Dr. Stavros G. Stavrinides

ERASMUS+ Coordinator for the School of Science and Technology

Course(s):

Teaching Hours and Credit Allocation: 30 Hours, 6 Credits
Course Assessment: Exam & Coursework

Cryptography has been an invaluable tool for information protection for centuries. Until the 1970s, cryptography was almost exclusively found in diplomatic, military and government applications. During the 1980s, the financial and telecommunications industries deployed hardware cryptographic devices. The first mass-market cryptographic application was the digital mobile phone system of the late 1980s. Today, everyone uses cryptography daily. This course introduces theoretical and practical modern cryptography and data protection principles in computer security. Threat and vulnerability assessment, encryption techniques (symmetric and asymmetric keys, public and secret key encryption, digital signatures etc.) are some of the topics that will be covered. Finally, more exotic (for the time being) but promising approaches, like chaotic and quantum cryptography, are introduced.

Aims

This course aims in providing solid knowledge in of cryptology, a domain where computer science, mathematics and electronic engineering are intersecting. Comprehensive knowledge on the theoretical foundations of the area (fundamental principles, elements, and protocols) is offered. Besides this and considering the extremely fast developments (old algorithms are broken and withdrawn and new algorithms and protocols emerge), the addition of new developments and advanced protocols provide with more fancy material. Topics that are relevant to cryptography practitioners today are introduced and explored by the most practical approach (step by step introduction to the basic concepts and judiciously chosen algorithms and protocols). Finally, the course prompts to further reading, for those who want to expand and deepen their knowledge.

Learning Outcomes

On completing the course students will be able to:

  • Understand the essential mathematics behind contemporary cryptographic schemes.
  • Implement basic cryptographic protocols (by hand as a proof of concept and utilizing the coding).
  • Understand and estimate the limits/performance of the cryptographic power of various protocols.
  • Understand the limits of breaking code approaches.
  • Come in touch with new, “out of the box” cryptographic approaches.
  • Have the background needed, to understand the upcoming methods and approaches in the area.

Content

  • Introduction to Cryptography and Data Security
  • Symmetric Cryptography
  • Stream Ciphers
  • The Data Encryption Standard (DES) and Alternatives
  • The Advanced Encryption Standard (AES)
  • About Block Ciphers
  • Asymmetric Cryptography
  • Introduction to Public-Key Cryptography
  • The RSA Cryptosystem
  • Public-Key Cryptosystems Based on the Discrete Logarithm Problem
  • Hash Functions
  • Message Authentication Codes (MACs)
  • Chaotic Encryption
  • Basics of Quantum Cryptography

Instructor(s): Prof. M. Drakaki, Dr. S.G. Stavrinides
Teaching Hours and Credit Allocation: 30 Hours, 6 Credits
Course Assessment: Exam & Coursework

Time series analysis has been established as a tool for understanding and representing data associated with complex real-life problems. Time series analysis, modelling and forecasting has been widely applied to solve practical problems in a wide range of scientific disciplines including natural, social and political sciences, economics and engineering. Early revolutionary works on applications of time series analysis by using mathematical linear models have demonstrated the suitability of the linear time series methodology in understanding and representing dynamic real time series data. On the other hand, the paradigm of deterministic chaos has influenced thinking in many fields of science. Chaotic systems show rich and surprising mathematical structures. In the applied sciences, deterministic chaos provides a striking explanation for irregular behaviour and anomalies in systems which do not seem to be inherently stochastic. The most direct link between chaos theory and the real world is the analysis of time series from real systems in terms of nonlinear dynamics. Experimental technique and data analysis have seen such dramatic progress that, by now, most fundamental properties of nonlinear dynamical systems have been observed in the laboratory. Great efforts are being made to exploit ideas from chaos theory wherever the data displays more structure than can be captured by traditional methods. Problems of this kind are typical in biology and physiology but also in geophysics, economics, and many other sciences.

