Kostas Diamantaras - Research

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Department of Information and Electronic Engineering
International Hellenic University

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Machine Learning

− Publications

  • Session-Based Recommendations for e-Commerce with Graph-Based Data Modeling

    M. DelianidiM. Delianidi, K. I. Diamantaras, M. Salampasis, D. TektonidisD. Tektonidis,

    Applied Sciences, 13(1):394. https://doi.org/10.3390/app13010394, 2023

  • Machine learning methods for results merging in patent retrieval

    V. StamatisV. Stamatis, M. Salampasis, K. I. Diamantaras,

    Data Technologies and Applications, Emerald Publishing, https://doi.org/10.1108/DTA-06-2021-0156, 2023

  • A Flexible Session-Based Recommender System for e-Commerce

    M. Salampasis, A. KatsalisA. Katsalis, Th. SiomosTh. Siomos, M. DelianidiM. Delianidi, D. TektonidisD. Tektonidis, K. ChristantonisK. Christantonis, P. KaplanoglouP. Kaplanoglou, I. KaraveliI. Karaveli, C. BourlisC. Bourlis, K. I. Diamantaras,

    Applied Sciences, 13(5), pp. 3347, MDPI, https://doi.org/10.3390/app13053347, 2023

  • An ensemble framework for patent classification

    E. KamateriE. Kamateri, M. Salampasis, K. I. Diamantaras,

    World Patent Information, vol. 75, 102233, https://doi.org/10.1016/j.wpi.2023.102233, 2023

  • Reinforcement Learning for Motion Policies in Mobile Relaying Networks

    S. EvmorfosS. Evmorfos, K. I. Diamantaras, A. Petropulu,

    IEEE Transactions on Signal Processing, vol. 70, pp. 850-861, Jan. 2022

  • KT-Bi-GRU: Student Performance Prediction with a Recurrent Knowledge Tracing Neural Network

    M. DelianidiM. Delianidi, K. I. Diamantaras,

    TechRxiv, 2022, DOI:10.36227/techrxiv.20055545.v2

  • Embeddings Methods for Next-Item and Last-Basket Session-Based Recommendations

    M. Salampasis, Th. SiomosTh. Siomos, A. KatsalisA. Katsalis, K. I. Diamantaras, D. TektonidisD. Tektonidis,

    Int. J. Machine Learning and Computing, Vol.12(4): 136-142, DOI:10.18178/ijmlc.2022.12.4.1092, 2022

  • Automated Single-Label Patent Classification using Ensemble Classifiers

    E. KamateriE. Kamateri, V. StamatisV. Stamatis, K. I. Diamantaras, M. Salampasis,

    arXiv preprint arXiv:2203.03552, 2022

  • Energy Management in Microgrids Using Model Predictive Control Empowered with Artificial Intelligence

    D. TrigkasD. Trigkas, G. GravanisG. Gravanis, K. I. Diamantaras, S. VoutetakisS. Voutetakis, S. PapadopoulouS. Papadopoulou,

    Chemical Engineering Transactions, vol. 94, pp. 961-966, DOI: 10.3303/CET2294160, 2022

  • Semantically Enriched Augmented Reality Applications: a Proposed System Architecture and a Case Study

    G. LampropoulosG. Lampropoulos, E. KeramopoulosE. Keramopoulos, K. I. Diamantaras,

    International Journal of Recent Contributions from Engineering, Science & IT (iJES), vol. 10, no. 1, DOI:10.3991/ijes.v10i01.27463, 2022

  • NLP-Theatre: Employing Speech Recognition Technologies for Improving Accessibility and Augmenting the Theatrical Experience

    A. KatsalisA. Katsalis, K. ChristantonisK. Christantonis, C. TsioustasC. Tsioustas, P. KaplanoglouP. Kaplanoglou, M. Kaliakatsos-PapakostasM. Kaliakatsos-Papakostas, A. KatsamanisA. Katsamanis, K. I. Diamantaras, V. KatsourosV. Katsouros, E. FotineaE. Fotinea, D. PangaD. Panga,

    In Proceedings of IntelliSys Intelligent Systems Conference, pp. 507-526. Springer, Cham, 2023.

  • Exploring Classification in Open and Closed Eyes EEG Data for People with Cognitive Disorders

    I. ChouvardaI. Chouvarda, L. MpaltadorosL. Mpaltadoros, I. BoutzionaI. Boutziona, G. TsakonasG. Tsakonas, M. TsolakiM. Tsolaki, K. I. Diamantaras,

    In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS, pp. 298-305, ISBN 978-989-758-552-4, DOI: 10.5220/0011010100003123, 2022Β 

  • Diagnosis of Alzheimer’s disease and Mild Cognitive Impairment using EEG and Recurrent Neural Networks

    G. GkeniosG. Gkenios, K. LatsiouK. Latsiou, K. I. Diamantaras, I. ChouvardaI. Chouvarda, M. TsolakiM. Tsolaki,

    In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 3179-3182. IEEE, https://doi.org/10.1109/EMBC48229.2022.9871302, 2022.

  • Comparison of RNN and Embeddings Methods for Next-item and Last-basket Session-based Recommendations

    M. Salampasis, Th. SiomosTh. Siomos, A. KatsalisA. Katsalis, K. I. Diamantaras, K. ChristantonisK. Christantonis, M. DelianidiM. Delianidi, P. KaplanoglouP. Kaplanoglou, I. KaraveliI. Karaveli, G. Stalidis,

    in 13th Int. Conf. Machine Learning and Computing (ICMLC 2021), pp. 477-484, Shenzhen, China, 26 Feb.-1 March 2021

  • Student Performance Prediction Using Dynamic Neural Models

    M. DelianidiM. Delianidi, K. I. Diamantaras, G. ChrysogonidisG. Chrysogonidis, V. NikiforidisV. Nikiforidis,

    in Proc. 14th Int. Conf. Educational Data Mining, I-Han (Sharon) Hsiao, Shaghayegh (Sherry) Sahebi, Francois Bouchet, Jill-Jenn Vie (eds), pp. 46-54, June 29 - July 2, 2021

  • Predicting shopping intent of e-commerce users using LSTM recurrent neural networks

    K. I. Diamantaras, M. Salampasis, A. KatsalisA. Katsalis, K. ChristantonisK. Christantonis,

    In Proceedings 10th Int. Conf. Data Science, Technology and Applications (DATA2021), ISBN 978-989-758-521-0, pages 252-259. July 6-8, 2021, DOI: 10.5220/0010554102520259

  • Fault Detection and Diagnosis for Non-Linear Processes empowered by Dynamic Neural Networks

    G. GravanisG. Gravanis, I. DragogiasI. Dragogias, K. PapakiriakosK. Papakiriakos, C. ZiogouC. Ziogou, K. I. Diamantaras,

    Computers and Chemical Engineering, vol. 156, DOI:10.1016/j.compchemeng.2021.107531, Jan 2022, available online 30 September 2021.

