Xidian University

About: Xidian University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Antenna (radio) & Synthetic aperture radar. The organization has 32099 authors who have published 38961 publications receiving 431820 citations. The organization is also known as: University of Electronic Science and Technology at Xi'an & Xīān Diànzǐ Kējì Dàxué.

Construction and Block Error Rate Analysis of Polar Codes Over AWGN Channel Based on Gaussian Approximation

Abstract: Polar codes are usually constructed to minimize the upper bound of a block error ratio (BLER). In this paper, we discuss the estimation of the exact BLERs of polar codes as well as the construction of polar codes. Assuming that successive cancellation (SC) decoding is employed, we present a method for estimating the exact BLER of polar codes with the help of Gaussian approximation (GA). A new algorithm is proposed to construct polar codes, aimed at minimizing the exact BLER, instead of the upper bound of the BLER. Analysis indicates that the new method is less complex than the existing methods. It is also shown that the estimation results match the simulations well.

Planning Bike Lanes based on Sharing-Bikes' Trajectories

Abstract: Cycling as a green transportation mode has been promoted by many governments all over the world. As a result, constructing effective bike lanes has become a crucial task for governments promoting the cycling life style, as well-planned bike paths can reduce traffic congestion and decrease safety risks for both cyclists and motor vehicle drivers. Unfortunately, existing trajectory mining approaches for bike lane planning do not consider key realistic government constraints: 1) budget limitations, 2) construction convenience, and 3) bike lane utilization. In this paper, we propose a data-driven approach to develop bike lane construction plans based on large-scale real world bike trajectory data. We enforce these constraints to formulate our problem and introduce a flexible objective function to tune the benefit between coverage of the number of users and the length of their trajectories. We prove the NP-hardness of the problem and propose greedy-based heuristics to address it. Finally, we deploy our system on Microsoft Azure, providing extensive experiments and case studies to demonstrate the effectiveness of our approach.

Adaptive OFDM Integrated Radar and Communications Waveform Design Based on Information Theory

Abstract: To improve the effectiveness of limited spectral resources, an adaptive orthogonal frequency division multiplexing integrated radar and communications waveform design method is proposed. First, the conditional mutual information (MI) between the random target impulse response and the received signal, and the data information rate (DIR) of frequency selective fading channel are formulated. Then, with the constraint on the total power, the optimization problem, which simultaneously considers the conditional MI for radar and DIR for communications, is devised, and the analytic solution is derived. With low transmit power, the designed integrated waveform outperforms the fixed waveform (i.e., equal power allocation). Finally, several simulated experiments are provided to verify the effectiveness of the designed waveform.

Lightweight Image Super-Resolution with Information Multi-distillation Network

Abstract: In recent years, single image super-resolution (SISR) methods using deep convolution neural network (CNN) have achieved impressive results. Thanks to the powerful representation capabilities of the deep networks, numerous previous ways can learn the complex non-linear mapping between low-resolution (LR) image patches and their high-resolution (HR) versions. However, excessive convolutions will limit the application of super-resolution technology in low computing power devices. Besides, super-resolution of any arbitrary scale factor is a critical issue in practical applications, which has not been well solved in the previous approaches. To address these issues, we propose a lightweight information multi-distillation network (IMDN) by constructing the cascaded information multi-distillation blocks (IMDB), which contains distillation and selective fusion parts. Specifically, the distillation module extracts hierarchical features step-by-step, and fusion module aggregates them according to the importance of candidate features, which is evaluated by the proposed contrast-aware channel attention mechanism. To process real images with any sizes, we develop an adaptive cropping strategy (ACS) to super-resolve block-wise image patches using the same well-trained model. Extensive experiments suggest that the proposed method performs favorably against the state-of-the-art SR algorithms in term of visual quality, memory footprint, and inference time. Code is available at \url{this https URL}.