Automatic detection of quartz glass subsurface defects by laser scattering method based on an ellipsoidal mirror.

With the improvement of quality requirements of optical components, the detection of subsurface defects of optical components has become a key technology. The existing detection methods still have some limitations in detection depth and detection efficiency. In this paper, a defect scattering light collection method based on ellipsoidal mirror model is used to analyze the scattering light collection efficiency under different experimental conditions theoretically, and the favorable conditions for improving the scattering light collection are proposed. After simulation verification, the use of ellipsoidal reflectors to collect scattered light can effectively avoid the impact of surface defects compared to lenses. At the same time, an experimental system based on this method is set up to filter the stray light by mean filtering method. The system detected three scratches (2 µm in width and 252 nm in depth) on the underside of a piece of quartz glass. The results show that the system can clearly detect the subsurface defects of optical components.

A GAN-based anomaly detector using multi-feature fusion and selection.

In numerous applications, abnormal samples are hard to collect, limiting the use of well-established supervised learning methods. GAN-based models which trained in an unsupervised and single feature set manner have been proposed by simultaneously considering the reconstruction error and the latent space deviation between normal samples and abnormal samples. However, the ability to capture the input distribution of each feature set is limited. Hence, we propose an unsupervised and multi-feature model, Wave-GANomaly, trained only on normal samples to learn the distribution of these normal samples. The model predicts whether a given sample is normal or not by its deviation from the distribution of normal samples. Wave-GANomaly fuses and selects from the wave-based features extracted by the WaveBlock module and the convolution-based features. The WaveBlock has proven to efficiently improve the performance on image classification, object detection, and segmentation tasks. As a result, Wave-GANomaly achieves the best average area under the curve (AUC) on the Canadian Institute for Advanced Research (CIFAR)-10 dataset (94.3%) and on the Modified National Institute of Standards and Technology (MNIST) dataset (91.0%) when compared to existing state-of-the-art anomaly detectors such as GANomaly, Skip-GANomaly, and the skip-attention generative adversarial network (SAGAN). We further verify our method by the self-curated real-world dataset, the result show that our method is better than GANomaly which only use single feature set for training the model.

Photothermal vortex interferometer with azimuthal complex spectra analysis for the measurement of laser-induced nanoscale thermal lens dynamics.

A photothermal vortex interferometer (PTVI) is proposed to fill the gap of full-field measurement of the laser-induced nanoscale thermal lens dynamics of optical elements. The PTVI produces a multi-ring petal-like interferogram by the coaxial coherent superposition of the high-order conjugated Laguerre-Gaussian beams. The non-uniform optical path change (OPC) profile resulting from the thermal lens causes the petals of the interferogram at the different radii to shift by the different azimuths. To demodulate such an interferogram, an azimuthal complex spectra analysis is presented by using a camera with a pixelated multi-ring pattern written on its sensor to extract multiple azimuthal intensity profiles synchronously from the interferogram. Therefore, the OPC profile can be determined dynamically from the complex spectra of the azimuthal intensity profiles at the main frequency components. An analytical thermophysical model of the thermal lens is given, and the basic principle of the azimuthal complex spectra analysis is revealed. A proof-of-concept experiment is demonstrated using a N-BK7 glass sample heated by a pump laser. The results verified that the PTVI achieves the measurement accuracy of 47 pm with a standard deviation of 358 pm (3σ) and can be used for full-field measurement of the nanoscale OPC profile caused by the thermal lens dynamics. Due to the picometer-scale accuracy of the PTVI, the absorption coefficient and thermal diffusivity of the glass sample were determined to be A0 = 0.126 m-1 and D = 5.63 × 10-7 m2 s-1, respectively, which agree with the nominal ones of A0 = 0.129 m-1 and D = 5.17 × 10-7 m2 s-1. Although the PTVI is only suitable for measuring the rotationally symmetric OPC, it shows less computation burden and hardware complexity, and it is proved to be a highly sensitive and effective tool in studying optical, thermo-physical, and mechanical properties of optical elements.

The influence of arm positions on mechanical dyssynchrony measured by gated myocardial perfusion imaging.

