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ABSTRACT Recently, there has been research on using cognitive radio to increase the effectiveness of spectrum resources. Secondary users (SUs) sense, identify, and access frequency bands presently unoccupied by primary users (PUs). Indeed, the precise communication methods of PUs remain consistently unknown to SUs. Designing an appropriate system for spectrum monitoring along with access is necessary to address the challenging problem of guaranteeing the quality of service (QoS) for PUs and satisfying the demand of SUs for spectrum resources. When several SUs and different channels are available, spectrum allocation and sharing must be considered. This work proposes a system for allocating spectrum to multiple users and sharing multiple channels for SUs dealing with PUs with uncertain behavior. The study focuses on analyzing the interference caused to PUs by the dynamic access of SUs. The analysis of the interference produced by SUs is conducted using renewal theory. Queuing theory determines the highest average data rate achievable by SUs while considering the limitations imposed by interference to PUs and the need to maintain stability in the secondary network. Comprehensive simulations validate the efficacy of this approach. [ABSTRACT FROM AUTHOR]

Ever since Computer Vision was introduced, humanity has seen various ways to detect or classify objects of various types. Depending upon the context in consideration, the performances of models vary with respect to their evolution or even upon the nature of the data in hand. The classification of front or rear views in vehicles forms an integral part when we go ahead with deciding whether a given vehicle is moving in the correct lane. In the context of Indian streets, we have various challenges like rural unmarked roads, faded markings, shaded situations from poles or trees, etc. Hence instead of detecting lanes, an alternative way is to detect whether the vehicle(s) ahead is facing toward or away from our vehicle. Various deep learning architectures have been proposed in this aspect to detect or classify objects like the networks from Visual Geometry Group, You Only Look Once, Inception Networks, Residual Networks, etc. In this paper, we have performed a comparative analysis of performance on various versions of You Only Look Once for its evolution over time. [ABSTRACT FROM AUTHOR]