postoperative monitoring based on changes of the thermal image color palette will provide a comparison that can be used to determine if perfusion across the microanastomosis is sustained.Results:An overview of how to use a smartphone in concert with a thermal imaging camera is outlined.Dynamic infrared thermography represents a thermal stress necessary with a smartphone to better identify donor site"hot spots".Conclusion:Smartphone thermography is an inexpensive and expeditious means for identification of"hot spots"that correlate with perforators that would suffice to insure perfusion to a free perforator flap.However,since perforator caliber and course cannot be determined,this should be considered to be only a complementary adjunct for conventional methods.Nevertheless,its simplicity will overall improve the safer design,harvest,and subsequent monitoring of free flaps.

Oversample Reconstruction Based on a Strong Inter-Diagonal Matrix for an Optical Microscanning Thermal Microscope Imaging System

Based on a strong inter-diagonal matrix and Taylor series expansions,an oversample reconstruction method was proposed to calibrate the optical micro-scanning error. The technique can obtain regular 2 ×2 microscanning undersampling images from the real irregular undersampling images,and can then obtain a high spatial oversample resolution image. Simulations and experiments show that the proposed technique can reduce optical micro-scanning error and improve the system＇s spatial resolution. The algorithm is simple,fast and has low computational complexity. It can also be applied to other electro-optical imaging systems to improve their spatial resolution and has a widespread application prospect.

Gait Recognition System in Thermal Infrared Night Imaging by Using Deep Convolutional Neural Networks

Gait is an essential biomedical feature that distinguishes individuals through walking.This feature automatically stimulates the need for remote human recognition in security-sensitive visual monitoring applications.However,there is still a lack of sufficient accuracy of gait recognition at night,in addition to taking some critical factors that affect the performances of the recognition algorithm.Therefore,a novel approach is proposed to automatically identify individuals from thermal infrared(TIR)images according to their gaits captured at night.This approach uses a new night gait network(NGaitNet)based on similarity deep convolutional neural networks(CNNs)method to enhance gait recognition at night.First,the TIR image is represented via personal movements and enhanced body skeleton segments.Then,the state-space method with a Hough transform is used to extract gait features to obtain skeletal joints′angles.These features are trained to identify the most discriminating gait patterns that indicate a change in human identity.To verify the proposed method,the experimental results are performed by using learning and validation curves via being connected by the Visdom website.The proposed thermal infrared imaging night gait recognition(TIRNGaitNet)approach has achieved the highest gait recognition accuracy rates(99.5%,97.0%),especially under normal walking conditions on the Chinese Academy of Sciences Institute of Automation infrared night gait dataset(CASIA C)and Donghua University thermal infrared night gait database(DHU night gait dataset).On the same dataset,the results of the TIRNGaitNet approach provide the record scores of(98.0%,87.0%)under the slow walking condition and(94.0%,86.0%)for the quick walking condition.

Fault Diagnosis of Industrial Motors with Extremely Similar Thermal Images Based on Deep Learning-Related Classification Approaches

Induction motors(IMs)typically fail due to the rate of stator short-circuits.Because of the similarity of the thermal images produced by various instances of short-circuit and the minor interclass distinctions between categories,non-destructive fault detection is universally perceived as a difficult issue.This paper adopts the deep learning model combined with feature fusion methods based on the image’s low-level features with higher resolution and more position and details and high-level features with more semantic information to develop a high-accuracy classification-detection approach for the fault diagnosis of IMs.Based on the publicly available thermal images(IRT)dataset related to condition monitoring of electrical equipment-IMs,the proposed approach outperforms the highest training accuracy,validation accuracy,and testing accuracy,i.e.,99%,100%,and 94%,respectively,compared with 8 benchmark approaches based on deep learning models and 3 existing approaches in the literature for 11-class IMs faults.Even the training loss,validation loss,and testing loss of the eleven deployed deep learning models meet industry standards.

