Advancing automated pupillometry:a practical deep learning model utilizing infrared pupil images

AIM:To establish pupil diameter measurement algorithms based on infrared images that can be used in real-world clinical settings.METHODS:A total of 188 patients from outpatient clinic at He Eye Specialist Shenyang Hospital from Spetember to December 2022 were included,and 13470 infrared pupil images were collected for the study.All infrared images for pupil segmentation were labeled using the Labelme software.The computation of pupil diameter is divided into four steps:image pre-processing,pupil identification and localization,pupil segmentation,and diameter calculation.Two major models are used in the computation process:the modified YoloV3 and Deeplabv 3+models,which must be trained beforehand.RESULTS:The test dataset included 1348 infrared pupil images.On the test dataset,the modified YoloV3 model had a detection rate of 99.98% and an average precision(AP)of 0.80 for pupils.The DeeplabV3+model achieved a background intersection over union(IOU)of 99.23%,a pupil IOU of 93.81%,and a mean IOU of 96.52%.The pupil diameters in the test dataset ranged from 20 to 56 pixels,with a mean of 36.06±6.85 pixels.The absolute error in pupil diameters between predicted and actual values ranged from 0 to 7 pixels,with a mean absolute error(MAE)of 1.06±0.96 pixels.CONCLUSION:This study successfully demonstrates a robust infrared image-based pupil diameter measurement algorithm,proven to be highly accurate and reliable for clinical application.

Determination of Melamine by Infrared Spectroscopy Based on Nonlinear Modeling

[Objective] The aim was to develop a nonlinear model of quantitative analysis of melamine content by infrared spectroscopy and provide theoretical basis for the nondestructive detection of melamine. [Method] According to dynamics,mathematical modeling and optimization theory,linear and nonlinear models were respectively set up by taking an absorption peak of 1 550 cm-1 as characteristic absorption peak. [Result] The correlation coefficient of nonlinear model was 0.922 7 and the recovery was 96%,which showed that the nonlinear model was more accurate than linearity model with correlation coefficient of 0.904 9 and recovery of 557%. [Conclusion] It is feasible to determine melamine content by using the nonlinear model quantitatively.

QUANTUM MECHANICAL MODEL AND SIMULATION OF GaAs/AlGaAs QUANTUM WELL INFRARED PHOTO-DETECTOR-ⅠOPTICAL ASPECTS

A complete quantum mechanical model for GaAs?AlGaAs quantum well infrared photodetectors(QWIPs) is presented here. The model consisted of four parts: (1) Starting with the description of the electromagnetic field of the infrared radiation in the QWIP, effective component of the vector potential <| A z |> along the QWIP growth direction ( z axis) due to the optical diffraction grating was calculated. (2) From the wave transmissions and the occupations of the electronic states, it was discussed that the dark current in the QWIP is determined by the drift diffusion current of carriers thermally excited from the ground sublevel in the quantum well to extended states above the barrier. (3) The photocurrent was investigated by the optical transition (absorption coefficient between the ground state to excited states due to the nonzero <| A z |> ). (4) By studying the inter diffusion of the Al atoms across the GaAs?AlGaAs heterointerfaces,the mobility of the drift diffusion carriers in the excited states was calculated, so the measurement results of the dark current and photocurrent spectra can be explained theoretically. With the complete quantum mechanical descriptions of (1 4), QWIP device design and optimization are possible.

Geometry model construction in infrared image theory simulation of buildings

Geometric model construction is the basis of infrared image theory simulation. Taking the construction of the geometric model of one building in Harbin as an example, this paper analyzes the theoretical groundings of simplification and principles of geometric model construction of buildings. It then discusses some particular treatment methods in calculating the radiation transfer coefficient in geometric model construction using the Monte Carlo Method.

