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Jan Kowalczyk
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presentation/LATEX_Presentation-TUG-SPSC.tex
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presentation/LATEX_Presentation-TUG-SPSC.tex
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% **************************************************************************************************
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% ** SPSC Presentation Template
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% **************************************************************************************************
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% based upon "Presentation Template TUG (simple)" by Christoph Klug
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%
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% ***** Authors *****
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% Daniel Arnitz
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% Signal Processing and Speech Communication Laboratory (SPSC)
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% Graz University of Technology (TU Graz), Austria
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%
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% ***** Changelog *****
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% 0.1 2010-02-17
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%
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% ***** Todo *****
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% - replace "quadrat.ps" by a color box
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% - revise header/footer definitions
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% **************************************************************************************************
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%----------------------------------------------------------------------
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\documentclass[aspectratio=169]{beamer}
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\usepackage[USenglish]{babel}
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\usepackage{graphicx,psfrag,subfigure}
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\usepackage{color,colortbl}
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\usepackage{amsmath,amssymb,amstext,bm}
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\usepackage{cite}
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\usefonttheme[onlymath]{serif}
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\usepackage{ulem,xspace}
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\graphicspath{{./plots/}{./drawings/}{./logos/}{./pictures/}}
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%----------------------------------------------------------------------
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%----------------------------------------------------------------------
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\title{Lidar Degradation Quantification\\Master's Thesis}
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\author{Jan Kowalczyk}
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\institute[SPSC]{Signal Processing and Speech Communication Laboratory}
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\date{Nov 14, 2024}
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%----------------------------------------------------------------------
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%----------------------------------------------------------------------
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% math
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\newcommand{\mtx}[2]{\left[\begin{array}{#1}#2\end{array}\right]}%vector/matrix
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\newcommand{\vu}[2]{\mbox{$#1\,\text{#2}$}}% value-unit
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\newcommand{\vm}[1]{\underline{\bm{#1}}}% vector/matrix highlighting
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\newcommand{\E}[1]{\text{E}\!\left\{#1\right\}}% expectation operator
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\newcommand{\var}[1]{\text{var}\!\left\{#1\right\}}% variance operator
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\newcommand{\avgvar}[1]{\overline{\text{var}}\!\left\{#1\right\}}% average variance operator
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\renewcommand{\Re}[1]{\text{Re}\!\left\{#1\right\}}% real part
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\renewcommand{\Im}[1]{\text{Im}\!\left\{#1\right\}}% imaginary part
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\newcommand{\conj}{^\ast}% conjugate complex
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% reminders
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\newcommand{\reminder}[1]{\colorbox{red}{#1}\xspace}
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\newcommand{\rem}{\reminder{(...)}}
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\newcommand{\comment}[1]{\colorbox{yellow}{#1}\xspace}
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%----------------------------------------------------------------------
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%----------------------------------------------------------------------
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\definecolor{LightGray}{gray}{0.9}
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\definecolor{MedGray}{gray}{0.45}
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\def\textboxcolor{LightGray}
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%----------------------------------------------------------------------
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\setbeamerfont{caption name}{size=\tiny}
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\newcommand{\whiteframe}{{\setbeamercolor{background canvas}{bg=white}\frame[plain]{}}}
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\newcommand{\blackframe}{{\setbeamercolor{background canvas}{bg=black}\frame[plain]{}}}
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%----------------------------------------------------------------------
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%----------------------------------------------------------------------
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\setbeamertemplate{headline}{%
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\hspace{0.01\textwidth}\begin{minipage}[t]{0.88\textwidth}
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\vspace{0.1cm}
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\begin{tabular}{p{0.6\textwidth}p{0.217\textwidth}p{0.18\textwidth}}
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TU Graz - \insertinstitute&&\hfill%
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%\includegraphics[height=11pt]{CompanyLogo}\hspace*{0.005\textwidth}
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\includegraphics[height=11pt]{logo_ohne_text}\\
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\hline\\
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\end{tabular}
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\end{minipage}}
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%----------------------------------------------------------------------
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\setbeamertemplate{footline}{%
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\addtolength\extrarowheight{-2mm}% make square flushed with both hlines; this will only affect the footer
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\hspace{0.01\textwidth}\begin{minipage}[t]{0.88\textwidth}%
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\begin{tabular}{p{0.309\textwidth}p{0.35\textwidth}p{0.2\textwidth}p{0.1\textwidth}}%
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\rowcolor[rgb]{0.8,0.8,0.8}\hline%
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\raggedright\begin{minipage}{0.3cm}\includegraphics[width=0.30cm]{quadrat}\end{minipage}\ \insertauthor &%
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\centering \ \usebeamerfont{date in head/foot}\insertshortdate{}& &%
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page \insertframenumber{}/\inserttotalframenumber\\
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\hline\\
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\end{tabular}
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\end{minipage}}
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%----------------------------------------------------------------------
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\setbeamerfont{caption name}{size=\tiny}
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%----------------------------------------------------------------------
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%----------------------------------------------------------------------
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\begin{document}
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%**********************************************************************
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%**********************************************************************
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\maketitle
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\begin{frame}[t]{Organization}
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\begin{itemize}
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\item Jan Kowalczyk, ICE Student
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\item Supervisor: Franz Pernkopf, Univ.-Prof. Dipl.-Ing. Dr.mont
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\item Cooperation: Virtual Vehicle Research GmbH\\(Advisor: Kailin Tong)
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\end{itemize}
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\end{frame}
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\begin{frame}[t]{Introduction}
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\begin{block}{Target}
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\begin{itemize}
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\item Lidar degradation quantification in harsh environments
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\end{itemize}
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\end{block}
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\begin{block}{Approach}
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\begin{itemize}
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\item Task is formulated as an anomaly detection problem
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\item Anomaly detection is a process of finding patterns in a dataset that deviate significantly from normal behavior
