Georgios Giannakopoulos

The rapid digitalization of communication systems has elevated Interactive Voice Response (IVR) technologies to become critical interfaces for customer engagement. With Artificial Intelligence (AI) now driving these platforms, ensuring secure, compliant, and ethically designed development practices is more imperative than ever. AI-powered IVRs leverage Natural Language Processing (NLP) and Machine Learning (ML) to personalize interactions, automate service delivery, and optimize user experiences. However, these innovations expose systems to heightened risks, including data privacy breaches, AI decision opacity, and model security vulnerabilities.This paper analyzes the evolution of IVRs from static code-based designs to adaptive AI-driven systems, presenting a cybersecurity-centric perspective. We propose a practical governance framework that embeds agile security principles, compliance with global data legislation, and user-centric ethics. Emphasizing privacy-by-design, adaptive risk modeling, and transparency, the paper argues that ethical AI integration is not a feature but a strategic imperative. Through this multi-dimensional lens, we highlight how modern IVRs can transition from communication tools to intelligent, secure, and accountable digital frontlines resilient against emerging threats and aligned with societal expectations.

Interactive Voice Response (IVR) systems have experienced a remarkable evolution in recent years, transitioning from conventional code-driven development methods to more intuitive, widget-based approaches, and ultimately embracing Artificial Intelligence (AI) for automated IVR flow generation. This paper examines the progressive transformation of IVR development methodologies, emphasizing the technological revolution that is redefining the landscape and future direction of IVR systems. The discussion encompasses the historical background, prevailing trends, and emerging advancements in IVR technology, illustrating how AI integration streamlines the design process and significantly elevates customer interaction and satisfaction.

Monte Carlo (MC) simulations are the backbone of medical physics, especially radiation therapy, due to their high accuracy in modeling the interaction of ionizing radiation with biological tissues. These significantly improve the accuracy of dose calculations and thus are of primary importance for treatment planning and quality assurance. Still, a high computational cost remains one of the substantial limiting factors for their wider use in clinical applications. This paper investigates the advantages of MC-based dose calculations compared to conventional algorithms; it underlines their superior precision and computational complexity. A comparative evaluation of MC techniques and deep learning-based methodologies is carried out in terms of performance with respect to accuracy, computational efficiency, and robustness in heterogeneous media. Results are presented that show Monte Carlo simulations reach up to 99.2\% accuracy but are computationally intensive, requiring substantial optimization through AI-driven acceleration techniques. Recent developments, such as GPU acceleration and hybrid AI-MC approaches, have shown great promise to close the gap between accuracy and computational feasibility. In addition, this study has demonstrated how AI-based variance reduction techniques contribute to the optimization of MC simulations by gaining execution time with no loss of their intrinsic accuracy. Future research efforts should focus on the development and integration of deep learning models in MC simulations to further advance the efficiency of MC simulations. These results of this study illustrated that hybrid AI-MC frameworks may achieve advances in computational efficiency while maintaining reliability for clinical use and thus can make Monte Carlo simulations more feasible in real-time medical applications.

When organizations adopt systems thinking principles, systemic organizational change becomes more achievable through intelligent automation (IA). Implementing intelligent process automation--beyond the capabilities of robotic process automation--demands a departure from traditional, discrete-target methods, which often fail to unlock the full strategic potential of these advanced technologies. This study analyzes current research and real-world case studies to develop a system-based implementation model that emphasizes maintaining organizational interconnections, response cycles, and achieving equilibrium across all operational segments. Intelligent automation empowers organizations to develop a strategic vision by leveraging systems thinking methodologies, while simultaneously enhancing operational agility. This evolution elevates IT from a basic support function to a driver of enterprise-level strategic leadership. The research offers both theoretical and practical insights by aligning systems thinking principles with smart automation implementation, thereby providing IT leaders with a comprehensive framework for leveraging automation technologies as instruments of strategic business transformation.

The rapid digitalization of communication systems has elevated Interactive Voice Response (IVR) technologies to become critical interfaces for customer engagement. With Artificial Intelligence (AI) now driving these platforms, ensuring secure, compliant, and ethically designed development practices is more imperative than ever. AI-powered IVRs leverage Natural Language Processing (NLP) and Machine Learning (ML) to personalize interactions, automate service delivery, and optimize user experiences. However, these innovations expose systems to heightened risks, including data privacy breaches, AI decision opacity, and model security vulnerabilities. This paper analyzes the evolution of IVRs from static code-based designs to adaptive AI-driven systems, presenting a cybersecurity-centric perspective. We propose a practical governance framework that embeds agile security principles, compliance with global data legislation, and user-centric ethics. Emphasizing privacy-by-design, adaptive risk modeling, and transparency, the paper argues that ethical AI integration is not a feature but a strategic imperative. Through this multidimensional lens, we highlight how modern IVRs can transition from communication tools to intelligent, secure, and accountable digital frontlines-resilient against emerging threats and aligned with societal expectations.

