This paper proposes a novel combined optical-electronic simulation in an indoor environment consisting of four luminaires with tunable LEDs of different Correlated Color Temperatures (CCT). This paper investigates the ability to perform Visible Light Positioning (VLP) to identify the receiver positions in such a scenario with tunable LEDs. In this regard, the ray-tracing simulation, generating a list of rays consisting of optical power, CCT, and the corresponding wavelength of each ray, impinging on the receiver's surface, is combined with the simulation of an electronic receiver with wavelength depending sensitivity in Simulink/Simscape. This configuration allows us to evaluate the impact of tunable CCT on the electronic design, especially regarding optimizing certain parameters. In this work, we show how the number of unique values in an offline-fingerprinting map can be optimized, which is a crucial requirement for indoor positioning utilizing the fingerprinting method. With our outlined solution approach, a system-level tool is formed based on a precise and comprehensive optical-electronic simulation that allows for assessing VLP scenarios.

To support people’s wayfinding activities we propose a Visible Light Communication (VLC) cooperative system that supports guidance services and uses an edge/fog based architecture for wayfinding services. A mesh cellular hybrid structure is proposed. The dynamic navigation system is composed of several transmitters (ceiling luminaries) which send the map information and path messages required to wayfinding. The luminaires are equipped with one of two types of nodes: a “mesh” controller that connects with other nodes in its vicinity and can forward messages to other devices in the mesh, effectively acting like routers nodes in the network and a “mesh/cellular” hybrid controller, that is also equipped with a modem providing IP base connectivity to the central manager services. These nodes acts as borderrouter and can be used for edge computing. Mobile optical receivers, using joint transmission, collect the data at high frame rates, extracts theirs location to perform positioning and, concomitantly, the transmitted data from each transmitter. Each luminaire, through VLC, reports its geographic position and specific information to the users, making it available for whatever use. Bidirectional communication is implemented and the best route to navigate through venue calculated. The results show that the system makes possible not only the self-localization, but also to infer the travel direction and to interact with information received optimizing the route towards a static or dynamic destination.

In recent years, devices with wireless communication capabilities have generated a growing interest in indoor navigation. Indoor localization and proximity detection is becoming increasingly attractive due to the emergence of the Internet of Things (IoT) and the inherent end-to-end connectivity of billions of devices. In a closed space, GPS has poor, unreliable performance, requiring alternative techniques and wireless technologies. In this paper, we propose the use of Visible Light Communication (VLC) to support guidance and communication for signaling in an indoor environment. Research focuses mainly on the development of navigation VLC systems, transmission of control data information, and decoding techniques. The communication system uses RGB white LEDs as emitters and pinpin photodiodes with selective spectral sensitivity as receivers. Downlink communication is established between the infra-structure and the mobile user. The decoding strategy is based on accurate calibration of the output signal and uses bit error control methods to reduce the BER of the system. In this paper, we will describe the coding schemes and decoding algorithms, as well as the characteristics of transmitters and receivers.

This paper addresses the issues related to the Visible Light Communication (VLC) usage in vehicular communication applications. We propose a Visible Light Communication system based on Vehicle-to-Vehicle, Vehicle-to-Infrastructure and Infrastructure-to-Vehicle communications able to safely manage vehicles crossing through an intersection leveraging Edge of Things facilities. By using the streetlamps, street lights and traffic signaling to broadcast information, the connected vehicles interact with one another and with the infrastructure. By using joint transmission, mobile optical receivers collect data at high frame rates, calculate their location for positioning and, concomitantly, read the transmitted data from each transmitter. In parallel with this, an intersection manager coordinates traffic flow and interacts with the vehicles via Driver Agents embedded in them. A communication scenario is stablished and a “mesh/cellular” hybrid network configuration proposed. Data is encoded, modulated and converted into light signals emitted by the transmitters. As receivers and decoders, optical sensors with light filtering properties, are used. Bidirectional communication between the infrastructure and the vehicles is tested. To command the passage of vehicles crossing the intersection safely queue/request/response mechanisms and temporal/space relative pose concepts are used. Results show that the shortrange mesh network ensures a secure communication from street lamp controllers to the edge computer through the neighbor traffic light controller with active cellular connection and enables peer-to-peer communication, to exchange information between V-VLC ready connected cars. The innovative treatments for the congested intersections are related with the introduction of the split intersection. In the split intersection a congested two-way-two-way traffic light controlled intersection was transformed into two lighter intersections which facilitate a smoother flow with less driver delay by reducing the number of vehicle signal phases. Based on the results, the V-VLC system provides direct monitoring of critical points including queue formation and dissipation, relative speed thresholds and inter-vehicle spacing, increasing safety.

Human activity recognition (HAR) gained great interest in today’s research activities, especially in regard to demographic change. Especially when complex activities have to be recognized, HAR systems often rely on multiple sensors necessary to be worn by the user. In this work, we propose a novel approach for combining a segmented optical receiver with a single IMU device. By fusing IMU related real world experimental data with precise optical simulations of a segmented optical receiver we can not only determine the activity of the user, including complex movements like walk-up and walk-down, but can also determine the user´s location.

Avalanche photodiodes (APDs) are used in high-speed data communication and light detection and ranging (LIDAR) systems due to their high sensitivity and high speed. However, InAlAs and InP based APDs have relatively high excess noise because they have relatively similar electron and hole ionization coefficients (α and β respectively). Here, we report on an ultra-low excess noise material Al_0.85Ga_0.15As_0.56Sb_0.44 (hereafter AlGaAsSb) with a k value (β/α) of 0.01. The excess noise and multiplication measurements were performed on both random alloy (RA) p⁺-i-n⁺ and digital alloy (DA) grown p⁺-i-n⁺ diodes with depletion regions of 1020nm and 890nm respectively. The excess noise was found to be broadly similar in both RA and DA grown structures.

In this paper we analyze the possibilities of developing efficient and low-cost multichannel photonic sensor system using SiO₂:TiO₂ photonic integrated circuit (PIC) technology. We examine numerically the possibility of using optical ring resonator as a fundamental building block of the system. The most convenient ring resonator configuration is shown providing both relatively high sensitivity (125 nm/RIU) and relatively wide free spectral range (9.6 nm) large enough for serial connection of multiple rings. Our study is completed by the report of recent advances in fabrication technology obtained in our project.

