Property:References
From ISLAB/CAISR
This is a property of type Text.
Pages using the property "References"
Showing 25 pages using this property.
(previous 25) (next 25)" | |
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| "TROLL": a regenerating robot + | (self-detection) K. Gold, B. Scassellati, Using probabilistic reasoning over time to self-recognize, Robotics and Autonomous Systems (2008), doi:10.1016/j.robot.2008.07.006 (body schema) Mai Hikita, Sawa Fuke, Masaki Ogino, Takashi Minato and Minoru Asada. Visual attention by saliency leads cross-modal body representation. IROS - 2008. (anomaly detection) Takahiro Suzuki, Fumihiro Bessho, Tatsuya Harada and Yasuo Kuniyoshi. Visual Anomaly Detection under Temporal and Spatial Non-uniformity for News Finding Robot. IROS 2011. (self-augmentation) Luzius Brodbeck and Fumiya Iida. Enhanced Robotic Body Extension with Modular Units, IROS 2012. |
A | |
| A Decentralized IoT Infrastructure: A comparative study + | Oktian, Y.E., Witanto, E.N. and Lee, S.G., 2021. A Conceptual Architecture in Decentralizing Computing, Storage, and Networking Aspect of IoT Infrastructure. IoT, 2(2), pp.205-221. Kolhar, M., Al-Turjman, F., Alameen, A. and Abualhaj, M.M., 2020. A three layered decentralized IoT biometric architecture for city lockdown during COVID-19 outbreak. Ieee Access, 8, pp.163608-163617. Da Xu, L., Lu, Y. and Li, L., 2021. Embedding blockchain technology into IoT for security: a survey. IEEE Internet of Things Journal. |
| A decision support system for reducing false alarms in ICU + | 1. Liu C, Zhao L, Tang H, Li Q, Wei S, Li J. Life-threatening false alarm rejection in ICU: using the rule-based and multi-channel information fusion method. Physiological Measurement. 2016;37(8):1298. 2. Konkani A, Oakley B, Bauld TJ. Reducing hospital noise: a review of medical device alarm management. Biomedical Instrumentation & Technology. 2012;46(6):478-87. 3. Cvach M. Monitor alarm fatigue: an integrative review. Biomedical Instrumentation & Technology. 2012;46(4):268-77. 4. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet. Circulation. 2000;101(23):e215. |
| Activity monitoring for AAL + | Beth Logan et al. A Long-Term Evaluation of Sensing Modalities for Activity Recognition. Ubiquitous Computing. Lecture Notes in Computer Science vol. 4717, pp. 483-50, 2007. Juan Carlos Augusto, Hideyuki Nakashima, Hamid Aghajan. Ambient Intelligence and Smart Environments: A State of the Art. Handbook of Ambient Intelligence and Smart Environments, pp 3-31, 2010. |
| Acumen Robot Model Series + | http://www.acumen-language.org/ http://en.wikipedia.org/wiki/SCARA SCARA |
| Adaptive warning field system + | SAS2-project, http://islab.hh.se/mediawiki/SAS2 ROS - Robot Operating System, http://www.ros.org/ OpenCv - http://opencv.org/ Nemati, Hassan, Åstrand, Björn (2014). Tracking of People in Paper Mill Warehouse Using Laser Range Sensor. 2014 UKSim-AMSS 8th European Modelling Symposium, EMS 2014, Pisa, Italy, 21-23 October, 2014. Power, P. Wayne, and Johann A. Schoonees. "Understanding background mixture models for foreground segmentation." Proceedings image and vision computing New Zealand. Vol. 2002. 2002. |
| Agent and object detection and classification in a warehouse setting + | SAS2-project, http://islab.hh.se/mediawiki/SAS2 ROS - Robot Operating System, http://www.ros.org/ OpenCv - http://opencv.org/ Lalonde, Jean-Francois; Vandapel, Nicolas; Huber, Daniel; Hebert, Martial; Natural terrain classification using three-dimensional ladar data for ground robot mobility, Journal of Field Robotics, Vol. 23, No. 10, pp. 839 - 861, November, 2006 Mosberger, Rafael; Vision-based human detection from mobile machinery in industrial environments, Thesis, Örebro University, Sweden, 2016 Saarinen, Jari P.; Andreasson, Henrik; Stoyanov, Todor; Lilienthal, Achim J.; 3D normal distributions transform occupancy maps: An efficient representation for mapping in dynamic environments, The International Journal of Robotics Research, Vol 32, Issue 14, pp. 1627 – 1644, 2013 |
| Analysing comments (NLP) for Malware Analysis + | https://dl.acm.org/doi/pdf/10.1145/3564625.3567988 |
