Daily Archives: March 28, 2026

Authors: P.Sreesudha, CV.Kiranmaiee, P.Santhoshini, Nadia Shareen, Megha Chandana, Harini Vadla

Abstract: Real-time object detection and environmental awareness are essential components in assistive technologies for visually impaired individuals. Traditional mobility aids provide limited information about surrounding objects and their proximity, making independent navigation difficult in complex environments. In this work, an AI-based assistive vision system is proposed that integrates the YOLOv8 deep learning model for real-time object detection, distance estimation techniques for proximity awareness, and text-to-speech output for auditory feedback. The system captures input from a camera, detects and classifies multiple objects in the environment, estimates their distance from the user, and converts the detected object labels along with distance information into speech output. This enables visually impaired users to understand nearby obstacles and objects more effectively while moving in indoor and outdoor environments. The proposed approach offers a practical, low-cost, and efficient assistive solution by combining computer vision and artificial intelligence to enhance user safety, independence, and confidence. Experimental observations indicate that the system performs effectively for common object categories and provides meaningful audio guidance in real time

DOI: https://doi.org/10.5281/zenodo.19280231

 

Published by: vikaspatanker

Authors: Souvik Shil

Abstract: Rapid urbanization and land surface transformations significantly influence local thermal environments, leading to the intensification of Urban Heat Island (UHI) effects. This study analyses the spatio-temporal relationship between vegetation dynamics and Land Surface Temperature (LST) in English Bazar Municipality (EBM), West Bengal, over the period 2001–2025 using multi-temporal satellite data. The results indicate a consistent decline in vegetation cover, accompanied by a substantial increase in surface temperature. The mean LST increased from approximately 30.43°C in 2001 to 40.41°C in 2025, reflecting pronounced thermal intensification. A strong inverse relationship between NDVI and LST is observed, with low vegetation areas corresponding to higher temperatures. High-temperature zones have expanded notably in the central and eastern parts of the municipality, indicating the growth of UHI hotspots. The study demonstrates that vegetation loss and urban expansion are key drivers of rising surface temperature and UHI intensification, highlighting the need for climate-responsive urban planning and increased green cover to mitigate future thermal stress.

Published by: vikaspatanker

Authors: Siddhi Suryakant Shigwan, Charulata Manohar Talele, Prajakta Vilasrao Wankhade, Ms. A. P. Deshmukh

Abstract: This study focuses on the design and development of a digital platform that enhances the monitoring and management of female reproductive and overall health. The purpose of the study is to analyze how modern digital technologies and data-driven approaches can be utilized to develop an intelligent health tracking system that goes beyond traditional menstrual tracking applications. Conventional period tracking systems mainly record menstrual cycle dates and provide basic predictions for upcoming cycles. However, these systems often fail to consider the broader physiological, psychological, and lifestyle factors that influence women’s health.

DOI: https://doi.org/10.5281/zenodo.19279759

 

Published by: vikaspatanker

Authors: Ajay Sonawane, Pranav Babrekar, Aditya Pandagale, Himanshu Saindlya

Abstract: Brain tumours are life-threatening conditions that demand early and precise diagnosis to improve patient outcomes. While deep learning has significantly advanced automated medical imaging, conventional convolutional neural network (CNN) models often require large annotated datasets and intensive computation, limiting their applicability in clinical settings. In experiments, the quantum-augmented models achieved notable performance gains. The hybrid MobileNetV2 model achieved the highest validation accuracy of 95.79%, outperforming traditional CNN baselines while offering faster inference and reduced computational overhead. These results suggest that integrating quantum layers enhances feature representation and model robustness.