Thermal Infrared Salient Human Detection Model Combined with Thermal Features in Airport Terminal

Target detection in low light background is one of the main tasks of night patrol robots for airport terminal.However,if some algorithms can run on a robot platform with limited computing resources,it is difficult for these algorithms to ensure the detection accuracy of human body in the airport terminal. A novel thermal infrared salient human detection model combined with thermal features called TFSHD is proposed. The TFSHD model is still based on U-Net,but the decoder module structure and model lightweight have been redesigned. In order to improve the detection accuracy of the algorithm in complex scenes,a fusion module composed of thermal branch and saliency branch is added to the decoder of the TFSHD model. Furthermore,a predictive loss function that is more sensitive to high temperature regions of the image is designed. Additionally,for the sake of reducing the computing resource requirements of the algorithm,a model lightweight scheme that includes simplifying the encoder network structure and controlling the number of decoder channels is adopted. The experimental results on four data sets show that the proposed method can not only ensure high detection accuracy and robustness of the algorithm,but also meet the needs of real-time detection of patrol robots with detection speed above 40 f/s.

Model Development of p-i-n Germanium Devices for Infrared Detection

Germanium offers many benefits over groups III-V materials when used for infrared detection. Most importantly, germanium is compatible with Complementary Metal Oxide Semiconductor (CMOS) manufacturing which allows for a low-cost, high-throughput device, and it does not require cooling, which many III-V devices do. With the deposition of germanium directly on silicon, there will be a thermally induced strain due to the difference in thermal expansion coefficients between the two materials. When a biaxial tensile strain is present, the direct bandgap of germanium is lowered to ~0.77 eV and is capable of absorbing longer wavelengths. We have used a two-step deposition process to create a strained germanium film in order to fabricate a photodetector device. Our device has a dark current of 1.35 nA and a photocurrent of 22.5 nA at 1570 nm wavelength. Next, we developed a model in order to compare theoretical results with experimental results. The results indicate that the model is in close agreement with our measured data, and we are therefore able to use it to model future devices.

A New Sample-Selection and Modeling Method Based on Near-Infrared Spectroscopy and Its Industrial Application

Near-infrared( NIR) spectroscopy has been widely employed as a process analytical tool( PAT) in various fields; the most important reason for the use of this method is its ability to record spectra in real time to capture process properties. In quantitative online applications,the robustness of the established NIR model is often deteriorated by process condition variations,nonlinear of the properties or the high-dimensional of the NIR data set. To cope with such situation,a novel method based on principal component analysis( PCA) and artificial neural network( ANN) is proposed and a new sample-selection method is mentioned. The advantage of the presented approach is that it can select proper calibration samples and establish robust model effectively. The performance of the method was tested on a spectroscopic data set from a refinery process. Compared with traditional partial leastsquares( PLS),principal component regression( PCR) and several other modeling methods, the proposed approach was found to achieve good accuracy in the prediction of gasoline properties. An application of the proposed method is also reported.

Thermal,infrared spectroscopy and molecular modeling characterization of bone:An insight in the apatite-collagen type I interaction

An insight into the interaction of collagen type I with apatite in bone tissue was performed by using differential scanning calorimetry, Fourier transform infrared spectroscopy, and molecular modeling. Scanning electron microscopy shows that bone organic content incinerate gradually through the different temperatures studied. We suggest that the amide regions of the type I collagen molecule (mainly C=O groups of the peptide bonds) will be important in the control of the interactions with the apatite from bone. The amide I infrared bands of the collagen type I change when interacting to apatite, what might confirm our assumption. Bone tissue results in a loss of thermal stability compared to the collagen studied apart, as a consequence of the degradation and further combustion of the collagen in contact with the apatite microcrystals in bone. The thermal behavior of bone is very distinctive. Its main typical combustion temperature is at 360°C with a shoulder at 550°C compared to the thermal behavior of collagen, with the mean combustion peak at ca. 500°C. Our studies with molecular mechanics (MM+ force field) showed different interaction energies of the collagen-like molecule and different models of the apatite crystal planes. We used models of the apatite (100) and (001) planes;additional two planes (001) were explored with phosphate-rich and calcium-rich faces;an energetic preference was found in the latter case. We preliminary conclude that the peptide bond of collagen type I is modified when the molecule interacts with the apatite, producing a decrease in the main peak from ca. 500°C in collagen, up to 350°C in bone. The combustion might be related to collagen type I, as the ΔH energies present only small variations between mineralized and non-mineralized samples. The data obtained here give a molecular perspective into the structural properties of bone and the change in collagen properties caused by the interaction with the apatite. Our study can be useful to understand the biological synthesis of minerals as well as the organic-inorganic interaction and the synthesis of apatite implant materials.

