Analytical Solution of Projectile Motion in Mid-Air with Quadratic Resistance Law Using Taylor Series Method
https://www.iosrjournals.org/iosr-jap/papers/Vol14-issue6/Ser-1/E1406012533.pdf
My research on “Analytical solution on projectile motion in mid-air with quadratic resistance law using Taylor series method” was published in IOSR Journal of Applied Physics under the guidance of my schoolteacher, Ashish John, D.Y. Patil International School.
I examined the intricacies of projectile motion in mid-air, predicting the trajectory of an object in flight, factoring in variables like initial velocity and launch angle, and incorporating the influence of the quadratic resistance law. I have acquired a fascination with calculus. This stems from its role in unlocking a deeper understanding of how things change and evolve. Obtaining an extensive understanding of calculus has been a personal revelation, as I applied it, to unravel projectile motion complexities in my research. The precision and versatility of calculus proved instrumental in decoding the complicated trajectories of objects in mid-air, especially when employing the powerful tool of Taylor Series. Calculus, with its emphasis on instantaneous rates of change and accumulation of quantities, laid the groundwork for dissecting the dynamics of mid-air motion. The Taylor Series, as a method for approximating functions, proved indispensable in handling the complexities introduced by quadratic resistance.
An Analysis of Fabric Defect Detection techniques for Textile Industry Quality Control
https://ieeexplore.ieee.org/document/10235154
My research paper “An Analysis of Fabric Defect Detection techniques for Textile Industry Quality Control” was published in IEEE journal. I presented the paper in World Conference Communication and Computing (WCOF) 2023 and was accepted for publication. The research was conducted under the guidance of Ms. Reetu Jain.
In the course of my research on detecting defects in fabric, titled Deftecht, I initiated the data collection process by acquiring images, followed by pre-processing steps. Subsequently, the images were cropped and inputted into a machine learning model for comprehensive training. Using the python package, OpenCV. I implemented a method where non-defective regions in each image were isolated through masking, leaving the remaining parts blackened. These processed images serve as training data for Mask R-CNN, enabling it to identify and classify fabric defects. Mask R-CNN employs a set of bounding boxes, and takes an image as input in order to generate pixel segmentation masks for each object. This facilitates the precise identification of pixels corresponding to each object, enhancing the model’s ability to recognise and categorize fabric defects accurately.
Optimizing the Parameters of Acoustic Metamaterials for Quantum Simulations.
RESEARCH SCIENCE INSTITUTE PROGRAM, MASSACHUSETTS INSTITUTE OF TECHNOLOGY CAMBRIDGE, MASSACHUSETTS, US
RESEARCH INTERN | HOFFMAN LAB
Mentor: Prof. Jennifer Hoffman
Optimized the sharpness of the resonance peaks in an acoustic metamaterial by varying air cavity dimensions.
Simulated coupling of acoustic waves in air cavities to mimic electron wave hopping through nuclei.
Generated amplitude vs. frequency graphs, minimizing the FWHM value to enhance electron wave simulation.
Created a 3D Kagome-lattice model with 198 air cavities for clearer acoustic band structure.
Employed COMSOL Multiphysics to simulate the acoustic band structure and analyze flat bands and dispersion curves.
Achieved accurate flat bands, contributing useful data for flat-band system research, though dispersion curve issues suggested possible errors.
Exploring Flat Band Physics in Acoustic Metamaterials
HARVARD UNIVERSITY | CAMBRIDGE, MASSACHUSETTS, USA
TEAM MEMBER | HOFFMAN LAB
Mentor: Prof. Jennifer Hoffman
Created a new multi-cavity modelling method that reduced sound energy loss and implemented it for the modelling of the Kagome-lattice of air cavities
Enhanced the quality factor of the sound transmitting through the acoustic metamaterial, yielding sharper resonance peaks than previous lab models.
Assisted in refining the extended-Kagome lattice, analyzing the properties of cavity networks in-depth, addressing sound amplitude discrepancies between transducers, and noting greater sound loss due to waveguides.
Smart Load Carriers through IoT Integration: Revolutionizing Aid for the Downtrodden, 4th International Symposium on Electrical, Electronics and Information Engineering, (ISEEIE) 2024 Conference, University of Leicester, UK
Peer-reviewed and under-publication in Springer Journal, Institute of Electrical and Electronics Engineers (IEEE) Journal
Pioneered a QR code-based tracking system for autonomous navigation and load identification.
Optimized dual motor control with L298 and PWM for smooth, bidirectional movement and torque management.
Engineered a QR code recognition algorithm and dynamic navigation system.
Formulated Python-based algorithms for object detection and route optimization using OpenCV, pyzbar and NumPy modules
Integrated ultrasonic and infrared sensors for navigation and collision avoidance.
Implemented wireless communication modules for remote monitoring and automated operation with pre-programmed routes.