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Prof. Nikolaos Doulamis (M) received the Diploma in Electrical and Computer Engineering from the National Technical University of Athens (NTUA) with the highest honor (ranked first in his class) and the PhD degree in Electrical and Computer Engineering from NTUA. He joined the Image, Video and Multimedia Lab of NTUA as research assistant. His PhD thesis was supported by the Bodosakis Foundation Scholarship. He is currently Associate professor at the National Technical University of Athens. His work has been cited 61500 times. One of his works is cited as the “Doulamis Model” in the literature. He has authored more than 400 papers.
Modeling ionospheric variability throughout a proper total electron content (TEC) parameter estimation is a demanding, however, crucial, process for achieving better accuracy and rapid convergence in precise point positioning (PPP). In particular, the single-frequency PPP (SF-PPP) method lacks accuracy due to the difficulty of dealing adequately with the ionospheric error sources. In order to apply ionosphere corrections in techniques, such as SF-PPP, external information of global ionosphere maps (GIMs) is crucial. In this article, we propose a deep learning model to efficiently predict TEC values and to replace the GIM-derived data that inherently have a global character, with equal or better in accuracy regional ones. The proposed model is suitable for predicting the ionosphere delay at different locations of receiver stations. The model is tested during different periods of time, under different solar and geomagnetic conditions and for stations in various latitudes, providing robust estimations of the ionospheric activity at the regional level. Our proposed model is a hybrid model comprising of a 1-D convolutional layer used for the optimal feature extraction and stacked recurrent layers used for temporal time series modeling. Thus, the model achieves good performance in TEC modeling compared to other state-of-the-art methods.
Maria Kaselimi; Athanasios Voulodimos; Nikolaos Doulamis; Anastasios Doulamis; Demitris Delikaraoglou. Deep Recurrent Neural Networks for Ionospheric Variations Estimation Using GNSS Measurements. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -15.
AMA StyleMaria Kaselimi, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Demitris Delikaraoglou. Deep Recurrent Neural Networks for Ionospheric Variations Estimation Using GNSS Measurements. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-15.
Chicago/Turabian StyleMaria Kaselimi; Athanasios Voulodimos; Nikolaos Doulamis; Anastasios Doulamis; Demitris Delikaraoglou. 2021. "Deep Recurrent Neural Networks for Ionospheric Variations Estimation Using GNSS Measurements." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-15.
An increasing number of emerging applications in data science and engineering are based on multidimensional and structurally rich data. The irregularities, however, of high-dimensional data often compromise the effectiveness of standard machine learning algorithms. We hereby propose the Rank- $R$ Feedforward Neural Network (FNN), a tensor-based nonlinear learning model that imposes Canonical/Polyadic decomposition on its parameters, thereby offering two core advantages compared to typical machine learning methods. First, it handles inputs as multilinear arrays, bypassing the need for vectorization, and can thus fully exploit the structural information along every data dimension. Moreover, the number of the model’s trainable parameters is substantially reduced, making it very efficient for small sample setting problems. We establish the universal approximation and learnability properties of Rank- $R$ FNN, and we validate its performance on real-world hyperspectral datasets. Experimental evaluations show that Rank- $R$ FNN is a computationally inexpensive alternative of ordinary FNN that achieves state-of-the-art performance on higher-order tensor data.
Konstantinos Makantasis; Alexandros Georgogiannis; Athanasios Voulodimos; Ioannis Georgoulas; Anastasios Doulamis; Nikolaos Doulamis. Rank-R FNN: A Tensor-Based Learning Model for High-Order Data Classification. IEEE Access 2021, 9, 1 -1.
AMA StyleKonstantinos Makantasis, Alexandros Georgogiannis, Athanasios Voulodimos, Ioannis Georgoulas, Anastasios Doulamis, Nikolaos Doulamis. Rank-R FNN: A Tensor-Based Learning Model for High-Order Data Classification. IEEE Access. 2021; 9 ():1-1.
Chicago/Turabian StyleKonstantinos Makantasis; Alexandros Georgogiannis; Athanasios Voulodimos; Ioannis Georgoulas; Anastasios Doulamis; Nikolaos Doulamis. 2021. "Rank-R FNN: A Tensor-Based Learning Model for High-Order Data Classification." IEEE Access 9, no. : 1-1.
