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The Internet of Things (IoT) paradigm and the advanced wireless technologies of 5G and beyond are expected to enable diverse applications such as autonomous driving, industrial automation, and smart cities. These applications bring together a vast and diverse IoT device population that occupy radio frequency spectrum. Such a large number of wireless devices expose previously unheard-of threat surfaces in addition to the bandwidth shortage and throughput issues. Device identification is crucial in such scenarios not only to authenticate and authorize nodes, but also to employ different network services. One of the promising solutions for device identification is the use of radio frequency (RF) fingerprinting. Recently, wireless device identification using RF fingerprinting along with machine learning and deep learning technologies showed outstanding results in the recent contemporary domains. This paper presents a systematic literature review of RF fingerprinting identification of wireless devices by presenting the results as a graphical and tabular representation of statistical data obtained. Only experimental research papers were considered of over 130 journals and international conference papers that have been classified and evaluated from the year 2010 till date. This survey focuses on exploring the commonly used RF fingerprinting approaches, feature extraction and filtration techniques, and classification algorithm used in the device identification. Finally, open issues and challenges along with future directions have presented which were discovered during the process of analyzing the literature. [ABSTRACT FROM AUTHOR]

During the manufacturing process of printed circuit boards (PCBs), quality defects can occur, which can affect the performance and reliability of PCBs. Existing deep learning-based PCB defect detection methods are difficult to simultaneously achieve the goals of high detection accuracy, fast detection speed, and small number of parameters. Therefore, this paper proposes a PCB defect detection algorithm based on CDI-YOLO. Firstly, the coordinate attention mechanism (CA) is introduced to improve the backbone and neck network of YOLOv7-tiny, enhance the feature extraction capability of the model, and thus improve the accuracy of model detection. Secondly, DSConv is used to replace part of the common convolution in YOLOv7-tiny to achieve lower computing costs and faster detection speed. Finally, Inner-CIoU is used as the bounding box regression loss function of CDI-YOLO to speed up the bounding box regression process. The experimental results show that the method achieves 98.3% mAP on the PCB defect dataset, the detection speed is 128 frames per second (FPS), the parameters is 5.8 M, and the giga floating-point operations per second (GFLOPs) is 12.6 G. Compared with the existing methods, the comprehensive performance of this method has advantages. [ABSTRACT FROM AUTHOR]

Secure user authentication has grown importance in today’s modern culture. It is significant to authenticate the user identity in numerous consumer applications particularly financial transactions. Traditional authentication methods rely on easy-to-guess passwords, PIN numbers, or tokens with several security flaws, such as those printed on the back of credit cards for PIN numbers. As an alternative to current systems, biometric authentication techniques based on physical and behavioral characteristics have been proposed. Multibiometric systems, which combine several biometrics, are developed as a result of the difficulties that single-biometric authentication systems encountered in real-world applications including lack of precision and noisy data. The proposed system provides better performance and greater accuracy compared with other authentication techniques. The majority of them is inconvenient and demand complicated user interactions. This paper proposes Enhanced Elman Spike Neural Network along Glowworm Swarm Optimization (EESNN-GSO-AMT) for Multiple Transaction Authentication. The images are collected via SDUMLA-HMTalong CASIA V5 dataset. The pictures are provided to pre-processing to enhance the images quality utilizing Learnable Edge Collaborative Filter (LECF). The preprocessed images are fed to feature extraction using Adaptive and concise empirical wavelet transform (ACEWT) and the features are extracted such as entropy, homogeneity, energy and contrast. The extracting features are provided to EESNN classifier to categorize authorized or unauthorized persons. In general, the EESNN classifier does not express adapting optimization methods to determine ideal parameters to ensure accurately. Therefore, it is proposed to utilize the Glowworm Swarm Optimization to enhanceEESNN, which accurately categorizes the authorized and unauthorized person. The efficiency of the proposed approach is assessed usingsome metrics. The proposed EESNN-GSO-AMT method attains higher accuracy 20.54%, 21.76% and 23.89%; greater sensitivity 20.12% 20.34% and 21.43%; higher precision 23.34%, 22.68% and 24.34% are analyzed to the existing methods, like Optimal feature level fusion for safe human authentication in multimodal biometric scheme (OptGWO-AMT-FV), Joint attention network for finger vein authentication (JAnet-AMT-FV), Finger Vein Recognition Utilizing Deep Learning Technique (DCNN-AMT-FV) respectively. [ABSTRACT FROM AUTHOR]