Background: In routine procedures, patient's arms are positioned above their heads to avoid potential attenuation artifacts and reduced image quality during gated myocardial perfusion imaging (G-MPI). However, it is difficult to achieve this action in the acute period following pacemaker implantation. This study aimed to explore the influence of arm positioning on myocardial perfusion imaging (MPI) in different types of heart disease. Methods: This study was conducted retrospectively. A total of 123 patients were enrolled and underwent resting G-MPI using a standard protocol with arms positioned above their heads and again with their arms at their sides. All individuals were divided into 3 groups: the normal group, the obstructive coronary artery disease (O-CAD) group, and the dilated cardiomyopathy (DCM) group. The G-MPI data were measured by QGS software and Emory Reconstruction Toolbox, including left ventricular ejection fraction (LVEF), end-diastolic volume (EDV), end-systolic volume (ESV), extent, total perfusion deficit (TPD), summed rest score (SRS), scar burden, phase standard deviation (SD), and phase histogram bandwidth (BW). Results: In total, extent, TPD, EDV, ESV, LVEF, systolic SD, systolic BW, diastolic SD, and diastolic BW were all significantly different between the 2 arm positions (all P<0.01). On the Bland-Altman analysis, both EDV and ESV with the arm-down position were significantly underestimated (P<0.001). Meanwhile, TPD, extent, and LVEF with the arm-down position were significantly overestimated (P<0.05). Systolic SD, systolic BW, diastolic SD, and diastolic BW were systematically overestimated (P<0.001). In the DCM group (n=52), EDV, ESV, systolic SD, systolic BW, diastolic SD, and diastolic BW were identified as significantly different by the paired t-test between the 2 arm positions (P<0.05). In the O-CAD group (n=32), scar burden, ESV, LVEF, and diastolic BW were significantly different between the 2 arm positions (P<0.05). Conclusions: Systolic and diastolic dyssynchrony parameters and most left ventricular (LV) functional parameters were significantly influenced by arm position in both normal individuals and patients with heart failure (HF) with different pathophysiologies. More attention should be given to LV dyssynchrony data during clinical evaluation of cardiac resynchronization therapy (CRT) implantation procedure.

Picometer-scale optical vortex interferometer using azimuthal complex spectrum analysis.

An interferogram demodulation method based on azimuthal complex spectrum analysis is proposed for achieving picometer-scale accuracy with an optical vortex interferometer (OVI). The OVI uses conjugated p-radial-order Laguerre-Gaussian beams to produce a high-order petal-like interferogram. A camera with a multi-ring pattern written on its sensor is used to convert the interferogram into multiple azimuthal intensity profiles. A phase shift subjected to either uniform surface displacement or axisymmetric non-uniform surface deformation can be retrieved from the complex spectra of the azimuthal intensity profiles at the main frequency components. The experiment verified that the measurement error is 84 pm for a displacement of 10 nm and 0.359 nm for a deformation magnitude of 100 nm. The effect of surface misalignment on the measurement result is also discussed. The proposed method provides an effective and highly accurate method of interferogram demodulation for the OVI and extends the applicability of OVI from uniform surface displacement measurement to axisymmetric non-uniform surface deformation measurement.

Toward surface defect detection in electronics manufacturing by an accurate and lightweight YOLO-style object detector.

In electronics manufacturing, surface defect detection is very important for product quality control, and defective products can cause severe customer complaints. At the same time, in the manufacturing process, the cycle time of each product is usually very short. Furthermore, high-resolution input images from high-resolution industrial cameras are necessary to meet the requirements for high quality control standards. Hence, how to design an accurate object detector with real-time inference speed that can accept high-resolution input is an important task. In this work, an accurate YOLO-style object detector was designed, ATT-YOLO, which uses only one self-attention module, many-scale feature extraction and integration in the backbone and feature pyramid, and an improved auto-anchor design to address this problem. There are few datasets for surface detection in electronics manufacturing. Hence, we curated a dataset consisting of 14,478 laptop surface defects, on which ATT-YOLO achieved 92.8% mAP0.5 for the binary-class object detection task. We also further verified our design on the COCO benchmark dataset. Considering both computation costs and the performance of object detectors, ATT-YOLO outperforms several state-of-the-art and lightweight object detectors on the COCO dataset. It achieves a 44.9% mAP score and 21.8 GFLOPs, which is better than the compared models including YOLOv8-small (44.9%, 28.6G), YOLOv7-tiny-SiLU (38.7%, 13.8G), YOLOv6-small (43.1%, 44.2G), pp-YOLOE-small (42.7%, 17.4G), YOLOX-small (39.6%, 26.8G), and YOLOv5-small (36.7%, 17.2G). We hope that this work can serve as a useful reference for the utilization of attention-based networks in real-world situations.

Accurate Calibration of a Large Field of View Camera with Coplanar Constraint for Large-Scale Specular Three-Dimensional Profile Measurement.