Aims

This course aims in providing solid knowledge on a domain that is beneficial to those studying AI and machine learning. Timeseries analysis and forecasting is a domain where computer science, and coding meet mathematics, physics and other natural sciences, engineering, economics, finance and social sciences. Comprehensive knowledge on the theoretical foundations of the area (fundamental principles, elements etc.) is offered. The course includes timeseries analysis by utilizing both linear approaches and nonlinear dynamics. Both modules move towards the final goal which is timeseries forecasting for practical applications.

Learning Outcomes

On completing the course students will be able to:

· Understand the essential mathematics and algorithms behind contemporary timeseries analysis.

· Understand the methods utilized for forecasting the temporal evolution of dynamical systems.

· Implement timeseries analysis both by utilizing linear and nonlinear methods.

· Learn how to analyze, model and forecast time series data by using statistical software packages.

· Successfully implement timeseries modelling and forecasting.

· Understand and estimate the limits of proper and reliable forecasting.

· Have the background needed and experience, to understand the upcoming methods and approaches in the area.

Content

· Introduction to time series analysis

· Basic characteristics of stationary processes

· Time series models (ARMA, ARIMA, SARIMA)

· Time series forecasting

  • Short introduction to Chaos Theory

· Basic characteristics of nonlinear timeseries and their analysis

  • Reconstruction of phase space
  • Dimensions, entropies and other invariant metrics
  • Timeseries forecasting methods and models

Reading

· “Introduction to time series and forecasting” by Brockwell P.J. and Davis R.A., 3rd edition, Springer, 2016.

· “Introduction to Time Series Analysis and Forecasting” by D. C. Montgomery, C. L. Jennings, M. Kulahci, 2nd edition, Wiley, 2015.

· “Nonlinear Timeseries Analysis” by Holger Kantz and Thomas Schreiber (2 nd edition).

· “Elements of Nonlinear Timeseries Analysis and Forecasting” by Jan G. De Gooijer

Hours and Credit Allocation :  30 Hours, 6 Credits
Course Assessment :  Exam & Coursework

Learning outcomes

On completing the course, students will be able to:

  • Develop knowledge of embedded system & sensor networks.
  • Acquire a solid overview of the forthcoming technologies in the Internet of Things.
  • Understand the challenged faced by IoT devices in various application domains.
  • Familiarize with different technologies and standards.

Content

  • Embedded systems and real-time operating systems.
  • Programming languages for embedded systems.
  • Sensor networking and technologies.
  • Mobile sensing systems.
  • Smart grid & Intelligent Transportation Systems.

Hours and Credit Allocation :  30 Hours, 6 Credits
Course Assessment :  Exam & Coursework

Learning outcomes

On completing the course, students will be able to:

  • Develop knowledge of embedded system & sensor networks.
  • Acquire a solid overview of the forthcoming technologies in the Internet of Things.
  • Understand the challenged faced by IoT devices in various application domains.
  • Familiarize with different technologies and standards.

Content

  • Embedded systems and real-time operating systems.
  • Programming languages for embedded systems.
  • Sensor networking and technologies.
  • Mobile sensing systems.
  • Smart grid & Intelligent Transportation Systems.

Click here for further information

Dr. Stavros G. Stavrinides is a Physicist with a MSc in Electronics and a PhD in Chaotic Electronics, all awarded by Aristotle University of Thessaloniki, in Thessaloniki, Greece. He currently serves as a Faculty Member (ΕΔΙΠ) of the School of Science and Technology at International Hellenic University (IHU), Thessaloniki, Greece. He has taught in academia numerous topics in circuit theory and electronics, timeseries forecasting and data security. Previous academic positions, include serving as a Visiting Assistant Professor in the ECE Dept. at University of Cyprus (2012-2013); Adjunct Lecturer/Assistant Professor in Physics (2009-2011), Informatics Depts. (2008-2010) and the MSc on Electronic Physics (2006-2010) at Aristotle University of Thessaloniki, the Computer Science Department (2014-2017), the MSc on Informatics and Computational Biology (2014-2017) and the MSc in Financial Forecasting and Econophysics (2017-2021) at the University of Thessaly and the Department of Electrical Engineering T.E. (2008-2018) at the TEI of Eastern Macedonia and Thrace. He has also supervised several undergraduate and master theses, while he currently co-supervises an on-going PhD at the University of Balearic Islands in Spain.

