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Publications (7)
Millimeter-wave (mmWave) and terahertz (THz) communication systems require large antenna arrays and use narrow directive beams to ensure sufficient receive signal power. However, selecting the optimal beams for these large antenna arrays incurs a significant beam training overhead, making it challenging to support applications involving high mobili...
Millimeter-wave (mmWave) and terahertz (THz) communication systems adopt large antenna arrays to ensure adequate receive signal power. However, adjusting the narrow beams of these antenna arrays typically incurs high beam training overhead that scales with the number of antennas. Recently proposed vision-aided beam prediction solutions, which utili...
Low-rank and sparse decomposition based methods find their use in many applications involving background modeling such as clutter suppression and object tracking. While Robust Principal Component Analysis (RPCA) has achieved great success in performing this task, it can take hundreds of iterations to converge and its performance decreases in the pr...
p>In this paper, we consider the problem of removing clouds and recovering ground cover information from remote sensing images by proposing novel framework based on a deep unfolded and prior-aided robust principal component analysis (DUPA-RPCA) network. Clouds, together with their shadows, usually occlude ground-cover features in optical remote sen...
p>In this paper, we consider the problem of removing clouds and recovering ground cover information from remote sensing images by proposing novel framework based on a deep unfolded and prior-aided robust principal component analysis (DUPA-RPCA) network. Clouds, together with their shadows, usually occlude ground-cover features in optical remote sen...
Clouds, together with their shadows, usually occlude ground-cover features in optical remote sensing images. This hinders the utilization of these images for a range of applications such as earth observation, land-cover classification and urban planning. In this work, we propose a deep unfolded and prior-aided robust principal component analysis (D...
