publications | Stavros Nousias

2025

Elsevier

Design Healing framework for automated code compliance

Jiabin Wu, Stavros Nousias, and André Borrmann

Elsevier,Automation in Construction, 2025

@article{wu2025design,
  title = {Design Healing framework for automated code compliance},
  author = {Wu, Jiabin and Nousias, Stavros and Borrmann, Andr{\'e}},
  journal = {Elsevier,Automation in Construction},
  volume = {171},
  pages = {106004},
  year = {2025},
  publisher = {Elsevier},
  category_1 = {AIBE}
}

2024

arXiv

Text2BIM: Generating Building Models Using a Large Language Model-based Multi-Agent Framework

Changyu Du, Sebastian Esser, Stavros Nousias, and 1 more author

arXiv preprint arXiv:2408.08054, 2024

DOI Bib PDF Code

@article{du2024text2bim,
  title = {Text2BIM: Generating Building Models Using a Large Language Model-based Multi-Agent Framework},
  author = {Du, Changyu and Esser, Sebastian and Nousias, Stavros and Borrmann, Andr{\'e}},
  journal = {arXiv preprint arXiv:2408.08054},
  year = {2024},
  doi = {10.48550/arXiv.2408.08054},
  url = {https://arxiv.org/abs/2408.08054},
  category_1 = {AIBE},
}

Frontiers

Distributed intelligence in industrial and automotive cyberphysical systems: A review

Nikos Piperigkos, Alexandros Gkillas, Gerasimos Arvanitis, and 6 more authors

Frontiers in Robotics and AI, 2024

DOI Bib

@article{piperigkos2024distributed,
  title = {Distributed intelligence in industrial and automotive cyberphysical systems: A review},
  author = {Piperigkos, Nikos and Gkillas, Alexandros and Arvanitis, Gerasimos and Nousias, Stavros and Lalos, Aris and Fournaris, Apostolos and Radoglou-Grammatikis, Panagiotis and Sarigiannidis, Panagiotis and Moustakas, Konstantinos},
  journal = {Frontiers in Robotics and AI},
  doi = {10.3389/frobt.2024.1430740},
  url = {https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2024.1430740},
  volume = {11},
  year = {2024},
  publisher = {Frontiers},
  category_1 = {CPS}
}

arXiv

VectorGraphNET: Graph Attention Networks for Accurate Segmentation of Complex Technical Drawings

Andrea Carrara, Stavros Nousias, and André Borrmann

arXiv preprint arXiv:2410.01336, 2024

Bib

@article{carrara2024vectorgraphnet,
  title = {VectorGraphNET: Graph Attention Networks for Accurate Segmentation of Complex Technical Drawings},
  author = {Carrara, Andrea and Nousias, Stavros and Borrmann, Andr{\'e}},
  journal = {arXiv preprint arXiv:2410.01336},
  year = {2024},
  category_1 = {AIBE}
}

ASCE

Integrating pedestrian simulation into early building design: A deep learning approach for trajectory prediction

Patrick Berggold, Stavros Nousias, and Andre Borrmann

In i3CE 2024: 2024 ASCE International Conference on Computing in Civil Engineering, 2024

Bib

@inproceedings{berggold2024integrating,
  title = {Integrating pedestrian simulation into early building design: A deep learning approach for trajectory prediction},
  author = {Berggold, Patrick and Nousias, Stavros and Borrmann, Andre},
  booktitle = {i3CE 2024: 2024 ASCE International Conference on Computing in Civil Engineering},
  year = {2024},
  category_1 = {AIBE}
}

arXiv

Towards commands recommender system in BIM authoring tool using transformers

Changyu Du, Zihan Deng, Stavros Nousias, and 1 more author

arXiv preprint arXiv:2406.10237, 2024

Bib

@article{du2024towards,
  title = {Towards commands recommender system in BIM authoring tool using transformers},
  author = {Du, Changyu and Deng, Zihan and Nousias, Stavros and Borrmann, Andr{\'e}},
  journal = {arXiv preprint arXiv:2406.10237},
  year = {2024},
  category_1 = {AIBE}
}

arXiv

Coordinating robotized construction using advanced robotic simulation: The case of collaborative brick wall assembly

Mohammad Reza Kolani, Stavros Nousias, and André Borrmann

arXiv preprint arXiv:2405.17171, 2024

Bib

@article{kolani2024coordinating,
  title = {Coordinating robotized construction using advanced robotic simulation: The case of collaborative brick wall assembly},
  author = {Kolani, Mohammad Reza and Nousias, Stavros and Borrmann, Andr{\'e}},
  journal = {arXiv preprint arXiv:2405.17171},
  year = {2024},
  category_1 = {AIBE}
}

