A rotation speed measurement based on double-beam self-mixing speckle interference is presented. The self-mixing speckle signal is analyzed by a cross-correlation algorithm. The experimental results show that, compared with the Doppler frequency shift method, the proposed method can measure high speed at a low sampling rate.

Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). However, there is still a problem with this algorithm associated with the selection of relevant modes. To solve this problem, this paper proposes a novel signal-filtering method that combines VMD with Hausdorff distance (HD) in the VMD-HD method. A noisy signal is first decomposed into a given number K of band-limited intrinsic mode functions by VMD. The probability density function is then estimated for each mode. The aim of this method is to reconstruct the signal using the relevant modes, which are selected on the basis of noticeable similarities between the probability density function of the input signal and that of each mode. Various similarity measures are investigated and compared, and the HD is shown to offer the best performance. The results of filtering of simulation signals illustrate the validity of the proposed method when compared with EMD-based methods under comprehensive quantitative evaluation criteria. As a specific example, the proposed method is successfully used for filtering the pipeline leakage signal as evaluated by the de-trended fluctuation analysis algorithm.

We propose a sensor based on the integration of self-mixing interference with microfiber, for human health monitoring. Through the power variation of the reflected light from the microfiber which is embedded into polydimethylsiloxane (PDMS), we are able to accurately monitor pulse waves continuously and in real time, and human blood pressure is successfully monitored via the pulse transmission time (PTT). We measured systolic blood pressure (SBP) and diastolic blood pressure (DBP) in volunteers with a measurement error of -0.222±2.636mmHg and -1.056±2.198mmHg, respectively. Long-term health monitoring was also performed for several days, and measurements met the requirements of the American Association for the Advancement of Medical Devices (AAMI). The experimental results show that the sensor is capable of accurately monitoring multi-parameter information of human pulse and blood pressure, and has the characteristics of high sensitivity, non-invasive and long-term monitoring. This application of microfiber to the self-mixing sensor provides an effective solution for smart health monitoring, and has potential applications in disease prevention and healthcare.

Deep learning techniques have been widely used in pipeline leakage aperture identification. However, most are designed and implemented for offline data, with problems such as large parameters, high memory consumption, and poor noise immunity. To solve the problem, this paper presents a lightweight residual convolutional neural network (L-Resnet) applied to a real-time detection platform to achieve real-time identification of pipeline leakage apertures. Firstly, based on the depth separable technique, two different separable residual modules are constructed to realize the feature extraction of signals; then, a more efficient activation function is applied to the high-dimensional space to enhance the nonlinear capability of the model; after that, a lightweight attention mechanism is used to weight the features to distinguish the importance of different features; finally, the classification results are obtained by a classifier. The real-time detection platform consists of Jetson Nano, the signal acquisition module and the processing circuit. The results indicated that the method could accurately identify the pipeline leakage apertures in real time. Moreover, the number of parameters is only 14.71Kb, and the model has good computing efficiency and robustness compared to other methods.

Improving the ability to detect small leaks to prevent more severe accidents plays an extremely important role in the safe operation of pipelines. To tackle the issue of low diagnostic accuracy associated with single sensors for detecting small leaks, a multi-source multi-modal feature fusion method for gas pipeline leak detection was proposed. First, the collected data from multiple sensors were transformed into two-dimensional time-frequency images for input into the feature extraction network. Then, the dual-information fusion (DIF) module was introduced, incorporating the attention mechanism and multi-scale feature fusion to enhance the model’s feature expression capability, and fully interact with the multi-modal features. Secondly, the channel split multiscale convolution (CSMC) module was designed to accommodate the diversity of input data and improve the model’s generalization capability. The DIF and CSMC modules were cascaded and fused to produce the classification results through the fully connected layer. Finally, the effectiveness of the proposed method was assessed using pipeline leak data collected in the laboratory. The experimental results demonstrate that the proposed multi-modal deep learning model can effectively identify the small leak state in pipelines, exhibiting superior diagnostic performance when compared to the current mainstream image classification models.

A novel laser self-mixing interferometry vibration measurement algorithm based on the local maximum detection technique is proposed, that can reconstruct micro vibrations rapidly and easily. In this article, the principles of the local maximum detection algorithm are analyzed in detail, and the process of solving the window function is emphasized. The major advantage of the method is that it does not involve any complicated calculations and removes the need for optical/electromechanical components. The validity of the presented method is confirmed by means of simulated signals and demonstrated via several experiments for harmonic and aleatory motion.