Fiber Specklegram Sensor Research Articles

Fiber Specklegram Sensor Based on Reflective Mode Rotation in Few-Mode Fiber

Fiber Specklegram Sensor Based on Reflective Mode Rotation in Few-Mode Fiber

A low-cost Raspberry Pi based imaging system for analysis of fiber specklegram sensors

A low-cost Raspberry Pi based imaging system for analysis of fiber specklegram sensors

Advances in Optical Fiber Speckle Sensing: A Comprehensive Review

Optical fiber sensors have been studied, developed, and already used in the industry for more than 50 years due to their multiplexing capabilities, lightweight design, compact form factors, and electromagnetic field immunity. The scientific community continuously studies new materials, schemes, and architectures aiming to improve existing technologies. Navigating through diverse sensor technologies, including interferometry, intensity variation, nonlinear effects, and grating-based sensors, fiber specklegram sensors (FSSs) emerge as promising alternatives due to their simplicity and low cost. This review paper, emphasizing the potential of FSSs, contributes insights to the present state and future prospects for FSSs, providing a holistic view of advancements propelling FSSs to new frontiers of innovation. Subsequent sections explore recent research, technological trends, and emerging applications, contributing to a deeper understanding of the intricacies shaping the future of FFS sensor technologies.

A Novel Structure with Ultra Short Multimode Fiber for Fiber Specklegram Sensor and Its Application in Multi-Bending Sensing. Proof and Concept

A Novel Structure with Ultra Short Multimode Fiber for Fiber Specklegram Sensor and Its Application in Multi-Bending Sensing. Proof and Concept

Dynamic Multimode Fiber Specklegram Sensor Smart Bed Enabled by Deep Learning

Dynamic Multimode Fiber Specklegram Sensor Smart Bed Enabled by Deep Learning

Machine-Learning-Enabled Multimode Fiber Specklegram Sensors: A Review

Machine-Learning-Enabled Multimode Fiber Specklegram Sensors: A Review

A spatial domain multiplexing technology for fiber specklegram sensor

With the advancement of image processing and artificial intelligence technology, Fiber Specklegram Sensors (FSS) based on specklegram recognition have emerged as a novel sensing technology. The output speckles represent the interference between different transmission modes in the fiber, which vary with changes in fiber fluctuations such as the pressure on the fiber or ambient temperature. This sensor boasts high precision and low cost. However, when the optical fiber is required to perform multi-parameter or multi-position sensing simultaneously, the variety of specklegrams counterpart to external stimuli has become very large, significantly limiting the application scenarios of FSS. In this paper, a new structure based on side-coupling theory is proposed, where the output specklegram comprises a circular specklegram and an annular specklegram formed by end-coupling light and side-coupling light respectively. In this manner, the circular specklegram and annular specklegram can represent different sensing parameters, and the circular specklegram is completely unaffected by the annular specklegram, enabling multiplexed sensing. Multiplexed experiments for pressure and temperature sensing are conducted utilizing this structure, and deep learning technology is employed to process the different sets of specklegrams for regression prediction. The accuracy of pressure and temperature sensing tests reached 98.62% and 95.31% respectively, verifying the feasibility of this method for multiplexed sensing.

Bending recognition based on learning enhanced fiber specklegram sensor

In this paper, we propose and demonstrate a deep learning-enhanced fiber specklegram sensor for bending recognition. A segment of multimode fiber is used to sense bending, and tiny bending changes lead to significant variations in the speckle pattern generated at the distal end of the fiber. Principal component analysis (PCA) is utilized to optimize the collected samples and remove noise and redundant information by mining internal features, which makes the mapping relationship between the speckle image and corresponding curvature clearer and is conducive to reducing computational complexity. Back propagation neural network is employed to learn the mapping relationship between speckle image and curvature on the optimized dataset according to the optimized direction provided by PCA. The testing results show that the prediction error of the trained model for the learned bending state is 5.9 × 10−4 m−1, and the prediction speed is 0.05 ms per frame. The proposed scheme has a strong generalization ability and can be applied to predict bending states that have never been learned or seen with a prediction error of 3.8 × 10−2 m−1, which cannot be realized by the previously reported fiber specklegram sensor based on the classification neural network. The bending recognition scheme enhanced by deep learning proposed in this paper provides an enlightening reference for solving fiber sensing problems with deep learning methods, and it has the potential to be applied in more fields as a general scheme.

