Huawei

About: Huawei is a company organization based out in Shenzhen, China. It is known for research contribution in the topics: Terminal (electronics) & Signal. The organization has 41417 authors who have published 44698 publications receiving 343496 citations. The organization is also known as: Huawei Technologies & Huawei Technologies Co., Ltd..

A 2.5-D Angularly Stable Frequency Selective Surface Using Via-Based Structure for 5G EMI Shielding

Abstract: A novel broadband bandpass frequency-selective surface (FSS) designed for fifth generation (5G) EMI shielding is proposed in this paper. This new design employs the vertical vias into the 2-D periodic arrays, and such a single 2.5-D periodic layer of via-based structure is demonstrated to produce a highly stable angular response up to 60° for both TE and TM polarizations. By cascading two layers of such 2.5-D periodic arrays, the proposed FSS is able to obtain a broad passband as well as the wide out-of-band rejection. Moreover, it has a quite sharp band edge between the passband and the specified stopband. A corresponding equivalent circuit model (ECM) is further developed for better analysis of the operating principle. Finally, a prototype working at the center frequency of around 28 GHz is fabricated and measured. The main novelty of this paper is introducing the 2.5-D concept into designing a wideband FSS, and further reduce the unit size as well as improve the angular stability. Favorable agreement is achieved among the 3-D full-wave simulation, ECM and measurement. All these results demonstrate that the proposed FSS is a good candidate for 5G EMI shielding.

ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network

Abstract: Scene text detection and recognition has received increasing research attention. Existing methods can be roughly categorized into two groups: character-based and segmentation-based. These methods either are costly for character annotation or need to maintain a complex pipeline, which is often not suitable for real-time applications. Here we address the problem by proposing the Adaptive Bezier-Curve Network (ABCNet). Our contributions are three-fold: 1) For the first time, we adaptively fit arbitrarily-shaped text by a parameterized Bezier curve. 2) We design a novel BezierAlign layer for extracting accurate convolution features of a text instance with arbitrary shapes, significantly improving the precision compared with previous methods. 3) Compared with standard bounding box detection, our Bezier curve detection introduces negligible computation overhead, resulting in superiority of our method in both efficiency and accuracy. Experiments on arbitrarily-shaped benchmark datasets, namely Total-Text and CTW1500, demonstrate that ABCNet achieves state-of-the-art accuracy, meanwhile significantly improving the speed. In particular, on Total-Text, our realtime version is over 10 times faster than recent state-of-the-art methods with a competitive recognition accuracy. Code is available at this https URL

Learning Radio Resource Management in RANs: Framework, Opportunities, and Challenges

Abstract: In the fifth generation (5G) of mobile broadband systems, radio resource management (RRM) will reach unprecedented levels of complexity. To cope with the ever more sophisticated RRM functionalities and the growing variety of scenarios, while carrying out the prompt decisions required in 5G, this manuscript presents a lean RRM architecture that capitalizes on recent advances in the field of machine learning in combination with the large amount of data readily available in the network from measurements and system observations. The architecture consists of a learner (or a few), which learns RRM policies directly from the data gathered in the network using a single general-purpose learning framework, and a set of distributed actors, which execute RRM policies issued by the learner and repeatedly generate samples of experience. Thus, the complexity of RRM is shifted to the design of the learning framework, while the RRM algorithms derived from this framework are executed in a computationally efficient distributed manner at the radio access nodes. The potential of this approach is verified in a pair of pertinent scenarios, and future directions on applications of machine learning to RRM are discussed. Although we focus on a mobile broadband context, the concepts proposed hereafter extend to any radio access network technology where one can conceive the idea of a central learning unit gathering data from distributed actors.

Neural detection of semantic code clones via tree-based convolution

Abstract: Code clones are similar code fragments that share the same semantics but may differ syntactically to various degrees. Detecting code clones helps reduce the cost of software maintenance and prevent faults. Various approaches of detecting code clones have been proposed over the last two decades, but few of them can detect semantic clones, i.e., code clones with dissimilar syntax. Recent research has attempted to adopt deep learning for detecting code clones, such as using tree-based LSTM over Abstract Syntax Tree (AST). However, it does not fully leverage the structural information of code fragments, thereby limiting its clone-detection capability. To fully unleash the power of deep learning for detecting code clones, we propose a new approach that uses tree-based convolution to detect semantic clones, by capturing both the structural information of a code fragment from its AST and lexical information from code tokens. Additionally, our approach addresses the limitation that source code has an unlimited vocabulary of tokens and models, and thus exploiting lexical information from code tokens is often ineffective when dealing with unseen tokens. Particularly, we propose a new embedding technique called position-aware character embedding (PACE), which essentially treats any token as a position-weighted combination of character one-hot embeddings. Our experimental results show that our approach substantially outperforms an existing state-of-the-art approach with an increase of 0.42 and 0.15 in F1-score on two popular code-clone benchmarks (OJClone and BigCloneBench), respectively, while being more computationally efficient. Our experimental results also show that PACE enables our approach to be substantially more effective when code clones contain unseen tokens.