Conventional method for monitoring the IgG levels suffered from some apparent problems such as long assay time, multistep processing, and high overall cost. An effective and suitable optical platform for label-free biosensing has been investigated by the implementation of antibody/antigen immunoassays. Thus, the ultrasensitive detection of IgG levels can be achieved by exploiting the dispersion turning point (DTP) existed in the tapered two-mode fibers (TTMFs) due to the sensitivity will reach ±∞ on either side of the DTP. Tracking the resonant wavelength shift it was found that the fabricated TTMF device exhibited limits of detection (LOD) down up to concentrations of 10 fg/mL of IgG in PBS solution. Such immunosensors based on the DTP have great significance on trace detection of IgG due to simple detection scheme, quick response time, and miniaturation.

In recent years, the unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) has become a highly sought-after topic for its wide applications in the field of target recognition, detection, and tracking. However, SAR automatic target recognition (ATR) models based on deep neural networks (DNN) are suffering from adversarial examples. Generally, non-cooperators rarely disclose any information about SAR-ATR models, making adversarial attacks challenging. In this situation, we propose Transferable Adversarial Network (TAN) to attack these models with highly transferable adversarial examples. The proposed method improves the transferability via a two-player game, in which we simultaneously train two encoder-decoder models: a generator that crafts malicious samples through a one-step forward mapping from original data, and an attenuator that weakens the effectiveness of malicious samples by capturing the most harmful deformations. In particular, compared to traditional iterative methods, our approach is able to one-step map original samples to adversarial examples, thus enabling real-time attacks. Experimental results indicate that the proposed approach achieves state-of-the-art transferability with acceptable adversarial perturbations and minimum time costs compared to existing attack methods, i.e., it excellently realizes real-time transferable adversarial attacks.