Heat transfer and temperature evolution in underground mininginduced overburden fracture and ground fissures: Optimal time window of UAV infrared monitoring

Heat transfer and temperature evolution in overburden fracture and ground fissures are one of the essential topics for the identification of ground fissures via unmanned aerial vehicle(UAV) infrared imager. In this study, discrete element software UDEC was employed to investigate the overburden fracture field under different mining conditions. Multiphysics software COMSOL were employed to investigate heat transfer and temperature evolution of overburden fracture and ground fissures under the influence of mining condition, fissure depth, fissure width, and month alternation. The UAV infrared field measurements also provided a calibration for numerical simulation. The results showed that for ground fissures connected to underground goaf(Fissure Ⅰ), the temperature difference increased with larger mining height and shallow buried depth. In addition, Fissure Ⅰ located in the boundary of the goaf have a greater temperature difference and is easier to be identified than fissures located above the mining goaf. For ground fissures having no connection to underground goaf(Fissure Ⅱ), the heat transfer is affected by the internal resistance of the overlying strata fracture when the depth of Fissure Ⅱ is greater than10 m, the temperature of Fissure Ⅱ gradually equals to the ground temperature as the fissures’ depth increases, and the fissures are difficult to be identified. The identification effect is most obvious for fissures larger than 16 cm under the same depth. In spring and summer, UAV infrared identification of mining fissures should be carried out during nighttime. This study provides the basis for the optimal time and season for the UAV infrared identification of different types of mining ground fissures.

Human Stress Recognition from Facial Thermal-Based Signature:A Literature Survey

Stress is a normal reaction of the human organism which triggered in situations that require a certain level of activation.This reaction has both positive and negative effects on everyone’s life.Therefore,stress management is of vital importance in maintaining the psychological balance of a person.Thermal-based imaging technique is becoming popular among researchers due to its non-contact conductive nature.Moreover,thermal-based imaging has shown promising results in detecting stress in a non-contact and non-invasive manner.Compared to other non-contact stress detection methods such as pupil dilation,keystroke behavior,social media interaction and voice modulation,thermal-based imaging provides better features with clear boundaries and requires no heavy methodology.This paper presented a brief review of previous work on thermal imaging related stress detection in humans.This paper also presented the stages of stress detection based on thermal face signatures such as dataset type,thermal image face detection,feature descriptors and classification performance comparisons are presented.This paper can help future researchers to understand stress detection based on thermal imaging by presenting the popular methods previous researchers use for stress detection based on thermal images.

Device topological thermal management of β-Ga_(2)O_(3) Schottky barrier diodes

The ultra-wide bandgap semiconductor β gallium oxide(β-Ga_(2) O_(3)) gives promise to low conduction loss and high power for electronic devices. However, due to the natural poor thermal conductivity of β-Ga_(2) O_(3), their power devices suffer from serious self-heating effect. To overcome this problem, we emphasize on the effect of device structure on peak temperature in β-Ga_(2) O_(3) Schottky barrier diodes(SBDs) using TCAD simulation and experiment. The SBD topologies including crystal orientation of β-Ga_(2) O_(3), work function of Schottky metal, anode area, and thickness, were simulated in TCAD, showing that the thickness of β-Ga_(2) O_(3) plays a key role in reducing the peak temperature of diodes. Hence, we fabricated β-Ga_(2) O_(3) SBDs with three different thickness epitaxial layers and five different thickness substrates. The surface temperature of the diodes was measured using an infrared thermal imaging camera. The experimental results are consistent with the simulation results. Thus, our results provide a new thermal management strategy for high power β-Ga_(2) O_(3) diode.

The Relation between Mental Workload and Face Temperature in Flight Simulation

In this research, we study the relationship between mental workload and facial temperature of aircraft participants during a simulated takeoff flight. We conducted experiments to comprehend the correlation between work and facial temperature within the flight simulator. The experiment involved a group of 10 participants who played the role of pilots in a simulated A-320 flight. Six different flying scenarios were designed to simulate normal and emergency situations on airplane takeoff that would occur in different levels of mental workload for the participants. The measurements were workload assessment, face temperatures, and heart rate monitoring. Throughout the experiments, we collected a total of 120 instances of takeoffs, together with over 10 hours of time-series data including heart rate, workload, and face thermal images and temperatures. Comparative analysis of EEG data and thermal image types, revealed intriguing findings. The results indicate a notable inverse relationship between workload and facial muscle temperatures, as well as facial landmark points. The results of this study contribute to a deeper understanding of the physiological effects of workload, as well as practical implications for aviation safety and performance.