In the vision-based inspection of specular or shiny surfaces, we often compute the camera pose with respect to a reference plane by analyzing images of calibration grids, reflected in such a surface. To obtain high precision in camera calibration, the calibration target should be large enough to cover the whole field of view (FOV). For a camera with a large FOV, using a small target can only obtain a locally optimal solution. However, using a large target causes many difficulties in making, carrying, and employing the large target. To solve this problem, an improved calibration method based on coplanar constraint is proposed for a camera with a large FOV. Firstly, with an auxiliary plane mirror provided, the positions of the calibration grid and the tilt angles of the plane mirror are changed several times to capture several mirrored calibration images. Secondly, the initial parameters of the camera are calculated based on each group of mirrored calibration images. Finally, adding with the coplanar constraint between each group of calibration grid, the external parameters between the camera and the reference plane are optimized via the Levenberg-Marquardt algorithm (LM). The experimental results show that the proposed camera calibration method has good robustness and accuracy.

Rapid estimation approach for glycosylated serum protein of human serum based on the combination of deep learning and TD-NMR technology.

Rapid and precise estimation of glycosylated serum protein (GSP) of human serum is of great importance for the treatment and diagnosis of diabetes mellitus. In this study, we propose a novel method for estimation of GSP level based on the combination of deep learning and time domain nuclear magnetic resonance (TD-NMR) transverse relaxation signal of human serum. Specifically, a principal component analysis (PCA)-enhanced one-dimensional convolutional neural network (1D-CNN) is proposed to analyze the TD-NMR transverse relaxation signal of human serum. The proposed algorithm is proved by accurate estimation of GSP level for the collected serum samples. Furthermore, the proposed algorithm is compared with 1D-CNN without PCA, long short-term memory network (LSTM) and some conventional machine learning algorithms. The results indicate that PCA-enhanced 1D-CNN (PC-1D-CNN) has the minimum error. This study proves that proposed method is feasible and superior to estimate GSP level of human serum using TD-NMR transverse relaxation signals.

A phage-based magnetic relaxation switching biosensor using bioorthogonal reaction signal amplification for Salmonella detection in foods.

Phages are uniquely suited for bacterial detection due to their low cost and ability to recognize live bacteria. Herein, our work establishes the proof-of-concept detection of Salmonella in orange juice based on a phage-mediated portable magnetic relaxation switching (MRS) biosensor. The limit of quantification (LOQ) could reach 5 CFU/mL (95 % confidence interval [CI]: 4-7, N = 4) with a linear range of 102-108 CFU/mL, which has improved 10-fold than that without bioorthogonal signal amplification. The recovery rate of the phage-based MRS biosensor was 95.0 % (95 % confidence interval [CI]: 89.0 %-100.9 %, N = 6). The specificity of the phage-based MRS biosensor was 100 % without false-positive results. In addition, this sensor was able to detect <10 CFU per 25 mL of Salmonella in orange juice with 4-h pre-enrichment. The result from the phage-based MRS biosensor is consistent with that from the standard plate count method. This sensor provides a reliable and ultrasensitive detection platform for pathogens.

Diagnostic value of high sensitivity cardiac troponin T (hs-cTnT) in dialysis patients with myocardial infarction.

Background: As a sensitive diagnostic marker for myocardial infarction (MI) in people with normal renal function, elevated high sensitivity cardiac troponin T (hs-cTnT) was often found in chronic kidney disease (CKD) patients requiring dialysis. However, the accuracy of baseline hs-cTnT in the diagnosis of MI (including Type 1 MI (T1MI) and Type 2 MI (T2MI)) in dialysis patients is still controversial. The aim of this study was to retrospectively explore whether there were any clinical indices that could increase the predictive value of hs-cTnT on admission for MI occurrence in dialysis patients. Methods: Here, 136 patients with uremia who underwent regular dialysis with coronary angiography in the First Affiliated Hospital of Nanjing Medical University from August 2017 to October 2021 were enrolled. According to the coronary angiography results and the presence of clinical symptoms, the patients were divided into: (1). AMI group (n = 69; angiography positive) and Control group (n = 67; angiography negative); (2). T1MI group (n = 69; angiography positive), T2MI group (n = 7; angiography negative & symptomatic), and Control group (n = 60; angiography negative & asymptomatic). Results: Here, we found the mean hs-cTnT on admission in the Control group was much lower than that in the AMI group. Hs-cTnT alone had a mediocre predictive performance, with an AUROC of 0.7958 (95% CI: 0.7220, 0.8696). Moreover, the ROC curve of hs-cTnT combined with the Triglyceride (TG), Time of dialysis, and Albumin (Alb) showed a higher sensitivity area [0.9343 (95% CI: 0.8901, 0.9786)] than that of single hs-cTnT. Next, hs-cTnT combined with the TG, Time of dialysis, and Alb also presented a better performance in predicting T1MI [0.9150 (95% CI: 0.8678, 0.9621)] or T2MI (0.9167 [0.9167 (95% CI: 0.8427, 0.9906)] occurrences. Last, these combined variables could better distinguish patient between T1MI and T2MI group than hs-cTnT alone. Conclusions: On admission, a combination of hs-cTnT, TG, Time of dialysis, and Alb presented a higher sensitivity than hs-cTnT alone in predicting MI occurrence in dialysis patients, suggesting a better diagnostic approach for future clinical applications.

