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3. Comparative Analysis of Multiple Deep CNN Models for Waste Classification

Author

Gyawali, Dipesh, Regmi, Alok, Shakya, Aatish, Gautam, Ashish, and Shrestha, Surendra

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Waste is a wealth in a wrong place. Our research focuses on analyzing possibilities for automatic waste sorting and collecting in such a way that helps it for further recycling process. Various approaches are being practiced managing waste but not efficient and require human intervention. The automatic waste segregation would fit in to fill the gap. The project tested well known Deep Learning Network architectures for waste classification with dataset combined from own endeavors and Trash Net. The convolutional neural network is used for image classification. The hardware built in the form of dustbin is used to segregate those wastes into different compartments. Without the human exercise in segregating those waste products, the study would save the precious time and would introduce the automation in the area of waste management. Municipal solid waste is a huge, renewable source of energy. The situation is win-win for both government, society and industrialists. Because of fine-tuning of the ResNet18 Network, the best validation accuracy was found to be 87.8%., Comment: 6 pages, 13 figures

Published
2020

4. Analysis of Throughput Gain in Multi-tier Heterogeneous Networks

Author

Gachhadar, Anand, Maharjan, Ram Krishna, Shrestha, Surendra, Adhikari, Nanda Bikram, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nagar, Atulya K., editor, Singh Jat, Dharm, editor, Mishra, Durgesh Kumar, editor, and Joshi, Amit, editor

Published
2023
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16. Aesthetic Gingival Recontouring

Author

Bajracharya, Alina, primary, Shrestha, Surendra Man, additional, Pradhan, Ameena, additional, Aryal, Shreeya, additional, and Shrestha, Shilu, additional

Published
2023
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22. Divergent biophysical responses of western United States forests to wildfire driven by eco-climatic gradients.

Author

Shrestha, Surendra, Williams, Christopher A., Rogers, Brendan M., Rogan, John, and Kulakowski, Dominik

Subjects
FOREST fires, WILDFIRES, MODIS (Spectroradiometer), FOREST reserves, FIRE management, ECOLOGICAL succession, LEAF area index, ECOLOGICAL disturbances
Abstract

Understanding vegetation recovery after fire is critical for predicting vegetation-mediated ecological dynamics in future climates. However, information characterizing vegetation recovery patterns after fire and their determinants over large geographical extents is limited. This study uses Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) and albedo to characterize patterns of post-fire biophysical dynamics across the western United States (US) and further examines the influence of topo-climatic variables on the recovery of LAI and albedo at two different time horizons, 10 and 20 years post-fire, using a random forest model. Recovery patterns were derived for all wildfires that occurred between 1986 and 2017 across seven forest types and 21 level III ecoregions of the western US. We found differences in the characteristic trajectories of post-fire vegetation recovery across forest types and eco-climatic settings. In some forest types, LAI had recovered to only 60 %–70 % of the pre-fire levels by 25 years after the fire, while it recovered to 120 %–150 % of the pre-fire levels in other forest types, with higher absolute post-fire changes observed in forest types and ecoregions that had a higher initial pre-fire LAI. Our random forest results showed very little influence of fire severity on the recovery of both summer LAI and albedo at both post-fire time horizons. Post-fire vegetation recovery was most strongly controlled by elevation, with faster rates of recovery at lower elevations. Similarly, annual precipitation and average summer temperature had significant impacts on the post-fire recovery of vegetation. Full recovery was seldom observed when annual precipitation was less than 500 mm and average summer temperature was above the optimal range, i.e., 15–20 °C. Climate influences, particularly annual precipitation, were a major driver of post-fire summer albedo change through its impact on ecological succession. This study provides quantitative measures of primary controls that could be used to improve the modeling of ecosystem dynamics post-fire. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Accounting for albedo to identify climate positive tree cover restoration

Author

Cook-Patton, Susan, primary, Hasler, Natalia, additional, Williams, Christopher, additional, Denney, Vanessa Carrasco, additional, Ellis, Peter, additional, Shrestha, Surendra, additional, Hart, Drew Terasaki, additional, Wolff, Nicholas, additional, Yeo, Samantha, additional, Crowther, Thomas, additional, and Werden, Leland, additional

Published
2023
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25. Forest Types Show Divergent Biophysical Responses After Fire: Challenges to Ecological Modeling

Author

Shrestha, Surendra, Williams, Christopher A., Rogers, Brendan M., Rogan, John, and Kulakowski, Dominik

Abstract

Understanding vegetation recovery after fire is critical for predicting vegetation-mediated ecological dynamics in future climates. However, information characterizing vegetation recovery patterns after fire and their determinants are lacking over large geographical extents. This study uses Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) and albedo to characterize patterns of post-fire biophysical dynamics across the western United States (US) and further examines the influence of topo-climatic variables on the recovery of LAI and albedo at two different time intervals, 10 and 20 years post-fire, using a random forest model. Recovery patterns were derived for all wildfires that occurred between 1986 and 2017 across seven forest types and 21 level III ecoregions of the western US. We found differences in characteristic trajectories of post-fire vegetation recovery across forest types and ecoclimatic settings. LAI in some forest types recovered only 60 %–70 % by 25 years after fire while it recovered 120 % to 150 % of the pre-fire levels in other forest types, with higher absolute post-fire changes in forest types and ecoregions that had a higher initial pre-fire LAI. Our random forest results showed very little influence of fire severity on the recovery of both summer LAI and albedo at both post-fire time intervals. Post-fire vegetation recovery was most strongly controlled by elevation, with faster rates of recovery in lower elevations. Similarly, annual precipitation and average summer temperature had significant impacts on the post-fire recovery of vegetation. Full recovery was seldom observed when annual precipitation was less than 500 mm and average summer temperature was above the optimal range i.e., 15–20 °C. Climate influences, particularly annual precipitation, was a major driver of post-fire summer albedo change through its impact on ecological succession. This study provides quantitative measure of primary controls that could be used to improve the modelling of ecosystem dynamics post-fire.

Published
2023

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