This paper proposes a new photonic-based non-invasive device for managing of Diabetic Foot Ulcers (DFUs) for people suffering from diabetes. DFUs are one of the main severe complications of diabetes, which may lead to major disabilities, such as foot amputation, or even to the death. The proposed device exploits hyperspectral (HSI) and thermal imaging to measure the status of an ulcer, in contrast to the current practice where invasive biopsies are often applied. In particular, these two photonic-based imaging techniques can estimate the biomarkers of oxyhaemoglobin (HbO2) and deoxyhaemoglobin (Hb), through which the Peripheral Oxygen Saturation (SpO2) and Tissue Oxygen Saturation (StO2) is computed. These factors are very important for the early prediction and prognosis of a DFU. The device is implemented at two editions: the in-home edition suitable for patients and the PRO (professional) edition for the medical staff. The latter is equipped with active photonic tools, such as tuneable diodes, to permit detailed diagnosis and treatment of an ulcer and its progress. The device is enriched with embedding signal processing tools for noise removal and enhancing pixel accuracy using super resolution schemes. In addition, a machine learning framework is adopted, through deep learning structures, to assist the doctors and the patients in understanding the effect of the biomarkers on DFU. The device is to be validated at large scales at three European hospitals (Charité–University Hospital in Berlin, Germany; Attikon in Athens, Greece, and Victor Babes in Timisoara, Romania) for its efficiency and performance.
Anastasios Doulamis; Nikolaos Doulamis; Aikaterini Angeli; Andreas Lazaris; Siri Luthman; Murali Jayapala; Günther Silbernagel; Adriane Napp; Ioannis Lazarou; Alexandros Karalis; Richelle Hoveling; Panagiotis Terzopoulos; Athanasios Yamas; Panagiotis Georgiadis; Richard Maulini; Antoine Muller. A Non-Invasive Photonics-Based Device for Monitoring of Diabetic Foot Ulcers: Architectural/Sensorial Components & Technical Specifications. Inventions 2021, 6, 27 .
AMA StyleAnastasios Doulamis, Nikolaos Doulamis, Aikaterini Angeli, Andreas Lazaris, Siri Luthman, Murali Jayapala, Günther Silbernagel, Adriane Napp, Ioannis Lazarou, Alexandros Karalis, Richelle Hoveling, Panagiotis Terzopoulos, Athanasios Yamas, Panagiotis Georgiadis, Richard Maulini, Antoine Muller. A Non-Invasive Photonics-Based Device for Monitoring of Diabetic Foot Ulcers: Architectural/Sensorial Components & Technical Specifications. Inventions. 2021; 6 (2):27.
Chicago/Turabian StyleAnastasios Doulamis; Nikolaos Doulamis; Aikaterini Angeli; Andreas Lazaris; Siri Luthman; Murali Jayapala; Günther Silbernagel; Adriane Napp; Ioannis Lazarou; Alexandros Karalis; Richelle Hoveling; Panagiotis Terzopoulos; Athanasios Yamas; Panagiotis Georgiadis; Richard Maulini; Antoine Muller. 2021. "A Non-Invasive Photonics-Based Device for Monitoring of Diabetic Foot Ulcers: Architectural/Sensorial Components & Technical Specifications." Inventions 6, no. 2: 27.
Recent studies indicate that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 identification. In this paper, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia-infected area segmentation in CT images for the detection of COVID-19. Traditional methods for CT scan segmentation exploit a supervised learning paradigm, so they (a) require large volumes of data for their training, and (b) assume fixed (static) network weights once the training procedure has been completed. Recently, to overcome these difficulties, few-shot learning (FSL) has been introduced as a general concept of network model training using a very small amount of samples. In this paper, we explore the efficacy of few-shot learning in U-Net architectures, allowing for a dynamic fine-tuning of the network weights as new few samples are being fed into the U-Net. Experimental results indicate improvement in the segmentation accuracy of identifying COVID-19 infected regions. In particular, using 4-fold cross-validation results of the different classifiers, we observed an improvement of 5.388 ± 3.046% for all test data regarding the IoU metric and a similar increment of 5.394 ± 3.015% for the F1 score. Moreover, the statistical significance of the improvement obtained using our proposed few-shot U-Net architecture compared with the traditional U-Net model was confirmed by applying the Kruskal-Wallis test (p-value = 0.026).
Athanasios Voulodimos; Eftychios Protopapadakis; Iason Katsamenis; Anastasios Doulamis; Nikolaos Doulamis. A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images. Sensors 2021, 21, 2215 .
AMA StyleAthanasios Voulodimos, Eftychios Protopapadakis, Iason Katsamenis, Anastasios Doulamis, Nikolaos Doulamis. A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images. Sensors. 2021; 21 (6):2215.
Chicago/Turabian StyleAthanasios Voulodimos; Eftychios Protopapadakis; Iason Katsamenis; Anastasios Doulamis; Nikolaos Doulamis. 2021. "A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images." Sensors 21, no. 6: 2215.
