Unified complex-valued high-resolution frequency epresentation with nsattention for iological ognition

High-resolution frequency domain analysis is pivotal in a wide range of critical applications, including physiological signal processing, radar target detection, and communication systems. In this study, we present a complex-valued neural network designed for accurate estimation of frequency components encompassing both magnitude and phase, the Unified-Complex High-Resolution Frequency Representation Module (UHFreq). This method generates comprehensive high-resolution frequency domain representations, addressing key limitations in current approaches that typically capture only amplitude information, omit crucial phase details, and suffer from low resolution in frequency domain outputs. Furthermore, conventional methods for physiological signal detection and recognition require meticulous preprocessing steps, including demodulation and filtering. In response to these challenges, we propose UHFreq-based Vital Sign Status Detection Network (UVSD-Net), an application example of UHFreq, which classifies different human physiological states starting from raw radar echoes. This model utilizes the UHFreq structure as the frontend for the frequency domain representation of physiological signals from raw radar echoes. The UVSD-Net architecture incorporates a dual-pathway design: one pathway processes frequency domain features via UHFreq, while the other applies time domain amplitude and phase information from the raw radar signals. Furthermore, a weight redistribution mechanism is introduced across the different feature domains to enhance cross-domain feature integration and interaction. This comprehensive end-to-end framework offers a robust approach for analyzing time domain original signals and enables effective execution of downstream tasks.