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..

Method, apparatus, and system for time synchronization

Abstract: A method, apparatus, and system for time synchronization are disclosed. The method comprising: obtaining a master sending time stamp, a slave receiving time stamp, a slave sending time stamp, and a master receiving time stamp; and adjusting the time of the slave clock according to the offset calculated from the time stamps to synchronize with the clock time of the master clock. With the present invention, in passband transmission systems that transmit signals continuously in units of symbols, the time synchronization is implemented between the master clock and the slave clock.

Network coding meets information-centric networking: an architectural case for information dispersion through native network coding

Abstract: User behavior in the Internet has changed over the recent years towards being driven by exchanging and accessing information. Many advances in networking technologies have utilized this change by focusing on the content of an exchange rather than on the endpoints exchanging the content, in particular to better support mobility. Network coding and information-centric networking are two examples of these trends, each being developed largely independently thus far. This paper brings these areas together at the internetworking layer. We outline opportunities for applying network coding in a novel and performance-enhancing way that could push forward the case for information-centric networking itself.

Novel Techniques to Suppress the Common-Mode EMI Noise Caused by Transformer Parasitic Capacitances in DC–DC Converters

Abstract: This paper proposes novel electromagnetic interference (EMI) suppression techniques for dc-dc converters. For low-voltage high-current applications, windings are paralleled and interleaved. Although low conduction loss and low leakage inductance can be achieved, the winding capacitances are considerably increased, thereby deteriorating the converter's EMI and soft-switching performances. To solve these problems, a novel balanced choke concept is proposed, as well as other techniques. The advantages of the proposed concepts and strategies are verified and demonstrated on a 1-kW 1-MHz 400-V/12-V LLC resonant converter prototype. More than 52-dB noise attenuation and 75% equivalent winding capacitance reduction are achieved. Hence, EMI and soft-switching performances are significantly improved.

An Identity Management and Authentication Scheme Based on Redactable Blockchain for Mobile Networks

Abstract: More and more users are eager to obtain more comprehensive network services without revealing their private information. Traditionally, in order to access a network, a user is authorized with an identity and corresponding keys, which are generated and managed by the network operator. All users' personally identifying information are centralized stored by the network operator. However, this approach makes users lose the control of their personally identifying information. Users are concerned about who can access these sensitive data and whether they have been compromised. In this paper, we propose a blockchain-based identity management and authentication scheme for mobile networks, where users' identifying information are controlled by the users themselves. Our scheme let users generate their self-sovereign identities (SSIs) and corresponding public keys and private keys. The private key used to authenticate the user's identifying information is only known to the user. We use blockchain to record SSIs and public keys of legitimate user, and adopt chameleon hash to delete illegal users' information on the blockchain, while keeping the block head unchanged. Furthermore, other service providers can obtain the user's SSI and public key and authenticate users by querying the blockchain. Experimental results confirm that our scheme can greatly reduce the revocation overhead and communication overhead.

Ultra-High-Definition Image Dehazing via Multi-Guided Bilateral Learning

Abstract: Convolutional neural networks (CNNs) have achieved significant success in the single image dehazing task. Unfortunately, most existing deep dehazing models have high computational complexity, which hinders their application to high-resolution images, especially for UHD (ultra-high-definition) or 4K resolution images. To address the problem, we propose a novel network capable of real-time dehazing of 4K images on a single GPU, which consists of three deep CNNs. The first CNN extracts haze-relevant features at a reduced resolution of the hazy input and then fits locally-affine models in the bilateral space. Another CNN is used to learn multiple full-resolution guidance maps corresponding to the learned bilateral model. As a result, the feature maps with high-frequency can be reconstructed by multi-guided bilateral upsampling. Finally, the third CNN fuses the high-quality feature maps into a dehazed image. In addition, we create a large-scale 4K image dehazing dataset to support the training and testing of compared models. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art dehazing approaches on various benchmarks.