Category Archives: Uncategorized

Authors: Ashok Kumar Kanagala

Abstract: The proliferation of AI-native architectures has introduced autonomous, model-driven systems with unprecedented capabilities and complex security challenges. These systems, often deployed across multi-agent pipelines and edge environments, expand the attack surface and exhibit dynamic, unpredictable behaviors that traditional security frameworks fail to address. Despite emerging research on AI robustness and alignment, comprehensive strategies for proactively securing agentic AI remain underdeveloped. This paper investigates the operationalization of Zero Trust principles in AI-native architectures, aiming to provide a forward-looking framework for resilient and accountable systems. The proposed approach integrates continuous model verification, alignment assurance with transparency tooling, lifecycle-integrated security validation, and autonomous red-teaming to proactively identify and mitigate vulnerabilities. Key findings indicate that embedding self-assessing mechanisms, standardizing behavioral benchmarks, and applying cross-layer defenses significantly enhance system resilience and reduce dependency on reactive interventions. This research contributes a structured methodology for securing autonomous AI, advancing both practical and theoretical understanding of AI-native security in complex, adaptive environments.

DOI: http://doi.org/10.5281/zenodo.18137101

Published by: vikaspatanker

Authors: Mrs.S. Dhivya,, Kanika N, Kavipriya A, Madhumitha S, Preethi S

Abstract: This This report presents the project titled “Donar Hub,” a web-based platform developed to connect individuals in need with donors and volunteers through a structured and accessible system. The platform enables the sharing of essential resources such as food, clothes, books, and medical assistance. Users can create Request Help and Offer Help posts, which are categorized and searchable to ensure efficient matching of needs and available support. Donar Hub aims to reduce resource wastage while promoting social responsibility and community collaboration. To ensure authenticity and prevent misuse, the system incorporates Aadhaar-based identity verification for users. Requests related to medical assistance require verification of medical reports or hospital-issued documents before approval. This validation mechanism enhances trust, transparency, and security within the platform. The application is designed with intuitive forms and a user-friendly interface, making it accessible to users with varying levels of digital literacy.

Published by: vikaspatanker

Authors: Shaun Paul Moses, Vignesh. S

Abstract: Another emerging danger in the world of cybersecurity is the term steganography, which means concealing secret data in digital form, because concealed messages can be easily transferred to a different information exchange format. Other modalities such as audio steganography possess unique features that make it difficult to detect such signals, such as the temporal-frequency properties and audio signals are high dimensional. This project offers a DLDA, Deep Learning Based Detection System Stegware in Audio Files, that will inform whether the audio sample is a real cover or it is a stegware, i.e. it has embedded data in it. The system employs improved methods of feature extraction like Spectrogram Analysis and Mel-Frequency Cepstral Coefficients (MFCCs) to identify requisite frequency, amplitude and temporal indications to identify stegmodifications. The CNNs and LSTMs process subsequently learn a discriminative feature (CNNs) and temporal patterns (LSTM) that occurs between normal and manipulated audio. Training and testing are done using a dataset of clean audiofiles and audiofiles with various modifications done using steganography. The performance is measured by the accuracy, precision, recall and F1-score and the system has been found to be very reliable with accuracy of 97.8 and very few false detections. In the experimental results, it is seen that the model works fairly well when noise and compression is introduced, indicating its strength in the real world. Overall, the framework that is created due to the research effectively applies deep learning to offer a scalable, automated and accurate method of audio steganalysis, which is an outstanding achievement that can provide cybersecurity, digital forensics and secure communications as the number of illegal data transmission via audio channels decreases.

Published by: vikaspatanker

Authors: K. Nagarathna, Mallikarjun Aralimard

Abstract: This paper presents the design and implementation of a simple Convolutional Neural Network (CNN) using Tensor Flow for binary image classification. The proposed model classifies 5×5 pixel images into two categories: images containing the pattern of an 'X' and images that do not. The study demonstrates dataset generation, model architecture, training, and evaluation, highlighting the effectiveness of CNNs for pattern recognition tasks.

DOI: http://doi.org/10.5281/zenodo.18129745

Published by: vikaspatanker

Authors: Dr. Osman Zenk

Abstract: As is known, memcapacitors, which are memory elements, are electrical circuit elements ex-pected to revolutionize various research and many engineering fields thanks to their variable capacitance and non-volatile memory effects. Although there is not yet enough scientific research on memcapacitors in power electronics systems, they have serious scientific discovery potential, especially in terms of reducing output voltage ripple, increasing voltage stability, and improving energy efficiency. In this study, theoretical and simulation results performed in the Matlab/Simulink environment are presented, showing that adding a memcapacitor to the output of a commonly used buck-boost converter, a type of DC-DC converter, significantly reduces the output voltage ripple. In the study, a memcapacitor emulator that can be implemented using commercial components is first proposed and validated. Then, this emulator was used to exam-ine the steady-state output voltage ripple and transient response of the buck-boost converter. The results show that the use of a load-controlled memcapacitor can reduce the output voltage ripple by up to 96%.

