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Since most studies for estimating an angle-of-arrival (AOA) based on the antenna array have considered the antenna array with a single configuration, they are not proper to simultaneously estimate AOAs of multiple signals with various frequencies. In this paper, we introduce a cascade AOA estimation technique consisting of CAPON and Beamspace Multiple Signal Classification (MUSIC), based on a Combined Array Antenna (CAA) with Uniform Rectangular Frame Array (URFA) and Uniform Circular Array (UCA), for enhancing the above problem. In addition, we provide the computational complexity analysis for showing the low computational complexity of this technique comparing to the conventional technique.

Colored contact lenses have gained popularity in recent years. However, their production process is plagued by low efficiency, which is attributed to the complex nature of the lens color patterns. The manufacturing process involves multiple complex steps that can introduce defects or inconsistencies into the contact lenses. Moreover, manual inspection of a considerable number of contact lenses that are produced inefficiently in terms of consistency and quality by humans is prevalent. Alternatively, automatic optical inspection (AOI) systems have been developed to perform quality-control checks on colored contact lenses. However, their accuracy is limited due to the increasing complexity of the lens color patterns. To address these issues, convolutional neural networks have been used to detect and classify defects in colored contact lenses. This study aims to provide a comprehensive guide for AOI systems using artificial intelligence in the colored contact lens manufacturing process, including the benefits and challenges of using these systems. Further, future research directions to achieve a classification accuracy of >95%, which is the human recognition rate, are explored.