The design and analysis of a dual-band hexagonal microstrip patch antenna loaded with an E-shaped slot and a Hexagonal Defected Ground Structure (DGS) is proposed. The antenna is realized on a low-cost FR-4 epoxy substrate with a dielectric constant of 4.4 and a thickness of 1.6 mm. The radiating element comprises a hexagonal patch featuring modified edges and a central E-shaped slot. To implement the DGS, a hexagonal slot with a circumradius of 12.75 mm is introduced in the ground plane. Simulation results obtained using Ansys HFSS validate the antenna's operation at resonant frequencies of 4.025 GHz and 6.65 GHz, with bandwidths of 0.883 GHz (3.52–4.41 GHz) and 0.519 GHz (6.44–6.94 GHz), respectively. The proposed antenna reaches a maximum gain of 5.04 dBi at 6.65 GHz. Hence, the design is applicable to the current requirements of wireless communication.

Keywords : Hexagonal Microstrip Patch Antenna, E-Shaped Slot, DGS, and Dual-Band.

Authors : Ramana Reddy

Title : Double Band E-Shaped Slotted Microstrip Hexagonal Patch Antenna

Volume/Issue : 2026;8(2 (March - April))

Page No : 30 - 35

To improve the efficiency of outsourced storage systems, secure deduplication mechanisms have been introduced. Among these, AES-based encryption is widely used, where packet-level messages are encrypted using a key derived from the message itself. This enables the detection of duplicate data, since identical plaintexts generate identical ciphertexts under the same deterministic setting. AES also supports combined encryption functionality and provides strong security assurances. Cloud computing has emerged as a practical solution for storing and sharing massive volumes of data over networks. Although several approaches have been proposed to ensure data security in distributed storage environments, many of them are closely tied to ciphertext-based techniques. Therefore, this paper presents a cloud-based data collection, sharing, and controlled dissemination scheme designed to preserve multi-owner privacy. In the proposed model, the data owner can securely share sensitive information with a group of authorized users through the cloud.

Keywords : Cross-modal generation, ASR, vision-language understanding, TTS, low latency, Privacy.

Authors : P. Srinivasa Rao

Title : Scalable Deduplication for Privacy-Preserving Media Sharing in Mobile Cloud Environments

Volume/Issue : 2026;8(2 (March - April))

Page No : 21 - 29

We propose a unified multimodal framework, Universal Multi-Modal Generation Enabling Any-to-Any Transformation, that enables seamless transformation between text, audio, and image inputs and outputs. The system integrates three core capabilities: speech understanding using Whisper, visual understanding through LLaVA, and speech synthesis via PyTorch-based text-to-speech models. All modules are deployed on-premise using Docker, providing a privacy-centric execution environment and reducing operational overhead associated with cloud processing. The framework supports advanced workflows including document/PDF-to-text extraction, text-to-speech conversion, and image-driven description generation, thereby enabling accessible and interactive multimodal content pipelines. The implementation emphasizes efficient orchestration and inference to meet real-time constraints. Experimental results across multiple cross-modal tasks demonstrate robust accuracy and consistently low latency, suggesting that local, containerized multimodal systems can deliver scalable performance for practical applications. The proposed approach is particularly relevant to accessibility, education, and content creation, where rapid modality conversion and data privacy are essential.

Keywords : Cross-Modal Generation, ASR, Vision-Language Understanding, TTS, Low Latency, Privacy

Authors : Ramakrishna Kolikipogu

Title : A Privacy-Preserving Universal Multimodal Framework for Real-Time Any-to-Any Transformation

Volume/Issue : 2026;8(2 (March - April))

Page No : 21 - 24

Academic institutions have become prominent targets for evolving cyber threats, including ransom ware, credential theft, data manipulation attacks, and intellectual-property exploitation. Traditional centralized security infrastructures used in universities are increasingly inadequate due to distributed learning environments, large user populations, and heterogeneous digital ecosystems. This paper proposes a block chain-enabled cyber security framework designed to enhance trust, transparency, and security in academic networks. A permissioned block chain model is implemented to secure academic credentials, research data, and access-control logs. Experimental evaluation using hyper ledger Fabric demonstrates improvements in integrity assurance, tamper resistance, and detection of unauthorized record modification. Comparative performance testing with conventional database systems shows enhanced traceability and reduced vulnerability to manipulation. The results indicate that block chain technologies can effectively strengthen cyber Defense mechanisms in academic environments and provide a trustworthy foundation for secure digital academic ecosystems.

Keywords : Block Chain, Cyber Security, DLT , Ransom ware Defense, Hyper ledger Fabric, Zero-Trust Architecture.

Authors : YEGIREDDI RAMESH

Title : Blockchain for Cybersecurity : Strengthening Trust, Transparency, and Threat Resilience

Volume/Issue : 2026;8(2 (March - April))

Page No : 10 - 20

In the state of affairs of industrialization, requirements of the international strength crisis, including environmental pollution and smart grid connectivity for power networks continue enhancement to deeply built-in to shape network the Power Internet using Artificial Intelligence. Hence, it is wished aimed at power systems, proceedings technology, AI, and science along with research. Because of this, this research paper is an intelligent way to analyze synthetic wise methods and their software in strength network communications. Initially, it recommends based on AI’s creating process automatically connect core technologies based on working importance and later enchanting the concerned software of Neural Network community technological know-how in inspecting and predicting wind power, based on the situation factors out that AI strategies can be utilized to strengthen an internet lookup on communicate too frequently asset device scheduling and the personal aspect of the electrical energy market. To conclude, it outcomes in the future improvement of the power Internet connectivity and decreased troubles of unexpected situations have occurred during the working environment that resolves an Artificial Intelligence technological know-how justifies interest in the strength Internet.

Keywords : Smart Energy, Global Energy Crisis, Internet, Energy Internet, AI, ANN, Machine Learning.

Authors : T Murali Mohan

Title : Internet Enhanced Smart Energy Network Efficiency using Artificial Intelligence

Volume/Issue : 2026;8(2 (March - April))

Page No : 1 - 9