ASCE

Employing graph neural networks for construction drawing content recognition

Andrea Carrara, Stavros Nousias, and A Borrmann

In i3CE 2024: 2024 ASCE International Conference on Computing in Civil Engineering, 2024

Bib

@inproceedings{carrara2024employing,
  title = {Employing graph neural networks for construction drawing content recognition},
  author = {Carrara, Andrea and Nousias, Stavros and Borrmann, A},
  booktitle = {i3CE 2024: 2024 ASCE International Conference on Computing in Civil Engineering},
  year = {2024},
  category_1 = {AIBE}
}

2023

ACM TMM

Deep saliency mapping for 3D meshes and applications

Stavros Nousias, Gerasimos Arvanitis, Aris Lalos, and 1 more author

ACM Transactions on Multimedia Computing, Communications and Applications, 2023

DOI Bib PDF Code

@article{nousias2023deep,
  title = {Deep saliency mapping for 3D meshes and applications},
  author = {Nousias, Stavros and Arvanitis, Gerasimos and Lalos, Aris and Moustakas, Konstantinos},
  journal = {ACM Transactions on Multimedia Computing, Communications and Applications},
  volume = {19},
  number = {2},
  pages = {1--22},
  year = {2023},
  doi = {10.1145/3550073},
  url = {https://dl.acm.org/doi/10.1145/3550073},
  publisher = {ACM New York, NY},
  category_1 = {GDL}
}

IEEE Access

AI sound recognition on asthma medication adherence: Evaluation with the RDA benchmark suite

Dimitris Nikos Fakotakis, Stavros Nousias, Gerasimos Arvanitis, and 2 more authors

IEEE Access, 2023

DOI Bib PDF Code

@article{fakotakis2023ai,
  title = {AI sound recognition on asthma medication adherence: Evaluation with the RDA benchmark suite},
  author = {Fakotakis, Dimitris Nikos and Nousias, Stavros and Arvanitis, Gerasimos and Zacharaki, Evangelia I and Moustakas, Konstantinos},
  journal = {IEEE Access},
  volume = {11},
  pages = {13810--13829},
  year = {2023},
  doi = {10.1109/ACCESS.2023.3258400},
  url = {https://ieeexplore.ieee.org/document/10040762},
  publisher = {IEEE},
  category_1 = {MLHM}
}

IEEE Access

Accelerating deep neural networks for efficient scene understanding in multi-modal automotive applications

Stavros Nousias, Erion-Vasilis Pikoulis, Christos Mavrokefalidis, and 1 more author

IEEE Access, 2023

DOI Bib PDF

@article{nousias2023accelerating,
  title = {Accelerating deep neural networks for efficient scene understanding in multi-modal automotive applications},
  author = {Nousias, Stavros and Pikoulis, Erion-Vasilis and Mavrokefalidis, Christos and Lalos, Aris S},
  journal = {IEEE Access},
  doi = {10.1109/ACCESS.2023.3258400},
  url = {https://ieeexplore.ieee.org/document/10073550},
  volume = {11},
  pages = {28208--28221},
  year = {2023},
  publisher = {IEEE},
  category_2 = {MCA}
}

2022

Springer
A new clustering-based technique for the acceleration of deep convolutional networks

Erion Vasilis Pikoulis, Christos Mavrokefalidis, Stavros Nousias, and 1 more author

Springer,Deep Learning Applications, Volume 3, 2022

Abs Bib

Deep learning and especially the use of Deep Neural Networks (DNNs) provide impressive results in various regression and classification tasks. However, to achieve these results, there is a high demand for computing and storing resources. This becomes problematic when, for instance, real-time mobile applications are considered, in which the involved (embedded) devices have limited resources. A common way of addressing this problem is to transform the original large pre-trained networks into new smaller models, by utilizing Model Compression and Acceleration (MCA) techniques. Within the MCA framework, we propose a clustering-based approach that is able to increase the number of employed centroids/representatives, while, at the same time, having an acceleration gain compared to conventional, k-means-based approaches. This is achieved by imposing a special structure to the employed representatives, which is enabled by the particularities of the problem at hand. Moreover, the theoretical acceleration gains are presented and the key system hyper-parameters that affect that gain are identified. Extensive evaluation studies carried out using various state-of-the-art DNN models trained in image classification and object detection validate the effectiveness of the proposed method in MCA tasks.
@article{pikoulis2022new, title = {A new clustering-based technique for the acceleration of deep convolutional networks}, author = {Pikoulis, Erion Vasilis and Mavrokefalidis, Christos and Nousias, Stavros and Lalos, Aris S}, journal = {Springer,Deep Learning Applications, Volume 3}, pages = {123--150}, year = {2022}, url = {https://link.springer.com/chapter/10.1007/978-981-16-3357-7_5}, publisher = {Springer Singapore}, category_1 = {MCA} }

