no code implementations • 30 Nov 2018 • Shahin Khobahi, Naveed Naimipour, Mojtaba Soltanalian, Yonina C. Eldar
Machine learning, and more specifically deep learning, have shown remarkable performance in sensing, communications, and inference.
1 code implementation • 27 Nov 2019 • Shahin Khobahi, Mojtaba Soltanalian
Parameterized mathematical models play a central role in understanding and design of complex information systems.
no code implementations • 10 Dec 2019 • Shahin Khobahi, Arindam Bose, Mojtaba Soltanalian
Parameterized mathematical models play a central role in understanding and design of complex information systems.
no code implementations • 17 Dec 2019 • Shahin Khobahi, Arindam Bose, Mojtaba Soltanalian
In radar systems, unimodular (or constant-modulus) waveform design plays an important role in achieving better clutter/interference rejection, as well as a more accurate estimation of the target parameters.
no code implementations • 3 Feb 2020 • Chirag Agarwal, Shahin Khobahi, Arindam Bose, Mojtaba Soltanalian, Dan Schonfeld
The lack of interpretability in current deep learning models causes serious concerns as they are extensively used for various life-critical applications.
1 code implementation • 13 Feb 2020 • Arindam Bose, Shahin Khobahi, Mojtaba Soltanalian
In this paper, we investigate the joint optimization of the waveform covariance matrix and the antenna position vector for a MIMO radar system to approximate a given transmit beam-pattern, as well as to minimize the cross-correlation between the probing signals at a number of given target locations.
no code implementations • 9 Mar 2020 • Naveed Naimipour, Shahin Khobahi, Mojtaba Soltanalian
The problem of phase retrieval has been intriguing researchers for decades due to its appearance in a wide range of applications.
no code implementations • 15 Nov 2020 • Zahra Esmaeilbeig, Shahin Khobahi, Mojtaba Soltanalian
In this work, we consider the application of model-based deep learning in nonlinear principal component analysis (PCA).
no code implementations • 21 Dec 2020 • Naveed Naimipour, Shahin Khobahi, Mojtaba Soltanalian
Exploring the idea of phase retrieval has been intriguing researchers for decades, due to its appearance in a wide range of applications.
1 code implementation • 5 Feb 2021 • Shahin Khobahi, Nir Shlezinger, Mojtaba Soltanalian, Yonina C. Eldar
The need to recover high-dimensional signals from their noisy low-resolution quantized measurements is widely encountered in communications and sensing.
no code implementations • 13 Mar 2022 • Yiming Zeng, Shahin Khobahi, Mojtaba Soltanalian
The proposed deep architecture is able to learn an alternative sensing matrix by taking advantage of the underlying unfolded algorithm such that the resulting learned recovery algorithm can accurately and quickly (in terms of the number of iterations) recover the underlying compressed signal of interest from its one-bit noisy measurements.