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Sub-Nyquist radars require fewer measurements, facilitating low-cost design, flexible resource allocation, etc. By applying compressed sensing (CS) method, such radars achieve close performance to traditional Nyquist radars. However in low signal-to-noise ratio (SNR) scenarios, detecting weak targets is challenging: low probability of detection and many spurious targets could occur in the recovery results of traditional CS method.

Multi-channel sparse blind deconvolution refers to the problem of learning an unknown filter by observing its circulant convolutions with multiple input signals that are sparse. It is challenging to learn the filter efficiently due to the bilinear structure of the observations with respect to the unknown filter and inputs, leading to global ambiguities of identification. We propose a novel approach based on nonconvex optimization over the sphere manifold by minimizing a smooth surrogate of the sparsity-promoting loss function.

Trellis quantization as structured vector quantizer is able to improve
the rate-distortion performance of traditional scalar quantizers. As such,
it has found its way into the JPEG~2000 standard, and also recently as an
option in HEVC. In this paper, a trellis quantization option for JPEG XS is
considered and analyzed; JPEG~XS is a low-complexity, low-latency high-speed
"mezzanine" codec for Video over IP transmission in professional
production environments and industrial applications where high compression

Generative models have recently received considerable attention in the field of compressive sensing. If an image belongs to the range of a pretrained generative network, we can recover it from its compressive measurements by estimating the underlying compact latent code. In practice, all the pretrained generators have certain range beyond which they fail to generate reliably. Recent researches show that convolutional generative structures are biased to generate natural images.

The design of sampling set (DoS) for bandlimited graph signals (GS) has been extensively studied in recent years, but few of them

Conventional analog–to–digital converters (ADCs) are limited in dynamic range. If a signal exceeds some prefixed threshold, the ADC saturates and the resulting signal is clipped, thus becoming prone to aliasing artifacts. Recent developments in ADC design allow to overcome this limitation: using modulo operation, the so called self-reset ADCs fold amplitudes which exceed the dynamic range. A new (unlimited) sampling theory is currently being developed in the context of this novel class of ADCs.