This article explores the integration of artificial intelligence (AI) and green engineering to address environmental challenges. It covers foundational principles of green engineering and AI's role in enhancing sustainable practices across sectors. The paper examines applications in sustainable energy systems, smart grids, and renewable energy, as well as AI's impact on sustainable design, manufacturing, and urban planning. It also addresses challenges including energy consumption, data privacy, and ethics. The article emphasizes interdisciplinary collaboration and provides insights for researchers, policymakers, and industry professionals working on environmental sustainability. It offers a comprehensive overview of the current state and future directions of AI-driven green engineering.

This paper presents a comprehensive study on the design and implementation of Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) digital filters tailored for low-power modem applications. With the increasing demand for energy-efficient digital communication systems in Internet of Things (IoT) and mobile technologies, optimizing filters for power and performance is vital. FIR filters were designed using the windowing method, particularly the Hamming window, while IIR filters employed the bilinear transformation method to ensure stability and spectral accuracy. MATLAB was used for algorithmic design and frequency response analysis, whereas SIMULINK provided a dynamic environment to simulate real-time performance under varying signal conditions. The hardware realization was accomplished using VHDL, with synthesis and implementation carried out on FPGA platforms using Xilinx ISE. FIR filters were structured around a tap delay line architecture, offering inherent stability and linear phase characteristics, while IIR filters, though more complex due to feedback components, demonstrate superior power efficiency and sharper frequency cutoff characteristics. This study further explores the trade-offs between signal fidelity, computational complexity, and power consumption, providing insights through mathematical formulations, simulation results, and FPGA synthesis reports. Results indicate that FIR filters are advantageous in applications where linear phase response and stability are crucial, whereas IIR filters are preferable for constrained environments demanding minimal power usage. The comparative analysis suggests that filter selection should be application-specific, and future work may explore hybrid implementations that combine the advantages of both architectures for enhanced performance in next-generation communication systems.

This paper presents a low-power modem realized using Quadrature Phase Shift Keying (QPSK) modulation and Infinite Impulse Response (IIR) filters on a Xilinx FPGA. The architecture, designed for low-power wireless communication in mobile handsets, IoT, and battery-powered applications, incorporates 16−bit fixed-point IIR filters with power consumption of 40 mW. Adaptive voltage scaling reduces energy by another 15%, and clock gating and Dynamic Voltage Scaling (DVS) reduce total power consumption by up to 30%. Hardware-in-the-Loop (HIL) Bit Error Rate (BER) testing and in-situ power measurements confirm 98 mW total system power at 12 dB SNR, with a BER of 1.2 × 10−6. Compared to Finite Impulse Response (FIR)-based implementations, the new modem has 35% less latency and improved computational efficiency. The FPGA design is scalable and parallel-processing capable, making it easily implementable in real-time applications such as sensor networks, mobile communication, and SDR systems. The QPSK modulation is very efficient in terms of spectra, transmitting two bits per symbol and yet being extremely robust in noisy environments. The integration of the IIR filter also enhances signal quality by filtering out noise and optimizing the use of hardware resources. A comparative analysis explains the modem’s higher power efficiency, lower computational complexity, and superior performance over state-of-the-art designs. Future research efforts will consider adaptive modulation schemes, i.e., Orthogonal Frequency Division Multiplexing (OFDM), for spectral efficiency enhancement, and machine learning-based dynamic power management and hardware acceleration for further optimization. The suggested design provides a power-saving solution for next generation wireless communication, especially in systems with constrained energy resources.

