| Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps I ©aulienė, L ©ukienė, G Daunys, G Valiulis, L Vaitkevičius, P Matavulj, ... Atmospheric Measurement Techniques 12 (6), 3435-3452, 2019 | 102 | 2019 |
| RealForAll: real-time system for automatic detection of airborne pollen D Teąendić, D Boberić Krstićev, P Matavulj, S Brdar, M Panić, V Minić, ... Enterprise Information Systems 16 (5), 1793391, 2022 | 35 | 2022 |
| Towards European automatic bioaerosol monitoring: comparison of 9 automatic pollen observational instruments with classic Hirst-type traps JM Maya-Manzano, F Tummon, R Abt, N Allan, L Bunderson, B Clot, ... Science of the Total Environment 866, 161220, 2023 | 19 | 2023 |
| Why should we care about high temporal resolution monitoring of bioaerosols in ambient air? M Smith, P Matavulj, G Mimić, M Panić, Ł Grewling, B ©ikoparija Science of the Total Environment 826, 154231, 2022 | 10 | 2022 |
| Real-time automatic detection of starch particles in ambient air B ©ikoparija, P Matavulj, G Mimić, M Smith, Ł Grewling, Z Podraąčanin Agricultural and Forest Meteorology 323, 109034, 2022 | 7 | 2022 |
| Advanced CNN architectures for pollen classification: Design and comprehensive evaluation P Matavulj, M Panić, B ©ikoparija, D Teąendić, M Radovanović, S Brdar Applied Artificial Intelligence 37 (1), 2157593, 2023 | 6 | 2023 |
| Integration of reference data from different Rapid-E devices supports automatic pollen detection in more locations P Matavulj, A Cristofori, F Cristofolini, E Gottardini, S Brdar, B Sikoparija Science of The Total Environment 851, 158234, 2022 | 6 | 2022 |
| Do we need continuous sampling to capture variability of hourly pollen concentrations? B Sikoparija, G Mimić, P Matavulj, M Panić, I Simović, S Brdar Aerobiologia 36, 3-7, 2020 | 5 | 2020 |
| Domain adaptation with unlabeled data for model transferability between airborne particle identifiers P Matavulj, S Brdar, M Racković, B ©ikoparija, IN Athanasiadis 17th International Conference on Machine Learning and Data Mining MLDM 2021 …, 2021 | 4 | 2021 |
| Manual and automatic quantification of airborne fungal spores during wheat harvest period I Simović, P Matavulj, B ©ikoparija Aerobiologia 39 (2), 227-239, 2023 | 2 | 2023 |
| Interseasonal transfer learning for crop mapping using Sentinel-1 data M Pandľić, D Pavlović, P Matavulj, S Brdar, O Marko, V Crnojević, ... International Journal of Applied Earth Observation and Geoinformation 128 …, 2024 | 1 | 2024 |
| Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy S Brdar, M Panić, P Matavulj, M Stanković, D Bartolić, B ©ikoparija Scientific Reports 13 (1), 3205, 2023 | 1 | 2023 |
| Classification accuracy and compatibility across devices of a new Rapid-E+ flow cytometer B Sikoparija, P Matavulj, I Simovic, P Radisic, S Brdar, V Minic, ... EGUsphere 2024, 1-36, 2024 | | 2024 |
| Integration of data from different rapid e-devices supports pollen classification in more locations P Matavulj, A Cristofori, F Cristofolini, E Gottardini, S Brdar, B Sikoparija One health Paestum 2022: 5th MedPalyos Symposium. 16th AIA Congress (Italian …, 2022 | | 2022 |
| High temporal resolution monitoring of Ambrosia pollen in ambient air L Grewling, P Matavulj, G Mimić, M Panić, M Smith, B ©ikoparija | | 2022 |
| Detection of starch rain in ambient air of Novi Sad, Serbia B ©ikoparija, P Matavulj, G Mimić, M Smith, L Grewling, Z Podraąčanin | | 2021 |
