Anzahl der Treffer: 15
Erstellt: Sun, 16 Jan 2022 13:13:53 +0100 in 0.1316 sec

Franke, Henning; Kucera, Paul; Kuners, Julian; Reinhold, Tom; Grabmann, Martin; Mäder, Patrick; Seeland, Marco; Gläser, Georg;
Trash or treasure? Machine-learning based PCB layout anomaly detection with AnoPCB. - In: SMACD/ PRIME 2021, (2021), S. 48-51

Ravi Kumar, Varun; Yogamani, Senthil; Milz, Stefan; Mäder, Patrick;
FisheyeDistanceNet++: self-supervised fisheye distance estimation with self-attention, robust loss function and camera view generalization. - In: Electronic imaging, ISSN 2470-1173, Bd. 2021 (2021), S. 181-1-181-10

FisheyeDistanceNet [1] proposed a self-supervised monocular depth estimation method for fisheye cameras with a large field of view (> 180˚). To achieve scale-invariant depth estimation, FisheyeDistanceNet supervises depth map predictions over multiple scales during training. To overcome this bottleneck, we incorporate self-attention layers and robust loss function [2] to FisheyeDistanceNet. A general adaptive robust loss function helps obtain sharp depth maps without a need to train over multiple scales and allows us to learn hyperparameters in loss function to aid in better optimization in terms of convergence speed and accuracy. We also ablate the importance of Instance Normalization over Batch Normalization in the network architecture. Finally, we generalize the network to be invariant to camera views by training multiple perspectives using front, rear, and side cameras. Proposed algorithm improvements, FisheyeDistanceNet++, result in 30% relative improvement in RMSE while reducing the training time by 25% on the WoodScape dataset. We also obtain state-of-the-art results on the KITTI dataset, in comparison to other self-supervised monocular methods.

Fischer, Kai; Simon, Martin; Ölsner, Florian; Milz, Stefan; Groß, Horst-Michael; Mäder, Patrick;
StickyPillars: robust and efficient feature matching on point clouds using graph neural networks. - In: IEEE Xplore digital library, ISSN 2473-2001, (2021), S. 313-323

Robust point cloud registration in real-time is an important prerequisite for many mapping and localization algorithms. Traditional methods like ICP tend to fail without good initialization, insufficient overlap or in the presence of dynamic objects. Modern deep learning based registration approaches present much better results, but suffer from a heavy runtime. We overcome these drawbacks by introducing StickyPillars, a fast, accurate and extremely robust deep middle-end 3D feature matching method on point clouds. It uses graph neural networks and performs context aggregation on sparse 3D key-points with the aid of transformer based multi-head self and cross-attention. The network output is used as the cost for an optimal transport problem whose solution yields the final matching probabilities. The system does not rely on hand crafted feature descriptors or heuristic matching strategies. We present state-of-art art accuracy results on the registration problem demonstrated on the KITTI dataset while being four times faster then leading deep methods. Furthermore, we integrate our matching system into a LiDAR odometry pipeline yielding most accurate results on the KITTI odometry dataset. Finally, we demonstrate robustness on KITTI odometry. Our method remains stable in accuracy where state-of-the-art procedures fail on frame drops and higher speeds.

Mohapatra, Sambit; Yogamani, Senthil; Gotzig, Heinrich; Milz, Stefan; Mäder, Patrick;
BEVDetNet: Bird's Eye View LiDAR point cloud based real-time 3D object detection for autonomous driving. - In: IEEE Xplore digital library, ISSN 2473-2001, (2021), S. 2809-2915

3D object detection based on LiDAR point clouds is a crucial module in autonomous driving particularly for long range sensing. Most of the research is focused on achieving higher accuracy and these models are not optimized for deployment on embedded systems from the perspective of latency and power efficiency. For high speed driving scenarios, latency is a crucial parameter as it provides more time to react to dangerous situations. Typically a voxel or point-cloud based 3D convolution approach is utilized for this module. Firstly, they are inefficient on embedded platforms as they are not suitable for efficient parallelization. Secondly, they have a variable runtime due to level of sparsity of the scene which is against the determinism needed in a safety system. In this work, we aim to develop a very low latency algorithm with fixed runtime. We propose a novel semantic segmentation architecture as a single unified model for object center detection using key points, box predictions and orientation prediction using binned classification in a simpler Bird's Eye View (BEV) 2D representation. The proposed architecture can be trivially extended to include semantic segmentation classes like road without any additional computation. The proposed model has a latency of 4 ms on the embedded Nvidia Xavier platform. The model is 5X faster than other top accuracy models with a minimal accuracy degradation of 2% in Average Precision at IoU = 0.5 on KITTI dataset.

Rabe, Martin; Milz, Stefan; Mäder, Patrick;
Development methodologies for safety critical machine learning applications in the automotive domain: a survey. - In: IEEE Xplore digital library, ISSN 2473-2001, (2021), S. 129-141

Enabled by recent advances in the field of machine learning, the automotive industry pushes towards automated driving. The development of traditional safety-critical automotive software is subject to rigorous processes, ensuring its dependability while decreasing the probability of failures. However, the development and training of machine learning applications substantially differs from traditional software development. The processes and methodologies traditionally prescribed are unfit to account for specifics like, e.g., the importance of datasets for a development. We perform a systematic mapping study surveying methodologies proposed for the development of machine learning applications in the automotive domain. We map the identified primary publications to a general machine learning-based development process and preliminary assess their maturity. The reviewss goal is providing a holistic view of current and previous research contributing to ML-aware development processes and identifying challenges that need more attention. Additionally, we list methods, network architectures, and datasets used within these publications. Our meta-study identifies that model training and model V&V received by far the most research attention accompanied by the most mature evaluations. The remaining development phases, concerning domain specification, data management, and model integration, appear underrepresented and in need of more thorough research. Additionally, we identify and aggregate typically methods applied when developing automated driving applications like models, datasets and simulators showing the state of practice in this field.

