Magnetic resonance imaging (MRI) is a powerful imaging method that provides rich anatomical information in clinical applications, leading to more accurate diagnosis and pathological analysis. However, MRI acquisition is limited by the hardware performance and scan time, making it challenging to obtain complete high-quality images. In recent years, MRI reconstruction algorithms using deep learning (DL) have demonstrated good capabilities in improving image quality and accelerating image acquisition. Thus, a new complex-valued dual-domain dilated convolution neural network (C3DNet) providing fast and accurate MRI reconstruction is proposed in this paper. Unlike existing DL-based methods, the developed C3DNet uses complex-valued convolution to extract complex-valued features from the k-space and image-domain data separately and perform dual-domain feature fusion. Additionally, we use dilated convolution to expand the features’ receptive field and thus capture contextual information. To fully use the prior knowledge, we utilize two data consistency (DC) methods and apply them several times to both the k-space and image-domain feature maps. Experimental results demonstrate that our proposed method outperforms several state-of-the-art algorithms regarding imaging results and computation time.

The recognition of small unmanned aerial vehicle (UAV) has important application value. The very challenging problems in small UAV recognition are recognition accuracy of traditional methods and interpretability of deep learning-based methods. High-resolution radar sensors provide an attractive choice for this issue. In this paper, we propose a multi-feature fusion method for small UAV recognition based on high-resolution radar sensor, which can combine the structure features and micro-Doppler features of small UAV. A dataset contains two different type of small UAV targets, namely four-rotor UAV and two-rotor helicopter, is constructed for training and testing. The recognition accuracy of these two small UAVs based on the measured high-resolution radar data reached 98.5%. The experimental results demonstrate that the proposed multi-feature fusion method is effective for small UAV recognition.

Following the great success of deep learning in signal processing, Many models based on real-valued convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been proposed for automatic modulation classification (AMC). However, the modulation signal is not only temporally dependent but also complex-valued data. The real-valued deep learning models treat the real and imaginary parts of the complex-valued modulation signal as two independent real-valued inputs, which destroy the structure of the raw signal data and make the model more uninterpretable. Thus, this paper proposes a novel complex-valued parallel convolutional recurrent neural network (CPCRNN) specifically for AMC. CPCRNN combines parallel complex-valued CNN and RNN with redesigned complex-valued activation function and complex-valued max pooling. The model directly feeds the complex-valued raw signal to the complex-valued CNN to obtain the complex-valued feature maps, which are then transformed into amplitude and phase and fed to the RNN. Our model can first extract the complex-valued features of the modulation signal with complex-valued CNNs, and then extract the temporal features of the modulation signal with RNNs. CPCRNN achieved an overall accuracy of 62.29% and 69.93% on the benchmark datasets RadioML2016.10A and RadioML2018.01-simple, respectively, outperforming all the state-of-the-art algorithms.

Transcripts generated by automatic speech recognition (ASR) systems usually have poor readability caused by lacking of punctuation and containing a large portion of dis-fluency. Existing methods for automatic punctuation restoration have obtain great improvements by finetuning large pre-trained language models (LMs). However, large amount of well-formatted written language are used in pre-training and finetuning LMs, leaving a mismatch between the training and application. In this paper, we modify the ELECTRA model with disfluency generator and multi-task discriminator for automatic punctuation restoration. The generator dynamically inject disfluency into the input training data, and the discriminator is trained to distinguish disfluency from generator outputs and predict punctuation marks. Experimental results demonstrate that our proposed method significantly outperforms the baselines based on the English IWSLT dataset and our newly collected Chinese dataset.

Radar-based sensors provide an attractive choice for hand gesture recognition (HGR). The very challenging problems in radar-based HGR are radar echo data preprocessing and recognition accuracy. In this paper, we propose a convolutional neural network (CNN) for dynamic HGR based on a millimeter-wave Frequency Modulated Continuous Wave (FMCW) radar which operates at 77GHz. Six different dynamic hand gestures are designed and the time-frequency analysis of micro-Doppler signatures are adopted as the input to CNN. The measured data of the dynamic hand gestures are collected in different experimental scenarios. The recognition accuracy of the six gestures based on the measured data reached 95.2%. The experimental results demonstrate that the proposed method is effective in the measured data and the micro-Doppler signature is effective for dynamic HGR.