document.bib

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@Report{Lawrence2020,
  author = {Kai Lawrence},
  date   = {2020},
  url = {https://github.com/klawr/deepmech/tree/master/reports/srp},
  title  = {Student Research Project},
  type   = {resreport},
}

@WWW{Josefsson2018,
  author  = {Simon Josefsson},
  date    = {2018},
  title   = {base64},
  url     = {https://www.gnu.org/software/coreutils/manual/html_node/base64-invocation.html},
  urldate = {2020-04-01},
}

@WWW{MaterialUI2020,
  author = {Material-UI},
  date   = {2020-12-15},
  title  = {Material-UI},
  url    = {https://material-ui.com/},
}

@WWW{Abramov2021,
  author = {Dan Abramov},
  date   = {2021-01-08},
  title  = {React-Redux},
  url    = {https://react-redux.js.org/},
}

@WWW{Microsoft2021,
  author = {Microsoft},
  date   = {2021-02-19},
  title  = {Windows Presentation Foundation},
  url    = {https://docs.microsoft.com/en-us/dotnet/desktop/wpf/?view=netframeworkdesktop-4.8},
}

@WWW{Microsoft2021a,
  author = {Microsoft},
  date   = {2021-02-19},
  title  = {Windows UI Library},
  url    = {https://docs.microsoft.com/en-us/windows/apps/winui/winui3/},
}

@WWW{Chollet2019,
  author  = {François Chollet},
  title   = {Keras},
  url     = {https://keras.io/},
  urldate = {2019-10-09},
  year    = {2019},
}

@Article{Long2014,
  author      = {Jonathan Long and Evan Shelhamer and Trevor Darrell},
  date        = {2014-11-14},
  title       = {Fully Convolutional Networks for Semantic Segmentation},
  eprint      = {1411.4038},
  eprintclass = {cs.CV},
  eprinttype  = {arXiv},
  abstract    = {Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Our key insight is to build "fully convolutional" networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet, the VGG net, and GoogLeNet) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task. We then define a novel architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20\% relative improvement to 62.2\% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes one third of a second for a typical image.},
  file        = {:http\://arxiv.org/pdf/1411.4038v2:PDF},
  keywords    = {cs.CV},
}

@WWW{pandas2021,
  author = {pandas},
  date   = {2021-02-20},
  title  = {pandas},
  url    = {https://pandas.pydata.org/},
}

@WWW{Google2021,
  author = {Google},
  date   = {2021-02-20},
  title  = {TensorBoard},
  url    = {https://www.tensorflow.org/tensorboard/},
}

@WWW{Google2021a,
  author = {Google},
  date   = {2021-02-20},
  title  = {Tensorflow.js},
  url    = {https://www.tensorflow.org/js},
}

@WWW{Joyent2021,
  author = {Joyent},
  date   = {2021-02-21},
  title  = {Node.js},
  url    = {https://nodejs.org/},
}

@InProceedings{Goessner2019b,
  author    = {Gössner, Stefan},
  booktitle = {13. Kolloquium-Getriebetechnik - Tagungsband},
  date      = {2019},
  title     = {Ebene Mechanismenmodelle als Partikelsysteme - ein neuer Ansatz},
  editor    = {Corves, Burkhard and Wenger, Philippe and H{\"u}sing, Mathias},
  isbn      = {978-3-8325-4979-4},
  location  = {Cham},
  pages     = {169-180},
  publisher = {Springer International Publishing},
  abstract  = {With the growing importance of distance education comes an increasing demand for web-based tools for analysis and simulation of mechanisms.},
}

@InProceedings{Goessner2019a,
  author    = {Gössner, Stefan},
  booktitle = {EuCoMeS 2018},
  date      = {2019},
  title     = {Fundamentals for Web-Based Analysis and Simulation of Planar Mechanisms},
  editor    = {Corves, Burkhard and Wenger, Philippe and H{\"u}sing, Mathias},
  isbn      = {978-3-319-98020-1},
  location  = {Cham},
  pages     = {215--222},
  publisher = {Springer International Publishing},
  abstract  = {With the growing importance of distance education comes an increasing demand for web-based tools for analysis and simulation of mechanisms.},
}

@WWW{Goessner2021a,
  author = {Stefan Gössner},
  date   = {2021-02-21},
  title  = {mec2},
  url    = {https://github.com/goessner/mec2},
}

@WWW{Goessner2021,
  author = {Stefan Gössner},
  date   = {2021-02-21},
  title  = {g2},
  url    = {https://github.com/goessner/g2},
}

@WWW{Facebook2021,
  author = {Facebook},
  date   = {2021-02-21},
  title  = {React.js},
  url    = {https://reactjs.org/},
}

@WWW{Babel2021,
  author = {Babel},
  date   = {2021-02-21},
  title  = {babeljs},
  url    = {https://babeljs.io/},
}

@WWW{Hermann2021,
  author = {Tobias Hermann},
  date   = {2021-02-21},
  title  = {frugally-deep},
  url    = {https://github.com/Dobiasd/frugally-deep},
}

@WWW{SixLabors2021,
  author = {Six Labors},
  date   = {2021-02-21},
  title  = {ImageSharp},
  url    = {https://sixlabors.com/products/imagesharp/},
}

@Article{Bochkovskiy2020,
  author      = {Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao},
  date        = {2020-04-23},
  title       = {YOLOv4: Optimal Speed and Accuracy of Object Detection},
  eprint      = {2004.10934},
  eprintclass = {cs.CV},
  eprinttype  = {arXiv},
  abstract    = {There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy. Practical testing of combinations of such features on large datasets, and theoretical justification of the result, is required. Some features operate on certain models exclusively and for certain problems exclusively, or only for small-scale datasets; while some features, such as batch-normalization and residual-connections, are applicable to the majority of models, tasks, and datasets. We assume that such universal features include Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT) and Mish-activation. We use new features: WRC, CSP, CmBN, SAT, Mish activation, Mosaic data augmentation, CmBN, DropBlock regularization, and CIoU loss, and combine some of them to achieve state-of-the-art results: 43.5\% AP (65.7\% AP50) for the MS COCO dataset at a realtime speed of ~65 FPS on Tesla V100. Source code is at https://github.com/AlexeyAB/darknet},
  file        = {:http\://arxiv.org/pdf/2004.10934v1:PDF},
  keywords    = {cs.CV, eess.IV},
}

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