Mahecha, Miguel; Rzanny, Michael Carsten; Kraemer, Guido; Mäder, Patrick; Seeland, Marco; Wäldchen, Jana;
Crowd-sourced plant occurrence data provide a reliable description of macroecological gradients. - In: Ecography, ISSN 1600-0587, Bd. 44 (2021), 8, S. 1131-1142

Deep learning algorithms classify plant species with high accuracy, and smartphone applications leverage this technology to enable users to identify plant species in the field. The question we address here is whether such crowd-sourced data contain substantial macroecological information. In particular, we aim to understand if we can detect known environmental gradients shaping plant co-occurrences. In this study we analysed 1 million data points collected through the use of the mobile app Flora Incognita between 2018 and 2019 in Germany and compared them with Florkart, containing plant occurrence data collected by more than 5000 floristic experts over a 70-year period. The direct comparison of the two data sets reveals that the crowd-sourced data particularly undersample areas of low population density. However, using nonlinear dimensionality reduction we were able to uncover macroecological patterns in both data sets that correspond well to each other. Mean annual temperature, temperature seasonality and wind dynamics as well as soil water content and soil texture represent the most important gradients shaping species composition in both data collections. Our analysis describes one way of how automated species identification could soon enable near real-time monitoring of macroecological patterns and their changes, but also discusses biases that must be carefully considered before crowd-sourced biodiversity data can effectively guide conservation measures.

Cierpka, Christian; Otto, Henning; Poll, Constanze; Hüther, Jonas; Jeschke, Sebastian; Mäder, Patrick;
SmartPIV: flow velocity estimates by smartphones for education and field studies. - In: Experiments in fluids, ISSN 1432-1114, Bd. 62 (2021), 8, 172, S. 1-13

In this paper, a smartphone application is presented that was developed to lower the barrier to introduce particle image velocimetry (PIV) in lab courses. The first benefit is that a PIV system using smartphones and a continuous wave (cw-) laser is much cheaper than a conventional system and thus much more affordable for universities. The second benefit is that the design of the menus follows that of modern camera apps, which are intuitively used. Thus, the system is much less complex and costly than typical systems, and our experience showed that students have much less reservations to work with the system and to try different parameters. Last but not least the app can be applied in the field. The relative uncertainty was shown to be less than 8%, which is reasonable for quick velocity estimates. An analysis of the computational time necessary for the data evaluation showed that with the current implementation the app is capable of providing smooth live display vector fields of the flow. This might further increase the use of modern measurement techniques in industry and education.

Mäder, Patrick; Kuschke, Tobias; Janke, Mario;
Reactive auto-completion of modeling activities. - In: IEEE transactions on software engineering, ISSN 1939-3520, Bd. 47 (2021), 7, S. 1431-1451

Assisting and automating software engineering tasks is a state-of-the-art way to support stakeholders of development projects. A common assistance function of IDEs is the auto-completion of source code. Assistance functions, such as auto-completion, are almost entirely missing in modeling tools though auto-completion in general gains continuously more importance in software development. We analyze a users performed editing operations in order to anticipate modeling activities and to recommend appropriate auto-completions for them. Editing operations are captured as events and modeling activities are defined as complex event patterns, facilitating the matching by complex-event-processing. The approach provides adapted auto-completions reactively upon each editing operation of the user. We implemented the RapMOD prototype as add-in for the modeling tool Sparx Enterprise Architect . A controlled user experiment with 37 participants performing modeling tasks demonstrated the approach's potential to reduce modeling effort significantly. Users having auto-completions available for a modeling scenario performed the task 27 percent faster, needed to perform 56 percent less actions, and perceived the task 29 percent less difficult.

Mäder, Patrick; Boho, David; Rzanny, Michael Carsten; Seeland, Marco; Wittich, Hans Christian; Deggelmann, Alice; Wäldchen, Jana;
The Flora Incognita app - interactive plant species identification. - In: Methods in ecology and evolution, ISSN 2041-210X, Bd. 12 (2021), 7, S. 1335-1342

Being able to identify plant species is an important factor for understanding biodiversity and its change due to natural and anthropogenic drivers. We discuss the freely available Flora Incognita app for Android, iOS and Harmony OS devices that allows users to interactively identify plant species and capture their observations. Specifically developed deep learning algorithms, trained on an extensive repository of plant observations, classify plant images with yet unprecedented accuracy. By using this technology in a context-adaptive and interactive identification process, users are now able to reliably identify plants regardless of their botanical knowledge level. Users benefit from an intuitive interface and supplementary educational materials. The captured observations in combination with their metadata provide a rich resource for researching, monitoring and understanding plant diversity. Mobile applications such as Flora Incognita stimulate the successful interplay of citizen science, conservation and education.

