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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.

Radio-frequency identification (RFID) communication system known as auto-id has experienced tremendous growth worldwide, considering as a flexible mechanism for identification of objects. Degradation of data transmission due to external interference and interference of RFID system itself (tags and reader collision) is one of the most important factors to restrict the development of the RFID system and has a direct bearing on the performance of the whole system. In order to eliminate the tag collision problem in an RFID system variety of different anti-collision algorithms have been proposed by many researchers for the successful adoption of RFID on a massive scale. Synthesizing much anti-collision algorithm and conducting in-depth analysis, we propose a new combined primary digital transmission technique based on time division (TD) and gold code to efficiently suppress tag-to-tag interference.

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