@conference{inproceedingsc,

title = {Call Admission Control under a Probabilistic Bandwidth Reservation Policy and Handover Queueing in Mobile Hotspots},

author = {Marinos Vlasakis and Irene Keramidi and Ioannis Moscholios and Panagiotis Sarigiannidis},

url = {https://www.researchgate.net/publication/364234461_Call_Admission_Control_under_a_Probabilistic_Bandwidth_Reservation_Policy_and_Handover_Queueing_in_Mobile_Hotspots},

doi = {10.1109/CSNDSP54353.2022.9907974},

isbn = {978-1-6654-1044-1},

year  = {2022},

date = {2022-07-01},

pages = {263-268},

abstract = {In this paper we study a mobility-aware call admission control algorithm in a mobile hotspot. More specifically, we consider a vehicle which has an access point of a fixed capacity and may alternate between stop and moving phases. In the stop phase, the vehicle services new and handover calls. To prioritize handover calls a probabilistic bandwidth reservation (BR) policy is considered where a fraction of the capacity is reserved for handover calls. Based on this policy, new calls may enter the reservation space with a predefined probability. In addition, handover calls have the option to wait in a queue of finite size if there are no available resources at the time of their arrival. In the moving phase, the vehicle services only new calls under the classical complete sharing policy. In both phases, calls arrive according to a Poisson process, require a single bandwidth unit for their acceptance in the system and have an exponentially distributed service time. To analytically determine the various performance measures such as call blocking probabilities an efficient iterative algorithm is proposed.},

keywords = {admission, handover, hotspot, loss, mobility, queueing, Reservation},

pubstate = {published},

tppubtype = {conference}

}

@conference{9844034,

title = {Dynamic Risk Assessment and Certification in the Power Grid: A Collaborative Approach},

author = {Athanasios Liatifis and Pedro Ruzafa Alcazar and Panagiotis Radoglou Grammatikis and Dimitris Papamartzivanos and Sofianna Menesidou and Thomas Krousarlis and Molinuevo Martin Alberto and Iñaki Angulo and Antonios Sarigiannidis and Thomas Lagkas and Vasileios Argyriou and Antonio Skarmeta and Panagiotis Sarigiannidis},

url = {https://www.researchgate.net/publication/362464616_Dynamic_Risk_Assessment_and_Certification_in_the_Power_Grid_A_Collaborative_Approach},

doi = {10.1109/NetSoft54395.2022.9844034},

isbn = {978-1-6654-0694-9},

year  = {2022},

date = {2022-06-27},

booktitle = {2022 IEEE 8th International Conference on Network Softwarization (NetSoft)},

pages = {462-467},

abstract = {The digitisation of the typical electrical grid introduces valuable services, such as pervasive control, remote monitoring and self-healing. However, despite the benefits, cybersecurity and privacy issues can result in devastating effects or even fatal accidents, given the interdependence between the energy sector and other critical infrastructures. Large-scale cyber attacks, such as Indostroyer and DragonFly have already demonstrated the weaknesses of the current electrical grid with disastrous consequences. Based on the aforementioned remarks, both academia and industry have already designed various cybersecurity standards, such as IEC 62351. However, dynamic risk assessment and certification remain crucial aspects, given the sensitive nature of the electrical grid. On the one hand, dynamic risk assessment intends to re-compute the risk value of the affected assets and their relationships in a dynamic manner based on the relevant security events and alarms. On the other hand, based on the certification process, new approach for the dynamic management of the security need to be defined in order to provide adaptive reaction to new threats. This paper presents a combined approach, showing how both aspects can be applied in a collaborative manner in the smart electrical grid.},

keywords = {certification, Cybersecurity, energy, Honeypot, Power Grid, Risk Assessment, Software Defined Networking},

pubstate = {published},

tppubtype = {conference}

}

@conference{9837727,

title = {A Cloud Computing web-based application for Smart Farming based on microservices architecture},

author = {Vasileios Moysiadis and Konstantinos Tsakos and Panagiotis Sarigiannidis and Euripides G M Petrakis and Achilles D Boursianis and Sotirios K Goudos},

