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• A novel water transparency calculation algorithm is introduced, employing deep neural network and image processing technology. This algorithm obviates the need for a water gauge, allowing the calculation of water transparency solely through the analysisof Secchi disk videos. Notably, it demonstrates heightened detection efficiency and success rates. • An enhanced Siamese tracker, termed SiamDCFF (Siamese tracker based on dual correlation feature fusion), is proposed for Secchi disk recognition. Distinguishing features of SiamDCFF include a dual correlation module and a feature fusion module. On the Secchi disk dataset, SiamDCFF achieves the highest success rate, outperforming other methods detailed in this paper. • The simplified 3D-ResNet [41] is leveraged to assess the state of the Secchi disk, transforming the critical position determination problem into an optimization challenge. The objective function involves maximizing the number of visible and invisible Secchi disk images on both sides of the critical position. This method facilitates adaptive critical position determination. • A novel Gated Recurrent Unit based on auto-regression (ARGRU) is proposed, taking into account the motion characteristics of the Secchi disk. ARGRU achieves a higher success detection rate for water transparency compared to the water gauge recognition method. Notably, ARGRU outperforms RNN [42], GRU, and LSTM [43] on the dataset presented in this paper. Water transparency is traditionally assessed through visual inspection using a lowered Secchi disk (SD) into the water, with the disappearance depth of the SD recorded as the measure of transparency. However, manual and visually-dependent frequency measurements of the SD render this process labor-intensive and time-consuming. This paper presents a comprehensive machine-vision-based algorithm designed for the automatic computation of water transparency using Secchi disk videos. The algorithm leverages multiple deep neural networks, including an enhanced Siamese tracking model, a 3D-ResNet, and an improved GRU network. Trained models directly identify the SD in the video, calculating water transparency by processing pixel information. Experimental results demonstrate the algorithm's efficacy in estimating water transparency across diverse natural environments, achieving commendable accuracy (MAE = 3.6 cm MSE = 21.5 cm RMSE = 4.6 cm), rapid processing speed (average 6.87 s), and robust stability. In comparison with the former water-gauge-based algorithm, our proposed algorithm exhibits heightened efficiency and a superior detection success rate. [ABSTRACT FROM AUTHOR]

• This paper suggests a grating Moiré fringe-based error correction approach that uses an artificial neural network (ANN) and the quartile method (QM). • The approach uses QM for data cleaning operations to eliminate outliers and creates the ANN model with a structure of fully connected layer-normalization layer-fully connected layer for regression training in order to achieve lightweight and stability. • The proposed QM-ANN error correction model's mean absolute error (MAE) can reach 3.3 nm once the QM data has been cleaned and the ANN model has been trained. Many interference factors of the complex and changeable working environment of the grating sensor are the main factors affecting the accuracy; a grating Moiré fringe error correction model based on the quartile method (QM) and artificial neural network (ANN) is proposed in this paper. Firstly, the grating sensor's signals in the working process are collected, and three kinds of interference signals (temperature, humidity and vibration) from the grating sensor working environment are collected simultaneously. Secondly, the QM is used to detect outliers from the collected data and delete data outliers directly. Subsequently, the processed data are input into the ANN model for training. The model is lightweight and stable, because it adopts the cascaded structure of full connection layer-normalization layer-full connection layer, which does not need deploy the complex convolution layer module etc. Finally, the corrected data are output after being processed by the QM-ANN error correction model. The experimental results show that the mean absolute error (MAE) of the proposed QM-ANN error correction model is better than QM-convolutional neural network (CNN), QM-long short-term memory (LSTM) and ANN, CNN, LSTM models without QM. It shows that the proposed model has better robustness and practicability. [ABSTRACT FROM AUTHOR]

• An ISSS positioning algorithm based on Bluetooth AOA technology is proposed, and the Angle estimation effect of this algorithm for uniform linear array (ULA) and uniform rectangular array (URA) antennas is experiment. • To account for two common sources of indoor interference. We reduce the coherent interference with signals using the spatial smoothing technique and the interference of transient occlusion with signals using the DBSCON technique, respectively. The layout of positioning device in different indoor Spaces and the algorithm of location calculation under different layout are discussed. The positioning accuracy of these algorithms is evaluated by the experimental data. This paper investigates the Bluetooth direction finding technique and combines it with the improved signal subtraction subspace (ISSS) algorithm for precise indoor positioning. First, the phase offset caused by the electronic switch is solved by the phase compensation algorithm. Then, the ISSS algorithm is proposed by combining the spatial smoothing techniques algorithm, the density-based spatial clustering of applications with noise (DBSCON) techniques and the signal subtraction subspace (SSS), which reduces the interference of coherent signals and caused by moving between people in the room. Finally, different placement methods for receivers in different spatial shapes are proposed. In this paper, the positioning accuracy of the algorithm is tested using Silicon Labs' Bluetooth device. The results show that the average positioning errors are 0.92 m for single receivers and 0.30 m for dual receivers in a room size of 3 m * 3 m. Therefore, the method has a promising application in achieving accurate indoor positioning. [ABSTRACT FROM AUTHOR]

