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Machine learning reveals the control mechanics of an insect wing hinge

Insects constitute the most species-rich radiation of metazoa, a success that is due to the evolution of active flight. Unlike pterosaurs, birds and bats, the wings of insects did not evolve from legs1, but are novel structures that are attached to the body via a biomechanically complex hinge that transforms tiny, high-frequency oscillations of specialized power muscles into the sweeping back-and-forth motion of the wings2. The hinge consists of a system of tiny, hardened structures called sclerites that are interconnected to one another via flexible joints and regulated by the activity of specialized control muscles. Here we imaged the activity of these muscles in a fly using a genetically encoded calcium indicator, while simultaneously tracking the three-dimensional motion of the wings with high-speed cameras. Using machine learning, we created a convolutional neural network3 that accurately predicts wing motion from the activity of the steering muscles, and an encoder–decoder4 ....

United Kingdom , International Conference On Neural Networks Vol , Curran Associates , International Conference On Engineering , How Diptera , International Conference , Neural Networks , Insect Morphology , Arthropod Relationships , Neural Information Processing Systems , Kalman Filtering , John Wiley ,

A non-mathematical introduction to Kalman Filters for programmers

Read my manifesto on Code as an alternative to Mathematics. Code for this article can be found on this Colab Notebook should you choose to follow along. Why Kalman Filters? Kalman filters are ingenius. If you have never heard of them, then a very intuitive (and arguably reductive) way to think about them is to ....

Colab Notebook , Kalman Filters , Linear Algebra , Probability Theory , Kalman Filter , Kalman Filtering , Bell Curve ,

Mission Critical GNSS Training

Online GNSS Courses with Real-Time Engagement Take advantage of these upcoming courses taught by leaders in their fields. Course 122: GPS Fundame ....

Remote Training , Chris Hegarty , Inertial Systems , Kalman Filtering , Alan Pue , Michael Vaujin ,