Anastasia Pentari

Orcid: 0000-0003-2823-5584

According to our database1, Anastasia Pentari authored at least 12 papers between 2019 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
Using the recurrence plots as indicators for the recognition of Parkinson's disease through phonemes assessment.
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023

Respiration and heartbeat rates estimation using IR-UWB non-contact radar sensor recordings: A pre-clinical study.
Proceedings of the Workshops of the EDBT/ICDT 2023 Joint Conference, 2023

Multi-channel CNN-based emotion recognition using recurrence plot representations of speech.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2023

2022
Changes in resting-state functional connectivity in neuropsychiatric lupus: A dynamic approach based on recurrence quantification analysis.
Biomed. Signal Process. Control., 2022

Graph denoising of impulsive EEG signals and the effect of their graph representation.
Biomed. Signal Process. Control., 2022

Investigating Graph-based Features for Speech Emotion Recognition.
Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics, 2022

2020
Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks.
J. Imaging, 2020

Graph-based Denoising of EEG Signals in Impulsive Environments.
Proceedings of the 28th European Signal Processing Conference, 2020

A Study on the Effect of Distinct Adjacency Matrices for Graph Signal Denoising.
Proceedings of the 20th IEEE International Conference on Bioinformatics and Bioengineering, 2020

2019
Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement.
Sensors, 2019

Sparse Representations on DW-MRI: A Study on Pancreas.
Proceedings of the 19th IEEE International Conference on Bioinformatics and Bioengineering, 2019

Hyperspectral Image Compression and Super-Resolution Using Tensor Decomposition Learning.
Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019


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