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Specific emitter identification (SEI) is gaining popularity since it can distinguish different individuals in same type of radar emitter under complex electromagnetic environment. However, classification of signals is still a challenging task when the feature has low physical representation. In this work, we propose a compressed sensing mask feature in ambiguity domain, which can significantly improve the recognition rate of civil flight radar emitters.

The problem of load estimation from sensor signals holds significance in the field of intelligent manufacturing. The goal of this work is to estimate the axial and spindle load values in a Computer Numerical Control machine from input sensor readings like spindle speed, feed rate, tool positions, etc. This can be viewed as a standard regression problem. Here, we propose a novel deep learning based regression technique that incorporates regression within the stacked autoencoder framework.

Signal processing techniques can create new applications for the data captured by existing sensor systems. Decades old sensor technology for monitoring the structural health of a building can serve a new role as a novel source of indoor localization data. Specifically, when a person's footstep-generated floor vibrations can be detected and located then it is possible to locate persons moving within a building. This emergent cyber-physical system holds the potential for an ambient localization service.