Composite materials offer significant performance advantages due to their lightweight, high-strength, and high stiffness. This led to their adoption in several industrial sectors with particular emphasis on the aerospace industry which has undergone a transformation towards a composite-dominated new standard. In order to respond to the increased demand, it is mandatory to focus on an efficient and well-controlled curing cycle of the resin, which will lead to a significant reduction of cost and an increase in production speed. We investigate, a photonic solution, able of measure key monitoring values that facilitate optimization of the curing process. Simulation and evaluation results on a bragg grating based photonic integrated sensor, developed in 220 nm Silicon-onInsulator platform, are presented. A multi-sensor deployment is considered, enabling monitoring of the temperature and the refractive index of the resin. Serially coupled bragg grating photonic elements will enable concurrent monitoring of both temperature and refractive index. Several bragg configurations have been investigated and experimentally evaluated, specifically regular and phase-shifted ones. Both TE and TM polarization operation sensors that have been designed and fabricated, will be presented. Their sensitivity on resin temperature and refractive index variation will be discussed, resulting in a comparative study outlining the benefits and disadvantages of each solution. Refractive index sensors are realized by employing post-processing etching techniques on Multi-Project-Wafer run fabricated silicon chips, on top of the periodic bragg grating element. The comparative study takes into consideration TE and TM polarization operation, regular and phase-shifted bragg grating configuration elements, while evaluating their sensitivity in temperature and refractive index variations. Temperatures considered are in the range of 27°C to 200°C, while refractive index values lay between 1.5 and 1.6. A Figure-of-Merit is proposed to facilitate the selection of multi-sensor deployment for specific temperature and refractive index ranges.

This study concerns the development of sensors based on integrated resonators elements for monitoring of the dynamic processes of sedimentation and drying of various bloods. The different bloods studied were sampled respectively from poultry, goats and humans with various characteristics in terms of red blood cell sizes, viscosities and densities. The analyzes relating to the sedimentation rates, the viscosity measurements by rheometer and the images of the red blood cells were carried out by different methods and apparatus for all the blood studied. These latest corroborated by our specific resonant measurements on integrated sensors allow us to converge and conclude that the device and photonic chip sensor produced can discriminate the different bloods, with in the 1st order, an equivalent and differential viscosity measurements.

An optical switch configuration is demonstrated as a promising approach for the detection of proteins based on preliminary work consisting of the detection of variations in the bulk refractive index. The device comprises a corrugated dielectric grating covered by chrome and gold, featured by a high depth-to-period ratio, allowing switching SPR detection. This grating is placed inside a fluidic cell through which solutions are injected. Bulk sensing is demonstrated by the successive injection of four mixtures of water and glycerol in different concentrations. The refractive indices of these solutions have a 2×10^-3 RIU increment. Their detection has been obtained with a signal-to-noise ratio of 735.

This research reports concept, realization and testing of an opto-microfluidic lab-on-a-chip fully integrated in lithium niobate. Such a device aims at the integration between optical sensing and microfluidics, a perspective with many fields of application like micro-analytical chemistry and biomedical analysis. The integration is achieved herein by arranging several parallel waveguides with a T-junction of two micrometric channels on the same LiNbO3 surface. While the Tjunction enables the generation of microdroplets of a liquid sample, light can be brought to the channel by the waveguides once one of them is lighted from its input. Thanks to this configuration, an intensity signal can be collected through the waveguides to a photodiode, resulting in the detection of a time signal linked to the shape and composition of the flowing microdroplets. In this research, after introducing the detection process, particular attention is paid to the experimental test of the device sensing performance. As an example, the light absorption due to the introduction of a protein dispersion in a concentration reactive dye is reported. By comparing two droplet classes of different content and same shape, the two related mean signals are clearly discriminated, showing a significant sensitivity enhancement for two different protein concentrations. These results prove the device as a fully efficient system for content analyses, merging low consumption of the liquid sample with an effective detection strategy.

We present a new method for detecting single nanoparticles using a sensor based on a whispering gallery mode resonator submerged in aqueous solutions. A free-space diode laser excites whispering-gallery mode resonances by focusing it on the edge of the microresonator. Its emission frequency is then locked to a resonant mode in order to track any change induced by the interaction of the microsphere with nanoparticles, which can be suspended in the surrounding liquid medium. A theoretical analysis based on some seminal work, together with preliminary noise source evaluation, indicates that frequency shifts down to the order of hundreds of kHz are measurable, thus allowing to detect single nanoparticles. Further upgrades of the experimental scheme aimed at precise nanoparticle sizing and positioning are discussed.

This paper discusses an application of machine-learning solution for processing of the dynamical sensing responses collected with a multiplexed microresonator detector. Performance of a long short-term memory network (LSTM) out of bidirectional and dropout layers is analyzed on example of the experimental data collected for a temporal gradient of the local refractive index. We experimentally demonstrate the possibility for analyte parameters prediction with accuracy of >99% based on a set of complex non-linear highly specific time sequences of the intensities radiated by the microcavities which is obtained within a timescale 4 times shorter than required to reach the steady state. Optimization possibilities in terms of the number of microresonator signals to consider for the LSTM network training along with the complexity of its architecture are analyzed.

We have created a highly sensitive humidity sensor that is based on glycerol droplet. Glycerol is hygroscopic and has a specific glycerol-water ratio for every relative humidity (RH) %. It is cheap and environmentally friendly. We use the droplet as a whispering gallery mode (WGM) microresonator. WGMs are known for their high Q factors, which lead to high sensitivity and precision. As RH changes, the resonant wavelength shifts due to a change in the droplet’s radius and refractive index. We have successfully created an experimental set-up and original data analysis method that allow us to follow the resonant wavelength shift in real-time. Results show that the sensor has an average sensitivity of 2.85 nm/% RH in the 50–70 % RH range, it is stable and has a long lifetime. To further investigate the properties of the glycerol droplet sensor, we tested its selectivity and tried two coupling methods (free-space and tapered fiber). We decided to test the sensor’s response to two different gases – ethanol and acetone. Results show that glycerol is highly selective and does not absorb ethanol/acetone molecules, meaning that it can be used for trustworthy humidity measurements.

In this work, the protein content alteration in cancerous versus healthy exosomes is used as the figure of merit in an early stage, label-free, non-invasive, liquid biopsy based cancer diagnosis biosensing method. To do so, an anisotropic effective refractive index is assigned to the exosome, which determines its protein content, according to a minute 3D model of the exosome based on the mean field theory. The modeled exosomes are then positioned in the vicinity of a polymer coated spherical microresonator, capable of distinguishing such slight refractive index alteration. Such ultra-sensitivity is achieved due to adding the shell as it reduces the radiation loss together with the mode volume. Results show that, increasing the amount of protein content in the exosome by even 1%, as a potential cancer biomarker, concludes to 3.5fm wavelength shift in the whispering gallery resonance of the microresonator, and accordingly facilitating early stage cancer diagnosis in a label-free manner.

Room temperature-operable mid-infrared (MIR) photodetectors have drawn significant attention due to their potential applications in imaging, security and sensing. The unique and tunable optoelectronic properties of graphene make it an attractive platform for designing tunable and broadband photodetectors. This work demonstrates a tunable mid-infrared photodetector using Graphene Nanoribbons (GNR) operated in the 5 – 12 μm range. We used the tunable plasmonic properties of graphene nanoribbons to model the photodetector. We numerically compute the generation rate using the plasmon-enhanced optical absorption in the GNR. We show a peak extinction of ~35% in the structure with GNR of width 50 nm and Fermi energy 0.3 eV is due to plasmonic resonances. The computed generation rate determines the photoresponse current in the GNR-based FET. The proposed structure shows a ~ 40-fold improvement in the peak photoresponse current in patterned structure over unpatterned structure in the wavelength 5 – 12 μm. Hence the tunable plasmonic resonances and the width dependent bandgap of GNRs enable the realization of room-temperature operable broadband MIR photodetector.

