Imperial College London
Portrait Picture

Dr. Ayush Bhandari

Faculty of Engineering, Department of Electrical and Electronic Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6233a.bhandari Website

 
 
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Location

 

802Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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42 results found

Bhandari A, 2022, Back in the US-SR: unlimited sampling and sparse super-resolution with Its hardware validation, IEEE Signal Processing Letters, Vol: 29, Pages: 1047-1051, ISSN: 1070-9908

The Unlimited Sensing Framework (USF) is a digital acquisition protocol that allows for sampling and reconstruction of high dynamic range signals. By acquiring modulo samples, the USF circumvents the clipping or saturation problem that is a fundamental bottleneck in conventional analog-to-digital converters (ADCs). In the context of the USF, several works have focused on bandlimited function classes and recently, a hardware validation of the modulo sampling approach has been presented. In a different direction, in this paper we focus on non-bandlimited function classes and consider the well-known super-resolution problem; we study the recovery of sparse signals (Dirac impulses) from low-pass filtered, modulo samples. Taking an end-to-end approach to USF based super-resolution, we present a novel recovery algorithm (US-SR) that leverages a doubly sparse structure of the modulo samples. We derive a sampling criterion for the US-SR method. A hardware experiment with the modulo ADC demonstrates the empirical robustness of our method in a realistic, noisy setting, thus validating its practical utility.

Journal article

Florescu D, Krahmer F, Bhandari A, 2022, The Surprising Benefits of Hysteresis in Unlimited Sampling: Theory, Algorithms and Experiments, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 70, Pages: 616-630, ISSN: 1053-587X

Journal article

Florescu D, Bhandari A, 2022, Time Encoding via Unlimited Sampling: Theory, Algorithms and Hardware Validation, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 70, Pages: 4912-4924, ISSN: 1053-587X

Journal article

Fernandez-Menduina S, Krahmer F, Leus G, Bhandari Aet al., 2022, Computational Array Signal Processing via Modulo Non-Linearities, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 70, Pages: 2168-2179, ISSN: 1053-587X

Journal article

Beckmann M, Bhandari A, Krahmer F, 2022, The Modulo Radon Transform: Theory, Algorithms, and Applications, SIAM JOURNAL ON IMAGING SCIENCES, Vol: 15, Pages: 455-490, ISSN: 1936-4954

Journal article

Bhandari A, Krahmer F, Poskitt T, 2021, Unlimited sampling from theory to practice: fourier-prony recovery and prototype ADC, IEEE Transactions on Signal Processing, Vol: 70, Pages: 1131-1141, ISSN: 1053-587X

Following the Unlimited Sampling strategy to alleviate the omnipresent dynamic range barrier, we study the problem of recovering a bandlimited signal from point-wise modulo samples, aiming to connect theoretical guarantees with hardware implementation considerations. Our starting point is a class of non-idealities that we observe in prototyping an unlimited sampling based analog-to-digital converter. To address these non-idealities, we provide a new Fourier domain recovery algorithm. Our approach is validated both in theory and via extensive experiments on our prototype analog-to-digital converter, providing the first demonstration of unlimited sampling for data arising from real hardware, both for the current and previous approaches. Advantages of our algorithm include that it is agnostic to the modulo threshold and it can handle arbitrary folding times. We expect that the end-to-end realization studied in this paper will pave the path for exploring the unlimited sampling methodology in a number of real world applications.

Journal article

Bouis V, Krahmer F, Bhandari A, 2021, Multidimensional Unlimited Sampling: A Geometrical Perspective, 28th European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 2314-2318, ISSN: 2076-1465

Conference paper

Bhandari A, Beckmann M, Krahmer F, 2021, The Modulo Radon Transform and its Inversion, 28th European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 770-774, ISSN: 2076-1465

Conference paper

Fernandez-Menduina S, Krahmer F, Leus G, Bhandari Aet al., 2021, DoA Estimation via Unlimited Sensing, 28th European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 1866-1870, ISSN: 2076-1465

Conference paper

Bhandari A, Krahmer F, Raskar R, 2020, On unlimited sampling and reconstruction, IEEE Transactions on Signal Processing, Vol: 69, Pages: 3827-3839, ISSN: 1053-587X

