Terahertz (THz) radiation has recently received a lot of interests in science and technology. The potential of orbital angular momentum (OAM) of light in the THz region is a novel idea for researchers with increasing applications in broadband communications. THz waveguides have rarely been reported to generate and guide OAM modes. In this paper, we propose for the first time hollow-core optical fibers consist of circular air-hole arrays for guiding THz OAM modes with the topological charge order up to l=3. Using numerical simulations, two low-loss transmission windows over 0.47 0.65 THz (window1) and 0.95 1.20 THz (window2) are recognized to guide OAM modes. Results show that both windows of the THz fiber exhibit the flat near-zero dispersion as well as low confinement loss (~ 10-3 dB/cm) with simultaneously high purity of the OAM modes (higher than 90%). Finally, two designs of THz optical fibers consist of one and two rings of air-hole arrays in their cladding are examined and it is shown that difference of the confinement loss and the modal purity between two designs are negligible. This implies that hollow-core optical fibers consist of one ring of air-hole array in their cladding are very promising for efficient transmission of OAM modes in the THz region.

A simple design of a D-shaped photonic crystal fiber sensor with 10 layers of graphene on the surface plasmon of gold is proposed for tuning refractive index (RI) sensitivity in the visible and IR regions. The proposed structure has been simulated by optimizing the mesh size and the method of finite-difference eigenmode. By tuning the chemical potential of graphene ( μ c ), we realize tunable and enhanced RI sensing for a wide RI range from 1.35 to 1.42. The maximum wavelength sensitivities of the proposed sensor are 5200 nm/RIU, 6000 nm/RIU, and 7500 nm/RIU for μ c = 0.8 e v , 1.0 ev, and 1.2 ev, respectively. Further, sensor characteristic parameters such as amplitude sensitivity, resolution, and figure of merit have been investigated for different chemical potentials. All dramatic changes in sensor parameters show an advancement plan to control and tune RI-based optical sensors assisted by graphene.

We introduce a method to measure the Rayleigh signature of an optical fiber, which is defined as the frequency dependence of the backscattering from each position along its length. The method uses the Short-Frequency Fourier transform to extract frequency-dependent time-resolved information from the spectrum of the detected pulse response in an optical time-domain reflectometry (OTDR) setup. The Rayleigh signature obtained can be used to implement broadband high-sensitivity high-linearity distributed acoustic sensors (DAS) that are immune to signal fading problems commonly affecting other OTDR-based systems. Rayleigh signature interrogation has been widely applied in optical frequency-domain reflectometry sensors and some specialized OTDR configurations. However, to our knowledge, this is the first technique that enables its general use in conventional single-pulse coherent OTDR setups, as well as in other time-domain systems that measure the impulse response of a fiber, such as those that utilize pulse compression. We experimentally demonstrate the method in a conventional heterodyne-detection OTDR DAS and also in a enhanced-performance pulse compression setup employing phase-coded waveforms. Measurements in a 50 km fiber with spatial resolution 2 m and a sensitivity of 113 $\text{p}\epsilon /\sqrt{\text{Hz}}$ demonstrate the capabilities of the technique.

We propose and experimentally validate an automatic urban water leakage detection architecture that leverages dark fiber links already deployed in telecommunication networks in underground conduits in the vicinity of water pipelines. The sensing stage relies on a differential-phase coherent optical time-domain reflectometry interrogator enhanced with optical pulse compression to improve sensitivity. Building on this vibration acquisition stage, automatic leakage detection algorithms are implemented by searching for leak-induced activity in the frequency domain, which is well suited to revealing leakage-related features. After acquiring a baseline calibration to characterize normal-condition vibrations at each sensing position, leakage candidates are identified by comparing distribution-based metrics computed over multiple measurements against the corresponding baseline statistics. Two automatic leakage detection strategies are developed. First, low-complexity feature-based metrics are implemented, enabling continuous monitoring with minimal computational requirements. Second, an autoencoder-based anomaly detection technique is introduced, which also relies on location-specific normal-condition calibration but reduces the dependence on prior knowledge of the expected leakage vibration signatures. A real-world field trial on an urban network demonstrates reliable detection and localization using controlled leak events generated in the field, with measurements performed over a 17 km sensing fiber and an effective spatial resolution of 2.6 m. Benchmarking against a commercial punctual electro-acoustic leak detector yields consistent trends. Overall, the proposed system could complement existing technologies by enabling automated, continuous city-scale monitoring over already deployed dark fiber infrastructure.

Surface plasmon resonance (SPR) sensors based on fiber optics have demonstrated strong potential in recent years for detecting refractive index (RI), temperature, and pressure. Among these, RI sensors have primarily focused on sensing aqueous solutions, particularly for biomedical applications. In contrast, gas sensing using similar configurations has received less attention, largely due to the challenge of accurately detecting the low RI range of gases. In this work, we report an SPR-based fiber sensor capable of detecting RI values in the range of 1.00 to 1.10, which encompasses nearly all gases. A practical and simple SPR sensor design is proposed using a ring-core fiber, where a thin gold layer is coated on the surface of the central air hole filled with the gas sample. Simulation results show a linear shift in spectral response with a wavelength sensitivity of 795 nm/RIU and a resolution on the order of 10 ⁻⁴ RIU, enabling accurate discrimination of gas samples. Furthermore, the sensor exhibits a maximum amplitude sensitivity of approximately 401/RIU, indicating that a small change in the analyte RI (e.g., 0.01 RIU) results in a 40% variation in transmitted intensity, thus supporting cost-effective, intensity-based interrogation methods.

A novel processing and classification framework for the automatic detection and classification of pedestrians, bicycles, and cars in urban environments is proposed and experimentally demonstrated. The proposed processing pipeline combines high-sensitivity distributed acoustic sensing, automatic trajectory extraction, physically interpretable spectral feature extraction, and supervised machine-learning-based pattern recognition. The technique leverages pre-existing telecommunication fibers installed beneath or close to sidewalks and roadways. Coherent optical time-domain reflectometry enhanced with optical pulse compression provides the sensitivity and spatial resolution required to detect mobility-induced vibrations using these non-dedicated fibers, thereby avoiding sensing-cable deployment or pavement retrofitting. A detailed analysis of the signals extracted along the detected trajectories is performed in the spatial-frequency and equivalent-frequency domains. Controlled walking, running, and cycling measurements, together with passenger-vehicle observations under normal traffic conditions, reveal reproducible spectral characteristics associated with the underlying pavement-loading mechanisms. In particular, the analysis identifies four complementary properties: spatial loading periodicity, periodic loading peak prominence, initial spectral decay, and broadband spectral-energy distribution. Seven physically interpretable features derived from these properties are subsequently supplied to a support vector machine classifier. The processing and classification framework is evaluated using measurements acquired from an existing optical fiber in a real urban environment containing simultaneous sidewalk and road activity. When classification is restricted to manually verified bicycle, car, and pedestrian trajectories, a balanced accuracy of 98.3% is achieved. These results demonstrate the potential of physically interpretable spectral analysis of DAS measurements for continuous, spatially distributed, and privacy-preserving urban mobility monitoring.