Deep learning convolutional neural networks generally involve multiple-layer, forward-backward propagation machine-learning algorithms that are computationally costly. In this work, we demonstrate an alternative scheme to convolutional neural nets that reconstructs an original image from its optically preprocessed, Fourier-encoded pattern. The scheme is much less computationally demanding and more noise robust, and thus suited for high-speed and low-light imaging. We introduce a vortex phase transform with a lenslet-array to accompany shallow, dense, “small-brain” neural networks. Our single-shot coded-aperture approach exploits the coherent diffraction, compact representation, and edge enhancement of Fourier-transformed spiral phase gradients. With vortex encoding, a small brain is trained to deconvolve images at rates 5–20 times faster than those achieved with random encoding schemes, where greater advantages are gained in the presence of noise. Once trained, the small brain reconstructs an object from intensity-only data, solving an inverse mapping without performing iterations on each image and without deep learning schemes. With vortex Fourier encoding, we reconstruct MNIST Fashion objects illuminated with low-light flux ( 5 nJ / cm 2 ) at a rate of several thousand frames per second on a 15 W central processing unit. We demonstrate that Fourier optical preprocessing with vortex encoders achieves similar accuracies and speeds 2 orders of magnitude faster than convolutional neural networks.

We studied the degradation of thermal conductivity in single crystal sapphire (α-Al2O3) irradiated by 167 MeV Xe swift heavy ions (SHIs) over the multiple fluences in the range of 1012–1014 ions/cm2. Thermal conductivity was measured primarily in the cross-plane direction using a noncontact ultrafast optical pump-probe technique called picosecond time domain thermoreflectance (TDTR). Multiple samples with variable ion fluences allowed us to probe distinct regions resulting from different regimes of microstructure evolution caused by electronic energy loss. By tuning the penetration depth of the thermal waves using different modulation frequencies, two regions with distinct conductivities were identified and the values of which were found to be consistent with phonon-mediated thermal transport models while the microstructure was confirmed by electron microscopy characterization. These damaged regions were determined to be a several micrometer thick ion track region and several tens of nanometer-thick amorphous layer present only above 5.0 × 1013 ions/cm2. These results demonstrate the applicability of TDTR to resolve thermal transport behavior in SHI irradiated oxides having nonhomogeneous damage profile on a nanometer scale. The presented approach facilitates future studies aiming at resolving the impact of distinct damage resulting from electronic and nuclear stopping regimes under irradiation.

A two-dimensional monolayer multi-scaled polyaniline inverse opal film is fabricated and exhibits efficient polarization filtering, which separates s- and p-polarized light for polarization sensing and imaging.

We demonstrate diffractal spatial multiplexing for wireless communication for moving transceivers. The approach leverages free-space diffraction with optical demodulation schemes. Exponentially-enhanced data rates are achieved without tracking.

Speed, generalizability, and robustness are fundamental issues for building lightweight computational cameras. Here we demonstrate generalizable image reconstruction with the simplest of hybrid machine vision systems: linear optical preprocessors combined with nohidden-layer, "small-brain" neural networks. Surprisingly, such simple neural networks are capable of learning the image reconstruction from a range of coded diffraction patterns using two masks. We investigate the possibility of generalized or "universal training" with these small brains. Neural networks trained with sinusoidal or random patterns uniformly distribute errors around a reconstructed image, whereas models trained with a combination of sharp and curved shapes (the phase pattern of optical vortices) reconstruct edges more boldly. We illustrate variable convergence of these simple neural networks and relate learnability of an image to its singular value decomposition entropy (SVD-entropy) of the image. We also provide heuristic experimental results. With thresholding, we achieve robust reconstruction of various disjoint datasets. Our work is favorable for future real-time low-SWaP hybrid vision: we reconstruct images on a 15W laptop CPU with 15k fps: faster by a factor of 3 than previously reported results and 3 orders of magnitude faster than convolutional neural networks.