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The advancement of Internet of Things (IoT) has enhanced the extensive usage of QR code images in various computer vision applications. Nonetheless, this has also brought forth several technical challenges. In particular, the logistics sorting system often encounters issues such as a low recognition rate and slow processing speed when dealing with QR code images under complex lighting conditions like uneven illumination. To address these difficulties, a method that focuses on achieving a fast adaptive binarization of QR code images through dynamic illumination equalization was proposed. First, an algorithm based on edge enhancement to obtain the position detection patterns within QR code images was applied, which enabled the acquisition of structural features in uneven illumination. Subsequently, QR code images with complex lighting conditions can achieve a fast adaptive binarization through dynamic illumination equalization. As for method validation, the experiments were performed on the two datasets that include QR code images influenced by strong light, weak light, and different shadow degrees. The results disclosed the benefits of the proposed method compared to the previous approaches; it produced superior recognition rates of 78.26–98.75% in various cases through commonly used decoders (Wechat and Zxing), with a faster processing speed of 0.0164 s/image, making it a proper method to satisfy real-time requirements in practical applications, such as a logistics sorting system. [ABSTRACT FROM AUTHOR]

This paper proposes the BP-CM teaching model to solve the problems of lagging classroom feedback, poor learning initiative of students and students rapidly forgetting what they have learned in Internet of Things (IoT) hardware technology courses. This BP-CM(BOPPPS, PAD, cyclic memory, memory system) teaching model is based on BOPPPS(Bridge in, Objective, Pre-assessment, Participatory learning, Post-assessment, and Summary), PAD(Presentation, Assimilation, and Discussion), and the memory system model. This paper compares the BP-CM teaching model with the traditional teaching model by having research participants teach and learn separately in each teaching model. Taking the Microcontroller Unit (MCU) course as an example, this paper introduces each link of the teaching design of this model. It also explains the design method and intention of each link. Finally, the practical results indicate that the BP-CM teaching model proposed in this article positively impacts students' learning initiative and memory. In addition, teachers can obtain timely feedback using the BP-CM model, and the model significantly improves students' knowledge and ability levels. It enhances the teaching quality of IoT hardware technology courses and is conducive to cultivating students' sustainable learning abilities. This research not only promotes education and teaching reform but also provides a reference case. [ABSTRACT FROM AUTHOR]

As the fundamental element of the Internet of Things, the QR code has become increasingly crucial for connecting online and offline services. Concerning e-commerce and logistics, we mainly focus on how to identify QR codes quickly and accurately. An adaptive binarization approach is proposed to solve the problem of uneven illumination in warehouse automatic sorting systems. Guided by cognitive modeling, we adaptively select the block window of the QR code for robust binarization under uneven illumination. The proposed method can eliminate the impact of uneven illumination of QR codes effectively whilst meeting the real-time needs in the automatic warehouse sorting. Experimental results have demonstrated the superiority of the proposed approach when benchmarked with several state-of-the-art methods. [ABSTRACT FROM AUTHOR]