Infrared Physics & Technology最新文献

Superconductor-insulator-superconductor (SIS) mixers are the most sensitive coherent detectors in the terahertz region and are extensively utilized in astronomical research and atmospheric observation. In order to improve the detection accuracy of frequency, reduce the loss of signal in the transmission path, and reduce noise temperature, it is important to design a signal coupling circuit with high coupling efficiency. In this paper, with the help of a high-frequency structure simulator, a signal coupling circuit was designed based on a waveguide NbN-based SIS mixer at 650 GHz. The return loss and embedding impedance of this SIS mixer were investigated from 600 to 720 GHz. The effects of the SIS mixer chip’s bow-tie probe angle, substrate thickness, and chip slot height were calculated and analyzed. The results show that the return loss is lower than -16 dB in the 600 to 720 GHz band, and the embedding impedance is around 60 Ω, which can provide a good reference for the development of waveguide SIS mixers.

As a real saturable absorber (SA) based on the nonlinear multimode interference (NL-MMI) effect, the devices based on the few-mode fiber possess numerous exciting characteristics due to their all-fiber structure, straightforward manufacturing process. In this paper, by employing a SA with the SMF-FMF-SMF structure in a linear cavity, we demonstrate a stable mode-locked Tm-doped fiber laser. At the pump power of 1 W, a single noise-like pulse (NLP) with a pedestal pulse duration of 28 ps and coherent peak width of 0.77 ps is generated at a central wavelength of 1940 nm with a 3 dB bandwidth of 10.51 nm. The stable noise-like pulse can be sustained from the lasing pump threshold up to the maximum pump power of 2.2 W without experiencing pulse splitting. The experiment results in a peak average output power of 116 mW, along with a corresponding maximum pulse energy of 24.73 nJ at a repetition frequency of 4.69 MHz. The high stability of our fiber laser is confirmed by the signal-to-Noise Ratio (SNR) of 63 dB, and its enduring stability is additionally validated over an 8-hour period. The experimental findings suggest that the SMF-FMF-SMF structure has significant potential in the simple and robust all-fiber mode-locked lasers operating in the noise-like pulse regime.

Abnormalities in infrared radiation temperature (IRT) often accompany the loading In this investigation, stress experiments and splitting tests were conducted on sandstone to investigate the IRT characteristics of sandstone under various loading conditions. The results shows that the Maximum Infrared Radiation Temperature (MAXIRT) increases by up to 5 °C, while in splitting tests, the minimum infrared radiation temperature (MINIRT) decreases by approximately 1.5 °C. Compared to the average infrared radiation temperature (AIRT), MAXIRT is more sensitive to compressive stress, whereas (MINIRT) is more responsive to tensile stress. The correlation between IRT indicators and stress were further analyzed. The findings reveal a positive correlation between IRT and compressive stress. At low stress levels, the correlation coefficient fluctuates between −0.4 and 0.6, generally showing low correlation. As stress increases, the correlation coefficient rises, reaching above 0.8, indicating a high correlation. Tensile stress exhibits a negative correlation, with a consistent trend. Additionally, a statistical analysis of the frequency distribution of IRT under different stress conditions was conducted, followed by hypothesis testing. The results demonstrate that the probability distribution of IRT during loading follows a skewed normal distribution. The changes in skewness can be divided into three stages: initial fluctuation, stable variation, and abrupt failure. Under compressive stress, a right-skewed distribution is observed, with skewness exceeding 3.5 before fracture and reaching above 10 near fracture. Under tensile stress, a left-skewed distribution is noted, with skewness reaching −4.5 near fracture. These findings contribute to the assessment of rock stress states and provide early warning information for predicting rock failure.

We present an experimental demonstration of a pulse-state switchable Tm-doped fiber laser, mode-locked using linear-cavity nonlinear polarization rotation (LNPR). Compared to previous LNPR lasers with free-space structures, our laser cavity employs an all-fiber design, which enhances the laser flexibility. The laser operates in three states within a large anomalous dispersion, including conventional soliton, multi-pulse, and noise-like pulse states. This research provides an effective solution for realizing a multifunctional Tm-doped fiber laser with a simple and stable configuration, making it both appealing and promising for various practical applications.

