publications | Stavros Nousias

2025

ASCE JCCE

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

Andrea Carrara, Stavros Nousias, and André Borrmann

Journal of Computing in Civil Engineering, 2025

Abs DOI

This study introduces a new approach to extracting vector data from technical drawings in Portable Document Format (PDF) format and analyzing them semantically by employing graph attention networks. The proposed method involves converting PDF files into Scalable Vector Graphics (SVG) format and creating a feature-rich graph representation, which captures the relationships between vector entities using geometrical information. We then apply a graph attention transformer with hierarchical label definition to achieve accurate line-level semantic segmentation. Our approach is evaluated on two data sets, including the public FloorplanCAD data set, which achieves state-of-the-art results on weighted F1-score (89%), surpassing existing methods. Our vector-based method offers a scalable large-scale technical drawing analysis solution, requiring significantly less graphical processing unit (GPU) power than does current state-of-the-art techniques. Our method performs better semantic segmentation tasks, effectively extracting meaningful information from technical drawings. This enables reliable line-level classification, even for complex drawings such as architectural floorplans with overlapping structural and annotation layers, and opens up new applications, such as automated drawing processing. Additionally, our method can improve existing workflows in the Architecture, Engineering, and Construction (AEC) industry, including automated building information modeling (BIM) and construction planning. Technical drawings are essential for communication in the AEC industries; however, manual analysis is often time-consuming and error prone. This study presents an efficient method for automating the segmentation and analysis of technical drawings using graph-based machine-learning techniques. Converting vector data from commonly used PDF or computer-aided design (CAD) files into feature-rich graph representations enables precise and scalable classification of drawing elements, such as walls, windows, symbols, and annotations. A key advantage of this method is its efficiency, requiring limited computational resources. Unlike traditional machine learning models that rely on computationally intensive pixel-based approaches, this framework uses lightweight graph neural networks, making it accessible for professionals without access to high-performance hardware. This efficiency allows it to process large and complex technical drawings on standard GPUs or even in cloud-based environments, ensuring broader adoption across diverse organizational scales. In practice, this approach reduces project documentation time, improves accuracy, and streamlines workflows such as BIM. It is particularly beneficial for large-scale projects with intricate technical drawings, for which traditional methods struggle to maintain precision. This research demonstrates the transformative potential of AI-driven tools to make data-driven construction and planning more efficient and accessible to a wider range of practitioners.

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},
  doi = {10.1016/j.autcon.2025.106004},
  url = {https://www.sciencedirect.com/science/article/pii/S0926580525000445},
  year = {2025},
  publisher = {Elsevier},
  category_1 = {AIBE}
}

EG-ICE

Exploring Building Energy Performance Prediction Using Graph Neural Networks

A. Dalach, Z. Wang, S. Nousias, and A. Borrmann

In Proc. of the 32nd Int. Conference on Intelligent Computing in Engineering (EG-ICE), Jul 2025

Best Paper Award DOI Bib PDF

The paper received the Best Paper Award at EG-ICE Workshop 2025. View award certificate.

@inproceedings{dalach2025exploring,
  author = {Dalach, A. and Wang, Z. and Nousias, S. and Borrmann, A.},
  title = {Exploring Building Energy Performance Prediction Using Graph Neural Networks},
  booktitle = {Proc. of the 32nd Int. Conference on Intelligent Computing in Engineering (EG-ICE)},
  year = {2025},
  month = jul,
  category_1 = {AIBE},
  doi = {10.17868/strath.00093246},
  url = {https://mediatum.ub.tum.de/node?id=1783397},
}

ICML WCUA

BIMgent: Towards Autonomous Building Modeling via Computer-use Agents

Zihan Deng, Changyu Du, Stavros Nousias, and André Borrmann

In ICML 2025 Workshop on Computer-use Agents, Jun 2025

DOI Bib PDF

@inproceedings{deng2025bimgent,
  title = {BIMgent: Towards Autonomous Building Modeling via Computer-use Agents},
  author = {Deng, Zihan and Du, Changyu and Nousias, Stavros and Borrmann, Andr{\'e}},
  booktitle = {ICML 2025 Workshop on Computer-use Agents},
  doi = {10.48550/arXiv.2506.07217},
  month = jun,
  year = {2025},
  url = {https://openreview.net/forum?id=vQCFwXAl2A},
  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 André Borrmann

arXiv preprint arXiv:2408.08054, Jun 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, Stavros Nousias, Aris Lalos, Apostolos Fournaris, Panagiotis Radoglou-Grammatikis, Panagiotis Sarigiannidis, and Konstantinos Moustakas

Frontiers in Robotics and AI, Jun 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}
}

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, Jun 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 André Borrmann

arXiv preprint arXiv:2406.10237, Jun 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, Jun 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, Jun 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 Konstantinos Moustakas

ACM Transactions on Multimedia Computing, Communications and Applications, Jun 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, Evangelia I Zacharaki, and Konstantinos Moustakas

