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Dr. Athanasios Voulodimos
Department of Informatics & Computer Engineering, University of West Attica, Athens 12243, Greece

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0 Artificial Intelligence
0 Computer Vision
0 Machine Learning
0 Multimedia Analysis
0 Image & signal processing

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Journal article
Published: 01 July 2021 in IEEE Transactions on Geoscience and Remote Sensing
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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.

ACS Style

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 Style

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 (99):1-15.

Chicago/Turabian Style

Maria 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.

Journal article
Published: 13 April 2021 in IEEE Access
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Konstantinos 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.

Journal article
Published: 22 March 2021 in Sensors
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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).

ACS Style

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 Style

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 (6):2215.

Chicago/Turabian Style

Athanasios 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.

Journal article
Published: 18 December 2020 in IEEE Open Journal of Signal Processing
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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.

ACS Style

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 Style

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 (99):1-16.

Chicago/Turabian Style

Maria 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.

Preprint content
Published: 16 December 2020
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Iason 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.

Conference paper
Published: 07 December 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Iason 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.

Journal article
Published: 20 November 2020 in Applied Sciences
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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.

ACS Style

Eftychios Protopapadakis; Ioannis Rallis; Anastasios Doulamis; Nikolaos Doulamis; Athanasios Voulodimos. Unsupervised 3D Motion Summarization Using Stacked Auto-Encoders. Applied Sciences 2020, 10, 8226 .

AMA Style

Eftychios 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 Style

Eftychios Protopapadakis; Ioannis Rallis; Anastasios Doulamis; Nikolaos Doulamis; Athanasios Voulodimos. 2020. "Unsupervised 3D Motion Summarization Using Stacked Auto-Encoders." Applied Sciences 10, no. 22: 8226.

Conference paper
Published: 07 September 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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Computational Geometry is a field of study whose full comprehension by students requires a combination of mathematical, algorithmic and application-oriented approaches. Due to the inherently visual nature of geometrical problems as well as to the complexity of related algorithms in terms of data structures and concepts employed, algorithm visualization can provide significant added value to the learning process, by helping shorten the cognitive distance gap between concept and visualization. In this paper, we describe CGVis, a visualization-based interactive educational platform for Computational Geometry algorithms. The paper explains the major design decisions adopted and describes the platform’s main features and functionality in detail. The platform has been evaluated in real-world settings by capturing postgraduate students’ response and feedback regarding usefulness, usability and user experience, as well as by measuring the platform’s educational effectiveness.

ACS Style

Athanasios Voulodimos; Paraskevas Karagiannopoulos; Ifigenia Drosouli; Georgios Miaoulis. CGVis: A Visualization-Based Learning Platform for Computational Geometry Algorithms. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 288 -302.

AMA Style

Athanasios Voulodimos, Paraskevas Karagiannopoulos, Ifigenia Drosouli, Georgios Miaoulis. CGVis: A Visualization-Based Learning Platform for Computational Geometry Algorithms. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():288-302.

Chicago/Turabian Style

Athanasios Voulodimos; Paraskevas Karagiannopoulos; Ifigenia Drosouli; Georgios Miaoulis. 2020. "CGVis: A Visualization-Based Learning Platform for Computational Geometry Algorithms." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 288-302.

Journal article
Published: 21 May 2020 in Sustainability
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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.

ACS Style

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 Style

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 (10):4223.

Chicago/Turabian Style

Anastasios 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.

Other
Published: 11 May 2020
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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.

ACS Style

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 Style

Athanasios 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 Style

Athanasios Voulodimos; Eftychios Protopapadakis; Iason Katsamenis; Anastasios Doulamis; Nikolaos Doulamis. 2020. "Deep learning models for COVID-19 infected area segmentation in CT images." , no. : 1.

Journal article
Published: 25 April 2020 in Remote Sensing
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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.

ACS Style

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 Style

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 (9):1354.

Chicago/Turabian Style

Maria 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.

Journal article
Published: 17 February 2020 in IEEE Transactions on Smart Grid
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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.

ACS Style

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 Style

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 (4):3054-3067.

Chicago/Turabian Style

Maria 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.

Journal article
Published: 19 October 2019 in Entertainment Computing
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Georgios 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.

Original article
Published: 23 August 2019 in The Visual Computer
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Recently, several educational game platforms have been proposed in the literature for choreographic training. However, their main limitation is that they fail to provide a quantitative assessment framework of a performing choreography against a groundtruth one. In this paper, we address this issue by proposing a machine learning framework exploiting deep learning paradigms. In particular, we introduce a long short-term memory network with the main capability of analyzing 3D captured skeleton feature joints of a dancer into predefined choreographic postures. This pose identification procedure is capable of providing a detailed (fine) evaluation score of a performing dance. In addition, the paper proposes a choreographic summarization architecture based on sparse modeling representative selection (SMRS) in order to abstractly represent the performing choreography through a set of key choreographic primitives. We have modified the SMRS algorithm in a way to extract hierarchies of key representatives. Choreographic summarization provides an efficient tool for a coarse quantitative evaluation of a dance. Moreover, hierarchical representation scheme allows for a scalable assessment of a choreography. The serious game platform supports advanced visualization toolkits using Labanotation in order to deliver the performing sequence in a formal documentation.

