We report on stimulated Raman scattering in an approximately 1-meter-long hollow-core photonic crystal fiber filled with hydrogen gas under pressure. Light was guided and confined in the 15-micrometer-diameter hollow core by a two-dimensional photonic bandgap. Using a pulsed laser source (pulse duration, 6 nanoseconds; wavelength, 532 nanometers), the threshold for Stokes (longer wavelength) generation was observed at pulse energies as low as 800 ± 200 nanojoules, followed by a coherent anti-Stokes (shorter wavelength) generation threshold at 3.4 ± 0.7 microjoules. The pump-to-Stokes conversion efficiency was 30 ± 3% at a pulse energy of only 4.5 microjoules. These energies are almost two orders of magnitude lower than any other reported energy, moving gas-based nonlinear optics to previously inaccessible parameter regimes of high intensity and long interaction length.

Experimental demonstration of the frequency shift of photonic bandgaps due to refractive index scaling using D₂O-filled hollow-core photonic crystal fibers is presented. The results confirm a simple scaling law for bandgaps in fibers in which the low-index medium is varied.

Pure rotational stimulated Raman scattering spectra containing nine strong spectral components were generated from a $\sim 11\mathrm{m}$ long hollow-core photonic crystal fiber filled with hydrogen and pumped with nanosecond pulses having energies around 100–300 nJ. Observation of both transient and steady-state scattering threshold behavior is reported. Passage from the transient to the steady state is observed with a pulse as long as 14 ns. Convenient analytical expressions for energy and power threshold are deduced for the present configuration.

In this letter, we report for the first time (to the best of our knowledge) the ability of a conventional hollow-core photonic crystal fiber (HC-PCF) to guide light over a wide bandwidth by selectively filling the cladding of the fiber with polymeric material. The transmission spectrum of the new fiber was recorded using a supercontinuum source while the measured near-field profile, combined with the numerical analysis, verified the existence of a single white fundamental guided mode in the air-core. The main advantage of the proposed method is that broadband guidance in air can be easily achieved, avoiding complex and sophisticated designs as well as fabrication of the fiber cladding structure, thus opening new prospects for the HC-PCF.

This paper presents COGNIFOG, an innovative framework under development that is designed to leverage decentralized decision-making, machine learning, and distributed computing to enable autonomous operation, adaptability, and scalability across the IoT–edge–cloud continuum. The work emphasizes Continuous Integration/Continuous Deployment (CI/CD) practices, development, and versatile integration infrastructures. The described methodology ensures efficient, reliable, and seamless integration of the framework, offering valuable insights into integration design, data flow, and the incorporation of cutting-edge technologies. Through three real-world trials in smart cities, e-health, and smart manufacturing and the development of a comprehensive QuickStart Guide for deployment, this work highlights the efficiency and adaptability of the COGNIFOG platform, presenting a robust solution for addressing the complexities of next-generation computing environments.

In the era of ubiquitous computing, the challenges imposed by the increasing demand for real-time data processing, security, and energy efficiency call for innovative solutions. The emergence of fog computing has provided a promising paradigm to address these challenges by bringing computational resources closer to data sources. Despite its advantages, the fog computing characteristics pose challenges in heterogeneous environments in terms of resource allocation and management, provisioning, security, and connectivity, among others. This paper introduces COGNIFOG, a novel cognitive fog framework currently under development, which was designed to leverage intelligent, decentralized decision-making processes, machine learning algorithms, and distributed computing principles to enable the autonomous operation, adaptability, and scalability across the IoT–edge–cloud continuum. By integrating cognitive capabilities, COGNIFOG is expected to increase the efficiency and reliability of next-generation computing environments, potentially providing a seamless bridge between the physical and digital worlds. Preliminary experimental results with a limited set of connectivity-related COGNIFOG building blocks show promising improvements in network resource utilization in a real-world-based IoT scenario. Overall, this work paves the way for further developments on the framework, which are aimed at making it more intelligent, resilient, and aligned with the ever-evolving demands of next-generation computing environments.