  • Double Deep Q Learning with Prioritized Replay for Mobile Relay Beamforming Networks

    S. EvmorfosS. Evmorfos, A. Petropulu, K. I. Diamantaras,

    to appear in Asilomar Conference on Signals, Systems, and Computers, Oct. 31th - Nov 3rd, Pacific Grove, CA, USA, 2021

  • Enhancing the functionality of augmented reality using deep learning, semantic web and knowledge graphs: a review

    G. LampropoulosG. Lampropoulos, E. KeramopoulosE. Keramopoulos, K. I. Diamantaras,

    Visual Informatics, Volume 4, Issue 1, pp. 32-42, Elsevier, March 2020, https://doi.org/10.1016/j.visinf.2020.01.001

  • Q-Learning Based Predictive Relay Selection for Optimal Relay Beamforming

    A. DimasA. Dimas, K. I. Diamantaras, A. Petropulu,

    in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP-2020), Barcelona, May 4-8, 2020

  • Dynamic k-NN classification based on subspace homogeneity

    S. Ougiaroglou, G. Evangelidis, K. I. Diamantaras,

    24th European Conference on Advances in Databases and Information Systems (ADBIS-2020), (virtual conference) August 25-27, 2020

  • Multidimensional Factor and Cluster analysis vs embedding-based learning for personalised supermarket offer recommendations

    G. Stalidis, Th. SiomosTh. Siomos, P. KaplanoglouP. Kaplanoglou, A. KatsalisA. Katsalis, M. DelianidiM. Delianidi, I. KaraveliI. Karaveli, K. I. Diamantaras,

    in Data Analysis and Rationality in a Complex World, (Edited by Theodore Chadjipadelis, Berthold Lausen, Angelos Markos, Tae Rim Lee, Angela Montanari, Rebecca Nugent), Springer , DOI:10.1007/978-3-030-60104-1, 2021

  • Embeddings Methods for Next item and Last basket Session based Recommendations

    M. Salampasis, Th. SiomosTh. Siomos, A. KatsalisA. Katsalis, K. I. Diamantaras, K. ChristantonisK. Christantonis, M. DelianidiM. Delianidi, P. KaplanoglouP. Kaplanoglou, I. KaraveliI. Karaveli, G. Stalidis,

    in 7th International Conference on Artificial Intelligence (ICOAI-2020), Full virtual conference, 14-16 Oct. 2020

  • A Graph-based Method for Session-based Recommendations

    M. DelianidiM. Delianidi, M. Salampasis, K. I. Diamantaras, Th. SiomosTh. Siomos, A. KatsalisA. Katsalis, I. KaraveliI. Karaveli,

    Proc. 24nd Pan-Hellenic Conference on Informatics with International Participation (PCI 2020), Virtual conference, pp. 264-267, DOI:10.1145/3437120.3437321, 20-22 Nov. 2020

  • Towards exploration and evaluation of sleep staging classification schemes for healthy and patient subjects

    C. Timplalexis, D. ChasanidisD. Chasanidis, I. ChouvardaI. Chouvarda, K. I. Diamantaras,

    EAI Endorsed Transactions on Bioengineering and Bioinformatics (BEBI), European Alliance for Innovation (EAI), 2020. DOI: 10.4108/eai.19-10-2020.166665

  • Side-Channel-Based Code-Execution Monitoring Systems: A Survey

    Y. HanY. Han, I. ChristoudisI. Christoudis, K. I. Diamantaras, S. Zonouz, A. Petropulu,

    IEEE Signal Processing Magazine, vol. 36, no. 2, pp. 22-35, March 2019

  • Behind the Cues: A benchmarking study for Fake News Detection

    G. GravanisG. Gravanis, A. Vakali, K. I. Diamantaras, P. KaradaisP. Karadais,

    Expert Systems with Applications, vol. 128, pp. 201-213, Elsevier, August 2019, doi: https://doi.org/10.1016/j.eswa.2019.03.036

  • Μηχανική Μάθηση

    Κ. Διαμαντάρας, Δ. Μπότσης,

    Κλειδάριθμος, 2019

  • Design and implementation of an open source Greek POS Tagger and Entity Recognizer using spaCy

    E. PartalidouE. Partalidou, E. Spyromitros-XioufisE. Spyromitros-Xioufis, S. DoropoulosS. Doropoulos, S. Vologiannidis, K. I. Diamantaras,

    IEEE/WIC/ACM Int. Conf. Web Intelligence, Thessaloniki, Greece, Oct. 14-17, 2019

  • Optimal Mobile Relay Beamforming via Reinforcement Learning

    K. I. Diamantaras, A. Petropulu,

    Proc. IEEE Int. Workshop on Machine Learning for Signal Processing, (MLSP-2019), Pittsburgh, PS, USA, Oct. 13-16, 2019

  • Fast tree-based classification via homogeneous clustering

    G. PardisG. Pardis, K. I. Diamantaras, S. Ougiaroglou, G. Evangelidis,

    in: Yin H., Camacho D., Tino P., Tallon-Ballesteros A., Menezes R., Allmendinger R. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2019. Lecture Notes in Computer Science, vol 11871, pp. 514-524, Springer, Cham, https://doi.org/10.1007/978-3-030-33607-3_55

  • Multidimensional data analysis of shopping records towards knowledge-based recommendation techniques

    G. Stalidis, P. KaplanoglouP. Kaplanoglou, K. I. Diamantaras,

    16th Conference of the Int. Federation of Classification Societies (IFCS2019), Thessaloniki, 26-29 August, 2019

  • Classification of Sleep Stages for Healthy Subjects and Patients With Minor Sleep Disorders

    C. Timplalexis, K. I. Diamantaras, I. ChouvardaI. Chouvarda,

    Proc. 19th IEEE Int. Conf. BioInformatics and BioEngineering (BIBE2019), Athens, Greece, 28-20 Oct., 2019

  • A Reinforcement Learning Approach for Mobile Beamforming

    A. DimasA. Dimas, K. I. Diamantaras, A. Petropulu,

    Asilomar Conference on Signals, Systems and Computers, Pacific Grove, 2019

  • CLAss-specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification

    Y. Yu, K. I. Diamantaras, T. McKelvey, S. Y. Kung,

    IEEE Transactions on Neural Networks and Learning Systems, vol. 29 , no 2 , Feb. 2018.

  • Exploring the effect of Data Reduction on Neural Network and Support Vector Machine Classification

    S. Ougiaroglou, K. I. Diamantaras, G. Evangelidis,

    Neurocomputing, vol. 280, pp. 101-110, Elsevier, March 2018.