To augment situational awareness capabilities for high-frequency disturbances, the modern power system necessitates the implementation of high-sampling rates for real-time Synchro-Waveform (SW) measurements. Nevertheless, the nonlinear nature of data collected from SW measurement devices poses significant challenges to the communication and storage capacities of data centers. To effectively tackle this issue, this paper proposes a Multi-stage Hybrid Coding (MHC) method for the lossless compression of SW data. The MHC method utilizes a well-structured two-stage approach to achieve efficient compression. In the first stage, a variable frame-based method is designed to reduce redundant information and achieve initial compression using multiple periodic recursive compression. Subsequently, the dictionary-based method, specifically the Lempel–Ziv–Markov chain algorithm, is seamlessly integrated to further compress the SW data. Experimental and numerical analyses are conducted using both simulated and real-world source data, with real-time performance validation in SW measurement units. The obtained results convincingly demonstrate that the proposed method enables online compression of both event and non-event SW data, achieving an impressive reduction of over 73.4% in data space, while maintaining a data loss ratio lower than 0.4% for high-density SW data. • Multiple periodic recursive method is proposed to eliminate repetitive information. • A multi-stage hybrid coding method is implemented for the lossless compression. • A series of synchro-waveform events are collected to evaluate the performance. [ABSTRACT FROM AUTHOR]

• Nonuniform E-field is restored by measuring E-FISH signals along probing laser. • Convergence of inversion algorism related to measuring system parameter is given. • Accuracy of inversion algorism is validated via experimental investigations. • Robustness of inversion algorism in resisting measurement noise is confirmed. The electric field-induced second-harmonic generation (E-FISH) method can offer high spatiotemporal resolution in electric field measurement, but faces challenge in reconstructing the inhomogeneous electric field along the probing laser. This paper presents an inversion method to reconstruct the electric field profile by using E-FISH signals measured from multiple points along the probing laser. The relationship between the condition number of the inversion coefficient matrix and the parameters of E-FISH measurement system is clarified. The inversion method is validated through experimental investigations conducted in three different electrode configurations, namely the plate-to-plate gap, the rod-to-rod gap, and the rod-plate gap. With the inhomogeneity coefficient varies from 1 to 10.7, the maximum deviation between the measured electric field and the finite element simulation is less than 6%. This method does not rely on priori assumptions and electrode configurations, and exhibits good robustness in resisting the effect of measurement noise on the inversion accuracy. [ABSTRACT FROM AUTHOR]

• Proposes an Adaptive Kernel Correlation Filter algorithm improving tracking precision. • A binocular error correction method improves the synchronization precision of cameras. • A motion capture system evaluates and compensates for binocular measurement errors. • The reconstructed models' average error and similarity are 1.352 mm and 0.996, respectively. • The results contribute to the deformation measurement of flexible mechanisms. In order to measure the kinematic parameters and deformation of flapping wings precisely, this paper presents a wing motion reconstruction method utilizing a binocular vision system (BVS) comprising two action cameras. We designed an Adaptive Kernel Correlation Filter algorithm based on target scaling prediction for wing reconstruction to improve target tracking accuracy and robustness. A binocular error correction method with a startup time difference-induced error model was proposed, which helps the action cameras achieve the same high-precision synchronization as the industrial cameras with external trigger mode. Moreover, an infrared motion capture system is applied to assess and compensate for errors in the BVS. The experimental results show that the maximum value of the average errors of the BVS is 1.352 mm, and the minimum similarity value of the two wings is 0.996. The results contribute to the deformation measurement and optimization of soft materials such as the flapping wings. [ABSTRACT FROM AUTHOR]

The acquisition of large atomic-force-microscopy (AFM) scans at nanoscale resolutions can take hours and produce datasets with millions of pixels, which is time consuming and computationally expensive to analyze. In this paper, we present an approach to speedup this process by using a computer-vision algorithm, namely the Noise2Noise algorithm, to reconstruct high-resolution, low scan speed AFM data from high-speed, noisy, sparsely sampled AFM data. This algorithm is trained on various noise types to reproduce different sources of experimental noises encountered during the acquisition of AFM data. Our results demonstrate that a sparse, uniform AFM scan of 20 × 20 μ m at 128 × 128 pixel resolution can be processed within seconds, and the output image is comparable to a higher quality raw AFM data scan which required 30 minutes or more to collect and process manually, reducing not only the acquisition and analysis time, but also the size of the data being collected. • High resolution, low scan speed AFM images are reconstructed from higher scan speeds data. • The Noise2Noise algorithm is used to map noisy AFM images to clean AFM images. • Several noise modes are used to train the algorithm to mimic realistic noises in AFM data. • Process of raw and fast scan data corrections leads to up to 70% improvement in image quality. [ABSTRACT FROM AUTHOR]

