Publications related to A Learning-Based Framework for Quantized Compressed Sensing

SVM Active Learning Approach for Image Classification Using Spatial Information

In the last few years, active learning has been gaining growing interest in the remote sensing community in optimizing the process of training sample collection for supervised image classification. Current strategies formulate the active learning problem i ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Kinematics of age mixing in advection-dispersion models

Andrea Rinaldo, Paolo Benettin, Gianluca Botter

This paper investigates age mixing processes arising in advection-dispersion models, where large-scale travel and residence time distributions can be explicitly calculated based on the underlying velocity field. In particular, we analyze spatially integrat ...

American Geophysical Union2013

Sparse image reconstruction on the sphere: implications of a new sampling theorem

Jean-Philippe Thiran, Pierre Vandergheynst, Dimitri Nestor Alice Van De Ville, Yves Wiaux, Gilles Puy, Jason Douglas McEwen

A new sampling theorem on the sphere has been developed recently, reducing the number of samples required to represent a band-limited signal by a factor of two for equiangular sampling schemes. For signals sparse in a spatially localised measure, such as i ...

Institute of Electrical and Electronics Engineers2013

Design of a parallel low power flash A/D converter for the sub-sampling IR-UWB receiver

Catherine Dehollain, Shenjie Wang

This paper presents the design of a dual-channel 4-bit analog-to-digital converter (ADC) for the sub-sampling impulse radio ultra-wideband receiver with the sampling rate of 2.112 GS/s. The ADC's specifications are optimized at the system level. Two parall ...

Springer Verlag2013

Sampling and reconstructing diffusion fields in presence of aliasing

Martin Vetterli, Juri Ranieri

The reconstruction of a diffusion field, such as temperature, from samples collected by a sensor network is a classical inverse problem and it is known to be ill-conditioned. Previous work considered source models, such as sparse sources, to regularize the ...

2013

Learning Ridge Functions With Randomized Sampling In High Dimensions

Volkan Cevher, Hemant Tyagi

We study the problem of learning ridge functions of the form f(x) = g(aT x), x ∈ ℝd, from random samples. Assuming g to be a twice continuously differentiable function, we leverage techniques from low rank matrix recovery literature to derive a uniform app ...

IEEE2012

Sampling Trajectories for Mobile Sensing

Martin Vetterli, Jayakrishnan Unnikrishnan

Classical sampling theory for sampling and reconstructing bandlimited fields in

\Re^d

addresses the problem of sampling on lattice points. We consider a generalization of this problem, in which one samples the field along

1

-dimensional spatial trajecto ...

2011

Learning Real-Time Perspective Patch Rectification

Pascal Fua, Vincent Lepetit

We propose two learning-based methods to patch rectification that are faster and more reliable than state-of-the-art affine region detection methods. Given a reference view of a patch, they can quickly recognize it in new views and accurately estimate the ...

Springer Verlag2011

Distributed Sampling of Signals Linked by Sparse Filtering: Theory and Applications

Martin Vetterli, Olivier Roy, Yue Lu, Ali Hormati

We study the distributed sampling and centralized reconstruction of two correlated signals, modeled as the input and output of an unknown sparse filtering operation. This is akin to a Slepian-Wolf setup, but in the sampling rather than the lossless compres ...

2010

Non-linear interpolation and adaptive sampling of scattering parameters

Michael Mattes

The accurate and efficient sampling of scattering parameters is addressed. A popular sampling technique is the uniform sampling combined with a straight-line interpolation for representing the continuous variation of the observable. However, this sampling ...

2010

A Learning-Based Framework for Quantized Compressed Sensing

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SVM Active Learning Approach for Image Classification Using Spatial Information

Kinematics of age mixing in advection-dispersion models

Sparse image reconstruction on the sphere: implications of a new sampling theorem

Design of a parallel low power flash A/D converter for the sub-sampling IR-UWB receiver

Sampling and reconstructing diffusion fields in presence of aliasing

Learning Ridge Functions With Randomized Sampling In High Dimensions

Sampling Trajectories for Mobile Sensing

Learning Real-Time Perspective Patch Rectification

Distributed Sampling of Signals Linked by Sparse Filtering: Theory and Applications

Non-linear interpolation and adaptive sampling of scattering parameters

Design of a parallel low power flash A/D converter for the sub-sampling IR-UWB receiver

SVM Active Learning Approach for Image Classification Using Spatial Information

Kinematics of age mixing in advection-dispersion models

Learning Ridge Functions With Randomized Sampling In High Dimensions

Sampling Trajectories for Mobile Sensing

Sparse image reconstruction on the sphere: implications of a new sampling theorem

Non-linear interpolation and adaptive sampling of scattering parameters

Sampling and reconstructing diffusion fields in presence of aliasing

Learning Real-Time Perspective Patch Rectification

Distributed Sampling of Signals Linked by Sparse Filtering: Theory and Applications