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)Q | |
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| Quantifying exercise-induced muscle fatigue by machine learning + | Yousif, Hayder A., et al. "Assessment of muscles fatigue based on surface EMG signals using machine learning and statistical approaches: a review." IOP conference series: materials science and engineering. Vol. 705. No. 1. IOP Publishing, 2019. Karlik, Bekir. "Machine learning algorithms for characterization of EMG signals." International Journal of Information and Electronics Engineering 4.3 (2014): 189. Rampichini, S., Vieira, T. M., Castiglioni, P., & Merati, G. (2020). Complexity analysis of surface electromyography for assessing the myoelectric manifestation of muscle fatigue: A review. Entropy, 22(5), 529. Carroll, T. J., Taylor, J. L., & Gandevia, S. C. (2017). Recovery of central and peripheral neuromuscular fatigue after exercise. Journal of Applied Physiology, 122(5), 1068-1076. Yousefi, J., & Hamilton-Wright, A. (2014). Characterizing EMG data using machine-learning tools. Computers in biology and medicine, 51, 1-13. |
| Quantum Machine Learning models for predicting disease + | 1. Tiwari, P., Dehdashti, S., Obeid, A. K., Marttinen, P., & Bruza, P. (2022). Kernel method based on non-linear coherent states in quantum feature space. Journal of Physics A: Mathematical and Theoretical, 55(35), 355301. 2. Laxminarayana, N., Mishra, N., Tiwari, P., Garg, S., Behera, B. K., & Farouk, A. (2022). Quantum-Assisted Activation for Supervised Learning in Healthcare-based Intrusion Detection Systems. IEEE Transactions on Artificial Intelligence. 3. Tiwari, P., & Melucci, M. (2018, October). Towards a quantum-inspired framework for binary classification. In Proceedings of the 27th ACM international conference on information and knowledge management. 4. Zhang, Y., Liu, Y., Li, Q., Tiwari, P., Wang, B., Li, Y., ... & Song, D. (2021). CFN: a complex-valued fuzzy network for sarcasm detection in conversations. IEEE Transactions on Fuzzy Systems, 29(12), 3696-3710. 5. Moreira, C., Tiwari, P., Pandey, H. M., Bruza, P., & Wichert, A. (2020). Quantum-like influence diagrams for decision-making. Neural Networks, 132, 190-210. |
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| RaspberryPiVolvoLogger + | http://www.raspberrypi.org/ http://lnxpps.de/rpie/ http://islab.hh.se/mediawiki/index.php/ReDi2Service http://www.youtube.com/watch?v=KJ5hMkWPEGY |
| Real-time Motion Analysis using Inertial Sensors + | Chia Bejarano, N.; Ambrosini, E.; Pedrocchi, A.; Ferrigno, G.; Monticone, M.; Ferrante, S., "A Novel Adaptive, Real-Time Algorithm to Detect Gait Events From Wearable Sensors," in Neural Systems and Rehabilitation Engineering, IEEE Transactions on , vol.23, no.3, pp.413-422, May 2015 J. Lee and E. Park, “Quasi real-time gait event detection using shank-attached gyroscopes,” Med. & Bio. Eng. & Comp., vol. 49, no. 6, pp. 707–712, 2011. |
| Real-time bladder scanner + | TBD |
| Reconfigurable Orbital angular momentum (OAM) antenna for High-Speed Wireless Communications + | 1. Li, Weiwen, et al. "A reconfigurable second-order OAM patch antenna with simple structure." IEEE Antennas and Wireless Propagation Letters 19.9 (2020): 1531-1535. 2. Kang, Le, et al. "A mode-reconfigurable orbital angular momentum antenna with simplified feeding scheme." IEEE Transactions on Antennas and Propagation 67.7 (2019): 4866-4871. 3. Kang, Le, et al. "An OAM-mode reconfigurable array antenna with polarization agility." IEEE Access 8 (2020): 40445-40452. 4. Wu, Jie, et al. "A broadband electronically mode-reconfigurable orbital angular momentum metasurface antenna." IEEE Antennas and Wireless Propagation Letters 18.7 (2019): 1482-1486. |
