16 accepted papers
In this paper, we introduce a novel self-calibrating integrate-and-fire time encoding machine (S-IF-TEM) that enables simultaneous parameter estimation and signal reconstruction during sampling, thereby effectively mitigating mismatch effects. The proposed framework is developed over a new practical…
Integrate-and-fire time-encoding machines (IF-TEMs) are an alternative to conventional uniform sampling, where the signals are measured based on their variations. While IF-TEMs excel in energy efficiency through their event-driven sampling approach, they often oversample significantly to ensure accu…
Modulo-folding ADCs (MF-ADCs) offer a potential alternative to conventional ADCs by requiring fewer bits. However, the algorithms that follow an MF-ADC typically require an unfolding method, which demands significant oversampling. In this paper, we explore the problem of symbol detection in digital…
The challenge of sensor selection involves choosing a subset of the available measurements to estimate data while aiming to minimize estimation errors. Traditionally, convex relaxation methods with solvers like the projected subgradient (PSG) algorithm have been employed to address this problem. How…
Compressive sensing (CS) techniques for estimating the direction-of-arrival (DoA) stand apart from traditional approaches due to their ability to derive DoA information from just a single snapshot, eliminating the need for a large number of snapshots. This research addresses the challenge of adaptiv…
In this study, we consider a variant of unlabelled sensing where the measurements are sparsely permuted, and additionally, a few correspondences are known. We present an estimator to solve for the unknown vector. We derive a theoretical upper bound on the ℓ<inf xmlns:mml="http://www.w3.org/1998/Math…
In the problem of sensor selection, observations from L out of N sensors are chosen for data estimation, with the objective of minimizing an error metric. It is well known that a greedy selection (GS) method can achieve an error metric which is not worse than 1/e from that of the optimal algorithm,…
Modulo folding can be used to sample high-dynamic range signals without increasing the dynamic range of the sampler. Specifically, folding is used prior to sampling and then the folded signal is sampled. After sampling, unfolding algorithms are used to compute the true samples (up to a constant fact…
In practice, Analog-to-Digital Converter (ADC) is used to perform sampling. A practical bottleneck of ADC is its lower dynamic range, leading to loss of information. To address this issue, researchers suggested folding operation on the signal using a modulo operator before passing it as an input to…
Recovering multiple measurement vectors (MMVs) with common sparse support from compressed measurements is an important problem in many applications. Theoretical results and practical algorithms that use rank properties of the MMVs have been shown to require fewer measurements. These methods use the…
Finite-rate-of-innovation (FRI) signals are ubiquitous in radar, ultrasound, and time of flight imaging applications. In this paper, we propose a model-based deep learning approach to jointly design the subsampling and reconstruction of FRI signals. Specifically, our framework is a combination of a…
Two important attributes of analog to digital converters (ADCs) are its sampling rate and dynamic range. The sampling rate should be greater than or equal to the Nyquist rate for bandlimited signals with bounded energy. It is also desired that the signals’ dynamic range should be within that of the…
Sub-Nyquist sampling is used in modern high-resolution pulse-Doppler radar systems to reduce system resources and improve resolution. Xampling with Doppler focusing is utilized to implement these sub-Nyquist radar systems. Signal recovery involves iterative optimization requiring large computational…
We consider a continuous-time sparse multichannel blind deconvolution problem. The signal at each channel is expressed as the convolution of a common source signal and its impulse response given as a sparse filter. The objective is to identify these sparse filters from sub-Nyquist samples of channel…
We propose a learned-structured unfolding neural network for the problem of compressive sparse multichannel blind-deconvolution. In this problem, each channel’s measurements are given as convolution of a common source signal and sparse filter. Unlike prior works where the compression is achieved eit…
A typical finite-rate-of-innovation (FRI) signal reconstruction scheme is based on the measurement of uniform samples in time/frequency domain, and the application of the annihilating filter on the measured samples. We propose a continuous-time annihilation framework for a class of FRI signals. In p…