IEEE ICPS

Coarse-to-fine defect detection of heritage 3D models using a CNN learning approach

Gerasimos Arvanitis, Stavros Nousias, Aris S Lalos, and 1 more author

In 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS), 2022

DOI Bib

@inproceedings{arvanitis2022coarse,
  title = {Coarse-to-fine defect detection of heritage 3D models using a CNN learning approach},
  author = {Arvanitis, Gerasimos and Nousias, Stavros and Lalos, Aris S and Moustakas, Konstantinos},
  booktitle = {2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS)},
  pages = {1--6},
  doi = {10.1109/ICPS56567.2022.9899335},
  url = {https://ieeexplore.ieee.org/abstract/document/9816869},
  year = {2022},
  organization = {IEEE},
  category_1 = {HTG}
}

IEEE Dataport

Respiratory and Drug Actuation Dataset

Stavros Nousias, Gerasimos Arvanitis, Aggeliki Anastasiou, and 1 more author

2022

DOI Bib

@misc{nousias2022respiratory,
  title = {Respiratory and Drug Actuation Dataset},
  author = {Nousias, Stavros and Arvanitis, Gerasimos and Anastasiou, Aggeliki and Moustakas, Konstantinos},
  journal = {https://ieee-dataport.org/documents/respiratory-and-drug-actuation-dataset},
  number = {10.21227/g84h-ma24},
  year = {2022},
  doi = {10.21227/g84h-ma24},
  url = {https://ieee-dataport.org/documents/respiratory-and-drug-actuation-dataset},
  category_1 = {MLHM}
}

PhD
Patient-specific modelling, simulation and real time processing for respiratory diseases

Stavros Nousias

Department of Electrical and Computer Engineering, School of Engineering, University of Patras, Patras, Greece. , 2022

Abs DOI Bib PDF Slides

Asthma is a common chronic disease of the respiratory system causing significant disability and societal burden. It affects more than 300 million more people worldwide, while more than 100 million people will likely have asthma by 2025. The price of asthma varies greatly from nation to nation. Mean yearly cost can be estimated to 1900 EUR in Europe and $3100 in the United States. Managing asthma involves controlling symptoms, preventing exacerbations, and maintaining lung function. Improved asthma control is reduces the risk of exacerbations and lung function impairment while reducing the direct costs of asthma care and indirect costs associated with reduced productivity. Understanding the complex dynamics of the pulmonary system and the lung’s response to disease is fundamental to the advancement of Asthma treatment. Computational models of the respiratory system seek to provide a theoretical framework to understand the interaction between structure and function. Their application can improve pulmonary medicine by a patient-specific approach to medicinal methodologies optimizing the delivery given the personalized geometry and personalized ventilation patterns. A three-fold objective is addressed within this dissertation. The first part refers to the comprehension of pulmonary pathophysiology and the mechanics of Asthma and subsequently of constrictive pulmonary conditions in general. The second part refers to the design and implementation of tools that facilitate personalized medicine to improve delivery and effectiveness. Finally, the third part refers to the self-management of the condition, meaning that medical personnel and patients have access to tools and methods that allow the first party to easily track the course of the condition and the second party, i.e. the patient to easily self-manage it alleviating the significant burden from the health system.
@phdthesis{nousias2022patient, title = {Patient-specific modelling, simulation and real time processing for respiratory diseases}, author = {Nousias, Stavros}, year = {2022}, school = {Department of Electrical and Computer Engineering, School of Engineering, University of Patras, Patras, Greece. }, doi = {10.12681/eadd/52290}, url = {https://www.didaktorika.gr/eadd/handle/10442/52290?locale=en}, category_1 = {BME}, category_2 = {PHD}, category_3 = {MLHM}, }

Code

Revisiting audio pattern recognition for asthma medication adherence: evaluation with the RDA benchmark suite.

