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 a ...
Using high level coordination primitives allows enhanced expressiveness of component-based frameworks to cope with the inherent complexity of present-day systems designs. Nonetheless, their distributed implementation raises multiple issues, regarding both ...
The parareal algorithm seeks to extract parallelism in the time-integration direction of time-dependent differential equations. While it has been applied with success to a wide range of problems, it suffers from some stability issues when applied to non-di ...
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 asymptoticall ...
Euclidean distance matrices (EDMs) are central players in many diverse fields including psychometrics, NMR spectroscopy, machine learning and sensor networks. However, they are not often exploited in signal processing. In this thesis, we analyze attributes ...
We investigate Farley’s CAT(0) cubical model for Thompson’s group F (we adopt the classical language of F, using binary trees and piecewise linear maps, avoiding the one of diagram groups and pictures). Main results include: in general, Thompson’s group el ...