doi = {10.1109/MOCAST54814.2022.9837727},

isbn = {978-1-6654-6717-9},

year  = {2022},

date = {2022-06-08},

booktitle = {2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST)},

pages = {1-5},

abstract = {The agriculture sector is envisioning a revolution of traditional farming supported by Information and Communications Technologies (ICT) and Cloud Computing is one of them. This tendency is called Smart Farming and promises to boost productivity while reducing production costs and chemical inputs. Cloud Computing aims to provide the necessary resources and the central orchestration of all devices involved in a Smart Farming scenario. To achieve high scalability, usability and performance in Cloud-based applications, we have to move from a monolithic development approach to microservices architecture using cutting edge technologies like containerisation. This paper presents a Smart Farming application based on Cloud Computing that promises to provide useful information to agronomists and farmers to support their decisions based on measurements from ground sensors and images captured from UAVs or ground cameras. Our implementation is based on microservices architecture using Docker Containers as the virtualisation technology. Each microservice runs on a different container and communicates through a RESTful API interface. The proposed architecture is highly scalable in future upgrades and promises high performance and security.},

keywords = {Cloud Computing, Containerisation, Microservices, smart farming},

pubstate = {published},

tppubtype = {conference}

}

@conference{9837653,

title = {Autism detection in High-Functioning Adults with the application of Eye-Tracking technology and Machine Learning},

author = {Konstantinos-Filippos Kollias and Christine K Syriopoulou-Delli and Panagiotis Sarigiannidis and George F Fragulis},

url = {https://www.researchgate.net/publication/362340239_Autism_detection_in_High-Functioning_Adults_with_the_application_of_Eye-Tracking_technology_and_Machine_Learning},

doi = {10.1109/MOCAST54814.2022.9837653},

isbn = {978-1-6654-6717-9},

year  = {2022},

date = {2022-06-08},

booktitle = {2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST)},

pages = {1-4},

abstract = {High-Functioning Autism Detection in Adults is significantly difficult compared with early Autism Spectrum Disorder (ASD) diagnosis with severe symptoms. ASD diagnosis is usually achieved by behavioural instruments relying on subjective rather on objective criteria, whereas advances in research indicate cutting -edge methods for early assessment, such as eye-tracking technology, machine learning, Internet of Things (IoT), and other assessment tools. This study suggests the detection of ASD in high-functioning adults with the contribution of Transfer Learning. Decision Trees, Logistic Regression and Transfer Learning were applied on a dataset consisting of high-functioning ASD adults and controls, who looked for information within web pages. A high classification accuracy was achieved regarding a Browse (80.50%) and a Search (81%) task showing that our method could be considered a promising tool regarding automatic ASD detection. Limitations and suggestions for future research are also included.},

keywords = {eye-tracking, High-Functioning Autism detection, IoT, machine learning, Transfer learning, web},

pubstate = {published},

tppubtype = {conference}

}

@conference{9837541,

title = {Music Deep Learning: A Survey on Deep Learning Methods for Music Processing},

author = {Lazaros Alexios Iliadis and Sotirios P Sotiroudis and Kostas Kokkinidis and Panagiotis Sarigiannidis and Spiridon Nikolaidis and Sotirios K Goudos},

url = {https://www.researchgate.net/publication/333014972_Deep_Learning_Techniques_for_Music_Generation_-_A_Survey},

doi = {10.1109/MOCAST54814.2022.9837541},

isbn = {978-1-6654-6717-9},

year  = {2022},

date = {2022-06-08},

booktitle = {2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST)},

pages = {1-4},

abstract = {Deep Learning has emerged as a powerful set of computational methods achieving great results in a variety of different tasks. Music signal processing, a field with rich commercial applications, seems to benefit too from this data-driven approach. In this paper a review of the state of the art Deep Learning methods applied on music signal processing is provided. A special focus is given in music information retrieval and music generation. In addition, possible future research directions are discussed.},