ND Fakotakis, Stavros Nousias, Gerasimos Arvanitis, and 2 more authors

2022

Bib Code

@misc{fakotakis2022revisiting,
  title = {Revisiting audio pattern recognition for asthma medication adherence: evaluation with the RDA benchmark suite.},
  author = {Fakotakis, ND and Nousias, Stavros and Arvanitis, Gerasimos and Zacharaki, Evangelia I and Moustakas, Konstantinos},
  journal = {https://gitlab.com/vvr/monitoring-medication-adherence/rda-benchmark},
  year = {2022},
}

2021

IEEE ICPS
Accelerating deep neural networks for efficient scene understanding in automotive cyber-physical systems

Stavros Nousias, Erion Vasilis Pikoulis, Christos Mavrokefalidis, and 1 more author

In 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), 2021

Abs Bib

Automotive Cyber-Physical Systems (ACPS) have attracted a significant amount of interest in the past few decades, while one of the most critical operations in these systems is the perception of the environment. Deep learning and, especially, the use of Deep Neural Networks (DNNs) provides impressive results in analyzing and understanding complex and dynamic scenes from visual data. The prediction horizons for those perception systems are very short and inference must often be performed in real time, stressing the need of transforming the original large pre-trained networks into new smaller models, by utilizing Model Compression and Acceleration (MCA) techniques. Our goal in this work is to investigate best practices for appropriately applying novel weight sharing techniques, optimizing the available variables and the training procedures towards the significant acceleration of widely adopted DNNs. Extensive evaluation studies carried out using various state-of-the-art DNN models in object detection and tracking experiments, provide details about the type of errors that manifest after the application of weight sharing techniques, resulting in significant acceleration gains with negligible accuracy losses.
@inproceedings{nousias2021accelerating, title = {Accelerating deep neural networks for efficient scene understanding in automotive cyber-physical systems}, author = {Nousias, Stavros and Pikoulis, Erion Vasilis and Mavrokefalidis, Christos and Lalos, Aris S}, booktitle = {2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)}, pages = {63--69}, year = {2021}, organization = {IEEE}, category_1 = {MCA} }

arXiv

Empowering cyberphysical systems of systems with intelligence

Stavros Nousias, Nikos Piperigkos, Gerasimos Arvanitis, and 3 more authors

arXiv preprint arXiv:2107.02264, 2021

Bib

@article{nousias2021empowering,
  title = {Empowering cyberphysical systems of systems with intelligence},
  author = {Nousias, Stavros and Piperigkos, Nikos and Arvanitis, Gerasimos and Fournaris, Apostolos and Lalos, Aris S and Moustakas, Konstantinos},
  journal = {arXiv preprint arXiv:2107.02264},
  year = {2021},
}

IEEE VLSI-SoC

Accelerating 3D scene analysis for autonomous driving on embedded AI computing platforms

Stavros Nousias, Erion-Vasilis Pikoulis, Christos Mavrokefalidis, and 2 more authors

In 2021 IFIP/IEEE 29th International Conference on Very Large Scale Integration (VLSI-SoC), 2021

Bib

@inproceedings{nousias2021acceleratinh,
  title = {Accelerating 3D scene analysis for autonomous driving on embedded AI computing platforms},
  author = {Nousias, Stavros and Pikoulis, Erion-Vasilis and Mavrokefalidis, Christos and Lalos, Aris S and Moustakas, Konstantinos},
  booktitle = {2021 IFIP/IEEE 29th International Conference on Very Large Scale Integration (VLSI-SoC)},
  pages = {1--6},
  year = {2021},
  organization = {IEEE},
  category_2 = {MCA}
}

2020

PLoS ONE

AVATREE: An open-source computational modelling framework modelling Anatomically Valid Airway TREE conformations

Stavros Nousias, Evangelia I. Zacharaki, and Konstantinos Moustakas

PLoS ONE, 2020

DOI Bib Code

@article{nousias2020avatree,
  title = {AVATREE: An open-source computational modelling framework modelling Anatomically Valid Airway TREE conformations},
  author = {Nousias, Stavros and Zacharaki, Evangelia I. and Moustakas, Konstantinos},
  journal = {PLoS ONE},
  volume = {15},
  number = {4},
  doi = {10.1371/journal.pone.0230259},
  url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0230259},
  pages = {e0230259},
  year = {2020},
  category_1 = {BME}
}

Sensors

Deep CNN sparse coding for real time inhaler sounds classification

Vaggelis Ntalianis, Nikos Dimitris Fakotakis, Stavros Nousias, and 4 more authors

Sensors, 2020

Bib

@article{ntalianis2020deep,
  title = {Deep CNN sparse coding for real time inhaler sounds classification},
  author = {Ntalianis, Vaggelis and Fakotakis, Nikos Dimitris and Nousias, Stavros and Lalos, Aris S and Birbas, Michael and Zacharaki, Evangelia I and Moustakas, Konstantinos},
  journal = {Sensors},
  volume = {20},
  number = {8},
  pages = {2363},
  year = {2020},
  publisher = {MDPI},
}

