http://hdl.handle.net/11189/1911 2026-08-12T10:05:05Z 2026-08-12T10:05:05Z Gupta, Gunjan Mishra, Vikash Kumar Pragya, Nidhi http://hdl.handle.net/11189/10637 2026-07-29T20:53:25Z 2025-01-01T00:00:00Z

Title: A study on remote sensing image classification Authors: Gupta, Gunjan; Mishra, Vikash Kumar; Pragya, Nidhi Abstract: Remote Sensing Images (RSIs) are rich in quantitative and qualitative information. A single RSI is worth millions of acres of land area. The RSI is only valuable once mapped into Land Use and Land Cover (LULC). The LULC is achieved through different techniques; classification is one such method which classifies the terrestrial features into respective classes and labels them. This study focuses on Remote Sensing Image classification and different classification methods.

2025-01-01T00:00:00Z Mishra, Vikash Kumar Gupta, Gunjan Pragya, Nidhi Aleem, Abdul Kumar, Vimal http://hdl.handle.net/11189/10631 2026-07-28T20:50:00Z 2025-01-01T00:00:00Z

Title: A study on image segmentation and segmentation methods Authors: Mishra, Vikash Kumar; Gupta, Gunjan; Pragya, Nidhi; Aleem, Abdul; Kumar, Vimal Abstract: The image is a composition of many pixels. These pixels include two pieces of information: Coordinate or position and intensity value. The image includes several objects; extracting the crucial objects from the image is critical. Based on the similarity of patterns, classes, groups, and segments of contained objects in the image can be created. Assigning the labels to the pixels is necessary to make the image more informative for analyzing features and decision-making. This study addresses segmentation techniques for extracting objects of interest from the image.

2025-01-01T00:00:00Z Tiwari, Bhawna Gupta, Sindhu Hak Balyan, Vipin http://hdl.handle.net/11189/10328 2025-11-13T20:46:20Z 2024-01-01T00:00:00Z

Title: Performance analysis of denim-based body-worn UWB antenna for classification of human activities Authors: Tiwari, Bhawna; Gupta, Sindhu Hak; Balyan, Vipin Abstract: The application-centric approach of classification techniques implemented for recognition of human movements and activities is sought after in present scenario. Recognizing and classifying human movements and activities enables the utility and development of diverse applications, comprising continuous health and fitness status observation, real-time posture detection, assisted living, robotics, context-enabled gaming, etc. The major emphasis of this work is to demonstrate the suitability of compact ultra-wideband antenna as a wearable device for classifying fifteen human body activities. This research paper presents the performance analysis of flexible textile substrate-based compact wearable antenna. Machine learning algorithms are implemented on observed body activities datasets observed from on body antennas for classification.

2024-01-01T00:00:00Z Okkers, Dale Balyan, Vipin http://hdl.handle.net/11189/10327 2025-11-13T20:46:28Z 2024-01-01T00:00:00Z

Title: High-gain X-band patch antenna for spaceborne synthetic aperture radar applications Authors: Okkers, Dale; Balyan, Vipin Abstract: This paper presents a high-gain X-band rectangular microstrip patch antenna with edge feed for spaceborne Synthetic Aperture Radar (SAR) applications. The increasing demand for high-resolution Earth Observation (EO) Satellite imagery prompts a growing necessity for high-gain antennas. PTFE (Teflon) substrate with a relative permittivity of 2.1 is used in this design which ensures high-frequency performance and resilience to space radiation. This substrate also delivers impedance stability, minimizing signal and dielectric losses. The substrate height has been set to 3 mm, a choice deemed more efficient for antenna performance. The antenna was simulated using HFSS with a fractional bandwidth (FBW) of 8.57% (8.7–9.48 GHz), which is depicted by the reflection coefficient (S11). A return loss of − 17.1 dB and a high gain of 8.1591 dBi is simulated at the resonant frequency (9 GHz). The proposed antenna has good impedance matching which makes it suitable for SAR applications.

2024-01-01T00:00:00Z