An overview of fiber laser sensing is presented. The design and the characteristics of distributed feedback (DFB) fiber lasers for high performance sensing applications are described. Demodulation techniques based on unbalanced fiber interferometer are discussed, especially for the noise level, the dynamic range, and the crosstalk in dense-wavelength-division multiplexing. Finally, the fiber laser sensing system configurations and field demonstrations for different applications are illustrated.

The surface defect detection plays an important role in industrial production and directly affects production efficiency and product quality. In this article, we focus on the surface defect detection of heat sink and propose a method based on a Ghost-SE light U-Net (GSLU-Net). The GSLU-Net is a novel combination of the lightweight convolution module, self-attention mechanism, and fully convolutional network (FCN). It has a symmetrical architecture inspired by the U-Net. By introducing the Ghost module, which can generate feature maps with cheap operations, we reduce the computation cost while maintaining high accuracy. The Ghost module uses an ordinary convolution with small kernels to obtain original feature maps and then a depthwise convolution to generate more feature maps. The squeeze-and-excitation (SE) block is also introduced to improve the representational power of the network and further improve accuracy. It can adaptively recalibrate channelwise feature responses at a slight additional computational cost. Fewer downsampling layers and more skip connections enable this network to retain more location information and have stronger detection capabilities for tiny targets than previous FCNs. The ablation experiments validate that our improvements have played their due role. Then, the comparative experiments demonstrate that our network outperforms the state-of-the-art FCNs. The GSLU-Net reaches an accuracy of 97.96% at a speed of 14.115 ms per image on the dataset of heat sink surface defect, achieving a balance of efficiency and accuracy. The GSLU-Net also outperforms other networks on open datasets, further demonstrating the superiority of the GSLU-Net.

In recent years, video-based physiological parameter measurement has gained significant attention because of its non-contact and low-cost characteristics. While supervised learning-based methods have achieved state-of-the-art performance, less attention has been paid to the reliability of the reference labels. In most studies, finger pulse oximeters are commonly used to record photoplethysmography (PPG) signals, serving as ground truth rather than utilizing real facial PPG signals. However, the appropriateness of this substitution for reference labels has been scarcely investigated. In this paper, we introduce a contact PPG acquisition system to acquire contact PPG signals of faces and fingers simultaneously and analyze their similarities and differences quantitatively. Experimental results reveal substantial disparities in morphology and phase between the PPG signal collected from the finger (Finger-PPG) and the PPG signal collected from the face (Facial-PPG). To eliminate the above differences, we propose a new framework: A Contrastive learning-based method is used to generate PPG signals (Contrast-PPG) to enhance Finger-PPG as reference labels. A 3DCNN-based network is used as a PPG estimator. This framework combines supervised and unsupervised methods to further improve performance. Extensive experiments are conducted proving that the corrected PPG signals are closer to the real facial PPG signal, effectively improving the performance and generalization ability of the model.

The refractive index measurement of seawater has proven significance in oceanography, while an optical heterodyne interferometer is an important, highly accurate, tool used for seawater refractive index measurement. However, for practical seawater refractive index measurement, the refractive index of seawater needs to be monitored for long periods of time, and the influence of drift error on the measurement results for these cases cannot be ignored. This paper proposes a drift error compensation algorithm based on wavelet decomposition, which can adaptively separate the background from the signal, and then calculate the frequency difference to compensate for the drift error. It is suitable for unstable signals, especially signals with large differences between the beginning and the end, which is common in actual seawater refractive index monitoring. The authors identify that the primary cause of drift error is the frequency instability of the acousto-optic frequency shifter (AOFS), and the actual frequency difference was measured through experimentation. The frequency difference was around 0.1 Hz. Simulation experiments were designed to verify the effectiveness of the algorithm, and the standard deviation of the optical length of the results was on the scale of 10⁻⁸ m. Liquid refractive index measurement experiments were carried out in a laboratory, and the measurement error was reduced from 36.942% to 0.592% after algorithm processing. Field experiments were carried out regarding seawater refractive index monitoring, and the algorithm-processing results are able to match the motion of the target vehicle. The experimental data were processed with different algorithms, and, according to the comparison of the results, the proposed algorithm performs better than other existing drift error elimination algorithms.

Seawater density is an important physical property in oceanography that affects the accuracy of calculations such as gravity fields and tidal potentials and the calibration of acoustic and optical oceanographic sensors. In related studies, constant density values are frequently used, which can introduce significant errors. Therefore, this study employs a basic convolutional neural network model to construct a comprehensive model showing the seawater density distribution across the globe. The model takes into account depth, latitude, longitude, and month as inputs. Numerous real seawater datasets were used to train the model, and it has been shown that the model has an absolute mean error and root mean square error of less than 1 kg/m³ in 99% of the test set samples. The model effectively demonstrates the influence of input parameters on the distribution of seawater density. In this paper, we present a newly developed global model for distributing seawater density which is both comprehensive and accurate, surpassing previous models. The utilization of the model presented in this paper for estimating seawater density can minimize errors in theoretical ocean models and serve as a foundation for designing and analyzing ocean exploration systems.