Barnkob, Rune; Cierpka, Christian; Chen, Minqian; Sachs, Sebastian; Mäder, Patrick; Rossi, Massimiliano;
Defocus particle tracking : a comparison of methods based on model functions, cross-correlation, and neural networks. - In: Measurement science and technology, ISSN 1361-6501, Bd. 32 (2021), 9, 094011, insges. 14 S.

Defocus particle tracking (DPT) has gained increasing importance for its use to determine particle trajectories in all three dimensions with a single-camera system, as typical for a standard microscope, the workhorse of todays ongoing biomedical revolution. DPT methods derive the depth coordinates of particle images from the different defocusing patterns that they show when observed in a volume much larger than the respective depth of field. Therefore it has become common for state-of-the-art methods to apply image recognition techniques. Two of the most commonly and widely used DPT approaches are the application of (astigmatism) particle image model functions (MF methods) and the normalized cross-correlations between measured particle images and reference templates (CC methods). Though still young in the field, the use of neural networks (NN methods) is expected to play a significant role in future and more complex defocus tracking applications. To assess the different strengths of such defocus tracking approaches, we present in this work a general and objective assessment of their performances when applied to synthetic and experimental images of different degrees of astigmatism, noise levels, and particle image overlapping. We show that MF methods work very well in low-concentration cases, while CC methods are more robust and provide better performance in cases of larger particle concentration and thus stronger particle image overlap. The tested NN methods generally showed the lowest performance, however, in comparison to the MF and CC methods, they are yet in an early stage and have still great potential to develop within the field of DPT.

Kumar, Varun Ravi; Klingner, Marvin; Yogamani, Senthil; Milz, Stefan; Fingscheidt, Tim; Mäder, Patrick;
SynDistNet: self-supervised monocular fisheye camera distance estimation synergized with semantic segmentation for autonomous driving. - In: 2021 IEEE Winter Conference on Applications of Computer Vision, (2021), S. 61-71

State-of-the-art self-supervised learning approaches for monocular depth estimation usually suffer from scale ambiguity. They do not generalize well when applied on distance estimation for complex projection models such as in fisheye and omnidirectional cameras. This paper introduces a novel multi-task learning strategy to improve self-supervised monocular distance estimation on fisheye and pinhole camera images. Our contribution to this work is threefold: Firstly, we introduce a novel distance estimation network architecture using a self-attention based encoder coupled with robust semantic feature guidance to the decoder that can be trained in a one-stage fashion. Secondly, we integrate a generalized robust loss function, which improves performance significantly while removing the need for hyperparameter tuning with the reprojection loss. Finally, we reduce the artifacts caused by dynamic objects violating static world assumptions using a semantic masking strategy. We significantly improve upon the RMSE of previous work on fisheye by 25% reduction in RMSE. As there is little work on fisheye cameras, we evaluated the proposed method on KITTI using a pinhole model. We achieved state-of-the-art performance among self-supervised methods without requiring an external scale estimation.

Ravi Kumar, Varun; Yogamani, Senthil; Rashed, Hazem; Sitsu, Ganesh; Witt, Christian; Leang, Isabelle; Milz, Stefan; Mäder, Patrick;
OmniDet: surround view cameras based multi-task visual perception network for autonomous driving. - In: IEEE Robotics and automation letters, ISSN 2377-3766, Bd. 6 (2021), 2, S. 2830-2837

Surround View fisheye cameras are commonly deployed in automated driving for 360˚ near-field sensing around the vehicle. This work presents a multi-task visual perception network on unrectified fisheye images to enable the vehicle to sense its surrounding environment. It consists of six primary tasks necessary for an autonomous driving system: depth estimation, visual odometry, semantic segmentation, motion segmentation, object detection, and lens soiling detection. We demonstrate that the jointly trained model performs better than the respective single task versions. Our multi-task model has a shared encoder providing a significant computational advantage and has synergized decoders where tasks support each other. We propose a novel camera geometry based adaptation mechanism to encode the fisheye distortion model both at training and inference. This was crucial to enable training on the WoodScape dataset, comprised of data from different parts of the world collected by 12 different cameras mounted on three different cars with different intrinsics and viewpoints. Given that bounding boxes is not a good representation for distorted fisheye images, we also extend object detection to use a polygon with non-uniformly sampled vertices. We additionally evaluate our model on standard automotive datasets, namely KITTI and Cityscapes. We obtain the state-of-the-art results on KITTI for depth estimation and pose estimation tasks and competitive performance on the other tasks. We perform extensive ablation studies on various architecture choices and task weighting methodologies. A short video at https://youtu.be/xbSjZ5OfPes provides qualitative results.

Hofmann, Martin; Mäder, Patrick;
Synaptic scaling - an artificial neural network regularization inspired by nature. - In: IEEE transactions on neural networks and learning systems, ISSN 2162-237X, (2021), S. 1-15

Seeland, Marco; Mäder, Patrick;
Multi-view classification with convolutional neural networks. - In: PLOS ONE, ISSN 1932-6203, Bd. 16 (2021), 1, e0245230, insges. 17 S.