[Effects of paramagnetic material on the characteristics of low field nuclear magnetic resonance signals and water measurement in different textured soils.] / é¡ºç£ç‰©è´¨å¯¹åœŸå£¤ä½Žåœºæ ¸ç£å ±æŒ¯ä¿¡å·ç‰¹å¾åŠå«æ°´é‡æµ‹å®šçš„å½±å“.

Nuclear magnetic resonance (NMR) technology has been applied in soil science due to the characte-ristics of high efficiency, rapidity, no damage to soil structure, and harmlessness to the human body. However, the effect of the presence of paramagnetic materials in soils on the characteristics of NMR signals was still unclear. In this study, we investigated the effects of paramagnetic material on the low field nuclear magnetic (LF-NMR) signals and soil water content measurement in soils with different texture. The results showed that the LF-NMR signal of soil water could reach about 150, while that of all the solid materials including soil minerals, organic matter and microbes was less than 0.3, which was relatively negligible. Compared with the NMR signals produced by solid materials in soils, soil texture and paramagnetic material had stronger impact on the measured LF-NMR signals of soil water. LF-NMR equipment had a relaxation time monitoring blind area, and the loss of NMR signal was mainly due to the acceleration of the relaxation process of hydrogen protons in water by magnetic materials, resulting in extremely fast LF-NMR signals feed back by water in small pores that could not be captured by monitoring equipment. For loamy fluvo-aquic soil (1.2%) and clay loamy black soil (1.3%) with low paramagnetic material contents, the loss of LF-NMR signals was not large, which was linearly related to soil water content. For clayey red soil with high content of clay (45.3%) and paramagnetic materials (4.0%), a part of the LF-NMR signals would be lost in the measurement, and the monitored LF-NMR signal was not linearly related to the soil water content. In addition, external addition of paramagnetic materials (3.0 g·L-1 MnCl2 solution) would further reduce the LF-NMR signals that could be monitored in black and red soils. The maximum signal loss rates of black soil and red soil were 41.0% and 46.7%, respectively, which greatly changed the quantitative relationship between it and soil water content. Therefore, the influence of paramagnetic materials on the LF-NMR signals should be reduced first through correction when using LF-NMR to measure the water content of clay soil with rich internal paramagnetic materials (>1.3%) or external addition of paramagnetic materials. Our results would provide valuable insights into the study of soil water content measurement and soil pore structure analysis using low field nuclear magnetic resonance technology.

Development of a confocal line-scan laser scattering probe for dark-field surface defects detection of transmissive optics.

Dark-field detection has long been used to identify micron/submicron-sized surface defects benefiting from the broadening effect of the actual defect size caused by light scattering. However, the back-side scattering of a transmissive optical slab is inevitably confused with the front-side scattering phenomenon, resulting in deterioration of the signal-to-noise ratio (SNR) of the scattering signal and false alarms for real defect detection. To this end, a confocal line-scan laser scattering probe equipped with optical sectioning ability is proposed to separate the back-side scattering from the front-side scattering. The optical sectioning ability is realized through a confocal light scattering collector, which overcomes the restriction imposed on the numerical aperture (NA) and the field of view (FOV), reaching an FOV length of 90 mm and NA of 0.69. The line-scan principle of the probe protects itself from crosstalk because it produces only a laser spot on the tested surface in an instant. Experimental results verified that the probe has a line-scan length of 90 mm with a uniformity better than 98%, an rms electronic noise of 3.4 mV, and an rms background noise of 6.4 mV with laser on. The probe can reject the false back-side scattering light for a 2 mm thick fused silica slab at 17.1 dB SNR and operate at a high imaging efficiency of 720 mm2/s with a minimum detectability limit of 1.4 µm at 12 dB SNR. This work put forward an effective method with great application value for submicron-sized defect detection in transmissive optics.

