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Code for the conference paper "Streaming Data through the IoT via Actor-Based Semantic Routing Trees" from VLIOT@VLDB 2021.

License: MIT License

CMake 0.77% C++ 6.23% Python 1.23% Shell 0.27% Makefile 0.06% TeX 35.68% Jupyter Notebook 55.77%
actor-model data-streaming edge-computing fog-computing iot network-topology paper semantic-routing-trees vldb-conference vliot

rime-data-streaming-iot's Introduction

Hi ๐Ÿ‘‹, I'm Johannes

Your Friendly Neighborhood Data Scientist!

Languages and Tools โš’๏ธ

python cplusplus pytorch

Projects ๐Ÿ’ป

If you are really interested in my work, below are all projects I did while studying. In chronological order, so please don't be too judgy on my earlier code. We all had to start somewhere ๐Ÿ˜œ. Will be cool memories in the future. PEP8 is my friend, I swear.

  • Learn SQL and MapReduce (click).
  • Implement an Extended Kalman Filter for pose estimation of a car in Apache Flink (click).
  • Bachelor Thesis: Research and benchmark a decentral way of organizing nodes in a data streaming engine (click). Published at VLIoT@VLDB 2021 (paper).
  • Find new clusters of Customers in a real-world dataset from Robert Bosch GmbH. Visualize Results and pitch them to management. Closed source.
  • Predict the filling level of glass containers based on a real dataset from a German recycling company. Mostly time series data. Visualize Results and pitch them to management. Closed source.
  • Pandemic Package: automatically detect damaged packages in a warehouse based on video data in real-time. Lots of CNNs and bounding boxes.
  • SciQuack: Answer free-text questions about scientific papers by highlighting specific passages in the papers. Uses database and knowledge graph. Lots of deep learning and NLP. Includes website as frontend. Closed source.
  • Compress 2d and 3d videos from autonomous vehicles for remote assistance using deep learning (click). Using GANs and VAEs on camera and lidar video data for real-time processing. Published in ML4AD@NeurIPS 2021 (paper).
  • Optimize energy usage and storage, predict energy production in smart grids (click). Mostly time series data.
  • Learn Vulkan & GLSL basics, build custom general-purpose shaders (click). Out of my comfort zone (I was the only non-computer-graphics guy) and painful at times, but totally worth it.
  • SmartHomeBuddy: identify smart home devices in your home network using machine learning. Including PoC of a complete app. Three-month scholarship by the German Bundesnachrichtendienst.
  • Cofvefe Scale: Fun little arduino project where the goal was to design an information appliance while applying the design thinking process. My appliance turned out to be a coffee scale. Very innovation, much wow.
  • Master Thesis: Data-efficient Representation Learning for Cell Organelle Segmentation in Electron Microscopy Images. Using self-supervised learning and transformers. Lots of optimization for low-powered hardware. Currently closed source for reasons, but working on open-sourcing.

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