keywords = {Deep Learning, Music Generation, Music Information Retrieval, Music Signal Processing},

pubstate = {published},

tppubtype = {conference}

}

@conference{9837645,

title = {Comparative Performance of Algorithmic Techniques for Optimizing Dual-Band Rectifier},

author = {Maria S Papadopoulou and Achilles D Boursianis and Argyrios Chatzopoulos and Panagiotis Sarigiannidis and Spyridon Nikolaidis and Sotirios K Goudos},

url = {https://www.researchgate.net/publication/362330125_Comparative_Performance_of_Algorithmic_Techniques_for_Optimizing_Dual-Band_Rectifier},

doi = {10.1109/MOCAST54814.2022.9837645},

isbn = {978-1-6654-6717-9},

year  = {2022},

date = {2022-06-08},

booktitle = {2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST)},

pages = {1-4},

abstract = {Radio Frequency (RF) energy harvesting (EH) is a technique to replenish the source of wireless sensor networks (WSNs). Also, many interdisciplinary fields in the Internet-of-Things (IoT) era use RF-EH, like precision agriculture, biomedical, and robotics. Over the years, various designs have been presented in the literature operating in multi- or wide-band frequencies. Usually, a designed system is optimized using specific goals and optimization parameters to obtain maximization in power conversion efficiency (PCE). In this work, a dual-band RF rectifier system that resonates in the Wi-Fi frequency bands of 2.45 GHz and 5.8 GHz is presented. The proposed system is optimized using four optimization techniques, namely the Gradient algorithm, the Minimax algorithm, the Simulated Annealing, and the Genetic algorithm. A set of comparative results is presented to assess the performance of each technique and to obtain the feasible solution of the proposed design. Numerical results demonstrate that a 42.8% efficiency is achieved, having a 16 dBm input power and a 1.7 kΩ output resistance load.},

keywords = {genetic algorithm, impedance matching, Optimization technique, rectenna, rectifier, RF energy harvesting, wireless sensor network},

pubstate = {published},

tppubtype = {conference}

}

@conference{inproceedingse,

title = {Real Time Sign Language Translation Systems: A review study},

author = {Maria Papatsimouli and Konstantinos Kollias and Lazaros Lazaridis and George S Maraslidis and Heracles Michailidis and Panagiotis Sarigiannidis and George Fragulis},

url = {https://www.researchgate.net/publication/362331604_Real_Time_Sign_Language_Translation_Systems_A_review_study},

doi = {10.1109/MOCAST54814.2022.9837666},

isbn = {978-1-6654-6717-9},

year  = {2022},

date = {2022-06-08},

pages = {1-4},

abstract = {There are people who cannot communicate in the same way with others. Deaf and hard-of-hearing people use sign languages for their communication with other people. Sign languages are also used for the communication between deaf and non-deaf people, including different types of hand gestures and facial expressions for communication and emotional expression. Sign language recognition and gesture-based controls are applications that are used by gesture recognition technologies, and it is a fact that this technology has reduced the communication gap, while these systems are used for converting gestures to text or speech. The focus of our research is to analyze real-time sign language translators that are used for language translation. Sign Language Translation Systems that were developed from 2017 to

2021 are analysed in this paper.},

keywords = {Application Program Interfaces, Handicapped aids, IoT, Sign Language, Sign Language Recognition},

pubstate = {published},

tppubtype = {conference}

}

@conference{9837760,

title = {Smart Refrigeration Equipment based on IoT Technology for Reducing Power Consumption},

author = {Kyriakos Koritsoglou and Maria S Papadopoulou and Achilles D Boursianis and Panagiotis Sarigiannidis and Spyridon Nikolaidis and Sotirios K Goudos},

url = {https://www.researchgate.net/publication/362329831_Smart_Refrigeration_Equipment_based_on_IoT_Technology_for_Reducing_Power_Consumption},