We present a novel approach for hyperspectral snapshot imaging. Compared to methods containing Fabry-Perot or absorption filters on each of the sensors pixels our approach relies on diffractive optics. The sensor can be realized in a particularly cost-effective and simple way. Another advantage is the possibility of a flexible adaptation to the respective measurement task. The spatio-spectral resolution and also the spectral measuring range can be adjusted and even varied over the sensor. Therefore, it becomes possible to measure certain parts of the scene with high spatial resolution and other parts with high spectral resolution or a different wavelength range. In initial tests, the system was able to detect spectral shifts of less than two 0.5 nm, within a measuring range of 190 nm. Besides potential applications in medicine or agriculture, the system can also be used in surface metrology. For this purpose, we present the realization of an area-based snapshot chromatic confocal sensor, which can perform 2.5D measurements with only one image acquisition.

Precision agriculture has evolved over the years to meet the growing demand for agricultural productivity with limited resources, and with it, the smart irrigation techniques are also gaining traction. Management of water is critical since it is one of the most significant components of the photosynthetic process and hence an indicator of crop health and yield. Due to the high sensitivity of Terahertz (THz) radiation towards the presence of water, in current work, the moisture content in leaves from a Capsicum annuum plant is approximated using THz time-domain imaging. To overcome the effect of approximations and limitations in theoretical models, this work aims to generalize the prediction of moisture content in plants by simulating drought stress in an un-watered plant through a detached leaf. This is achieved through analysis of time-lapsed THz imaging of several leaves by employing a machine learning approach. The THz images are processed to retrieve pixels corresponding only to the flat lamina without the veins because of their morphological differences. The process is repeated for all instances of images as the leaf dries up. For predicting the moisture content in the leaf, transmittance of the selected pixels at selected frequencies ranging from 0.4-2.1 THz are used to train supervised machine learning regression (SMLR) model. Standard error of estimate (SEE) used for performance analysis of Decision Tree, Random Forest and Support Vector regression models show that as the drought sets in and the leaves dry up, the prediction accuracy improves.

This work considers the use of an optical fiber sensor, employing a Gd₂O₂S:Tb inorganic scintillator, for applications in LDR brachytherapy for prostate cancer. Gd₂O₂S:Tb is characterized by a scintillation decay time of ~500 μs, implying that each primary gamma interaction produces a series of single photons, requiring the use of adequate detectors, such as Silicon Photomultipliers (SiPMs). These devices suffer from a significant Dark Count Rate (DCR), undermining system sensitivity. This work reports the result of a feasibility study where identical SiPMs, but different packages, are compared. Specifically, a room temperature SiPM in a ceramic package and a TE-cooled SiPM in a TO8 package. In the former, the optical fiber is in direct contact with the sensor surface, while in the latter there is a separation of ~3 mm. The signal, measured as Photon Count Rate (PCR), in excess of the DCR, was measured in a water phantom at distances of 5 mm and 30 mm from an I¹²⁵ source. For the TE-cooled SiPM, the DCR dropped by ~96% as expected, and the PCR dropped by ~80%, compared to the room-temperature SiPM, due to reduced light acceptance. However, incorporating an optical coupling system into the TE-cooled SiPM, to improve acceptance, resulted in sensitivity increases of 332% and 296% at distances of 5 mm and 30 mm respectively, compared to the room-temperature SiPM. It is hoped that these improvements in sensitivity, will allow for accurate monitoring of the dose-rate from LDR sources, within the clinically relevant treatment volume for prostate cancer.

FLASH-Radiotherapy (FLASH-RT) is an emerging radiotherapy technique delivering ionizing radiation beam at ultrahigh dose rates (UHDR), typically ≥40 Gy/s. Animal studies have demonstrated the safety and efficacy of the technique in killing tumor cells while significantly reducing radiation toxicity in normal tissues, compared to conventional radiotherapy (dose-rate exposure <0.03 Gy/s). A reliable real-time dosimeter system is crucial for the characterization of the so-called ‘FLASH-effect’ and an accurate beam delivery. Standard dosimeters for conventional radiotherapy saturate at this high-intensity field or cannot provide real-time measurements. In previous work, optical fiber inorganic scintillating detectors (ISDs) showed excellent linearity with shutter exposure time and tube current, indicating scintillating signals independent of the dose and dose rate, respectively. This study aims to benchmark the performance of the ISD with plastic scintillating detectors (PSDs) for an ultrahigh dose-rate x-ray beam irradiation. Relative scintillator output, signal linearity with dose and dose rate, signal-to-noise ratio (SNR), signal stability and reliability were evaluated for all detectors. In a UHDR x-ray beam irradiation, the ISDs produced a larger SNR than the PSDs. All detectors showed good linearity with tube current (R2 < 0.975) and shutter exposure (R² >0.999). Gd₂O₂S:Tb showed excellent repeatability (coefficient of variation (CV) <0.1%) compared to other detectors, while the PSDs resulted in the highest reliability for a UHDR beam measurement with a CV of <0.1%. A further investigation regarding the positioning uncertainty of the ISDs during irradiation due to the detector’s angular dependency and the optimal design of the scintillator detectors for UHDR applications are required.

Optical fibre scintillation detectors have proven to be a viable alternative in clinical radiation dosimetry. By obtaining active readouts, scintillators can be used to give real time measurements for various clinical and pre-clinical applications from radiology to radiation therapy. Gd₂O₂S:Pr is of interest as a scintillator due to it offering a much higher light output than other scintillators, organic scintillators in particular. Temperature dependence is exhibited by a number of organic scintillators, such as BCF-60; this however needs to be established for the inorganic scintillator Gd₂O₂S:Pr. This study therefore aims to characterize the temperature dependence of Gd₂O₂S:Pr, using the HYPERSCINT Research Platform 200. The detector was immersed in water and the temperature varied from 22.7°C to 49.0°C using a hotplate and temperature controller. Five spectra were recorded and averaged at each of the seven temperatures, in approximately 5°C increments. A decrease in total photon count with temperature was observed of 0.22%/°C between 346 and 631 nm with a decrease of 0.38 in the full width at half maximum at the photopeak of 513 nm. A method of correcting for temperature is necessary in the use of Gd₂O₂S:Pr as a detection material in environments where the temperature differs significantly from the calibration temperature.

