It is with great pride and heartfelt joy that I announce the successful public defence of Mr. Panagiotis Argyrakis’ PhD thesis, titled: "Signal and Data Processing, Time Series Analysis of Permanent and Portable GNSS Stations, and Multipath Effect with Application to Geosciences."

This extraordinary research explored cutting-edge methodologies in GNSS data processing, with significant contributions to understanding seismic phenomena and geosciences. Mr. Argyrakis’ work has already led to notable academic publications and applications, exemplifying the rigor and innovation characteristic of top-tier doctoral research.

As his supervisor at the Department of Informatics and Telecommunications, University of the Peloponnese, Greece, I had the privilege of witnessing Mr. Argyrakis’ journey of intellectual growth and dedication to his field. I am immensely proud of his achievements and the impact his work has already made.

The advisory committee, composed of Prof. George Tsoulos, Dr. Athanasios Ganas, and myself, guided and supported Mr. Argyrakis throughout this journey. Today, the 7-member examination committee (Nikolaos Sagias, George Tsoulos, Athanasios Ganas, John Moscholios, George Glentis, Petros Bithas, Ioannis Dokas) unanimously graded his thesis as Excellent, recognizing the depth and breadth of his contributions.

I extend my sincerest gratitude to all members of the examination and advisory committees for their invaluable insights and support.

Finally, I offer my heartfelt congratulations to Mr. Panagiotis Argyrakis. His hard work and excellence serve as an inspiration to his peers and the academic community. I am confident that he will continue to achieve great things in his future endeavors.

Let us all join in celebrating this remarkable achievement!

University of Peloponnese
Digital Communications and Systems Lab:
hashtag#PhD hashtag#thesis hashtag#PhDDefense hashtag#ThesisDefense hashtag#DoctoralDefense hashtag#Doctorate hashtag#AcademicMilestone hashtag#PhDJourney

I recently completed the Machine Learning with Python course offered by IBM SkillsBuild.

The Machine Learning with Python course by IBM on SkillBuild is designed to provide a comprehensive introduction to machine learning using Python. Here are some key highlights:

• Foundational Concepts: The course begins with an introduction to machine learning, covering the basics of supervised and unsupervised learning, as well as linear and non-linear regression.
• Classification Techniques: You will learn about various classification algorithms, including K-Nearest Neighbors (KNN), decision trees, and logistic regression.
• Clustering Methods: The course covers different clustering techniques such as k-means, hierarchical clustering, and DBSCAN.
• Hands-On Learning: Emphasis is placed on practical experience, using Python libraries like SciPy and scikit-learn. You will apply your knowledge through labs and projects.
• Final Project: By the end of the course, you will build, evaluate, and compare several machine learning models using different algorithms.
• Career Skills: Completing the course will equip you with job-ready skills and a certificate to showcase your competency in machine learning

This course is suitable for those looking to advance their data science career or get started in machine learning and deep learning.

Certification

🎉I am thrilled to announce that I have been included in the prestigious list of the top 2% most influential researchers globally for the 6th year in a row. This esteemed recognition, compiled by scientists at Stanford University, USA, was published by Elsevier on Sept. 16, 2024.

I extend my heartfelt gratitude to all my co-authors, both within and outside the University of Peloponnese, for their unwavering dedication, effort, and hard work, which have been instrumental in achieving this milestone. Thank you for being an essential part of this journey! 🙏✨

Ioannidis, John P.A. (2024), “August 2024 data-update for "Updated science-wide author databases of standardized citation indicators"”, Elsevier Data Repository, V7, doi: 10.17632/btchxktzyw.7

An article titled Enhancing 5G Performance: A Standalone System Platform with Customizable Features has been published in the AEU - International Journal of Electronics and Communications.

Congratulations to my PhD student Emmanouil-Zafeirios Bozis and all my co-authors, Michael Batistatos, George Xilouris, Akis Kourtis, and Tasos Kourtis.

The paper details the experimental outcomes of the integrated 5G platform utilizing SDR hardware and the most recent OpenAir Interface software, focusing on the key performance indicators of bandwidth and latency.

Here is a brief summary:

• Flexible 5G Platform: The paper proposes a standalone 5G system platform based on Open Air Interface software, which is fully reprogrammable and customizable.
• Improved Usability: The platform uses Linux shell scripts to simplify the implementation of different connectivity scenarios, speeding up the process.
• Performance Metrics: Achieved a mean Round Trip Time (RTT) latency of 6.78 ms with a 2 ms DL/UL Transmission periodicity, suitable for low-latency applications.
• Future Work: Plans to expand the system capabilities with additional hardware and software to test MIMO transmission and wider bandwidth parts.