doi = {10.1109/MOCAST54814.2022.9837760},

isbn = {978-1-6654-6717-9},

year  = {2022},

date = {2022-06-08},

booktitle = {2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST)},

pages = {1-4},

abstract = {In developed countries, companies active in the food retail sector consume about 20% of the total power required for cooling and are therefore ranked among them with the largest energy footprint after the industries. For this reason, refrigeration equipment manufacturers are increasingly focusing on methods that will reduce the power consumption required for its efficient operation. The solutions that are currently implemented mainly focus on interventions in the development phase of freezer units (better insulation, variable frequency compressors, etc.), which, however, do not adequately address the problem. This paper presents the development of an IoT-based technology device which, using machine learning techniques, aims to reduce the power consumption of refrigeration equipment and consecutively, the energy footprint of food retailers.},

keywords = {artificial intelligence (AI) algorithms, embedded system, Internet of Thing (IoT), smart refrigerator, wireless sensor network (WSN)},

pubstate = {published},

tppubtype = {conference}

}

@conference{9837524,

title = {Rectifier circuit design for 5G energy harvesting applications},

author = {Ioannis D Bougas and Maria S Papadopoulou and Achilles D Boursianis and Panagiotis Sarigiannidis and Spyridon Nikolaidis and Sotirios. K Goudos},

url = {https://www.researchgate.net/publication/362327796_Rectifier_circuit_design_for_5G_energy_harvesting_applications},

doi = {10.1109/MOCAST54814.2022.9837524},

isbn = {978-1-6654-6717-9},

year  = {2022},

date = {2022-06-08},

booktitle = {2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST)},

pages = {1-4},

abstract = {The need for electronic devices usage has risen significantly over the years. This has in turn generated greater demands for electricity and in addition for green energy sources. These include Radio-Frequency (RF) energy harvesting. In this concept we design a rectifier circuit for RF to DC conversion suitable for operation at sub-6 GHz 5G bands. Such a circuit can be used to supply low-power electronic devices. The proposed rectifier works at the frequency band FR1 of 5G cellular network and more specifically at 3.5 GHz. The most important problem in the RF energy harvesters is low system efficiency, something that limits the popularity of the power harvest. The proposed design is found to be highly efficient in its current form. Numerical results show that the system in a single-tone signal provides maximum power conversion efficiency equal to 42.5% when the load of the circuit is 1.1 KΩ and the input power reaches 9 dBm. The presented rectifier circuit performs better or equally with similar designs in the literature.},

keywords = {5G, impedance matching network, power conversion efficiency, radio frequency energy harvesting, rectifier, voltage doubler, voltage multiplier, wireless power transfer},

pubstate = {published},

tppubtype = {conference}

}

@conference{9858306,

title = {Towards 6G: Deep Learning in Cell-Free Massive MIMO},

author = {Lazaros Alexios Iliadis and Zaharias D Zaharis and Sotirios P Sotiroudis and Panagiotis Sarigiannidis and George K Karagiannidis and Sotirios K Goudos},

url = {https://www.researchgate.net/publication/362924666_Towards_6G_Deep_Learning_in_Cell-Free_Massive_MIMO},

doi = {10.1109/BlackSeaCom54372.2022.9858306},

isbn = {978-1-6654-9749-7},

year  = {2022},

date = {2022-06-06},

booktitle = {2022 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)},

pages = {269-273},

abstract = {Massive Multiple-Input-Multiple-Output (MIMO) technology is considered a crucial part of the fifth generation (5G) telecommunications systems. However, moving towards sixth generation (6G) wireless networks, novel solutions have to be incorporated into the current telecommunications' systems. Cell-free Massive MIMO and especially the user-centric approach, seems to be the most promising idea to this direction at this moment. Nevertheless, there are many open issues to be resolved. Deep Learning has been successfully applied to a wide range of problems in many different fields, including wireless communications. In this paper, a review of the state-of-the-art Deep Learning methods applied to Cell-free Massive MIMO communications systems is provided. In addition future research directions are discussed.},

keywords = {6G, Cell-free massive MIMO, Deep Learning, User-centric cell-free massive MIMO},

pubstate = {published},

tppubtype = {conference}

}