Yann Barbotin

Learn More
We consider the problem of estimating sparse communication channels in the MIMO context. In small to medium bandwidth communications, as in the current standards for OFDM and CDMA communication systems (with bandwidth up to 20 MHz), such channels are individually sparse and at the same time share a common support set. Since the underlying physical channels(More)
Fluorescence microscopy is widely used to determine the subcellular location of proteins. Efforts to determine location on a proteome-wide basis create a need for automated methods to analyze the resulting images. Over the past ten years, the feasibility of using machine learning methods to recognize all major subcellular location patterns has been(More)
We consider the joint estimation of multipath channels obtained with a set of receiving antennas and uniformly probed in the frequency domain. This scenario fits most of the modern outdoor communication protocols for mobile access [1] or digital broadcasting [2] among others. Such channels verify a Sparse Common Support property (SCS) which was used in [3](More)
We propose an algorithm (SCS-FRI) to estimate multipath channels with Sparse Common Support (SCS) based on Finite Rate of Innovation (FRI) sampling. In this setup, theoretical lower-bounds are derived, and simulation in a Rayleigh fading environment shows that SCS-FRI gets very close to these bounds. We show how to apply SCS-FRI to OFDM and CDMA downlinks.(More)
The present paper proposes and studies an algorithm to estimate channels with a sparse common support (SCS). It is a generalization of the classical sampling of signals with Finite Rate of Innovation (FRI) [1] and thus called SCS-FRI. It is applicable to OFDM and WalshHadamard coded (CDMA downlink) communications since SCS-FRI is shown to work not only on(More)
A central problem in signal processing and communications is to design signals that are compact both in time and frequency. Heisenberg’s uncertainty principle states that a given function cannot be arbitrarily compact both in time and frequency, defining an “uncertainty” lower bound. Taking the variance as a measure of localization in time and frequency,(More)
A fast computational method is given for the Fourier transform of the polyharmonic B-spline autocorrelation sequence in d dimensions. The approximation error is exponentially decaying with the number of terms taken into account. The algorithm improves speed upon a simple truncated-sum approach. Moreover, it is virtually independent of the spline's order.(More)
For a given time or frequency spread, one can always find continuous-time signals, which achieve the Heisenberg uncertainty principle bound. This is known, however, not to be the case for discrete-time sequences; only widely spread sequences asymptotically achieve this bound. We provide a constructive method for designing sequences that are maximally(More)
In the last lecture, we introduced the streaming model. The goal of this model is to help us develop algorithms that are useful in the scenarios where we want to process large amounts of data while having only very limited storage (and processing time) at our disposal. The motivating example here is a network router. This device has an extremely small(More)
  • 1