Characterization of Automotive Aluminum AA356 by Photo Thermal Radiometric Spectroscopy

Photothermal radiometric spectroscopy was employed for characterizing an aluminum automotive piston with a steel insert. Amplitude and phase thermal property differences allowed the mapping of both metals. Thermal images were generated by using table stages x-y movement. The thermal wave penetrated more deeply in aluminum than in steel. Results demonstrated that this relatively unknown, non-intrusively, non-contact, and non-destructive technique can be used for characterizing a wide variety of metals and other materials.

Utilization of Thermal Infrared Image for Inversion of Winter Wheat Yield and Biomass

The present paper utilizes thermal infrared image for inversion of winter wheat yield and biomass with different technology of irrigation(drip irrigation,sprinkler irrigation,flood irrigation).It is the first time that thermal infrared image is used for predicting the winter wheat yield and biomass.The temperature of crop and background was measured by thermal infrared image.It is necessary to get the crop background separation index(CBSIL,CBSIH),which can be used for distinguishing the crop value from the image.CBSIL and CBSIH(the temperature when the leaves are wet adequately;the temperature when the stomata of leaf is closed completely) are the threshold values.The temperature of crop ranged from CBSIL to CBSIH.Then the ICWSI was calculated based on relevant theoretical method.The value of stomata leaf has strong negative correlation with ICWSI proving the reliable value of ICWSI.In order to construct the high accuracy simulation model,the samples were divided into two parts.One was used for constructing the simulation model,the other for checking the accuracy of the model.Such result of the model was concluded as:(1) As for the simulation model of soil moisture,the correlation coefficient(R2) is larger than 0.887 6,the average of relative error(Er) ranges from 13.33% to 16.88%;(2) As for the simulation model of winter wheat yield,drip irrigation(0.887 6,16.89%,-0.12),sprinkler irrigation(0.970 0,14.85%,-0.12),flood irrigation(0.969 0,18.87%,0.18),with the values of R2,Er and CRM listed in the parentheses followed by the individual term.(3) As for winter wheat biomass,drip irrigation(0.980 0,13.70%,0.13),sprinkler irrigation(0.95,13.15%,-0.14),flood irrigation(0.970 0,14.48%,-0.13),and the values in the parentheses are demonstrated the same as above.Both the CRM and Er are shown to be very low values,which points to the accuracy and reliability of the model investigated.The accuracy of model is high and reliable.The results indicated that thermal infrared image can be used potentially for inversion of winter wheat yield and biomass.

Application of Spectral Angle Mapper Classification to Discriminate Hydrothermal Alteration in Southwest Birjand, Iran, Using Advanced Spaceborne Thermal Emission and Reflection Radiometer Image Processing

The purpose of this study is to evaluate the Spectral Angle Mapper （SAM） classification method for determining the optimum threshold （maximum spectral angle） to unveil the hydrothermal mineral assemblages related to mineral deposits. The study area indicates good potential for Cu-Au porphyry, epithermal gold deposits and hydrothermal alteration well developed in arid and semiarid climates, which makes this region significant for Advanced Spaceborne Thermal Emission and Reflection Radiometer （ASTER） image processing analysis. Given that achieving an acceptable mineral mapping requires knowing the alteration patterns, petrochemistry and petrogenesis of the igneous rocks while considering the effect of weathering, overprinting of supergene alteration, overprinting of hypogene alteration and host rock spectral mixing, SAM classification was implemented for argillic, sericitic, propylitic, alunitization, silicification and iron oxide zones of six previously known mineral deposits： Maherabad, a Cu-Au porphyry system; Sheikhabad, an upper part of Cu-Au porphyry system; Khoonik, an Intrusion related Au system; Barmazid, a low sulfidation epithermal system; Khopik, a Cu-Au porphyry system; and Hanish, an epithermal Au system. Thus, the investigation showed that although the whole alteration zones are affected by mixing, it is also possible to produce a favorable hydrothermal mineral map by such complementary data as petrology, petrochemistry and alteration patterns.