Super-Resolution Generative Adversarial Network Based on the Dual Dimension Attention Mechanism for Biometric Image Super-Resolution

There exist many types of intelligent security sensors in the environment of the Internet of Things (IoT) and cloud computing. Among them, the sensor for biometrics is one of the most important types. Biometric sensors capture the physiological or behavioral features of a person, which can be further processed with cloud computing to verify or identify the user. However, a low-resolution (LR) biometrics image causes the loss of feature details and reduces the recognition rate hugely. Moreover, the lack of resolution negatively affects the performance of image-based biometric technology. From a practical perspective, most of the IoT devices suffer from hardware constraints and the low-cost equipment may not be able to meet various requirements, particularly for image resolution, because it asks for additional storage to store high-resolution (HR) images, and a high bandwidth to transmit the HR image. Therefore, how to achieve high accuracy for the biometric system without using expensive and high-cost image sensors is an interesting and valuable issue in the field of intelligent security sensors. In this paper, we proposed DDA-SRGAN, which is a generative adversarial network (GAN)-based super-resolution (SR) framework using the dual-dimension attention mechanism. The proposed model can be trained to discover the regions of interest (ROI) automatically in the LR images without any given prior knowledge. The experiments were performed on the CASIA-Thousand-v4 and the CelebA datasets. The experimental results show that the proposed method is able to learn the details of features in crucial regions and achieve better performance in most cases.

Research on testing method of solid-state disk used in intelligent security field

This paper summarizes the specific requirement of SSD in intelligent security field and comparison of related lifespan and endurance test method.

Design of Intelligent Security Management and Control System for a Large Nuclear Facility

There are many kinds of safety control sub-systems of a large nuclear facility, and their information is isolated from each other, so the overall safety control efficiency is low. To solve this problem, based on the original security control sub-system, an intelligent security management and control system is designed by using the technology of system integration and multi-sensor cooperative scheduling. Firstly, the web service interfaces of each security control sub-system are developed, and the interconnection and information sharing of each security control sub-system are realized through these interfaces. Then, based on the multi-sensor system cooperative optimization scheduling technology and the improved ant colony algorithm, the information synthesis and security cooperative optimization scheduling of heterogeneous multi-sensor messages are realized. The highly integrated intelligent security management and control system reduces the alarm omission rate and false alarm rate of the safety control and improves the efficiency of the security control.