When optical fibre dosimetry systems are exposed to ionizing radiation, unwanted Cerenkov radiation and fluorescent light are produced in the fibre itself during irradiation. A number of techniques have been used to eliminate or minimize these effects. In this study time discrimination technique was used, by measuring the signal of an inorganic scintillation detector between linac pulses, after the stem effect signal has decayed to successfully eliminate the contribution of Cerenkov radiation. Dosimetric properties, including the repeatability of the ISD system response and angular dependence of the system, were tested. Percentage depth dose profiles were measured for different field sizes and compared to ion chamber measurements. The result of this study shows that the ISD system has good repeatability of the output signal when exposed to high and low radiation doses with a maximum deviation of 0.55% and 1.10%, respectively. However, the system showed a strong angular dependence in the azimuthal plane due to the detector shape. Additionally, the system overestimates the dose when measuring PDDs, this effect decreased with the decrease in field sizes.

In this work, an algorithm for the optimization of the design of an optical fiber bundle displacement sensor for Tip Clearance and Tip Timing measurements is presented. The former is based on the gaussian beam mathematical approach, and implements a fiber arrangement comprised of a transmitting single-mode fiber and two concentric rings of receiving multimode fibers. The software includes a graphical user interface that allows modifying relevant geometrical parameters of the sensor and fiber characteristics, and enables monitoring in real time the effects of such modifications. Results indicate that in order to achieve the most sensitive design with the largest operating range, the key parameters are the gain configuration, the distance between the receiving fiber rings and the radii of receiving fibers, and that the best fitting sensor configuration is a tradeoff among the aforementioned characteristics as it is heavily dependent on the measurement constraints. The results obtained by the simulation tool were validated in good agreement with the calibration curve of the manufactured optical fiber bundle displacement sensor in a scaled rotor.

A single-mode fiber (SMF) may be transformed into a distributed sensor using optical backscattering reflectometry (OBR). OBR interrogates Rayleigh scattering appearing in each section of optical fibers by tracking reflections and generating spectral signatures. When fiber experiences temperature and strain variations, OBR shows changes in terms of wavelength shift. The OBR operation works for single sensing fiber. Using multiple fibers on OBR leads to detection difficulties since the parallel backscattering cannot be distinguished. The use of optical switch increases the time of the scanning that is problematic in real-time clinical operations. Our solution is high scattering nanoparticles doped fiber (NPDF). The core of the fiber is doped with MgO nanoparticles that significantly increases the backscattering power of the fiber. In other words, NPDF backscattering power is 40 dB higher than ordinary single-mode fiber (SMF). A pair of NPDF and SMF can be used to build multiple fiber configurations with discrimination of each sensor in 2mm at NPDF location. This setup is suitable in a medical shape sensing environment, especially in epidural anesthesia. In epidural anesthesia, the needle is inserted through the spine of the patient till it reaches the epidural space and then the anesthetic fluid is delivered. The method is based on a doctor’s perception of strain and has a 12-13% failure rate. The technique can be improved by adding a guidance system with optical fibers. In this article, we present a system of four NPDF fibers along the epidural needle and evaluate performance on a medical phantom. The fibers are glued perpendicular to each other at 90 degrees on all four sides of the needle. These four distributed strain values enable the formation of the 3D shape of the needle. The shape information can be monitored so that the needle can be guided to achieve the epidural space.

Biosensing using optical fibers allows the detection of low concentrations of analytes. To exploit this property, we study a cost-effective fiber-based solution to target the presence of Plasmodium falciparum histidine-rich protein 2 (PfHRP2), a biomarker for malaria diagnosis. In this work, unclad multimode optical fiber probes (400 µm core diameter) are coated with a thin gold film to excite Surface Plasmon Resonance (SPR), yielding high sensitivity to surrounding medium and more precisely to target-bioreceptor interactions. They are connected to a portable white light source and a spectrometer for read-out. The covalent immobilization of anti-PfHRP2 antibodies on gold and its passivation allowed specific detection in different types of media. The use of secondary antibodies as amplifiers in a sandwich assay also allowed us to improve the sensitivity of the method. Different blocking agents (fish gelatin, bovine serum albumin and casein) were studied to optimize the surface selectivity. The detection of PfHRP2 was first calibrated using spiked proteins into phosphate buffer saline (PBS) and then tested in vitro using fresh cultures of Plasmodium falciparum. The shifts observed with our technique were also compared with results obtained from an ELISA (gold-standard technique for detection and quantification of targets), and commercial paper-based lateral-flow assays, usually used on field. As malaria affects many people worldwide, improvement and multiplexing of this technique can be a perspective to overcome the limitations of currently available point-of-care tests in the future.

In the last few years, the use of machine learning has emerged in the field of distributed fiber optic sensors as a promising approach to enhance their performance and provide new capabilities. In this study, we use machine le arning for simultaneous measurements of temperature and humidity in polyimide (PI)-coated optical fibers based on Brillouin Brillouin optical frequency domain analysis (BOFDA). Different non-linear machine learning algorithms are employed, namely polynomial regression, decision trees and artificial neural networks (ANNs), and their discrimination performance is benchmarked against that of the conventional linear regression. The performance is evaluated using leave-one-out cross-validation to ensure that the models are reliable and able to generalize well on new data. We show that nonlinear machine learning algorithms outperform the conventional linear regression and thus could pave the way towards simultaneous cost-effective tempera ture and humidity distributed sensing, which has the potential to find attractive new applications in the field of civil and geotechnical engineering, from structural health monitoring of dikes and bridges to subsea cables and long pipelines corrosion detection.

Nanoparticle doped optical fibres are a new topic of interest in fibre optic strain sensing. In our current work, we find that the plasmon resonance spectral peak shift of embedded gold NPs can be used as a novel strain detection method. When the refractive index change of the optical fibre under strain change is close to zero, the shape change induced plasmon resonance spectral peak shift will become quite important. In this work, we analysed the plasmon resonance spectral peak shift only caused by NP morphing for gold NPs diameters from 10 nm to 400 nm based on the T-matrix method on the high performance computing cluster.

We present a novel approach which could be useful for real-time all-fiber measurements of magnetic fields applied either longitudinally or transversally. We used a microstructured optical fiber, whose suspended core is coated with a nanocomposite magneto-optic thin film. It is obtained by doping a sol-gel solution with cobalt ferrite nanoparticles based on an innovative microfluidic deposition process. When any magnetic field is applied, several effects are induced by the appearance of off-diagonal elements in the nanocomposite material permittivity tensor. It results in a variation of the emerging light polarization state measured for the C-band telecom spectral range, at 1550 nm. One of the best-known magneto-optic effects is the Faraday rotation, observed with longitudinal magnetic fields. Therefore, our functionalization greatly exalts this effect already present in commercial fibers due to the Verdet constant of silica. A 300mT and 1 cm long longitudinal magnetic field creates up to 15° of rotation compared to 0.1° for a classical optical fiber, while for a transverse magnetic field, conventional optical fibers are almost totally insensitive to it. However, in our configuration different off-diagonal elements of permittivity tensor will be activated causing non-reciprocal variation of the propagation constant of the eigenmodes as well as another process leading to a reciprocal magneto-refractive effect. It results in an additional polarization variation of several degrees per centimeter in transversal magnetic field configuration. The interest of such a highly sensitive functionalized optical fiber, is found where conventional magnetic probes cannot be used. Above all, the optical fibers are relatively unaffected by radiations, unlike conventional electronic sensors, so it is conceivable to use them in these exclusion zones where the sensitive part is deported from the electronic measurement system.