DOI: https://doi.org/10.5281/zenodo.19279172

Published by: vikaspatanker

Authors: R.L Aarthi, Dr. S. Selvi

Abstract: The Vedic Multiplier, derived from the ancient Urdhva-Tiryakbhyam sutra, provides an efficient and structured approach to perform high-speed multiplication, which is a fundamental operation in digital signal processing, image processing, embedded systems, and VLSI applications. Conventional multipliers such as array or Wallace tree multipliers, although accurate, often require large hardware resources and suffer from increased delay due to complex carry propagation paths, limiting their suitability for low-power and small-scale designs. In this project, a Vedic multiplier is designed and implemented in Verilog HDL, incorporating Ripple Carry Adder (RCA) optimization for the accumulation stage to reduce design complexity and ensure consistent performance. The design process covers the implementation of basic modules including AND, OR, Half Adder (HA), Full Adder (FA), and Ripple Carry Adder (RCA), which are then combined to form 2-bit and 4- bit Vedic multipliers. By leveraging the RCA for final addition, the architecture minimizes hardware overhead while maintaining reliable accuracy across test cases. Simulation and functional verification were carried out using industry-standard EDA tools, and results validate the correctness of multiplication operations for various inputs with low area utilization and moderate delay. The optimized Vedic multiplier demonstrates efficient trade-offs in terms of area and delay, establishing it as a simple yet effective solution for arithmetic-intensive applications in energy-constrained embedded systems and FPGA-based platforms, with scalability potential for higher bit-width multipliers. Furthermore, the simplicity of the RCA- based approach makes the proposed architecture highly adaptable for classroom learning, research, and prototyping environments where clarity and resource efficiency are essential. While advanced adders such as Carry Lookahead or Carry Save adders may provide lower propagation delay in large-scale multipliers, the Ripple Carry Adder offers a favourable balance of low power consumption, reduced complexity, and straightforward implementation, making it especially effective for small-to-medium bit-width operations. This highlights the practicality of the proposed design as a baseline for further optimization, with the potential to extend towards pipelined or parallel Vedic multiplier architectures suitable for real-time signal processing and embedded computing applications.

DOI: https://doi.org/10.5281/zenodo.19276670

Published by: vikaspatanker

Authors: Mr. Anne Murali Krishna, Dr. M. A. Rasheed, Dr. N.Y. Raju

Abstract: Healthcare and pharmaceutical organizations depend on robust and efficient supply chains to ensure continuous access to essential medicines and medical products. Recent global disruptions have exposed critical vulnerabilities in conventional supply chain structures, underscoring the urgent need for strategic transformation. This study investigates the influence of supply chain resilience, risk management practices, and collaborative strategies on operational efficiency within healthcare and pharmaceutical organizations. A quantitative research approach was employed, with primary data collected from 180 supply chain professionals; this sample also served as a pilot study to validate the research instrument. Reliability and validity were established through appropriate statistical tests. Data were analyzed using descriptive statistics, correlation analysis, and multiple regression techniques. The findings reveal that resilience-focused supply chain strategies have a significant positive impact on operational efficiency and overall performance. The study offers empirical evidence and practical insights to support the development of more resilient healthcare and pharmaceutical supply chains in increasingly uncertain and dynamic environments.

DOI: https://doi.org/10.5281/zenodo.19276170

Published by: vikaspatanker

Authors: Dr.M. Lalithamigai, Harshini S, Srinithi A

Abstract: An AI studies team has taken artificial intelligence as a way for robots to enter into a new realm of technology in which they are no longer programmed with hard rules and can be adaptive, learn based on their surroundings, and therefore have the ability to evolve through their experiences. Robots that are being created using AI technology will provide robots with the ability to learn via machine learning (ML), computer vision, fusing sensor data through sensor fusion algorithms, and making decisions using algorithms suitable for the individual use cases. The work done by researching teams in these areas has been explored in this publication, including how this technology has changed and improved due to AI, as well as how it has changed the way we think of Robots and how they can manage tasks without requiring human input. The main body of research focuses on how Autonomy in Artificial Intelligence will change multiple industries, which include but are not limited to—(i.e.) Healthcare & Medical, Manufacturing, Transportation, Space Exploration, etc.

DOI: https://doi.org/10.5281/zenodo.19275480

 

Published by: vikaspatanker