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As we all know, the change of mode interference caused by the curvature change in multi-mode fiber (MMF) can be well represented as a fiber specklegram recorded by CCD (Charge-coupled Device). However, it's difficult to identify the different bending occurred in several points in short distance. The paper proposes plastic fiber bending sensors can be used to detect the multi-point bending without adding any hardware. The convolutional neural network was used to classify the output speckles under different bending states. Specklegrams from fiber with three sensitization areas can be recognized by the neural network with a bending interval of 15°,10° and 5° with an accuracy rate of 99.2%, 96.1% and 93.5% respectively. Compared with traditional multi-point distributed sensors, this method is lower cost and easier to operate. The method proposed in this paper can find applications in distinguishing the status of certain structures, such as robotic arms and some disabled auxiliary equipment.

An image camouflage encryption scheme based on fiber specklegram and polarization multiplexing technology is proposed and demonstrated via simulation. The camouflaged original image is coupled into the multimode fiber (MMF) through different polarized light fields, and the specklegram caused by strong intermodal interference provides excellent chaos and randomness required for encryption. Since the optical fiber specklegram has a strong dependence on the configuration, transmission matrixes used as the key have high security and unpredictability. The results demonstrate the huge keyspace and excellent key sensitivity of the scheme. The ability to resist key disclosure and differential attack is analyzed. In addition, the scheme shows great security and reliability in several common noise attacks. • An image camouflage encryption scheme based on fiber specklegram and polarization multiplexing technology. • Intermode interference in multimode fiber providing chaos and randomness. • Polarization transmission matrixes keys achieving high security and unpredictability. • Performing good security and reliability in several common noise attacks with huge key spaces. [ABSTRACT FROM AUTHOR]

In this work, we present a compact and ultrasensitive bio-molecule sensor based on fiber specklegram, achieving a DNA detection sensitivity of 10 −16  M. To our knowledge, the new device is the most sensitive fiber-optic DNA sensor reported to date. Importantly, the well-known temperature cross-sensitivity issue in many fiber-optic devices has been entirely removed by the introduction of a reference fiber-optic channel embedded in microfluidic channels. In the experiment, we demonstrate label-free DNA hybridization detection at different concentrations, where normalized intensity inner product of the collected specklegrams are calculated and used for determining the DNA concentration. The result shows the exact ssDNA concentration can be precisely determined with a single experiment in 60 min. The new fiber sensor presents distinctive advantages over traditional bio-sensors in terms of sensitivity, precision and cost, which may find many practical applications in health and medicine, e.g., point-of-care testing and disease diagnosis. [ABSTRACT FROM AUTHOR]

A weight recognition scheme is proposed based on the analysis of plastic optical fiber (POF) specklegrams. A simple experiment is performed for data acquisition comprising specklegram images corresponding to different external applied weights ranging from 0 to 3 kg in steps of 0.5 kg on the POF. These specklegram images are further split into training, validation, and test datasets for employment in the convolutional neural network (CNN). The model is trained and validated with a sufficient number of epochs (iterations) to obtain optimal training and validation accuracies (>90 %); it is then used for recognizing (classifying) the unseen test dataset images. A user-defined CNN model is optimized using four different optimizers: Adam, AdaMax, Nadam, and RMSProp. The recognition (or test) accuracy of these optimizers is compared. The Nadam optimizer has the highest recognition accuracy of 93.1% for increasing weights and 91.9% for decreasing weights. Furthermore, we investigate the impact of two parameters, namely temperature effect and information loss of specklegrams, on the model's weight recognition accuracy. The temperature effect is studied for finite temperatures ranging from 29°C to 35°C with 3°C step fluctuations around the ambient temperature of 25°C. We find that the maximum deviation in recognition accuracy is about 1.2%. To quantify the information content (speckles) in each blocked specklegram, Shannon entropy (SE) is estimated. These images are then used in our existing CNN model with the Nadam optimizer to evaluate recognition performance with blocking. With increasing blocking, we observe a decrease in SE and recognition accuracy. This analysis suggests that, even with information loss in the specklegrams at ≤20 % blocking, a good representation of weights with high recognition accuracy (>80 %) can be obtained. The presence of a small physical obstacle in front of the imaging system can cause information loss in specklegrams to manifest in practical scenarios. [ABSTRACT FROM AUTHOR]

• A bending recognition scheme based on specklegrams with high accuracies. • A motorized translation stage is used to get specklegrams with various bending. • CNN based deep learning is used to classify specklegrams. • The scheme shows potential a single MMF acts as an independent status monitor. Since the curvature induced variations of mode interference in multimode fiber (MMF) can be well represented by the fiber specklegrams, a bending recognition scheme based on the analysis of MMF specklegrams is proposed and verified. Amounts of specklegrams from the facet of MMF under different bendings were detected and used for training and testing in an image recognition algorithm based on deep learning. Good recognition results are provided by the specklegrams from MMF with diameter being 105 and 200 μ m , for which the average accuracies of bending status recognition are respectively 92.8% and 96.6%. Because specklegram can represent the status of the whole section of MMF, such scheme indicates the capability to distinguish the specklegrams even when the MMF is under complicated bending. In this sense, the scheme presents the potential of a single MMF being used as a bending indicator or status monitor independently, which may find applications in distinguishing the status of certain structures, such as robotic arms, mechanical fingers and some disabled auxiliary equipments. [ABSTRACT FROM AUTHOR]