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Neole, Bhumika
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Electrical & Electronics Engineering

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Optimizing neural radiance field: a comprehensive review of the impact of different optimizers on neural radiance fields Pinjarkar, Latika; Nittala, Aditya; P. Mattada, Mahantesh; Pinjarkar, Vedant; Neole, Bhumika; Kogundi Math, Manisha
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8315

Abstract

Neural radiance field (NeRF) is a form of deep learning model that may be used to depict 3D scenes from a collection of photos. It has been demonstrated that NeRF can produce photorealistic photographs of fresh perspectives on a scene even from a small number of input images. However, the optimizer that is employed can have a significant impact on the quality of the final reconstruction. Finding an effective optimizer is one of the biggest challenges while learning NeRF models. The optimizer is responsible for making changes to the model's parameters to minimize the discrepancy between the model's predictions and the actual data. We cover the many optimizers that have been used to train NeRF models in this study. We present research results contrasting the effectiveness of multiple optimizers and examine the benefits and drawbacks of each optimizer. For training NeRF models, four different optimizers viz. Adaptive moment estimation (Adam), AdamW, root mean square propagation (RMSProp), and adaptive gradient (Adagrad) are trained. The most effective optimizer for a given assignment will vary depending on a variety of elements, including the size of the dataset, the complexity of the scene, and the level of accuracy that is required.
Drone-based high-resolution air pollution monitoring: a comprehensive system and field evaluation Neole, Bhumika; Vyawahare, Shreerang; Pinjarkar, Latika; Kohli, Tanishq; Sah, Parimal; Panchore, Meena
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8592

Abstract

This paper presents a novel air pollution monitoring system designed for drone deployment, featuring a specialized payload comprising sensor suites and processing components. The upper half of the payload accommodates MQ series sensors and an SDS011 particulate matter (PM) sensor, strategically positioned to provide comprehensive coverage of various air pollutants. Processing boards, including an Arduino and ESP8266 node micro controller unit (NodeMCU), facilitate data collection, transmission, and connectivity to a designated cloud platform for real-time monitoring and analysis. Additionally, the payload incorporates air pumps for pollution mitigation and relay modules for remote control. Field tests conducted in suburban and industrial areas evaluated the system's efficacy in capturing subtle air quality variations and responding to pollution spikes. Analysis of ground-level and airborne data provided insights into sensor performance and system adaptability across diverse environments. Overall, the proposed system demonstrates promising potential as a comprehensive solution for high-resolution air pollution monitoring, with implications for enhancing public health interventions and environmental management strategies.
Co-Authors Kogundi Math, Manisha Kohli, Tanishq Nittala, Aditya P. Mattada, Mahantesh Panchore, Meena Pinjarkar, Latika Pinjarkar, Vedant Sah, Parimal Vyawahare, Shreerang