Ryan A. McCarthy
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View article: Machine Learning in Acoustics: A Review and Open-source Repository
Machine Learning in Acoustics: A Review and Open-source Repository Open
Acoustic data provide scientific and engineering insights in fields ranging from bioacoustics and communications to ocean and earth sciences. In this review, we survey recent advances and the transformative potential of machine learning (M…
View article: Deep-ocean macrofaunal assemblages on ferromanganese and phosphorite-rich substrates in the Southern California Borderland
Deep-ocean macrofaunal assemblages on ferromanganese and phosphorite-rich substrates in the Southern California Borderland Open
Mineral-rich hardgrounds, such as ferromanganese (FeMn) crusts and phosphorites, occur on seamounts and continental margins, gaining attention for their resource potential due to their enrichment in valuable metals in some regions. This st…
View article: Subaerial Profiles at Two Beaches: Equilibrium and Machine Learning
Subaerial Profiles at Two Beaches: Equilibrium and Machine Learning Open
Shoreline position (e.g., beach width) is a critical component of flooding and overtopping forecasts but difficult to predict accurately. We model beach width changes with a supervised machine learning (ML) approach informed by equilibrium…
View article: Wide-Area Debris Field and Seabed Characterization of a Deep Ocean Dump Site Surveyed by Autonomous Underwater Vehicles
Wide-Area Debris Field and Seabed Characterization of a Deep Ocean Dump Site Surveyed by Autonomous Underwater Vehicles Open
Disposal of industrial and hazardous waste in the ocean was a pervasive global practice in the 20th century. Uncertainty in the quantity, location, and contents of dumped materials underscores ongoing risks to marine ecosystems and human h…
View article: Autonomous learning and interpretation of channel multipath scattering using braid manifolds in underwater acoustic communications
Autonomous learning and interpretation of channel multipath scattering using braid manifolds in underwater acoustic communications Open
In this work, we explore machine learning through a model-agnostic feature representation known as braiding, that employs braid manifolds to interpret multipath ray bundles. We generate training and testing data using the well-known BELLHO…
View article: Employing and Interpreting a Machine Learning Target-Cognizant Technique for Analysis of Unknown Signals in Multiple Reaction Monitoring
Employing and Interpreting a Machine Learning Target-Cognizant Technique for Analysis of Unknown Signals in Multiple Reaction Monitoring Open
The aim of this interdisciplinary work is a robust signal processing and autonomous machine learning framework to associate well-known (target) as well as any potentially unknown (non-target) peaks present within gas chromatography-mass sp…
View article: Signal Processing Methods to Interpret Polychlorinated Biphenyls in Airborne Samples
Signal Processing Methods to Interpret Polychlorinated Biphenyls in Airborne Samples Open
The main contribution of this interdisciplinary work is a robust computational framework to autonomously discover and quantify previously unknown associations between well-known (target) and potentially unknown (non-target) toxic industria…
View article: Multi-beam uncoordinated random access MAC for underwater communication networks
Multi-beam uncoordinated random access MAC for underwater communication networks Open
We consider a multi-beam directional network where the nodes have digital antenna arrays capable of performing post-reception digital beamforming without prior knowledge of angle-of-arrival. Post-reception digital beamforming has greatly a…