Fiber specklegram torsion sensor based on residual network

A novel fiber specklegram torsion sensor based on multimode fiber (MMF) is proposed and experimentally demonstrated in this paper. The Residual Network (ResNet) is used to establish the relationship between the changing process of the specklegram and the torsion angle of the MMF. The experimental results indicate that the sensor exhibits the advantages of high accuracy and large measurement range. Within a torsion angle range of 0–360°, the prediction error is within ±2° for 99.1% of the specklegrams in test set. By analyzing the decorrelation process of the specklegrams from different kinds of MMFs, we find that the difference in torsion angle prediction accuracy of different MMFs stems from the decorrelation angle of the specklegram, and the larger decorrelation angle of specklegram holds higher torsion angle sensing accuracy of the trained ResNet. Meanwhile, the difference of the decorrelation angle of specklegram is attributed to the difference of the fiber normalized frequency. This analytical process may be applicable to the analysis of other specklegram based sensing schemes and the selection of dataset for fiber specklegram sensor. The possibility of transfer learning is also investigated to show an improved portability and reusability. In addition, the fiber specklegram torsion sensor owns other merits such as easy fabrication and good robustness, which shows its wide range of potential applications.

Demonstration of a Learning-Empowered Fiber Specklegram Sensor Based on Focused Ion Beam Milling for Refractive Index Sensing.

We report a simple and robust fiber specklegram refractive index sensor with a multimode fiber-single mode fiber-multimode fiber structure based on focused ion beam milling. In this work, a series of fluid channels are etched on the single-mode fiber by using focused ion beam milling to enhance the interaction between light and matter, and a deep learning model is employed to demodulate the sensing signal according to the speckle patterns collected from the output end of the multimode fiber. The feasibility and effectiveness of the proposed scheme were verified by rigorous experiments, and the test results showed that the demodulation accuracy and speed could reach 99.68% and 4.5 ms per frame, respectively, for the refractive index range of 1.3326 to 1.3679. The proposed sensing scheme has the advantages of low cost, easy implementation, and a simple measurement system, and it is expected to find applications in various chemical and biological sensing.

The Optimization of Multimode Fiber Speckle Sensor for Microvibration

A vibration sensing system with optical fiber speckles is demonstrated and optimized with different optical fiber diameters and speckle statistical algorithms. The types of fiber diameter and material lead to a different performance of fiber specklegram sensor (FSS), which has been experimentally explored. The signal intensity, demodulated from the speckles, is different when using multimode fibers with different diameters. At the same time, the sensing effect of different fibers depends on the speckle statistical algorithms. Accordingly, we use different statistical methods in theory and experiment to analyze the influence of fiber diameter and speckle statistical methods on the sensing performance. A vibration sensing system with optimized performance is achieved by the optimized types of optical fiber and the corresponding optimized algorithms, which are promising for sensing weak vibration, such as detecting.

Spatially-resolved bending recognition based on a learning-empowered fiber specklegram sensor.

Fiber specklegram sensors do not rely on complex fabrication processes and expensive sensor interrogation schemes and provide an alternative to routinely used fiber sensing technologies. Most of the reported specklegram demodulation schemes focus on correlation calculation based on statistical properties or classification according to features, resulting in limited measurement range and resolution. In this work, we propose and demonstrate a learning-empowered spatially resolved method for fiber specklegram bending sensors. This method can learn the evolution process of speckle patterns through a hybrid framework constructed by a data dimension reduction algorithm and regression neural network, which can simultaneously identify the curvature and perturbed position according to the specklegram, even for the unlearned curvature configuration. Rigorous experiments are performed to verify the feasibility and robustness of the proposed scheme, and the results show that the prediction accuracy for the perturbed position is 100%, and the average prediction errors for the curvature of the learned and unlearned configurations are 7.79 × 10-4 m-1 and 7.02 × 10-2 m-1, respectively. The proposed method promotes the application of fiber specklegram sensors in the practical scene and provides insights for the interrogation of sensing signals by deep learning.