| Reinforcement Learning with Adaptive Representation Learning + | IS A GOOD REPRESENTATION SUFFICIENT FOR SAMPLE EFFICIENT REINFORCEMENT LEARNING?, Simon S. Du, Sham M. Kakade, 2020 Learning State Representations for Query Optimization with Deep Reinforcement Learning, Jennifer Ortiz, Magdalena Balazinska, Johannes Gehrke, S. Sathiya Keerthi, 2018 State Representation Learning for Control: An Overview, Timothée Lesort, Natalia Díaz-Rodríguez, Jean-François Goudou, and David Filliat, 2018 |
| Representation Learning for Deviation Detection + | Bengio, Yoshua, Aaron Courville, and Pascal Vincent. "Representation learning: A review and new perspectives." IEEE transactions on pattern analysis and machine intelligence 35.8 (2013): 1798-1828. Jaeger, Herbert. "Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the" echo state network" approach. GMD-Forschungszentrum Informationstechnik, 2002. Jaeger, Herbert, et al. "Optimization and applications of echo state networks with leaky-integrator neurons." Neural networks 20.3 (2007): 335-352. Lukoševičius, Mantas. "A practical guide to applying echo state networks." Neural networks: Tricks of the trade. Springer Berlin Heidelberg, 2012. 659-686. Wang, Lin, Zhigang Wang, and Shan Liu. "An effective multivariate time series classification approach using echo state network and adaptive differential evolution algorithm." Expert Systems with Applications 43 (2016): 237-249. Li, Decai, Min Han, and Jun Wang. "Chaotic time series prediction based on a novel robust echo state network." IEEE Transactions on Neural Networks and Learning Systems 23.5 (2012): 787-799. Krause13, André Frank, et al. "Evolutionary Optimization of Echo State Networks: multiple motor pattern learning." (2010). Marco Rigamonti et al., "Echo State Network for the Remaining Useful Life Prediction of a Turbofan Engine." Third European Conference of the Prognostics and Health Management Society 2016, Bilbao, Spain, 5-8 July, 2016. PHM Society, 2016. Chen, Huanhuan, Peter Tiňo, and Xin Yao. "Cognitive fault diagnosis in Tennessee Eastman Process using learning in the model space." Computers & Chemical Engineering 67 (2014): 33-42. Quevedo, Joseba, et al. "Combining learning in model space fault diagnosis with data validation/reconstruction: Application to the Barcelona water network." Engineering Applications of Artificial Intelligence 30 (2014): 18-29. Fan, Yuantao, et al. "Predicting Air Compressor Failures with Echo State Networks." Third European Conference of the Prognostics and Health Management Society 2016, Bilbao, Spain, 5-8 July, 2016. PHM Society, 2016. |
| Representation Learning for Fault Detection and Prognosis + | Bengio, Yoshua, Aaron Courville, and Pascal Vincent. "Representation learning: A review and new perspectives." IEEE transactions on pattern analysis and machine intelligence 35.8 (2013): 1798-1828. Jaiswal, Ashish, et al. "A survey on contrastive self-supervised learning." Technologies 9.1 (2020): 2. Liu, Xiao, et al. "Self-supervised learning: Generative or contrastive." IEEE Transactions on Knowledge and Data Engineering (2021). Wan, Chuan, et al. "Representation Learning for Fault Diagnosis with Contrastive Predictive Coding." 2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS). IEEE, 2021. Jiang, Guoqian, et al. "Stacked multilevel-denoising autoencoders: A new representation learning approach for wind turbine gearbox fault diagnosis." IEEE Transactions on Instrumentation and Measurement 66.9 (2017): 2391-2402. Xiao, Dengyu, et al. "Unsupervised deep representation learning for motor fault diagnosis by mutual information maximization." Journal of Intelligent Manufacturing 32.2 (2021): 377-391. Li, Guoqiang, et al. "Self-supervised learning for intelligent fault diagnosis of rotating machinery with limited labeled data." Applied Acoustics 191 (2022): 108663. Wang, Tian, et al. "Data-driven prognostic method based on self-supervised learning approaches for fault detection." Journal of Intelligent Manufacturing 31.7 (2020): 1611-1619. Quevedo, Joseba, et al. "Combining learning in model space fault diagnosis with data validation/reconstruction: Application to the Barcelona water network." Engineering Applications of Artificial Intelligence 30 (2014): 18-29. Fan, Yuantao, et al. "Predicting Air Compressor Failures with Echo State Networks." Third European Conference of the Prognostics and Health Management Society 2016, Bilbao, Spain, 5-8 July, 2016. PHM Society, 2016. |