Dunker, Susanne; Motivans, Elena; Rakosy, Demetra; Boho, David; Mäder, Patrick; Hornick, Thomas; Knight, Tiffany M.;
Pollen analysis using multispectral imaging flow cytometry and deep learning. - In: The new phytologist, ISSN 1469-8137, Bd. 229 (2021), 1, S. 593-606

Pollen identification and quantification are crucial but challenging tasks in addressing a variety of evolutionary and ecological questions (pollination, paleobotany), but also for other fields of research (e.g. allergology, honey analysis or forensics). Researchers are exploring alternative methods to automate these tasks but, for several reasons, manual microscopy is still the gold standard. In this study, we present a new method for pollen analysis using multispectral imaging flow cytometry in combination with deep learning. We demonstrate that our method allows fast measurement while delivering high accuracy pollen identification. A dataset of 426 876 images depicting pollen from 35 plant species was used to train a convolutional neural network classifier. We found the best-performing classifier to yield a species-averaged accuracy of 96%. Even species that are difficult to differentiate using microscopy could be clearly separated. Our approach also allows a detailed determination of morphological pollen traits, such as size, symmetry or structure. Our phylogenetic analyses suggest phylogenetic conservatism in some of these traits. Given a comprehensive pollen reference database, we provide a powerful tool to be used in any pollen study with a need for rapid and accurate species identification, pollen grain quantification and trait extraction of recent pollen.



Anzahl der Treffer: 12
Erstellt: Sun, 16 Jan 2022 13:13:46 +0100 in 0.0910 sec

Döring, Ulf; Sommer, Oliver; Fincke, Sabine;
First experiences in the generation of reasonable feedback for Java beginners. - In: INTED 2020, (2020), S. 4383-4389

Ravi Kumar, Varun; Yogamani, Senthil; Bach, Markus; Witt, Christian; Milz, Stefan; Mäder, Patrick;
UnRectDepthNet: self-supervised monocular depth estimation using a generic framework for handling common camera distortion models. - In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (2020), S. 8177-8183

Boho, David; Rzanny, Michael Carsten; Wäldchen, Jana; Nitsche, Fabian; Deggelmann, Alice; Wittich, Hans Christian; Seeland, Marco; Mäder, Patrick;
Flora Capture: a citizen science application for collecting structured plant observations. - In: BMC bioinformatics, ISSN 1471-2105, 21 (2020), article number 576, Seite 1-11

Digital plant images are becoming increasingly important. First, given a large number of images deep learning algorithms can be trained to automatically identify plants. Second, structured image-based observations provide information about plant morphological characteristics. Finally in the course of digitalization, digital plant collections receive more and more interest in schools and universities.

Reinhold, Tom; Seeland, Marco; Grabmann, Martin; Paintz, Christian; Mäder, Patrick; Gläser, Georg;
Ain't got time for this? : reducing manual evaluation effort with Machine Learning based Grouping of Analog Waveform Test Data. - In: ANALOG 2020, (2020), S. 47-52

Gonzalez, Danielle; Rath, Michael; Mirakhorli, Mehdi;
Did you remember to test your tokens?. - In: 2020 IEEE/ACM 17th International Conference on Mining Software Repositories, (2020), S. 232-242

Authentication is a critical security feature for confirming the identity of a system's users, typically implemented with help from frameworks like Spring Security. It is a complex feature which should be robustly tested at all stages of development. Unit testing is an effective technique for fine-grained verification of feature behaviors that is not widely-used to test authentication. Part of the problem is that resources to help developers unit test security features are limited. Most security testing guides recommend test cases in a "black box" or penetration testing perspective. These resources are not easily applicable to developers writing new unit tests, or who want a security-focused perspective on coverage. In this paper, we address these issues by applying a grounded theory-based approach to identify common (unit) test cases for token authentication through analysis of 481 JUnit tests exercising Spring Security-based authentication implementations from 53 open source Java projects. The outcome of this study is a developer-friendly unit testing guide organized as a catalog of 53 test cases for token authentication, representing unique combinations of 17 scenarios, 40 conditions, and 30 expected outcomes learned from the data set in our analysis. We supplement the test guide with common test smells to avoid. To verify the accuracy and usefulness of our testing guide, we sought feedback from selected developers, some of whom authored unit tests in our dataset.

Holtmann, Jörg; Steghöfer, Jan-Philipp; Rath, Michael; Schmelter, David;
Cutting through the jungle: disambiguating model-based traceability terminology. - In: 28th IEEE International Requirements Engineering Conference, (2020), S. 8-19

Ravi Kumar, Varun; Hiremath, Sandesh Athni; Bach, Markus; Milz, Stefan; Witt, Christian; Pinard, Clément; Yogamani, Senthil; Mäder, Patrick;
FisheyeDistanceNet : self-supervised scale-aware distance estimation using monocular fisheye camera for autonomous driving. - In: 2020 IEEE International Conference on Robotics and Automation (ICRA), (2020), S. 574-581

Rath, Michael; Mäder, Patrick;
Request for comments: conversation patterns in issue tracking systems of open-source projects. - In: The 35th Annual ACM Symposium on Applied Computing, (2020), S. 1414-1417

Rath, Michael; Tomova, Mihaela Todorova; Mäder, Patrick;
SpojitR: intelligently link development artifacts. - In: SANER '20, (2020), S. 652-656

König, Jörg; Chen, Minqian; Rösing, Wiebke; Boho, David; Mäder, Patrick; Cierpka, Christian;
On the use of a cascaded convolutional neural network for three-dimensional flow measurements using astigmatic PTV. - In: Measurement science and technology, ISSN 1361-6501, Volume 31 (2020), number 7, 074015, 14 Seiten

