In recent years, the advancement of deep learning technology has led to excellent performance in synthetic aperture radar (SAR) automatic target recognition (ATR) technology. However, due to the interference of speckle noise, the task of classifying SAR images remains challenging. To address this issue, a multi-scale local–global feature fusion network (MFN) integrating a convolution neural network (CNN) and a transformer network was proposed in this study. The proposed network comprises three branches: a CovNeXt-SimAM branch, a Swin Transformer branch, and a multi-scale feature fusion branch. The CovNeXt-SimAM branch extracts local texture detail features of the SAR images at different scales. By incorporating the SimAM attention mechanism to the CNN block, the feature extraction capability of the model was enhanced from the perspective of spatial and channel attention. Additionally, the Swin Transformer branch was employed to extract SAR image global semantic information at different scales. Finally, the multi-scale feature fusion branch was used to fuse local features and global semantic information. Moreover, to overcome the problem of poor accuracy and inefficiency of the model due to empirically determined model hyperparameters, the Bayesian hyperparameter optimization algorithm was used to determine the optimal model hyperparameters. The model proposed in this study achieved average recognition accuracies of 99.26% and 94.27% for SAR vehicle targets under standard operating conditions (SOCs) and extended operating conditions (EOCs), respectively, on the MSTAR dataset. Compared with the baseline model, the recognition accuracy has been improved by 12.74% and 25.26%, respectively. The results demonstrated that Bayes-MFN reduces the inter-class distance of the SAR images, resulting in more compact classification features and less interference from speckle noise. Compared with other mainstream models, the Bayes-MFN model exhibited the best classification performance.

Nasal obstruction is a common symptom of nasal conditions, with nasal resistance being a crucial physiological indicator for assessing severity. However, traditional rhinomanometry faces challenges with interference, limited automation, and unstable measurement results. To address these issues, this research designed a nasal resistance measurement system based on multi-sensor fusion of pressure and flow. The system comprises lower computer hardware for acquiring raw pressure–flow signals in the nasal cavity and upper computer software for segmenting and filtering effective respiratory cycles and calculating various nasal resistance indicators. Meanwhile, the system’s anti-interference capability was assessed using recall, precision, and accuracy rates for respiratory cycle recognition, while stability was evaluated by analyzing the standard deviation of nasal resistance indicators. The experimental results demonstrate that the system achieves recall and precision rates of 99% and 86%, respectively, for the recognition of effective respiratory cycles. Additionally, under the three common interference scenarios of saturated or weak breaths, breaths when not worn properly, and multiple breaths, the system can achieve a maximum accuracy of 96.30% in identifying ineffective respiratory cycles. Furthermore, compared to the measurement without filtering for effective respiratory cycles, the system reduces the median within-group standard deviation across four types of nasal resistance measurements by 5 to 18 times. In conclusion, the nasal resistance measurement system developed in this research demonstrates strong anti-interference capabilities, significantly enhances the automation of the measurement process and the stability of the measurement results, and offers robust technical support for the auxiliary diagnosis of related nasal conditions.

The nasal cavity three-dimensional structure reconstruction is significant for the diagnosis and treatment of nasal and nasopharynx diseases. Currently, most systems for nasal cavity three-dimensional structure reconstruction are based on a long-source acoustic tube. However, the long-source acoustic tube has the weaknesses of low portability and high cost. To address these issues, a nasal cavity three-dimensional structure reconstruction system based on a short-source acoustic tube was designed in this research. The designed system comprises the lower computer of a nasal acoustic signal acquisition device and the upper computer of nasal acoustic signal analysis software. The lower computer of a nasal acoustic signal acquisition device consists of a signal processing unit, a detection unit, a microcontroller, and a USB communication interface. It is used to collect the original acoustic signal in the nasal cavity. The upper computer of nasal acoustic signal analysis software consists of the nasal cavity acoustic signal preprocessing method and the nasal cavity three-dimensional structure hierarchical reconstruction method. The nasal cavity acoustic signal preprocessing method is used to eliminate the DC offset component and suppress the high-frequency noise. Then, the nasal cavity three-dimensional structure hierarchical reconstruction method is used to establish the relationship between the cross-sectional area of the nasal cavity and the depth of the nasal cavity. In order to verify the effectiveness of the system designed in this research, the straight tube model and the simulated nasal cavity model were selected as test subjects, and the depth–cross-sectional area was selected as the evaluation indicator of the accuracy of the nasal cavity three-dimensional structure reconstruction result. The experimental results show that the root mean squared error and the mean relative error of the depth–cross-sectional area were reduced from 0.6284 cm${}^2$ to 0.0201 cm${}^2$ and 47.6467% to 1.8248%, respectively, under the straight tube model by using the nasal cavity acoustic signal preprocessing method. The root mean squared error and the mean relative error of the depth–cross-sectional area were reduced from 4.7730 cm${}^2$ to 0.1510 cm${}^2$ and 107.8128% to 3.3096%, respectively, under the simulated nasal cavity model. Meanwhile, this system can increase the effective measurement depth of the nasal cavity from 7 cm to 13 cm based on the preprocessing method. The results demonstrated that the designed system can not only reconstruct and visualize a nasal cavity three-dimensional structure with high precision but also generate comprehensive test reports. Therefore, this system can provide a basis for further auxiliary diagnosis of the disease and facilitate doctors in explaining the physiological structure of the nasal cavity to patients.