IEEE TII

Fast mesh denoising with data driven normal filtering using deep variational autoencoders

Stavros Nousias, Gerasimos Arvanitis, Aris S Lalos, and 1 more author

IEEE Transactions on Industrial Informatics, 2020

DOI Bib Code

@article{nousias2020fast,
  title = {Fast mesh denoising with data driven normal filtering using deep variational autoencoders},
  author = {Nousias, Stavros and Arvanitis, Gerasimos and Lalos, Aris S and Moustakas, Konstantinos},
  journal = {IEEE Transactions on Industrial Informatics},
  volume = {17},
  number = {2},
  doi = {10.1109/TII.2020.3000491},
  url = {https://ieeexplore.ieee.org/document/9110709},
  pages = {980--990},
  year = {2020},
  publisher = {IEEE},
  category_1 = {GDL},
}

IEEE Access

A saliency aware CNN-based 3D model simplification and compression framework for remote inspection of heritage sites

Stavros Nousias, Gerasimos Arvanitis, Aris S Lalos, and 4 more authors

IEEE Access, 2020

DOI Bib

@article{nousias2020saliency,
  title = {A saliency aware CNN-based 3D model simplification and compression framework for remote inspection of heritage sites},
  author = {Nousias, Stavros and Arvanitis, Gerasimos and Lalos, Aris S and Pavlidis, George and Koulamas, Christos and Kalogeras, Athanasios and Moustakas, Konstantinos},
  journal = {IEEE Access},
  volume = {8},
  doi = {10.1109/ACCESS.2020.3023167},
  url = {https://ieeexplore.ieee.org/document/9193917},
  pages = {169982--170001},
  year = {2020},
  publisher = {IEEE},
  category_1 = {GDL}
}

IEEE INDIN
Efficient automated U-Net based tree crown delineation using UAV multi-spectral imagery on embedded devices

Kostas Blekos, Stavros Nousias, and Aris S Lalos

In 2020 IEEE 18th International Conference on Industrial Informatics (INDIN), 2020

Abs Bib

Delineation approaches provide significant benefits to various domains, including agriculture, environmental and natural disasters monitoring. Most of the work in the literature utilize traditional segmentation methods that require a large amount of computational and storage resources. Deep learning has transformed computer vision and dramatically improved machine translation, though it requires massive dataset for training and significant resources for inference. More importantly, energy-efficient embedded vision hardware delivering real-time and robust performance is crucial in the aforementioned application. In this work, we propose a U-Net based tree delineation method, which is effectively trained using multi-spectral imagery but can then delineate single-spectrum images. The deep architecture that also performs localization, i.e., a class label corresponds to each pixel, has been successfully used to allow training with a small set of segmented images. The ground truth data were generated using traditional image denoising and segmentation approaches. To be able to execute the proposed DNN efficiently in embedded platforms designed for deep learning approaches, we employ traditional model compression and acceleration methods. Extensive evaluation studies using data collected from UAVs equipped with multi-spectral cameras demonstrate the effectiveness of the proposed methods in terms of delineation accuracy and execution efficiency.
@inproceedings{blekos2020efficient, title = {Efficient automated U-Net based tree crown delineation using UAV multi-spectral imagery on embedded devices}, author = {Blekos, Kostas and Nousias, Stavros and Lalos, Aris S}, booktitle = {2020 IEEE 18th International Conference on Industrial Informatics (INDIN)}, volume = {1}, pages = {541--546}, year = {2020}, organization = {IEEE}, category_1 = {MCA} }

2019

ENTSC

Exploiting gamification to improve eco-driving behaviour: The GamECAR approach

Stavros Nousias, Christos Tselios, Dimitris Bitzas, and 5 more authors

Electronic Notes in Theoretical Computer Science, 2019

Bib

@article{nousias2019exploiting,
  title = {Exploiting gamification to improve eco-driving behaviour: The GamECAR approach},
  author = {Nousias, Stavros and Tselios, Christos and Bitzas, Dimitris and Amaxilatis, Dimitris and Montesa, Javier and Lalos, Aris S and Moustakas, Konstantinos and Chatzigiannakis, Ioannis},
  journal = {Electronic Notes in Theoretical Computer Science},
  volume = {343},
  pages = {103--116},
  year = {2019},
  publisher = {ACM},
}

IEEE ETFA

Assessment of medication adherence in respiratory diseases through deep sparse convolutional coding