This paper presents a systematic methodology for enhancing microstrip patch antenna (MPA) performance at 1.3 GHz for Phase Alternating Line (PAL) television broadcasting systems. Through the integration of slot-loaded patch geometries, substrate optimization, and array configurations, the proposed design achieves an 8.1 dBi gain and a 108 MHz bandwidth, representing improvements of 30% and 50% respectively, over conventional designs [1], [2]. The approach combines analytical modeling in MATLAB with full-wave electromagnetic simulations using CST Microwave Studio and is validated through precision measurements of fabricated prototypes. Key innovations include the implementation of a U-shaped slot for multi-resonant operation (∆f = 45 MHz per iteration), strategic selection of RT/Duroid 5880 substrate (ϵr = 2.2), and a 1 × 4 phased array configuration incorporating a defected ground structure (DGS) [5]. Experimental verification demonstrates 82% radiation efficiency and −22 dB cross-polarization isolation, fulfilling PAL TV specifications while maintaining compact dimensions (58 × 58 × 1.6 mm) [6], [7]. MATLAB and CST simulations analyze the antenna’s performance, including the reflection coefficient (S11) [11], [12]. The optimized MPA achieves a gain of 8 dBi with a bandwidth exceeding 100 MHz, aligning with the operational requirements of PAL TV applications [3], [13]. Future work will explore adaptive configurations and alternative substrate materials [14], [15].

The computational complexity of Discrete Fourier Transform (DFT) algorithms plays a pivotal role in signal processing, influencing their applicability in various domains. This paper investigates three prominent Fast Fourier Transform (FFT) algorithms: Radix-2, Radix-4, and Bluestein, with a focus on their computational efficiency and suitability for different sequence lengths. MATLAB implementations were developed to optimize these algorithms, reducing the number of multiplications and additions required during runtime. A comparative analysis reveals that Radix-2 and Radix-4 algorithms are highly efficient for power-of-two and power-offour data lengths, respectively, while the Bluestein algorithm provides unparalleled flexibility for arbitrary sequence lengths, including primes. The study demonstrates the trade-offs associated with each algorithm, highlighting their strengths and limitations. Radix-4 achieves greater efficiency over Radix-2 for longer sequences, while Bluestein eliminates the need for zero-padding at the cost of increased computational complexity. This research offers valuable insights into the selection of FFT algorithms based on application-specific requirements and data characteristics, laying the groundwork for further optimization and hybrid algorithm development. The findings underscore the enduring importance of FFTs in addressing the computational demands of modern signal processing tasks.

Internet Protocol Television (IPTV) is a transformative approach to delivering audio and video services through high-speed Internet networks, enabling direct access to television content via home computers or set-top boxes. Despite its promising advantages, including flexibility, interactivity, and bundled services such as triple play (voice, Internet, and TV) and quadruple play (adding mobile services), IPTV is still in its development phase. Key challenges include achieving a Quality of Service (QoS) comparable to traditional broadcasters, addressing limited bandwidth, and overcoming a lack of standardization among service providers. This paper explores the technical, operational, and consumer-oriented aspects of IPTV. It discusses data compression techniques, protocols like IGMP and RTSP, and the role of advanced codecs like H.264 in ensuring efficient data transmission. The study also examines the distinctions between IPTV and open-network Internet TV, the importance of security and privacy, and the emergence of new business opportunities through targeted advertising and interactive services. Although IPTV is unlikely to completely replace traditional broadcasting, it is poised to play an important role in shaping the future of television by offering personalized, secure, and scalable viewing experiences.

The aim of this paper is to study the performance of a matched filter for baseband binary transmission when subjected to Gaussian noise. A BER performance simulation model is created which consists of a BPSK encoder, a transmission SQRC filter, a Gaussian noise generator, a receive SQRT filter and a BPSK decoder. A Monte Carlo simulation technique is used to determine the dependency of BER performance on the signal to noise ratio. A MATLAB program and a MATLAB SIMULINK program was written to generate the required data. The MATLAB results show good agreement with statistical theoretical predictions.

The increasing use of mobile phones worldwide has raised concerns about potential health risks associated with exposure to high-frequency radiation emitted by these devices. This study explores the short-term and potential long-term health effects of mobile phone usage, particularly focusing on risks such as cancer, nervous system disorders, and electromagnetic hypersensitivity. Key research conducted by organizations such as the World Health Organization (WHO) and the Mobile Telecommunications and Health Research (MTHR) program is reviewed, highlighting inconclusive evidence linking mobile phone radiation to health problems. Children and young users are identified as particularly vulnerable to potential risks, given their developing physiology and higher susceptibility to radiation absorption. Experimental studies investigating biological mechanisms, cognitive function, and hypersensitivity have largely found no conclusive evidence of harm, though long-term effects remain under-researched due to the relatively recent widespread adoption of mobile phones. The research article emphasizes the need for adherence to rigorous research methodologies, including randomized, double-blind experiments, and standardized statistical analyses to ensure reliable conclusions. Recommendations for reducing exposure, such as limiting mobile phone usage and adopting hands-free solutions, are provided as precautionary measures. While no definitive causal link has been established between mobile phone use and adverse health outcomes, ongoing research and cautious usage remain essential to safeguarding public health.

