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Damian Ledziński
Researcher at University of Technology and Life Sciences in Bydgoszcz
Publications - 24
Citations - 182
Damian Ledziński is an academic researcher from University of Technology and Life Sciences in Bydgoszcz. The author has contributed to research in topics: Chordal graph & Mobile QoS. The author has an hindex of 6, co-authored 22 publications receiving 109 citations.
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Journal ArticleDOI
Molecular Dynamic Analysis of Hyaluronic Acid and Phospholipid Interaction in Tribological Surgical Adjuvant Design for Osteoarthritis.
TL;DR: The results indicate that the hyaluronic acid radius of gyration time evolution is both pH- and phospholipid concentration-dependent, and dipalmitoylphosphatidylcholine remains an adjuvant candidate for certain clinical situations.
Proceedings ArticleDOI
Assessing Measurements of QoS for Global Cloud Computing Services
Jens Myrup Pedersen,Tahir Riaz,Joaquim Celestino Júnior,Bozydar Dubalski,Damian Ledziński,Ahmed Patel +5 more
TL;DR: This paper investigates if latency in terms of simple Ping measurements can be used as an indicator for other QoS parameters such as jitter and throughput, and shows some correlation between latency and throughput.
Journal ArticleDOI
ECG Signal Classification Using Deep Learning Techniques Based on the PTB-XL Dataset.
TL;DR: In this paper, a deep neural network was developed for the automatic classification of primary ECG signals, which was carried out on the data contained in a PTB-XL database, and three neural network architectures were proposed: the first based on the convolutional network, the second on SincNet, and the third on the CNN with additional entropy-based features.
Journal ArticleDOI
Anomalous Behavior of Hyaluronan Crosslinking Due to the Presence of Excess Phospholipids in the Articular Cartilage System of Osteoarthritis.
Piotr Bełdowski,Piotr Weber,Tomasz Andrysiak,Wayne K. Augé,Damian Ledziński,Tristan De Leon,Adam Gadomski +6 more
TL;DR: The results demonstrated that phospholipids affect the crosslinking mechanisms of hyaluronic acid significantly and the influence is higher during pathological conditions.
Journal ArticleDOI
Study of the Few-Shot Learning for ECG Classification Based on the PTB-XL Dataset
TL;DR: The Few-Shot Learning (FSL) applicability for ECG signal proximity-based classification proved to have higher accuracy than the softmax- based classification network, and the proposed network achieved better results in classifying five different disease classes thanSoftmax-based counterparts.