Vaggelis Ntalianis, Stavros Nousias, Aris S Lalos, and 3 more authors

In 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2019

Bib

@inproceedings{ntalianis2019assessment,
  title = {Assessment of medication adherence in respiratory diseases through deep sparse convolutional coding},
  author = {Ntalianis, Vaggelis and Nousias, Stavros and Lalos, Aris S and Birbas, Michael and Tsafas, Nikolaos and Moustakas, Konstantinos},
  booktitle = {2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)},
  pages = {1657--1660},
  year = {2019},
  organization = {IEEE},
}

Springer

Enhancing an eco-driving gamification platform through wearable and vehicle sensor data integration

Christos Tselios, Stavros Nousias, Dimitris Bitzas, and 5 more authors

In Ambient Intelligence: 15th European Conference, AmI 2019, Rome, Italy, November 13–15, 2019, Proceedings 15, 2019

Bib

@inproceedings{tselios2019enhancing,
  title = {Enhancing an eco-driving gamification platform through wearable and vehicle sensor data integration},
  author = {Tselios, Christos and Nousias, Stavros and Bitzas, Dimitris and Amaxilatis, Dimitrios and Akrivopoulos, Orestis and Lalos, Aris S and Moustakas, Konstantinos and Chatzigiannakis, Ioannis},
  booktitle = {Ambient Intelligence: 15th European Conference, AmI 2019, Rome, Italy, November 13--15, 2019, Proceedings 15},
  pages = {344--349},
  year = {2019},
  organization = {Springer International Publishing},
}

IEEE IISA

Fluid-structure interaction simulation framework for cerebral aneurysm wall deformation

Giorgos Papoulias, Stavros Nousias, and Konstantinos Moustakas

In 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), 2019

DOI Bib

@inproceedings{papoulias2019fluid,
  title = {Fluid-structure interaction simulation framework for cerebral aneurysm wall deformation},
  author = {Papoulias, Giorgos and Nousias, Stavros and Moustakas, Konstantinos},
  booktitle = {2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)},
  pages = {1--7},
  year = {2019},
  organization = {IEEE},
  doi = {10.1109/IISA.2019.8900781},
  category_1 = {BME},
}

IEEE SA

Sparse modeling and optimization tools for energy efficient and reliable IoT

Stavros Nousias, Aris S Lalos, Athanasios Kalogeras, and 3 more authors

In 2019 First International Conference on Societal Automation (SA), 2019

DOI Bib

@inproceedings{nousias2019sparse,
  title = {Sparse modeling and optimization tools for energy efficient and reliable IoT},
  author = {Nousias, Stavros and Lalos, Aris S and Kalogeras, Athanasios and Alexakos, Christos and Koulamas, Christos and Moustakas, Konstantinos},
  booktitle = {2019 First International Conference on Societal Automation (SA)},
  pages = {1--4},
  year = {2019},
  organization = {IEEE},
  doi = {10.1109/SA47457.2019.8938029},
  category_1 = {MCDI}
}

IEEE BIBE

Recognition of breathing activity and medication adherence using LSTM neural networks

Dionysis Pettas, Stavros Nousias, Evangelia I Zacharaki, and 1 more author

In 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), 2019

Bib

@inproceedings{pettas2019recognition,
  title = {Recognition of breathing activity and medication adherence using LSTM neural networks},
  author = {Pettas, Dionysis and Nousias, Stavros and Zacharaki, Evangelia I and Moustakas, Konstantinos},
  booktitle = {2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)},
  pages = {941--946},
  year = {2019},
  organization = {IEEE},
}

IEEE BIA

Coping with missing data in an unobtrusive monitoring system for office workers

Stavros Nousias, Giwrgos Papoulias, Otilia Kocsis, and 3 more authors

In 2019 International Conference on Biomedical Innovations and Applications (BIA), 2019

DOI Bib

@inproceedings{nousias2019coping,
  title = {Coping with missing data in an unobtrusive monitoring system for office workers},
  author = {Nousias, Stavros and Papoulias, Giwrgos and Kocsis, Otilia and Cabrita, Miriam and Lalos, Aris S and Moustakas, Konstantinos},
  booktitle = {2019 International Conference on Biomedical Innovations and Applications (BIA)},
  pages = {1--4},
  year = {2019},
  organization = {IEEE},
  doi = {10.1109/BIA48344.2019.8967465},
  category_1 = {MCDI}
}

