Huawei

About: Huawei is a company organization based out in Shenzhen, China. It is known for research contribution in the topics: Terminal (electronics) & Node (networking). 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..

System and methods for session management

Abstract: A network architecture and methods of managing packet data unit (PDU) sessions in a network are provided. The methods include PDU session establishment procedures, PDU session modification procedures, PDU session state transfer procedures, PDU session release procedures, and user equipment (UE) handover procedures.

Program setting adjustments based on activity identification

Abstract: An electronic device monitors accelerations using an inertial sensor. The electronic device identifies a current motion state based on the accelerations. The electronic device determines an application that subscribes to a motion state identification service and notifies the application of the current motion state.

Antenna Decoupling by Common and Differential Modes Cancellation

Abstract: In this article, a general decoupling method based on a new perspective of common mode (CM) and differential mode (DM) cancellation is proposed. For two closely spaced antennas, the mutual coupling effect can be analyzed and solved by exciting them simultaneously with in-phase (CM) and out-of-phase (DM) signals. It is theoretically proved that, if CM and DM impedances are the same, the mutual coupling effect between two separated antennas can be totally eliminated. Therefore, we can solve the coupling problem by CM and DM impedance analysis and exploit the unique field properties of characteristic modes to assist in antenna decoupling in a physical intuitive way. To validate the feasibility of this method, two practical design examples, including the decoupling between closely spaced dipole antennas and planar inverted-F antennas, are proposed. Both design examples have demonstrated that the proposed method can provide a systemic design guideline for antenna decoupling and achieve better decoupling performance compared to the conventional decoupling techniques. We forecast the proposed decoupling scheme, with a simplified decoupling procedure, has great potential for the applications of antenna arrays and multi-input multi-output (MIMO) systems.

DeepLPF: Deep Local Parametric Filters for Image Enhancement

Abstract: Digital artists often improve the aesthetic quality of digital photographs through manual retouching. Beyond global adjustments, professional image editing programs provide local adjustment tools operating on specific parts of an image. Options include parametric (graduated, radial filters) and unconstrained brush tools. These highly expressive tools enable a diverse set of local image enhancements. However, their use can be time consuming, and requires artistic capability. State-of-the-art automated image enhancement approaches typically focus on learning pixel-level or global enhancements. The former can be noisy and lack interpretability, while the latter can fail to capture fine-grained adjustments. In this paper, we introduce a novel approach to automatically enhance images using learned spatially local filters of three different types (Elliptical Filter, Graduated Filter, Polynomial Filter). We introduce a deep neural network, dubbed Deep Local Parametric Filters (DeepLPF), which regresses the parameters of these spatially localized filters that are then automatically applied to enhance the image. DeepLPF provides a natural form of model regularization and enables interpretable, intuitive adjustments that lead to visually pleasing results. We report on multiple benchmarks and show that DeepLPF produces state-of-the-art performance on two variants of the MIT-Adobe 5k dataset, often using a fraction of the parameters required for competing methods.

Opportunistic Routing in Low Duty-Cycle Wireless Sensor Networks

Abstract: Opportunistic routing is widely known to have substantially better performance than unicast routing in wireless networks with lossy links. However, wireless sensor networks are usually duty cycled, that is, they frequently enter sleep states to ensure long network lifetime. This renders existing opportunistic routing schemes impractical, as they assume that nodes are always awake and can overhear other transmissions. In this article we introduce ORW, a practical opportunistic routing scheme for wireless sensor networks. ORW uses a novel opportunistic routing metric, EDC, that reflects the expected number of duty-cycled wakeups that are required to successfully deliver a packet from source to destination. We devise distributed algorithms that find the EDC-optimal forwarding and demonstrate using analytical performance models and simulations that EDC-based opportunistic routing results in significantly reduced delay and improved energy efficiency compared to traditional unicast routing. In addition, we evaluate the performance of ORW in both simulations and testbed-based experiments. Our results show that ORW reduces radio duty cycles on average by 50p (up to 90p on individual nodes) and delays by 30p to 90p when compared to the state-of-the-art.