A high performance fiber-optic current sensor (FOCS) based on Faraday rotation in a toroidal sensing coil is proposed and demonstrated. The sensor performance has been improved by forming a toroidal sensing head to experience large magnetomotive force for a very small signal of electric current. In order to improve the sensor performance even further, the effective optical path length is increased by making a fiber coil, and the operating wavelength has been shifted to shorter wavelength (1064 nm) compared to the conventional telecom wavelengths (1550 nm) to benefit from a higher value of Verdet constant in conventional single mode fibers. Several toroid-core structures have been simulated using finite element analysis (COMSOL Multiphysics) to obtain enhanced sensitivity. The FOCS design includes an optimized 3D printed core structure along with toroidal windings and fiber loop inside it. Faraday rotator mirror (FRM) compensates for the birefringence along the sensing arm of the setup, while laser amplitude modulation is implemented using an electro-optic modulator (EOM) to enhance the signal to noise ratio at a particular modulated frequency. The developed FOCS set-up with four layers of copper wire windings in toroidal sensing head configuration is capable of detecting low currents of the order of 50 mA within a tested dynamic range of operation 0-10A. Detection of even lower order current (as low as several mA) could be achieved by tuning the design of sensing head.

We report on an on-field CO₂ sensing experiment based on a rapidly modulated optical parametric oscillator (OPO). This OPO is pumped by a mode-locked fibre laser source delivering 120 ps pulse laser with a spectral width of about 0.03 nm at a repetition frequency of 40 MHz and an average power of 5W. The output wavelength of the fibre laser pump source can be rapidly modulated resulting in a modulated mid-IR signal. This modulated signal around 2.7 μm was used for CO₂ detection during a field experiment by a deported (~100m) sampling method.

A strategy for the detection of H₂O₂ as a milk adulterant using a single shot membrane sensor, is presented. Direct quantitative evaluation of H₂O₂ in raw, skimmed, semi-skimmed and whole milk was carried out based on a chemiluminescence reaction with luminol. For H₂O₂ water solutions a linear response was attained from 0.0001% to 0.007 %w/w, with a limit of detection of 3×10^-5 %w/w. A coefficient of determination, R² , greater than 0.97 was achieved, with a relative standard deviation (RSD) not exceeding 10%. In the analyzed milk samples, the lowest H₂O₂ concentration detected was 0.001%w/w for raw and for skim milk and 0.002%w/w for, semi-skimmed and whole milk. The presented method is original, sensitive, rapid, and cost-effective. Due to the achieved sensitivity the method has great potential to be used for H₂O₂ detection in diverse areas, such as environmental monitoring and food quality.

Gold-coated tilted fiber Bragg gratings (TFBGs) have been extensively studied over the past years, particularly for biosensing purposes. Surface Plasmon Resonance (SPR) is generated through the deposition of a gold layer of appropriate thickness onto the grating region. The combination of SPR and TFBG permits to create a comb-like spectrum of narrow-band cladding mode resonances, which is usually demodulated by tracking the change of optical features of a selected peak. Here, for the first time to the best of our knowledge, a twenty-fold more sensitive demodulation technique based on the intersection of the upper and lower envelopes of gold-coated TFBG spectra is presented. This method has been successfully applied in biosensing for the detection of HER2 (Human Epidermal Growth Factor Receptor-2) proteins, a crucial breast cancer biomarker. Some practical improvements have also been proposed and assessed: first, a uniform FBG has been superimposed on the TFBG to reduce the read-out wavelength span to 10 nm instead of 70 nm, while keeping the temperature-compensated measurements; second, a microfluidic system has been designed and integrated to inject the samples towards the sensor at controlled flow rates. All these novelties make this sensing platform even more attractive and promising for use in practical applications.

We report on highly reflective fiber Bragg gratings in photonic crystal fibers (PCF) that excite two types of cladding mode resonances. We consider two hexagonal lattice PCF structures with a similar air-filling fraction (0.4) but different lattice pitch values (2.5 and 3.6 μm). We demonstrate both experimentally and numerically that the lattice parameters of the microstructure influence the spectral location of the resonances and the spectral span that they occupy. For the PCF with the lattice pitch of 2.5 μm, we demonstrate its application for surface plasmon-enhanced refractometry of waterbased solutions with potential for biosensing applications.

Gold-coated tilted fiber Bragg gratings (Au-TFBG) are promising platforms leading to the development of highly sensitive biosensors. The spectral resonance of the surface plasmon polariton carried by the gold layer interface depends on the refractive index of the surrounding medium. Once covered with bioreceptors, the p-polarized mode spectrum can be used to detect the change in refractive index induced by the analyte-bioreceptor interactions. In practice, a polarization controller is used to extract its insertion loss spectrum. Usual demodulation techniques track the evolution of the insertion loss spectral attenuation instead of the phase evolution. Indeed, to extract the phase, the polarization controller needs to be removed, and the p-polarized mode must be retrieved by other means. In this paper, a new demodulation technique based on phase evolution with a sensitivity of several thousands of degrees/RIU is presented. Using the complete transfer matrix function (Jones’s matrix), the amplitudes and phases for both p and s polarized modes are mathematically exact for each wavelength considered. Thanks to this procedure, every characteristic like phase and amplitude field evolution can be retrieved. The physical platform sensitivity is measured using LiCl solutions and the fiber is functionalized with anti-HER2 (Human Epidermal Growth Factor Receptor-2,a breast cancer marker) aptamers. Phase demodulation is utilized to extract the refractive index modification induced by all the process. Biosensing experiments on specific antiaptamer/HER2 interactions are also tested for concentrations of HER2 proteins at 1μg/mL.

We report on the inscription of fiber Bragg grating in perfluorinated polymer optical fiber (CYTOP) by femtosecond pulses laser at 400 nm with phase mask technique and at 800 nm with two direct inscription methods: point-by-point and line-byline. We analyze the stability of the gratings after inscription and study their sensing properties. To discriminate between the properties of the CYTOP fiber and the influence of the over-clad, we consider gratings produced in CYTOP fiber with and without the over-clad, and we determine their sensitivities to temperature, humidity and strain.

