Authors - Daniel H. M. Marques, Luiz A. P. Silva, Andson M. Balieiro, Mohammed B. Alshawki, Alexandre J. R. Serres, Dalton C. G. Valadares Abstract - The evolution towards 5G and Beyond (5G/B5G) standardization, and even the development of 5G, can be accelerated by using accessible, high-fidelity emulation environments to validate emerging network architectures. For instance, Network Slicing is an aspect that can especially benefit from these tools. However, defining an emulation platform that is compatible with research objectives can be challenging. So, this work aims to compare two different Mobile Network emulation setups: one using Open5GS to emulate the Core Network and UERANSIM for the implementation of the Radio Access Network (RAN) and User Equipment (UE), and another using an OpenAirInterface (OAI) End-to- End implementation. Furthermore, this work aims to fill relevant gaps in the academic literature, addressing implementation obstacles at a granular level and the architectural trade-offs necessary to stabilize these environments. To that end, we identified and resolved operational friction points such as kernel-level GPRS Tunneling Protocol User Plane (GTP-U) conflicts, slice identity alignment, and Physical layer (PHY) timing sensitivities in virtualized radio frequency simulators through two deployment frameworks. Our analysis shows that, while the Open5GS/UERANSIM stack offers better agility for Core Network prototyping, OAI offers a more flexible and feature-rich framework, suitable for researching advanced RAN features, albeit with greater configuration complexity. By documenting troubleshooting protocols and architectural comparisons, this work serves as a practical guide for researchers migrating from 5G simulation to 5G/B5G-ready emulation test environments.
Authors - Aditya D. Nandgirwar, Deepika Burte, Rashmi Malvankar, Rupali Vairagade, Shwetambari Borade, Sandeep M. Chitalkar Abstract - Another significant development in intelligent system development is the notion of Agentic Artificial Intelligence (Agentic AI): as passively generative models continue becoming acts-oriented entities capable of perceiving their environment, thinking about goals, and planning and executing more complex tasks without necessarily involving humans. Unlike the traditional artificial intelligence systems, whose main input is the fixed input, agentic AI systems respond to objectives, are adaptive in their decision-making and are able to interact with other tools, environments and fellow agents. Thus agentic systems are increasingly being used in diverse technical disciplines, including software development and cybersecurity, healthcare, finances, robotics and enterprise automation. The paper provides a thorough description of agentic AI, its theoretical basis, architectural design components, implementation plans, security issues and applications. We take a look at intelligent agent development and explain fundamental concepts in the designing of intelligent agents such as the reasoning, the strategy to plan, the management of memory, and collaboration of intelligent agents. Moreover, we examine leading agentic AI systems and platforms that facilitates development and coordination of autonomous systems.
Authors - A.N. Gachahi, L.W. Gachahi Abstract - Antennas are fundamentally important in all modern wireless communication systems. Their design directly influences the performance, efficiency, and size of wireless devices. A core limitation of existing antenna designs is their reliance on spatial resonance where antenna size is proportional to a fraction of the wavelength of the signal they are intended to transmit or receive. Here, we present the feasibility of using single-layer capacitors (SLCs) as radiating elements for antenna applications. The SLC approach exploits displacement currents and the temporal resonance inherent to capacitive structures. A theoretical framework is established using Maxwell’s equations and circuit-level analysis to explain how electromagnetic fields arise around SLCs. An experimental setup using an array of 90 ceramic capacitors is constructed, and a magnetic field sensor is used to measure radiated fields across a range of frequencies. The antenna is then modeled in MATLAB, and simulations are performed to evaluate radiation patterns and impedance characteristics. Results confirm field generation consistent with theoretical predictions. Integration into ESP32-C3 Wi-Fi modules is demonstrated. Impedance mismatch is identified as a key limitation and addressed through a resistive matching network, improving signal consistency under varying distance conditions. The theoretical, experimental and simulation results in this set-up confirm that SLCs can serve as efficient, compact antenna elements, with practical implications for RF systems where size, cost, and integration constraints are critical.
Authors - PARVIZ FIRUDIN OQLU KAZIMI, KAZIM ASAD OQLU KAZIMLI Abstract - Objective: The targeted modeling of social information in global, regional, and local projects, and the use of information influence for both progressive and aggressive purposes, constitutes an activity in the field of information engineering. It is important to study the multifaceted and complex scientific and theoretical foundations of this activity and discuss them in a broad academic community. Theoretical Foundations: It is inappropriate to limit information engineering to technical, technological, and software issues; it is important to study the scientific, theoretical, and experimental aspects of information influence at various levels using modern technologies. Research Methods: This study presents considerations regarding methods for targeted information modeling in education, culture, and information-intensive fields. The proposed considerations are intended to standardize a number of processes, identify aggressive elements in some areas, and, in some cases, consider innovative information modeling. The proposed concept can be compared with a number of sociological analytical models. However, for the first time, it is proposed that the essence of information, the study of the aspects of thesauri influence, and the application of modern technologies will gain greater relevance. This study addresses the application of our theoretical work, known as "information
Authors - Vishakha Shinde, Himangi Pande Abstract - The rapid growth of digital communication platforms, cloud multimedia sharing and AI-driven visual systems has increased the demand for secure image ownership verification and multimedia authentication. Conventional watermarking approaches often suffer from limited robustness against compression, geometric distortion and adversarial attacks. Recent advances in deep learning and cryptographic protection mechanisms have enabled the development of intelligent hybrid watermarking frameworks with improved robustness, adaptive embedding and secure ownership verification. This paper presents a comprehensive review of secure image watermarking techniques integrating deep learning architectures and cryptographic security schemes. The study analyzes CNN-, autoencoder-, GAN- and diffusion-based watermarking frameworks along with encryption-assisted watermark embedding strategies, attack resilience mechanisms and lightweight watermarking systems for edge– IoT environments. Benchmark datasets, performance metrics, loss functions and quantitative comparisons of existing frameworks are also discussed. The comparative analysis indicates that hybrid deep learning–cryptographic watermarking methods significantly improve robustness, authentication reliability and resistance against signal-processing, geometric and adversarial attacks. However, computational complexity, scalability and real-time deployment constraints remain major challenges. Finally, the paper discusses future research
Authors - Meghali Kalyankar, Prashant Lahane Abstract - As deepfake generation technologies have rapidly advanced, establishing authenticity for multimedia content on digital platforms has become a major challenge[1]. Current deepfake detection approaches primarily rely on unimodal analysis and face challenges in encoding joint AV inconsistencies, temporal consistency, and adversarial attacks. To overcome these problems, a novel Multimodal Attention and Adversarial Deepfake Network (MMAD-Net) framework for robust audiovisual deepfake detection is proposed. The proposed solution adopts a framework that combines fine-grained visual feature extraction provided by VideoMAE v2 [2], audio representation learning provided by HuBERT [3] and temporal dependency modeling provided by TimeSformer [4] to model the fine-grained spatial and temporal inconsistency in manipulated media. In addition, a Multimodal CoAttention Transformer (MCAT) is used for better cross-modal interaction between audio and visual streams, and a hybrid HOA-COA optimization scheme optimizes discriminative feature representations to ensure better feature separation and remove redundancy. Adversarial Consistency Training (ACT) is embedded in the learning process to enhance adversarial robustness against adversarial perturbation and unseen adversarial manipulation. FakeAVCeleb and Celeb-DF are used for testing the proposed model with several performance metrics. Experimental results show that MMAD-Net can provide stable and general detection performance while maintaining a high level of robustness for current multimodal deepfake detection methods.