Investigation of 1H nuclear magnetic resonance relaxometry to screen metabolic syndrome and diabetes.

The purpose of this study was to investigate and compare the abilities of 1H nuclear magnetic resonance (NMR) transverse relaxation constant time (T2) and longitudinal relaxation constant time (T1) to screen people at the risk of diabetes and metabolic syndrome. Human blood samples were collected for NMR detection and biochemical examinations. Bivariate correlations, categorical analyses were performed to explore the relationship between NMR relaxation time and metabolic biomarkers. Results show that NMR relaxation time of human serum correlated well with some biomarkers associated with diabetes and metabolic syndrome. Statistically significant differences in NMR relaxation time between subjects with normal and poor metabolic health were observed. NMR relaxation time, especially T2, can be used to screen people at risk of diabetes and metabolic syndrome.

Spectral preprocessing to improve accuracy of quantitative detection of elemental Cr in austenitic stainless steel by laser-induced breakdown spectroscopy.

The production and consumption of austenitic stainless steel account for about 70% of stainless steel worldwide. The content of chromium (Cr) must be accurately detected and controlled to form a stable austenite structure and obtain strong properties in production. Laser-induced breakdown spectroscopy (LIBS) can be used to detect the Cr content of austenitic stainless steel in a complex production process. However, LIBS signals may be weak and unstable because the experimental signals are seriously affected by noise, self-absorption, the matrix effect, and the instability of the shot-to-shot signal, rendering the quantitative detection results inaccurate and unstable. The spectral-preprocessing methods of baseline correction and denoising can improve the accuracy of quantitative detection of LIBS. An improved segmented Hermite cubic-interpolation method is proposed herein to correct the baseline offset and produce baseline signals that are smooth and convergent (to overcome the Runge phenomenon). Empirical mode decomposition (EMD) based on the wavelet method is proposed to remove LIBS noise; this is done by exploiting the adaptivity of EMD to refine the wavelet-scaling coefficients. Compared with other denoising methods, the proposed method has good denoising evaluation indices and stability and, thus, effectively removes the noise. To verify detection accuracy, the internal standard quantitative method is used to detect the Cr content, and a cyclic-inversion prediction method is designed to verify detection stability. The results show that the correlation coefficient of the calibration curve is improved, the root-mean-square error is reduced, and the average relative error of the predicted Cr content decreases from 10.46% to 3.858%.

A novel inversion method of 2D TD-NMR signals based on realizing unconstrained maximization of objective function.

The inversion of time-domain nuclear magnetic resonance (TD-NMR) signals is an ill-posed problem, which presents enormous challenges for the inversion algorithm. We propose a novel inversion method that converts conventional minimum objective function with non-negative constraints into an unconstrained maximization problem in the inversion of TD-NMR signals. Hence, the objective function becomes a differentiable concave function that can be solved more easily. The validity of the proposed method was verified by the uncertainty estimation of NMR inversion spectra with different signal-to-noise ratios (SNR). Through the inversion of simulated 2D D-T2 and T1-T2 signals under different SNR, the proposed method was proved to be less sensitive to noise than the conventional inversion method. We use the proposed method to study the migrations of oil and water in shales, the components change in shale could be identified and quantified according to the 2D T1-T2 inversion spectra. The proposed method was also used to analyze the hydration process of cement. The 2D T1-T2 inversion spectra could distinctly present the component of tiny volume with short relaxation time, and the migration regularity of capillary water, gel water, and bound water could also be found. In conclusion, the proposed method could be a reliable method to invert TD-NMR signals, especially the identification of the 2D NMR signals with a short relaxation time in low SNR.

Optimized radio frequency coil for noninvasive magnetic resonance relaxation detection of human finger.

