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The present paper investigates the dynamic behaviors of a bubble restricted by two parallel plates near an elliptical wall. The typical experimental phenomena of the bubble are recorded employing the high-speed photography and a theoretical Kelvin impulse model is established. The impacts of the spatial position and the curvature of the wall on the bubble collapse behaviors are quantitatively investigated through the theoretical model and verified against the experimental results. The Kelvin impulse intensity and the direction during the bubble collapse process are compared and discussed for different elliptical-shaped walls. The main conclusions include: (1) During the bubble collapse process, the phenomenon of the bubble uneven splitting is discovered. (2) At different spatial positions and wall curvatures, the bubble collapse jet angle, movement distance, and velocity are in good agreement with the theoretical Kelvin impulse predictions. (3) As the short-to-long axis ratio increases, the differences in the distributions of the Kelvin impulse intensity and the direction near the elliptical wall gradually become larger, and the range of the influence of the impulse intensity expands.

The deep neural network has achieved remarkable recognition performance in synthetic aperture radar (SAR) automatic target recognition (ATR) by extracting the discriminative features from massive SAR images. Due to the sensitivity of SAR image to the observation aspect, the multiview ATR method could enhance the robustness of feature representation and improve the recognition performance. However, existing multiview ATR methods suffer from increasing complex structure and heavy computation when the number of input images grows. An adaptive multiview fusion network based on image attention (IA-AMF-Net) compatible with variable number of input images is proposed for SAR ATR in this article. In IA-AMF-Net, first, the depthwise separable convolution is employed to extract the classification features from multiple SAR input images in parallel with the lightweight attribute. Second, the channel feature weight vector of each image is generated and concatenated by applying the squeeze-and-excitation operation to the extracted features. The image attention weights for feature fusion are calculated through softmax normalizing the concatenated channel feature weights of input images. At last, the extracted features from multiview SAR images are fused by the obtained image attention weights. The dimension of fused feature keeps constant regardless of the number of input images, and the attention to the classification features of interested images is enhanced. Experimental results on the moving and stationary target acquisition and recognition dataset show that IA-AMF-Net achieves superior recognition performance under various operating conditions with fewer parameters and lower computational load compared to the other networks.

In the present paper, the dynamic behaviors of a bubble collapsing at the symmetrical positions of the double cylinders within a narrow gap are qualitatively and quantitatively investigated. Using a high-speed photographic technique, the morphological evolution of a bubble near the double cylinders in a two-dimensional flow field is explored and qualitatively demonstrated. The mechanism by which the position of the bubble affects its dynamics is revealed. At the symmetrical position of the double cylinders, the bubble’s dimensionless abscissa shows significant impacts on the collapse behaviors, and its increase weakens the bubble deformation and strengthens the centroid movement.

In the increasingly complex electromagnetic environment, main lobe jamming significantly degrades the performance of a wideband radar system. To mitigate this issue, this paper developed a wideband main lobe jamming suppression algorithm based on a distributed array radar. Firstly, this algorithm utilizes eigen-projection matrix processing in the main array to cancel out the main lobe jamming for main lobe maintenance, and then suppresses side lobe jamming through null constraint beamforming. Subsequently, the large aperture of the full array is leveraged to form a narrow beam directed toward the main lobe interference. Finally, joint beamforming using the minimum mean square error criterion is employed. In scenarios where both main lobe jamming and side lobe jamming exist, this algorithm can adaptively cancel main lobe wideband jamming, suppress side lobe wideband jamming, and effectively control the significant loss of the desired wideband signals caused by main lobe jamming within a smaller angular range. Simulation results validate the effectiveness of the algorithm.

Natural speech plays a pivotal role in communication and interactions between human beings. The prosody of natural speech, due to its high ecological validity and sensitivity, has been acoustically analyzed and more recently utilized in machine learning to identify individuals with autism spectrum disorders (ASDs). In this meta-analysis, we evaluated the findings of empirical studies on acoustic analysis and machine learning techniques to provide statistically supporting evidence for adopting natural speech prosody for ASD detection. Using a random-effects model, the results observed moderate-to-large pooled effect sizes for pitch-related parameters in distinguishing individuals with ASD from their typically developing (TD) counterparts. Specifically, the standardized mean difference (SMD) values for pitch mean, pitch range, pitch standard deviation, and pitch variability were 0.3528, 0.6744, 0.5735, and 0.5137, respectively. However, the differences between the two groups in temporal features could be unreliable, as the SMD values for duration and speech rate were only 0.0738 and −0.0547. Moderator analysis indicated task types were unlikely to influence the final results, whereas age groups showed a moderating role in pooling pitch range differences. Furthermore, promising accuracy rates on ASD identification were shown in our analysis of multivariate machine learning studies, indicating averaged sensitivity and specificity of 75.51% and 80.31%, respectively. In conclusion, these findings shed light on the efficacy of natural prosody in identifying ASD and offer insights for future investigations in this line of research.