Browsing by Advisor "Subramani, Deepak N"
Now showing items 1-6 of 6
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Deep Convolutional and Generative Networks for Ocean Synoptic Feature Extraction and Super Resolution from Remotely Sensed Images
Accurate extraction of Synoptic Ocean Features and Downscaling of Ocean Features is crucial for climate studies and the operational forecasting of ocean systems. With the advancement of space and sensor technologies, the ... -
Fast and Scalable Algorithms for Intelligent Routing of Autonomous Marine Agents
Autonomous marine agents play a pivotal role in diverse ocean applications. These agents serve as indispensable instruments for acquiring crucial environmental information. They are used to explore and monitor harsh ... -
Investigation of sea level anomalies along the Indian coastline using tide gauge data: a physics-guided machine learning approach
The Indian coastline falls within low-elevation coastal zones that are densely populated. Recent observations indicate, with high confidence, that the rate of sea level rise in the In- dian Ocean has been accelerating ... -
Investigation of the Indian Summer Monsoon Rainfall Using Statistical and Machine Learning Techniques
The Indian Summer Monsoon is an important atmospheric phenomenon, marked by a characteristic seasonal wind reversal pattern, delivering 70 to 90% of the annual rainfall to the Indian subcontinent. Monsoon rain profoundly ... -
Quantifying the past and future variability in the Bay of Bengal using statistical and deep learning methods
The Bay of Bengal, the world's largest bay, along with the Andaman Sea, a peripheral sea situated in the southeastern part of the bay, is crucial to the economic and maritime security of India. Understanding the dynamics ... -
Theory and Algorithms for sequential non-Gaussian Bayesian filtering and estimation
Seamless integration of dynamical system models with sparse measurements, called as Data Assimilation, is important in many applications like weather forecasting, socio-economics, navigation, and beyond. In order to produce ...

