For significant memory concern (SMC) and mild cognitive impairment (MCI), their classification performance is limited by confounding features, diverse imaging protocols, and limited sample size. To address the above limitations, we introduce a dual-modality fused brain connectivity network combining resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), and propose three mechanisms in the current graph convolutional network (GCN) to improve classifier performance. First, we introduce a DTI-strength penalty term for constructing functional connectivity networks. Stronger structural connectivity and bigger structural strength diversity between groups provide a higher opportunity for retaining connectivity information. Second, a multi-center attention graph with each node representing a subject is proposed to consider the influence of data source, gender, acquisition equipment, and disease status of those training samples in GCN. The attention mechanism captures their different impacts on edge weights. Third, we propose a multi-channel mechanism to improve filter performance, assigning different filters to features based on feature statistics. Applying those nodes with low-quality features to perform convolution would also deteriorate filter performance. Therefore, we further propose a pooling mechanism, which introduces the disease status information of those training samples to evaluate the quality of nodes. Finally, we obtain the final classification results by inputting the multi-center attention graph into the multi-channel pooling GCN. The proposed method is tested on three datasets (i.e., an ADNI 2 dataset, an ADNI 3 dataset, and an in-house dataset). Experimental results indicate that the proposed method is effective and superior to other related algorithms, with a mean classification accuracy of 93.05% in our binary classification tasks. Our code is available at: https://github.com/Xuegang-S.

With the popularity of unmanned aerial vehicles (UAVs), how to conduct automatic and effective detection to prevent unauthorized flying has become an important issue. The conventional constant false alarm rate (CFAR) detector based on radar signal has shown advantages in moving target detection. However, the CFAR-based detectors are strongly dependent on some manual experience, such as the ambient noise distribution estimation and the detection windows’ size selection, and usually suffered poor performance on small UAV detection due to the weak signal. Inspired by the success of deep learning (DL) on natural scene object detection, this article tries to explore a DL-based method for UAV detection in pulse-Doppler radar. Concretely, we propose a convolutional neural network (CNN) with two heads: one for the classification of the input range-Doppler map patch into target present or target absent and the other for the regression of offset between the target and the patch center. Then, based on the output of the network, a nonmaximum suppression (NMS) mechanism composed of probability-based initial recognition, distribution density-based recognition, and voting-based regression is developed to reduce false alarms as well as control the false alarms. Finally, experiments on both simulated data and real data are carried out, and it is shown that the proposed method can locate the target more accurately and achieve a much lower false alarm rate at a comparable detection rate than CFAR.

Acoustic vector sensor (AVS) has been recently researched and developed for acoustic wave capturing and signal processing. Conventional array generally employs spatially displayed sensors for signal enhancement, source localisation, target tracking, etc. However, the large size usually limits its implementations on some portable devices. AVS which generally includes one omni‐directional sensor and three orthogonally co‐located directional sensors has been recently introduced. An AVS is able to provide the four‐dimensional information of sound field in space: the acoustic pressure and its three‐dimensional particle velocities. A compact assembled AVS could be as small as a match head and the weight can be <50 g. Benefits from these properties, AVS tends to be more attractive for exploitation and commercialisation than conventional sensor array. To have a well understanding of the research progress on AVS, an overview on its recent developments is first given in this study. Then, discussions of challenges on AVS and extensions on its possible future prospects are presented.