The new generation of distributed optical sensors with improved interrogation, multiplexing, and acquisition techniques with the possibility of performing measurements with high spatial resolution over tens of kilometers of optical fiber has led to the accumulation of a vast volume of data that can present a big challenge to process and store all this data. Looking for simple solutions to this problem, we present in this paper a data compression method for distributed temperature sensors. This compression approach performs the spatial derivative of the temperature signal, constituting a simple and effective method to remove redundant information. Also, this compression methodology is suitable for temperature data, as it follows thermal variations over time and can be applied to any temperature profile with multiple thermal events along the sensing fiber, whether in heating or cooling circumstances. Tests performed with a large amount of experimental data showed that an average compression ratio of 1.5× can be obtained by removing redundant spatial temperature variations without losing spatial resolution.
Raman-based distributed temperature sensor (RDTS) devices have grown dramatically over the past two decades, partially driving the optical sensor industry. Over nearly four decades, most academic investigations about RDTS have focused on developing distributed sensor devices operating at the wavelength of 1550 nm, given the low loss of standard single-mode fibers in this spectral region. Certainly, the wavelength of 1550 nm is ideal for long-range sensing applications. However, at this wavelength, the signal-to-noise ratio (SNR) of RDTS systems is degraded, given the low intensity of the measured signals. Looking for simple solutions to improve the SNR of this sensing technology, we discuss in this paper an RDTS operating at the spectral region of 840 nm as an alternative for short-range distributed temperature sensing applications delivering an improved SNR.
Measuring cortisol levels as a stress biomarker is essential in many medical conditions associated with a high risk of metabolic syndromes such as anxiety and cardiovascular diseases, among others. One technology that has a growing interest in recent years is fiber optic biosensors that enable ultrasensitive cortisol detection. Such interest is allied with progress being achieved in basic interrogation, accuracy improvements, and novel applications. The development of improved cortisol monitoring, with a simplified manufacturing process, high reproducibility, and low cost, are challenges that these sensing mechanisms still face, and for which solutions are still needed. In this paper, a comprehensive characterization of a D-shaped fiber optic immunosensor for cortisol detection based on surface plasmon resonance (SPR) enabled by gold coating is reported. Specifically, the sensor instrumentation and fabrication processes are discussed in detail, and a simulation with its complete mathematical formalism is also presented. Moreover, experimental cortisol detection tests were performed for a detection range of 0.01 to 100 ng/mL, attaining a logarithmic sensitivity of 0.65 ± 0.02 nm/log(ng/mL) with a limit of detection (LOD) of 1.46 ng/mL. Additionally, an investigation of signal processing is also discussed, with the main issues addressed in order to highlight the best way to extract the sensing information from the spectra measured with a D-shaped sensor.
In this work, we analyze different types of recurrent neural networks (RNNs) working under several different parameters to best model the nonlinear optical dynamics of pulse propagation. Here we studied the propagation of picosecond and femtosecond pulses under distinct initial conditions going through 13 m of a highly nonlinear fiber and demonstrated the application of two RNNs returning error metrics such as normalized root mean squared error (NRMSE) as low as 9%. Those results were further extended for a dataset outside the initial pulse conditions used on the RNN training, and the best-proposed network was still able to achieve a NRMSE below 14%. We believe that this study can contribute to a better understanding of building RNNs employed for modeling nonlinear optical pulse propagation and of how the peak power and nonlinearity affect the prediction error.
Aquaculture is a fundamental sector of the food industry nowadays. However, to become a sustainable and more profitable industry, it is necessary to monitor several associated parameters, such as temperature, salinity, ammonia, potential of hydrogen, nitrogen dioxide, bromine, among others. Their regular and simultaneous monitoring is expected to predict and avoid catastrophes, such as abnormal fish mortality rates. In this paper, we propose a novel anomaly detection approach for the early prediction of high fish mortality based on a multivariate Gaussian probability model. The goal of this approach is to determine the correlation between the number of daily registered physicochemical parameters of the fish tank water and the fish mortality. The proposed machine learning model was fitted with data from the weaning and pre-fattening phases of Senegalese sole (Solea senegalensis) collected over 2018, 2019, and 2020. This approach is suitable for real-time tracking and successful prediction of up to 80% of the high fish mortality rates. To the best of our knowledge, the proposed anomaly detection approach is the first time studied and applied in the framework of the aquaculture industry.
Aquaculture/instrumentation , Fishes/physiology , Animals , Mortality , Probability
This paper proposes and experimentally demonstrates a linearization technique for interferometric fiber sensors. From a 2D reconstruction of the interference spectra and subsequent denoising process, relevant improvements in linearity and range are obtained for both angle and liquid level sensors. This linearization technique can be easily implemented on any graphical interface of different types of interferometric sensors without requiring modification of the sensor physical structure, which makes it a low-cost solution. In this regard, this approach finds a wide field of applications. With the appropriate modifications, it can potentially be applied to other non-interferometric sensors that have moderate linearity and operating range.
In this paper, we propose a new, to the best of our knowledge, technique based on the measurement and analysis of the intensity of the interference pattern as an alternative approach for interrogating liquid-level interferometric fiber sensors. This interrogation is based on calculations that can take into account a vast number of peaks and dips of an interferometric spectrum, allowing the use of such devices as distributed sensors capable of measuring longer-level ranges. Here, liquid-level measurements of up to 120 mm were experimentally obtained with high linearity and a sensitivity of $ - {0.042}\;{\rm dB/mm}$-0.042dB/mm.
In this paper, we report for the first time, to the best of our knowledge, the experimental generation of dark pulses in the 1.5 µm band from a passively $Q$Q-switched fiber laser employing graphite oxide as the saturable absorber, generating tunable microsecond pulses with kHz repetition rates. The graphite oxide samples were obtained by recycling the graphite present in Li-ion batteries used in cell phones through a chemical separation and oxidation process. Sample characterization employing x-ray diffraction, solid-state $ ^{{13}}{\rm C} $13C nuclear magnetic resonance, and Raman spectroscopy showed that the produced graphite oxide exhibited a homogeneously oxidized structure. Dark pulse emission could be observed at a relatively low pump threshold of 35 mW in a short 20 m laser cavity, indicating that the graphite oxide acted as a saturable absorber, significantly enhancing the nonlinearity of the laser cavity. Additionally, dark pulse operation was demonstrated at a high stability with a signal-to-noise ratio of 56 dB and a pulse-to-pulse timing jitter of 159.84 fs.