  • Predicting bulk density using pedotransfer functions for soils in the Upper Anthemountas basin, Greece

    S. SevastasS. Sevastas, D. GasparatosD. Gasparatos, D. Botsis, I. SiarkosI. Siarkos, K. I. Diamantaras, G. BilasG. Bilas,

    Geoderma Regional, vol. 14,Β Elsevier, September 2018.

  • Margin-based Sample Filtering for Image Classification using Convolutional Neural Networks

    P. KaplanoglouP. Kaplanoglou, K. I. Diamantaras,

    Proc. Int. Conf. Image Processing (ICIP 2018), pp. 1-5, Athens, Greece, Oct. 2018

  • Comparison of Stochastic and Machine Learning Models in Streamflow Forecasting

    D. Botsis, P. Latinopoulos, K. I. Diamantaras,

    in Proc. Int. Conf. Protection and Restoration of the Environment XIV (PRE XIV), Thessaloniki, Greece, July 2018

  • Parallel Pattern Classification Utilizing CPU Based Kernelized Slackmin Algorithm

    G. A. Papakostas, K. I. Diamantaras, Th. Papadimitriou,

    Journal of Parallel and Distributed Computing, vol. 99, pp. 90-99, Elsevier, January 2017

  • Sentiment Analysis Leveraging Emotions and Word Embeddings

    M. Giatsoglou, M. VozalisM. Vozalis, K. I. Diamantaras, A. Vakali, G. Sarigiannidis, K. Ch. Chatzisavvas,

    Expert Systems with Applications, vol. 69, pp. 214-224, Elsevier, 1 March 2017

  • Airfare Prices Prediction Using Machine Learning Techniques

    K. TziridisK. Tziridis, Th. KalampokasTh. Kalampokas, G. A. Papakostas, K. I. Diamantaras,

    in Proc. 25th European Signal Processing Conf. (EUSIPCO 2017), Kos, Greece, pp. 1036-1039, 28 Aug-2 Sept. 2017

  • Kernel Subspace Methods for Pattern Classification

    Y. Yu, K. I. Diamantaras, T. McKelvey, S. Y. Kung,

    in Adaptive Learning Methods for Nonlinear System Modeling, D. Comminiello, J. Principe (eds), pp. 127-147, Elsevier, 2018

  • Adaptive Margin Slack Minimization in RKHS for Classification

    Y. Yu, K. I. Diamantaras, T. McKelvey, S. Y. Kung,

    in Proceedings, 41st IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP2016), pp. 2319-2323, Shanghai, China, March 2016

  • Efficient Support Vector Machine Classification using Prototype Selection and Generation

    S. Ougiaroglou, K. I. Diamantaras, G. Evangelidis,

    in Proc. Artificial Intelligence Applications and Innovations (AIAI 2016), pp. 328-340, Thessaloniki, 16-18 Sept. 2016

  • Data Privacy Protection By Kernel Subspace Projection And Generalized Eigenvalue Decomposition

    K. I. Diamantaras, S. Y. Kung,

    in Proc. IEEE Int. Workshop Machine Learning for Signal Processing, (MLSP2016) , Vietri Sul Mare, Italy, Sept. 13-16, 2016

  • Enhanced Distance Subset Approximation Using Class-Specific Subspace Kernel Rerpesentation for Kernel Approximation

    Y. Yu, K. I. Diamantaras, T. McKelvey, S. Y. Kung,

    in Proc. IEEE Int. Workshop Machine Learning for Signal Processing, (MLSP2016) , Vietri Sul Mare, Italy, Sept. 13-16, 2016

  • Market Sentiment and Exchange Rate Directional Forecasting

    V. Plakandaras, Th. Papadimitriou, P. Gogas, K. I. Diamantaras,

    Algorithmic Finance, vol. 4, no. 1-2, DOI: 10.3233/AF-150044, 2015

  • A Comparison of Machine Learning Techniques for Customer Churn Prediction

    T. Vafeiadis, K. I. Diamantaras, G. Sarigiannidis, K. Ch. Chatzisavvas,

    Simulation Modelling Practice and Theory, vol. 55, pp. 1-9, doi:10.1016/j.simpat.2015.03.003, Elsevier, 2015

  • Machine Learning Sentiment Prediction based on Hybrid Document Representation

    P. Stalidis, M. Giatsoglou, K. I. Diamantaras, G. Sarigiannidis, K. Ch. Chatzisavvas,

    arXiv:1511.09107 [cs.CL]

  • Multiclass Ridge-adjusted Slack Variable Optimization Using Selected Basis for Fast Classification

    Y. Yu, K. I. Diamantaras, T. McKelvey, S. Y. Kung,

    in proceedings 22nd European Signal Processing Conference (EUSIPCO 2014), Lisbon, Portugal, Sept. 2014

  • Efficient Binary Classification Through Energy Minimisation of Slack Variables

    M. Kotti, K. I. Diamantaras,

    Neurocomputing, Elsevier, DOI: 10.1016/j.neucom.2014.07.013, 2014

  • Ridge-adjusted Slack Variable Optimization for Supervised Classification

    Y. Yu, K. I. Diamantaras, T. McKelvey, S. Y. Kung,

    in Proc. IEEE Int. Workshop Machine Learning for Signal Processing (MLSP-2013), Southampton, Sept. 2013.

  • Binary Classification by Minimizing the Mean Squared Slack

    K. I. Diamantaras, M. Kotti,

    in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP-2012), pp. 2057-2060, Kyoto, Japan, March, 2012

  • Investigation of the Effect of Interception and Evapotranspiration on the Rainfall-runoff Relationship Using Bayesian Networks

    D. Botsis, P. Latinopoulos, K. I. Diamantaras,

    in Proc. Protection and Restoration of the Environment XI, Thessaloniki, Greece, July 2012

  • Towards Minimizing the Energy of Slack Variables for Binary Classification

    M. Kotti, K. I. Diamantaras,

    in Proc. EUSIPCO 2012 (20th European Signal Processing Conference), pp. 644-648, Bucharest, Romania, August 27-31, 2012

  • Rainfall-runoff modeling using support vector regression and artificial neural networks

    D. Botsis, P. Latinopoulos, K. I. Diamantaras,

    in Proc. 12th International Conference on Environmental Science and Technology (CEST2011), Rhodes, Greece, 8 - 10 September 2011.

