Underwater acoustic communications (UACs) are demanded in a wide range of marine applications. However, one of the main challenges for UACs is unexpected interferences from nearby acoustic activities, especially dynamic and short-period interferences (called shot interference), which are hard to detect, estimate, and mitigate over time- and frequency-selective acoustic channels. In this paper, we propose a novel filter that mitigates the interference effects for both single-carrier and multi-carrier systems without any prior knowledge of the interference. An Adaptive Sliding Window Interference Detection (ASWID) algorithm is developed to detect the location and the number of interfered symbols. Meanwhile, a least trimmed squares (LTS) equalizer is presented for the shot interference estimation and mitigation based on robust regression. The proposed algorithms are evaluated through numerical simulations and real channel measurements. Our results show that the proposed designs can detect and mitigate shot interferences effectively for single-carrier and multi-carrier UAC systems.

sEMG-based gesture recognition is widely applied in human-machine interaction system by its unique advantages. However, the accuracy of recognition drops significantly as electrodes shift. Besides, in applications such as VR, virtual hands should be shown in reasonable posture by self-calibration. We propose an armband fusing sEMG and IMU with autonomously adjustable gain, and an extended spatial transformer convolutional neural network (EST-CNN) with feature enhanced pretreatment (FEP) to accomplish both gesture recognition and self-calibration via a one-shot processing. Different from anthropogenic calibration methods, spatial transformer layers (STL) in EST-CNN automatically learn the transformation relation, and explicitly express the rotational angle for coarse correction. Due to the shape change of feature pattern as rotational shift, we design the fine tuning layer (FTL) which is able to regulate rotational angle within 45°. By combining STL, FTL and IMU-based posture, EST-CNN is able to calculate non-discretized angle, and achieves high resolution of posture estimation based on sparse sEMG electrodes. Experiments collect frequently-used 3 gestures of 4 subjects in equidistant angles to evaluate EST-CNN. The results under electrodes shift show that the accuracy of gesture recognition is 97.06%, which is 5.81% higher than CNN, the fitness between estimated and true rotational angle is 99.44%.

Objective: Major depressive disorder (MDD) and bipolar disorder (BD) are serious chronic disabling mental and emotional disorders, with symptoms that often manifest atypically in children and adolescents, making diagnosis difficult without objective physiological indicators. Therefore, we aimed to objectively identify MDD and BD in children and adolescents by exploring their voiceprint features. Methods: This study included a total of 150 participants, with 50 MDD patients, 50 BD patients, and 50 healthy controls aged between 6 and 16 years. After collecting voiceprint data, chi-square test was used to screen and extract voiceprint features specific to emotional disorders in children and adolescents. Then, selected characteristic voiceprint features were used to establish training and testing datasets with the ratio of 7:3. The performances of various machine learning and deep learning algorithms were compared using the training dataset, and the optimal algorithm was selected to classify the testing dataset and calculate the sensitivity, specificity, accuracy, and ROC curve. Results: The three groups showed differences in clustering centers for various voice features such as root mean square energy, power spectral slope, low-frequency percentile energy level, high-frequency spectral slope, spectral harmonic gain, and audio signal energy level. The model of linear SVM showed the best performance in the training dataset, achieving a total accuracy of 95.6% in classifying the three groups in the testing dataset, with sensitivity of 93.3% for MDD, 100% for BD, specificity of 93.3%, AUC of 1 for BD, and AUC of 0.967 for MDD. Conclusion: By exploring the characteristics of voice features in children and adolescents, machine learning can effectively differentiate between MDD and BD in a population, and voice features hold promise as an objective physiological indicator for the auxiliary diagnosis of mood disorder in clinical practice.

Synthetic aperture radar (SAR) images of ships have complex background interference, multi-scale targets, and irregular distribution characteristics. However, existing mainstream SAR ship detection algorithms rely on manually designed hyperparameters. This results in poor robustness, which makes it difficult to effectively balance the detection accuracy and speed. To solve these problems, a novel anchor-free SAR ship detection algorithm based on self-balancing position attention is proposed. First, a lightweight feature extraction backbone (GhostVS-Net) is designed by FOCUS, Ghost, and separable convolution modules to extract feature information. This helps to extract the contour features of ships and suppress unrelated interference, making it more suitable for the scattering characteristics of SAR images. Second, a self-balancing position attention (SBPA) module is designed, which balances the local image features under multiple receptive fields, and aggregates the global position information and spatial context information. The proposed SBPA module considers the characteristics of background, scale, and distribution of SAR images to obtain greatly improved positioning accuracy. Finally, the feature pyramid network (FPN) is applied to fuse the scale context information, and further improve the accuracy of detection. Experimental results on the SSDD and HRSID datasets show that the proposed network bears feature of accurate detection and strong robustness, with the mean average precision (mAP) reaches 99.72% and 95.30%, which reveals that it outperforms all current state-of-the-art algorithms with super high performances.

Ammonia (NH ₃ ) has a significant impact on human health, merely 300 ppm of which could pose a risk of irreversible harm to human life. Size and sensitivity are two primary factors for real-time sensing of NH ₃ . Through dry grinding, we develope a novel 61 MHz quartz crystal microbalance (QCM) sensor with dimensions of only 5.0 mm in length and 3.2 mm in width. In this study, the MXene/ZnO heterojunction solution is created using the liquid phase method, and it is uniformly sprayed onto the surface of the QCM. The large specific surface area of the MXene/ZnO heterojunction provides additional adsorption sites for NH ₃ detection, enhancing sensitivity across a wide dynamic range and offering superior selectivity towards ammonia. At a temperature of 25 °C, the sensitivity of the MXene/ZnO sensor for detecting NH ₃ is 1.319 Hz/ppm, surpassing that of the ZnO sensor (0.166 Hz/ppm). Furthermore, the sensor’s recovery time is significantly reduced from typically 300 s under normal conditions to 40 s after exposure to ultraviolet (UV) light. The proposed QCM sensor boasts advantages of compact size, high sensitivity, short recovery time, and exceptional selectivity for ammonia, which presents a novel solution for detecting harmful gases like NH ₃ at room temperature.