Distributed temperature sensors operating at 840 nm for short-range sensing applications.

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.

Spatial-derivative-based compression approach for distributed temperature data.

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.

Design and analysis of recurrent neural networks for ultrafast optical pulse nonlinear propagation.

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%.

Label-free plasmonic immunosensor for cortisol detection in a D-shaped optical fiber.

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.

Prediction of fish mortality based on a probabilistic anomaly detection approach for recirculating aquaculture system facilities.

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.

2D denoising technique for the linearization of interferometric fiber sensors.

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.

Stable dark pulses produced by a graphite oxide saturable absorber in a fiber laser cavity.

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.

Optical spectral intensity-based interrogation technique for liquid-level interferometric fiber sensors.

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.

NARX neural network model for strong resolution improvement in a distributed temperature sensor.

This paper proposes an approach to process the response of a distributed temperature sensor using a nonlinear autoregressive with external input neural network. The developed model is composed of three steps: extraction of characteristics, regression, and reconstruction of the signal. Such an approach is robust because it does not require knowledge of the characteristics of the signal; it has a reduction of data to be processed, resulting in a low processing time, besides the simultaneous improvement of spatial resolution and temperature.