Many applications in chemistry, biology and medicine use microfluidic devices to separate, detect and analyze samples on a miniaturized size-level. Fluid flows evolving in channels of only several tens to hundreds of micrometers in size are often of a 3D nature, affecting the tailored transport of cells and particles. To analyze flow phenomena and local distributions of particles within those channels, astigmatic particle tracking velocimetry (APTV) has become a valuable tool, on condition that basic requirements like low optical aberrations and particles with a very narrow size distribution are fulfilled. Making use of the progress made in the field of machine vision, deep neural networks may help to overcome these limiting requirements, opening new fields of applications for APTV and allowing them to be used by nonexpert users. To qualify the use of a cascaded deep convolutional neural network (CNN) for particle detection and position regression, a detailed investigation was carried out starting from artificial particle images with known ground truth to real flow measurements inside a microchannel, using particles with uni- and bimodal size distributions. In the case of monodisperse particles, the mean absolute error and standard deviation of particle depth-position of less than and about 1 [my]m were determined, employing the deep neural network and the classical evaluation method based on the minimum Euclidean distance approach. While these values apply to all particle size distributions using the neural network, they continuously increase towards the margins of the measurement volume of about one order of magnitude for the classical method, if nonmonodisperse particles are used. Nevertheless, limiting the depth of measurement volume in between the two focal points of APTV, reliable flow measurements with low uncertainty are also possible with the classical evaluation method and polydisperse tracer particles. The results of the flow measurements presented herein confirm this finding. The source code of the deep neural network used here is available on https://github.com/SECSY-Group/DNN-APTV.

Janke, Mario; Kuschke, Tobias; Mäder, Patrick;
A definition-by-example approach and visual language for activity patterns in engineering disciplines. - In: PLOS ONE, ISSN 1932-6203, Bd. 15 (2020), 1, e0226877, insges. 28 S.



Anzahl der Treffer: 20
Erstellt: Sun, 16 Jan 2022 13:13:35 +0100 in 0.1328 sec

Döring, Ulf; Fincke, Sabine;
Scoring schemes for multiple-choice tests. - In: INTED 2019, (2019), S. 5835-5844

Döring, Ulf;
Aktuelle Möglichkeiten des Einsatzes von Struktogrammen als anschauliches Hilfsmittel beim Programmierenlernen. - In: Diversität und kulturelle Vielfalt - Differenzieren, Individualisieren - oder Integrieren?, (2019), S. 249-254

Döring, Ulf;
Ansatz zur automatischen Generierung von Java-OOP-Aufgaben inkl. Bewertungsschemen. - Bonn : Gesellschaft für Informatik e.V. (GI). - 1 Online-Ressource (4 Seiten)Publikation entstand im Rahmen der Veranstaltung: Proceedings of the Fourth Workshop "Automatische Bewertung von Programmieraufgaben" (ABP 2019) : 8. und 9. Oktober 2019, Essen / Sven Strickroth, Michael Striewe, Oliver Rod (Hrsg.). - Bonn : Gesellschaft für Informatik e.V. (GI), 2019. - Seite 63-66

Mohamed, Mohamed Elamir; Gotzig, Heinrich; Zöllner, Raoul; Mäder, Patrick;
A convolution neural network based machine learning approach for ultrasonic noise suppression with minimal distortion. - In: 2019 IEEE International Ultrasonics Symposium (IUS), (2019), S. 1629-1634

Vehar, Darko; Nestler, Rico; Franke, Karl-Heinz;
Scene based camera pose estimation in Manhattan worlds. - In: Photonics and education in measurement science 2019, (2019), S. 111440L-1-111440L-9

Rath, Michael; Mäder, Patrick;
Structured information in bug report descriptions - influence on IR-based bug localization and developers. - In: Software quality journal, ISSN 1573-1367, Bd. 27 (2019), 3, S. 1315-1337

Rath, Michael; Tomova, Mihaela Todorova; Mäder, Patrick;
Selecting open source projects for traceability case studies. - In: Requirements engineering: foundation for software quality, (2019), S. 229-242

Brix, Torsten; Döring, Ulf;
15 Jahre Digitale Mechanismen- und Getriebebibliothek. - In: Tagungsband 13. Kolloquium Getriebetechnik, (2019), S. 3-18

Döring, Ulf; Artelt, Benedikt;
On the usefulness of animated structograms in teaching algorithms and programming. - In: Mobile technologies and applications for the internet of things, (2019), S. 34-46

Döring, Ulf; Artelt, Benedikt;
Web-based tools for interactive training in implementing JAVA methods. - In: INTED 2019, (2019), S. 3974-3979

Cierpka, Christian; Mäder, Patrick;
SmartPIV - Smartphone-based flow visualization for education :
SmartPIV - Strömungsvisualisierung mit dem Smartphone in der Lehre. - In: Experimentelle Strömungsmechanik, (2019), S. 23.1-23.7

Kuang, Hongyu; Gao, Hui; Hu, Hao; Ma, Xiaoxing; Lü, Jian; Mäder, Patrick; Egyed, Alexander;
Using frugal user feedback with closeness analysis on code to improve IR-based traceability recovery. - In: ICPC 2019, (2019), S. 369-379

Artelt, Benedikt; Brix, Torsten; Döring, Ulf;
THEDI - the first online editor for the IFToMM dictionary. - In: Advances in mechanism and machine science, (2019), S. 3511-3519