  • Προσομοίωση βροχόπτωσης – απορροής με τη χρήση της παλινδρόμησης των μηχανών διανυσμάτων υποστήριξης

    Δ. Μπότσης, Π. Λατινόπουλος, Κ. Διαμαντάρας,

    Πρακτικά 15ου Πανελλήνιου Δασολογικού συνεδρίου, Καρδίτσα, 16-19 Οκτωβρίου 2011

  • Greek Named Entities Recognition using Support Vector Machines, Maximum Entropy Models and Onetime

    I. MichailidisI. Michailidis, K. I. Diamantaras, S. VasileiadisS. Vasileiadis, Y. FrereY. Frere,

    in Proceesings 5th International Conference on Language Resources and Evaluation (LREC), pp. 47-52, Genova, Italy, 24-26 May, 2006

  • A Very Fast and Efficient Linear Classification Algorithm

    K. I. Diamantaras, I. MichailidisI. Michailidis, S. VasileiadisS. Vasileiadis,

    in Proc. 2005 IEEE Int. Workshop on Machine Learning for Signal Processing (MLSP-2005), Mystic, CT, USA, Sept. 2005

  • Greek Named Entity Recognition using Support Vector Machines

    I. MichailidisI. Michailidis, K. I. Diamantaras, S. VasileiadisS. Vasileiadis,

    in Proceedings 7th International Conference in Greek Linguistics (ICGL), York, UK, 8-10 Sept, 2005

  • Neural Classifiers Using One-Time Updating

    K. I. Diamantaras, M. G. StrintzisM. G. Strintzis,

    IEEE Trans. Neural Networks, vol. 9, no 3, pp. 436-447, May 1998.

  • ECG Analysis Using Non-linear PCA Neural Networks for Ischemia Detection

    T. StamkopoulosT. Stamkopoulos, K. I. Diamantaras, N. MaglaverasN. Maglaveras, M. G. StrintzisM. G. Strintzis,

    IEEE Transactions on Signal Processing, vol. 46, no 11, pp. 3058-3067, Nov. 1998.

  • ECG Pattern Recognition and Classification Using Non-linear Transformations and Neural Networks: A Review

    N. MaglaverasN. Maglaveras, T. StamkopoulosT. Stamkopoulos, K. I. Diamantaras, C. PappasC. Pappas, M. G. StrintzisM. G. Strintzis,

    International Journal of Medical Informatics, vol. 52, pp. 191-208, 1998.

  • Ischemia Classification Techniques Using An Advanced Neural Network Algorithm

    T. StamkopoulosT. Stamkopoulos, N. MaglaverasN. Maglaveras, K. I. Diamantaras, M. G. StrintzisM. G. Strintzis,

    in Proc. IEEE Computers in Cardiology, Lund, Sweden, Sept. 7-10, 1997.

  • Nonlinear Neural Classifiers with Synaptic Weight Commitment

    K. I. Diamantaras, M. G. StrintzisM. G. Strintzis,

    in Proceedings IEEE International Symposium on Circuits and Systems (ISCAS-97), pp. 653-656, Hong-Kong, June 9-12, 1997.

  • ST Segment Nonlinear Principal Component Analysis for Ischemia Detection

    K. I. Diamantaras, T. StamkopoulosT. Stamkopoulos, N. MaglaverasN. Maglaveras, M. G. StrintzisM. G. Strintzis,

    in Proceedings IEEE Computers in Cardiology 1996, pp. 493-496, Indianapolis, IN, Sept. 8-11, 1996.

Signal Processing

− Publications

  • Learning to Select for Mimo Radar Based on Hybrid Analog-Digital Beamforming

    Z. XuZ. Xu, F. Liu, K. I. Diamantaras, C. Masouros, A. Petropulu,

    2021 IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), pp. 8228-8232, Toronto, 6-11 June, 2021

  • Innovative Applications of Natural Language Processing and Digital Media in Theatre and Performing Arts

    C. TsioustasC. Tsioustas, D. PetratouD. Petratou, M. Kaliakatsos-PapakostasM. Kaliakatsos-Papakostas, V. KatsourosV. Katsouros, K. ChristantonisK. Christantonis, K. I. Diamantaras, M. LoupisM. Loupis,

    In Proceedings of the ENTRENOVA-ENTerprise REsearch InNOVAtion Conference, Virtual Conference, vol. 6, pp. 84-96, 10-12 September 2020 . Zagreb: IRENET-Society for Advancing Innovation and Research in Economy.

  • Analyzing Data into Quantized Components

    K. I. Diamantaras, Th. Papadimitriou, K. GoulianasK. Goulianas,

    in Proc. IEEE Int. Conference on Acoustics, Speech and Signal Processing (ICASSP-2014), pp. 6252-6256, Florence, Italy, May 2014

  • Multi-Input Single-Output Nonlinear Blind Separation of Binary Sources

    K. I. Diamantaras, G. VranouG. Vranou, Th. Papadimitriou,

    IEEE Trans. Signal Processing, vol. 61, no. 11, pp. 2866-2873, June 2013

  • Blind Identification of PAM-MIMO Systems Based on the Distribution of Output Differences

    K. I. Diamantaras, Th. Papadimitriou,

    in Proc. 21st European Signal Processing Conference, EUSIPCO Marrakech, Morocco, 9-13 Sept., 2013

  • Blind separation of multiple binary sources from one nonlinear mixture

    K. I. Diamantaras, Th. Papadimitriou, G. VranouG. Vranou,

    in Proc. Int. Conf. Acoustics, Speech, Signal Processing (ICASSP-2011), pp. 2108-2111, Prague, Czechia, May 2011.

  • Blind Separation of Three Binary Sources From One Nonlinear Mixture

    K. I. Diamantaras, Th. Papadimitriou,

    in Proc. 2010 Int. Workshop on Machine Learning for Signal Processing, MLSP-2010, Kittila, Finland, August 29-Sept. 1. 2010

  • Separating two Binary Sources from a Single Nonlinear Mixture

    K. I. Diamantaras, Th. Papadimitriou,

    in Proc. Int. Conf. Acoustics Speech Signal Processing (ICASSP 2010), Dallas, TX, March 14-19, 2010

  • Blind Deconvolution of Multi-Input Single-Output Systems Using the Distribution of Point Distances

    K. I. Diamantaras, Th. Papadimitriou,

    Journal Signal Processing Systems, Springer, Volume 65, Number 3, Pages 525-534, Oct. 2011, DOI 10.1007/s11265-010-0555-9, published online Nov. 2010.

  • Blind MISO Deconvolution Using the Distribution of Output Differences

    K. I. Diamantaras, Th. Papadimitriou,

    in Proc. 2009 Int. Workshop on Machine Learning for Signal Processing, MLSP-2009, Grenoble, France, Sept. 2009.

  • Histogram Based Blind Identification and Source Separation from Linear Instantaneous Mixtures

    K. I. Diamantaras, Th. Papadimitriou,

    Lecture Notes on Computer Sience (LNCS) vol. 5441, Proc. 8th Int. Conference on Independent Component Analysis and Blind Signal Separation (ICA2009), Paraty, Brazil, March 15-18, pp. 227-234, Springer, 2009.