Noninvasive NMR measurement of human tissues, such as fingers, to achieve early detection for metabolic diseases is of important significance. The NMR relaxation measurements have a wide application prospect due to simplicity, portability, and low cost, as the static magnetic field is not required to be highly homogeneous. However, the inhomogeneous radiofrequency (RF) magnetic field (B1) results in errors in the magnetic resonance relaxation times. This is inevitable in in-vivo localized human tissue measurements with a portable MR scanner, as signals from tissues close to the edge of RF coil are excited with a different B1 field amplitude. A novel RF coil termed T coil with high B1 field homogeneity is presented. Numerical simulation and phantom measurements were implemented. The novel RF coil was compared with a regular solenoid coil and a variable width coil. In-vivo experiments were performed. The T coil has a better B1 field homogeneity than the regular solenoid coil and the variable width coil, producing more accurate magnetic resonance relaxation times. Improved detection accuracy has been achieved with the T coil. This work may promote the development of noninvasive human tissue diagnosis based on NMR relaxation methods.

Sensitive Oxidation of Sorbitol-Mediated Fe2+ by H2O2: A Reliable TD-NMR Method for Clinical Blood Glucose Detection.

The clinical challenge of high-accuracy blood glucose detection schemes is to overcome the detection error caused by the background interferences in different individuals. H2O2 as the specific product of glucose oxidation can be involved in the Fe2+/Fe3+ conversion and detected by the time-domain nuclear magnetic resonance (TD-NMR) method sensitively. But, in clinical applications, the oxidation of Fe2+ is susceptible to the complex sample substrates. In this work, we sorted out two kinds of possible interference mechanisms of Fe2+ oxidation in the NMR blood glucose detection method and proposed a feasible scheme that uses sorbitol to weaken the adverse effects of interference. We found that sorbitol-mediated Fe2+ can greatly enhance the sensitivity of the T2 value to H2O2. The chain reaction caused by sorbitol can significantly amplify the efficiency of Fe2+ oxidation at the same concentration of H2O2. Thereby, we can achieve the higher dilution multiple of serum samples to reduce the amount of interfering substances involved in the Fe2+/Fe3+ conversion. We justified the accuracy and availability of our method by successfully detecting and confirming the correlation between the T2 decrease and glucose concentration of the serum samples collected from 16 subjects. The sorbitol-Fe2+ glucose detection method with high sensitivity can be further combined with miniature NMR analyzers to satisfy the calibration requirements of glucose monitoring in diabetic patients instead of frequent medical visits.

Automatic defect inspection of thin film transistor-liquid crystal display panels using robust one-dimensional Fourier reconstruction with non-uniform illumination correction.

Automatic inspection of micro-defects of thin film transistor-liquid crystal display (TFT-LCD) panels is a critical task in LCD manufacturing. To meet the practical demand of online inspection of a one-dimensional (1D) line image captured by the line scan visual system, we propose a robust 1D Fourier reconstruction method with the capability of automatic determination of the period Δx of the periodic pattern of a spatial domain line image and the neighboring length r of the frequency peaks of the corresponding frequency domain line image. Moreover, to alleviate the difficulty in the discrimination between the defects and the non-uniform illumination background, we present an effective way to correct the non-uniform background using robust locally weighted smoothing combined with polynomial curve fitting. As a proof-of-concept, we built a line scan visual system and tested the captured line images. The results reveal that the proposed method is able to correct the non-uniform illumination background in a proper way that does not cause false alarms in defect inspection but also preserves complete information about the defects in terms of the brightness and darkness as well as the shape, indicating its distinct advantage in defect inspection of TFT-LCD panels.

Sensitivity enhancement in photothermal interferometry by balanced detection of the complex response to moving excitation.

The sensitivity of photothermal detection relies on both the magnitude of the response of a sample to excitation and the way the response is sensed. We propose a highly sensitive photothermal interferometry by addressing the above two issues. One is the use of moving excitation to enable a different manner in sample heating and cooling, which results in a strong thermoelastic response of the sample. The other is the use of a balanced Mach-Zehnder interferometer with a defocused probe beam to sense the complex response induced by the phase delays taking place at the sample surface and in the surrounding air. The method was verified experimentally with a Nd-doped glass to have 68-fold sensitivity enhancement over the classical photothermal common-path interferometry.

High-Performance Ultra-Thin Spectrometer Optical Design Based on Coddington's Equations.

A unique method to design a high-throughput and high-resolution ultrathin Czerny-Turner (UTCT) spectrometer is proposed. This paper reveals an infrequent design process of spectrometers based on Coddington's equations, which will lead us to develop a high-performance spectrometer from scratch. The spectrometer is composed of cylindrical elements except a planar grating. In the simulation design, spot radius is sub-pixel size, which means that almost all of the energy is collected by the detector. The spectral resolution is 0.4 nm at central wavelength and 0.75 nm at edge wavelength when the width of slit is chosen to be 25 µm and the groove density is 900 lines/mm.