Ling Zhang

TL;DR: An inductance-based method is utilized to calculate the port priority fist, and afterwards deep reinforcement learning (DRL) with deep neural network (DNN) is applied to optimize the assignment of decaps on the prioritized locations.

TL;DR: The feasibility of sparse sampling is mathematically proved, and it is shown that increasing number of scanning points increases the signal-to-noise ratio of reconstructed images, and a nearest neighbor interpolation method is applied in the real-time processing to estimate the radiated power through the scanning plane.

TL;DR: This paper presents an improved decap-selection algorithm based on deep reinforcement learning (DRL), which seeks the minimum number of decaps through a self-exploration training to satisfy a given target impedance, and demonstrates the feasibility of achieving decent performance with pre-trained knowledge for more complicated engineering tasks in the future.

TL;DR: The paper shows that sub-Nyquist is achievable and suggests uniform sampling is superior to nonuniform, in contrast to other reported uses of microwave imaging and care should be taken if the source reconstruction is based on uniform 2-D DFT.

TL;DR: The resulting I-V curve for the PN junction, using the deep learning solver presented in this work, shows a perfect match to the I-v curve obtained using the finite difference method, with the advantage of being 10 times faster at every time step.