Photonics

A compact offset-coupled hybrid fiber interferometer (OCHFI) is designed and experimentally demonstrated for simultaneous measurement of salinity and temperature. The sensor integrates multimode fiber (MMF) and offset no-core fiber (NCF) through an intermediate single-mode fiber (SMF), producing distinct interference patterns for multi-parameter sensing. The optimal SMF length was determined through COMSOL simulations (version 6.2) and fixed at 50 cm to achieve stable and well-separated interference dips. Fast Fourier Transform analysis confirmed that the modal behavior originates from the single-mode-multimode-single-mode (SMS) and single-mode-no-core-single-mode (SNS) segments. Experimentally, Dip 1 exhibits salinity sensitivity of 0.62206 nm/‰, while Dip 2 shows temperature sensitivity of 0.09318 nm/°C, both with linearity (R² > 0.99), excellent repeatability, and stability, with fluctuations within 0.15 nm over 60 min. To remove cross-sensitivity, both the transfer matrix method and an Artificial Neural Network (ANN) model were employed. The ANN approach significantly enhanced prediction accuracy (R² = 0.9999) with RMSE improvement approximately 539-fold for salinity and 56-fold for temperature, compared with the analytical model. The proposed OCHFI sensor provides a compact, low-cost, and intelligent solution for precise simultaneous salinity and temperature measurement, with strong potential for applications in marine, chemical, and industrial process control.

Optical fiber biosensors have evolved into powerful tools for non-invasive biomedical analysis. While foundational principles are well-established, recent years have marked a paradigm shift, driven by advancements in nanomaterials, fabrication techniques, and data processing. This review provides a focused overview of these emerging trends, critically analyzing the innovations that distinguish the current generation of optical fiber biosensors from their predecessors. We begin with a concise summary of fundamental sensing principles, including Surface Plasmon Resonance (SPR) and Fiber Bragg Gratings (FBGs), before delving into the latest breakthroughs. Key areas of focus include integrating novel 2D materials and nanostructures to dramatically enhance sensitivity and advancing synergy with Lab-on-a-Chip (LOC) platforms. A significant portion of this review is dedicated to the rapid expansion of clinical applications, particularly in early cancer detection, infectious disease diagnostics, and continuous glucose monitoring. We highlight the pivotal trend towards wearable and in vivo sensors and explore the transformative role of artificial intelligence (AI) and machine learning (ML) in processing complex sensor data to improve diagnostic accuracy. Finally, we address the persistent challenges—biocompatibility, long-term stability, and scalable manufacturing—that must be overcome for widespread clinical adoption and commercialization, offering a forward-looking perspective on the future of this dynamic field.

Optical Wireless Communication (OWC) technologies are emerging as promising complements to radio-frequency systems, offering high bandwidth, spatial confinement, and license-free operation. Within this domain, Visible Light Communication (VLC) and Optical Camera Communication (OCC) represent two distinct paradigms with divergent performance and security profiles. While VLC leverages LED-photodiode links for high-speed data transfer, OCC exploits ubiquitous image sensors to decode modulated light patterns, enabling flexible but lower-rate communication. Despite their potential, both remain vulnerable to various attacks, including eavesdropping, jamming, spoofing, and privacy breaches. This work applies—and extends—the Risk Matrix (RM) methodology to systematically evaluate the security of VLC and OCC across reconnaissance, denial, and exploitation phases. Unlike prior literature, which treats VLC and OCC separately and under incompatible threat definitions, we introduce a unified, domain-specific risk framework that maps empirical channel behavior and attack feasibility into a common set of impact and likelihood indices. A normalized risk rank (NRR) is proposed to enable a direct, quantitative comparison of heterogeneous attacks and technologies under a shared reference scale. By quantifying risks for representative threats—including war driving, Denial of Service (DoS) attacks, preshared key cracking, and Evil Twin attacks—our analysis shows that neither VLC nor OCC is intrinsically more secure; rather, their vulnerabilities are context-dependent, shaped by physical constraints, receiver architectures, and deployment environments. VLC tends to concentrate confidentiality-driven exposure due to optical leakage paths, whereas OCC is more sensitive to availability-related degradation under adversarial load. Overall, the main contribution of this work is the first unified, standards-aligned, and empirically grounded risk-assessment framework capable of comparing VLC and OCC on a common security scale. The findings highlight the need for technology-aware security strategies in future OWC deployments and demonstrate how an adapted RM methodology can identify priority areas for mitigation, design, and resource allocation.