Osamah Alsalman

Fiber Bragg gratings (FBGs) have been one of the most widely used optical fiber sensors in both scientific and industrial applications due to their unique advantages of high sensitivity, ease of signal transduction, and capabilities for remote operation and multiplexing. However, the majority of sensing systems based on FBGs can only achieve quasi-distributed sensing along the fiber under test, leaving dark zones in between discrete FBG elements. In this work, we propose and demonstrate a microwave photonics enabled approach for the interrogation of cascaded FBGs to achieve spatially distributed sensing. The core of the system includes an incoherent optical frequency-domain reflectometry module, with the assistance of a dispersive element and a joint time-frequency domain demodulation strategy. By measuring the electrical frequency response of the FBG arrays, followed by the joint time-frequency domain analysis, both the FBG elements and the optical fiber sections connecting the FBGs can be used as sensor devices based on a wavelength-to-delay-mapping technique and interferometry, respectively. Proof-of-concept experiments with a 10-FBG array (10 cm spacing) demonstrated strain sensitivities of –2.37 kHz/μϵ (interferometric channel) and –0.335 ps/μϵ (wavelength-to-delay channel), corresponding to strain resolutions of ∼10 μϵ and ∼0.9 μϵ, respectively. The approach is further shown to support hybrid WDM–TDM interrogation, offering scalability and compatibility with weak FBG arrays.

Displacement is a pivotal physical parameter, and advancements in displacement sensor technology have enabled the creation of a diverse array of physical and mechanical sensors through seamless integration with mechanical transducers. In this study, we introduce a displacement sensing technique leveraging an extrinsic Fabry-Perot interferometer (EFPI) assisted microwave photonic filter. By translating displacement-induced variations in the EFPI’s optical reflection into peak frequency shifts within its frequency response, we achieve large-dynamic-range displacement measurements with outstanding signal quality and demodulation ease. Proof-of-concept demonstrations showcase a substantial 5 mm range with a remarkable sensitivity of 1.148 GHz/mm, achieved using a basic single-mode fiber-based EFPI with a movable external mirror. Besides, nm-scale displacement monitoring is experimentally validated through a phase-measurement approach. Notably, the sensitivity and dynamic range can be tailored by adjusting the microwave photonic filter system’s parameter, i.e., the dispersion parameter of the dispersive element in the system. Furthermore, we introduce a strategy to enhance the dynamic range further by employing a virtual frequency down-conversion technique with an additional interferometer. This proposed technique opens avenues for developing a series of high-sensitivity and broad operating-range physical and mechanical sensors based on a simplified EFPI measurement configuration.

This paper presents GreenMU, an innovative proposed novel framework designed to address the two main challenges: energy efficiency as one of the main research components and detection performance in intrusion detection systems. In the proposed research paper study, by integrating advanced machine learning techniques such as random forest classifier and support vector machines classifier with knowledge distillation and adaptive energy-aware optimization, GreenMU achieves a balanced trade-off between computational efficiency and cybersecurity accuracy. The proposed MUGuard algorithm is at the framework’s core, which dynamically adjusts computational complexity based on real-time actual energy constraints and the evolving threat landscape. Extensive simulations conducted on the KDD 1999 dataset demonstrate that GreenMU achieves a detection accuracy close to 99%, significantly surpassing standard baseline models while reducing energy consumption by 31%. Furthermore, the framework improves computational efficiency, reducing processing time by 15% and making it highly effective for resource-constrained environments such as IoT and edge computing. This research paper study highlights the potential of green artificial intelligence in advancing cybersecurity, providing a scalable, sustainable, and high-performing solution to modern intrusion detection challenges.

Aiming at realizing high-efficiency distributed strain sensing through optical frequency domain reflectometry (OFDR), this paper introduces a fast demodulation algorithm to determine strain-induced spectral shifts from coarse Rayleigh backscattering (RBS) cross-correlation spectra. The proposed approach employs an enhanced Buneman frequency estimation (BFE) algorithm, enabling direct spectral shift analysis across coarse signals. By applying this algorithm, the need for dense interpolation in the conventional cross-correlation demodulation process—typically required for a finer spectral sampling interval but at the cost of demodulation efficiency—can be eliminated. Both theoretical analysis and experimental investigation reveal the equivalence of the BFE and conventional algorithms in terms of underlying physical principles and resultant demodulation noise distribution, establishing a strong foundation for adopting the BFE algorithm to improve demodulation efficiency without sacrificing performance metrics. Demonstration experiments using identical hardware configurations show that the proposed BFE method enhances demodulation efficiency by over 17 times without sacrificing other performance metrics, confirming its effectiveness and practicality. This advancement holds promise for improving the practical application of OFDR-based distributed sensing systems.

Microwave displacement sensors have garnered significant research interest in recent years and have found successful applications in industrial automation and aerospace engineering. However, most microwave sensors are limited to measuring in-plane displacement, with the assumption that out-of-plane displacement remains constant during operation. In this work, we propose and experimentally demonstrate a novel concept for 2-D microdisplacement sensing that simultaneously measures both in-plane and out-of-plane displacement. We developed a proof-of-concept sensor system based on a custom-made coaxial cable resonator (CCR) serving as the stator and a rubber-metal heterogeneous plate as the movable part. The in-plane and out-of-plane micromovements of the plate relative to the CCR’s open-end plane were successfully detected by monitoring the fundamental resonant frequency of the CCR. Additionally, machine learning-based analysis was employed to decouple the CCR’s responses to the 2-D displacement, enabling independent and accurate quantification of both in-plane and out-of-plane displacements. Our strategy combines the heterogeneous plate, which adds spatial information, with machine learning analysis for response decoupling, thereby paving the way for the development of multidimensional sensing systems using a simple 1-D sensor device. The resulting 2-D displacement sensor has the potential to be extended for measuring other 2-D physical and mechanical parameters, such as vibration and acceleration.

Frequency domain reflectometry (FDR) and interferometry are two widely used investigative techniques that have been instrumented using optical and microwave devices for a diverse array of sensing applications. In this paper, we present a highly sensitive liquid-level sensor using a custom hollow coaxial cable transmission line. Measurements of liquid levels with high sensitivity and resolution can be achieved by both FDR and interferometry-based measurements using the same sensor device. We show that FDR-based measurements are more straightforward for liquid-level sensing and are suitable for both small and large variations of liquid level, while interferometry-based measurements provide slightly higher measurement resolution than the FDR-based measurements, but require additional data processing and are only suitable for sensing small variations of liquid level. We experimentally demonstrate that the FDR-based measurements exhibit a linear response over a large dynamic range of 122 mm with an estimated resolution of 5 μm. The capability of the proposed sensing system for measurements of multiple levels of a stratified liquid (oil+water) is also investigated based on FDR measurements. The presented work suggests that the two different measurement techniques, namely FDR and interferometry, can be flexibly applied to the proposed coaxial probe for liquid-level sensing, based on different applications.