In this paper, we propose a Stack Auto-encoder (SAE)-Driven and Semi-Supervised (SSL)-Based Deep Neural Network (DNN) to extract buildings from relatively low-cost satellite near infrared images. The novelty of our scheme is that we employ only an extremely small portion of labeled data for training the deep model which constitutes less than 0.08% of the total data. This way, we significantly reduce the manual effort needed to complete an annotation process, and thus the time required for creating a reliable labeled dataset. On the contrary, we apply novel semi-supervised techniques to estimate soft labels (targets) of the vast amount of existing unlabeled data and then we utilize these soft estimates to improve model training. Overall, four SSL schemes are employed, the Anchor Graph, the Safe Semi-Supervised Regression (SAFER), the Squared-loss Mutual Information Regularization (SMIR), and an equal importance Weighted Average of them (WeiAve). To retain only the most meaning information of the input data, labeled and unlabeled ones, we also employ a Stack Autoencoder (SAE) trained under an unsupervised manner. This way, we handle noise in the input signals, attributed to dimensionality redundancy, without sacrificing meaningful information. Experimental results on the benchmarked dataset of Vaihingen city in Germany indicate that our approach outperforms all state-of-the-art methods in the field using the same type of color orthoimages, though the fact that a limited dataset is utilized (10 times less data or better, compared to other approaches), while our performance is close to the one achieved by high expensive and much more precise input information like the one derived from Light Detection and Ranging (LiDAR) sensors. In addition, the proposed approach can be easily expanded to handle any number of classes, including buildings, vegetation, and ground.
Eftychios Protopapadakis; Anastasios Doulamis; Nikolaos Doulamis; Evangelos Maltezos. Stacked Autoencoders Driven by Semi-Supervised Learning for Building Extraction from near Infrared Remote Sensing Imagery. Remote Sensing 2021, 13, 371 .
AMA StyleEftychios Protopapadakis, Anastasios Doulamis, Nikolaos Doulamis, Evangelos Maltezos. Stacked Autoencoders Driven by Semi-Supervised Learning for Building Extraction from near Infrared Remote Sensing Imagery. Remote Sensing. 2021; 13 (3):371.
Chicago/Turabian StyleEftychios Protopapadakis; Anastasios Doulamis; Nikolaos Doulamis; Evangelos Maltezos. 2021. "Stacked Autoencoders Driven by Semi-Supervised Learning for Building Extraction from near Infrared Remote Sensing Imagery." Remote Sensing 13, no. 3: 371.
Separating the household aggregated energy consumption signal into its additive sub-components (the power signal from individual appliances), is called energy (power) disaggregation or non intrusive load monitoring (NILM). NILM resembles the signal source separation problem and poses several challenges, not only as an ill-posed problem, but also, due to unsteady appliance signatures, abnormal behaviour that is usually detected in appliances operation and the existence of noise in the aggregated signal. In this paper, we propose a EnerGAN++, a model based on Generative Adversarial Networks for robust energy disaggregation. We attempt to unify the autoencoder (AE) and GAN architectures into a single framework, in which the autoencoder achieves a non-linear power signal source separation. EnerGAN++ is trained adversarially using a novel discriminator, to enhance robustness to noise. The discriminator performs sequence classification, using a recurrent convolutional neural network to handle the temporal dynamics of an appliance energy consumption time series. In particular, the proposed architecture of the discriminator leverages the ability of Convolutional Neural Networks (CNN) in rapid processing and optimal feature extraction, among with the need to infer the data temporal character and time dependence. Experimental results indicate the proposed method's superiority compared to the current state of the art.
Maria Kaselimi; Nikolaos Doulamis; Athanasios Voulodimos; Anastasios D. Doulamis; Eftychios Protopapadakis. EnerGAN++: A Generative Adversarial Gated Recurrent Network for Robust Energy Disaggregation. IEEE Open Journal of Signal Processing 2020, 2, 1 -16.
AMA StyleMaria Kaselimi, Nikolaos Doulamis, Athanasios Voulodimos, Anastasios D. Doulamis, Eftychios Protopapadakis. EnerGAN++: A Generative Adversarial Gated Recurrent Network for Robust Energy Disaggregation. IEEE Open Journal of Signal Processing. 2020; 2 (99):1-16.
Chicago/Turabian StyleMaria Kaselimi; Nikolaos Doulamis; Athanasios Voulodimos; Anastasios D. Doulamis; Eftychios Protopapadakis. 2020. "EnerGAN++: A Generative Adversarial Gated Recurrent Network for Robust Energy Disaggregation." IEEE Open Journal of Signal Processing 2, no. 99: 1-16.
We introduce a deep learning framework that can detect COVID-19 pneumonia in thoracic radiographs, as well as differentiate it from bacterial pneumonia infection. Deep classification models, such as convolutional neural networks (CNNs), require large-scale datasets in order to be trained and perform properly. Since the number of X-ray samples related to COVID-19 is limited, transfer learning (TL) appears as the go-to method to alleviate the demand for training data and develop accurate automated diagnosis models. In this context, networks are able to gain knowledge from pretrained networks on large-scale image datasets or alternative data-rich sources (i.e. bacterial and viral pneumonia radiographs). The experimental results indicate that the TL approach outperforms the performance obtained without TL, for the COVID-19 classification task in chest X-ray images.
Iason Katsamenis; Eftychios Protopapadakis; Athanasios Voulodimos; Anastasios Doulamis; Nikolaos Doulamis. Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images. 2020, 1 .
