Multi-Channel Attentive Feature Fusion for Radio Frequency Fingerprinting

Radio frequency fingerprinting (RFF) is a promising device authentication technique for securing the Internet of things. It exploits the intrinsic and unique hardware impairments of the transmitters for RF device identification. In real-world communication systems, hardware impairments across transmitters are subtle, which are difficult to model explicitly. Recently, due to the superior performance of deep learning (DL)-based classification models on real-world datasets, DL networks have been explored for RFF. Most existing DL-based RFF models use a single representation of radio signals as the input. Multi-channel input model can leverage information from different representations of radio signals and improve the identification accuracy of the RF fingerprint. In this work, we propose a novel multi-channel attentive feature fusion (McAFF) method for RFF. It utilizes multi-channel neural features extracted from multiple representations of radio signals, including IQ samples, carrier frequency offset, fast Fourier transform coefficients and short-time Fourier transform coefficients, for better RF fingerprint identification. The features extracted from different channels are fused adaptively using a shared attention module, where the weights of neural features from multiple channels are learned during training the McAFF model. In addition, we design a signal identification module using a convolution-based ResNeXt block to map the fused features to device identities. To evaluate the identification performance of the proposed method, we construct a WiFi dataset, named WFDI, using commercial WiFi end-devices as the transmitters and a Universal Software Radio Peripheral (USRP) as the receiver. ..

Magnetically driven formation of 3D freestanding soft bioscaffolds.

3D soft bioscaffolds have great promise in tissue engineering, biohybrid robotics, and organ-on-a-chip engineering applications. Though emerging three-dimensional (3D) printing techniques offer versatility for assembling soft biomaterials, challenges persist in overcoming the deformation or collapse of delicate 3D structures during fabrication, especially for overhanging or thin features. This study introduces a magnet-assisted fabrication strategy that uses a magnetic field to trigger shape morphing and provide remote temporary support, enabling the straightforward creation of soft bioscaffolds with overhangs and thin-walled structures in 3D. We demonstrate the versatility and effectiveness of our strategy through the fabrication of bioscaffolds that replicate the complex 3D topology of branching vascular systems. Furthermore, we engineered hydrogel-based bioscaffolds to support biohybrid soft actuators capable of walking motion triggered by cardiomyocytes. This approach opens new possibilities for shaping hydrogel materials into complex 3D morphologies, which will further empower a broad range of biomedical applications