相关论文: Machine Learning Models to Identify Promising Nest…
Hollow-core fibers are inherently multimode, making it crucial to filter out higher-order modes within the shortest possible fiber length for applications such as high speed coherent communications and fiber optic gyroscopes. However,…
Hollow-core anti-resonant fibers (HC-ARFs) have proven to be an indispensable platform for various emerging applications due to their unique and extraordinary optical properties. However, accurately estimating the propagation loss of nested…
Dedicated analog neurocomputing circuits are promising for high-throughput, low power consumption applications of machine learning (ML) and for applications where implementing a digital computer is unwieldy (remote locations; small, mobile,…
Understanding the impact of the cladding tube structure on the overall guiding performance is crucial for designing single-mode, wide-band, and ultra low-loss nested hollow-core anti-resonant fiber (HC-ARF). Here we thoroughly investigate…
Advancements in quantum communication and sensing require improved optical transmission that ensures excellent state purity and reduced losses. While free-space optical communication is often preferred, its use becomes challenging over long…
Artificial intelligence and machine learning models deployed on edge devices, e.g., for quality control in Additive Manufacturing (AM), are frequently small in size. Such models usually have to deliver highly accurate results within a short…
Hollow-core negative curvature fibers can confine light within air core and have small nonlinearity and dispersion and high damage threshold, thereby attracting a great deal of interest in the field of hollow core fibers. However, reducing…
Hollow-core anti-resonant fibers (HC-ARFs) have earned great attention in the fiber optics community due to their remarkable light-guiding properties and broad application spectrum. Particularly nested HC-ARFs have recently reached…
We experimentally demonstrate an acoustically modulated antiresonant nanoweb hollow-core fiber (N-HCF) for the first time. The N-HCF contains two off-center air cores with a diameter difference of 5 microns, separated by a nanoweb of…
In telecommunications, Autonomous Networks (ANs) automatically adjust configurations based on specific requirements (e.g., bandwidth) and available resources. These networks rely on continuous monitoring and intelligent mechanisms for…
We propose and theoretically analyse a novel hollow-core photonic crystal fibre (PCF) that is engineered so as to strongly suppress higher order modes, i.e., to provide robust LP$_{01}$ single-mode guidance in all the wavelength ranges…
In order to achieve the dual goals of privacy and learning across distributed data, Federated Learning (FL) systems rely on frequent exchanges of large files (model updates) between a set of clients and the server. As such FL systems are…
Neural Architecture Search (NAS) is increasingly popular to automatically explore the accuracy versus computational complexity trade-off of Deep Learning (DL) architectures. When targeting tiny edge devices, the main challenge for DL…
The millimeter-wave (mm-wave) radio-over-fiber (RoF) systems have been widely studied as promising solutions to deliver high-speed wireless signals to end users, and neural networks have been studied to solve various linear and nonlinear…
Achieving robust single-polarization guidance in hollow-core fibers has remained a longstanding challenge, limiting their integration into precision photonic systems. Here, we report the first experimental realization of a low-loss,…
We propose a lensed-fiber based coupling strategy for low-loss interconnection between single-mode fibers and anti-resonant fibers. By optimizing structural and geometric parameters, the design simultaneously achieves high coupling…
Neural Fields (NF) have gained prominence as a versatile framework for complex data representation. This work unveils a new problem setting termed \emph{Meta-Continual Learning of Neural Fields} (MCL-NF) and introduces a novel strategy that…
Machine learning applied to architecture design presents a promising opportunity with broad applications. Recent deep reinforcement learning (DRL) techniques, in particular, enable efficient exploration in vast design spaces where…
This paper presents a hardware-efficient deep neural network (DNN), optimized through hardware-aware neural architecture search (HW-NAS); the DNN supports the classification of session-level encrypted traffic on resource-constrained…
A novel convolution neural network model, abbreviated NL-CNN is proposed, where nonlinear convolution is emulated in a cascade of convolution + nonlinearity layers. The code for its implementation and some trained models are made publicly…