Abstract: This patent presents an integrated multi-sensor fusion system designed to detect and assess drone threats in maritime environments, with a focus on offshore facilities. The system utilizes various sensors, including TDOA, sound, video, and GIS data, processed by specialized neural networks. These data are fused using a Multifaceted Neural Network (MFNN) for comprehensive threat evaluation. A Drone Risk Level (DRL) algorithm assigns risk levels based on drone characteristics. The system addresses maritime-specific challenges, incorporates data augmentation and transfer learning, and offers a robust solution for safeguarding economic, environmental, and national security interests in offshore regions.

Abstract: This patent presents an integrated system for monitoring coastal ecosystems, focusing on mangroves and corals in Gulf region-specific environments. It utilizes a Multi-Faceted Neural Network (MFNN) to process diverse data sources, offering insights into mangrove health, carbon sequestration potential, coral health, disease spread prediction, and site selection for mangrove restoration. The system employs various training and optimization techniques to ensure precision and adaptability, including backpropagation, regularization, fine-tuning with regional datasets, and data augmentation. It introduces mathematical formulas for evaluating mangrove canopies and coral ecosystems, addressing disease prediction and optimal planting site selection for mangrove restoration. The system's interconnection with carbon storage research enhances its scientific foundation for assessing overall carbon storage in mangrove ecosystems.

Abstract: The present invention relates to a sophisticated maritime emission reporting and compliance system utilizing advanced Long Short-Term Memory (LSTM) neural networks. This innovative system seamlessly integrates with shipboard systems, ensuring accurate data collection and processing. It harnesses data from Intrusion Detection Systems (IDS), on-board sensors, navigation systems, Automatic Identification System (AIS), and remote sensing devices. The collected data undergoes rigorous analysis, producing comprehensive reports aligning with EU Emission Trading System guidelines, obligatory for ships operating within the EU from 2024. This invention guarantees meticulous compliance and streamlined reporting for maritime emissions.

Abstract: Method, system, and non-transitory computer readable storage medium are provided for creating a shelf bathymetric map. In some embodiments, a satellite image with a shelf area and related altimeter data for at least a number of points of the shelf area are obtained. The shelf area in the satellite image and depth values for the shelf area from said altimeter data are selected. The selected depth values are associated with points of the shelf area based on geo-coordinates. A training dataset and a testing dataset are created from the associated points. A neural network is trained on the training dataset. The neural network is tested on the testing dataset. Inference of the neural network is adjusted based on testing results. A depth value for each satellite image pixel of the shelf area is predicted by the neural network.

Abstract: A method for detecting a biological marker in a sample, which comprises a complex mixture of molecules, from a patient comprising exposing a detection site having bound monoclonal antibodies specific for the biological marker to the sample, exposing the detection site to a detectably labeled reporter molecule, which is substantially identical to the biological marker, and assessing the degree of binding at the detection site by the reporter molecule; reporter molecules; haptens; and monoclonal antibodies.