Fiber Bragg gratings (FBGs) have already proven their efficiency in axle counting when distributed along a railway track and bring advantages with respect to competing sensors. In this work, two relevant originalities are proposed to broaden the state-of-the-art solutions. First, the strain distribution in the rail cross-section is studied to identify the sensitivity, depending on the charge and the position. Secondly, the sensor head, composed of four wavelength-division-multiplexed FBGs in a single optical fiber, is deployed along the railway and interrogated by a small smart read-out device. Two FBGs are used for the train direction determination and the remaining two bring redundancy to reach safety integrity level (SIL) 4. The smart interrogator has been especially developed for this work and is composed of a vertical-cavity surface-emitting laser (VCSEL) and a photodiode driven by a high-speed microprocessor. The useful information (i.e. the number of counted axles) can be wireless communicated. On-field experiments confirm that this approach offers an easier installation process and a democratization of the technology.

A fiber Bragg gratings (FBGs) based system, constituted by six sensors, for wheelchair users muscle effort monitoring was proposed. Each sensor consists in one FBG embedded in epoxy resin, which was secured to Kinesio tape through a 3D printed connection system. After the approval of the Ethics and Deontology Committee and the Data Protection Officer of the University of Aveiro (Portugal), the sensors were implemented to evaluate wheelchair users muscle effort. The sensors were placed on the biceps, deltoids, and triceps (three sensors in each arm) of four wheelchairs users’ volunteers, which were asked to perform several exercises. The arms’ muscle effort required was estimated through the FBGs wavelength shift, which was related with the deformation of the epoxy resin during some of the wheelchair users’ daily movements: varying the typical used hand patterns on horizontal plane (pattern A, B and D); vertical and inclined dips; and going up and down a ramp. The results reveal that on the horizontal plane, the movement characterized by minor hand swings in relation to the wheelchair rim (pattern A), requires a smaller muscle effort, and the dips were the exercise requested to wheelchair users which demand the highest and most sudden muscle effort applied in the arms. The proposed system may be used to monitor and quantify the muscle effort related to any movement, aiding on the choice of techniques to promote the reduction of the muscle fatigue, and therefore contributing to the improvement of wheelchair user quality of life.

The authors report the use of FBGs to monitor the infusion process of the Resin Transfer Molding (RTM). Composite structure can be very large, ensuring that the resin has reached every part of the mold during the infusion process becomes a critical step to ensure the structural integrity and minimize failure. During the infusion stage of the process, the FBG undergoes a blue wavelength to red wavelength shift. The magnitude of wavelength shifts depends upon the location and depth of the sensors along with the viscosity of resin (we used a resin-like material with similar viscous properties) being used for the infusion. The observed wavelength shifts varied from ~10pm to ~400pm, which is small but still significant and readily measurable also showing reasonable repeatability for all experiments conducted with the same conditions. Evidence will be presented to show that viscous force is a major factor in explaining the observed FBG wavelength shifts. Due to the small wavelength shifts of the FBG sensors, this allows the opportunity to embed a dense population of sensors within a single structure, hence ensuring a satisfactory spatial resolution to monitor the resin flow front to ensure complete impregnation of the reinforcement.

Fiber Bragg gratings can be used to monitor temperature or strain in harsh environments. We investigate the effect of Xrays on type III gratings – also called void gratings –which are known for their capacity to withstand high temperatures. The tested gratings are inscribed in a SMF28 germanosilicate optical fiber using the point-by-point method and a frequency-doubled Yb femtosecond laser emitting at 515 nm. The tested FBGs are separated in two groups depending on their reflectivity levels (Low/High). Half of each group is pre-annealed at a temperature of 750°C during 30 min. We have irradiated all the gratings up to 100 kGy(SiO₂) at a dose-rate of 10 Gy/s at two different irradiation temperatures: 25°C and 150°C. For all the irradiations, the grating radiation response is identical independently of the chosen writing and preannealing conditions. When the irradiation is performed at 25°C, a Bragg wavelength shift of 10 pm is observed for all the gratings, which represents an error of less than 1°C at the total dose of 100 kGy while at 150°C the Bragg peak shift only of less than 4 pm corresponding to an error of 0.3°C.

We report on the development of two optical fiber sensors for agricultural applications. Specifically, a Fabry Perot optical fiber sensor with a hydroscopic photo- resin cavity is developed for monitoring oak barrel stave moisture evolution towards the development of a sensor for assessment of oak barrels used in wine aging. Furthermore, an optical fiber long period grating of extended length (~9 cm) is used as line sensor for tracing sprayed copper chlorophyllin water droplet distribution aiming to detect spraying drift during drone chemical pesticide operations.

Near-infrared (NIR) spectroscopy has acquired widespread adoption in various sectors as a result of its benefits over other analytical techniques, the most notable of which is the ability to record spectra for solid samples without any prior manipulation. Furthermore, advances in instrumentation have led to the creation of compact and high-speed spectrometers that can be used in a variety of scenarios, including hazardous materials identification. Fourier Transform NIR (FT-NIR) technology is one of the most useful tools for onsite analysis of chemical and biological substances. Herein, we propose a compact, portable FT-NIR spectroscopic sensor for field measurements, based on commercial broadband light source and spectrometer for detection of chemical precursors of explosives. We mainly focus on four compounds, ammonium nitrate, potassium nitrate, sodium nitrate and urea, some of the best-known chemical precursors of explosives with NIR content. A customized spectral library is constructed, including the forementioned substances under different environmental conditions. We emphasize on two basic factors that can affect the NIR spectra: the relative humidity and the ambient temperature. For the unknown spectrum identification, we evaluate prediction models which involve the use of Random Forest and Support Vector Machine, as well as the Hit Quality Index (HQI) value. The FT-NIR spectroscopic sensor additionally includes an integrated communication module that provides measurement spectra and results to a novel edge computing platform, called DECIoT. We demonstrate the operation of the FT-NIR spectroscopic sensor in real settings under humidity, straight sunlight, and temperature fluctuations, achieving maximum accuracy of 0.96.

Optical spectra, and particularly fluorescence spectra, contain a large quantity of information about the substances and their interaction with the environment. It is of great interest, therefore, to try to extract as much of this information as possible, as optical measurements can be easy, non-invasive, and can happen in-situ making the data collection a very appealing method of gathering knowledge. Artificial neural networks are known for their feature extraction capabilities and are therefore well suited for this challenge. In this work, inspired by convolutional neural network (CNN) architectures in 2D and their success with images, a novel approach using one-dimensional convolutional neural networks (1D-CNN) is used to extract information on the measured spectra by using explainability techniques. The 1D-CNN architecture has as input the entire fluorescence spectrum and takes advantage in its design of prior knowledge about the instrumentation and sample characteristics as, for example, spectrometer resolution or the expected number of relevant features in the spectrum. Even if network performance is good, it remains an open question if the features used for the predictions make sense from a physical and chemical point of view and if they match what is known from existing studies. This work studies the output of the convolutional layers, known as feature maps, to understand which features the network has effectively used for the predictions, and thus which part of the measured spectra contains the relevant information about the phenomena at the basis of what has to be predicted. The proposed approach is demonstrated by applying it to the determination of the UV absorbance at 232 nm, K232, from fluorescence spectra using a dataset of 18 Spanish olive oils, which were chemically analysed from certified laboratories. The 1D-CNN successfully predicts the parameter K232 and enables, by studying feature maps, the clear identification of the relevant spectral features. The main contributions of this work are two. Firstly, it describes how designing the neural network architecture with prior knowledge (spectrometer resolution, etc.) will help the network in learning features that have a clear connection to the chemical composition of the substances, and thus are clearly explainable. Secondly, it shows how, in the case of olive oil, the identified features match perfectly the relevant features known from existing previous studies, thus confirming that the network is learning from the underlying chemical process.

