Fourier-domain correlation approaches have been successful in a variety of image comparison approaches but fail when the scenes, patterns, or objects in the images are distorted. Here, we utilize the sequential training of shallow neural networks on Fourier-preprocessed video to infer 3-D movement. The bio-inspired pipeline learns x, y, and z-direction movement from high-frame-rate, low-resolution, Fourier-domain preprocessed inputs (either cross power spectra or phase correlation data). Our pipeline leverages the high sensitivity of Fourier methods in a manner that is resilient to the parallax distortion of a forward-facing camera. Via sequential training over several path trajectories, models generalize to predict the 3-D movement in unseen trajectory environments. Models with no hidden layer are less accurate initially but converge faster with sequential training over different flightpaths. Our results show important considerations and trade-offs between input data preprocessing (compression) and model complexity (convergence).
Langmuir-Blodgett troughs provide an excellent system to deposit monolayer films onto flat and curved substrates. However, most trough designs use motorized barriers to compact the film, and it is difficult to fully eliminate the capillary waves and striations on deposited films caused by motorized barriers. Here, we present an inexpensive design for a benchtop LB trough that compresses the film without motorized barriers; instead, it is the trough's geometry that compresses the film in a drainage basin. We demonstrate this approach with a 3D printed drainage basin and with self-assembled polystyrene colloidal films on a range of 3D glass substrates: a jar, a bulb, and a compressor tube. We provide a mathematical formalism to coat 3D objects with arbitrary size and shape; especially with facile 3D printing, this concept may be extended in a cheap and modular approach.
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