Morgan State University

Arlene Cole-Rhodes

Peter Taiwo

Greig Richmond

A scale-based, convolutional, long-short term memory (LSTM) network is developed for image deblurring. Multi-scale information is obtained using dilated convolutions shared between scales using recurrent connections resulting in low-parameter count and to deblur an image without the use of prior information. Effectiveness is evaluated with industry standard datasets. Results show that a comparable sharp image can be recovered more efficiently even with a significant reduction in the total number of network parameters.

Image deblurring using a multi-layer LSTM network

Disclosed herein are methods and systems for processing signals from multiple users at an antenna array, and to provide beamforming for transmitting to those multiple users, and more particularly for channel estimation and wireless signal recovery in wireless networks carrying transmissions in the millimeter wave frequency bands to enable such beamforming. Such methods and systems enable MIMO communications at millimeter wave frequencies for multiple users communicating with a MIMO antenna system, such as a massive MIMO multi-antenna system (multi-antenna arrays that consist of hundreds of antenna elements). Such methods and systems may characterize the communications link (i.e., channel) at that frequency band, and directly provide a precoding matrix for beam steering towards a particular user that is in communication with the antenna system.

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Bel Air, MD, United States of America

Arlene Cole-Rhodes