Category Archives: Uncategorized

Authors: Santhosh Babu K C, Chirakshitha S, Eashanya K R, Eashanya K R, Ganavi A S, Gowthami G

Abstract: Efficient digital filtering is critical for modern signal processing applications. This work presents an Adaptive Distributed Arithmetic (ADA)-based FIR filter designed for single-channel and scalable multi-channel configurations on FPGA. The proposed design incorporates error- controlled approximation and optimized computation to reduce LUT usage, power consumption, and processing delay. Implementation using the Xilinx Vivado environment demonstrates improved area efficiency and speed while maintaining acceptable signal quality. The results indicate that the ADA approach is well- suited for low-power, high-throughput FPGA-based DSP applications.

DOI:

Published by: vikaspatanker

Authors: Veena Vanamane, S Zuha Afsheen, Thrisha M, Preksha Malipatil, Chirasvi S N

Abstract: In many industrial applications, maintaining a steady water level in tank systems is crucial. The design and analysis of proportional (P), proportional-integral (PI), and proportional-integral-derivative (PID) controllers for a single-tank water level control system are presented in this study. MATLAB/Simulink is used to create and construct a mathematical simulation model. Rise time, settling time, overshoot, and steady-state error are used to assess the controllers. The P controller has steady-state error, the PI controller decreases error but increases overshoot, and the PID controller offers ideal performance with better stability and quicker reaction, making it appropriate for efficient water level control, according to the results.

Published by: vikaspatanker

Authors: Dr. A Senthilkumar, Dr. Tintu George, Dr. Ginne M James

Abstract: Achromatopsia is a rare inherited retinal disorder characterized by the absence of cone photoreceptor function, resulting in color blindness, photophobia, nystagmus, and reduced visual acuity. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have transformed ophthalmic diagnostics and opened new avenues for early detection and treatment planning. This review paper presents a comprehensive analysis of achromatopsia, focusing on its clinical features, genetic basis, diagnostic approaches and therapeutic developments, with a strong emphasis on AI-driven methodologies. The paper also explores the integration of AI in retinal imaging, genotype–phenotype correlation, and gene therapy optimization. Finally, challenges, limitations and future research directions are discussed.

DOI:

Published by: vikaspatanker

Authors: N. Santosh Kumar, U. Reshma, V. Sandhya, S. Santhi vardhan

Abstract: This project presents an innovative Battery Management System (BMS) for electric vehicles, leveraging Arduino UNO to ensure optimal battery performance, safety, and longevity. The system is designed to monitor critical parameters such as voltage, current, temperature, and state of charge (SOC), while also assessing the state of health (SOH) of the battery. The BMS supports both fast and slow charging modes, intelligently managing charging processes to prevent overcharging and thermal runaway. User-friendly interfaces, including real-time data displays, offer intuitive insights into battery status, empowering users with actionable information. This project combines cost-effective hardware with comprehensive safety and performance monitoring, making it a significant step toward safer and more efficient electric vehicle batteries. Real-time SOC/SOH evaluation, and versatile charging management, which collectively advance the capabilities of conventional battery management solutions.

Published by: vikaspatanker

Authors: N.Santosh Kumar, V.Kavya, M.Harshitha, P.SudheerKumar, V.Karthik

Abstract: Train collisions are one of the critical safety concerns in railways, which could result from human error, signal failure, communication delay, or reduced line of sight owing to complex terrain. This paper proposes a low-cost intelligent TCPS designed using ESP32 microcontrollers, ultrasonic sensors, ESP-NOW wireless communication, and an automatic servo-based braking mechanism. The trackside unit constantly monitors the movement of the train through two ultrasonic sensors and calculates real-time distances for potential head-on collision detection. When the system identifies a threat, it issues an instantaneous emergency braking signal to the onboard units, which trigger the servo-driven brake assembly. Further, the proposed system is integrated with regenerative braking to recover the kinetic energy for recharging the lithium-ion battery supply present in the system. Experimental testing on a prototype railway track has shown high detection accuracy, quick wireless communication, and reliable automatic braking. The proposed system gives a scalable, modular, and energy-efficient alternative to conventional railway safety mechanisms that can be integrated with state-of-the-art signalling and AI-based prediction in future applications.

Published by: vikaspatanker

Authors: Riya Srivastava, Dr. Shilpi Aggarwal

Abstract: This study delves into the diverse perceptions of mental health, with a particular focus onSchizophrenia, across different generational cohorts. By examining how perceptions have evolved over decades, from the "Gen Z" cohort to older generations, this research aims to broaden our understanding of the disorder and its impact on individuals and society. The study encompasses an extensive analysis of Schizophrenia, covering its historical evolution, contemporary awareness, and societal attitudes. Through a comparative lens, it investigates how perceptions of Schizophrenia and the resulting social distance vary among individuals of diverse ages. Employing a mixed-methods approach, primarily utilizing an online survey, this research captures a comprehensive picture of mental health literacy and stigma across these generations. This work contributes valuable, actionable data to the field of mental health advocacy and education. Ultimately, it advocates for a more inclusive and supportive society, where mental health is understood, accepted, and supported across all generations.