IEEE Access, Jun 2023

Abs DOI Bib PDF Code

Asthma is a common, usually long-term respiratory disease with negative impact on global society and economy. Treatment involves using medical devices (inhalers) that distribute medication to the airways and its efficiency depends on the precision of the inhalation technique. There is a clinical need for objective methods to assess the inhalation technique, during clinical consultation. Integrated health monitoring systems, equipped with sensors, enable the recognition of drug actuation, embedded with sound signal detection, analysis and identification from intelligent structures, that could provide powerful tools for reliable content management. Health monitoring systems equipped with sensors, embedded with sound signal detection, enable the recognition of drug actuation and could be used for effective audio content analysis. This paper revisits sound pattern recognition with machine learning techniques for asthma medication adherence assessment and presents the Respiratory and Drug Actuation (RDA) Suite (https://gitlab.com/vvr/monitoring-medication-adherence/rda-benchmark) for benchmarking and further research. The RDA Suite includes a set of tools for audio processing, feature extraction and classification procedures and is provided along with a dataset, consisting of respiratory and drug actuation sounds. The classification models in RDA are implemented based on conventional and advanced machine learning and deep networks’ architectures. This study provides a comparative evaluation of the implemented approaches, examines potential improvements and discusses on challenges and future tendencies.
@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.2023.3243547}, 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 Aris S Lalos

IEEE Access, Jun 2023

Abs DOI Bib PDF

Environment perception constitutes one of the most critical operations performed by semi- and fully- autonomous vehicles. In recent years, Deep Neural Networks (DNNs) have become the standard tool for perception solutions owing to their impressive capabilities in analyzing and modelling complex and dynamic scenes, from (often multi-modal) sensory inputs. However, the well-established performance of DNNs comes at the cost of increased time and storage complexity, which may become problematic in automotive perception systems due to the requirement for a short prediction horizon (as in many cases inference must be performed in real-time) and the limited computational, storage, and energy resources of mobile systems. 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, improving both their storage and execution efficiency. Within the MCA framework, in this paper, we investigate the application of two state-of-the-art weight-sharing MCA techniques, namely a Vector Quantization (VQ) and a Dictionary Learning (DL) one, as well as two novel extensions, towards the acceleration and compression of widely used DNNs for 2D and 3D object-detection in automotive applications. Apart from the individual (uni-modal) networks, we also present and evaluate a multi-modal late-fusion algorithm for combining the detection results of the 2D and 3D detectors. Our evaluation studies are carried out on the KITTI Dataset. The obtained results lend themselves to a twofold interpretation. On the one hand, they showcase the significant acceleration and compression gains that can be achieved via the application of weight sharing on the selected DNN detectors, with limited accuracy loss, as well as highlight the performance differences between the two utilized weight-sharing approaches. On the other, they demonstrate the substantial boost in detection performance obtained by combining the outcome of the two unimodal individual detectors, using the proposed late-fusion-based multi-modal approach. Indeed, as our experiments have shown, pairing the high-performance DL-based MCA technique with the loss-mitigating effect of the multi-modal fusion approach, leads to highly accelerated models (up to approximately 2.5× and 6× for the 2D and 3D detectors, respectively) with the performance loss of the fused results ranging in most cases within single-digits figures (as low as around 1% for the class “cars”).
@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 Aris S Lalos

Springer,Deep Learning Applications, Volume 3, Jun 2022

Abs DOI 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}, doi = {10.1007/978-981-16-3357-7_5}, 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 Konstantinos Moustakas

In 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS), Jun 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},
  category_2 = {GDL}
}

IEEE Dataport

Respiratory and Drug Actuation Dataset

Stavros Nousias, Gerasimos Arvanitis, Aggeliki Anastasiou, and Konstantinos Moustakas

Jun 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. , Jun 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}, }
PhD

Doctoral Dissertation Defense Presentation on Patient-specific computational modelling, simulation and real-time processing for respiratory diseases

Stavros Nousias

Jun 2022

DOI Slides

Code

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

ND Fakotakis, Stavros Nousias, Gerasimos Arvanitis, Evangelia I Zacharaki, and Konstantinos Moustakas

Jun 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 Aris S Lalos

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

Abs DOI 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}, doi = {10.1109/ICPS49255.2021.9468126}, category_1 = {MCA} }

arXiv

Empowering cyberphysical systems of systems with intelligence

Stavros Nousias, Nikos Piperigkos, Gerasimos Arvanitis, Apostolos Fournaris, Aris S Lalos, and Konstantinos Moustakas

arXiv preprint arXiv:2107.02264, Jun 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, Aris S Lalos, and Konstantinos Moustakas

In 2021 IFIP/IEEE 29th International Conference on Very Large Scale Integration (VLSI-SoC), Jun 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, Jun 2020

DOI Bib Code Slides

@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, Aris S Lalos, Michael Birbas, Evangelia I Zacharaki, and Konstantinos Moustakas

Sensors, Jun 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 Konstantinos Moustakas

IEEE Transactions on Industrial Informatics, Jun 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, George Pavlidis, Christos Koulamas, Athanasios Kalogeras, and Konstantinos Moustakas