| Representation of Complex Data Types for Machine Learning + | Statistical Relational Learning Knowledge Representation |
| Resolving Class Imbalance using Generative Adversarial Networks + | NIPS 2016 Tutorial on GANs https://arxiv.org/pdf/1701.00160.pdf Effective data generation for imbalanced learning using Conditional Generative Adversarial Networks https://www.researchgate.net/publication/319672232_Effective_data_generation_for_imbalanced_learning_using_Conditional_Generative_Adversarial_Networks BAGAN: Data Augmentation with Balancing GAN https://arxiv.org/abs/1803.09655 InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets https://arxiv.org/pdf/1606.03657.pdf |
| Robot Artwork + | -robot artwork Michael Raschke, Katja Mombaur, Alexander Schubert. An optimisation-based robot platform for the generation of action paintings. Int. J. Arts and Technology, Vol. 4, No. 2, 2011 181 -emotion recognition from eeg Yuan-Pin Lin, Chi-Hong Wang, Tzyy-Ping Jung, Tien-Lin Wu, Shyh-Kang Jeng, Jeng-Ren Duann, and Jyh-Horng Chen. EEG-Based Emotion Recognition in Music Listening. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 57, NO. 7, JULY 2010 |
| Robot Cooking + | -robot cooking Christian Østergaard Laursen, Søren Pedersen, Timothy Merritt, Ole Caprani. Robot-Supported Food Experiences: Exploring Aesthetic Plating with Design Prototypes. In J.T.K.V. Koh et al. (Eds.): Cultural Robotics 2015, LNAI 9549, pp. 107–130, 2016. DOI: 10.1007/978-3-319-42945-8 10 Springer International Publishing Switzerland 2016.012.241 -common sense acquisition Rakesh Gupta, Mykel J. Kochenderfer. Common Sense Data Acquisition for Indoor Mobile Robots. ROBOTICS |
| Robotic First aid response + | (first aid teleoperated robots) http://www.uasvision.com/2014/10/29/ambulance-drone-with-integrated-defibrillator/ http://www.technologyreview.com/news/411865/a-robomedic-for-the-battlefield/ (fall detection example) Simin Wang, Salim Zabir, Bastian Leibe. Lying Pose Recognition for Elderly Fall Detection (breathing recognition) Phil Corbishley and Esther Rodriguez-Villegas. 2008. Breathing Detection: Towards a Miniaturized, Wearable, Battery-Operated Monitoring System. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 55, NO. 1, JANUARY 2008 |
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| Security analysis of IIoT connectivity protocols + | Eldefrawy, Mohamed, Ismail Butun, Nuno Pereira, and Mikael Gidlund. "Formal security analysis of LoRaWAN." Computer Networks 148 (2019): 328-339. Gebremichael, Teklay, Lehlogonolo PI Ledwaba, Mohamed H. Eldefrawy, Gerhard P. Hancke, Nuno Pereira, Mikael Gidlund, and Johan Akerberg. "Security and Privacy in the Industrial Internet of Things: Current Standards and Future Challenges." IEEE Access 8 (2020): 152351-152366. Patel, C. and Doshi, N., 2020. A Novel MQTT Security framework In Generic IoT Model. Procedia Computer Science, 171, pp.1399-1408. Singh, V.K. and Sharan, H.O., 2019. Security Analysis and Improvements to IoT Communication Protocols-CoAP. |
| Short-Term Energy Demand Forecasting + | Hong, Wei-Chiang. Intelligent Energy Demand Forecasting. Vol. 10. Springer, 2013. Ghofrani, M., et al. "Smart meter based short-term load forecasting for residential customers." North American Power Symposium (NAPS), 2011. IEEE, 2011. http://www.sciencedirect.com/science/article/pii/S1877050914011053 |
