[D] Nousias, S., 2022. Patient-specific modelling, simulation and real time processing for respiratory diseases, Doctoral dissertation, University of Patras. Polytechnical School. Department of Electrical and Computer Engineering. http://dx.doi.org/10.12681/eadd/52290
[J10] Nousias, S., Pikoulis, E.V., Mavrokefalidis, C. and Lalos, A.S., July 2023. Accelerating Deep Neural Networks for Efficient Scene Understanding in Multi-Modal Automotive Applications. IEEE Access, 11, pp.28208-28221. https://doi.org/10.1109/ACCESS.2023.3258400
[J9] Fakotakis, D.N., Nousias, S., Arvanitis, G., Zacharaki, E.I. and Moustakas, K., 2023. AI Sound Recognition on Asthma Medication Adherence: Evaluation With the RDA Benchmark Suite. IEEE Access, 11, pp.13810-13829.https://doi.org/10.1109/ACCESS.2023.3243547
[J8] Nousias, S., Arvanitis, G., Lalos, A. and Moustakas, K., 2022. Deep Saliency Mapping for 3D Meshes and Applications. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM). https://doi.org/10.1145/3550073
[J7] Nousias, S., Arvanitis, G., Lalos, A.S. and Moustakas, K., 2020. Fast mesh denoising with data driven normal filtering using deep variational autoencoders. IEEE Transactions on Industrial Informatics, 17(2), pp.980-990. https://doi.org/10.1109/TII.2020.3000491
[J6] Nousias, S., Arvanitis, G., Lalos, A.S., Pavlidis, G., Koulamas, C., Kalogeras, A. and Moustakas, K., 2020. A saliency aware CNN-Based 3D Model simplification and compression framework for remote inspection of heritage sites. IEEE Access, 8, pp.169982-170001. https://doi.org/10.1109/ACCESS.2020.3023167
[J5] Nousias, S., Zacharaki, E.I. and Moustakas, K., 2020. AVATREE: An open-source computational modelling framework modelling Anatomically Valid Airway TREE conformations. PloS one, 15(4), p.e0230259. https://doi.org/10.1371/journal.pone.0230259
[J4] Ntalianis, V., Fakotakis, N.D., Nousias, S., Lalos, A.S., Birbas, M., Zacharaki, E.I. and Moustakas, K., 2020. Deep CNN Sparse Coding for Real Time Inhaler Sounds Classification. Sensors, 20(8), p.2363. https://doi.org/10.3390/s20082363
[J3] Nousias, S., Tselios, C., Bitzas, D., Amaxilatis, D., Montesa, J., Lalos, A.S., Moustakas, K. and Chatzigiannakis, I., 2019. Exploiting gamification to improve eco-driving behaviour: The GamECAR approach. Electronic Notes in Theoretical Computer Science, 343, pp.103-116. https://doi.org/10.1016/j.entcs.2019.04.013
[J2] Nousias, S., Lalos, A.S., Arvanitis, G., Moustakas, K., Tsirelis, T., Kikidis, D., Votis, K. and Tzovaras, D., 2018. An mHealth system for monitoring medication adherence in obstructive respiratory diseases using content based audio classification. IEEE Access, 6, pp.11871-11882. https://doi.org/10.1109/ACCESS.2018.2809611
[J1] Lalas A, Nousias S, Kikidis D, Lalos A, Arvanitis G, Sougles C, Moustakas K, Votis K, Verbanck S, Usmani O, Tzovaras D. Substance deposition assessment in obstructed pulmonary system through numerical characterization of airflow and inhaled particles attributes. BMC Med Inform Decis Mak. 2017 Dec 20;17(Suppl 3):173. doi: https://doi.org/10.1186/s12911-017-0561-y . PMID: 29297393; PMCID: PMC5751792.
