3 citations · 6 across the 14 of their papers we have counts for
5 papers · 1 filter
Sample Abundance for Signal Processing: A Brief Introduction
Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian
This paper reports, by way of introduction, on the advances made by our group and the broader signal processing community on the concept of sample abundance; a phenomenon that natu…
From Quasi-Isometric Embeddings to Finite-Volume Property: A Theoretical Framework for Quantized Matrix Completion
Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian
We delve into the impact of memoryless scalar quantization on matrix completion. Our primary motivation for this research is to evaluate the recovery performance of nuclear norm mi…
Matrix Completion from One-Bit Dither Samples
Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian
We explore the impact of coarse quantization on matrix completion in the extreme scenario of dithered one-bit sensing, where the matrix entries are compared with time-varying thres…
Low-rank Matrix Sensing With Dithered One-Bit Quantization
Farhang Yeganegi, Arian Eamaz, Mojtaba Soltanalian
We explore the impact of coarse quantization on low-rank matrix sensing in the extreme scenario of dithered one-bit sampling, where the high-resolution measurements are compared wi…
Harnessing the Power of Sample Abundance: Theoretical Guarantees and Algorithms for Accelerated One-Bit Sensing
Arian Eamaz, Farhang Yeganegi, Deanna Needell +1
One-bit quantization with time-varying sampling thresholds (also known as random dithering) has recently found significant utilization potential in statistical signal processing ap…