AMA StyleIason Katsamenis, Eftychios Protopapadakis, Athanasios Voulodimos, Anastasios Doulamis, Nikolaos Doulamis. Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images. . 2020; ():1.
Chicago/Turabian StyleIason Katsamenis; Eftychios Protopapadakis; Athanasios Voulodimos; Anastasios Doulamis; Nikolaos Doulamis. 2020. "Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images." , no. : 1.
Customer Experience (CX) is monitored through market research surveys, based on metrics like the Net Promoter Score (NPS) and the customer satisfaction for certain experience attributes (e.g., call center, website, billing, service quality, tariff plan). The objective of companies is to maximize NPS through the improvement of the most important CX attributes. However, statistical analysis suggests that there is a lack of clear and accurate association between NPS and the CX attributes’ scores. In this paper, we address the aforementioned deficiency using a novel classification approach, which was developed based on logistic regression and tested with several state-of-the-art machine learning (ML) algorithms. The proposed method was applied on an extended data set from the telecommunication sector and the results were quite promising, showing a significant improvement in most statistical metrics.
Ioannis Markoulidakis; Ioannis Rallis; Ioannis Georgoulas; George Kopsiaftis; Anastasios Doulamis; Nikolaos Doulamis. A Machine Learning Based Classification Method for Customer Experience Survey Analysis. Technologies 2020, 8, 76 .
AMA StyleIoannis Markoulidakis, Ioannis Rallis, Ioannis Georgoulas, George Kopsiaftis, Anastasios Doulamis, Nikolaos Doulamis. A Machine Learning Based Classification Method for Customer Experience Survey Analysis. Technologies. 2020; 8 (4):76.
Chicago/Turabian StyleIoannis Markoulidakis; Ioannis Rallis; Ioannis Georgoulas; George Kopsiaftis; Anastasios Doulamis; Nikolaos Doulamis. 2020. "A Machine Learning Based Classification Method for Customer Experience Survey Analysis." Technologies 8, no. 4: 76.
Corrosion detection on metal constructions is a major challenge in civil engineering for quick, safe and effective inspection. Existing image analysis approaches tend to place bounding boxes around the defected region which is not adequate both for structural analysis and prefabrication, an innovative construction concept which reduces maintenance cost, time and improves safety. In this paper, we apply three semantic segmentation-oriented deep learning models (FCN, U-Net and Mask R-CNN) for corrosion detection, which perform better in terms of accuracy and time and require a smaller number of annotated samples compared to other deep models, e.g. CNN. However, the final images derived are still not sufficiently accurate for structural analysis and prefabrication. Thus, we adopt a novel data projection scheme that fuses the results of color segmentation, yielding accurate but over-segmented contours of a region, with a processed area of the deep masks, resulting in high-confidence corroded pixels.
Iason Katsamenis; Eftychios Protopapadakis; Anastasios Doulamis; Nikolaos Doulamis; Athanasios Voulodimos. Pixel-Level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contour Segmentation. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 160 -169.
AMA StyleIason Katsamenis, Eftychios Protopapadakis, Anastasios Doulamis, Nikolaos Doulamis, Athanasios Voulodimos. Pixel-Level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contour Segmentation. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():160-169.
Chicago/Turabian StyleIason Katsamenis; Eftychios Protopapadakis; Anastasios Doulamis; Nikolaos Doulamis; Athanasios Voulodimos. 2020. "Pixel-Level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contour Segmentation." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 160-169.
In this paper, a deep stacked auto-encoder (SAE) scheme followed by a hierarchical Sparse Modeling for Representative Selection (SMRS) algorithm is proposed to summarize dance video sequences, recorded using the VICON Motion capturing system. SAE’s main task is to reduce the redundant information embedding in the raw data and, thus, to improve summarization performance. This becomes apparent when two dancers are performing simultaneously and severe errors are encountered in the humans’ point joints, due to dancers’ occlusions in the 3D space. Four summarization algorithms are applied to extract the key frames; density based, Kennard Stone, conventional SMRS and its hierarchical scheme called H-SMRS. Experimental results have been carried out on real-life dance sequences of Greek traditional dances while the results have been compared against ground truth data selected by dance experts. The results indicate that H-SMRS being applied after the SAE information reduction module extracts key frames which are deviated in time less than 0.3 s to the ones selected by the experts and with a standard deviation of 0.18 s. Thus, the proposed scheme can effectively represent the content of the dance sequence.
Eftychios Protopapadakis; Ioannis Rallis; Anastasios Doulamis; Nikolaos Doulamis; Athanasios Voulodimos. Unsupervised 3D Motion Summarization Using Stacked Auto-Encoders. Applied Sciences 2020, 10, 8226 .
AMA StyleEftychios Protopapadakis, Ioannis Rallis, Anastasios Doulamis, Nikolaos Doulamis, Athanasios Voulodimos. Unsupervised 3D Motion Summarization Using Stacked Auto-Encoders. Applied Sciences. 2020; 10 (22):8226.
