Specific emitter identification technology has always been a hot issue for the vital research of radio-related departments in various countries. However, due to the sharp increase in the types of radiation sources and the complexity of electromagnetic space, identifying individual radiation sources has become more challenging. In order to provide a convenient and effective radiation source identification method, this paper proposes an emitter individual identification research method based on the characteristics of synchronization signal rising edges. With the help of artificial intelligence technology, the method proposed in this paper has achieved a very high individual identification rate of radiation sources by using the characteristics of synchronization signal rising edges and deep neural networks.
Image quality assessment (IQA) has always been an active research topic since the birth of the digital image. Actually, the arrival of deep learning has made IQA more promising. However, most state-of-the-art no-reference (NR) IQA methods require regression training on distorted images or extracted features with subjective image scores, which makes them suffer from insufficient reference image content and training samples with subjectively scoring due to timeconsuming and laborious subjective testing. Furthermore, most convolutional neural networks (CNN)-based methods generally transform original images into patches to accommodate fixed-size input of CNN, which often alter the image's data and introduce noise into the neural network. This paper aims to solve the above problems by adopting new strategies and proposes a novel NRIQA method based on deep CNN. Specifically, first, we obtain image data with diverse image content, multiple image sizes, and reasonable distortion by crawling, filtrating, and degrading numerous publicly licensed high-quality images from the Internet. Then, we score all the images using an excellent full-reference (FR) IQA algorithm, thereby artificially construct a large objective IQA database. Next, we design a deep CNN, which can accept input images of original sizes from our database instead of patches, then we train the model with the FRIQA index as training objective thus propose the opinionunaware( OU) NRIQA method. Finally, the experiment results show that our method achieves excellent performance, which outperforms state-of-the-art OU-NRIQA models and is comparable to most of the traditional opinion-aware NRIQA methods, even some FRIQA methods on standard subjective IQA databases.
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