Beijing University of Posts and Telecommunications

About: Beijing University of Posts and Telecommunications is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: MIMO & Quality of service. The organization has 39576 authors who have published 41525 publications receiving 403759 citations. The organization is also known as: BUPT.

α-Ga2O3 Nanorod Array–Cu2O Microsphere p–n Junctions for Self-Powered Spectrum-Distinguishable Photodetectors

Abstract: Most of the photodetectors can measure all of the light illumination with a wavelength below the absorption edge of the detector materials, while they cannot distinguish the different waveband. Her...

T-C3D: Temporal Convolutional 3D Network for Real-Time Action Recognition

Abstract: Video-based action recognition with deep neural networks has shown remarkable progress. However, most of the existing approaches are too computationally expensive due to the complex network architecture. To address these problems, we propose a new real-time action recognition architecture, called Temporal Convolutional 3D Network (T-C3D), which learns video action representations in a hierarchical multi-granularity manner. Specifically, we combine a residual 3D convolutional neural network which captures complementary information on the appearance of a single frame and the motion between consecutive frames with a new temporal encoding method to explore the temporal dynamics of the whole video. Thus heavy calculations are avoided when doing the inference, which enables the method to be capable of real-time processing. On two challenging benchmark datasets, UCF101 and HMDB51, our method is significantly better than state-of-the-art real-time methods by over 5.4% in terms of accuracy and 2 times faster in terms of inference speed (969 frames per second), demonstrating comparable recognition performance to the state-of-the-art methods. The source code for the complete system as well as the pre-trained models are publicly available at https://github.com/tc3d.

Optimal Energy-Efficient Power Allocation for OFDM-Based Cognitive Radio Networks

Abstract: This letter investigates the energy-efficient power allocation for orthogonal frequency division multiplexing (OFDM) based cognitive radio networks (CRNs). The problem is to maximize the energy-efficiency measured using the "throughput per Joule" metric subject to the total transmit power and interference constraints. It is then transformed into an equivalent convex problem using parametric programming. Furthermore, an optimal iterative algorithm based on convex optimization theory and parametric programming is proposed. The numerical results show that the proposed optimal algorithm can achieve higher energy-efficiency than that obtained by solving the original problem directly because of its non-convexity. Energy-efficiency maximization can also achieve a good tradeoff between capacity and energy in CRNs.