We directly transfer optical information around arbitrarily-shaped, fully-opaque occlusions that partially or entirely block the line-of-sight between the transmitter and receiver apertures. An electronic neural network (encoder) produces an encoded phase representation of the optical information to be transmitted. Despite being obstructed by the opaque occlusion, this phase-encoded wave is decoded by a diffractive optical network at the receiver. We experimentally validated our framework in the terahertz spectrum by communicating images around different opaque occlusions using a 3D-printed diffractive decoder. This scheme can operate at any wavelength and be adopted for various applications in emerging free-space communication systems.
KEYWORDS: Free space optics, Diffusers, Education and training, Deep learning, 3D modeling, Optical transmission, Neural networks, Mathematical optimization, Light sources and illumination, Image transmission
We report an optical diffractive decoder with an electronic encoder network to facilitate the accurate transmission of optical information of interest through unknown random phase diffusers along the optical path. This hybrid electronic-optical model was trained via supervised learning, and comprises a convolutional neural network-based encoder and jointly-trained passive diffractive layers. After their joint-training using deep learning, our hybrid model can accurately transfer optical information even in the presence of unknown phase diffusers, generalizing to new random diffusers never seen before. We experimentally validated this framework using a 3D-printed diffractive network, axially spanning <70λ, where λ=0.75mm is the illumination wavelength.
Free-space optical information transfer through diffusive media is critical in many applications, such as biomedical devices and optical communication, but remains challenging due to random, unknown perturbations in the optical path. We demonstrate an optical diffractive decoder with electronic encoding to accurately transfer the optical information of interest, corresponding to, e.g., any arbitrary input object or message, through unknown random phase diffusers along the optical path. This hybrid electronic-optical model, trained using supervised learning, comprises a convolutional neural network-based electronic encoder and successive passive diffractive layers that are jointly optimized. After their joint training using deep learning, our hybrid model can transfer optical information through unknown phase diffusers, demonstrating generalization to new random diffusers never seen before. The resulting electronic-encoder and optical-decoder model was experimentally validated using a 3D-printed diffractive network that axially spans <70λ, where λ = 0.75 mm is the illumination wavelength in the terahertz spectrum, carrying the desired optical information through random unknown diffusers. The presented framework can be physically scaled to operate at different parts of the electromagnetic spectrum, without retraining its components, and would offer low-power and compact solutions for optical information transfer in free space through unknown random diffusive media.
We report an electronic encoder (formed by a convolutional neural network) and a diffractive decoder (formed by spatially-structured diffractive layers) that are jointly optimized using deep learning to project super-resolved images at the output plane using a low-resolution spatial-light modulator (SLM). This diffractive super-resolution display performs ~4x pixel super-resolution, corresponding to a ~16x increase in the space-bandwidth product. This diffractive display was experimentally demonstrated using 3D-printed diffractive decoders operating at the THz spectrum. Diffractive super-resolution image displays can be used to build compact, low-power, and computationally efficient HR projectors operating at visible wavelengths and other parts of the electromagnetic spectrum.
We present a field-portable and high-throughput imaging flow-cytometer, which performs phenotypic analysis of microalgae using image processing and deep learning. This computational cytometer weighs ~1.6kg, and captures holographic images of water samples containing microalgae, flowing in a microfluidic channel at a rate of 100mL/h. Automated analysis is performed by extracting the spatial and spectral features of the reconstructed images to automatically identify/count the target algae within the sample, using image processing and convolutional neural networks. Changes within the measured features and the composition of the microalgae can be rapidly analyzed to reveal even minute deviations from the normal state of the population.
Current state-of-the-art technology for in-vitro diagnostics employ laboratory tests such as ELISA that consists of a multi-step test procedure and give results in analog format. Results of these tests are interpreted by the color change in a set of diluted samples in a multi-well plate. However, detection of the minute changes in the color poses challenges and can lead to false interpretations. Instead, a technique that allows individual counting of specific binding events would be useful to overcome such challenges. Digital imaging has been applied recently for diagnostics applications. SPR is one of the techniques allowing quantitative measurements. However, the limit of detection in this technique is on the order of nM. The current required detection limit, which is already achieved with the analog techniques, is around pM. Optical techniques that are simple to implement and can offer better sensitivities have great potential to be used in medical diagnostics. Interference Microscopy is one of the tools that have been investigated over years in optics field. More of the studies have been performed in confocal geometry and each individual nanoparticle was observed separately. Here, we achieve wide-field imaging of individual nanoparticles in a large field-of-view (~166 μm × 250 μm) on a micro-array based sensor chip in fraction of a second. We tested the sensitivity of our technique on dielectric nanoparticles because they exhibit optical properties similar to viruses and cells. We can detect non-resonant dielectric polystyrene nanoparticles of 100 nm. Moreover, we perform post-processing applications to further enhance visibility.
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