AI Medical Compendium Journal:
Optics letters

Showing 41 to 50 of 56 articles

Surveillance of few-mode fiber-communication channels with a single hidden layer neural network.

Optics letters
Multi- and few-mode fibers (FMFs) promise to enhance the capacity of optical communication networks by orders of magnitude. The key for this evolution was the strong advancement of computational approaches that allowed inherent complex light transmis...

Photonic reservoir computer based on frequency multiplexing.

Optics letters
Reservoir computing is a brain-inspired approach for information processing, well suited to analog implementations. We report a photonic implementation of a reservoir computer that exploits frequency domain multiplexing to encode neuron states. The s...

Broad-spectrum diffractive network via ensemble learning.

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We propose a broad-spectrum diffractive deep neural network (BS-DNN) framework, which incorporates multiwavelength channels of input lightfields and performs a parallel phase-only modulation using a layered passive mask architecture. A complementary ...

Experimental performance of deep learning channel estimation for an X-ray communication-based OFDM-PWM system.

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A deep learning channel estimation scheme in orthogonal frequency division multiplexing for X-ray communication (XCOM) is studied. The scheme uses simulated and detected data obtained with different working parameters and numbers of pilots as trainin...

Accurate OSNR monitoring based on data-augmentation-assisted DNN with a small-scale dataset.

Optics letters
Deep neural networks (DNNs) have been successfully applied for accurate optical signal-to-noise ratio (OSNR) monitoring. However, the performance of OSNR monitoring substantially degrades when a mega dataset is inaccessible. Here, we demonstrate an a...

Deep learning multi-shot 3D localization microscopy using hybrid optical-electronic computing.

Optics letters
Current 3D localization microscopy approaches are fundamentally limited in their ability to image thick, densely labeled specimens. Here, we introduce a hybrid optical-electronic computing approach that jointly optimizes an optical encoder (a set of ...

Deep-learning-based approach for strain estimation in phase-sensitive optical coherence elastography.

Optics letters
In this Letter, a deep-learning-based approach is proposed for estimating the strain field distributions in phase-sensitive optical coherence elastography. The method first uses the simulated wrapped phase maps and corresponding phase-gradient maps t...

Neuromorphology in-sensor computing architecture based on an optical Fourier transform.

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We propose an object recognition architecture relying on a neural network algorithm in optical sensors. Precisely, by applying the high-speed and low-power Fourier transform operation in the optical domain, we can transfer the high-cost part of the t...

Temporal and spectral unmixing of photoacoustic signals by deep learning.

Optics letters
Improving the imaging speed of multi-parametric photoacoustic microscopy (PAM) is essential to leveraging its impact in biomedicine. However, to avoid temporal overlap, the A-line rate is limited by the acoustic speed in biological tissues to a few m...

Digital holographic deep learning of red blood cells for field-portable, rapid COVID-19 screening.

Optics letters
Rapid screening of red blood cells for active infection of COVID-19 is presented using a compact and field-portable, 3D-printed shearing digital holographic microscope. Video holograms of thin blood smears are recorded, individual red blood cells are...