Property:References

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Showing 20 pages using this property.
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Merging Clothoids with B-Splines +
http://en.wikipedia.org/wiki/B-spline http://en.wikipedia.org/wiki/Clothoid  +
Mining For Meanings In Robot Maps +
Pronobis, Andrzej, and Rajesh PN Rao. "Learning Deep Generative Spatial Models for Mobile Robots." arXiv preprint arXiv:1610.02627 (2016). Khalil, Wisama, and Etienne Dombre. Modeling, identification and control of robots. Butterworth-Heinemann, 2004. Shahbandi, Saeed Gholami, Björn Åstrand, and Roland Philippsen. "Semi-supervised semantic labeling of adaptive cell decomposition maps in well-structured environments." Mobile Robots (ECMR), 2015 European Conference on. IEEE, 2015.  +
Model Heterogeneity in Federated Learning +
Advances and Open Problems in Federated Learning: https://hal.inria.fr/hal-02406503/document FedML: A Research Library and Benchmark for Federated Machine Learning: https://arxiv.org/pdf/2007.13518.pdf  +
Model Volvo Truck Lifetime Repair History +
http://www.sciencedirect.com/science/article/pii/S000437029800023X http://www.pomdp.org/index.shtml http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.129.7714 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.1619 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.335.8737  +
Model behaviour of agents in a warehouse setting +
SAS2-project, http://islab.hh.se/mediawiki/SAS2 ROS - Robot Operating System, http://www.ros.org/ OpenCv - http://opencv.org/ Lidström, Kristoffer; Situation-Aware vehicles – supporting the next generation of cooperative traffic system, PhD thesis, Örebro university, 2012. Lundström, Jens; Järpe, Eric; Verikas, Antanas; Detecting and exploring deviating behaviour of smart home residents, Expert systems with applications., 55, s. 429-440, 2016 Lidström, Kristoffer; Larsson, Tony; Act normal: using uncertainty about driver intentions as a warning criterion, 16th World Congress on Intelligent Transportation Systems (ITS WC), 21-25 September, 2009, Stockholm, Sweden Lidström, Kristoffer; Model-based Estimation of Driver Intentions Using Particle Filtering, Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems Beijing, China, October 12-15, 2008  +
Model-Based Testing of Zero-Copy Protocols +
1. Jan Tretmans. Model-based testing and some steps towards test-based modelling. In Marco Bernardo and Valérie Issarny, editors, Formal Methods for Eternal Networked Software Systems, LNCS 6659, pages 297–326. Springer, 2011. 2. Wojciech Mostowski, Thomas Arts, and John Hughes. Modelling of Autosar Libraries for Large Scale Testing. Proceedings, 2nd Workshop on Models for Formal Analysis of Real Systems (MARS), EPTCS 244, 2017. 3. ICEORYX home page: https://iceoryx.io/ 4. ALEX.AI home page: https://www.apex.ai/  +
Modeling patient trajectories using different representation learning techniques +
Attention is all you need: https://arxiv.org/abs/1706.03762 Bert: Pre-training of deep bidirectional transformers for language understanding: https://arxiv.org/abs/1810.04805 BEHRT: transformer for electronic health records: https://www.nature.com/articles/s41598-020-62922-y MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning: https://arxiv.org/abs/2107.09288 Heterogeneous Similarity Graph Neural Network on Electronic Health Records: https://arxiv.org/abs/2101.06800 Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer: https://arxiv.org/abs/1906.04716 Variationally Regularized Graph-based Representation Learning for Electronic Health Records: https://arxiv.org/pdf/1912.03761.pdf  +
Multi-robot trajectory adaptation using a state-time elastic band approach +
tbd  +
Multiband RF Rectifier for Self-Powered IoT Devices +
1. Muhammad, Surajo, et al. "Harvesting Systems for RF Energy: Trends, Challenges, Techniques, and Tradeoffs." Electronics 11.6 (2022): 959. 2. Rotenberg, Samuel A., et al. "Efficient rectifier for wireless power transmission systems." IEEE Transactions on Microwave Theory and Techniques 68.5 (2020): 1921-1932. 3. Vu, Hong Son, et al. "Multiband ambient RF energy harvesting for autonomous IoT devices." IEEE Microwave and Wireless Components Letters 30.12 (2020): 1189-1192. 4. Eid, A., et al. "Flexible w-band rectifiers for 5g-powered IoT autonomous modules." 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting. IEEE, 2019.  +
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Name of the new projectEstimating agricultural development indicators over large areas from satellite images – an approach using convolutional neural networks and transfer learning +
Xie, M., N. Jean, M. Burke, D. Lobell & S. Ermon (2015) Transfer learning from deep features for remote sensing and poverty mapping. arXiv preprint arXiv:1510.00098. Jean, N., M. Burke, et al (2016) Combining satellite imagery and machine learning to predict poverty. Science, 353, 790-794.  +
Network-assisted positioning in confined spaces using 802.11 Access Layer information +
IEEE 802.11az Indoor Positioning with mmWave (https://doi.org/10.48550/arXiv.2303.05996)  +
O
Object Movement Prediction for Autonomous Cars +
https://motchallenge.net https://github.com/abhineet123/Deep-Learning-for-Tracking-and-Detection https://arxiv.org/pdf/1909.07707.pdf  +
