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This research system enables simplified dynamic access to unrelated data sources through the internet.
Feature Selection Methods for Zero-Shot Learning of Neural Activity
This MATLAB code may be used to replicate the results published in the manuscript (reference 1), building zero-shot prediction models to classify previously unseen stimuli through neural signals and semantic attributes. The code incorporates feature extraction, feature selection, training, and testing through leave-one-class-out cross-validation, and the analysis of the results concentrating on the feature selection variations. For more information, please refer to reference 2.
Material for Courses and Tutorials in Model-Based Systems Engineering
Model-based systems engineering is the formalized application of modeling to support system requirements, design, analysis, verification, and validation activities beginning in the conceptual design phase and continuing throughout development and later life-cycle phases.
Small Fixed-Wing UAS State Estimation Examples (SUAS Code)
SUAS Code is a set of heavily commented MATLAB® routines that provide example methods for performing state estimation (i.e., position and orientation) of a fixed-wing unmanned aircraft system (UAS) using GPS, gyros, accelerometers, and a magnetometer as sensors. The code is meant as a companion to the Johns Hopkins APL Technical Digest article “Fundamentals of Small Unmanned Aircraft Flight.”