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Classification via Incoherent Subspaces

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Frame Domain Signal Processing: Framework and Applications

Martin Vetterli, Amina Chebira

Besides basis expansions, frames representations play a key role in signal processing. We thus consider the problem of frame domain signal processing, which is more complex and challenging than transform domain processing. Examples of such processing aboun ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

Dictionary Learning Based Dimensionality Reduction for Classification

Pierre Vandergheynst, Karin Schnass

In this article we present a signal model for classification based on a low dimensional dictionary embedded into the high dimensional signal space. We develop an alternate projection algorithm to find the embedding and the dictionary and finally test the c ...

2008

Signal Processing for Communications

Martin Vetterli, Paolo Prandoni

With a novel, less classical approach to the subject, the authors have written a book with the conviction that signal processing should be taught to be fun. The threatment is therefore less focused on the mathematics and more on the conceptual aspects, the ...

EPFL Press, CRC2008

Dimensionality Reduction for Distributed Estimation in the Infinite Dimensional Regime

Martin Vetterli, Olivier Roy

Distributed estimation of an unknown signal is a common task in sensor networks. The scenario usually envisioned consists of several nodes, each making an observation correlated with the signal of interest. The acquired data is then wirelessly transmitted ...

Institute of Electrical and Electronics Engineers2008

Dimensionality Reduction with Adaptive Approximation

Pascal Frossard, Effrosyni Kokiopoulou

In this paper, we propose the use of (adaptive) nonlinear approximation for dimensionality reduction. In particular, we propose a dimensionality reduction method for learning a parts based representation of signals using redundant dictionaries. A redundant ...

2007

Algorithmic aspects of sparse approximations

Philippe Jost

Typical tasks in signal processing may be done in simpler ways or more efficiently if the signals to analyze are represented in a proper way. This thesis deals with some algorithmic problems related to signal approximation, more precisely, in the novel fie ...

EPFL2007

Hybrid continuous-discrete-time multi-bit delta-sigma A/D converters with auto-ranging algorithm

Sergio Pesenti

In wireless portable applications, a large part of the signal processing is performed in the digital domain. Digital circuits show many advantages. The power consumption and fabrication costs are low even for high levels of complexity. A well established a ...

EPFL2007

Image Replica Detection based on Binary Support Vector Classifier

Touradj Ebrahimi

In this paper, we present a system for image replica detection. More specifically, the technique is based on the extraction of 162 features corresponding to texture, color and gray-level characteristics. These features are then weighted and statistically n ...

2005

Multimodal Analysis Using Redundant Parametric Decompositions

Pierre Vandergheynst, Gianluca Monaci, Oscar Divorra Escoda

In this work we explore the potentialities of a representational framework based on Matching Pursuit (MP) for the decomposition of audio-visual signals over redundant dictionaries. It is relatively easy for a human to correctly interpret a scene consisting ...

2004

From error probability to information theoretic signal and image processing

Torsten Butz

The signal processing community is increasingly interested in using information theoretic concepts to build signal processing algorithms for a variety of applications. A general theory on how to apply the mathematical concepts of information theory to the ...

EPFL2003