An Intra-BRNN and GB-RVQ Based END-TO-END Neural Audio Codec

Recently, neural networks have proven to be effective in performing speech coding task at low bitrates. However, under-utilization of intra-frame correlations and the error of quantizer specifically degrade the reconstructed audio quality. To improve the coding quality, we present an end-to-end neural speech codec, namely CBRC (Convolutional and Bidirectional Recurrent neural Codec). An interleaved structure using 1D-CNN and Intra-BRNN is designed to exploit the intra-frame correlations more efficiently. Furthermore, Group-wise and Beam-search Residual Vector Quantizer (GB-RVQ) is used to reduce the quantization noise. CBRC encodes audio every 20ms with no additional latency, which is suitable for real-time communication. Experimental results demonstrate the superiority of the proposed codec when comparing CBRC at 3kbps with Opus at 12kbps.

Intelligent Reflecting Surface Aided Wireless Communication Systems: Joint Location and Passive Beamforming Design

Intelligent reflecting surface (IRS) has been widely studied in recent years, it has emerged as a new technology which can reflect the incident signal by intelligently configuring the reflection elements, thus changing the signal propagation environment, enhancing the signals users desire and suppressing the interference between users. In this paper, we study an IRS aided multi-users wireless communication where the base station (BS) sends a variety of signals, each user receives desired signals. In order to guarantee the fairness of wireless communications, we need to maximize the minimum rates of users, subject to the power constraint of BS and the phase constraint of IRS. Prior works on IRS mainly consider optimizing BS beamforming and IRS passive beamforming, this paper also aims to optimize the IRS location. The considered problem is shown to be non-convex, we decompose the problem into two subproblems, transforming the two subproblems into a lower bound problem and using alternating optimization (AO) and successive convex approximation (SCA) to solve them, respectively. Finally, the two subproblems are optimized alternately to make the objective function value converge in an acceptable range. Simulation results verify the convergence results of our proposed algorithm, and the performance improvement compared with the benchmark scheme in wireless communication system.

Left Ventricle Contouring in Cardiac Images Based on Deep Reinforcement Learning

Medical image segmentation is one of the important tasks of computer-aided diagnosis in medical image analysis. Since most medical images have the characteristics of blurred boundaries and uneven intensity distribution, through existing segmentation methods, the discontinuity within the target area and the discontinuity of the target boundary are likely to lead to rough or even erroneous boundary delineation. In this paper, we propose a new iterative refined interactive segmentation method for medical images based on agent reinforcement learning, which focuses on the problem of target segmentation boundaries. We model the dynamic process of drawing the target contour in a certain order as a Markov Decision Process (MDP) based on a deep reinforcement learning method. In the dynamic process of continuous interaction between the agent and the image, the agent tracks the boundary point by point in order within a limited length range until the contour of the target is completely drawn. In this process, the agent can quickly improve the segmentation performance by exploring an interactive policy in the image. The method we proposed is simple and effective. At the same time, we evaluate our method on the cardiac MRI scan data set. Experimental results show that our method has a better segmentation effect on the left ventricle in a small number of medical image data sets, especially in terms of segmentation boundaries, this method is better than existing methods. Based on our proposed method, the dynamic generation process of the predicted contour trajectory of the left ventricle will be displayed online at https://github.com/H1997ym/LV-contour-trajectory.