Demodulation of Fiber Specklegram Curvature Sensor Using Deep Learning

In this paper, a learning-based fiber specklegram sensor for bending recognition is proposed and demonstrated. Specifically, since the curvature-induced variations of mode interference in optical fibers can be characterized by speckle patterns, Resnet18, a classification model based on convolutional neural network architecture with excellent performance, is used to identify the bending state and disturbed position simultaneously according to the speckle patterns collected from the distal end of the multimode fiber. The feasibility of the proposed scheme is verified by rigorous experiments, and the test results indicate that the proposed sensing system is effective and robust. The accuracy of the trained model is 99.13%, and the prediction speed can reach 4.75 ms per frame. The scheme proposed in this work has the advantages of low cost, easy implementation, and a simple measurement system and is expected to find applications in distributed sensing and bending identification in complex environments.

Agar-based soft tactile transducer with embedded optical fiber specklegram sensor

Tactile transducers monitor physical variables like force and texture through direct contact, featuring indispensable in human–computer interaction and biomedical applications. This work proposes an optical fiber tactile sensor based on specklegram analysis. A soft matrix made of agar embeds a concatenated multimode/single-mode/multimode fiber structure. Forces exerted over the tactile surface disturb the probe mechanically, modulating the output speckle patterns for further processing through image correlation techniques. Experiments evaluated the sensor response as a function of the probe position and the agar matrix composition. Static and dynamic characterizations yielded average force and spatial resolutions of 0.006 N and 0.0008 mm, respectively, with a time response of 34.7 ms, exceeding most of the available optical fiber tactile devices. Compared with intensity and spectral approaches, the specklegram analysis provides high sensitivity (∼1.65 N−1) and relies on a straightforward interrogation system. Furthermore, as a biocompatible, degradable, and easy-to-manipulate material, the agar-based transducer shows perspectives for developing artificial skins and implantable sensors.

Comparison of Ballistocardiogram Processing Methods Based on Fiber Specklegram Sensors

The ballistocardiogram (BCG) is a graphic representation of the movements of the body associated with cardiac activity. In this article, a 10-min BCG has been captured for ten different volunteers with a polymer optical fiber (POF) specklegram sensor. This transducer, which is composed of a charge-coupled device (CCD) camera, a laser emitting diode, and two meters of POF, allows capturing the BCG by analyzing how the induced speckle pattern changes over time. These changes are related to cardiac activity. Several processing methods have been compared to determine which method achieves the best performance: complex cepstrum, power of spectral density (PSD), Pam–Tompkins algorithm, wavelet, autocorrelation, Savitzky–Golay filter, mean absolute deviation, and Hilbert transform. Accuracy and resource consumption have been characterized and compared for these methods. Hilbert, PSD, and Savitzky–Golay exhibit both small errors and computational costs. This article describes a baseline for the main frequency determination of POF-based BCG signals in real-time.

Optical fiber specklegram sensor for multi-point curvature measurements.

We present a multi-point curvature sensor based on optical fiber specklegram measurements. Apart from the current approaches, the proposed system uses an ordinary multimode fiber excited with visible light as a reflection-type probe. Besides, this method discretizes the waveguide into segments connected by joints and assumes sequential bend events, simplifying the specklegram referencing for correlation analyses and avoiding laborious deep learning processing. Sensor characterization yielded a linear response with ∼1.3∘ resolution for single curvatures, whereas shape prediction experiments in the plane resulted in maximum errors of ∼3.5∘ and ∼5.4mm for angular and linear positioning, respectively. Furthermore, exploratory tests indicated errors <2.3∘ regarding probe curvatures in the space. This research introduces a feasible, straightforward alternative to the available shape sensors, enabling applications in medical probes and soft robotics.

