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Depth estimation is an essential component of computer vision applications for environment perception, 3D reconstruction and scene understanding. Among the available methods, self-supervised monocular depth estimation is noteworthy for its cost-effectiveness, ease of installation and data accessibility. However, there are two challenges with current methods. Firstly, the scale factor of self-supervised monocular depth estimation is uncertain, which poses significant difficulties for practical applications. Secondly, the depth prediction accuracy for high-resolution images is still unsatisfactory, resulting in low utilization of computational resources. We propose a novel solution to address these challenges with three specific contributions. Firstly, an interleaved depth network skip-connection structure and a new depth network decoder are proposed to improve the depth prediction accuracy for high-resolution images. Secondly, a data vertical splicing module is suggested as a data enhancement method to obtain more non-vertical features and improve model generalization. Lastly, a scale recovery module is proposed to recover the accurate absolute depth without additional sensors, which solves the issue of uncertainty in the scale factor. The experimental results demonstrate that the proposed framework significantly improves the prediction accuracy of high-resolution images. In particular, the novel network structure and data vertical splicing module contribute significantly to this improvement. Moreover, in a scenario where the camera height is fixed and the ground is flat, the effect of scale recovery module is comparable to that achieved by using ground truth. Overall, the RSANet framework offers a promising solution to solve the existing challenges in self-supervised monocular depth estimation. [ABSTRACT FROM AUTHOR]

Optical Wireless Communications (OWCs) can play vital role in providing communication in healthcare infrastructure's, where contagious patients are admitted or treated as well as other patients are coming without any side effects as of radio frequency communication. In this paper, we propose high-speed cost-efficient free space optical (FSO) system for transmission of information (20 Gbps) inside healthcare premises comprising of number of buildings by integrating mode division multiplexing (MDM) scheme. The MDM scheme uses Laguerre Gaussian (LG) 00 and 01 modes whereas a simple On off Key (OOK) encoding scheme is employed for the proposed MDM-FSO system. The reported results show the efficient transmission of 20 Gbps data over 27 km of FSO link under clear atmospheric condition. Moreover, the proposed MDM-FSO link is evaluated under the impact of different fog conditions—low, medium and heavy. [ABSTRACT FROM AUTHOR]