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\end{itemize}
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\end{block}
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\begin{block}{Key Ideas}
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\begin{itemize}
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\item Teach anomaly detection model to interpret good sensor data as the norm and degraded data as an anomaly
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\item Model then interprets the deviation from the norm as an anomaly score
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\end{itemize}
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\end{block}
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\end{frame}
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\begin{frame}[t]{DeepSAD~\cite{DBLP:journals/corr/abs-1906-02694} - Anomaly Detection Algorithm}
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\begin{block}{General}
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\begin{itemize}
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\item Learning based anomaly detection model that learns normal data's distribution so it can infer how strongly new data deviates from that norm
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\item Semi-supervised Training: Mostly unlabeled data (normal and abnormal) with some labelled data to improve performance
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\end{itemize}
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\end{block}
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\begin{block}{Background - Autoencoder}
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\includegraphics[width=0.9\textwidth]{autoencoder}
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\end{block}
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\end{frame}
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\begin{frame}[t]{DeepSAD~\cite{DBLP:journals/corr/abs-1906-02694} - Algorithm Details}
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\begin{enumerate}
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\item Pre-Training: Learn how to represent the most significant parts of data
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\begin{itemize}
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\item Autoencoder is used to learn latent space representation
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\end{itemize}
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\item Define the normal data target
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\begin{itemize}
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\item Hypersphere center (c) is defined as the mean of data's latent representation
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\end{itemize}
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\item Training: Minimize distance from hypersphere center
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\begin{itemize}
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\item Encoder weights are adjusted using backpropagation to minimize mean squared distance from c
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\item This forces the network to extract common factors of variation which are most stable within the dataset so normal data points get mapped near the hypersphere center and anomalies further away
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\item Labeled samples are utilized during this training (maximize distance for labeled anomalies)
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\end{itemize}
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\item Inference: Distance between new data and c is interpreted as an anomaly score
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\end{enumerate}
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\end{frame}
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\begin{frame}[t]{Dataset~\cite{Kyuroson_2023}}
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\begin{columns}[T] % align columns
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\begin{column}{.63\textwidth}
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\begin{itemize}
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\item Wheel based robotic sensor platform
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\item Ouster OS1-32 lidar 360°, camera (stereo, IR)
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\begin{itemize}
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\item also IMU, mmWave radar, 2nd lidar
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\end{itemize}
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\item Environment is sub-terranean
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\item Degradation is produced by smoke machine vapor
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\begin{itemize}
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\item No real smoke
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\item No ground truth for degradation
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\end{itemize}
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\item Approximately 16k normal lidar scans + 2k degraded lidar scans
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\item The 3D lidar data is projected onto a 2D image for DeepSAD
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\end{itemize}
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\end{column}%
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\hfill%
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\begin{column}{.33\textwidth}
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\includegraphics[width=\textwidth]{sensorplatform}
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\end{column}%
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\end{columns}
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\end{frame}
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\begin{frame}[t]{Video Demos}
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\begin{itemize}
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\item Dataset demo
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\item Results demo
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\end{itemize}
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\end{frame}
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\begin{frame}[t]{Results}
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\centering
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\includegraphics[height=0.9\textheight]{roc}
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\end{frame}
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%\begin{frame}{Outline} \tableofcontents \end{frame} % no shading here!
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%%----------------------------------------------------------------------
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%%----------------------------------------------------------------------
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%\AtBeginSection[]{% Do nothing for \section*
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%\frame{\frametitle{Outline}\tableofcontents[currentsection]}%sectionstyle=show/shaded,subsectionstyle=show/show/shaded
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%}
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%%----------------------------------------------------------------------
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%%----------------------------------------------------------------------
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%\begin{frame}[t]{\textsc{Intro -- }About this Template}
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% \begin{block}{Important Facts}
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% \begin{itemize}
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% \item this template is based upon "Presentation Template TUG (simple)" by Christoph Klug
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% \item and it is still under development (but nonetheless functional).
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% \end{itemize}
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% \end{block}
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% \begin{block}{Important Contents}
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% This template contains several macros intended to simplify your work, for example value/unit combinations preventing linebreaks (e.g., \vu{4}{GHz}), black/white frames, and loads of math operators $$\vm{x} = \E{\mtx{cc}{3 & 2 \\ y & z(t)}}$$ like the ones above.
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% \end{block}
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%\end{frame}
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%----------------------------------------------------------------------
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\begin{frame}[t]{References}\tiny
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\bibliography{mybib}{}
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\bibliographystyle{plain}
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%\begin{thebibliography}{9}
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%\bibitem{IEEEtran:parameters_freq_selective_channels}
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%K.~Witrisal, Y.-H. Kim, and R.~Prasad, \newblock ``A new method to measure parameters of
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% frequency-selective radio channels using power measurements,'' \newblock \textit{{IEEE}
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% Trans. Commun.}, vol.~49, no.~10, pp. 1788--1800, Oct. 2001,
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% 10.1109/26.957401.
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%
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%}
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%
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%\end{thebibliography}
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\end{frame}
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%----------------------------------------------------------------------
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%\blackframe
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%----------------------------------------------------------------------
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%----------------------------------------------------------------------
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%\cite{...}
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%\bibliographystyle{IEEEtran}
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%\bibliography{IEEEabrv,...}
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\end{document}
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