Tiago Almeida
Befattning: Doktorand Organisation: Institutionen för naturvetenskap och teknikE-post: tiago.almeida@oru.se
Telefon: 019 303725
Rum: T2226
Publikationer
Artiklar i tidskrifter |
Konferensbidrag |
Artiklar i tidskrifter
- Gutiérrez Maestro, E. , Almeida, T. R. d. , Schaffernicht, E. & Martinez Mozos, O. (2023). Wearable-Based Intelligent Emotion Monitoring in Older Adults during Daily Life Activities. Applied Sciences, 13 (9).
- Almeida, T. , Santos, V. , Martinez Mozos, O. & Lourenco, B. (2021). Comparative Analysis of Deep Neural Networks for the Detection and Decoding of Data Matrix Landmarks in Cluttered Indoor Environments. Journal of Intelligent and Robotic Systems, 103 (1).
Konferensbidrag
- Rodrigues de Almeida, T. & Martinez Mozos, O. (2023). Likely, Light, and Accurate Context-Free Clusters-based Trajectory Prediction. Konferensbidrag vid 26th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2023), Bilbao, Bizkaia, Spain, September 24-28, 2023.
- Almeida, T. , Rudenko, A. , Schreiter, T. , Zhu, Y. , Gutiérrez Maestro, E. , Morillo-Mendez, L. , Kucner, T. P. , Martinez Mozos, O. & et al. (2023). THÖR-Magni: Comparative Analysis of Deep Learning Models for Role-Conditioned Human Motion Prediction. I: 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). Konferensbidrag vid IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, Paris, France, October 2-6, 2023. (ss. 2192-2201). IEEE.
- Almeida, T. R. d. , Gutiérrez Maestro, E. & Martinez Mozos, O. (2022). Context-free Self-Conditioned GAN for Trajectory Forecasting. I: Wani, MA; Kantardzic, M; Palade, V; Neagu, D; Yang, L; Chan, KY, 21st IEEE International Conference on Machine Learning and Applications. ICMLA 2022 Proceedings. Konferensbidrag vid 21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA), Nassau, Bahamas, December 12-14, 2022. (ss. 1218-1223). IEEE.
- Schreiter, T. , Almeida, T. R. d. , Zhu, Y. , Gutiérrez Maestro, E. , Morillo-Mendez, L. , Rudenko, A. , Kucner, T. P. , Martinez Mozos, O. & et al. (2022). The Magni Human Motion Dataset: Accurate, Complex, Multi-Modal, Natural, Semantically-Rich and Contextualized. Konferensbidrag vid 31st IEEE International Conference on Robot & Human Interactive Communication, Naples, Italy, August 29 - September 2, 2022.