• A spatial ray tracing method in non-uniform refractive index field based on Hamiltonian algorithm was proposed. • The non-uniform refractive index field was reconstructed by multi-angle Tomography Schlieren method. • Error compensation method for visual measurement in the non-uniform refractive index space was realized. In complex large-scale industrial environments, light rays no longer propagate in a straight line due to the influence of non-uniform refractive index, which will lead to large measurement errors in visual measurement. In order to accurately compensate the error, this paper firstly quantitatively analyzes the influence of non-uniform refractive index distribution on visual measurement. Then the symplectic matrix ray-tracing method of Hamiltonian optics is used to reconstruct spatial light in the reverse direction with known light propagation end point and spatial refractive index distribution. Thus the traditional model of light propagation along a straight line in visual measurement can be changed and the measurement error introduced by non-uniform refractive index field can be corrected. Finally, the proposed Hamiltonian ray tracing method was verified through the spatial non-uniform refractive index field reconstructed by Background Oriented Schlieren(BOS) method. The experimental results verified the effectiveness and feasibility of this method. [ABSTRACT FROM AUTHOR]

• A novel iteratively reweighted accurate sparse low-rank (IRASLR) matrix estimation algorithm is proposed. • An equivalent variant of minimax concave penalty (MCP) is constructed. • The equivalent weighted MCP and equivalent weighted matrix γ - n o r m are defined with their proximal operators provided. • The simulation and experiment signal proved that the proposed IRASLR method shows better reconstruction accuracy. Bearing fault diagnosis is significant to ensure the safe functioning of mechanical systems. One of the key issues in bearing fault diagnosis is the accurate and effective extraction of fault characteristic features. In order to further explore the construction of penalty function, further alleviate the problem of bias estimation and enhance the capabilities of the sparse low-rank (SLR) method in effectively diagnosing bearing faults, in this paper, an iteratively reweighted accurate sparse low-rank (IRASLR) matrix estimation algorithm is proposed. Taking the sparse and low-rank properties of fault transient signals in time–frequency domain into account, an optimization problem is formulated. Two penalty functions, derived from a core minimax-concave penalty (MCP), induce sparse and low-rank properties, respectively. Specifically, to facilitate the design of weighting schemes, we construct an equivalent variant of MCP (EVMCP). By separately extending EVMCP to matrix elements and singular values, we obtain equivalent weighted MCP and equivalent weighted matrix γ - n o r m. Finally, based on the proximal operators of the two penalty functions, we deduce an IRASLR algorithm under the alternating direction method of the multipliers (ADMM) framework. The performance of IRASLR is verified by processing simulated and experimental signals, while the superiority of the algorithm is confirmed through comparative experiments. [ABSTRACT FROM AUTHOR]

• L-S/DSC algorithm realizes real-time 3D strain/displacement field reconstruction. • This algorithm has good applicability and suits beam and bracket arm structures. • This algorithm avoids the difficulty of selecting the mode order in traditional modal theory. • Reconstructed full-field strains and displacements with very high accuracy. The bracket arm structure, subjected to long-term vertical levitation and transversal guidance forces, is a key load-bearing component of high-speed maglev trains. It is crucial to monitor strain, displacement , and load in real time to evaluate structural health. However, it is almost impossible to measure directly at the service site of trains due to the intricacy of the bracket arm structure and the constrained installation area. Hence, it is an effective and feasible approach to reconstruct the strain/displacement field using a limited amount of strain information from FBG sensors. In order to reconstruct the strain/displacement field and identify the load on complicated structures, a load–strain/displacement superposition conversion algorithm is proposed in this paper. The modal coordinate and the mode-shape matrix are swapped out for the load weight and the load–strain/displacement matrix, respectively. Thus, it gets around the difficulty of choosing the mode order in traditional modal theory. In addition, the algorithm can achieve full-field reconstruction of both strain and displacement by measuring only part of the nodal strain response, reducing the redundancy of the traditional algorithm. Finally, numerical simulations and experimental tests were carried out to verify the feasibility of the proposed method for field reconstruction and load identification. [ABSTRACT FROM AUTHOR]