Related papers: Photonic systolic array for all-optical matrix-mat…
Optical phase measurement is critical for many applications and traditional approaches often suffer from mechanical instability, temporal latency, and computational complexity. In this paper, we describe compact phase sensor arrays based on…
The integration of silicon photonics (SiPh) and phase change materials (PCMs) has created a unique opportunity to realize adaptable and reconfigurable photonic systems. In particular, the nonvolatile programmability in PCMs has made them a…
This publication focuses on the study of silicon photomultipliers (SiPMs) in view of a reconstruction of the incident photon flux in the regime of highly non-linear response. SiPMs are semiconductor based light detectors compiled of…
Rapid and low-power control over the direction of a radiating light field is a major challenge in photonics and a key enabling technology for emerging sensors and free-space communication links. Current approaches based on bulky motorized…
This paper presents a memory efficient architecture that implements the Multi-Scale Line Detector (MSLD) algorithm for real-time retinal blood vessel detection in fundus images on a Zynq FPGA. This implementation benefits from the FPGA…
Realizing multiply resonant photonic crystal cavities with large free spectral range is key to achieve integrated devices with highly efficient nonlinear response, such as frequency conversion, four-wave mixing, and parametric oscillation.…
Photonic integrated circuits offer a compact and stable platform for generating, manipulating, and detecting light. They are instrumental for classical and quantum applications. Imperfections stemming from fabrication constraints,…
In this paper, we introduce Holographic Spectral Multiplexing (HSM) as a novel technique to enable multiple-input multiple-output (MIMO) communication in optical networks. HSM uses the spectral space of ultrashort laser pulses to create…
Deep metric learning maps visually similar images onto nearby locations and visually dissimilar images apart from each other in an embedding manifold. The learning process is mainly based on the supplied image negative and positive training…
Multi-instance learning is common for computer vision tasks, especially in biomedical image processing. Traditional methods for multi-instance learning focus on designing feature aggregation methods and multi-instance classifiers, where the…
Harnessing the full complexity of optical fields requires complete control of all degrees-of-freedom within a region of space and time -- an open goal for present-day spatial light modulators (SLMs), active metasurfaces, and optical phased…
We present a design and the optimization of an irregular-shaped ring-pair magnet array that generates a 1D monotonic field pattern for 2D head imaging in a low-field portable MRI system. The magnet rings are discretized into fan-shaped ring…
Active incoherent millimeter-wave (AIM) imaging is a recently developed technique that has been shown to generate fast millimeter-wave imaging using sparse apertures and Fourier domain sampling. In these systems, spatial frequency sampling…
Photonic Lanterns (PLs) are tapered waveguides that gradually transition from a multi-mode fiber geometry to a bundle of single-mode fibers (SMFs). They can efficiently couple multi-mode telescope light into a multi-mode fiber entrance at…
Random matrices are fundamental in photonic computing because of their ability to model and enhance complex light interactions and signal processing capabilities. In manipulating classical light, random operations are utilized for random…
High-speed image acquisition in light microscopy is essential for a wide range of applications, including observing dynamic biological processes and enabling high-throughput sample analysis. However, traditional imaging speeds are often…
Real-world data such as digital images, MRI scans and electroencephalography signals are naturally represented as matrices with structural information. Most existing classifiers aim to capture these structures by regularizing the regression…
Over the past few years, silicon photonics-based computing has emerged as a promising alternative to CMOS-based computing for Deep Neural Networks (DNN). Unfortunately, the non-linear operations and the high-precision requirements of DNNs…
Additive manufacturing techniques enable the fabrication of functional microstructures with mechanical and chemical properties tailored to their intended use. One common technique is stereolithography, which has recently been augmented in…
In recent years, with the rapid development of electro-optic modulators, optical computing has become a potential excellent candidate for various computing tasks. New structures and devices for optical computing are emerging one after…