His research interests include, non-exhaustively:

  • Design of analog and mixed-signal electronic circuits;
  • Chaotic electronics and their applications, including chaotic synchronization (with emphasis on secure data transmission and cryptography);
  • Nonlinear dynamics of electronic devices;
  • Memristors and memristive circuits;
  • Hardware-based security with emphasis on the Internet of Things and edge computing applications,
  • Circuits and systems based on Stochastic Logic (Stochastic Computing)
  • Education and Econophysics.

He has participated, as a researcher or coordinator, in several nationally and internationally (EU, NATO) funded projects, while he served as the STSM Committee Chair in the COST-IC1401 action on memristors. Dr. Stavrinides has been member of numerous Conference Committees (Program/Organizing/Scientific), while he chaired the 4th International Interdisciplinary Symposium on Chaos and Complex Systems (CCS2019). He has been reviewer in more than 80 prestigious scientific journals and conferences, as well as research projects, edited two books and guest edited six journal special issues. Finally, he is an IEEE Senior Member and member of the Chua Memristor Center.

                  

Currently open special issue calls for papers

Publicity regarding Dr Stavrinides research activities

Selected Publications

Edited Books

   

Book Chapters

  • Digital information transmission using discrete chaotic signal N. Anagnostopoulos, A.N. Miliou, S.G. Stavrinides, A.S. Dmitriev, E.V. Efremova In “Chaos Synchronization and Cryptography for Secure Communications: Applications for Encryption” edited by Santo Banerjee. Chapter 19, pp. 439-462, IGI Global, ISBN: 978-1615207374, May 2010.
  • The route from synchronization to de-synchronization of chaotic operating circuits and systems G. Stavrinides, A.N. Anagnostopoulos In “Applications of Nonlinear Dynamics and Chaos in Science and Engineering – Vol. 3” edited by Santo Banerjee and Lamberto Rondoni. Chapter 9, pp. 229-275, Springer, ISBN: 978-3642340161, February 2013.
  • Implementing memristor emulators in hardware S.G. Stavrinides, R. Picos, F. Corinto, Carola de Benito In “Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications” edited by Christos K. Volos and Viet-Thanh Pham. Chapter 2, pp. 17-40, Springer, ISBN: 978-0128211847, July 2021.
  • Applications of nanotechnology in alzheimer disease Maria Chountoulesi, Nikolaos Naziris, Anna Gioran, Aristeidis Papagiannopoulos, Barry R. Steele, Maria Micha-Screttas, Stavros G. Stavrinides, Michael Hanias, Niki Chondrogianni, Stergios Pispas, Cécile Arbez-Gindre, and Costas Demetzos In “Handbook of Computational Neurodegeneration” Edited by Panayiotis Vlamos, Ilias S. Kotsireas and Ioannis Tarnanas. Accepted for publication, Springer, ISBN: 978-3319759234, 2024.

