We are conducting research activities in various fields. Below you can find some topics and relevant publications.
Mosquito recognition and vector counts
There are many mosquito vector-borne diseases, such as malaria, dengue, or Zika. It is essential to be able to control the mosquito populations, which requires reliable recognition of individual species in the wild. We are performing various research activities in this field, see the following publications.
- Yin, M.S., Haddawy, P., Ziemer, T. et al.A deep learning-based pipeline for mosquito detection and classification from wingbeat sounds. Multimed Tools Appl 82, 5189–5205 (2023). https://doi.org/10.1007/s11042-022-13367-0
- Dinarte Vasconcelos, Myat Su Yin, Fabian Wetjen, Alexander Herbst, Tim Ziemer, Anna Förster, Thomas Barkowsky, Nuno Nunes, and Peter Haddawy. 2021. Counting Mosquitoes in the Wild: An Internet of Things Approach. In Proceedings of the Conference on Information Technology for Social Good (GoodIT ’21). Association for Computing Machinery, New York, NY, USA, 43–48. https://doi.org/10.1145/3462203.3475914
- Su Yin M, Bicout DJ, Haddawy P, Schöning J, Laosiritaworn Y, et al. (2021) Added-value of mosquito vector breeding sites from street view images in the risk mapping of dengue incidence in Thailand. PLOS Neglected Tropical Diseases 15(3): e0009122. https://doi.org/10.1371/journal.pntd.0009122
- Myat Su Yin, Peter Haddawy, Borvorntat Nirandmongkol, Tup Kongthaworn, Chanaporn Chaisumritchoke, Akara Supratak, Chaitawat Sa-ngamuang, and Patchara Sriwichai. 2021. A Lightweight Deep Learning Approach to Mosquito Classification from Wingbeat Sounds. In Proceedings of the Conference on Information Technology for Social Good (GoodIT ’21). Association for Computing Machinery, New York, NY, USA, 37–42. https://doi.org/10.1145/3462203.3475908
W. L. Passos, C. da S. Barreto, G. M. Araujo, U. Haque, S. L. Netto, and E. A. B. da Silva, “Toward improved surveillance of Aedes aegypti breeding grounds through artificially augmented data”, Engineering Applications of Artificial Intelligence, vol. 123, part C, paper #106488, pp. 1-12, Aug. 2023. DOI: 10.1016/j.engappai.2023.106488
W. L. Passos, A. A. de Lima, G. Araujo, S. L. Netto, and E. A. B. da Silva, “Automatic detection of Aedes aegypti breeding grounds based on deep networks with spatio-temporal consistency”, Computers, Environment and Urban Systems, vol. 93, paper# 101754, Apr. 2022. DOI: 10.1016/j.compenvurbsys.2021.101754.
Digital health applications
We are developing various tools to support the tracking and reporting of infectious diseases. Furthermore, we work together wiht hospitals to support them with digitalization of their services and processes, including disease diagnosis.
- Florian Vögtle, Peter Haddawy, Myat Su Yin, Thomas Barkowsky, Dominique Bicout, Mores Prachyabrued, Saranath Lawpoolsri, A Collaborative Platform Supporting Distributed Teams in Visualization and Analysis of Infectious Disease Data, Proc. 10th IEEE Int’l Conf. on Healthcare Informatics, Rochester, Minnesota, June 2022. https://ieeexplore.ieee.org/document/9874627
Medical training support
We are developing tools to support the training of doctors, inclduing surgeons and dentists. Methods include mainly state of the art visualization and virtual reality.
- M. Kaluschke, M. S. Yin, P. Haddawy, S. Suebnukarn and G. Zachmann, “The Impact of 3D Stereopsis and Hand-Tool Alignment on Effectiveness of a VR-based Simulator for Dental Training,” 2022 IEEE 10th International Conference on Healthcare Informatics (ICHI), Rochester, MN, USA, 2022, pp. 449-455, 10.1109/ICHI54592.2022.00067.