Jnaneshwar Das

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This paper presents an oceanographic toolchain that can be used to generate multi-vehicle robotic surveys for large-scale dynamic features in the coastal ocean. Our science application targets Harmful Algal Blooms (HABs) which have significant societal impact to coastal communities yet are poorly understood ecologically. Bloom patches can be large spatially(More)
Dynamic positioning is an important application for marine vehicles that do not have the luxury of anchoring or mooring themselves. Such vehicles are usually large and have arrays of thrusters that allow for controllability in the sway as well as the surge and yaw axes. Most smaller boats however, are underactuated and do not possess control in the sway(More)
Addressing the challenges of feeding the burgeoning world population with limited resources requires innovation in sustainable, efficient farming. The practice of precision agriculture offers many benefits towards addressing these challenges, such as improved yield and efficient use of such resources as water, fertilizer and pesticides. We describe the(More)
Marine phenomena such as algal blooms can be detected using in situ measurements onboard autonomous underwater vehicles (AUVs), but understanding plankton ecology and community structure requires retrieval and analysis of water specimens. This process requires shipboard or manual sample collection, followed by onshore lab analysis which is time-consuming.(More)
More than 70% of our earth is covered by water, yet we have explored less than 5% of the aquatic environment. Aquatic robots, such as autonomous underwater vehicles (AUVs), and their supporting infrastructure play a major role in the collection of oceanographic data. To make new discoveries and improve our overall understanding of the ocean, scientists must(More)
aRobotic Embedded Systems Laboratory, Department of Computer Science busCLAB, Department of Biological Sciences cCaron Lab, Department of Biological Sciences University of Southern California, Los Angeles, CA 90089, USA dSouthern California Coastal Water Research Project, Costa Mesa, CA eDipartimento di Automazione, Elettromagnetismo, Ingegneria(More)
In recent years, ocean scientists have started to employ many new forms of technology as integral pieces in oceanographic data collection for the study and prediction of complex and dynamic ocean phenomena. One area of technological advancement in ocean sampling if the use of Autonomous Underwater Vehicles (AUVs) as mobile sensor platforms. Currently, most(More)
This paper describes a fruit counting pipeline based on deep learning that accurately counts fruit in unstructured environments. Obtaining reliable fruit counts is challenging because of variations in appearance due to illumination changes and occlusions from foliage and neighboring fruits. We propose a novel approach that uses deep learning to map from(More)
Studying ocean processes often requires observations made in a Lagrangian frame of reference, that is, a frame of reference moving with a feature of interest [1]. Often, the only way to understand a process is to acquire measurements at sufficient spatial and temporal resolution within a specific feature while it is evolving. Examples of coastal ocean(More)