DOI: https://doi.org/10.5281/zenodo.18127037

Published by: vikaspatanker

Authors: Maram Mounika, Rakshitha L, Ramya R, Ramya Shree D, Dr Manasa P

Abstract: Inertial Navigation Systems (INS) are mission- critical subsystems in naval platforms, providing continuous nav- igation information independent of Global Navigation Satellite Systems (GNSS). In contested or GNSS-denied environments, the reliability of INS data directly impacts navigation accuracy, weapon control, and overall mission effectiveness. This paper presents the design and implementation of a real-time INS monitoring platform developed for Indian Naval shipboard appli- cations. The proposed system acquires high-speed navigation data from dual Ring Laser Gyro (RLG) based INS units operating at 10 Hz and 100 Hz through RS-422 interfaces. Using industrial- grade USB-to-serial hardware and a LabVIEW-based software framework, the system performs real-time data acquisition, frame validation, parameter extraction, visualization, logging, and replay. Experimental results demonstrate reliable, lossless data capture and synchronized monitoring of heading, roll, and pitch from forward and aft INS units, validating the effectiveness of the platform for onboard monitoring, testing, and post-mission analysis.

Published by: vikaspatanker

Authors: Girish Kishor Ingavale

Abstract: The exponential growth in computational power and industrial processes has led to an increased demand for efficient cooling solutions in data centers. Traditional air-cooling systems are becoming inadequate due to their limitations in managing high thermal loads and their high energy consumption. In response to these challenges, Direct-to-Chip Liquid Cooling Systems (D2C LCS) have emerged as a promising alternative for thermal management in high-density computing environments. This study investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) technologies to optimize the performance of D2C LCS in rack-mounted data center setups. The primary objective of this research is to develop and implement AI-driven models that can predict temperature and fluid flow within D2C LCS, thereby enabling the optimization of cooling strategies. By leveraging advanced algorithms such as Linear Regression and Support Vector Machine, the study aims to enhance thermal efficiency and reduce the energy consumption of data centers. Experimental data was collected from a simulated data center environment equipped with D2C LCS. The data was used to train and validate ML models, ensuring their accuracy and reliability in real-world applications. The results demonstrate that AI-optimized cooling strategies can achieve a 15% reduction in temperature and a 20% decrease in energy consumption compared to traditional air-cooling systems. The findings of this study highlight the significant benefits of integrating AI and ML technologies with D2C LCS for thermal management in data centers. The predictive models and optimized cooling strategies presented herein provide a robust framework for improving the efficiency and sustainability of data center operations. Future research directions include the development of more advanced AI models and the implementation of real-time monitoring systems to further enhance the performance of D2C LCS.

DOI: http://doi.org/10.5281/zenodo.18125704

Published by: vikaspatanker

Authors: Dr. Chetana Reddy, Divya K, Madhumitha B, Maithra K, Melisha K Sunny

Abstract: Power transformers must work well and be reliable to keep the flow of electricity stable and uninterrupted. Standard periodic maintenance often does not find problems in their early stages, which can lead to insulation breakdown, oil breakdown, thermal stress, overloading, and unexpected outages. This paper proposes an IoT-based Transformer Health Monitoring System to address these limitations. The system can continuously and in real time monitor important operating parameters. The system uses sensors for temperature, oil level, load current, and input voltage that are connected to a microcontroller. The microcontroller processes the data and sends it to a cloud-based monitoring platform. The data analyzed by the IOT platform ensures early fault detection for maintenance planning. To support predictive maintenance, the suggested framework provides threshold-based alert notifications, historical logging, real-time data visualization, and remote access. The system creates automated alerts to stop overheating, insulation failure, and possible transformer failures when anomalous conditions are identified. This system monitors multiple transformers at different distributed substation. This IoT- enabled strategy prolongs transformer lifespan, lowers maintenance costs, minimizes downtime, and improves operational safety. The solution offers a scalable architecture for intelligent monitoring across substations and distribution networks and is in line with efforts to modernize smart grids.

Published by: vikaspatanker

Authors: Abhishek Rawat, Basant Agarwal

Abstract: In recent years, extensive research has been conducted on vibrations in air-craft. Vibration can cause some serious failure in the structure. The increase in disquisition in vibration has led to taking design considerations in the bod-ies. This paper specifically focuses on the vibration in bodies causing defor-mation in aircraft wing. Modal analysis of two different wing is done using Fi-nite Element Analysis. Three different material namely: Aluminium Alloy, Copper Alloy and Titanium Alloy have been incorporated to describe the ef-fects of free vibration on different wings.

Published by: vikaspatanker

Authors: Pranav Dhondibhau Gawade, Sarthak Vivek Sagare, Sujan Anna Kambale, Piyush Vinod Chaudhary, Neelam N Kavale

Abstract: The proposed Smart Community Health Monitoring and Early Warning System for rural areas offers a transformative, cost-effective alternative to expensive, sensor-dependent technologies by prioritizing syndromic surveillance and community-led data collection. Recognizing that traditional IoT infrastructure often fails in remote regions due to high maintenance costs and power instability, this model empowers community health workers to act as "human sensors," manually reporting clinical symptoms like fever and diarrhea via an offline-capable mobile interface. By integrating these health reports with periodic, low-cost chemical water testing, the system utilizes a centralized analytical engine to run statistical aberration detection algorithms that compare real-time trends against historical baselines. This proactive framework identifies potential pathogenic outbreaks at their nascent stage, triggering a tiered Early Warning System (EWS) that alerts local authorities through automated SMS and voice calls. Ultimately, this research demonstrates that public health resilience is not solely dependent on high-tech hardware but can be achieved through strategic data management, community participation, and smart analytics, providing a scalable and sustainable blueprint for disease prevention in resource-constrained environments globally.

Published by: vikaspatanker