Analysis of Protein Content in Panicum miliaceum L. Using Near Infrared Transmittance Spectroscopy

[Objective] To explore a method for the rapid determination of protein con- tent in grains of Panicum miliaceum L. [Method] The near infrared transmittance spec- troscopy （NITS） was used to build the mathematical models for the quantitative analy- sis of protein content in the grains. Four combinations of treatment that first derivative and second derivative were respectively combined with partial least squares （PLS） and modified partial least squares （MPLS） were set to compare their effects on the original transmission spectrum. [Result] The predicting effects of the 4 combinations were similar. The optimal combination was first derivative with MPLS, in which the average determination coefficient of validation （RSQ） was 0.880 6, correlation coeffi- cient of cross validation （1-VR） was 0.857 0, standard error of calibration （SEC） was 0.342 4, standard error of cross validation （SECV） was 0.375 1, and the standard er- ror of prediction （SEP） was 0.454. [Conclusion] The constructed NITS model is a rapid way for the determination of protein content in grains of P. miliaceum.

Computer Simulation of Compensation Method for Infrared Focal Plane Array

A major motivation for this work is to investigate a method of computer simulation for compensating fixed pattern noise of the infrared focal plane arrays. A mathematical model of the output signal of focal plane array was established; a compensating algorithm utilizing reference source was derived and simulating programs were designed. The images of compensating process verify the influence of nonuniformity of responsibility and offset on fixed pattern noise. The result show that simulating method of investigating compensation technology for focal plane arrays is feasible, the generated images and methods can be used to the study of image recognition.

Determination of Protein and Starch Content in Whole Maize Kernel by Near Infrared Reflectance Spectroscopy

Using 128 bulk-kernel samples of inbred lines and hybrids, a study was conducted toinvestigate the feasibility and method of measuring protein and starch contents inintact seeds of maize by near infrared reflectance spectroscopy (NIRS). The chemometricalgorithms of partial least square (PLS) regression was used. The results indicated thatthe calibration models developed by the spectral data pretreatment of firstderivative+multivariate scattering correction within the spectral region of 10000-4000cm-1, and first derivative + straight line subtraction in 9000-4000cm-1 were thebest for protein and starch, respectively. All these models yielded coefficients ofdetermination of calibration (R2cal) above 0.97, while R2cv and R2val of cross and externalvalidation ranged from 0.92 to 0.95, respectively; however, the root of mean squareerrors of calibration, cross and external validation (RMSEE, RMSECV and RMSEP) werebelow 1(ranged 0.3-0.7),respectively. This study demonstrated that it is feasible touse NIRS as a rapid, accurate, and none-destructive technique to predict protein andstarch contents of whole kernel in the maize quality improvement program.

Experimental Study of Deformation of Sur- rounding Rock with Infrared Radiation

According to the practical conditions of coal roadway in Changcun Coal Mine of Lu＇an Mining Group, the deformation of rock surrounding roadway was experimentally studied by means of thermal infrared （TIP,） imaging system in the process of confined compressions. It is found that the model surface TIR temperature （TIRT） changes with the increase of load. Furthermore, TIRT changes non-synchronously in different ranges such as the roof, floor, wall, corners and bolted ranges. The TIRT is higher in the location of stress concentration and bolted ranges than that in the location of stress relaxation and broken ranges. The interaction ranges of bolt and rock are determined preliminarily according to the corresponding relationship of TIRT fields and the strain fields of the surrounding rock. The new method of TIR image processing has been proved to be effective for the study of bolt support and observation of roadway stability under mine pressure.