IEEE INDIN

Fast mesh denoising with data driven normal filtering using deep autoencoders

Stavros Nousias, Gerasimos Arvanitis, Aris S Lalos, and 1 more author

In 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 2019

Bib

@inproceedings{nousias2019fast,
  title = {Fast mesh denoising with data driven normal filtering using deep autoencoders},
  author = {Nousias, Stavros and Arvanitis, Gerasimos and Lalos, Aris S and Moustakas, Konstantinos},
  booktitle = {2019 IEEE 17th International Conference on Industrial Informatics (INDIN)},
  volume = {1},
  pages = {260--263},
  year = {2019},
  organization = {IEEE},
}

IEEE ICME

Mesh saliency detection using convolutional neural networks

Stavros Nousias, Gerasimos Arvanitis, Aris S Lalos, and 1 more author

In 2020 IEEE international conference on multimedia and expo (ICME), 2019

DOI Bib Slides

@inproceedings{nousias2020mesh,
  title = {Mesh saliency detection using convolutional neural networks},
  author = {Nousias, Stavros and Arvanitis, Gerasimos and Lalos, Aris S and Moustakas, Konstantinos},
  booktitle = {2020 IEEE international conference on multimedia and expo (ICME)},
  pages = {1--6},
  year = {2019},
  doi = {10.1109/ICME46284.2020.9102796},
  organization = {IEEE},
  category_1 = {GDL}
}

EPS

Gamification of EcoDriving Behaviours through Intelligent Management of dynamic car and driver information

Nousias Stavros, Aris S Lalos, Christos Tselios, and 5 more authors

OPPORTUNITIES AND CHALLENGES for European Projects (EPS Portugal 2017/2018 2017), 2019

Bib

@article{stavros2019gamification,
  title = {Gamification of EcoDriving Behaviours through Intelligent Management of dynamic car and driver information},
  author = {Stavros, Nousias and Lalos, Aris S and Tselios, Christos and Bitzas, Dimitris and Amaxilatis, Dimitris and Chatzigiannakis, Ioannis and Gerasimos, Arvanitis and Moustakas, Konstantinos},
  journal = {OPPORTUNITIES AND CHALLENGES for European Projects (EPS Portugal 2017/2018 2017)},
  pages = {100--123},
  year = {2019},
}

2018

IEEE Access

An mHealth system for monitoring medication adherence in obstructive respiratory diseases using content based audio classification

Stavros Nousias, Aris S Lalos, Gerasimos Arvanitis, and 5 more authors

IEEE Access, 2018

DOI Bib

@article{nousias2018mhealth,
  title = {An mHealth system for monitoring medication adherence in obstructive respiratory diseases using content based audio classification},
  author = {Nousias, Stavros and Lalos, Aris S and Arvanitis, Gerasimos and Moustakas, Konstantinos and Tsirelis, Triantafillos and Kikidis, Dimitrios and Votis, Konstantinos and Tzovaras, Dimitrios},
  journal = {IEEE Access},
  volume = {6},
  pages = {11871--11882},
  year = {2018},
  publisher = {IEEE},
  doi = {10.1109/ACCESS.2018.2809611},
  url = {https://ieeexplore.ieee.org/document/8302483},
  category_1 = {MLHM}
}

HCCAI

3-Class Prediction of Asthma Control Status Using a Gaussian Mixture Model Approach

Gerasimos Arvanitis, Otilia Kocsis, Aris S Lalos, and 3 more authors

In Proceedings of the 10th Hellenic Conference on Artificial Intelligence, 2018

Bib

@inproceedings{arvanitis20183,
  title = {3-Class Prediction of Asthma Control Status Using a Gaussian Mixture Model Approach},
  author = {Arvanitis, Gerasimos and Kocsis, Otilia and Lalos, Aris S and Nousias, Stavros and Moustakas, Konstantinos and Fakotakis, Nikos},
  booktitle = {Proceedings of the 10th Hellenic Conference on Artificial Intelligence},
  pages = {1--2},
  year = {2018},
}

IEEE CAMAD

Uncertainty management for wearable iot wristband sensors using laplacian-based matrix completion

Stavros Nousias, Christos Tselios, Dimitris Bitzas, and 3 more authors

In 2018 IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 2018

DOI Bib

@inproceedings{nousias2018uncertainty,
  title = {Uncertainty management for wearable iot wristband sensors using laplacian-based matrix completion},
  author = {Nousias, Stavros and Tselios, Christos and Bitzas, Dimitris and Lalos, Aris S and Moustakas, Konstantinos and Chatzigiannakis, Ioannis},
  booktitle = {2018 IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)},
  pages = {1--6},
  year = {2018},
  organization = {IEEE},
  doi = {10.1109/CAMAD.2018.8515001},
  category_1 = {MCDI}
}