Multimode optical fiber specklegram smart bed sensor array.

.SignificanceMonitoring the movement and vital signs of patients in hospitals and other healthcare environments is a significant burden on healthcare staff. Early warning systems using smart bed sensors hold promise to relieve this burden and improve patient outcomes. We propose a scalable and cost-effective optical fiber sensor array that can be embedded into a mattress to detect movement, both sensitively and spatially.AimProof-of-concept demonstration that a multimode optical fiber (MMF) specklegram sensor array can be used to detect and image movement on a bed.ApproachSeven MMFs are attached to the upper surface of a mattress such that they cross in a array. The specklegram output is monitored using a single laser and single camera and movement on the fibers is monitored by calculating a rolling zero-normalized cross-correlation. A image is formed by comparing the signal at each crossing point between two fibers.ResultsThe MMF sensor array can detect and image movement on a bed, including getting on and off the bed, rolling on the bed, and breathing.ConclusionsThe sensor array shows a high sensitivity to movement, which can be used for monitoring physiological parameters and patient movement for potential applications in healthcare settings.

Machine learning for sensing with a multimode exposed core fiber specklegram sensor.

Fiber specklegram sensors (FSSs) traditionally use statistical methods to analyze specklegrams obtained from fibers for sensing purposes, but can suffer from limitations such as vulnerability to noise and lack of dynamic range. In this paper we demonstrate that deep learning improves the analysis of specklegrams for sensing, which we show here for both air temperature and water immersion length measurements. Two deep neural networks (DNNs); a convolutional neural network and a multi-layer perceptron network, are used and compared to a traditional correlation technique on data obtained from a multimode fiber exposed-core fiber. The ability for the DNNs to be trained against a random noise source such as specklegram translations is also demonstrated.

Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensor

Fiber optic specklegram sensors use the modal interference pattern (or specklegram) to determine the magnitude of a disturbance. The most used interrogation methods for these sensors have focused on point measurements of intensity or correlations between specklegrams, with limitations in sensitivity and useful measurement range. To investigate alternative methods of specklegram interrogation that improve the performance of the fiber specklegram sensors, we implemented and compared two deep learning models: a classification model and a regression model. To test and train the models, we use physical-optical models and simulations by the finite element method to create a database of specklegram images, covering the temperature range between 0 °C and 100 °C. With the prediction tests, we showed that both models can cover the entire proposed temperature range and achieve an accuracy of 99.5%, for the classification model, and a mean absolute error of 2.3 °C, in the regression model. We believe that these results show that the strategies implemented can improve the metrological capabilities of this type of sensor.

Entropy analysis of optical fiber specklegram sensors

Optical fiber specklegrams originate from the interference between several guided light modes. Since these granular patterns are sensitive to changes in the fiber status, it is possible to retrieve an external stimulus by analyzing their morphology and spatial distribution. This paper evaluates the first and second-order entropies as metrics to interrogate fiber specklegram sensors. Firstly, the refractive index surrounding a no-core fiber is varied to modulate the number and average size of speckles. Secondly, a microbending transducer driven with controlled displacements creates deviations in the output speckle field. Then, the entropy data for each case is compared with Law’s energy texture and the zero-mean cross-correlation analyses, respectively. Conversely to the Shannon entropy, the joint and relative entropies process the spatial information of the image and corroborate the theoretical and experimental speckle count, yielding ∼0.02 refractive index resolution. Moreover, the relative entropy of the ratio between test and reference speckle field images agrees with the correlation coefficient, achieving ∼0.5 µm resolution. The results show that the second-order entropy is effective for interrogating fiber specklegrams, figuring as a straightforward alternative to the widespread texture processing and deep learning approaches.