research results

3D Machine learning and Computer Vision

2023

1. 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}
   }

2020

1. 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},
   }

2. 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}
   }

2019

1. 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}
   }

AI & Machine Learning in the Built Environment

2025

1. Elsevier

   Design Healing framework for automated code compliance

   Jiabin Wu, Stavros Nousias, and André Borrmann

   Elsevier,Automation in Construction, 2025

   Bib

   @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

1. 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},
   }

2. 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}
   }

3. 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}
   }

4. 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}
   }

5. 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}
   }

6. 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}
   }

Doctoral dissertation

Research in:

• Computational Modelling and Simulation in Biomedical Engineering
• AI and Machine Learning in Healthcare and Medicine

2022

1. 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},
   }

Computational Modelling and Simulation in Biomedical Engineering

2022

1. 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},
   }

2020

1. 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}
   }

2019

1. 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},
   }

2017

1. BMC

   

   Substance deposition assessment in obstructed pulmonary system through numerical characterization of airflow and inhaled particles attributes

   Antonios Lalas, Stavros Nousias, Dimitrios Kikidis, and 8 more authors

   BMC medical informatics and decision making, 2017

   DOI Bib PDF

   @article{lalas2017substance,
     title = {Substance deposition assessment in obstructed pulmonary system through numerical characterization of airflow and inhaled particles attributes},
     author = {Lalas, Antonios and Nousias, Stavros and Kikidis, Dimitrios and Lalos, Aris and Arvanitis, Gerasimos and Sougles, Christos and Moustakas, Konstantinos and Votis, Konstantinos and Verbanck, Sylvia and Usmani, Omar and others},
     journal = {BMC medical informatics and decision making},
     volume = {17},
     pages = {25--44},
     year = {2017},
     doi = {10.1186/s12911-017-0561-y},
     publisher = {BioMed Central},
     category_1 = {BME},
   }

Machine Learning for Healthcare and Medicine

2023

1. 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}
   }

2022

1. 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}
   }

2. 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},
   }

2018

1. 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}
   }

Efficient AI, Model Compression and Acceleration in DNNs

2023

1. 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

1. 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}
   }

2021

1. 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}
   }

2. 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

1. 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}
   }

Sparse Modelling for Data Imputation

2019

1. 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}
   }

2. 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}
   }

2018

1. 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}
   }

2. 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}
   }