QR codes are black and white two-dimensional bar code graphics that record data information, with fast readability and powerful storage capabilities. With the rapid development of mobile communication and the popularity of intelligent devices, the speed of information exchange has been greatly accelerated, QR codes are widely used in various areas of life. This paper proposes a defuzzification algorithm based on binary priori properties and conditions of black and white pixels to generate adversarial networks. The potential feature representation of the QR code image is extracted by the selfsupervised pretraining of the encoder, which is used for the training of the conditional generation adverssion network. The trained generator network can realize the pixel-level binary segmentation restoration of the degraded QR code image with different fuzzy degrees. The experimental results show that the proposed network framework can recover the QR code images with different degrees of degradation end to end, pixel level, and improve the recognition rate.
With the rapid development of sensors and machinery manufacturing, handling trolley has also gradually become a hot topic. This paper introduces the design of a single-handed control handling trolley based on real-time operating system RT-Thread, which has high application value, including using for daily life assistance of disabled people and non-contact small goods transmission and other fields. The trolley consists of a gesture acquisition terminal, a server transmission network and a trolley control terminal. The gesture acquisition terminal uses wearable devices to realize the gesture recognition. The uploaded data are sent to the trolley control terminal through the server transmission network. The trolley control terminal collects the video stream and transmits it to the server network auxiliary operator to implement the remote control. The system shows good control performance on the experimental platform.
As a method of geological disaster management, grouting has been widely used. At present, there is no good scheme on how to evaluate the grouting effect efficiently. In this paper, a dynamic monitoring system of grouting process based on ZigBee wireless network is proposed by monitoring the apparent resistivity change in the grouting area, which provides a basis for the evaluation of grouting effect. The monitoring system consists of a user control terminal, a ZigBee wireless network and a data acquisition terminal. The cascade of multiple data acquisition terminals realizes the expansion of area width, and the user control terminal can monitor multiple monitoring profiles through the ZigBee wireless network, so that the electrodes required for multiple monitoring profiles can be laid in the area to be measured at one time to dynamically monitor the apparent resistivity of the grouting area. After testing, the system can collect high-precision data to monitor the change of apparent resistivity in the grouting area, and can meet the rapid evaluation of filling effect in the grouting area.
Motion blur and ambient noise are the main reasons that affect quick response (QR) code recognition. In this paper, we propose a novel deep learning approach to deblur the QR codes and realize the effective recognition of deblurring QR codes by using generative adversarial networks (GANs). We estimate the blur kernel and ambient noise of the blur QR code in the dataset using GANs, so as to realize the transformation from the blur QR code image to the sharp image. We also propose an expansion method of QR codes dataset, and achieve better generalization performance of the model. The experimental results show that our approach can effectively estimate the blur kernel and ambient noise that can realize the deblurring of QR code.
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