DOI: https://doi.org/10.5281/zenodo.19728938

Published by: vikaspatanker

Authors: Yaminideavi A, Elakkiya N S

Abstract: The rapid expansion of urban populations has significantly intensified waste generation, straining the efficiency of traditional collection methods that rely on static, fixed schedules. Such conventional systems often result in overflowing bins, inefficient collection routes, and escalated operational costs. This paper proposes a comprehensive Long Range Wide Area Network (LoRaWAN) infrastructure designed to modernize Smart City waste management. Unlike existing single-task architectures, the proposed framework integrates a multi-tiered hierarchy of LoRaWAN device classes to manage services of varying complexity. At the foundational level, smart bins utilize ultrasonic sensors and low-power microcontrollers to monitor fill levels and environmental conditions. Higher-level smart drop-off containers facilitate user interaction and support asynchronous downlink queries for real-time data exchange. Data is transmitted via LoRa gateways to a centralized cloud-based dashboard, enabling municipal authorities to monitor bin status and dynamically optimize collection routes. Experimental results suggest that this scalable, energy-efficient IoT paradigm not only prevents bin overflow through automated threshold alerts but also reduces fuel consumption and environmental impact. The integration of diverse LoRaWAN node classes provides a robust, cost-effective solution for real-time urban process control within the Smart City ecosystem.

Published by: vikaspatanker

Authors: Dr. Dhanalakshmi S, B. Sasi Prabha

Abstract: In an era where financial transactions are increasingly digital, the threat of cyber fraud has become a growing concern for both institutions and individuals. With every swipe, click, or transfer, there's a risk that sensitive data could be exploited by attackers using sophisticated techniques. As fraudsters become smarter, our defenses must evolve too. This chapter presents a practical approach to fraud detection using MATLAB, focusing on a simple, transparent, and explainable rule-based system. Rather than relying on complex machine learning models that can act as "black boxes," this method uses intuitive rules based on transaction amount, time, and location to flag potentially fraudulent activity. The system is built with ease of implementation in mind, making it ideal for financial institutions looking for an interpretable starting point or a lightweight solution for early warning detection. The model is demonstrated on simulated transaction data, and its results are visualized clearly to show the difference between normal and suspicious behavior. By the end of this chapter, readers will not only understand how to build a basic fraud detection system in MATLAB, but also appreciate the importance of balancing technical rigor with real-world usability in cybersecurity efforts.

DOI: https://doi.org/10.5281/zenodo.19718898

Published by: vikaspatanker

Authors: Kriti.R. Shukla

Abstract: As of 2026, the biotechnology sector has undergone a fundamental paradigm shift from a traditional "wet-lab first" experimental model to an "in silico first" computational framework. This evolution is driven by the maturation of generative artificial intelligence (AI), geometric deep learning, and multi-modal foundational models. This article explores the current state of AI in biotechnology, focusing on protein engineering, generative chemistry, genomic interpretation, and bioprocess optimization. We examine how the integration of Large Language Models (LLMs) and diffusion-based generative models has accelerated the drug discovery pipeline, reduced R&D costs, and enabled the design of de novo biological systems with unprecedented precision.

Published by: vikaspatanker

Authors: Ms.S.Hari Priya, Akalya M, Anupriya S, Bala.G, Dhanusurya.S

Abstract: With the rapid growth of software development, platforms like GitHub have become essential for code sharing and collaboration. However, many developers, especially students and beginners, often upload code without proper security checks, leading to vulnerabilities such as hardcoded credentials, exposed API keys, and insecure coding practices. This project presents an AI-Based GitHub Security Scanner designed to automatically analyze repositories and identify potential security risks. The system integrates with GitHub to scan source code using a combination of static code analysis and AI-driven techniques. It detects common vulnerabilities, misconfigurations, and sensitive data exposure in real time. The AI component enhances detection accuracy by learning patterns from known security issues and suggesting improvements to developers. Additionally, the tool provides detailed reports and recommendations, helping users understand and fix vulnerabilities effectively. By automating security analysis, this project aims to improve coding practices, reduce risks, and promote secure software development. Overall, the proposed system offers a scalable and intelligent solution for early detection of security flaws in GitHub repositories, making it especially useful for students, developers, and organizations.

DOI: https://doi.org/10.5281/zenodo.19755384

Published by: vikaspatanker