ACS Style

Ioannis Rallis; Nikolaos Bakalos; Nikolaos Doulamis; Anastasios Doulamis; Athanasios Voulodimos. Bidirectional long short-term memory networks and sparse hierarchical modeling for scalable educational learning of dance choreographies. The Visual Computer 2019, 37, 47 -62.

AMA Style

Ioannis Rallis, Nikolaos Bakalos, Nikolaos Doulamis, Anastasios Doulamis, Athanasios Voulodimos. Bidirectional long short-term memory networks and sparse hierarchical modeling for scalable educational learning of dance choreographies. The Visual Computer. 2019; 37 (1):47-62.

Chicago/Turabian Style

Ioannis Rallis; Nikolaos Bakalos; Nikolaos Doulamis; Anastasios Doulamis; Athanasios Voulodimos. 2019. "Bidirectional long short-term memory networks and sparse hierarchical modeling for scalable educational learning of dance choreographies." The Visual Computer 37, no. 1: 47-62.

Journal article
Published: 16 August 2019 in Technologies
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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.

ACS Style

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 Style

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 (3):56.

Chicago/Turabian Style

Ioannis 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.

Journal article
Published: 19 June 2019 in IEEE Access
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Power consumption signals of household appliances are characterized by randomly occurring events (e.g. switch-on events), making timeseries modeling a demanding process. In this paper, we propose a CNN-based (Convolutional Neural Network) architecture with inputs and outputs formed as data sequences taking into consideration an appliance’s previous states for better estimation of its current state. Furthermore, the proposed model endows CNN models with a recurrent property in order to better capture energy signal interdependencies. Using a multi-channel CNN architecture fed with additional variables related to power consumption (current, reactive and apparent power), additionally to active power, overall performance, robustness to noise and convergence times are improved. Experimental results prove the proposed method’s superiority compared to the current state of the art.

ACS Style

Maria Kaselimi; Eftychios Protopapadakis; Athanasios Voulodimos; Nikolaos Doulamis; Anastasios Doulamis. Multi-Channel Recurrent Convolutional Neural Networks for Energy Disaggregation. IEEE Access 2019, 7, 81047 -81056.

AMA Style

Maria Kaselimi, Eftychios Protopapadakis, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis. Multi-Channel Recurrent Convolutional Neural Networks for Energy Disaggregation. IEEE Access. 2019; 7 (99):81047-81056.

Chicago/Turabian Style

Maria Kaselimi; Eftychios Protopapadakis; Athanasios Voulodimos; Nikolaos Doulamis; Anastasios Doulamis. 2019. "Multi-Channel Recurrent Convolutional Neural Networks for Energy Disaggregation." IEEE Access 7, no. 99: 81047-81056.

Journal article
Published: 16 May 2019 in Sensors
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The constantly increasing amount and availability of urban data derived from varying sources leads to an assortment of challenges that include, among others, the consolidation, visualization, and maximal exploitation prospects of the aforementioned data. A preeminent problem affecting urban planning is the appropriate choice of location to host a particular activity (either commercial or common welfare service) or the correct use of an existing building or empty space. In this paper, we propose an approach to address these challenges availed with machine learning techniques. The proposed system combines, fuses, and merges various types of data from different sources, encodes them using a novel semantic model that can capture and utilize both low-level geometric information and higher level semantic information and subsequently feeds them to the random forests classifier, as well as other supervised machine learning models for comparisons. Our experimental evaluation on multiple real-world data sets comparing the performance of several classifiers (including Feedforward Neural Networks, Support Vector Machines, Bag of Decision Trees, k-Nearest Neighbors and Naïve Bayes), indicated the superiority of Random Forests in terms of the examined performance metrics (Accuracy, Specificity, Precision, Recall, F-measure and G-mean).

ACS Style

Nikolaos Sideris; Georgios Bardis; Athanasios Voulodimos; Georgios Miaoulis; Djamchid Ghazanfarpour. Using Random Forests on Real-World City Data for Urban Planning in a Visual Semantic Decision Support System. Sensors 2019, 19, 2266 .

AMA Style

Nikolaos Sideris, Georgios Bardis, Athanasios Voulodimos, Georgios Miaoulis, Djamchid Ghazanfarpour. Using Random Forests on Real-World City Data for Urban Planning in a Visual Semantic Decision Support System. Sensors. 2019; 19 (10):2266.