  • Applying PCA Neural Models for the Blind Separation of Signals

    K. I. Diamantaras, Th. Papadimitriou,

    Neurocomputing, vol. 73, no. 1-3, pp. 3-9, Elsevier, 2009

  • Blind Separation of two multi-level sources from a single linear mixture

    K. I. Diamantaras,

    in Proc. 18th IEEE Int. Workshop on Machine Learning for Signal Processing (MLSP-2008), Cancun, Mexico, October, 2008

  • Blind System Identification: Instantaneous Mixtures of Binary Sources

    N. KofidisN. Kofidis, A. Margaris, K. I. Diamantaras, M. Roumeliotis,

    International Journal of Computer Mathematics, Volume 85, Issue 9, pages 1333 - 1340, September 2008

  • An Efficient Subspace Method for the Blind Identification of Multi-Channel FIR Systems

    K. I. Diamantaras, Th. Papadimitriou,

    IEEE Transactions on Signal Processing, vol. 56, no. 12, pp. 5833-5839, December 2008

  • Analytical Solution of the Blind identification Problem for Multichannel FIR Systems Based on Second Order Statistics

    K. I. Diamantaras, Th. Papadimitriou,

    in Proc. 2007 IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP-2007), vol. III, pp. 749-752, Honolulu, Hawaii, USA, April 2007

  • A Channel Deflation Approach for the Blind Deconvolution of a Complex FIR Channel With Real Input

    K. I. Diamantaras, Th. Papadimitriou, E. KotsialosE. Kotsialos,

    in Proc. 14 th European Signal Processing Conference, EUSIPCO 2006, Florence, Italy, Sept., 2006.

  • Channel Shortening Of Multi-Input Multi-Output Convolutive Systems With Binary Sources

    Th. Papadimitriou, K. I. Diamantaras,

    in Proc. 2006 IEEE Int. Workshop on Machine Learning for Signal Processing (MLSP-2006), pp. 271-276, Maynooth, Ireland, Sept. 2006.

  • Blind Multichannel Deconvolution Using Subspace-Based Single Delay Channel Deflation

    K. I. Diamantaras, Th. Papadimitriou,

    12th IEEE Digital Signal Processing Workshop (DSP'2006), Wyoming, USA, Sept. 2006.

  • A Clustering Approach for the Blind Separation of Multiple Finite Alphabet Sequences from a Single Linear Mixture

    K. I. Diamantaras,

    Signal Processing, Vol. 86, Issue 4, pp. 877-891, Elsevier, April 2006

  • Subspace-based Channel Shortening for the Blind Separation of Convolutive Mixtures

    K. I. Diamantaras, Th. Papadimitriou,

    IEEE Transactions on Signal Processing, vol. 54, no. 10, pp. 3669-3677, October 2006

  • Blind Deconvolution of Multi-Input Single-Output Systems with Binary Sources

    K. I. Diamantaras, Th. Papadimitriou,

    IEEE Transactions on Signal Processing, vol. 54, no. 10, pp. 3720-3731, October 2006

  • Blind Signal Processing Based on Data Geometric Properties

    K. I. Diamantaras,

    in New Directions in Statistical Signal Processing: From Systems to Brain, S. Haykin, J. Principe, T. Sejnowski, and J. McWhirter (eds), MIT Press, 2006

  • Blind Deconvolution of Multi-Input Single-Output Systems with Binary Sources

    K. I. Diamantaras, Th. Papadimitriou,

    in Proc. 2005 IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP-2005), vol. III, pp. 549-552, Philadelphia, PA, USA, March 2005

  • Blind Separation of Reflections Using the Image Mixtures Ratio

    K. I. Diamantaras, Th. Papadimitriou,

    in Proc. IEEE Int. Conf. Image Processing (ICIP-2005), vol. II, pp. 1034-1037, Genova, Italy, Sept. 2005

  • MIMO Blind Deconvolution Using Subspace-based Filter Deflation

    K. I. Diamantaras, Th. Papadimitriou,

    in Proc. 2004 IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP-2004), Montreal, May 2004.

  • Blind Deconvolution of SISO Systems with Binary Source based on Recursive Channel Shortening

    K. I. Diamantaras, Th. Papadimitriou,

    in Fifth Int. Conference on Independent Component Analysis and Blind Signal Separation (ICA2004), Lecture Notes in Computer Science, Vol. 3195, C.G. Puntonet and A. Prieto, Eds., Granada, Spain, pp. 548-553, Springer, September 2004

  • Oriented PCA and Blind Signal Separation

    K. I. Diamantaras, Th. Papadimitriou,

    in Proc. Fourth International Symposium on Independent Component Analysis and Blind Signal Separation (ICA-2003), pp. 609-614, Nara, Japan, April 2003

  • Blind Signal Separation Using Oriented PCA Neural Models

    K. I. Diamantaras, Th. Papadimitriou,

    in Proc. 2003 IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP-2003), vol. II, pp. 733-736, Hong-Kong, April 2003

  • Temporal Filtering and Oriented PCA Neural Networks for Blind Source Separation

    K. I. Diamantaras, Th. Papadimitriou,

    in Proc. 13th IEEE International Workshop on Neural Networks for Signal Processing, pp. 369-378, Toulouse France, Sept. 2003

  • On Blind Identifiability of FIR-MIMO Systems with Cyclostationary Inputs Using Second Order Statistics

    I. BradaricI. Bradaric, A. Petropulu, K. I. Diamantaras,

    IEEE Transactions on Signal Processing, vol. 51, no. 2, pp. 434-441, Feb. 2003.

  • Blind MIMO FIR Channel Identification Based on Second-Order Spectra Correlations

    I. BradaricI. Bradaric, A. Petropulu, K. I. Diamantaras,

    IEEE Transactions on Signal Processing, vol. 51, no. 6, pp. 1668-1674, June 2003.

  • On blind identifiability of FIR-MIMO systems with cyclostationary inputs using second order statistics

    I. BradaricI. Bradaric, A. Petropulu, K. I. Diamantaras,

    in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP-2002), pp. 2889-2892, Orlando, FL, May 2002.

  • Blind channel identification based on the geometry of the received signal constellation

    K. I. Diamantaras,

    IEEE Transactions on Signal Processing, vol. 50, no. 5, pp. 1133-1143, May 2002.

  • PCA Neural Models and Blind Signal Separation

    K. I. Diamantaras,

    International Joint Conference on Neural Networks (IJCNN-2001), Washington DC, July 15-19, 2001.

  • Blind source separation using Principal Component Neural Networks

    K. I. Diamantaras,

    International Conference on Artificial Neural Networks (ICANN-2001), Vienna, Austria, August 21-25, 2001.

  • A second-order statistics-based optimization approach for blind MIMO system identification

    I. BradaricI. Bradaric, A. Petropulu, K. I. Diamantaras,

    11th IEEE Workshop on Statistical Signal Processing (SSP-2001), Singapore, Aug. 6-10, 2001.

  • Blind Source Separation using Principal Component Neural Networks

    K. I. Diamantaras,

    in ICANN 2001, Lecture Notes in Computer Science vol. 2130, G. Dorffner, H. Bischof and K. Hornik (Eds.), pp. 515-520, Springer Verlag, 2001.