Döring, Ulf; Brix, Torsten; Artelt, Benedikt; Brandt-Salloum, Christiane;
Patents from the age of Prussian industrialization revived. - In: Advances in mechanism and machine science, (2019), S. 1223-1232

Rzanny, Michael Carsten; Mäder, Patrick; Degelmann, Alice; Chen, Minqian; Wäldchen, Jana;
Flowers, leaves or both? How to obtain suitable images for automated plant identification. - In: Plant methods, ISSN 1746-4811, 15 (2019), article number 77, Seite 1-11

Mohamed, Mohamed Elamir; Gotzig, Heinrich; Zöllner, Raoul; Mäder, Patrick;
A machine learning approach for detecting ultrasonic echoes in noisy environments. - In: 2019 IEEE 89th Vehicular Technology Conference (VTC Spring), (2019), insges. 6 S.

Cierpka, Christian; König, Jörg; Chen, Minqian; Boho, David; Mäder, Patrick;
On the use of machine learning algorithms for the calibration of astigmatism PTV. - In: 13th International Symposium on Particle Image Velocimetry, (2019), S. 772-781

Rath, Michael; Mäder, Patrick;
The SEOSS 33 dataset - requirements, bug reports, code history, and trace links for entire projects. - In: Data in Brief, ISSN 2352-3409, Volume 25 (2019), article 104005, Seite 1-12

Hofmann, Martin; Seeland, Marco; Mäder, Patrick;
Efficiently annotating object images with absolute size information using mobile devices. - In: International journal of computer vision, ISSN 1573-1405, Bd. 127 (2019), 2, S. 207-224

Seeland, Marco; Rzanny, Michael Carsten; Boho, David; Wäldchen, Jana; Mäder, Patrick;
Image-based classification of plant genus and family for trained and untrained plant species. - In: BMC bioinformatics, ISSN 1471-2105, Bd. 20 (2019), (3. Jan.), Article 4, insges. 13 S.



Anzahl der Treffer: 14
Erstellt: Sun, 16 Jan 2022 13:13:27 +0100 in 0.0968 sec

Meder, Julian; Brüderlin, Beat;
Screen Space Approximate Gaussian Hulls. - In: Eurographics Symposium on Rendering 2018 - Experimental Ideas & Implementations, (2018), S. 107-115

Meder, Julian; Brüderlin, Beat;
Hemispherical Gaussians for accurate light integration. - In: Computer vision and graphics, ISBN 978-3-030-00692-1, (2018), S. 3-15

Vehar, Darko; Nestler, Rico; Franke, Karl-Heinz;
Präzise Berechnung von Kameraposen in Manhattan-Welten. - In: 3D-NordOst 2018, (2018), S. 15-24

Dunke, Susanne; Boho, David; Wäldchen, Jana; Mäder, Patrick;
Combining high-throughput imaging flow cytometry and deep learning for efficient species and life-cycle stage identification of phytoplankton. - In: BMC ecology, ISSN 1472-6785, Bd. 18 (2018), 51, insges. 15 S.

Rath, Michael; Mäder, Patrick;
Influence of structured information in bug report descriptions on IR-based bug localization. - In: SEAA 2018, ISBN 978-1-5386-7383-6, (2018), S. 26-32

Wäldchen, Jana; Mäder, Patrick;
Machine learning for image based species identification. - In: Methods in ecology and evolution, ISSN 2041-210X, Bd. 9 (2018), 11, S. 2216-2225

Mäder, Patrick; Baumann, Tommy; Grüner, David;
Tool-supported methodology for the development of complex systems and complex software (SimDesign) :
Werkzeugunterstützte Methodik zur simulationsgetriebenen Entwicklung von komplexen Systemen und komplexer Software (SimDesign) : KMU-Innovativ Verbundvorhaben : Projektlaufzeit: 1. September 2014 - 31.Dezember 2017. - [Ilmenau] : [Technische Universität Ilmenau, Fachgebiet Softwaretechnik für sicherheitskritische Systeme]. - 1 Online-Ressource (17 Seiten, 1,30 MB)Förderkennzeichen BMBF 01IS14026A-B. - Verbund-Nummer 01155480

Krishnamurthy, Rohan; Meinel, Michael; Haupt, Carina; Schreiber, Andreas; Mäder, Patrick;
DLR secure software engineering. - In: 2018 ACM/IEEE 1st International Workshop on Security Awareness from Design to Deployment, ISBN 978-1-4503-5727-2, (2018), S. 49-50
Position and vision paper

Rath, Michael; Lo, David; Mäder, Patrick;
Analyzing requirements and traceability information to improve bug localization. - In: 2018 ACM/IEEE 15th International Conference on Mining Software Repositories, ISBN 978-1-4503-5716-6, (2018), S. 442-453

Tomova, Mihaela Todorova; Rath, Michael; Mäder, Patrick;
Poster: use of trace link types in issue tracking systems. - In: 2018 ACM/IEEE 40th International Conference on Software Engineering: companion proceeedings, ISBN 978-1-4503-5663-3, (2018), S. 181-182

Rath, Michael; Rendall, Jacob; Gio, Jin L. C.; Cleland-Huang, Jane; Mäder, Patrick;
Traceability in the wild: automatically augmenting incomplete trace links. - In: 2018 ACM/IEEE 40th International Conference on Software Engineering, ISBN 978-1-4503-5638-1, (2018), S. 834-845

Wäldchen, Jana; Rzanny, Michael Carsten; Seeland, Marco; Mäder, Patrick;
Automated plant species identification - trends and future directions. - In: PLoS Computational Biology, ISSN 1553-7358, Bd. 14 (2018), 4, e1005993, insges. 19 S.

