Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Eamaz, Arian"'
Deep learning models excel at capturing complex representations through sequential layers of linear and non-linear transformations, yet their inherent black-box nature and multi-modal training landscape raise critical concerns about reliability, robu
Externí odkaz:
http://arxiv.org/abs/2410.10984
Unrolled deep neural networks have attracted significant attention for their success in various practical applications. In this paper, we explore an application of deep unrolling in the direction of arrival (DoA) estimation problem when coarse quanti
Externí odkaz:
http://arxiv.org/abs/2405.09712
This paper investigates the effects of coarse quantization with mixed precision on measurements obtained from sparse linear arrays, synthesized by a collaborative automotive radar sensing strategy. The mixed quantization precision significantly reduc
Externí odkaz:
http://arxiv.org/abs/2403.08168
The design of sparse linear arrays has proven instrumental in the implementation of cost-effective and efficient automotive radar systems for high-resolution imaging. This paper investigates the impact of coarse quantization on measurements obtained
Externí odkaz:
http://arxiv.org/abs/2312.05423
We delve into the impact of memoryless scalar quantization on matrix completion. We broaden our theoretical discussion to encompass the coarse quantization scenario with a dithering scheme, where the only available information for low-rank matrix rec
Externí odkaz:
http://arxiv.org/abs/2311.05052
Intelligent reflecting surfaces (IRS) and their optimal deployment are the new technological frontier in sensing applications. Recently, IRS have demonstrated potential in advancing target estimation and detection. While the optimal phase-shift of IR
Externí odkaz:
http://arxiv.org/abs/2310.14443
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 threshold levels. In particular, instead of observing a subset of high-reso
Externí odkaz:
http://arxiv.org/abs/2310.03224
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 with random time-varying threshold levels. To recover the low-rank matri
Externí odkaz:
http://arxiv.org/abs/2309.04045
Modulo sampling and dithered one-bit quantization frameworks have emerged as promising solutions to overcome the limitations of traditional analog-to-digital converters (ADCs) and sensors. Modulo sampling, with its high-resolution approach utilizing
Externí odkaz:
http://arxiv.org/abs/2309.03982
One-bit quantization with time-varying sampling thresholds (also known as random dithering) has recently found significant utilization potential in statistical signal processing applications due to its relatively low power consumption and low impleme
Externí odkaz:
http://arxiv.org/abs/2308.00695