Chicago/Turabian StyleEftychios Protopapadakis; Ioannis Rallis; Anastasios Doulamis; Nikolaos Doulamis; Athanasios Voulodimos. 2020. "Unsupervised 3D Motion Summarization Using Stacked Auto-Encoders." Applied Sciences 10, no. 22: 8226.
This paper presents an approach for leveraging the abundance of images posted on social media like Twitter for large scale 3D reconstruction of cultural heritage landmarks. Twitter allows users to post short messages, including photos, describing a plethora of activities or events, e.g., tweets are used by travelers on vacation, capturing images from various cultural heritage assets. As such, a great number of images are available online, able to drive a successful 3D reconstruction process. However, reconstruction of any asset, based on images mined from Twitter, presents several challenges. There are three main steps that have to be considered: (i) tweets’ content identification, (ii) image retrieval and filtering, and (iii) 3D reconstruction. The proposed approach first extracts key events from unstructured tweet messages and then identifies cultural activities and landmarks. The second stage is the application of a content-based filtering method so that only a small but representative portion of cultural images are selected to support fast 3D reconstruction. The proposed methods are experimentally evaluated using real-world data and comparisons verify the effectiveness of the proposed scheme.
Anastasios Doulamis; Athanasios Voulodimos; Eftychios Protopapadakis; Nikolaos Doulamis; Konstantinos Makantasis. Automatic 3D Modeling and Reconstruction of Cultural Heritage Sites from Twitter Images. Sustainability 2020, 12, 4223 .
AMA StyleAnastasios Doulamis, Athanasios Voulodimos, Eftychios Protopapadakis, Nikolaos Doulamis, Konstantinos Makantasis. Automatic 3D Modeling and Reconstruction of Cultural Heritage Sites from Twitter Images. Sustainability. 2020; 12 (10):4223.
Chicago/Turabian StyleAnastasios Doulamis; Athanasios Voulodimos; Eftychios Protopapadakis; Nikolaos Doulamis; Konstantinos Makantasis. 2020. "Automatic 3D Modeling and Reconstruction of Cultural Heritage Sites from Twitter Images." Sustainability 12, no. 10: 4223.
Recent studies indicated that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 detection. In this work, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia infected area segmentation in CT images for the detection of COVID-19. We explore the efficacy of U-Nets and Fully Convolutional Neural Networks in this task using real-world CT data from COVID-19 patients. The results indicate that Fully Convolutional Neural Networks are capable of accurate segmentation despite the class imbalance on the dataset and the man-made annotation errors on the boundaries of symptom manifestation areas, and can be a promising method for further analysis of COVID-19 induced pneumonia symptoms in CT images.Impact StatementFully Convolutional Neural Networks appear to be an accurate segmentation method in CT scans for COVID-19 pneumonia and could assist in the detection as a fast and cost-effective option.
Athanasios Voulodimos; Eftychios Protopapadakis; Iason Katsamenis; Anastasios Doulamis; Nikolaos Doulamis. Deep learning models for COVID-19 infected area segmentation in CT images. 2020, 1 .
AMA StyleAthanasios Voulodimos, Eftychios Protopapadakis, Iason Katsamenis, Anastasios Doulamis, Nikolaos Doulamis. Deep learning models for COVID-19 infected area segmentation in CT images. . 2020; ():1.
Chicago/Turabian StyleAthanasios Voulodimos; Eftychios Protopapadakis; Iason Katsamenis; Anastasios Doulamis; Nikolaos Doulamis. 2020. "Deep learning models for COVID-19 infected area segmentation in CT images." , no. : 1.
The necessity of predicting the spatio-temporal phenomenon of ionospheric variability is closely related to the requirement of many users to be able to obtain high accuracy positioning with low cost equipment. The Precise Point Positioning (PPP) technique is highly accepted by the scientific community as a means for providing high level of position accuracy from a single receiver. However, its main drawback is the long convergence time to achieve centimeter-level accuracy in positioning. Hereby, we propose a deep learning-based approach for ionospheric modeling. This method exploits the advantages of Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) for timeseries modeling and predicts the total electron content per satellite from a specific station by making use of a causal, supervised deep learning method. The scope of the proposed method is to compare and evaluate the between-satellites ionospheric delay estimation, and to aggregate the Total Electron Content (TEC) outcomes per-satellite into a single solution over the station, thus constructing regional TEC models, in an attempt to replace Global Ionospheric Maps (GIM) data. The evaluation of our proposed recurrent method for the prediction of vertical total electron content (VTEC) values is compared against the traditional Autoregressive (AR) and the Autoregressive Moving Average (ARMA) methods, per satellite. The proposed model achieves error lower than 1.5 TECU which is slightly better than the accuracy of the current GIM products which is currently about 2.0–3.0 TECU.
Maria Kaselimi; Athanasios Voulodimos; Nikolaos Doulamis; Anastasios Doulamis; Demitris Delikaraoglou. A Causal Long Short-Term Memory Sequence to Sequence Model for TEC Prediction Using GNSS Observations. Remote Sensing 2020, 12, 1354 .
