Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets M Wielgosz, A Skoczeń, M Mertik Nuclear Instruments and Methods in Physics Research Section A: Accelerators …, 2017 | 103 | 2017 |
Predictive maintenance of induction motors using ultra-low power wireless sensors and compressed recurrent neural networks M Markiewicz, M Wielgosz, M Bocheński, W Tabaczyński, T Konieczny, ... IEEE Access 7, 178891-178902, 2019 | 50 | 2019 |
Mapping neural networks to FPGA-based IoT devices for ultra-low latency processing M Wielgosz, M Karwatowski Sensors 19 (13), 2981, 2019 | 47 | 2019 |
Roadmap on artificial intelligence and big data techniques for superconductivity M Yazdani-Asrami, W Song, A Morandi, G De Carne, J Murta-Pina, ... Superconductor Science and Technology 36 (4), 043501, 2023 | 37 | 2023 |
FPGA implementation of 64-bit exponential function for HPC E Jamro, K Wiatr, M Wielgosz 2007 International Conference on Field Programmable Logic and Applications …, 2007 | 32 | 2007 |
Methodologies of compressing a stable performance convolutional neural networks in image classification M Al-Hami, M Pietron, R Casas, M Wielgosz Neural Processing Letters 51 (1), 105-127, 2020 | 30 | 2020 |
Highly efficient structure of 64-bit exponential function implemented in FPGAs M Wielgosz, E Jamro, K Wiatr Reconfigurable Computing: Architectures, Tools and Applications: 4th …, 2008 | 30 | 2008 |
The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization M Wielgosz, M Mertik, A Skoczeń, E De Matteis Engineering Applications of Artificial Intelligence 74, 166-185, 2018 | 29 | 2018 |
Comparison of GPU and FPGA implementation of SVM algorithm for fast image segmentation M Pietron, M Wielgosz, D Zurek, E Jamro, K Wiatr Architecture of Computing Systems–ARCS 2013: 26th International Conference …, 2013 | 28 | 2013 |
FPGA implementaton of strongly parallel histogram equalization E Jamro, M Wielgosz, K Wiatr 2007 IEEE Design and Diagnostics of Electronic Circuits and Systems, 1-6, 2007 | 27 | 2007 |
For-instance: a uav laser scanning benchmark dataset for semantic and instance segmentation of individual trees S Puliti, G Pearse, P Surový, L Wallace, M Hollaus, M Wielgosz, R Astrup arXiv preprint arXiv:2309.01279, 2023 | 26 | 2023 |
Automated forest inventory: Analysis of high-density airborne LiDAR point clouds with 3D deep learning B Xiang, M Wielgosz, T Kontogianni, T Peters, S Puliti, R Astrup, ... Remote Sensing of Environment 305, 114078, 2024 | 25 | 2024 |
Retrain or not retrain?-efficient pruning methods of deep cnn networks M Pietron, M Wielgosz Computational Science–ICCS 2020: 20th International Conference, Amsterdam …, 2020 | 25 | 2020 |
Comparison of Hybrid Sorting Algorithms Implemented on Different Parallel Hardware Platforms D Żurek, M Pietroń, M Wielgosz, K Wiatr Computer Science 14 (4), 679--691, 2013 | 22* | 2013 |
FPGA implementation of the dynamic Huffman encoder E Jamro, M Wielgosz, K Wiatr IFAC Proceedings Volumes 39 (21), 60-65, 2006 | 22 | 2006 |
Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets M Wielgosz, A Skoczeń, M Mertik arXiv preprint arXiv:1702.00833, 2017 | 18 | 2017 |
FPGA–ARM heterogeneous system for high speed signal analysis E Jamro, M Wielgosz, S Bieniasz, W Cioch Solid State Phenomena 180, 207-213, 2012 | 18 | 2012 |
Point2Tree (P2T)—Framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest M Wielgosz, S Puliti, P Wilkes, R Astrup Remote Sensing 15 (15), 3737, 2023 | 17 | 2023 |
Protection of superconducting industrial machinery using RNN-based anomaly detection for implementation in smart sensor M Wielgosz, A Skoczeń, E De Matteis Sensors 18 (11), 3933, 2018 | 15 | 2018 |
Compression of convolutional neural network for natural language processing K Wróbel, M Karwatowski, M Wielgosz, M Pietroń, K Wiatr Computer Science 21 (1), 2020 | 13 | 2020 |