created proper title page

This commit is contained in:
Alex Blank
2025-03-14 13:51:47 +01:00
parent a085faaed8
commit e80eba8b50
3 changed files with 40 additions and 7 deletions
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%! Date = 3/6/25
% Preamble
\documentclass[11pt]{article}
\documentclass[a4paper, 12pt]{article}
% Packages
\usepackage{amsmath}
\usepackage[a4paper, margin=1in]{geometry}
\usepackage{graphicx}
\graphicspath{{resources/figures/}}
\usepackage[inksacpeformat=png]{svg}
\usepackage[style=ieee, backend=biber]{biblatex}
\addbibresource{../main.bib}
% Document
\begin{document}
\title{Finding Predictors for Human Ovulation with Attention Mechanisms}
\author{Alexander Blank}
\date{2025}
% university and faculty
\maketitle
\begin{titlepage}
\centering
{\large \textbf{Leipzig University}}\\[1.5cm]
{\large Faculty of Mathematics and Computer Science}\\[2cm]
\includegraphics[width=7cm]{leipzig_university_logo}\\[1cm] % Adjust size as needed
{\huge \textbf{Finding Predictors for Human Ovulation with Attention Mechanisms}}\\[1.5cm]
\textbf{Masters Thesis}\\[1cm]
\textbf{Author:}\\
Alexander Blank\\[0.5cm]
\textbf{Supervisor(s):}\\
Prof. XYZ, Dr. ABC\\[1.5cm]
\textbf{Date:} 2025\\[2cm]
\textbf{Department of XYZ}\\
\textbf{Leipzig University}
\end{titlepage}
\pagebreak
\begin{abstract}
This paper investigates the use of attention mechanisms to predict human ovulation. The results show that the attention mechanism is able to predict human ovulation with an accuracy of 95\%.
\end{abstract}
\pagebreak
\include{sections/introduction}
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@@ -84,4 +84,12 @@ detecting the slight temperature rise that follows ovulation.
Advances in wearable technology have further enabled continuous and automated temperature monitoring,
improving accessibility and usability\cite{alexander_fertilitatsmonitoring_2014, luo_detection_2020, yu_tracking_2022}.
\subsection{Technical Background}\label{subsec:technological_background}
\subsection{Technical Background}\label{subsec:technological_background}
\subsubsection{Time Series Analysis}\label{subsubsec:time_series_analysis}
Time series analysis is a fundamental tool for studying sequential data that evolves over time.
Unlike other data types, time series data has an inherent temporal order, where each data point is associated
with a timestamp, capturing its dependence on past values.
The goal of time series analysis is to model these dependencies and extract meaningful insights.
While related to signal processing, time series analysis focuses more on long-term trends and forecasting,
rather than frequency-domain characteristics.
\\