| Analysis of Ambient Sound in HINT + | Dekkers, G., Vuegen, L., van Waterschoot, T., Vanrumste, B., & Karsmakers, P. (2018). DCASE 2018 Challenge-Task 5: Monitoring of domestic activities based on multi-channel acoustics. arXiv preprint arXiv:1807.11246. Dekkers, G., Lauwereins, S., Thoen, B., Adhana, M. W., Brouckxon, H., Van den Bergh, B., ... & Karsmakers, P. (2017). The SINS database for detection of daily activities in a home environment using an acoustic sensor network. Detection and Classification of Acoustic Scenes and Events 2017. |
| Analyzing Human Motion using Inertial Sensors + | J. Rueterbories, E. G. Spaich, B. Larsen, and O. K. Andersen, “Methods for gait event detection and analysis in ambulatory systems,” Med. Eng. & Phys., vol. 32, no. 6, pp. 545–552, 2010. J. J. Kavanagh and H. B. Menz, “Accelerometry: A technique for quantifying movement patterns during walking,” Gait & Posture, vol. 28, no. 1, pp. 1–15, 2008. D. Lai, R. Begg, and M. Palaniswami, “Computational intelligence in gait research: A perspective on current app. And future challenges,” Info. Tech. in Biomed., IEEE Trans. on, vol. 13, no. 5, pp. 687–702, 2009. |
| Analyzing Privacy Policies (NLP) -- Malware Analysis + | https://www.usenix.org/system/files/sec22-manandhar.pdf https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7579770&tag=1 https://dl.acm.org/doi/pdf/10.1145/3442381.3450048 https://dspace.networks.imdea.org/bitstream/handle/20.500.12761/690/On_The_Ridiculousness_of_Notice_and_Consent_2019_EN.pdf?sequence=1 |
| Anomaly Detection on Truck Histograms + | Learning Low-Dimensional Representation of Bivariate Histogram Data https://ieeexplore.ieee.org/abstract/document/8464276 |
| Anomaly detection from IoT Time Series @AlfaLaval + | (1) https://www.youtube.com/watch?v=ZuydOEws92s <br /> (2) https://www.youtube.com/watch?v=sF2DeSPrGfc |
| Anomaly ranking of District Heating Substations + | M. Goldstein and S. Uchida, "A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data", PLOS ONE, vol. 11, no. 4, p. e0152173, 2016. P. Arjunan, H. Khadilkar, T. Ganu, Z. Charbiwala, A. Singh and P. Singh, "Multi-User Energy Consumption Monitoring and Anomaly Detection with Partial Context Information", Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments - BuildSys '15, 2015. D. Araya, K. Grolinger, H. ElYamany, M. Capretz and G. Bitsuamlak, "An ensemble learning framework for anomaly detection in building energy consumption", Energy and Buildings, vol. 144, pp. 191-206, 2017. S. Rayana and L. Akoglu, "Less is More", ACM Transactions on Knowledge Discovery from Data, vol. 10, no. 4, pp. 1-33, 2016. Huang, Huaming, "Rank Based Anomaly Detection Algorithms" (2013). Electrical Engineering and Computer Science - Dissertations.Paper 331. |
| Automated Inference regarding Goals in Elite Football Data + | https://www.worldatlas.com/articles/what-are-the-most-popular-sports-in-the-world.html https://www.alliedmarketresearch.com/football-market-A11328 Jordet, G., Aksum, K. M., Pedersen, D. N., Walvekar, A., Trivedi, A., McCall, A., ... & Priestley, D. (2020). Scanning, contextual factors, and association with performance in english premier league footballers: an investigation across a season. Frontiers in psychology, 11, 553813. Decroos, T., & Davis, J. (2019, September). Player vectors: Characterizing soccer players’ playing style from match event streams. In Joint European conference on machine learning and knowledge discovery in databases (pp. 569-584). Springer, Cham. |
| Automatic Generation of Realtime Machine Learning Architectures + | Subbaraj, H., 2020. Using Dataflow for Machine Learning Inference. Anderson, J., Alkabani, Y. and El-Ghazawi, T., 2019. Towards Energy-Quality Scaling in Deep Neural Networks. IEEE Design & Test. |
| Automatic Idea Detection from social media for Controlling and Preventing Healthcare-Associated Infections (with funding opportunity) + | Devlin, Jacob, et al. "Bert: Pre-training of deep bidirectional transformers for language understanding." arXiv preprint arXiv:1810.04805 (2018). Nguyen, Dat Quoc, Thanh Vu, and Anh Tuan Nguyen. "BERTweet: A pre-trained language model for English Tweets." arXiv preprint arXiv:2005.10200 (2020). Gould, Dinah, et al. "Electronic hand hygiene monitoring: accuracy, impact on the Hawthorne effect and efficiency." Journal of Infection Prevention 21.4 (2020): 136-143. Christensen, Kasper, et al. "How good are ideas identified by an automatic idea detection system?." Creativity and Innovation Management 27.1 (2018): 23-31. |