Carcinogenic contaminants in food are typically detected using sample-based and time-consuming chemical analysis. To offer a nondestructive alternative, we investigate the use of optical sensing methods for food safety evaluation, particularly considering broadband diffuse reflection spectroscopy and spatially resolved spectroscopy. As a case-study, the acrylamide sensing during potato processing is considered. For both spectroscopic methods, the broadband spectra (400-1700 nm) are recorded for different potato batches, showing acrylamide concentrations between 200 ppb and 2000 ppb, covering levels below and above the European regulation of 500 ppb. First, the raw spectra are evaluated enabling to identify the key spectral regions, being 400-525 nm, 750-1000 nm, 1220 nm, and 1440 nm, corresponding to different potato constituents such as riboflavin, starch, reducing sugar and water. Following, different data processing algorithms were applied maximizing the classification performance of the different batches. When considering the reflection spectroscopic data, a classification performance exceeding 92% could be obtained using Linear Discriminant Analysis, while with spatially resolved spectroscopy, a classification performance around 75% was obtained when considering the ratios of the diffuse and specular reflected light. Both techniques show a harvest-independent result, while coping with the large natural variation by considering the combined effect of different acrylamide precursors. In general, our results demonstrate that optical spectroscopy enables a successful acrylamide precursor sensing, allowing an accurate exclusion of potatoes unsuited for French fries production, complying with the European regulations.

The aim of this study was to develop mid-infrared chalcogenide sensor and perform its functionalization by polymers in order to detect various hydrocarbon pollutants in water and to ensure an efficient attenuation of the water absorption bands. Selenide waveguides were fabricated by radiofrequency magnetron sputtering on silicon substrates using two different glass target compositions, for cladding and guiding layers. A hydrophobic polymer was deposited on the surface of zinc selenide prisms to allow its characterization by ATR-FTIR (Attenuated Total Reflectance-Fourier Transform InfraRed) spectroscopy. Benzene, toluene and ortho-, meta- and para-xylenes in solutions of distilled water at concentrations ranging from 10 ppb to 20 ppm were simultaneously detected and the measured limit of detection was determined to be equal to 250 ppb. However, the limit of detection must be improved to meet environmental standards. To achieve this goal, metallic nanostructures were deposited on the surface of the chalcogenide waveguides to increase the sensitivity of the future optical sensor thanks to the plasmon resonance phenomena. Thus, the fabrication of a heterostructure composed of gold nanoparticles deposited by electron beam evaporation was performed on a slab selenide waveguide in order to assess SEIRA (Surface-Enhanced InfraRed Absorption) effect.

A novel Erbium-doped fiber laser sensor based on a sigma-type cavity configuration using a double-pass cascadedchirped long-period fiber grating (C-CLPG) as a reflective sensor section is proposed for a real-time displacement sensor, in which the double-pass C-CLPG is provided by returning the transmitted light of the C-CLPG with a Faraday rotator mirror (FRM) in the σ-branch of the cavity. The σ-branch with FRM-reflection realizes a compensation effect for the polarization fluctuations in the sensor section, a stable oscillation output during the sensing operation, and a suitable arrangement for remote sensing. In the experiment, we have successfully demonstrated a real-time displacement measurement based on bending characteristics of the C-CLPG, taking advantage of laser-type sensors.

Various parameters are measured to increase the safety of train operation and monitor the railway infrastructure. This contribution presents a monitoring system using an optical fiber Fabry-Pérot interferometer for speed measurement. The speed of the train was determined from the known distance of two sensors placed on the foot of the rail rather than the known geometry of the train. During the measurement campaign on the railway line in Slovakia, more than 70 train passages were determined. The speeds (from 15 km/h to 67 km/h) were divided into five groups and compared with the speeds determined by a piezoelectric-based sensor. Easy installation, a simple evaluation of the measured signal, and low sensor production costs, make the proposed sensor a good candidate for railway monitoring applications.

In this work, a solution to monitor users’ activity within an indoor scenario is proposed. It is based on non-wearable and non-invasive sensing, and it is specially fitted for the elders’ home monitoring. The localization of a person is estimated through an optical sensing network that detects the floor vibration produced by the footstep when walking. Optical fiber Bragg sensors are integrated within high sensitive accelerometers to detect such vibration. Three similar accelerometers were developed from which sensitivities of 269 pm/G, 225 pm/G, and 209 pm/G were found. Allied to vibration detection, an algorithm is employed to retrieve one’s position from the data. In preliminary localization tests, the system has demonstrated an accuracy under 5 cm over a 3.2 m² detection area, proving itself to be a promising solution for the targeted application.

Self-injection locking to an external fiber cavity is an efficient technique enabling drastic linewidth narrowing of semiconductor lasers. Recently, we have introduced a simple dual-frequency laser source that employs self-injection locking of a DFB laser in the external ring fiber cavity and Brillouin lasing in the same cavity. The laser performance characteristics are on the level of the laser modules commonly used with Brillouin Optical Time Domain Analysis (BOTDA). The use of a laser source operating two frequencies strongly locked through the Brillouin resonance simplifies the BOTDA system, avoiding the use of a broadband electrooptical modulator (EOM) and high-frequency electronics. In this work, in a direct comparison with the commercial BOTDA, we explore the capacity of our low-cost solution for BOTDA sensing, demonstrating distributed measurements of the Brillouin frequency shift in a 10 km sensing fiber with a 1.5 m spatial resolution.

The paper presents calculation, selection of components, and simulation of an optical scheme for a laser system developed for identifying small-sized metal objects for automated control systems. The relevance of the research results from the demand for developing a portable, cost efficient, non-contact optical control device based on the principles of measuring the reflected signal after the interaction of laser radiation with metals. The device inventory was analyzed, and the components of the optical system were selected. An optical scheme was developed, and the energy characteristics were calculated.

The fiber-optic current sensor (FOCS) will be installed in ITER to measure the plasma current for plasma control and machine protection. FOCS uses the Faraday effect in the fiber installed on the outer surface of the vacuum vessel. During plasma operation in ITER, vibrations may change polarization properties of optical fiber installed in the cryostat bridge, and it may affect the sensor accuracy. In this paper, we analyze the vibration effect on the FOCS measurement by applying the Jones matrix formalism. The effect of vibrations on the Jones matrix was addressed using an experimental set-up. A fiber-inserted helical shape metal tube was prepared according to the ITER cryostat bridge design. Vibrations were applied using a shaker, assuming a worst-case scenario of ITER operation. Using experimental data, we were able to estimate the influence of vibrations on the accuracy of plasma current measurement using FOCS. We have also estimated requirements for the spun fiber which is planned to be used for FOCS. It is concluded that it is not possible to satisfy the ITER requirements when using commercially Hi-Bi spun fibers, while a Lo-Bi fiber with a ratio of the linear beat length to the spun period of ~200 allows to achieve the goal.

