Publications related to Sampling and Reconstruction of Bandlimited Signals with Multi-Channel Time Encoding

Demonstration of Tunable Optical Aggregation of QPSK to 16-QAM Over Optically Generated Nyquist Pulse Trains Using Nonlinear Wave Mixing and a Kerr Frequency Comb

Tobias Kippenberg, Maxim Karpov, Martin Hubert Peter Pfeiffer, Arne Kordts, Fatemeh Alishahi, Ahmad Fallahpour, Cong Liu

A tunable and reconfigurable optical aggregation system is experimentally demonstrated. Optical Nyquist pulses are generated on multiple channels using a microresonator-based Kerr optical frequency comb and insertion of uniform lines by an intensity modula ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Multi-channel Time Encoding for Improved Reconstruction of Bandlimited Signals

Martin Vetterli, Adam James Scholefield, Karen Adam

Traditional sampling involves encoding a signal through (time, value)-pairs. In contrast, time encoding machines (TEMs) characterize a signal by recording time points which depend on the integral of the signal over time. We study multi-channel TEMs where c ...

2019

Demonstration of Kramers-Kronig Detection of Four 20-Gbaud 16-QAM Channels after 50-km Transmission Using Kerr Combs to Perform Shared Phase Estimation

Tobias Kippenberg, Maxim Karpov, Martin Hubert Peter Pfeiffer, Arne Kordts, Fatemeh Alishahi, Ahmad Fallahpour

We experimentally demonstrate KK detection of four 20-Gbaud 16-QAM channels after 50-km transmission using two soliton Kerr combs at transmitter and receiver. The frequency comb coherence enables a shared phase noise estimation among multiple channels. (C) ...

IEEE2019

Random sampling of bandlimited signals on graphs

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We study the problem of sampling k-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, i.e., independent of the graph structure, and its performa ...

Elsevier2018

Sampling at unknown locations, with an application in surface retrieval

Martin Vetterli, Benjamin Bejar Haro, Adam James Scholefield, Michalina Wanda Pacholska

We consider the problem of sampling at unknown locations. We prove that, in this setting, if we take arbitrarily many samples of a polynomial or real bandlimited signal, it is possible to find another function in the same class, arbitrarily far away from t ...

2017

Compressed delay-and-sum beamforming for ultrafast ultrasound imaging

Jean-Philippe Thiran, Yves Wiaux, Rafael Eduardo Carrillo Rangel, Adrien Georges Jean Besson, Olivier Bernard

The theory of compressed sensing (CS) leverages upon structure of signals in order to reduce the number of samples needed to reconstruct a signal, compared to the Nyquist rate. Although CS approaches have been proposed for ultrasound (US) imaging with prom ...

Ieee2016

Shapes from Pixels

Mitra Fatemi

In today's digital world, sampling is at the heart of any signal acquisition device. Imaging devices are ubiquitous examples that capture two-dimensional visual signals and store them as the pixels of discrete images. The main concern is whether and how th ...

EPFL2016

Random sampling of bandlimited signals on graphs

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We study the problem of sampling

k

-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, \ie, independent of the graph structure, and its perform ...

2015

Sampling based on timing: Time encoding machines on shift-invariant subspaces

Martin Vetterli, David Gontier

Sampling information using timing is an approach that has received renewed attention in sampling theory. The question is how to map amplitude information into the timing domain. One such encoder, called time encoding machine, was introduced by Lazar and To ...

Elsevier2014

Sampling High-Dimensional Bandlimited Fields on Low-Dimensional Manifolds

Martin Vetterli, Jayakrishnan Unnikrishnan

Consider the task of sampling and reconstructing a bandlimited spatial field in

\Re^2

using moving sensors that take measurements along their path. It is inexpensive to increase the sampling rate along the paths of the sensors but more expensive to incre ...

Institute of Electrical and Electronics Engineers2013

Sampling and Reconstruction of Bandlimited Signals with Multi-Channel Time Encoding

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Demonstration of Tunable Optical Aggregation of QPSK to 16-QAM Over Optically Generated Nyquist Pulse Trains Using Nonlinear Wave Mixing and a Kerr Frequency Comb

Multi-channel Time Encoding for Improved Reconstruction of Bandlimited Signals

Demonstration of Kramers-Kronig Detection of Four 20-Gbaud 16-QAM Channels after 50-km Transmission Using Kerr Combs to Perform Shared Phase Estimation

Random sampling of bandlimited signals on graphs

Sampling at unknown locations, with an application in surface retrieval

Compressed delay-and-sum beamforming for ultrafast ultrasound imaging

Shapes from Pixels

Random sampling of bandlimited signals on graphs

Sampling based on timing: Time encoding machines on shift-invariant subspaces

Sampling High-Dimensional Bandlimited Fields on Low-Dimensional Manifolds

Sampling at unknown locations, with an application in surface retrieval

Multi-channel Time Encoding for Improved Reconstruction of Bandlimited Signals

Demonstration of Kramers-Kronig Detection of Four 20-Gbaud 16-QAM Channels after 50-km Transmission Using Kerr Combs to Perform Shared Phase Estimation

Demonstration of Tunable Optical Aggregation of QPSK to 16-QAM Over Optically Generated Nyquist Pulse Trains Using Nonlinear Wave Mixing and a Kerr Frequency Comb

Sampling based on timing: Time encoding machines on shift-invariant subspaces

Sampling High-Dimensional Bandlimited Fields on Low-Dimensional Manifolds

Shapes from Pixels

Random sampling of bandlimited signals on graphs

Compressed delay-and-sum beamforming for ultrafast ultrasound imaging

Random sampling of bandlimited signals on graphs