Quantitative description of infrared radiation characteristics of preflawed sandstone during fracturing process

Previous studies show that infrared radiation temperature(IRT)abnormalities are always accompanied by the crack development in rocks under external loads.In this context,experiments were conducted on preflawed sandstone to investigate the infrared radiation characteristics during failure process.Two indicators were defined herein,i.e.coefficient of variation of IRT(CVIRT)and skewness of IRT(SIRT).The regression analysis shows that the IRT probability distributions during loading process fit the Gaussian model.The variations in the CVIRT are characterized by four stages:primary stage,steady stage,accelerating stage and post-peak stage.Besides,the variations in the SIRT are divided into three stages:primary stage,steady stage and failure and post-peak stage.The precursor point for preflawed rock failure is identified based on the CVIRTetime curve,with average precursor point of 83%of the peak stress.Compared with other IRT indicators,the proposed two IRT indicators have higher sensitivity to IRT abnormalities during failure process.Furthermore,the connection between the IRT indicators and the rock fracturing was investigated to interpret the IRT indicator abnormalities.Based on the Verhulst inverse function,a new quantitative model was presented to describe the primary stage,steady stage and accelerating stage of the CVIRTetime curve.The results obtained in this study can provide early-warning information for rock failure prediction.

Using Longwave Infrared Hyperspectral Imaging for a Quantitative Atmospheric Tracer Monitoring in Outdoor Environments

Quantitative real-time retrieval of concentration-lengths (CL) through gaseous plumes is an important tool for environmental monitoring, enabling remote monitoring of emissions from industrial facilities and risk assessment in scenarios of toxic gas releases. The adoption of LWIR (Long Wave InfraRed) hyperspectral imaging as a leading technique for remote gas plume detection paved the way for an introduction of a precise CL estimation and two-dimensional (2D) mapping. A novel methodology for evaluating and characterizing the performance of a retrieval algorithm is presented. The algorithm utilizes state-of-the-art retrieved hyperspectral 2D mapping and is applied on a series of localized atmospheric tracer gas (SF6) releases in monitored environmental conditions. The retrieved CL distributions are compared to a numeric atmospheric T&D (Transport and Diffusion) model. Satisfactory agreement between retrieved and simulated CL prediction is manifested, and the uncertainty involved is quantified. Possible sources for the remained discrepancies between retrieved and simulated CL values are characterized, and methods to minimize them are discussed.

Infrared Radiance Simulation and Application under Cloudy Sky Conditions Based on HIRTM

An algorithm based on hyperspectral infrared cloudy radiative transfer model (HIRTM) is introduced and a simulation method for infrared image of the generation geostationary meteorological satellite is proposed. Based on the parameters from weather research and forecast (WRF), such as the water content, atmospheric temperature, and humidity profile, the simulation data for the advanced Himawari imager (AHI) infrared radiative (IR) channels of Himawari-8 are obtained. Simulated results based on HIRTM agree well with the observed data. Further, the movement, development, and change of the cloud are well predicated. And the simulation of IR cloud image for the weather forecast has been obtained. This paper provides an improved method for evaluation and improvement of regional numerical model for weather forecast.

DIGITAL CONTOUR ENHANCEMENT OF INFRARED IMAGE

The digital contour enhancement techniques of infrared image are discussed. Emphasis is laid the thermal spread compensation method. On the basis of describing the theory of the method, a model is suggested. The concrete project based on the model for realizing digital contour enhancement of the infrared thermal image is put forward, and some test results are shown.

