Wireless Antenna Sensors for Biosimilar Monitoring Towards Cyber-Physical Systems : A Review of Current Trends and Future Prospects

The integration of wireless antenna sensors for cyber-physical systems has become increasingly prevalent in various biosimilar applications due to the escalating need for monitoring techniques that are efficient, accurate, and reliable. The primary objective of this comprehensive investigation is to offer a scholarly examination of the present advancements, challenges, and potentialities in the realm of wireless antenna sensor technology for monitoring biosimilars. Specifically, the focus will be on the current state of the art in wireless antenna sensor design, manufacturing, and implementation along with the discussion of cyber security trends. The advantages of wireless antenna sensors, including increased sensitivity, real-time data gathering, and remote monitoring, will next be discussed in relation to their use in a variety of biosimilar applications. Furthermore, we will explore the challenges of deploying wireless antenna sensors for biosimilar monitoring, such as power consumption, signal integrity, and biocompatibility concerns. To wrap things off, there will be a discussion about where this subject is headed and why collaborative work is essential to advancing wireless antenna sensor technology and its applications in biosimilar monitoring. Providing an in-depth overview of the present landscape and potential developments, this article aims to be an asset for academics and professionals in the fields of antenna sensors, biosimilar development, wireless communication technologies, and cyber physical systems.© 2023 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

Enhancing Workplace Safety: PPE_Swin—A Robust Swin Transformer Approach for Automated Personal Protective Equipment Detection

Accidents occur in the construction industry as a result of non-compliance with personal protective equipment (PPE). As a result of diverse environments, it is difficult to detect PPE automatically. Traditional image detection models like convolutional neural network (CNN) and vision transformer (ViT) struggle to capture both local and global features in construction safety. This study introduces a new approach for automating the detection of personal protective equipment (PPE) in the construction industry, called PPE_Swin. By combining global and local feature extraction using the self-attention mechanism based on Swin-Unet, we address challenges related to accurate segmentation, robustness to image variations, and generalization across different environments. In order to train and evaluate our system, we have compiled a new dataset, which provides more reliable and accurate detection of personal protective equipment (PPE) in diverse construction scenarios. Our approach achieves a remarkable 97% accuracy in detecting workers with and without PPE, surpassing existing state-of-the-art methods. This research presents an effective solution for enhancing worker safety on construction sites by automating PPE compliance detection