AMA StyleMaria Kaselimi, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Demitris Delikaraoglou. A Causal Long Short-Term Memory Sequence to Sequence Model for TEC Prediction Using GNSS Observations. Remote Sensing. 2020; 12 (9):1354.
Chicago/Turabian StyleMaria Kaselimi; Athanasios Voulodimos; Nikolaos Doulamis; Anastasios Doulamis; Demitris Delikaraoglou. 2020. "A Causal Long Short-Term Memory Sequence to Sequence Model for TEC Prediction Using GNSS Observations." Remote Sensing 12, no. 9: 1354.
Energy disaggregation, or Non-Intrusive Load Mon-itoring (NILM), describes various processes aiming to identify the individual contribution of appliances, given the aggregate power signal. In this paper, a non-causal adaptive context-aware bidirec-tional deep learning model for energy disaggregation is intro-duced. The proposed model, CoBiLSTM, harnesses the represen-tational power of deep recurrent Long Short-Term Memory (LSTM) neural networks, while fitting two basic properties of NILM problem which state of the art methods do not appropri-ately account for: non-causality and adaptivity to contextual fac-tors (e.g. seasonality). A Bayesian-optimized framework is intro-duced to select the best configuration of the proposed regression model, driven by a self-training adaptive mechanism. Further-more, the proposed model is structured in a modular way to ad-dress multi-dimensionality issues that arise when the number of appliances increases. Experimental results indicate the proposed method’s superiority compared to the current state of the art.
Maria Kaselimi; Nikolaos Doulamis; Athanasios Voulodimos; Eftychios Protopapadakis; Anastasios Doulamis. Context Aware Energy Disaggregation Using Adaptive Bidirectional LSTM Models. IEEE Transactions on Smart Grid 2020, 11, 3054 -3067.
AMA StyleMaria Kaselimi, Nikolaos Doulamis, Athanasios Voulodimos, Eftychios Protopapadakis, Anastasios Doulamis. Context Aware Energy Disaggregation Using Adaptive Bidirectional LSTM Models. IEEE Transactions on Smart Grid. 2020; 11 (4):3054-3067.
Chicago/Turabian StyleMaria Kaselimi; Nikolaos Doulamis; Athanasios Voulodimos; Eftychios Protopapadakis; Anastasios Doulamis. 2020. "Context Aware Energy Disaggregation Using Adaptive Bidirectional LSTM Models." IEEE Transactions on Smart Grid 11, no. 4: 3054-3067.
Obesity represents one of the most important health risks for a steadily increasing part of the population. The effort to prevent it or fight it is often hindered by the fact that a considerable percentage of individuals perceive their body image as healthier than it actually is. It is, therefore, essential to enhance awareness on actual body figure and weight state. In the current work we attempt to achieve this goal through visual feedback on the actual individual's image, increasing the impact by animating the transition from the initial state to the future outcome. The proposed module commences from a current body figure image and, relying on lifestyle and dietary information, gradually warps it to reflect expected changes. The mechanism relies on pure WebGL/Javascript, animating the transition at a selectable pace and to the extent chosen by the user, based on triangulation of the body figure outline and warping of the resulting polygon and corresponding texture according to input parameters. The WebGL/Javascript platform in combination with the absence of usage of external libraries allows for a small footprint of the module while offering high portability due to its native support by most modern day browsers.
Georgios Bardis; Yiannis Koumpouros; Nikolaos Sideris; Athanasios Voulodimos; Nikolaos Doulamis. WebGL enabled smart avatar warping for body weight animated evolution. Entertainment Computing 2019, 32, 100324 .
AMA StyleGeorgios Bardis, Yiannis Koumpouros, Nikolaos Sideris, Athanasios Voulodimos, Nikolaos Doulamis. WebGL enabled smart avatar warping for body weight animated evolution. Entertainment Computing. 2019; 32 ():100324.
Chicago/Turabian StyleGeorgios Bardis; Yiannis Koumpouros; Nikolaos Sideris; Athanasios Voulodimos; Nikolaos Doulamis. 2019. "WebGL enabled smart avatar warping for body weight animated evolution." Entertainment Computing 32, no. : 100324.
The convention for the safeguarding of Intangible Cultural Heritage (ICH) by UNESCO highlights the equal importance of intangible elements of cultural heritage to tangible ones. One of the most important domains of ICH is folkloric dances. A dance choreography is a time-varying 3D process (4D modelling), which includes dynamic co-interactions among different actors, emotional and style attributes, and supplementary elements, such as music tempo and costumes. Presently, research focuses on the use of depth acquisition sensors, to handle kinesiology issues. The extraction of skeleton data, in real time, contains a significant amount of information (data and metadata), allowing for various choreography-based analytics. In this paper, a trajectory interpretation method for Greek folkloric dances is presented. We focus on matching trajectories’ patterns, existing in a choreographic database, to new ones originating from different sensor types such as VICON and Kinect II. Then, a Dynamic Time Warping (DTW) algorithm is proposed to find out similarities/dissimilarities among the choreographic trajectories. The goal is to evaluate the performance of the low-cost Kinect II sensor for dance choreography compared to the accurate but of high-cost VICON-based choreographies. Experimental results on real-life dances are carried out to show the effectiveness of the proposed DTW methodology and the ability of Kinect II to localize dances in 3D space.