  • Blind Separation of Multiple Binary Sources using a Single Linear Mixture

    K. I. Diamantaras,

    in Proc. Int. Conf. Acoustics, Speech and Signal Processing (ICASSP-2000), vol. V, pp. 2889-2892, Istanbul, Turkey, June 2000.

  • Blind nxn MIMO System Estimation using Second Order Statistics

    I. BradaricI. Bradaric, A. Petropulu, K. I. Diamantaras,

    34th Annual Conference on Information Sciences and Systems (CISS-2000), Princeton University, March 2000.

  • Blind Separation of n Binary Sources from one Observation: A Deterministic Approach

    K. I. Diamantaras, E. ChassiotiE. Chassioti,

    in Proc. 2nd Int. Workshop on Independent Component Analysis and Blind Source Separation (ICA-2000), pp. 93-98, Helsinki, June 19-22, 2000.

  • Blind Two-Input-Two-Output FIR Channel Identification Based on Second-Order Statistics

    K. I. Diamantaras, A. Petropulu, B. ChenB. Chen,

    IEEE Transactions on Signal Processing, vol. 48, no. 2, pp.534-542, Feb. 2000.

  • Blind Two-Input-Two-Output FIR Channel Estimation and Source Separation

    K. I. Diamantaras, A. Petropulu,

    in Proc. IEEE Information Theory Workshop on Detection, Estimation, Classification and Imaging (DECI-99), Santa Fe, New Mexico (USA), February 24-26, 1999.

  • Blind Equalization of Multiuser CDMA Channels: A Frequency-Domain Approach

    K. I. Diamantaras, A. Petropulu,

    in the IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP-99), Phoenix, AZ, 1999.

  • Blind Equalization of Multiuser CDMA Channels

    K. I. Diamantaras, A. Petropulu,

    in Proc. 2nd IEEE Workshop on Signal Processing Advances in Wireless Communications, (SPAWC-99), Annapolis, MD, 1999.

  • Adaptive blind MIMO system identification using Principal Component neural models

    K. I. Diamantaras, A. Petropulu,

    in Proc. Int. Joint Conf. Neural Networks (IJCNN-99), Washington-DC, July 1999.

  • Error Analysis of a SOS-Based MIMO System Identification Algorithm

    B. ChenB. Chen, A. Petropulu, K. I. Diamantaras,

    in Proc. 33rd Asilomar Conf. on Signals Systems and Computers, Pacific Grove, CA, Oct. 24-27, 1999.

  • Blind Source Separation in the Presence of Measurement Noise

    K. I. Diamantaras, M. G. StrintzisM. G. Strintzis,

    in Proc. CISS'98 (Conference on Information Sciences and Systems), pp. 1001-1006, Princeton, NJ, March 1998.

  • Second Order Hebbian Neural Networks and Blind Source Separation

    K. I. Diamantaras,

    in Proc. EUSIPCO-98 (European Signal Processing Conference), vol. III, pp.1317-1320, Rhodes, Greece, Sept. 1998.

  • Asymmetric PCA Neural Networks for Adaptive Blind Source Separation

    K. I. Diamantaras,

    in Proc. IEEE Workshop on Neural Networks for Signal Processing (NNSP-98), pp. 103-112, Cambridge, UK, August 1998.

Parallel Processing

− Publications

  • Efficient Data Classification By Gpu-Accelerated Linear Mean Squared Slack Minimization

    G. A. Papakostas, K. I. Diamantaras,

    in Proc. IEEE Int. Workshop on Machine Learning for Signal Processing (MLSP2014), Reims France, Sept. 2014

  • Προγραμματισμός και Αρχιτεκτονική Συστημάτων Παράλληλης Επεξεργασίας

    Σ. Παπαδάκης, Κ. Διαμαντάρας,

    Κλειδάριθμος, 2012

  • A parallel implementation of the Natural Gradient BSS method using MPI

    A. Margaris, K. I. Diamantaras,

    2nd International Conference on Experiments/Process/System Modeling/Simulation & Optimization (2nd IC-EpsMsO), Athens, 4-7 July, 2007

  • A Linear Systolic Array for Real-Time Morphological Image Processing

    K. I. Diamantaras, S. Y. Kung,

    Journal of VLSI Signal Processing, Kluwer, vol. 17, no.1, pp. 43-55, Kluwer, September 1997.

  • Dynamic Programming Implementation on Array Processor Architectures

    K. I. Diamantaras, W. H. ChouW. H. Chou, S. Y. Kung,

    Journal of VLSI Signal Processing, vol. 13, no. 1, pp. 27-36, Kluwer, August 1996.

  • Implementing Dynamic Programming Algorithms for Signal and Image Processing on Array Processors

    W. H. ChouW. H. Chou, K. I. Diamantaras, S. Y. Kung,

    in VLSI Signal Processing VI, L. D. J. Eggermont et. al. (eds), pp. 289-297, Proceedings IEEE Workshop, Koningshof, Veldhoven, Netherlands, IEEE, NY, October 1993.

  • Scalable Architectures for Image Processing

    K. I. Diamantaras, A. ChihoubA. Chihoub, A. ZawadzkiA. Zawadzki,

    in Proc. SPIE Conf. Machine Vision Applications, Architectures, and System Integration II, pp. 2-13, Boston 1993.

  • Integrated Fast Implementation of Mathematical Morphology Operations in Image Processing

    K. I. Diamantaras, K. H. ZimmermannK. H. Zimmermann, S. Y. Kung,

    in Proc. IEEE Int. Symp. Circuits and Systems, pp. 1442-1445, New Orleans, May 1990.

  • Implementation of Neural Network Algorithms on the P3 Parallel Associative Processor

    K. I. Diamantaras, D. L. HeineD. L. Heine, I. D. SchersonI. D. Scherson,

    in Proc. Int. Conf. Parallel Processing (ICPP-90), pp. 247-250, St. Charles IL, August 13-17, 1990.

Image / Video Processing and Computer Vision

− Publications

  • Motion descriptors for semantic video indexing

    M. ZampoglouM. Zampoglou, Th. Papadimitriou, K. I. Diamantaras,

    in Proc. 2010 International Conference on Signal Processing and Multimedia Applications (SIGMAP), pp. 178-184, Athens, Greece, 26-28 July 2010

  • Integrating Motion and Color for Content Based Video Classification

    M. ZampoglouM. Zampoglou, Th. Papadimitriou, K. I. Diamantaras,

    in Proc. 2008 IAPR Workshop on Cognitive Information Processing, Santorini, Greece, June, 2008

  • Support Vector Machines Content-Based Video Retrieval based solely on Motion Information

    M. ZampoglouM. Zampoglou, Th. Papadimitriou, K. I. Diamantaras,

    in Proc. 17th IEEE Int. Workshop on Machine Learning for Signal Processing (MLSP-2007), Thessaloniki, Greece, August, 2007

  • Video Scene Segmentation using Spatial Contours and 3-D Robust Motion Estimation

    Th. Papadimitriou, K. I. Diamantaras, M. G. StrintzisM. G. Strintzis, M. Roumeliotis,

    IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 4, April 2004

  • Occlusion resistant object tracking

    E. LoutasE. Loutas, K. I. Diamantaras, I. Pitas,

    8th IEEE International Conference on Image Processing (ICIP-2001), Thessaloniki, Greece, Oct. 7-10, 2001.

