Step-change of the load in a DAB converter through direct changing of the frequency may cause temporal DC bias current easily, which could induce magnetic saturation, with associated noise, overheating, vibration etc. The proposed transient strategy in this article is to achieve instant switching frequency change by predicting the transformer current in the destined state. The transient overshoot current can be eliminated and total response time is reduced. Two methods with different transient processes are proposed and analyzed. Both simulation and experimental results validate the feasibility of the proposed methods, which may achieve fast transient step-load modulation in a frequency modulated DAB converter without introducing undesired overshoot and dc bias.

Scene flow represents the motion of points in the 3D space, which is the counterpart of the optical flow that represents the motion of pixels in the 2D image. However, it is difficult to obtain the ground truth of scene flow in the real scenes, and recent studies are based on synthetic data for training. Therefore, how to train a scene flow network with unsupervised methods based on real-world data shows crucial significance. A novel unsupervised learning method for scene flow is proposed in this paper, which utilizes the images of two consecutive frames taken by monocular camera without the ground truth of scene flow for training. Our method realizes the goal that training scene flow network with real-world data, which bridges the gap between training data and test data and broadens the scope of available data for training. Unsupervised learning of scene flow in this paper mainly consists of two parts: (i) depth estimation and camera pose estimation, and (ii) scene flow estimation based on four different loss functions. Depth estimation and camera pose estimation obtain the depth maps and camera pose between two consecutive frames, which provide further information for the next scene flow estimation. After that, we used depth consistency loss, dynamic-static consistency loss, Chamfer loss, and Laplacian regularization loss to carry out unsupervised training of the scene flow network. To our knowledge, this is the first paper that realizes the unsupervised learning of 3D scene flow from monocular camera. The experiment results on KITTI show that our method for unsupervised learning of scene flow meets great performance compared to traditional methods Iterative Closest Point (ICP) and Fast Global Registration (FGR). The source code is available at: https://github.com/IRMVLab/3DUnMonoFlow.

The limited battery capacity of current mobile devices and increasing amount of rich media content delivered over wireless networks have driven the latest research on energy efficient content delivery over wireless networks. Many energy-aware research solutions have been proposed involving traffic shaping, content adaptation, content sharing, etc. The existing solutions focus on the delivery application without considering application running environment and device features that pose different energy constraints on the whole content delivery process. This paper presents a Device characteristics-based differentiated Energy-efficient Adaptive Solution (DEAS) for video delivery over heterogeneous wireless networks. DEAS constructs an energy-oriented system profile including power signatures of various device components for each running application. Based on this profile, an energy efficient content delivery adaptation is performed for the current application. The proposed solution is evaluated by simulation-based testing and compared with other state of the art approaches in terms of performance and energy efficiency. The results show how DEAS outperforms the other well-known solutions.

Smart phones have gained great popularity all around the world, supporting rich media applications with Internet connectivity. Since increasing number of people own powerful mobile devices, ad-hoc WLANs can be deployed in public areas part of heterogeneous networks environments, as flexible and inexpensive alternatives to infrastructure-based approaches. However, these mobile devices are powered by battery with limited energy budgets, which introduces big energy-related challenges in ad-hoc WLAN routing algorithm design. This paper proposes a context-aware cross-layer energy-efficient adaptive routing algorithm for WLAN communications (AWERA) that performs energy-efficient context differentiated routing in wireless communications. It introduces a cross layer self-learning solution that monitors the context of device usage, and takes routing decisions based on current energy-oriented context. Compared with other state-of-the-art wireless routing protocols, simulation results show both better performance and energy efficiency context-based differentiation.