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106 V. Gogolou, K. Kozalakis, E. Koutroulis, G. Doumenis, and S. Siskos, “An ultra-low-power CMOS supercapacitor storage unit for energy harvesting applications,” Electronics, vol. 10, no. 17, Aug. 2021, doi: 10.3390/electronics10172097.2022-10-06
107 F. D. Kanellos, "Optimal scheduling and real-time operation of distribution networks with high penetration of plug-in electric vehicles," IEEE Systems Journal, vol. 15, no. 3, pp. 3938-3947, Sept. 2021, doi: 10.1109/JSYST.2020.3006002.2022-10-05
108 E. Grigoroudis, V. S. Kouikoglou, Y. A. Phillis, and F. D. Kanellos, “Energy sustainability: a definition and assessment model,” Oper. Res. Int. J., vol. 21, no. 3, pp. 1845–1885, Sep. 2021, doi: 10.1007/s12351-019-00492-2.2022-10-05
109 M. Žukovič and D. T. Hristopulos, “Ising model for interpolation of spatial data on regular grids,” Entropy, vol. 23, no. 10, Sep. 2021, doi: 10.3390/e23101270.2022-09-29
110 G. Batsis, P. Partsinevelos, and G. Stavrakakis, “A deep learning and GIS approach for the optimal positioning of wave energy converters,” Energies, vol. 14, no. 20, Oct. 2021, doi: 10.3390/en14206773.2022-09-29
111 N. Sifakis, K. Kalaitzakis, and T. Tsoutsos, “Integrating a novel smart control system for outdoor lighting infrastructures in ports,” Energy Convers. Manage., vol. 246, Oct. 2021, doi: 10.1016/j.enconman.2021.114684.2022-09-28
112 J. Han, D. Leykam, D. G. Angelakis, and J. Thingna, “Quantum transient heat transport in the hyperparametric oscillator,” Phys. Rev. A, vol. 104, no. 5, Nov. 2021, doi: 10.1103/PhysRevA.104.052220.2022-08-26
113 K. Thanjavur, D.T. Hristopulos, A. Babul, K. M. Yi and N. Virji-Babul, “Deep learning recurrent neural network for concussion classification in adolescents using raw electroencephalography signals: toward a minimal number of sensors,” Front. Hum. Neurosci., vol. 15, Nov. 2021, doi: 10.3389/fnhum.2021.734501.2022-08-26
114 E. Kosteletou, P.G. Simos, E. Kavroulakis, D. Antypa, T. G. Maris, A. P. Liavas, P. A. Karakasis and E. Papadaki, “Improving the sensitivity of task-related Functional Magnetic Resonance Imaging data using generalized canonical correlation analysis,” Front. Hum. Neurosci., vol. 15, Dec. 2021, doi: 10.3389/fnhum.2021.771668.2022-08-04
115 A. Rezaei, J. Lahtinen, F. Neugebauer, M. Antonakakis, M. C. Piastra, A. Koulouri, C. H. Wolters, and S. Pursiainen, "Reconstructing subcortical and cortical somatosensory activity via the RAMUS inverse source analysis technique using median nerve SEP data," NeuroImage, vol. 245, Dec. 2021. doi: 10.1016/j.neuroimage.2021.1187262022-08-04
116 M. Zhao, C. Roncoli, Y. Wang, N. Bekiaris-Liberis, J. Guo and S. Cheng, "Generic approaches to estimating freeway traffic state and percentage of connected vehicles with fixed and mobile sensing," IEEE Trans. Intell. Transp. Syst., early access, doi: 10.1109/TITS.2021.3121181.2022-08-04
117 A. A. Panagopoulos, F. Christianos, M. Katsigiannis, K. Mykoniatis, M. Pritoni, O. P. Panagopoulos, T. Peffer, G. Chalkiadakis, D. E. Culler, N. R. Jennings, and T. Lipman, “A low-complexity non-intrusive approach to predict the energy demand of buildings over short-term horizons,” Adv. Build. Energy Res., vol. 16, no. 2, pp. 202–213, Mar. 2022, doi: 10.1080/17512549.2020.1835712.2022-07-25
118 I. Flouris, N. Giatrakos, A. Deligiannakis, and M. Garofalakis, “Network-wide complex event processing over geographically distributed data sources,” Inf. Syst., vol. 88, Feb. 2020, doi: 10.1016/j.is.2019.1014422022-03-16
119 K. V. Blazakis, T. N. Kapetanakis, and G. S. Stavrakakis, “Effective electricity theft detection in power distribution grids using an adaptive neuro fuzzy inference system,” Energies, vol. 13, no. 12, June 2020, doi: 10.3390/en131231102022-03-02
120 C. Liaskos, S. Nie, A. Tsioliaridou, A. Pitsillides, S. Ioannidis and I. Akyildiz, "End-to-end wireless path deployment with intelligent surfaces using interpretable neural networks," IEEE Trans. Commun., vol. 68, no. 11, pp. 6792-6806, Nov. 2020. doi: 10.1109/TCOMM.2020.30125772022-01-07
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