 Journal papers

  • A digital non-autonomous chaotic oscillator suitable for information transmission G. Stavrinides, N.F. Karagiorgos, K. Papathanasiou, S. Nikolaidis and A.N. Anagnostopoulos IEEE TCAS-II: Express Briefs, 60(12), Article Number 6654269, pp. 887-891, 2013.
  • Using modern RF tools to predict chaotic behaviour of electronic circuits and systems G. Stavrinides, K. Papathanasiou and A.N. Anagnostopoulos International Journal of Electronics, 102(2), pp. 233-247, 2015.
  • Chaotic Behavior of Random Telegraph Noise in nanoscale UTBB FD-SOI MOSFETs H. Tassis, S. G. Stavrinides, M.P. Hanias, C.G. Theodorou, G. Ghibaudo and C.A. Dimitriadis IEEE Electron Device Letters, 38(4), pp. 517-520, 2017.
  • A Universal Physics-Based Model Describing COVID-19 Dynamics in Europe Υ. Contoyiannis, G. Stavrinides, M.P. Hanias, M. Kampitakis, P. Papadopoulos, R. Picos, S.M. Potirakis International Journal of Environmental Research and Public Health, 17(18), p. 6525, 2020.
  • Experimental evaluation of the dynamic route map in the reset transition of memristive ReRAMs Maldonado, Μ.Β. Gonzalez, F. Campabadal, F. Jimenez-Molinos, M.M. Al Chawa, S.G. Stavrinides, J.B. Roldan, R. Tezlaff, R. Picos, L.O. Chua, Chaos, Solitons & Fractals, 139, Art. No. 110288, 2020.
  • Observation of stochastic resonance for weak periodic magnetic field signal using chaotic system G. Silva, W. Korneta, S.G. Stavrinides, R. Picos, L.O. Chua Communications in Nonlinear Science and Numerical Simulation, 94, Article No. 105558, 2021.
  • “Stochastic Computing Implementation of Chaotic Systems” O. Camps, S.G. Stavrinides, R. Picos Mathematics, 9(4), Article No. 375, pp. 1-20, 2021.

Conference papers

  • An automated setup for the analysis of chaotic systems G. Stavrinides, Th. Laopoulos and A.N. Anagnostopoulos In Proceedings of the ΙΕΕΕ International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IEEE IDAACS 2005), pp. 628-632, September 2005, Sofia, Bulgaria.
  • Impulsive synchronization between two chaotic double-scroll circuits Volos, I.M. Kyprianidis, I. Stouboulos, S.G. Stavrinides, M. Ozer and A.Ν. Anagnostopoulos Chaos and Complex Systems: Procedings of the 4th International Interdisciplinary Symposium on Chaos and Complex Systems – Springer Complexity Series, pp. 469-474, CCS 2012, May 2012, Antalya, Turkey.
  • An initial approach in teaching Chaos Theory at school G. Stavrinides, M. Hanias, P. Konstantaki In Proceedings of the International Congress and Exhibition on Current Trends on Science and Technology Education (SCITEED 2014), pp. 70-76, April 2014, Lykia, Turkey.
  • A two-transistor non-ideal memristor emulator Kalomiros, S.G. Stavrinides, F. Corinto In Proc. IEEE International Conference on Modern Circuits and Systems Technology (IEEE-MOCAST2016), Article No. 7495164, May 2016, Thessaloniki, Greece.
  • “Design and implementation of passive memristor emulators using a charge-flux approach” M. M. Al Chawa, C. de Benito, M. Roca, R. Picos, S.G. Stavrinides In Proceedings of the IEEE International Symposium on Circuits & Systems 2018 (ΙΕΕΕ-ISCAS 2018), pp. 1-5, IEEE Xplore doi: 10.1109/ISCAS.2018.8351738, May 2018, Florence, Italy.
  • A Purely Digital Memristor Emulator based on a Flux-Charge Model Camps, M.M. Al Chawa, C. de Benito, M. Roca, S.G. Stavrinides, R. Picos and L.O. Chua In Proceedings of the 25th IEEE International Conference on Electronics Circuits and Systems (IEEE ICECS2018), 8618030, pp. 565-568, December 2018, Bordeaux, France.
  • Efficient Implementation of Memristor Cellular Nonlinear Networks using Stochastic Computing Camps, S. G. Stavrinides, R. Picos In Proceedings of the 2020 European Conference on Circuit Theory and Design (ECCTD), pp. 1-4, IEEE Xplore doi: 10.1109/ECCTD49232.2020.9218298, September 2020, Sofia, Bulgaria.

 

Working with: GMDH Streamline: Demand Planning Software