The authors therefore, acknowledge with thanks DSR for technical and financial support.

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Underwater wireless optical communication (UWOC) enables high-speed links in water for the optical Internet of Underwater Things (O-IoUT) networks. O-IoUT provides various marine applications, including ocean exploration, environmental monitoring, and underwater navigation. O-IoUT typically utilizes light-emitting diodes (LEDs) and different laser diodes (LDs) such as green/blue lasers to achieve efficient data communication in the underwater environment.The high-speed optical communication is limited up to a few tens of meters due to underwater channel impairments and misalignment between the transmitter (Tx) and the receiver (Rx). UWOC provides high-speed communications only in the line of sight conditions, and a small misalignment between the Tx and the Rx can degrade the system performance. In an attempt to understand and minimize this misalignment issue, we investigate how received power in a UWOC system depends on the transmitted beam's divergence angle. Simulation results are provided to show the effectiveness of the study by comparing the plane, Gaussian, and spherical beams. Monte Carlo simulations are utilized to determine the maximum allowable lateral offset between Tx and Rx for a given Tx divergence angle. The results provide an overview and design-based trade-off between different parameters such as lateral offset, the power received, and bandwidth of the channel. The proposed method improves not only the maximum allowed link-span but also the bandwidth of the channel for a given transmission distance.

Blind Source Separation (BSS) application is a delinquent issue in a complex reverberant environment with changing room geometric dimensions and an increasing number of speech sources. The BSS application issue is determined by the independent component analysis that usually manipulates higher-order statistical approaches. However, the permutation between desired speech sources remains a challenging issue for BSS applications. The permutation problem is been rectified by Independent Vector Analysis (IVA) for BSS applications in the frequency domain. The performance dependency of the IVA approach solely relies on the selection of appropriate source-prior to preserving the inter-frequency dependencies between the same speech source amongst different frequency bins. Therefore, a hybrid model for the IVA method is presented, which comprises of multivariate generalized Gaussian and super-Gaussian distribution source priors to model low as well as high amplitudes speech signals. The weights of the hybrid model between multivariate Gaussian and generalized Gaussian are assigned following the energy of the observed non-stationary speech mixture signal. In the simulations, different speech mixtures are generated from various speech sources by simulated room model. The proposed approach evaluates the blind separation performance in terms of signal-to-distortion ratio (SDR) and is compared with well-known BSS methods. The results show an improvement of the proposed methodology for non-stationary speech signals over the state-of-the-art IVA models having a fixed source prior.