Wittich, Hans Christian; Seeland, Marco; Wäldchen, Jana; Rzanny, Michael Carsten; Mäder, Patrick;
Recommending plant taxa for supporting on-site species identification. - In: BMC bioinformatics, ISSN 1471-2105, Bd. 19 (2018), (30. Mai), Article 190, insges. 17 S.

Wäldchen, Jana; Mäder, Patrick;
Plant species identification using computer vision techniques: a systematic literature review. - In: Archives of computational methods in engineering, ISSN 1886-1784, Bd. 25 (2018), 2, S. 507-543



Anzahl der Treffer: 11
Erstellt: Sun, 16 Jan 2022 13:13:20 +0100 in 0.0767 sec

Döring, Ulf; Fincke, Sabine;
Interaktive Ansätze zur Vermittlung von Programmierfähigkeiten im Rahmen des Ingenieurstudiums. - In: Digitalisierung in der Techniklehre, (2017), S. 171-176

Antakli, André; Moya, Pablo Alvarado; Brüderlin, Beat; Canzler, Ulrich;
Virtuelle Techniken und Semantic-Web : Stand der Wissenschaft und Technik. - In: Web-basierte Anwendungen Virtueller Techniken, ISBN 978-3-662-52956-0, (2017), S. 17-116

Jahn, Rainer; Kapusi, Daniel; Vehar, Darko; Nestler, Rico; Franke, Karl-Heinz;
Bewertung von Farbmustercodes zur flächigen, aktiven 3D-Erfassung. - In: 3D-NordOst 2017, (2017), S. 23-32

Färber, Markus; Ghiletiuc, Johannes; Schwarz, Peter; Brüderlin, Beat;
Echtzeit-Visualisierung von sehr großen Virtual- und Augmented-Reality-Szenen auf Smartphones und mobilen Tablet-Computern. - In: Entwerfen Entwickeln Erleben - Methoden und Werkzeuge in der Produktentwicklung, (2017), S. 251-265

Rzanny, Michael Carsten; Seeland, Marco; Wäldchen, Jana; Mäder, Patrick;
Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain. - In: Plant methods, ISSN 1746-4811, 13 (2017), article number 97, Seite 1-11

Rath, Michael; Rempel, Patrick; Mäder, Patrick;
The IlmSeven dataset. - In: 2017 IEEE 25th International Requirements Engineering Conference, (2017), S. 516-519

Rempel, Patrick; Mäder, Patrick;
Preventing defects: the impact of requirements traceability completeness on software quality. - In: IEEE transactions on software engineering, ISSN 1939-3520, Bd. 43 (2017), 8, S. 777-797

Kuschke, Tobias; Mäder, Patrick;
Poster: RapMOD - in situ auto-completion for graphical models. - In: 2017 IEEE/ACM 39th International Conference on Software Engineering companion, ISBN 978-1-5386-1589-8, (2017), S. 303-304

Kuang, Hongyu; Nie, Jia; Hu, Hao; Rempel, Patrick; Lü, Jian; Egyed, Alexander; Mäder, Patrick;
Analyzing closeness of code dependencies for improving IR-based traceability recovery. - In: SANER 2017, ISBN 978-1-5090-5501-2, (2017), S. 68-78

Seeland, Marco; Rzanny, Michael Carsten; Alaqraa, Nedal; Wäldchen, Jana; Mäder, Patrick;
Plant species classification using flower images - a comparative study of local feature representations. - In: PLOS ONE, ISSN 1932-6203, Bd. 12 (2017), 2, e0170629, insges. 29 S.



Anzahl der Treffer: 14
Erstellt: Sun, 16 Jan 2022 13:13:12 +0100 in 0.1174 sec

Meder, Julian; Brüderlin, Beat;
Fast depth image based rendering for synthetic frame extrapolation. - In: Journal of theoretical and applied computer science, ISSN 2299-2634, Bd. 10 (2016), 3, S. 3-18

Jahn, Rainer; Kapusi, Daniel; Franke, Karl-Heinz; Nestler, Rico;
Aktive Stereoskopie mittels Farbmusterkodierung. - In: 22. Workshop Farbbildverarbeitung, (2016), S. 183-196

Junger, Stephan; Nestler, Rico; Gäbler, Daniel;
Multispektraler CMOS-Sensor und dessen Eignungsbewertung für Lichtanwendungen. - In: 22. Workshop Farbbildverarbeitung, (2016), S. 9-21

Seeland, Marco; Rzanny, Michael Carsten; Alaqraa, Nedal; Thuille, Angelika; Wiesner, David; Wäldchen, Jana; Mäder, Patrick;
Description of flower colors for image based plant species classification. - In: 22. Workshop Farbbildverarbeitung, (2016), S. 145-154

Meder, Julian; Brüderlin, Beat;
Decoupling rendering and display using fast depth image based rendering on the GPU. - In: Computer Vision and Graphics, (2016), S. 61-72

