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When autonomous underwater vehicles (AUVs) perform underwater tasks, the absence of GPS position assistance can lead to a decrease in the accuracy of traditional navigation systems, such as the extended Kalman filter (EKF), due to the accumulation of errors. To enhance the navigation accuracy of AUVs in the absence of position assistance, this paper proposes an innovative navigation method that integrates a position correction model and a velocity model. Specifically, a velocity model is developed using a dynamic model and the Optimal Pruning Extreme Learning Machine (OP-ELM) method. This velocity model is trained online to provide velocity outputs during the intervals when the Doppler Velocity Log (DVL) is not updating, ensuring more consistent and reliable velocity estimation. Additionally, a position correction model (PCM) is constructed, based on a hybrid gated recurrent neural network (HGRNN). This model is specifically designed to correct the AUV’s navigation position when GPS data are unavailable underwater. The HGRNN utilizes historical navigation data and patterns learned during training to predict and adjust the AUV’s estimated position, thereby reducing the drift caused by the lack of real-time position updates. Experimental results demonstrate that the proposed VM-PCM-EKF algorithm can significantly improve the positioning accuracy of the navigation system, with a maximum accuracy improvement of 87.2% compared to conventional EKF algorithms. This method not only improves the reliability and accuracy of AUV missions but also opens up new possibilities for more complex and extended underwater operations.

In order to remedy the defects of single sensor in robustness, accuracy, and redundancy of target detection, this paper proposed a method for detecting obstacles in farmland based on the information fusion of a millimeter wave (mmWave) radar and a camera. Combining the advantages of the mmWave radar in range and speed measurement and the camera in type identification and lateral localization, a decision-level fusion algorithm was designed for the mmWave radar and camera information, and the global nearest neighbor method was used for data association. Then, the effective target sequences of the mmWave radar and the camera with successful data association were weighted to output, and the output included more accurate target orientation, longitudinal speed, and category. For the unassociated sequences, they were tracked as new targets by using the extended Kalman filter algorithm and were processed and output during the effective life cycle. Lastly, an experimental platform based on a tractor was built to verify the effectiveness of the proposed association detection method. The obstacle detection test was conducted under the ROS environment after solving the external parameters of the mmWave radar and the internal and external parameters of the camera. The test results show that the correct detection rate of obstacles reaches 86.18%, which is higher than that of a single camera with 62.47%. Furthermore, through the contrast experiment of the sensor fusion algorithms, the detection accuracy of the decision level fusion algorithm was 95.19%, which was higher than 4.38% and 6.63% compared with feature level and data level fusion, respectively.

NiCu alloy nanoparticle-loaded carbon nanofibers (NiCuCNFs) were fabricated by a combination of electrospinning and carbonization methods. A series of characterizations, including SEM, TEM and XRD, were employed to study the NiCuCNFs. The as-prepared NiCuCNFs were then mixed with laccase (Lac) and Nafion to form a novel biosensor. NiCuCNFs successfully achieved the direct electron transfer of Lac. Cyclic voltammetry and linear sweep voltammetry were used to study the electrochemical properties of the biosensor. The finally prepared biosensor showed favorable electrocatalytic effects toward hydroquinone. The detection limit was 90 nM (S/N = 3), the sensitivity was 1.5 µA µM−1, the detection linear range was 4 × 10−7–2.37 × 10−6 M. In addition, this biosensor exhibited satisfactory repeatability, reproducibility, anti-interference properties and stability. Besides, the sensor achieved the detection of hydroquinone in lake water.

Camphor sulfonic acid (CSA)-doped polyamide 6/polyaniline (PA6/PANI) composite nanofibers were fabricated using in situ polymerization of aniline under different CSA concentrations (0.02, 0.04, 0.06, 0.08 and 0.10 M) with electrospun PA6 nanofibers as templates. The structural, morphological and ammonia sensing properties of the prepared composite nanofibers were studied using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), four-point probe techniques, X-ray diffraction (XRD) and a home-made gas sensing test system. All the results indicated that the CSA concentration had a great influence on the sensing properties of CSA-doped PA6/PANI composite nanofibers. The composite nanofibers doped with 0.02 M CSA showed the best ammonia sensing properties, with a significant sensitivity toward ammonia (NH3) at room temperature, superior to that of the composite nanofibers doped with 0.04–0.10 mol/L CSA. It was found that for high concentrations of CSA, the number of PANI–H+ reacted with NH3 would not make up a high proportion of all PANI–H+ within certain limits. As a result, within a certain range even though higher CSA-doped PA6/PANI nanofibers had better conductivity, their ammonia sensing performance would degrade.

In order to weaken the influence of temperature on photoacoustic (PA) measurements and compensate PA signals with a proposed theoretical model, the relationship of PA signal amplitude with temperature, under the condition of different glucose concentrations and different light intensities, was studied in this paper. First, the theoretical model was derived from the theory of the PA effect. Then, the temperature characteristics of the PA signals were investigated, based on the analyses of the temperature-dependent Grüneisen parameter in glucose solution. Next, the concept of a PA temperature coefficient was proposed in this paper. The result of the theoretical analysis shows that this coefficient is linear to light intensity and irrelevant to the concentration of glucose solution. Furthermore, a new concept of a PA temperature coefficient of unit light intensity was proposed in this paper. This coefficient is approximately constant, with different light intensities and solution concentrations, which is similar to the thermal expansion coefficient. After calculation, the PA temperature coefficient by the unit light intensity of glucose solution is about 0.936 bar/K. Finally, relevant experiments were carried out to verify the theoretical analysis, and the PA temperature coefficient of the unit light intensity of glucose solution is about 0.04/°C. This method can also be used in sensors measuring concentrations in other aqueous solutions.