Phased array antennas provide the ability to electronically steer a beam, eliminating the need for mechanical adjustments [1]. While traditionally used in military applications, there is growing interest in their adoption across various fields [1,2]. Conformal antennas, a type of phased array, are designed for installation on curved or non-flat surfaces, enabling focused radio wave radiation [1,2]. These antennas can be integrated into various applications, including aerospace, wearable technology, vehicles, and modern mobile devices [2], while also reducing traditional antenna height to support the integration and coexistence of multiple radio technologies within a compact area [1,2]. Planar arrays, composed of elements with phase shifters in a matrix, are compact and cost-effective due to mass production via printed circuit technology [1–3]. These antennas, when mounted on rigid surfaces, exhibit robustness, provide beam deflection in two planes, and offer high gain with rapid beam-switching capabilities [1,3]. However, planar antennas can experience interference between feed lines and elements, often supporting narrow bandwidths and exhibiting relatively low radiation efficiency [1,3]. Conformal antennas, which are easily mounted on curved surfaces, are particularly suited for wearable applications, spacesuits, and aerospace designs [1,2,4]. By minimizing connection length, they bring electronics closer to the antenna elements, reducing signal loss while enhancing transmission power and receiver sensitivity, especially at higher frequencies [4]. Research into 3Dprinted conformal antennas has emerged as a significant field of study [1,5]. This paper presents the mathematical analysis of both planar and conformal antennas, covering key parameters such as gain, bandwidth, radiation efficiency, and mutual coupling for planar arrays, as well as the width and length calculations for rectangular microstrip patch antennas used in conformal designs [2,6–8]. Furthermore, the role of additive manufacturing in antenna development is highlighted, emphasizing its ability to produce antennas with complex geometries thereby revolutionizing conformal antenna design [1,9].

The evolution of Internet Protocol Television (IPTV) has transformed the landscape of digital broadcasting by leveraging high-speed internet connectivity to deliver high-quality multimedia content. IPTV provides a dynamic and interactive television experience through managed networks, ensuring superior Quality of Service (QoS) compared to open-network Internet TV. This study explores the technical infrastructure of IPTV, including its network architecture, data compression techniques, and the role of protocols such as IGMP and RTSP. It also examines security challenges, including encryption, digital rights management (DRM), and authentication mechanisms that safeguard IPTV services from unauthorized access and piracy. Moreover, the paper analyzes the distinctions between IPTV and open-network Internet TV, highlighting their respective advantages and limitations in terms of service control, bandwidth optimization, and content security. The integration of artificial intelligence (AI) and machine learning (ML) in IPTV enhances personalized content recommendations and predictive analytics, leading to improved user engagement and efficient network management. Additionally, emerging technologies such as 5G and cloud-based IPTV services are explored for their potential to further revolutionize the industry. While IPTV presents a robust alternative to traditional broadcasting, challenges such as bandwidth constraints, cybersecurity threats, and regulatory compliance remain significant. The study concludes that IPTV's future success will depend on advancements in network infrastructure, AI-driven optimizations, and strategic regulatory adaptations. As IPTV continues to evolve, hybrid models integrating IPTV and open-network streaming services are expected to enhance content accessibility, security, and overall user experience.

This publication presents the first intellectual output (IO1), entitled "Transnational Information Literacy Ecosystem Mapping - TILEM" in the form of a book. IO1 coordinated by the partner UNIWA-Greece, concerned four (4) tasks, the actions and the results of which are detailed in the chapters of the book.

This publication presents the first intellectual output (IO1), entitled "Transnational Information Literacy Ecosystem Mapping - TILEM" in the form of a book. IO1 coordinated by the partner UNIWA-Greece, concerned four (4) tasks, the actions and the results of which are detailed in the chapters of the book.

This publication presents the first intellectual output (IO1), entitled "Transnational Information Literacy Ecosystem Mapping - TILEM" in the form of a book. IO1 coordinated by the partner UNIWA-Greece, concerned four (4) tasks, the actions and the results of which are detailed in the chapters of the book.