Space-time adaptive processing (STAP) is a well-suited technique to detect slow-moving targets in the presence of a clutter-spreading environment. When considering the STAP system deployed with conformal radar array (CFA), the training data is range-dependent, which results in poor detection performance of traditional statistical-based algorithms. Current registration-based compensation (RBC) i...

Compressive spectral imaging (CSI) has emerged as an alternative image acquisition technology, which reduces the number of measurements at cost requiring a recovery process. In general, reconstruction methods are based on hand-crafted priors used regularizers in optimization algorithms or recent deep neural networks employed generator to learn non-linear mapping from low-dimensional compressed ...

The enormous amount of data that are generated by hyperspectral remote sensing images (HSI) combined with the spatial channel’s limited and fragile bandwidth creates serious transmission, storage, application challenges. HSI reconstruction based on compressed has become a frontier area, its effectiveness depends heavily exploitation sparse representation information correlation. In this paper, ...

We continue the study of AdS loop amplitudes in spectral representation and position space. compute finite coupling 4-point function space for large-$N$ conformal Gross Neveu model on $AdS_3$. The resummation bubble diagrams gives a result proportional to tree-level contact diagram. show that certain families fermionic Witten can be easily computed from their companion scalar diagrams. Thus, ma...

Recent advanced systems in the speech recognition domain use large Transformer neural networks that have been pretrained on massive data. General methods deep learning area frequently shared across various domains, and model can also be used effectively image. In this paper, we introduce a novel masking method for self-supervised representation with salt-and-pepper (S&P) mask which is commo...