  • Efficient Occlusion Handling Region Tracking

    E. LoutasE. Loutas, C. NikouC. Nikou, K. I. Diamantaras, I. Pitas,

    1st IEEE International Symposium on Signal Processing and Information Technology (ISSPIT-2001), Cairo, Egypt, Dec. 28-30, 2001.

  • A novel rigid object segmentation method based on multiresolution 3-D motion and luminance analysis

    Th. Papadimitriou, K. I. Diamantaras, M. G. StrintzisM. G. Strintzis,

    in Proc. Int. Conf. Image Processing (ICIP-2000), Vancouver, Canada, Sept. 10-13, 2000.

  • Robust Estimation of Rigid Body 3-D Motion Parameters based on Point Correspondences

    Th. Papadimitriou, K. I. Diamantaras, M. G. StrintzisM. G. Strintzis, M. Roumeliotis,

    IEEE Transactions on Circuits and Systems for Video Technology, vol. 10, no. 4, pp. 541-549, June 2000.

  • Optimal Reconstruction from Quantized Data

    K. I. Diamantaras, M. G. StrintzisM. G. Strintzis,

    Signal Processing-Image Communication, vol. 15, no. 9, pp. 811-815, Elsevier, July 2000.

  • Robust Estimation of Rigid Body 3-D Motion Parameters From Point Correspondences

    Th. Papadimitriou, K. I. Diamantaras, M. G. StrintzisM. G. Strintzis, M. Roumeliotis,

    in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP-99), Phoenix, AZ, 1999.

  • A New Image Sequence Segmentation Method based on Luminance and 3-D Motion Information

    Th. Papadimitriou, K. I. Diamantaras, M. G. StrintzisM. G. Strintzis, M. Roumeliotis,

    in Proc. Int. Workshop on Synthetic - Natural Hybrid Coding and Three Dimensional Imaging (IWSNHC3DI-99), pp. 53-57, Santorini, Greece, Sept. 15-17, 1999.

  • Optimal Transform Coding in the Presence of Quantization Noise

    K. I. Diamantaras, M. G. StrintzisM. G. Strintzis,

    IEEE Transactions on Image Processing, vol. 8, no. 11, pp. 1508-1515, Nov. 1999.

  • Optimal Reconstruction from Quantized Data

    K. I. Diamantaras, M. G. StrintzisM. G. Strintzis,

    Proceedings HERCMA-98.

  • Total Least Squares 3-D Motion Estimation

    K. I. Diamantaras, Th. Papadimitriou, M. G. StrintzisM. G. Strintzis, M. Roumeliotis,

    in Proceedings IEEE International Conf. Image Processing (ICIP-98), Chicago, Oct. 1998.

  • Camera Motion Parameter Recovery under Perspective Projection

    K. I. Diamantaras, M. G. StrintzisM. G. Strintzis,

    in Proc. IEEE Int. Conf. Image Processing (ICIP-96), vol. III, pp. 807-810, Lausanne, Switzerland, Sept. 16-19, 1996.

  • Resolving Motion Ambiguities

    K. I. Diamantaras, D. GeigerD. Geiger,

    in Advances in Neural Information Processing Systems 6 (NIPS-93), Denver, Colorado, J. Cowan, G. Tesauro, and J. Alspector (eds.), pp. 977-984, Morgan Kaufmann, 1994.

  • KLT-based coding of Stereo Image Sequences

    K. I. Diamantaras, N. GrammalidisN. Grammalidis, M. G. StrintzisM. G. Strintzis,

    presented in the International Workshop on Image Processing, Budapest, 1994, and published in the Journal of Communications, no. 45, pp. 20-23, May-June 1994.

  • Occlusion ambiguities in motion

    D. GeigerD. Geiger, K. I. Diamantaras,

    in Computer Vision - ECCV '94, Proc. 3rd European Conf. in Computer Vision, Sweden, J.-O. Eklundh (ed.), vol. I, pp. 175-180, published by Springer-Verlag, Lecture Notes in Computer Science Series, vol. 800, 1994.

  • Compressing Moving Pictures Using the APEX Neural Principal Component Extractor

    K. I. Diamantaras, S. Y. Kung,

    in Proc. 3rd IEEE Workshop Neural Networks for Signal Processing, IEEE, Baltimore, September 1993.

Neural Networks and Principal Component Analysis

− Publications

  • Solving polynomial systems using a fast adaptive back propagation-type neural network algorithm

    K. GoulianasK. Goulianas, A. Margaris, I. Refanidis, K. I. Diamantaras,

    European Journal of Applied Mathematics, vol. 29, issue 2, pp. 301-337, April 2018.

  • An Adaptive Learning Rate Backpropagation-type Neural Network for Solving n×n Systems of Non Linear Algebraic Equations

    K. GoulianasK. Goulianas, A. Margaris, I. Refanidis, K. I. Diamantaras,

    Mathematical Methods in the Applied Sciences, doi: 10.1002/mma.3715, Wiley, 2015

  • Estimating 2-cycle Fixed Points of Henon Map Using Backpropagation Neural Networks

    A. Margaris, K. GoulianasK. Goulianas, K. I. Diamantaras, Th. Papadimitriou,

    Mathematical Methods in the Applied Sciences, vol. 38, no. 2, pp. 263-273, John Wiley, 2015, Article first published online: 27 Dec. 2013, doi: 10.1002/mma.3064

  • Τεχνητά Νευρωνικά Δίκτυα

    Κ. Διαμαντάρας,

    Κλειδάριθμος, 2007

  • Optimal Linear Compression Under Unreliable Representation and Robust PCA Neural Models

    K. I. Diamantaras, K. Hornik, M. G. StrintzisM. G. Strintzis,

    IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 1186-1195, Sept. 1999.

  • Noisy PCA Theory and Application in Filter Bank Codec Design

    K. I. Diamantaras, M. G. StrintzisM. G. Strintzis,

    in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP-97), pp. 3857-3860, Munich, May 21-24, 1997.

  • Optimal Subband Coder Design Using Noisy PCA

    K. I. Diamantaras, M. G. StrintzisM. G. Strintzis,

    in Proc. First European Conference on Signal Analysis and Prediction (ECSAP-97), pp. 315-318, Prague, June 24-27, 1997.