Rempel, Patrick;
Continuous assessment of software traceability. - Ilmenau : Universitätsbibliothek, 2016. - 1 Online-Ressource (xi, 153 Seiten)
Technische Universität Ilmenau, Dissertation 2016

Die Nachvollziehbarkeit von Anforderungen ist wichtiges Qualitätsmerkmal der Softwareentwicklung. Für eine Vielzahl von Softwareentwicklungsaktivitäten ist die Nachvollziehbarkeit von Anforderungen eine notwenige Voraussetzung. Dazu gehören unter anderem die Analyse funktionaler Sicherheit, die Einflussanalyse, die Analyse des Abdeckungsgrades oder die Compliance. Für die Entwicklung sicherheitskritischer Softwaresysteme ist dieses Qualitätsmerkmal von besonderer Bedeutung. Daher wird dieses von entsprechenden Richtlinien zur Entwicklung sicherheitskritischer Software explizit vorgeschrieben. Obwohl die Relevanz der Nachvollziehbarkeit in Softwareprojekten allgemein bekannt ist, findet nur in wenigen Fällen eine systematische Planung zur Erreichung dieses Qualitätsmerkmals Anwendung. Häufig wird Nachvollziehbarkeit erst nachträglich umgesetzt. Daraus resultieren oft unvollständige Implementierungen der Nachvollziehbarkeit, die trotzdem als Grundlage für schwerwiegende Entscheidungen herangezogen werden. Aus diesem Grunde sollten die entsprechenden Implementierungen einer eingehenden Prüfung unterzogen werden, besonders im Rahmen der Entwicklung sicherheitskritischer Systeme. Dazu ist jedoch eine Vielzahl von Herausforderungen zu meistern. Zum einen hängt die Nachvollziehbarkeit von den projektspezifischen Zielen ab. Bei sicherheitskritischen Systemen müssen oft Vorgaben aus Richtlinien erfüllt werden. Auch die Nutzung der Nachvollziehbarkeit ist sehr stark von den jeweiligen Zielen abhängig. In dieser Arbeit wird ein Ansatz zur systematischen Prüfung von Softwareprojekten im Hinblick auf deren Nachvollziehbarkeit der Anforderungen vorgeschlagen. Eine notwendige Voraussetzung für den Prüfansatz ist die präzise Planung und Definition der Nachvollziehbarkeit von Anforderungen in einem Softwareprojekt. Daher wird im Rahmen dieser Arbeit ein entsprechender Planungsansatz präsentiert. Weiterhin wird ein analytisches Modell zur systematischen Prüfung der Nachvollziehbarkeit in Softwareprojekten präsentiert. Dieses Modell umfasst eine vollständige Klassifikation möglicher Fehlertypen. Außerdem werden Kriterien zur systematischen Erkennung dieser Fehler vorgeschlagen. Die Ergebnisse einer Expertenbefragung bestätigen die Vollständigkeit des analytischen Prüfmodells. Zudem wurde der vorgeschlagene Ansatz zur systematischen Prüfung der Nachvollziehbarkeit von Anforderungen in zwei Studien evaluiert. Dabei konnte der Nutzen des Ansatzes für die Entwicklung von sicherheitskritischer und nicht sicherheitskritischer Software nachgewiesen werden.

Kramer, Rüdiger; Döring, Ulf
Tool zur Unterstützung der bildorientierten Selektion von Patentdokumenten am Beispiel des XPAT Patent Viewers$HRüdiger Kramer, Ulf Döring. - In: Big Data - Chancen und Herausforderungen, ISBN 978-3-932488-20-7, (2016), S. 209-219

Wenzel, Kay; Fischer, Kai; Nestler, Rico; Machleidt, Torsten; Franke, Karl-Heinz;
Konzept einer LED-Lichtquelle zum Synthetisieren von spektral definierten Lichtstimmungen. - In: Proceedings des FWS 2015, (2016), S. 65-73

Nestler, Rico; Jahn, Rainer; Franke, Karl-Heinz; Junger, Stephan; Gäbler, Daniel;
Einsatz hyperspektraler Sensoren zur Überwachung intelligenter LED-basierter Lichtquellen - eine Fallstudie. - In: Proceedings des FWS 2015, (2016), S. 50-64

Prinke, Philipp; Nestler, Rico; Franke, Karl-Heinz;
Umsetzung einer farbwertbezogenen Kontrastanhebung zur robusten Segmentierung von medizinischen Hautbildern. - In: Proceedings des FWS 2015, (2016), S. 39-49

Linåker, Johan; Rempel, Patrick; Regnell, Björn; Mäder, Patrick;
How firms adapt and interact in open source ecosystems: analyzing stakeholder influence and collaboration patterns. - In: Requirements engineering: foundation for software quality, ISBN 978-3-319-30282-9, (2016), S. 63-81

Rempel, Patrick; Mäder, Patrick;
Continuous assessment of software traceability. - In: 2016 IEEE/ACM 38th IEEE International Conference on Software Engineering Companion, ISBN 978-1-4503-4205-6, (2016), S. 747-748

Mäder, Patrick; Egyed, Alexander;
The benefit of requirements traceability when evolving a software product: a controlled experiment. - In: Software Engineering 2016, (2016), S. 109-110

Rempel, Patrick; Mäder, Patrick;
A quality model for the systematic assessment of requirements traceability. - In: Software Engineering 2016, (2016), S. 37-38