Monocular depth estimation is an important dense prediction task for autonomous driving, robotic perception, and unmanned aerial vehicle navigation. Although recent deep networks have achieved impressive accuracy, many of them depend on large backbones and expensive context modeling modules, making deployment on resource-constrained platforms difficult. This paper proposes a lightweight supervised monocular depth estimation network that improves feature representation while maintaining a compact model size. The proposed framework adopts an efficient encoder and applies Dilated-Strip Enhancement blocks to multi-scale encoder features. By combining dilated depthwise convolution with horizontal and vertical strip convolutions, the proposed block enlarges the receptive field and captures directional spatial structures efficiently. A multi-scale fusion decoder further integrates enhanced features from different resolutions, and auxiliary supervision is used to guide intermediate depth representations. Experiments on NYU Depth V2 and KITTI show that the proposed model achieves competitive accuracy with only 4.2M parameters. In particular, the proposed method achieves strong performance on the KITTI benchmark, suggesting that the dilated-strip design is effective for outdoor scenes with elongated and directional structures.
Dun-Yu Hu, Han-Xiang Zhang, J. Gu et al.· 2026 IEEE Canadian Atlantic...· 0 citations
This paper presents the design and implementation of a robust adaptive control strategy for a Dual Active Bridge (DAB) converter used in More Electric Aircraft (MEA) applications. In MEA electrical architectures, a 270 V DC main bus must reliably supply regulated 28 V DC power for onboard systems such as avionics and battery subsystems. In response to this demand, a new controller based on a Fractional-Order Adaptive Super-Twisting Algorithm (FO-ASTA) integrated with a Model Reference Adaptive Control (MRAC) framework is proposed. The controller enhanced the robustness and chattering-free properties of super-twisting sliding mode control, the dynamic memory effects of fractional-order systems, and the real-time adaptability of MRAC.The proposed FO-ASTA controller integrated with the MRAC scheme is evaluated under steady-state conditions, load variations, reference changes, and parametric uncertainties. Moreover, its performance is compared with the Fractional-Order Sliding Mode Controller (FOSMC) and Fractional-Order Super-Twisting Algorithm (FOSTA) controllers. Simulation results on the circuit level confirm that the proposed controller outperforms the alternatives in terms of tracking accuracy, convergence speed, disturbance rejection, and control smoothness. This makes it a strong candidate for high-performance DC–DC conversion in next-generation MEA power systems.
Muhammad Arif Anwar, Wang Li, M. Balas et al.· INTERNATIONAL JOURNAL OF COM...· 0 citations
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