| Simulating Crowds for Traffic Safety Research + | http://gamma.cs.unc.edu/research/crowds/ http://www.coppeliarobotics.com/ |
| Smart App for PD + | Pérez-López, Carlos, et al. "Monitoring Motor Fluctuations in Parkinson’s Disease Using a Waist-Worn Inertial Sensor." Advances in Computational Intelligence. Springer International Publishing, 2015. 461-474. LeMoyne, Robert, and Timothy Mastroianni. "Use of Smartphones and Portable Media Devices for Quantifying Human Movement Characteristics of Gait, Tendon Reflex Response, and Parkinson’s Disease Hand Tremor." Mobile Health Technologies: Methods and Protocols (2015): 335-358. |
| Smart Home Simulation + | Teresa Garcia-Valverde, Francisco Campuzano, Emilio Serrano, Ana Villa, and Juan A. Botia. 2012. Simulation of human behaviours for the validation of Ambient Intelligence services: A methodological approach. J. Ambient Intell. Smart Environ. 4, 3 (August 2012), 163-181. Juan A. Botia, Ana Villa, Jose Palma, Ambient Assisted Living system for in-home monitoring of healthy independent elders, Expert Systems with Applications, Volume 39, Issue 9, July 2012, Pages 8136-8148. Pavel, M.; Jimison, H.B.; Wactlar, H.D.; Hayes, T.L.; Barkis, W.; Skapik, J.; Kaye, J., "The Role of Technology and Engineering Models in Transforming Healthcare," Biomedical Engineering, IEEE Reviews in , vol.6, no., pp.156,177, 2013. |
| Smart sensor + | Surya G. Nurzaman, Utku Culha, Luzius Brodbeck, Liyu Wang, Fumiya Iida. (2013) Active Sensing System with In Situ Adjustable Sensor Morphology. PLoS ONE 8(12):e84090. doi:10.1371/journal.pone.0084090 Robin R. Murphy. Dempster–Shafer Theory for Sensor Fusion in Autonomous Mobile Robots. IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, VOL. 14, NO. 2, APRIL 1998 197. |
| Social touch for robots + | You can read some papers by Breazeal and Dautenhahn about social robots. |
| Something to do with social robots + | Fasola, J., & Matarić, M. J. (2013). A socially assistive robot exercise coach for the elderly. Journal of Human-Robot Interaction, 2(2), 3-32. http://delivery.acm.org/10.1145/3110000/3109710/p3-fasola.pdf?ip=194.47.19.128&id=3109710&acc=OA&key=74F7687761D7AE37%2ED64FD9DC22ECC16B%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1540547143_b8ef87a85e37d78837ed72cdc958a5e1 Gold, K., & Scassellati, B. (2006). Learning acceptable windows of contingency. Connection Science, 18(2), 217-228. http://www.cs.yale.edu/homes/scaz/papers/Gold-ConnSci-06.pdf |
| Supervised/Unsupervised Electricity Customer Classification + | Schneider, Kevin P., et al. "Evaluation of conservation voltage reduction (CVR) on a national level." Pacific Northwest National Laboratory report (2010). |
| Surface normal estimation by Spiral Codes + | J. Bigun "Vision with Direction", chapter 11, Springer, 2016. |
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| Thermal Detection of Subtle Human Cues for a Robot Magic Performance + | Martin Cooney, & Alexey Vinel. “Magic in Human-Robot Interaction (HRI).” In the 34th annual workshop of the Swedish Artificial Intelligence Society (SAIS 2022), 2022. Cho, Y., Bianchi-Berthouze, N., Marquardt, N., & Julier, S. J. (2018, April). Deep thermal imaging: Proximate material type recognition in the wild through deep learning of spatial surface temperature patterns. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-13). Xu, Z., Wang, Q., Li, D., Hu, M., Yao, N., & Zhai, G. (2020). Estimating departure time using thermal camera and heat traces tracking technique. Sensors, 20(3), 782. Cooney, M., & Bigun, J. (2017). PastVision+: Thermovisual inference of recent Medicine intake by Detecting heated Objects and cooled lips. Frontiers in Robotics and AI, 4, 61. |