Ioannis Rallis; Eftychios Protopapadakis; Athanasios Voulodimos; Nikolaos Doulamis; Anastasios Doulamis; Georgios Bardis. Choreographic Pattern Analysis from Heterogeneous Motion Capture Systems Using Dynamic Time Warping. Technologies 2019, 7, 56 .
AMA StyleIoannis Rallis, Eftychios Protopapadakis, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Georgios Bardis. Choreographic Pattern Analysis from Heterogeneous Motion Capture Systems Using Dynamic Time Warping. Technologies. 2019; 7 (3):56.
Chicago/Turabian StyleIoannis Rallis; Eftychios Protopapadakis; Athanasios Voulodimos; Nikolaos Doulamis; Anastasios Doulamis; Georgios Bardis. 2019. "Choreographic Pattern Analysis from Heterogeneous Motion Capture Systems Using Dynamic Time Warping." Technologies 7, no. 3: 56.
Critical water infrastructure is susceptible to various types of major attacks, including direct, human-presence assaults and cyberattacks tampering with industrial control system (ICS) sensors and processes. As attacks become increasingly sophisticated and multifaceted, their timely detection becomes especially challenging and requires the exploitation of different data modalities, such as visual surveillance, channel state information (CSI) from Wi-Fi signals for human-presence detection, and ICS sensor data from the utility.
Nikolaos Bakalos; Athanasios Voulodimos; Nikolaos Doulamis; Anastasios Doulamis; Avi Ostfeld; Elad Salomons; Juan Caubet; Victor Jimenez; Pau Li. Protecting Water Infrastructure From Cyber and Physical Threats: Using Multimodal Data Fusion and Adaptive Deep Learning to Monitor Critical Systems. IEEE Signal Processing Magazine 2019, 36, 36 -48.
AMA StyleNikolaos Bakalos, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Avi Ostfeld, Elad Salomons, Juan Caubet, Victor Jimenez, Pau Li. Protecting Water Infrastructure From Cyber and Physical Threats: Using Multimodal Data Fusion and Adaptive Deep Learning to Monitor Critical Systems. IEEE Signal Processing Magazine. 2019; 36 (2):36-48.
Chicago/Turabian StyleNikolaos Bakalos; Athanasios Voulodimos; Nikolaos Doulamis; Anastasios Doulamis; Avi Ostfeld; Elad Salomons; Juan Caubet; Victor Jimenez; Pau Li. 2019. "Protecting Water Infrastructure From Cyber and Physical Threats: Using Multimodal Data Fusion and Adaptive Deep Learning to Monitor Critical Systems." IEEE Signal Processing Magazine 36, no. 2: 36-48.
BENEFFICE designed (eco-)system aims to reduce wasted electricity by incentivizing long-term consumption savings. It leverages Internet of Things enabled, low cost devices, which capture electricity use patterns at the level of clusters of devices and of each individual consumer. An energy behavior model correlates these patterns with optimal, personalized comfort levels and geographic and energy use contexts to determine optimal energy use behavior to reduce wastage of energy and to increase the use of renewable resources. Personalised, real-time motivational paths and challenges are contributing to deliver sustainable reductions of electricity consumption. Voluntarily engagement is achieved by the provision of monetary rewards -CO2 credits- in return of electricity savings and successful challenges. A novel ecosystem of like-minded actors of businesses who pay in CO2 credits and consumers who act for earning them is established.
Anastasia Garbi; Anna Malamou; Nassos Michas; Zisis Pontikas; Nikolaos Doulamis; Eftychios Protopapadakis; Thomas N. Mikkelsen; Konstantinos Kanellakis; Jean-Luc Baradat. BENEFFICE: Behaviour Change, Consumption Monitoring and Analytics with Complementary Currency Rewards. Proceedings 2019, 20, 12 .
AMA StyleAnastasia Garbi, Anna Malamou, Nassos Michas, Zisis Pontikas, Nikolaos Doulamis, Eftychios Protopapadakis, Thomas N. Mikkelsen, Konstantinos Kanellakis, Jean-Luc Baradat. BENEFFICE: Behaviour Change, Consumption Monitoring and Analytics with Complementary Currency Rewards. Proceedings. 2019; 20 (1):12.
Chicago/Turabian StyleAnastasia Garbi; Anna Malamou; Nassos Michas; Zisis Pontikas; Nikolaos Doulamis; Eftychios Protopapadakis; Thomas N. Mikkelsen; Konstantinos Kanellakis; Jean-Luc Baradat. 2019. "BENEFFICE: Behaviour Change, Consumption Monitoring and Analytics with Complementary Currency Rewards." Proceedings 20, no. 1: 12.
