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Trabalho de Álgebra Linear apresentado no CEFET/MG - Campus Timóteo

Diese Vorlage eignet sich für sprachliche W-Seminararbeiten an Gymnasien. Diese Vorlage ist so übersichtlich gestaltet, sodass der Schüler in der Datei Arbeit.tex nur seinen Inhalt schreibt. Die Präambel und das Titelblatt sind ausgelagert. In der Datei bausteine.tex finden sich Bausteine, die durch Copy/Paste in das Dokument übernommen werden können. Dies ersetzt aber nicht immer ein Nachschlagen in der Literatur oder im Internet... Angepasst für XeLaTeX 2022 Viel Spaß!

HoTT

Fakulteta za varnostne vede, Univerza v Mariboru - predloga zaključnega dela, UM FVV

This template is adapted from THU Beamer Theme and DLUT Beamer Theme, the contents, fonts, navigation bar, color matching and logo are modified. Note that smoothbars are replaced by miniframes as external themes. On the basis of mathematical formulas, tikz and linguex are added as examples.

LaTeX beamer theme for Northwest University students. Chinese support. Here is the GitHub page: https://github.com/starryious/NWU-latex-template

Unofficial poster template with gold and green colors of U of A.

Compressive Sensing is a Signal Processing technique, which gave a break through in 2004. The main idea of CS is, by exploiting the sparsity nature of the signal (in any domain), we can reconstruct the signal from very fewer samples than required by Shannon-Nyquist sampling theorem. Reconstructing a sparse signal from fewer samples is equivalent to solving a under-determined system with sparsity constraints. Least square solution to such a problem yield poor `results because sparse signals cannot be well approximated to a least norm solution. Instead we use l1 norm(convex) to solve this problem which is the best approximation to the exact solution given by l0 norm(non-convex). In this paper we plan to discuss three applications of CS in estimation theory. They are, CS based reliable Channel estimation assuming sparsity in the channel is known for TDS-OFDM systems[1]. Indoor location estimation from received signal strength (RSS) where CS is used to reconstruct the radio map from RSS measurements[2]. Identifying that subspace in which the signal of interest lies using ML estimation, assuming signal lies in a union of subspaces which is a standard sparsity assumption according to CS theory[3]. Index terms : Compressive Sensing, Indoor positioning, fingerprinting, radio map, Maximum likelihood estimation, union of linear subspaces, subspace recovery.