[C16] Pikoulis, E.V., Mavrokefalidis, C., Nousias, S. and Lalos, A.S., 2022. A new clustering-based technique for the acceleration of deep convolutional networks. In Deep Learning Applications, Volume 3 (pp. 123-150). Springer, Singapore. https://doi.org/10.1007/978-981-16-3357-7_5
[C15] Nousias, S., Pikoulis, E.V., Mavrokefalidis, C., Lalos, A.S. and Moustakas, K., 2021, October. Accelerating 3D scene analysis for autonomous driving on embedded AI computing platforms. In 2021 IFIP/IEEE 29th International Conference on Very Large Scale Integration (VLSI-SoC) (pp. 1-6). IEEE. https://doi.org/10.1109/VLSI-SoC53125.2021.9606990
[C14] Nousias, S., Pikoulis, E.V., Mavrokefalidis, C. and Lalos, A.S., 2021, May. Accelerating deep neural networks for efficient scene understanding in automotive cyber-physical systems. In 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) (pp. 63-69). IEEE. https://arxiv.org/abs/2107.09101
[C13] Nousias, S., Arvanitis, G., Lalos, A.S. and Moustakas, K., 2020, July. Mesh saliency detection using convolutional neural networks. In 2020 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1-6). IEEE. https://doi.org/10.1109/ICME46284.2020.9102796
[C12] Nousias, S., Arvanitis, G., Lalos, A.S. and Moustakas, K., 2019, July. Fast mesh denoising with data driven normal filtering using deep autoencoders. In 2019 IEEE 17th International Conference on Industrial Informatics (INDIN) (Vol. 1, pp. 260-263). IEEE. https://doi.org/10.1109/INDIN41052.2019.8972221
[C11] Papoulias, G., Nousias, S. and Moustakas, K., 2019, July. Fluid-structure interaction simulation framework for cerebral aneurysm wall deformation. In 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA) (pp. 1-7). IEEE. https://doi.org/10.1109/IISA.2019.8900781
[C10] Nousias, S., Lalos, A.S., Kalogeras, A., Alexakos, C., Koulamas, C. and Moustakas, K., 2019, September. Sparse modeling and optimization tools for energy efficient and reliable IoT. In 2019 First International Conference on Societal Automation (SA) (pp. 1-4). IEEE. https://doi.org/10.1109/SA47457.2019.8938029
[C9] Nousias, S., Papoulias, G., Kocsis, O., Cabrita, M., Lalos, A.S. and Moustakas, K., 2019, November. Coping with missing data in an unobtrusive monitoring system for office workers. In 2019 International Conference on Biomedical Innovations and Applications (BIA) (pp. 1-4). IEEE. https://doi.org/10.1109/BIA48344.2019.8967465
[C8] Tselios, C., Nousias, S., Bitzas, D., Amaxilatis, D., Akrivopoulos, O., Lalos, A.S., Moustakas, K. and Chatzigiannakis, I., 2019, November. Enhancing an eco-driving gamification platform through wearable and vehicle sensor data integration. In European Conference on Ambient Intelligence (pp. 344-349). Springer, Cham. https://doi.org/10.1007/978-3-030-34255-5_26
[C7] Ntalianis, V., Nousias, S., Lalos, A.S., Birbas, M., Tsafas, N. and Moustakas, K., 2019, September. Assessment of medication adherence in respiratory diseases through deep sparse convolutional coding. In 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1657-1660). IEEE. https://doi.org/10.1109/ETFA.2019.8869054
[C6] Pettas, D., Nousias, S., Zacharaki, E.I. and Moustakas, K., 2019, October. Recognition of breathing activity and medication adherence using lstm neural networks. In 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) (pp. 941-946). IEEE. https://doi.org/10.1109/BIBE.2019.00176
[C5] Nousias, S., Tselios, C., Bitzas, D., Lalos, A.S., Moustakas, K. and Chatzigiannakis, I., 2018, September. Uncertainty management for wearable iot wristband sensors using laplacian-based matrix completion. In 2018 IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) (pp. 1-6). IEEE. https://doi.org/10.1109/CAMAD.2018.8515001
[C4] Nousias, S., Tselios, C., Orfila, O., Jamson, S., Mejuto, P., Amaxilatis, D., Akrivopoulos, O., Chatzigiannakis, I., Lalos, A.S. and Moustakas, K., 2018, March. Managing nonuniformities and uncertainties in vehicle-oriented sensor data over next generation networks. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (pp. 272-277). IEEE. https://doi.org/10.1109/PERCOMW.2018.8480342
[C3] Arvanitis, G., Kocsis, O., Lalos, A.S., Nousias, S., Moustakas, K. and Fakotakis, N., 2018, July. 3-Class Prediction of Asthma Control Status Using a Gaussian Mixture Model Approach. In Proceedings of the 10th Hellenic Conference on Artificial Intelligence (pp. 1-2). https://dl.acm.org/doi/10.1145/3200947.3201056
[C2] Lalas, A., Kikidis, D., Votis, K., Tzovaras, D., Verbanck, S., Nousias, S., Lalos, A., Moustakas, K. and Usmani, O., 2016, December. Numerical assessment of airflow and inhaled particles attributes in obstructed pulmonary system. In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 606-612). IEEE. https://doi.org/10.1109/BIBM.2016.7822588
[C1] Nousias, S., Lakoumentas, J., Lalos, A., Kikidis, D., Moustakas, K., Votis, K. and Tzovaras, D., 2016, December. Monitoring asthma medication adherence through content based audio classification. In 2016 IEEE symposium series on computational intelligence (SSCI) (pp. 1-5). IEEE. https://doi.org/10.1109/SSCI.2016.7849898
