Acute ischemic stroke (AIS) has been a common threat to human health and may lead to severe outcomes without proper and prompt treatment. To precisely diagnose AIS, it is of paramount importance to quantitatively evaluate the AIS lesions. By adopting a convolutional neural network (CNN), many automatic methods for ischemic stroke lesion segmentation on magnetic resonance imaging (MRI) have been proposed. However, most CNN-based methods should be trained on a large amount of fully labeled subjects, and the label annotation is a labor-intensive and time-consuming task. Therefore, in this paper, we propose to use a mixture of many weakly labeled and a few fully labeled subjects to relieve the thirst of fully labeled subjects. In particular, a multifeature map fusion network (MFMF-Network) with two branches is proposed, where hundreds of weakly labeled subjects are used to train the classification branch, and several fully labeled subjects are adopted to tune the segmentation branch. By training on 398 weakly labeled and 5 fully labeled subjects, the proposed method is able to achieve a mean dice coefficient of on a test set with 179 subjects. The lesion-wise and subject-wise metrics are also evaluated, where a lesion-wise F1 score of 0.886 and a subject-wise detection rate of 1 are achieved.

The existence of high carrier frequency offset (CFO) and fading channels degrades the performance of communication systems in high mobility environments significantly. To address these challenges, in this paper, we propose an attention guided multi-task network for joint CFO and channel estimation in orthogonal frequency division multiplexing (OFDM) systems. Specifically, considering the correlation between CFO estimation and channel estimation, we construct a multi-task neural network framework which consists of a shared branch (SB), a CFO estimation branch (CFOEB) and a channel estimation branch (CEB). Among them, the SB extracts common features of two tasks, and the CFOEB and CEB make more detailed CFO and channel estimation, respectively. To further suppress the influence of CFO estimation error on channel estimation, we introduce an attention guided module (AGM) which completes the re-calibration of the channel dimension on the basis of different CFO characteristics. We train the whole network by an end-to-end paradigm and construct a suitable multi-task loss function to balance the losses between different tasks. Simulation results demonstrate that the proposed attention guided multi-task learning based joint CFO and channel estimation scheme outperforms the conventional methods and has greater robustness under various conditions.

Vehicular communication in high mobility environments has been widely explored in recent years. However, due to the existence of carrier frequency offset (CFO) and dynamic channel, the performance of vehicular communication systems over fast time-varying scenes drops severely. To address this problem, in this paper, we propose a simple and effective CRS-based joint CFO and channel estimation baseline method using deep learning (DL) for orthogonal frequency division multiplexing (OFDM) systems. Concretely, we construct a joint neural network (NN) architecture consisting of a CFO estimation network (CFOENet) based on fully connected layers and a channel estimation network (CENet) composed of convolutional layers. The proposed NN architecture can fully exploit the correlation of the cell reference signal (CRS), while learning the CFO characteristics and channel state information (CSI) changes simultaneously, which highly improves the pilot usage efficiency. We conduct adequate simulation experiments, and the results demonstrate that the proposed DL-based scheme can achieve better performance in terms of CFO estimation, channel estimation and overall system performance than conventional methods, while our method has stronger robustness and generalization ability under various channel conditions. The proposed joint CFO and channel estimation scheme has great potential in the field of the Internet of Vehicles.

Ultra-Reliable Low-Latency Communication (URLLC) has imposed significant challenges on the Internet of Things (IoT) networks due to its stringent Quality of Service (QoS) requirements. Conventionally, to support the latency and reliability requirements, the IoT devices have to leverage the transmission power, which significantly shortened the battery life. As a promising candidate for the future mobile communication systems, reconfigurable intelligent surface (RIS) is preferrable to be deployed in the IoT networks to reduce the power consumption, thanks to its ability in reconfiguring the propagation environment. In this paper, we focus on the design of an aerial RIS aided URLLC system where the RIS is deployed on an unmanned aerial vehicle (UAV). The system operates on the time division multiple access (TDMA) protocol, and the transmission power minimization problem is investigated. A low-complexity algorithm is proposed to jointly optimize the RIS phase shifts, transmission time duration, and the UAV location. Simulation results demonstrate that by deploying aerial RIS in the IoT network, the transmission power at the IoT device can be significantly reduced. The effect of the TDMA frame length is also discussed. When the TDMA frame length is large, the optimal transmission time duration could be much shorter than equal allocation, which implies that our proposed design is able to simultaneously reduce both the average power consumption and the transmission latency.

The secondary modulation with the Neumann-Hoffman code increases the possibility of bit sign transition. Unlike other GNSS signals, there is no pilot component for synchronization in BeiDou B1/B3 signals, which increases the complexity in acquisition. A previous study has shown that the delay and multiplication (DAM) method is able to eliminate the bit sign transition problem, but it only applies to pretty strong signals. In this paper, a DAM-based BeiDou signal acquisition approach, called variable length data accumulation (VLDA), is proposed to acquire weak satellite signals. Firstly, the performance of DAM method versus the different delays is analyzed. The DAM operation not only eliminates bit sign transition, but it also increases noise power. Secondly, long-term signal is periodically accumulated to improve signal intensity in order to acquire weak signals. While considering the Doppler frequency shift of ranging codes, the signal length must be compensated before accumulating long-term signal. Finally, the fast-Fourier-transform based parallel code phase algorithm are used for acquisition. The simulation results indicate that the proposed VLDA method has better acquisition sensitivity than traditional non-coherent integration method under the same calculation amount. The VLDA method only requires approximately 27.5% of calculations to achieve the same acquisition sensitivity (35 dBHz). What is more, the actual experimental results verify the feasibility of the VLDA method. It can be concluded that the proposed approach is an effective and feasible method for solving the bit sign transition problem.