Shannon's sampling theorem is one of the cornerstone topics that is well understood and explored, both mathematically and algorithmically. That said, practical realization of this theorem still suffers from a severe bottleneck due to the fundamental assumption that the samples can span an arbitrary range of amplitudes. In practice, the theorem is realized using so-called analog--to--digital converters (ADCs) which clip or saturate whenever the signal amplitude exceeds the maximum recordable ADC voltage thus leading to a significant information loss. In this paper, we develop an alternative paradigm for sensing and recovery, called the Unlimited Sampling Framework. It is based on the observation that when a signal is mapped to an appropriate bounded interval via a modulo operation before entering the ADC, the saturation problem no longer exists, but one rather encounters a different type of information loss due to the modulo operation. Such an alternative setup can be implemented, for example, via so-called folding or self-reset ADCs, as they have been proposed in various contexts in the circuit design literature. The key task that we need to accomplish in order to cope with this new type of information loss is to recover a bandlimited signal from its modulo samples. In this paper we derive conditions when perfect recovery is possible and complement them with a stable recovery algorithm. The sampling density required to guarantee recovery is independent of the maximum recordable ADC voltage and depends on the signal bandwidth only. Our recovery guarantees extend to measurements affected by bounded noise, which includes the case of round-off quantization. Numerical experiments validate our approach. For example, it is possible to recover functions with amplitudes orders of magnitude higher than the ADC's threshold from quantized modulo samples upto the unavoidable quantization error. Applications of the unlimited sampling paradigm can be found in a number of fields su

Journal article

Bhandari A, Conde MH, Loffeld O, 2020, One-Bit Time-Resolved Imaging, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Vol: 42, Pages: 1630-1641, ISSN: 0162-8828

Journal article

Bhandari A, Graf O, Krahmer F, Zayed Aet al., 2020, ONE-BIT SAMPLING IN FRACTIONAL FOURIER DOMAIN, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Publisher: IEEE, Pages: 9140-9144, ISSN: 1520-6149

Conference paper

Bhandari A, Krahmer F, 2020, HDR IMAGING FROM QUANTIZATION NOISE, IEEE International Conference on Image Processing (ICIP), Publisher: IEEE, Pages: 101-105, ISSN: 1522-4880

Conference paper

Beckmann M, Krahmer F, Bhandari A, 2020, HDR TOMOGRAPHY VIA MODULO RADON TRANSFORM, IEEE International Conference on Image Processing (ICIP), Publisher: IEEE, Pages: 3025-3029, ISSN: 1522-4880

Conference paper

Bhandari A, Zayed AI, 2019, Shift-invariant and sampling spaces associated with the special aline Fourier transform, APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS, Vol: 47, Pages: 30-52, ISSN: 1063-5203

Journal article

Bhandari A, Eldar YC, 2019, Sampling and super resolution of sparse signals beyond the Fourier domain, IEEE Transactions on Signal Processing, Vol: 67, Pages: 1508-1521, ISSN: 1053-587X

Recovering a sparse signal from its low-pass projections in the Fourier domain is a problem of broad interest in science and engineering and is commonly referred to as super resolution. In many cases, however, Fourier domain may not be the natural choice. For example, in holography, low-pass projections of sparse signals are obtained in the Fresnel domain. Similarly, time-varying system identification relies on low-pass projections on the space of linear frequency modulated signals. In this paper, we study the recovery of sparse signals from low-pass projections in the Special Affine Fourier Transform domain (SAFT). The SAFT parametrically generalizes a number of well-known unitary transformations that are used in signal processing and optics. In analogy to the Shannon's sampling framework, we specify sampling theorems for recovery of sparse signals considering three specific cases: 1) sampling with arbitrary, bandlimited kernels, 2) sampling with smooth, time-limited kernels, and 3) recovery from Gabor transform measurements linked with the SAFT domain. Our work offers a unifying perspective on the sparse sampling problem which is compatible with the Fourier, Fresnel, and Fractional Fourier domain-based results. In deriving our results, we introduce the SAFT series (analogous to the Fourier series) and the short-time SAFT, and study convolution theorems that establish a convolution-multiplication property in the SAFT domain.

Journal article

Graf O, Bhandari A, Krahmer F, 2019, ONE-BIT UNLIMITED SAMPLING, 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 5102-5106, ISSN: 1520-6149

Conference paper

Bhandari A, Krahmer F, 2019, On Identifiability in Unlimited Sampling, 13th International Conference on Sampling Theory and Applications (SampTA), Publisher: IEEE

Conference paper

Batenkov D, Bhandari A, Blu T, 2019, RETHINKING SUPER-RESOLUTION: THE BANDWIDTH SELECTION PROBLEM, 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 5087-5091, ISSN: 1520-6149

Conference paper

Bhandari A, Zayed AI, 2018, Convolution and Product Theorems for the Special Affine Fourier Transform, Frontiers in Orthogonal Polynomials and <i>q</i>-Series, Publisher: WORLD SCIENTIFIC, Pages: 119-137

Book chapter

Bhandari A, Krahmer F, Raskar R, 2018, Unlimited Sampling of Sparse Sinusoidal Mixtures, IEEE International Symposium on Information Theory (ISIT), Publisher: IEEE, Pages: 336-340

Conference paper

Bhandari A, Krahmer F, Raskar R, 2017, On unlimited sampling, 2017 International Conference on Sampling Theory and Applications (SampTA), Publisher: IEEE, Pages: 31-35