Obstacle Identification from 3D Data for AGVs in a Warehouse Environment +
Zhang, Hao, et al. "SVM-KNN: Discriminative nearest neighbor classification for visual category recognition." Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. Vol. 2. IEEE, 2006. Golovinskiy, Aleksey, Vladimir G. Kim, and Thomas Funkhouser. "Shape-based recognition of 3D point clouds in urban environments." Computer Vision, 2009 IEEE 12th International Conference on. IEEE, 2009. Rusu, Radu Bogdan, and Steve Cousins. "3d is here: Point cloud library (pcl)." Robotics and Automation (ICRA), 2011 IEEE International Conference on. IEEE, 2011. Nüchter, Andreas, and Joachim Hertzberg. "Towards semantic maps for mobile robots." Robotics and Autonomous Systems 56.11 (2008): 915-926. Lai, Kevin, and Dieter Fox. "Object recognition in 3D point clouds using web data and domain adaptation." The International Journal of Robotics Research 29.8 (2010): 1019-1037. Brostow, Gabriel J., et al. "Segmentation and recognition using structure from motion point clouds." Computer Vision–ECCV 2008. Springer Berlin Heidelberg, 2008. 44-57. Rusu, Radu Bogdan, et al. "Fast 3d recognition and pose using the viewpoint feature histogram." Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on. IEEE, 2010. Drost, Bertram, et al. "Model globally, match locally: Efficient and robust 3D object recognition." Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on. IEEE, 2010.  +
On control of robots in remote workspaces using lasers +
In the video clip, http://mobile-robotics.com/ragvald.php , a rate gyro is used to stabilized the heading of a mini UGV prototype.  +
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Pallet Detection and Mapping +
Belongie, Serge, Jitendra Malik, and Jan Puzicha. "Shape matching and object recognition using shape contexts." Pattern Analysis and Machine Intelligence, IEEE Transactions on 24.4 (2002): 509-522. Viola, Paul, and Michael Jones. "Rapid object detection using a boosted cascade of simple features." Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on. Vol. 1. IEEE, 2001. Lowe, David G. "Local feature view clustering for 3D object recognition." Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on. Vol. 1. IEEE, 2001. Lowe, David G. "Distinctive image features from scale-invariant keypoints." International journal of computer vision 60.2 (2004): 91-110. Bay, Herbert, et al. ”Speeded-up robust features (SURF).” Computer vision and image understanding 110.3 (2008): 346-359. Pinto, Nicolas, David D. Cox, and James J. DiCarlo. "Why is real-world visual object recognition hard?." PLoS computational biology 4.1 (2008): e27.  +
Peer Group Discovery in District Heating Substations and Heat Pumps for Self-Monitoring +
Y. Kim and S. Sohn, "Stock fraud detection using peer group analysis", Expert Systems with Applications, vol. 39, no. 10, pp. 8986-8992, 2012. D. Weston, D. Hand, N. Adams, C. Whitrow and P. Juszczak, "Plastic card fraud detection using peer group analysis", Advances in Data Analysis and Classification, vol. 2, no. 1, pp. 45-62, 2008. D. Weston, N. Adams, Y. Kim and D. Hand, "Fault Mining Using Peer Group Analysis", Challenges at the Interface of Data Analysis, Computer Science, and Optimization, pp. 453-461, 2012.  +
Piglets Detection and Counting using Deep Neural Networks +
Yolo: https://pjreddie.com/darknet/yolo/  +
Pose & distance of toy-vehicles in front by spirals +
https://www.youtube.com/watch?v=nixxFXZ-X3s  +
Positioning of the user at the HINT +
wireless communication, digital communication  +
Predicting Energy Consumption for Heavy-Duty Vehicles (in collaboration with Volvo) +
Koprinska, I., Wu, D., & Wang, Z. (2018, July). Convolutional neural networks for energy time series forecasting. In 2018 international joint conference on neural networks (IJCNN) (pp. 1-8). IEEE. Cao, D., Wang, Y., Duan, J., Zhang, C., Zhu, X., Huang, C., ... & Zhang, Q. (2020). Spectral temporal graph neural network for multivariate time-series forecasting. Advances in neural information processing systems, 33, 17766-17778. Geng, X., He, X., Xu, L., & Yu, J. (2022). Graph correlated attention recurrent neural network for multivariate time series forecasting. Information Sciences, 606, 126-142. Nan, S., Tu, R., Li, T., Sun, J., & Chen, H. (2022). From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus. Energy, 261, 125188. De Cauwer, C., Verbeke, W., Coosemans, T., Faid, S., & Van Mierlo, J. (2017). A data-driven method for energy consumption prediction and energy-efficient routing of electric vehicles in real-world conditions. Energies, 10(5), 608. Arastehfar, S., Matinkia, M., & Jabbarpour, M. R. (2022). Short-term residential load forecasting using graph convolutional recurrent neural networks. Engineering Applications of Artificial Intelligence, 116, 105358. Shchetinin, E. Y. (2018). Cluster-based energy consumption forecasting in smart grids. In Distributed Computer and Communication Networks: 21st International Conference, DCCN 2018, Moscow, Russia, September 17–21, 2018, Proceedings 21 (pp. 445-456). Springer International Publishing. Le, T., Vo, M. T., Kieu, T., Hwang, E., Rho, S., & Baik, S. W. (2020). Multiple electric energy consumption forecasting using a cluster-based strategy for transfer learning in smart building. Sensors, 20(9), 2668.  +
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