In this paper, we present WaterSpy, a project developing an innovative, compact, cost-effective photonic device for pervasive water quality sensing, operating in the mid-IR spectral range. The approach combines the use of advanced Quantum Cascade Lasers (QCLs) employing the Vernier effect, used as light source, with novel, fibre-coupled, fast and sensitive Higher Operation Temperature (HOT) photodetectors, used as sensors. These will be complemented by optimised laser driving and detector electronics, laser modulation and signal conditioning technologies. The paper presents the WaterSpy concept, the requirements elicited, the preliminary architecture design of the device, the use cases in which it will be validated, while highlighting the innovative technologies that contribute to the advancement of the current state of the art.
Nikolaos Doulamis; Athanasios Voulodimos; Anastasios Doulamis; Matthaios Bimpas; Aikaterini Angeli; Nikolaos Bakalos; Alessandro Giusti; Panayiotis Philimis; Antonio Varriale; Alessio Ausili; Sabato D’Auria; George Lampropoulos; Matthias Baer; Bernhard Schmauss; Stephan Freitag; Bernhard Lendl; Krzysztof Młynarczyk; Aleksandra Sosna-Głębska; Artur Trajnerowicz; Jarosław Pawluczyk; Mateusz Żbik; Jacek Kułakowski; Panagiotis Georgiadis; Stéphane Blaser; Nicola Bazzurro. WaterSpy: A High Sensitivity, Portable Photonic Device for Pervasive Water Quality Analysis. Sensors 2018, 19, 33 .
AMA StyleNikolaos Doulamis, Athanasios Voulodimos, Anastasios Doulamis, Matthaios Bimpas, Aikaterini Angeli, Nikolaos Bakalos, Alessandro Giusti, Panayiotis Philimis, Antonio Varriale, Alessio Ausili, Sabato D’Auria, George Lampropoulos, Matthias Baer, Bernhard Schmauss, Stephan Freitag, Bernhard Lendl, Krzysztof Młynarczyk, Aleksandra Sosna-Głębska, Artur Trajnerowicz, Jarosław Pawluczyk, Mateusz Żbik, Jacek Kułakowski, Panagiotis Georgiadis, Stéphane Blaser, Nicola Bazzurro. WaterSpy: A High Sensitivity, Portable Photonic Device for Pervasive Water Quality Analysis. Sensors. 2018; 19 (1):33.
Chicago/Turabian StyleNikolaos Doulamis; Athanasios Voulodimos; Anastasios Doulamis; Matthaios Bimpas; Aikaterini Angeli; Nikolaos Bakalos; Alessandro Giusti; Panayiotis Philimis; Antonio Varriale; Alessio Ausili; Sabato D’Auria; George Lampropoulos; Matthias Baer; Bernhard Schmauss; Stephan Freitag; Bernhard Lendl; Krzysztof Młynarczyk; Aleksandra Sosna-Głębska; Artur Trajnerowicz; Jarosław Pawluczyk; Mateusz Żbik; Jacek Kułakowski; Panagiotis Georgiadis; Stéphane Blaser; Nicola Bazzurro. 2018. "WaterSpy: A High Sensitivity, Portable Photonic Device for Pervasive Water Quality Analysis." Sensors 19, no. 1: 33.
Analysis of human motion is a field of research that attracts significant interest because of the wide range of associated application domains. Intangible Cultural Heritage (ICH), including the performing arts and in particular dance, is one of the domains where related research is especially useful and challenging. Effective keyframe selection from motion sequences can provide an abstract and compact representation of the semantic information encoded therein, contributing towards useful functionality, such as fast browsing, matching and indexing of ICH content. The availability of powerful 3D motion capture sensors along with the fact that video summarization techniques are not always applicable to the particular case of dance movement create the need for effective and efficient summarization techniques for keyframe selection from 3D human motion capture data sequences. In this paper, we introduce two techniques: a “time-independent” method based on k-means++ clustering algorithm for the extraction of prominent representative instances of a dance, and a physics-based technique that creates temporal summaries of the sequence at different levels of detail. The proposed methods are evaluated on two dance motion datasets and show promising results.
Athanasios Voulodimos; Ioannis Rallis; Nikolaos Doulamis. Physics-based keyframe selection for human motion summarization. Multimedia Tools and Applications 2018, 79, 3243 -3259.
AMA StyleAthanasios Voulodimos, Ioannis Rallis, Nikolaos Doulamis. Physics-based keyframe selection for human motion summarization. Multimedia Tools and Applications. 2018; 79 (5-6):3243-3259.
Chicago/Turabian StyleAthanasios Voulodimos; Ioannis Rallis; Nikolaos Doulamis. 2018. "Physics-based keyframe selection for human motion summarization." Multimedia Tools and Applications 79, no. 5-6: 3243-3259.