  • Robust Hebbian Learning and Noisy Principal Component Analysis

    K. I. Diamantaras,

    International Journal of Computer Mathematics, Gordon & Breach Science Publishers, vol. 68, no. 1-2, pp. 5-24, 1997.

  • Robust Principal Component Extracting Neural Networks

    K. I. Diamantaras,

    in Proc. Int. Conf. Neural Networks (ICNN-96), pp.74-77, Washington DC, June 3-6, 1996.

  • Adaptive Principal Component Extraction (APEX) and Applications

    S. Y. Kung, K. I. Diamantaras, J. S. TaurJ. S. Taur,

    IEEE Transactions on Signal Processing, pp. 1202-1217, vol. 42, no. 5, May 1994.

  • Multi-layer Neural Networks for Reduced-Rank Approximation

    K. I. Diamantaras, S. Y. Kung,

    IEEE Transactions on Neural Networks, pp. 684-697, vol. 5, no. 5, September 1994.

  • Cross-correlation Neural Network Models

    K. I. Diamantaras, S. Y. Kung,

    IEEE Transactions on Signal Processing, pp. 3218-3223, vol. 42, no. 11, November 1994.

  • Noisy Principal Component Analysis

    K. I. Diamantaras, K. Hornik,

    in Measurement '93, pp. 25-33, J. Volaufova and V. Witkowsky eds., Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia, May 1993.

  • Neural Networks for Extracting Unsymmetric Principal Components

    S. Y. Kung, K. I. Diamantaras,

    in Proc. 1st IEEE Workshop on Neural Networks for Signal Processing, pp. 50-59, Princeton, NJ, September 1991.

  • An Unsupervised Neural Model for Oriented Principal Component Extraction

    K. I. Diamantaras, S. Y. Kung,

    in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP-91), pp. 1049-1052, Toronto, Canada, May 14-17, 1991.

  • A Neural Network Learning Algorithm for Adaptive Principal Component Extraction (APEX)

    S. Y. Kung, K. I. Diamantaras,

    in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP-90), pp. 861-864, Albuquerque, NM., April 1990.

Other

− Publications

  • Augmented Reality and Gamification in Education: A Systematic Literature Review of Research, Applications, and Empirical Studies

    G. LampropoulosG. Lampropoulos, E. KeramopoulosE. Keramopoulos, K. I. Diamantaras, G. Evangelidis,

    Applied Sciences, vol. 12, no. 13, p. 6809, July 2022, DOI:10.3390/app12136809

  • Offers just for you: intelligent recommendation of personalised offers employing multidimensional statistical models

    G. Stalidis, K. I. Diamantaras,

    Proc. 7th Int. Conf. Contemporary Marketing Issues, pp. 297-299, Heraklion, Greece, 10-12 July, 2019

  • A Metaheuristic Bandwidth Allocation Scheme for FiWi Networks Using Ant Colony Optimization

    P. Sarigiannidis, M. Louta, G. Papadimitriou, P. Nicopolitidis, K. I. Diamantaras, I. Tinnirello, C. Verikoukis,

    22nd IEEE Symposium on Communications and Vehicular Technology (SCVT 2015), Luxembourg, November 24, 2015

  • Subspace design of low-rank estimators for higher-order statistics

    I. BradaricI. Bradaric, A. Petropulu, K. I. Diamantaras,

    Journal of the Franklin Institute, vol. 339, pp. 161-187, Elsevier, 2002.

  • Experimental User-Centered Evaluation of an Open Hypermedia System and Web Information Seeking Environments

    M. Salampasis, K. I. Diamantaras,

    Journal of Digital Information, vol. 2, issue 4, article no. 105, 31-May, 2002

  • Προσομοίωση στην εκπαίδευση προσωπικού με χρήση εικονικής πραγματικότητας

    Κ. Διαμαντάρας, Ι. ΚομπατσιάρηςΙ. Κομπατσιάρης, Μ. Γ. ΣτρίντζηςΜ. Γ. Στρίντζης,

    πρακτικά Διημερίδας Πληροφορικής & Επιχειρησιακής Έρευνας στις Ένοπλες Δυνάμεις, Πολεμικό Μουσείο, Αθήνα, 2-3 Νοεμβρίου, 1999.

  • Signal Integration and Tele-cooperation in a Critical Care Unit

    K. I. Diamantaras, E. BacharakisE. Bacharakis, I. KamilatosI. Kamilatos, M. G. StrintzisM. G. Strintzis, N. MaglaverasN. Maglaveras, A. ArmaganidisA. Armaganidis,

    in Proceedings IEEE EMBS-97 (Conference of the IEEE-Engineering in Medicine and Biology Society), Chicago, IL, Oct 30-Nov 2, 1997. (ppt presentation)

  • Critical Care Unit Information Integration System

    K. I. Diamantaras, T. D. DoukoglouT. D. Doukoglou, I. KamilatosI. Kamilatos, N. MaglaverasN. Maglaveras, M. G. StrintzisM. G. Strintzis, A. ArmaganidisA. Armaganidis,

    in Proc. 10th Int. Symp. Computer Assisted Radiology (CAR-96), pp. 480-483, Paris, June 26-29, Elsevier, 1996.

  • IHIS : An integrated hospital environment linking via LAN ICU with PACS and Biochemical Laboratories

    N. MaglaverasN. Maglaveras, M. G. StrintzisM. G. Strintzis, K. I. Diamantaras, T. D. DoukoglouT. D. Doukoglou, A. ArmaganidisA. Armaganidis, I. ChouvardaI. Chouvarda,

    in Proceedings IEEE Computers in Cardiology 1996, pp. 589-592, Indianapolis, IN, Sept. 8-11, 1996.

  • A New Transition Count for Testing of Logic Circuits

    K. I. Diamantaras, N. K. Jha,

    IEEE Transactions on CAD of ICAS, pp. 407-410, vol. 10, no. 3, March 1991.

  • A New Transition Count Method for Testing of Combinational Circuits

    K. I. Diamantaras, N. K. Jha,

    in Proceedings 13-th Int. Conf. Fault-Tolerant Systems and Diagnostics, pp. 218-224, Varna, Bulgaria, June 20-22, 1990.

  • A New Orthogonal Series Approach to State-Space Analysis of 1-D and 2-D Discrete Systems

    P. N. ParaskevopoulosP. N. Paraskevopoulos, K. I. Diamantaras,

    Proceedings IEE, Part G, vol. 137, no. 3, pp. 205-209, June 1990.

  • Algorithms for Characteristic Polynomial Assignment of 2-D Discrete Systems

    P. N. ParaskevopoulosP. N. Paraskevopoulos, K. I. Diamantaras,

    Proceedings IEE, Part D, vol. 137, no. 2, pp. 94-97, March 1990.

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