Shannon's sampling theorem provides a link between the continuous and the discrete realms stating that bandlimited signals are uniquely determined by its values on a discrete set. This theorem is realized in practice using so called analog-to-digital converters (ADCs). Unlike Shannon's sampling theorem, the ADCs are limited in dynamic range. Whenever a signal exceeds some preset threshold, the ADC saturates, resulting in aliasing due to clipping. The goal of this paper is to analyze an alternative approach that does not suffer from these problems. Our work is based on recent developments in ADC design, which allow for ADCs that reset rather than to saturate, thus producing modulo samples. An open problem that remains is: Given such modulo samples of a bandlimited function as well as the dynamic range of the ADC, how can the original signal be recovered and what are the sufficient conditions that guarantee perfect recovery? In this paper, we prove such sufficiency conditions and complement them with a stable recovery algorithm. Our results are not limited to certain amplitude ranges, in fact even the same circuit architecture allows for the recovery of arbitrary large amplitudes as long as some estimate of the signal norm is available when recovering. Numerical experiments that corroborate our theory indeed show that it is possible to perfectly recover function that takes values that are orders of magnitude higher than the ADC's threshold.

Conference paper

Bhandari A, Blu T, 2017, FRI SAMPLING AND TIME-VARYING PULSES: SOME THEORY AND FOUR SHORT STORIES, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Publisher: IEEE, Pages: 3804-3808, ISSN: 1520-6149

Conference paper

Bhandari A, Bourquard A, Raskar R, 2017, SAMPLING WITHOUT TIME: RECOVERING ECHOES OF LIGHT VIA TEMPORAL PHASE RETRIEVAL, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Publisher: IEEE, Pages: 3829-3833, ISSN: 1520-6149

Conference paper

Bhandari A, Raskar R, 2016, Signal processing for time-of-flight imaging sensors: an introduction to inverse problems in computational 3-D imaging, IEEE Signal Processing Magazine, Vol: 33, Pages: 45-58, ISSN: 1053-5888

Time-of-flight (ToF) sensors offer a cost-effective and realtime solution to the problem of three-dimensional (3-D) imaging-a theme that has revolutionized our sceneunderstanding capabilities and is a topic of contemporary interest across many areas of science and engineering. The goal of this tutorial-style article is to provide a thorough understanding of ToF imaging systems from a signal processing perspective that is useful to all application areas. Starting with a brief history of the ToF principle, we describe the mathematical basics of the ToF image-formation process, for both time- and frequency-domain, present an overview of important results within the topic, and discuss contemporary challenges where this emerging area can benefit from the signal processing community. In particular, we examine case studies where inverse problems in ToF imaging are coupled with signal processing theory and methods, such as sampling theory, system identification, and spectral estimation, among others. Through this exposition, we hope to establish that ToF sensors are more than just depth sensors; depth information may be used to encode other forms of physical parameters, such as, the fluorescence lifetime of a biosample or the diffusion coefficient of turbid/scattering medium. The MATLAB scripts and ToF sensor data used for reproducing figures in this article are available via the author?s webpage: http://www.mit.edu/~ayush/Code.

Journal article

Feigin M, Bhandari A, Izadi S, Rhemann C, Schmidt M, Raskar Ret al., 2016, Resolving Multipath Interference in Kinect: An Inverse Problem Approach, IEEE SENSORS JOURNAL, Vol: 16, Pages: 3419-3427, ISSN: 1530-437X

Journal article

Bhandari A, Wallace AM, Raskar R, 2016, Super-resolved time-of-flight sensing via FRI sampling theory, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE

Conference paper

Bhandari A, Eldar YC, 2016, A swiss army knife for finite rate of innovation sampling theory, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE

Conference paper

Bhandari A, Bourquard A, Izadi S, Raskar Ret al., 2016, TIME-RESOLVED IMAGE DEMIXING, 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 4483-4487, ISSN: 1520-6149

Conference paper

Bhandari A, Barsi C, Raskar R, 2015, Blind and reference-free fluorescence lifetime estimation via consumer time-of-flight sensors, Optica, Vol: 2, Pages: 965-973, ISSN: 2334-2536

Fluorescence lifetime imaging (FLI) is a popular method for extracting useful information that is otherwise unavailable from a conventional intensity image. Usually, however, it requires expensive equipment, is often limited to either distinctly frequency- or time-domain modalities, and demands calibration measurements and precise knowledge of the illumination signal. Here, we present a generalized time-based, cost-effective method for estimating lifetimes by repurposing a consumer-grade time-of-flight sensor. By developing mathematical theory that unifies time- and frequency-domain approaches, we can interpret a time-based signal as a combination of multiple frequency measurements. We show that we can estimate lifetimes without knowledge of the illumination signal and without any calibration. We experimentally demonstrate this blind, reference-free method using a quantum dot solution and discuss the method’s implementation in FLI applications.

Journal article

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