Unidentified Infrared Discrete Emission Bands

Unidentified Infrared emission bands (UIBs) are infrared discrete emissions from circumstellar regions, interstellar media (ISM), star-forming regions, and extragalactic objects for which the identity of the emitting materials is unknown. The main infrared features occur around peaks at 3.3, 6.2, 7.7, 8.6, 11.2, and 12.7 μm with the photon’s rest energy at the peaks 0.376, 0.200, 0.161, 0.144, 0.111, and 0.098 eV, respectively. The UIB emission phenomenon has been studied for about forty five years. The prevailing hypothesis is that the materials responsible for UIB are polycyclic aromatic hydrocarbon (PAH) molecules. PAHs are thought to be one of the main forms in which carbon exists in space. And yet, not a single member of this group of compounds had been identified in space definitively until now [1]. In frames of Hypersphere World-Universe Model (WUM), we introduced Dark Matter (DM) particles, named DIONs, with the rest energy 0.199 eV and an energy density of 68.8% of the total energy density of the World. DIONs compose Outer shells of DM Supercluster’s Cores—the main objects of the World [2]. In this paper, we give an explanation of UIB emission based on the self-annihilation of DM particles DIONs and biDIONs (DIONs pairs) with a rest energy about 0.38 eV that depends on the binding energy. To the best of our knowledge, WUM is the only cosmological model in existence that is consistent with UIB emission phenomenon.

Rapid Detection of Cement Raw Meal Composition Based on Near Infrared Spectroscopy

The composition of cement raw materials was detected by near-infrared spectroscopy.It was found that the BiPLS-SiPLS method selected the NIR spectral band of cement raw materials,and the partial least squares regression algorithm was adopted to establish a quantitative correction model of cement raw materials with good prediction effect.The root-mean-square errors of SiO_(2),Al_(2)O_(3),Fe_(2)O_(3) and CaO calibration were 0.142,0.072,0.034 and 0.188 correspondingly.The results show that the NIR spectroscopy method can detect the composition of cement raw meal rapidly and accurately,which provides a new perspective for the composition detection of cement raw meal.

NONINVASIVE OPTICAL IMAGING OF STAPHYLOCOCCUS AUREUS INFECTION IN VIVO USING AN ANTIMICROBIAL PEPTIDE FRAGMENT BASED NEAR-INFRARED FLUORESCENT PROBES

The diagnosis of bacterial infections remains a major challenge in medicine.Optical imaging of bacterial infection in living animals is usually conducted with genetic reporters such as lightemitting encymes or fluorescent proteins.However,there are many circumstances where genetic reporters are not applicable,and there is an urgent need for exogenous synthetic probes that can selectively target bacteria.Optical imaging of bacteria in vivo is much less developed than methods such as ndioimaging and MRI.Furthermore near infrared(NIR)dyes with emision wavelengths in the region of 650-900 rm can propagate through two or more centimeters of tissue and may enable deeper tisue imaging if sensitive detection techniques are employed.Here we constructed an antimicrobial peptide fragment UBI29-41-based near infrared fuorescent imaging probe.The probe is composed of UBI2941 conjugated to a near infraured dye ICG Der-02.UBI29-41 is a cationic antimicrobial peptide that targets the anionic surfaces of bacterial cells.The probe allows detection of Staphylococcus aureus infection(5×10^(7)cells)in a mouse local infection model using whole animal nearinfrared fuorescence imaging.Furthermore,we demonstrate that the UBI29-41-based imaging probe can selectively accumulate within bacteria.The significantly higher accumulation in bacterial infection suggests that UBI29-41-based imaging probe may be a promising imaging agent to detect bacterial infections.

Rapid Survey of Total Protein Content in Mycelia of Oyster Mushroom Based on Near Infrared Spectroscopy

Using the total protein content in mycelia of oyster mushroom cultured in plate medium as the index, the spectral information in 1 000-1 799 nm region was collected to establish a quantitative prediction model for the parameters of strains through partial least squares regression combined with chemometrics. The results showed that the optimal spectral pretreatment method was the combination of Savitzky-Golay smoothing＋Savitzky-Golay derivative＋MSC＋Mean-Centefing. Parameters of the quantitative model including RC, SEC, RP, SEP, MF, SEP /SEC were all in the reasonable regions. The correlation coefficient of the real value and predictive value of the model was 0.672 63. The prediction model had better reliability, robustness and predictive effects, so it could be used for protein content detection in mycelia.