| Automatic Machine Learning (AUTO-AUTO-ENCODER!) + | The following paper summarises the algorithm configuration in the different domain : http://aad.informatik.uni-freiburg.de/papers/16-AUTOML-AutoNet.pdf This paper presents the initial idea behind Bayesian optimization for estimating parameter: https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf Previous master thesis on applying autoencoder for histogram data: Robin Ng, “Efficient Implementation of Histogram Dimension Reduction using Deep Learning”, 2017. |
B | |
| Barcode mapping in warehouses + | AIMS-project, http://islab.hh.se/mediawiki/AIMS ROS - Robot Operating System, http://www.ros.org/ ZBar bar code reader, http://zbar.sourceforge.net/ Stampfer, D.; Lutz, M.; Schlegel, C., "Information driven sensor placement for robust active object recognition based on multiple views," Technologies for Practical Robot Applications (TePRA), 2012 IEEE International Conference on , vol., no., pp.133,138, 23-24 April 2012, doi: 10.1109/TePRA.2012.6215667 Karpischek, S., Michahelles, F., Fleisch, E., “my2cents: enabling research on consumer-product interaction”, Pers Ubiquit Comput (2012) 16:613–622, DOI 10.1007/s00779-011-0426-9 Han, Y., Sumi, Y., Matsumoto, Y., and And, N, “.Acquisition of Object Pose from Barcode for Robot Manipulation”, I. Noda et al. (Eds.): SIMPAR 2012, LNAI 7628, pp. 299–310, 2012. G Meng, S Darman, “Label and Barcode Detection in Wide Angle Image”, Master Thesis, Halmstad University, Sweden, http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-23979 |
| Beyond 5G baseband processing on a multicore architecture + | * Larsson, Erik G., et al. "Massive MIMO for next generation wireless systems." IEEE communications magazine 52.2 (2014): 186-195. * Malkowsky, Steffen. Massive MIMO: Prototyping, Proof-of-Concept and Implementation. Diss. University of Lund, 2019. |
| Biases in electronic health records + | 1. Verheij, Robert A., et al. "Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse." Journal of medical Internet research 20.5 (2018). 2. Gianfrancesco, Milena A., et al. "Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data." JAMA internal medicine (2018). 3. Johnson, Alistair EW, et al. "MIMIC-III, a freely accessible critical care database." Scientific data 3 (2016): 160035. |
| Body posture alignment feedback using xAI + | Vivek Anand Thoutam, Anugrah Srivastava, Tapas Badal, Vipul Kumar Mishra, G. R. Sinha, Aditi Sakalle, Harshit Bhardwaj, Manish Raj, "Yoga Pose Estimation and Feedback Generation Using Deep Learning", Computational Intelligence and Neuroscience, vol. 2022, Article ID 4311350, 12 pages, 2022. https://doi.org/10.1155/2022/4311350 Cooney, Martin & Pihl, J & Larsson, H & Orand, A & Aksoy, Eren. (2019). Exercising with an "Iron Man": Design for a Robot Exercise Coach for Persons with Dementia. 10.13140/RG.2.2.14286.61765. Chaudhari, Ajay, et al. "Yog-guru: Real-time yoga pose correction system using deep learning methods." 2021 International Conference on Communication information and Computing Technology (ICCICT). IEEE, 2021. https://doi.org/10.1109/ICCICT50803.2021.9509937 |
C | |
| Chess playing humanoid robot by vision + | https://www.youtube.com/watch?v=gXOkWuSCkRI |
| Comparative study of an automated testing coverage for a TCP/IP stack implementation + | 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 2017), Uppsala, Sweden, April 2017, Volume 244 of EPTCS. http://ceres.hh.se/mediawiki/images/b/bb/Mostowski_mars2017.pdf Thomas Arts and John Hughes (2016): How Well are Your Requirements Tested? In: 2016 IEEE International Conference on Software Testing, Verification and Validation, pp. 244–254, doi:10.1109/ICST.2016.23. |
| Comprehending low-dimensional manifolds of temporal data from the home + | Maaten, L. V. D., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(Nov), 2579-2605. Lundström, J., Järpe, E., & Verikas, A. (2016). Detecting and exploring deviating behaviour of smart home residents. Expert Systems with Applications, 55, 429-440. Rauber, P. E., Falcão, A. X., & Telea, A. C. (2016). Visualizing time-dependent data using dynamic t-SNE. Proc. EuroVis Short Papers, 2(5). Cheng, J., Liu, H., Wang, F., Li, H., & Zhu, C. (2015). Silhouette analysis for human action recognition based on supervised temporal t-sne and incremental learning. IEEE Transactions on Image Processing, 24(10), 3203-3217. |