Chicago/Turabian Style

Nikolaos Sideris; Georgios Bardis; Athanasios Voulodimos; Georgios Miaoulis; Djamchid Ghazanfarpour. 2019. "Using Random Forests on Real-World City Data for Urban Planning in a Visual Semantic Decision Support System." Sensors 19, no. 10: 2266.

Journal article
Published: 26 February 2019 in IEEE Signal Processing Magazine
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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.

ACS Style

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 Style

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 (2):36-48.

Chicago/Turabian Style

Nikolaos 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.

Article
Published: 07 February 2019 in Applied Intelligence
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In this paper, a crack detection mechanism for concrete tunnel surfaces is presented. The proposed methodology leverages deep Convolutional Neural Networks and domain-specific heuristic post-processing techniques to address a variety of challenges, including high accuracy requirements, low operational times and limited hardware resources, poor and variable lighting conditions, low textured lining surfaces, scarcity of training data, and abundance of noise. The proposed framework leverages the representational power of the convolutional layers of CNNs, which inherently selects effective features, thus obviating the need for the tedious task of handcrafted feature extraction. Additionally, the good performance rates attained by the proposed framework are acquired at a significantly lower execution time compared to other techniques. The presented mechanism was designed and developed as a core component of an autonomous robotic inspector deployed and validated in the tunnels of Egnatia Motorway in Metsovo, Greece. The obtained results denote the proposed approach’s superiority over a variety of methods and suggest a promising potential as a driver of autonomous concrete-lining tunnel-inspection robots.

ACS Style

Eftychios Protopapadakis; Athanasios Voulodimos; Anastasios Doulamis; Nikolaos Doulamis; Tania Stathaki. Automatic crack detection for tunnel inspection using deep learning and heuristic image post-processing. Applied Intelligence 2019, 49, 2793 -2806.

AMA Style

Eftychios Protopapadakis, Athanasios Voulodimos, Anastasios Doulamis, Nikolaos Doulamis, Tania Stathaki. Automatic crack detection for tunnel inspection using deep learning and heuristic image post-processing. Applied Intelligence. 2019; 49 (7):2793-2806.

Chicago/Turabian Style

Eftychios Protopapadakis; Athanasios Voulodimos; Anastasios Doulamis; Nikolaos Doulamis; Tania Stathaki. 2019. "Automatic crack detection for tunnel inspection using deep learning and heuristic image post-processing." Applied Intelligence 49, no. 7: 2793-2806.

Research article
Published: 17 January 2019 in Computational Intelligence and Neuroscience
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Accurate prediction of the seawater intrusion extent is necessary for many applications, such as groundwater management or protection of coastal aquifers from water quality deterioration. However, most applications require a large number of simulations usually at the expense of prediction accuracy. In this study, the Gaussian process regression method is investigated as a potential surrogate model for the computationally expensive variable density model. Gaussian process regression is a nonparametric kernel-based probabilistic model able to handle complex relations between input and output. In this study, the extent of seawater intrusion is represented by the location of the 0.5 kg/m3 iso-chlore at the bottom of the aquifer (seawater intrusion toe). The initial position of the toe, expressed as the distance of the specific line from a number of observation points across the coastline, along with the pumping rates are the surrogate model inputs, whereas the final position of the toe constitutes the output variable set. The training sample of the surrogate model consists of 4000 variable density simulations, which differ not only in the pumping rate pattern but also in the initial concentration distribution. The Latin hypercube sampling method is used to obtain the pumping rate patterns. For comparison purposes, a number of widely used regression methods are employed, specifically regression trees and Support Vector Machine regression (linear and nonlinear). A Bayesian optimization method is applied to all the regressors, to maximize their efficiency in the prediction of seawater intrusion. The final results indicate that the Gaussian process regression method, albeit more time consuming, proved to be more efficient in terms of the mean absolute error (MAE), the root mean square error (RMSE), and the coefficient of determination (R2).

ACS Style

George Kopsiaftis; Eftychios Protopapadakis; Athanasios Voulodimos; Nikolaos Doulamis; Aristotelis Mantoglou. Gaussian Process Regression Tuned by Bayesian Optimization for Seawater Intrusion Prediction. Computational Intelligence and Neuroscience 2019, 2019, 1 -12.

AMA Style

George Kopsiaftis, Eftychios Protopapadakis, Athanasios Voulodimos, Nikolaos Doulamis, Aristotelis Mantoglou. Gaussian Process Regression Tuned by Bayesian Optimization for Seawater Intrusion Prediction. Computational Intelligence and Neuroscience. 2019; 2019 ():1-12.

Chicago/Turabian Style

George Kopsiaftis; Eftychios Protopapadakis; Athanasios Voulodimos; Nikolaos Doulamis; Aristotelis Mantoglou. 2019. "Gaussian Process Regression Tuned by Bayesian Optimization for Seawater Intrusion Prediction." Computational Intelligence and Neuroscience 2019, no. : 1-12.