IEEE Access, Jun 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), Jun 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, Dimitris Amaxilatis, Javier Montesa, Aris S Lalos, Konstantinos Moustakas, and Ioannis Chatzigiannakis

Electronic Notes in Theoretical Computer Science, Jun 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, Michael Birbas, Nikolaos Tsafas, and Konstantinos Moustakas

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

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), Jun 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, Christos Alexakos, Christos Koulamas, and Konstantinos Moustakas

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

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

DOI Bib Slides

@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},
  doi = {10.1109/BIBE.2019.00176},
  url = {https://ieeexplore.ieee.org/document/8941960},
  category_1 = {MLHM},
}

IEEE BIA

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

Stavros Nousias, Giwrgos Papoulias, Otilia Kocsis, Miriam Cabrita, Aris S Lalos, and Konstantinos Moustakas

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

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

In 2020 IEEE international conference on multimedia and expo (ICME), Jun 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, Dimitris Bitzas, Dimitris Amaxilatis, Ioannis Chatzigiannakis, Arvanitis Gerasimos, and Konstantinos Moustakas

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

ENTSC

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

Stavros Nousias, Christos Tselios, Dimitris Bitzas, Dimitris Amaxilatis, Javier Montesa, Aris S Lalos, Konstantinos Moustakas, and Ioannis Chatzigiannakis

Electronic Notes in Theoretical Computer Science, Jun 2019

DOI PDF

Springer

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

Christos Tselios, Stavros Nousias, Dimitris Bitzas, Dimitrios Amaxilatis, Orestis Akrivopoulos, Aris S Lalos, Konstantinos Moustakas, and Ioannis Chatzigiannakis

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

DOI 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},
  doi = {10.1007/978-3-030-34255-5_26},
  category_1 = {Gamification},
}

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, Konstantinos Moustakas, Triantafillos Tsirelis, Dimitrios Kikidis, Konstantinos Votis, and Dimitrios Tzovaras

IEEE Access, Jun 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, Stavros Nousias, Konstantinos Moustakas, and Nikos Fakotakis

In Proceedings of the 10th Hellenic Conference on Artificial Intelligence, Jun 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, Aris S Lalos, Konstantinos Moustakas, and Ioannis Chatzigiannakis

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

Abs DOI Bib

Contemporary sensing devices provide reliable mechanisms for continuous process monitoring, accommodating use cases related to mHealth and smart mobility, by generating real-time data streams of numerous physiological and vital parameters. Such data streams can be later utilized by machine learning algorithms and decision support systems to predict critical clinical states and motivate users to adopt behaviours that improve the quality of their life and the society as a whole. However, in many cases, even when deployed over highly sophisticated, cutting-edge network infrastructure and deployment paradigms, data may exhibit missing values and non-uniformities due to various reasons, including device malfunction, deliberate data reduction for efficient processing, or data loss due to sensing and communication failures. This work proposes a novel approach to deal with missing entries in heart rate measurements. Benefiting from the low-rank property of the generated data matrices and the proximity of neighbouring measurements, we provide a novel method that combines classical matrix completion approaches with weighted Laplacian interpolation offering high reconstruction accuracy at fast execution times. Extensive evaluation studies carried out with real measurements show that the proposed methods could be effectively deployed by modern wristband-cloud computing systems increasing the robustness, the reliability and the energy efficiency of these systems.
@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, Jun 2018

Bib HTML

@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},
  category_1 = {Gamification},
}

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

Stavros Nousias, Christos Tselios, Olivier Orfila, Samantha Jamson, Pablo Mejuto, Dimitrios Amaxilatis, Orestis Akrivopoulos, Ioannis Chatzigiannakis, Aris S Lalos, and Konstantinos Moustakas

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

Abs DOI Bib

Detailed and accurate vehicle-oriented sensor data is considered fundamental for efficient vehicle-to-everything V2X communication applications, especially in the upcoming highly heterogeneous, brisk and agile 5G networking era. Information retrieval, transfer and manipulation in real-time offers a small margin for erratic behavior, regardless of its root cause. This paper presents a method for managing nonuniformities and uncertainties found on datasets, based on an elaborate Matrix Completion technique, with superior performance in three distinct cases of vehicle-related sensor data, collected under real driving conditions. Our approach appears capable of handling sensing and communication irregularities, minimizing at the same time the storage and transmission requirements of Multi-access Edge Computing applications.
@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, Dimitrios Tzovaras, Sylvia Verbanck, Stavros Nousias, A Lalos, Konstantinos Moustakas, and Omar Usmani

In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Jun 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, Jun 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, Dimitrios Kikidis, Konstantinos Moustakas, Konstantinos Votis, and Dimitrios Tzovaras

In 2016 IEEE symposium series on computational intelligence (SSCI), Jun 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, Antonios Lalas, Dimitrios Kikidis, Konstantinos Votis, Dimitrios Tzovaras, Omar Usmani, and Fan Chung

Jun 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},
}