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.

Fingerprint recognition is best known and generally used as a biometric technology because of their high acceptability, immutability, and uniqueness. A fingerprint consists of ridges and valleys pattern also known as furrows. These patterns fully develop in the mother's womb and remain constant throughout the whole lifetime of the individual. The ridge bifurcation and ridge termination are the main minutiae features that are extracted for identification of individuals in fingerprint recognition system. The aim of this paper is to enhance the performance of the fingerprint recognition systems using classifiers. To achieve the aim, fingerprints from the FV2002 database are used, before these fingerprints are evaluated, image enhancement and binarization is applied as a pre-processing on fingerprints, by combining many methods to build a database of fingerprint features having minutia marking and minutia feature extraction. The fingerprint recognition is presented by image classification using MATLAB classifiers, i.e., Decision Tree, Linear Discriminant Analysis, medium Gaussian support vector machine (MG-SVM), fine K-nearest neighbor, and bagged tree ensemble. The aim of this paper is to make a comparison between classifiers for performance enhancement of the fingerprint recognition system. The MG-SVM classifiers significantly give the highest verification rate of 98.90% among all classifies used.

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