Machine learning assisted Optical-SAR Radar for Target Classification
A new era of Synthetic Aperture Radar (SAR) has begun in recent years. Convolutional neural networks (CNNs) have garnered a lot of interest lately for their ability to analyze SAR data. This paper thoroughly studies the main subfields of SAR data analysis that CNNs have addressed, including segmentation, change detection, object identification, automatic target recognition, land use and land cover classification, and image denoising. Particular attention has been paid to useful methods like transfer learning and data augmentation. To overcome the issues of a high false alarm rate and the challenges of attaining high-performance detection using traditional approaches, a deep learning-based SAR target identification and classification method is proposed for target detection tasks in complex backdrops. An optical based approach is presented in this study for enhancing the security feature. A machine learning-based radar with better performance is suggested in light of the deep learning-based target models' problems with a high parameter count and memory usage. Even though there have been some significant advancements, deep learning research in radar is still mainly being tested in lab settings and is still in its theoretical stage. There are still a number of obstacles and potential restrictions in the application, including issues with dataset adequacy, robustness of the model, and electromagnetic modelling fidelity. Nonetheless, it is undeniable that deep learning technology will significantly advance radar. As a result, it is wise to recognize the field's current difficulties and potential future paths. Furthermore, it is hoped that this review will give readers fresh opportunities to investigate appropriate deep learning-based methods for radar applications. In this study, Long Short-Term Memory (LSTM) and SqueezeNet model are used for enhancing the accuracy of system designed.
Published by: Priyanka Shukla, Priti Singh, Ashutosh Dubey, Kritika Upadhyay, Pranshu Upadhyay
Author: Priyanka Shukla
Paper ID: V11I2-1454
Paper Status: published
Published: May 11, 2025
High-Frequency Capacitive Wireless Charging Using a 4-Plate Structure
Electric vehicles (EVs) are traditionally charged in a stationary position using wireless power transfer (WPT) systems. However, dynamic charging—where EVs are charged while in motion—offers a more flexible and efficient alternative. Most current dynamic charging systems are based on Inductive Power Transfer (IPT), which, despite its maturity, is limited by high costs and considerable eddy current losses associated with inductive coils. To address these limitations, this study proposes a high-power dynamic charging system utilizing Capacitive Power Transfer (CPT) for electric vehicle applications. The research focuses on the design and implementation of a capacitive-coupled WPT system, particularly emphasizing the significance of mutual capacitance in the coupler design. Mutual capacitance directly influences the power transfer capability and overall efficiency of the system. By exploring various coupler configurations and optimizing the capacitive plate design, the study aims to enhance the practicality and cost-effectiveness of dynamic EV charging through CPT technology.
Published by: Dr. Muthukannan S, Ananya H R, Deekshitha A V, Jeevitha N U, Ruchitha K C
Author: Dr. Muthukannan S
Paper ID: V11I2-1444
Paper Status: published
Published: May 11, 2025
Movies Recommendations System
Each of us needs entertainment to recharge our spirits and energy in this fast-paced world. Our confidence for work is restored by entertainment, and we work more ardently as a result. We can watch our favorite movies or listen to our favorite music to reenergize ourselves. Since finding chosen movies will take more and more time, which one cannot afford to waste, we can use more reliable movie recommendation algorithms to watch good movies online. In this paper, a hybrid approach that combines content-based filtering, collaborative filtering, using Support Vector Machine as a classifier, and genetic algorithm is presented in the proposed methodology. Comparative results are shown, showing that the proposed approach shows an improvement in the accuracy, quality, and scalability of the movie recommendation system than the pure approaches in three areas: accuracy, quality, and scalability. The advantages of both approaches are combined in a hybrid strategy, which also seeks to minimize their negative aspect
Published by: Kamakshi Bhardwaj
Author: Kamakshi Bhardwaj
Paper ID: V11I2-1422
Paper Status: published
Published: May 10, 2025
Design and Development of an Automated Hydroponics System Based on IoT with Data Logging
Integrating the Internet of Things (IoT) with hydroponic farming offers a powerful solution to the limitations of traditional hydroponic systems by introducing automation, real-time monitoring, and data-driven decision-making. Sensors continuously track environmental parameters, triggering automated adjustments to maintain optimal conditions. Data logging and analysis enable farmers to identify patterns, predict issues, and refine growing strategies. Remote access and control enhance scalability and reduce labor. This fusion aligns with precision agriculture principles, minimizing resource waste and promoting sustainable practices. The potential extends to large-scale commercial operations, where AI and machine learning can further optimize productivity. Ultimately, IoT-based hydroponics represents a significant advancement towards a more efficient, sustainable, and resilient agricultural future.
Published by: Manisha S, Karisni K, Gokul Raj V, Josphin M Sajith, Haritha M
Author: Manisha S
Paper ID: V11I2-1275
Paper Status: published
Published: May 10, 2025
Springless Suspension Using Bevel Gears
This paper presents an innovative approach to vehicle suspension by replacing conventional coil spring mech- anisms with a springless system based on bevel gears. Traditional suspension systems, while effective, often suffer from drawbacks such as component fatigue, frequent maintenance, and suboptimal energy efficiency, especially under rugged terrain conditions. To address these limitations, the proposed suspension design in- troduces a compact and mechanically simplified set-up comprising bevel gears positioned at right angles, a rotating shaft for torque transmission, and independent 12V DC motors for each wheel. This arrangement not only improves power transmission efficiency but also enables smoother and more responsive terrain adaptabil- ity, offering better control and improved ride comfort. The study includes a detailed system design, component selection, working principles, and analytical calculations to validate the feasibility of the concept. Simulations and prototype tests were carried out to assess suspension performance compared to traditional spring-based configurations. Key performance indicators such as vibration damping, mechanical wear, and torque distri- bution were analyzed to determine the practical advantages of the gear-based system. The results indicate a notable reduction in maintenance needs and mechanical complexity, along with increased durability under variable load conditions. Designed primarily for off-road and military grade applications, this suspension sys- tem demonstrates significant promise in terms of robustness, reliability and efficiency. In addition, the paper outlines the potential for future improvements, including noise reduction, long-term material durability, and the integration of intelligent feedback systems. In general, this study contributes to a forward-thinking solu- tion to the field of automotive engineering by introducing a low-maintenance high-performance alternative to traditional suspension designs.
Published by: Aditya Pol, Prof. Nikhil VS, Yash T Raut, Ninad Shinde
Author: Aditya Pol
Paper ID: V11I2-1436
Paper Status: published
Published: May 9, 2025
Multiverse Theories and Predictions
This thorough review explores multiple present-day multiverse theories along with their special theoretical underpinnings and different empirical predictions, in addition to multiple potential implications for basic physics. We assess how well Boltzmann, Quantum, and M-theory frameworks, which are multiple multiverse models, explain cosmological fine-tuning, vacuum state dynamics, and quantum decoherence. The paper combines several recent improvements in observational cosmology, high-energy particle physics, and quantum gravity. This combination is done to evaluate the evidentiary support for multiverse hypotheses. Possible observational signs and the methodological issues involved in assessing theories about areas beyond immediate empirical access are given consideration.
Published by: Shreesh Ghosh
Author: Shreesh Ghosh
Paper ID: V11I2-1432
Paper Status: published
Published: May 9, 2025