This study investigated the impact of different data fusion strategies on the performance of soluble solids content (SSC) prediction models based on near-infrared and mid-infrared spectroscopic techniques. In the data-level fusion approach, we applied standard normal variate and multiplicative scatter correction for pre-processing the NIR and MIR data. For the feature-level fusion, we utilized successive projections algorithm and competitive adaptive reweighted sampling to select informative wavelengths, and then applied direct orthogonal projection (DOP) for model transfer. The study employed a dataset of 150 honey samples to evaluate the impact of different data fusion strategies on model performance. To effectively evaluate model performance, we utilized the coefficient of R² and RMSEP as evaluation metrics. By comparing the results of data-level fusion, feature-level fusion and single-spectrum model transfer, the results showed that spectral data fusion improved the model transfer performance compared to the single-spectrum approach, with feature-level fusion exhibiting the most significant advantages. The effective variable selection techniques in feature-level fusion successfully removed a substantial amount of interfering data and significantly reduced noise influence, thereby improving the model accuracy. Specifically, the use of feature-level fusion improved the predictive model’s R² from 0.319 to 0.878 and reduced the RMSEP from 1.974 to 0.613°Brix, demonstrating the significant advantages of this approach in enhancing model transfer performance. The research findings provide important reference and theoretical support for future studies in the field of food quality assessment and other near-infrared spectroscopic data applications. This not only validates the effectiveness of the feature-level fusion approach, but also lays the foundation for establishing efficient and reliable predictive models.

By using the hydrofluoric acid uniform-etching and step-etching methods, a tilted fiber Bragg grating (TFBG) microfiber structure was fabricated, where the cladding diameter of the traditional TFBG was reduced to micrometer scale. The temperature sensing characteristics of the uniform-etched and step-etched microfiber TFBGs were experimentally studied and compared. The core mode of its transmission spectrum was used for sensing the temperature change; while the normalized area of cladding modes has been used for real-time monitoring the liquid level. The simultaneous measurement of temperature and liquid level has been experimentally demonstrated for the proposed microfiber TFBG. By combining the strong evanescent field of microfiber and the unique structural advantages of TFBG, the proposed microfiber TFBG has a great potential in developing the miniature optical fiber sensors.

To our knowledge, a compact diode-pumped Nd: KNaY (WO₄)₂ laser operating at 1.07 µm has been successfully achieved for the first time. The passively Q-switched Nd: KNaY (WO₄)₂ pulse laser with the narrowest pulse width of 6.23 ns and peak power of 1.34 kW was obtained by experiment, and its laser characteristics were studied in detail. The effect of Cr⁴⁺: YAG initial transmission for pulse width of Nd: KNaY (WO₄)₂ output laser was investigated by theoretical analysis and numerical simulation. Finally, it was found that the theoretical and experimental results are basically in agreement.

A theoretical and experimental investigation is conducted on a high repetition-rate side-pumped burst-mode Nd:YAG laser. The absorbed pumping power distribution and laser resonator are meticulously optimized to enlarge the area of the fundamental mode and improve thermal stability. With the 1 ms pumping duration and a pumping frequency of 10 Hz, the pulsed laser’s burst energy, peak power, and pulse width achieve 0.29 J, 347 kW, and 16 ns, respectively, with the Q-switched repetition rate of 50 kHz. The minimum coefficient of variation for the laser pulse train remains below 5 %. This study represents the first demonstration of a long-term reliable output from a high-energy, nanosecond-pulsed, side-pumped Nd:YAG burst laser operating at 50 kHz.

This paper presents a novel feature extraction method, enabling efficient training and deployment of neural networks for rapid identification of crack defects in Laser Array Spot Thermography (LAST). We trained the crack defect identification model based on pixel-level features, encoding each pixel as a feature vector using Frangi filter, and classifying them using a neural network. Experimental results demonstrate that Frangi features are an effective method for distinguishing cracks, speckles, and background noise interference in the experiment. Furthermore, the model only requires a small region of interest (ROI) as training samples to achieve effective training and efficient crack identification under the same detection conditions, allowing for rapid deployment in practical inspections.

Infrared and visible image fusion (IVIF) aims to preserve thermal radiation information from infrared images while integrating texture details from visible images. Thermal radiation information is mainly expressed through image intensities, while texture details are typically expressed through image gradients. However, existing dual-discriminator generative adversarial networks (GANs) often rely on two structurally identical discriminators for learning, which do not fully account for the distinct learning needs of infrared and visible image information. To this end, this paper proposes a novel GAN with a heterogeneous dual-discriminator network and an attention-based fusion strategy (GAN-HA). Specifically, recognizing the intrinsic differences between infrared and visible images, we propose, for the first time, a novel heterogeneous dual-discriminator network to simultaneously capture thermal radiation information and texture details. The two discriminators in this network are structurally different, including a salient discriminator for infrared images and a detailed discriminator for visible images. They are able to learn rich image intensity information and image gradient information, respectively. In addition, a new attention-based fusion strategy is designed in the generator to appropriately emphasize the learned information from different source images, thereby improving the information representation ability of the fusion result. In this way, the fused images generated by GAN-HA can more effectively maintain both the salience of thermal targets and the sharpness of textures. Extensive experiments on various public datasets demonstrate the superiority of GAN-HA over other state-of-the-art (SOTA) algorithms while showcasing its higher potential for practical applications.