Deep neural networks have revolutionized the field of image compressed sensing (CS) by
delivering unprecedented performance gains. Despite significant achievements, future …

Persistent wideband radio frequency (RF) surveillance and spectral analysis is increasingly
important, driven by the proliferation of wireless communication and RADAR technology …

In recent years, meaningful visual image encryption schemes that the plain image is
compressed and encrypted and then hidden into the carrier image have received increasing …

The fast expansion of photon detection technology has fertilized the rapid growth of single-
photon sensing and imaging techniques. While promising significant advantages over their …

Semantic communication (SemCom) demonstrates strong superiority over conventional bit-
level accurate transmission, by only attempting to recover the essential semantic information …

In most sensing processes, samples are acquired from a partial ensemble of measurements
taken from a general transform defined by physically variable parameters. To follow the …

Closed-loop architecture is widely utilized in automatic control systems and attains
distinguished dynamic and static performance. However, classical compressive sensing …

The use of deep unfolding networks in compressive sensing (CS) has seen wide success as
they provide both simplicity and interpretability. However, since most deep unfolding …

Compressed sensing is a signal processing technique used for signal reconstruction with
significantly smaller number of samples than the requirements of the Nyquist-Shannon …

Eye tracking is becoming an increasingly important task domain in emerging computing
platforms such as Augmented/Virtual Reality (AR/VR). Today's eye tracking system suffers …