ERCIM News

Gamecar: Gamifying selfmanagement of eco-driving

Aris S Lalos, Stavros Nousias, and Konstantinos Moustakas

ERCIM News, 2018

Bib

@article{lalos2018gamecar,
  title = {Gamecar: Gamifying selfmanagement of eco-driving},
  author = {Lalos, Aris S and Nousias, Stavros and Moustakas, Konstantinos},
  journal = {ERCIM News},
  volume = {115},
  pages = {46--47},
  year = {2018},
}

IEEE PerCom

Managing nonuniformities and uncertainties in vehicle-oriented sensor data over next generation networks

Stavros Nousias, Christos Tselios, Olivier Orfila, and 7 more authors

In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2018

DOI Bib

@inproceedings{nousias2018managing,
  title = {Managing nonuniformities and uncertainties in vehicle-oriented sensor data over next generation networks},
  author = {Nousias, Stavros and Tselios, Christos and Orfila, Olivier and Jamson, Samantha and Mejuto, Pablo and Amaxilatis, Dimitrios and Akrivopoulos, Orestis and Chatzigiannakis, Ioannis and Lalos, Aris S and Moustakas, Konstantinos},
  booktitle = {2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)},
  pages = {272--277},
  year = {2018},
  organization = {IEEE},
  doi = {10.1109/PERCOMW.2018.8480342},
  category_1 = {MCDI}
}

2016

IEEE BIBM

Numerical assessment of airflow and inhaled particles attributes in obstructed pulmonary system

Antonios Lalas, Dimitrios Kikidis, Konstantinos Votis, and 6 more authors

In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2016

Bib

@inproceedings{lalas2016numerical,
  title = {Numerical assessment of airflow and inhaled particles attributes in obstructed pulmonary system},
  author = {Lalas, Antonios and Kikidis, Dimitrios and Votis, Konstantinos and Tzovaras, Dimitrios and Verbanck, Sylvia and Nousias, Stavros and Lalos, A and Moustakas, Konstantinos and Usmani, Omar},
  booktitle = {2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
  pages = {606--612},
  year = {2016},
  organization = {IEEE}
}

Springer

Computational modeling for simulating obstructive lung diseases based on geometry processing methods

Stavros Nousias, Aris S Lalos, and Konstantinos Moustakas

In Digital Human Modeling: Applications in Health, Safety, Ergonomics and Risk Management: 7th International Conference, DHM 2016, Held as Part of HCI International 2016, Toronto, ON, Canada, July 17-22, 2016, Proceedings 7, 2016

Bib

@inproceedings{nousias2016computational,
  title = {Computational modeling for simulating obstructive lung diseases based on geometry processing methods},
  author = {Nousias, Stavros and Lalos, Aris S and Moustakas, Konstantinos},
  booktitle = {Digital Human Modeling: Applications in Health, Safety, Ergonomics and Risk Management: 7th International Conference, DHM 2016, Held as Part of HCI International 2016, Toronto, ON, Canada, July 17-22, 2016, Proceedings 7},
  pages = {100--109},
  year = {2016},
  organization = {Springer International Publishing}
}

IEEE SSCI

Monitoring asthma medication adherence through content based audio classification

Stavros Nousias, John Lakoumentas, Aris Lalos, and 4 more authors

In 2016 IEEE symposium series on computational intelligence (SSCI), 2016

Bib

@inproceedings{nousias2016monitoring,
  title = {Monitoring asthma medication adherence through content based audio classification},
  author = {Nousias, Stavros and Lakoumentas, John and Lalos, Aris and Kikidis, Dimitrios and Moustakas, Konstantinos and Votis, Konstantinos and Tzovaras, Dimitrios},
  booktitle = {2016 IEEE symposium series on computational intelligence (SSCI)},
  pages = {1--5},
  year = {2016},
  organization = {IEEE}
}

ERS

Computational modeling methods for simulating obstructive human lung diseases

Stavros Nousias, Aris Lalos, Konstantinos Moustakas, and 6 more authors

2016

Bib

@misc{nousias2016computationam,
  title = {Computational modeling methods for simulating obstructive human lung diseases},
  author = {Nousias, Stavros and Lalos, Aris and Moustakas, Konstantinos and Lalas, Antonios and Kikidis, Dimitrios and Votis, Konstantinos and Tzovaras, Dimitrios and Usmani, Omar and Chung, Fan},
  year = {2016},
  publisher = {European Respiratory Society},
}