One of many possible applications of a one-dimensional photonic crystal (1DPhC) is sensing utilizing the Bloch surface wave (BSW). In this paper, we present a method of relative humidity (RH) sensing based on the phase shift of the BSW supported by a truncated 1DPhC represented by a multilayered structure. The structure is composed of six TiO₂/SiO₂ bilayers with a termination layer of TiO₂. The BSW is excited by a total internal reflection of light in the Kretschmann configuration. A spectral interferometric technique is used to obtain a channeled spectrum due to projections of both reflected s and p polarized light waves. The phase is retrieved using a Windowed Fourier transform and spectral derivatives forming peaks are calculated. The sensing concept is based on tracking the derivative peak as a function of RH. A sensitivity to humidity of 0.028 nm/%RH and figure of merit of 0.0042 %RH₋₁ were determined.

In the contribution, we present the preliminary results of an investigation of the effect of a magnetic field applied on a thin magnetic fluid layer placed between two crossed plane polarizers on its transmittance in the visible spectral range. We used samples of two different thicknesses and a supercontinuum white light source for investigation. The magnetic field was generated by a solenoid with an iron core and applied parallel to the magnetic fluid surface and at the same time, perpendicular to the direction of light propagation. We changed the strength of the magnetic field, its direction with respect to the orientation of the polarization planes of the polarizers and recorded the intensity of light transmitted through the samples. A quite strong effect of the magnetic field on the transmittance of the magnetic fluid thin layer placed between two crossed plane polarizers was observed which is promising for sensor applications utilizing the presence of an external magnetic field.

Photodetectors comprising of InAs/(In, Ga)Sb Type-II Superlattice (T2SL) structures demonstrate excellent performance over bulk detectors, which mainly includes tunable bandgap and controllable photo-absorption. The T2SL exhibits completely distinct properties from its constituent materials. In particular, the thickness of InAs and GaSb considered in one period of the T2SL plays a key role in determining its photoresponse. In this work, we compare two different compositions of the T2SL structure, which have similar bandgaps, in order to analyze their electronic band properties and miniband characteristics. For this, 12ML/12ML and 11ML/7ML T2SLs are examined which have a similar bandgap of 0.17eV corresponding to the wavelength of 7.2μm and the ratios of InAs-to-GaSb widths are approximately 1 and 1.5, respectively. The bandgap and density-of-states (DOS) masses are obtained by employing the k.p method within the envelope function approximation and the E-k dispersion both in the in-plane and the out-of-plane directions are analyzed. To further gain microscopic insights, we examine the carrier localization, miniband, and spectral current properties of finite T2SL structures using the Keldysh nonequilibrium Green’s function (NEGF) method. The spatial separation of electrons and holes in InAs and GaSb layers can be elucidated via the local density of states. Furthermore, a higher finite interband overlap between the first conduction band (C1) and the first heavy hole band (HH1) is observed in an 11ML/7ML T2SL which indicates a stronger absorption. Also, to predict the carrier transport in these structures, we incorporate scattering processes via the momentum dephasing model and note a lesser broadened dark current spectra in the 12ML/12ML T2SL structure. This suggests a stronger localization of carriers and as a consequence, the dark tunneling current will be indeed be suppressed in this T2SL.

Olive oil is an important commodity in the world, and its demand has grown substantially in recent years. As of today, the determination of olive oil quality is based on both chemical analysis and organoleptic evaluation from specialized laboratories and panels of experts, thus resulting in a complex and time-consuming process. This work presents a new compact and low-cost sensor based on fluorescence spectroscopy and artificial neural networks that can perform olive oil quality assessment. The presented sensor has the advantage of being a portable, easy-to-use, and low-cost device, which works with undiluted samples, and without any pre-processing of data, thus simplifying the analysis to the maximum degree possible. Different artificial neural networks were analyzed and their performance compared. To deal with the heterogeneity in the samples, as producer or harvest year, a novel neural network architecture is presented, called here conditional convolutional neural network (CondCNN). The presented technology is demonstrated by analyzing olive oils of different quality levels and from different producers: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). The sensor classifies the oils in the three mentioned classes with an accuracy of 82%. These results indicate that the Cond-CNN applied to the data obtained with the low-cost luminescence sensor, can deal with a set of oils coming from multiple producers, and, therefore, showing quite heterogeneous chemical characteristics.

The chemical analysis of food is essential to monitor and guarantee its quality. The determination of the chemical parameters, like the concentration of particular substances, is performed by specialized laboratories and is a time-consuming and costly process. Therefore, alternative methods with easier handling are of great interest. Among these fluorescence spectroscopy offers great opportunities. Fluorescence spectra are one-dimensional arrays of values already successfully employed together with artificial neural networks for classification problems in chemistry, physics, and other fields. However, the extraction of specific quantities from the spectra poses a much harder challenge. This work analyzes and compares the ability of feed-forward neural networks (FFNN) and one-dimensional convolutional neural networks (1D-CNN) to extract relevant features from fluorescence spectra of olive oils. The results indicate that 1D-CNN, contrary to FFNN, successfully predicts the chemical parameters with high accuracy. The great advantages of the proposed method are: 1) the possibility of using optical methods instead of time-consuming chemical ones, like chromatography, 2) the lack of any special sample handling, like dilution and 3) the lack of any pre-processing of the data. The problem of small datasets, which may arise for novel techniques like the proposed one, is also addressed statistically by using the leave-one-out resampling technique.

The viscoelasticity of arterial walls is an important parameter to use when diagnosing cardiovascular disease. Recently, the index of arteriosclerosis has been clinically evaluated by looking at factors such as pulse transit time and ankle branchial pressure index. However, the indirect method does not reflect the actual real properties of arterial walls, which may lead to misdiagnosis. Therefore, just looking at pulse transit time is not a satisfactory parameter to evaluate arterial viscoelasticity. In this research, we used a Lamb wave velocity dispersion model to estimate the viscoelastic material properties of arterial phantoms. The arterial walls were composed of a thin layer plate merged in a water fluid in which the vibrations are induced by an external vibrator excitation such as a Lamb wave. A mechanical actuator was used to create a repetitive 50Hz to 200Hz low-frequency air-puff excitation to excite the harmonic mechanical waves in the latex pipe wall. The velocity of the traveling waves was measured by a fiber-based laser doppler vibrometer. The laser doppler vibrometer was the incorporation of a circulator which allowed for uni-directional transmission of electromagnetic waves, so that only the probe could achieve the purpose of emitting and receiving light. Moreover, the system was mounted on a linear translation stage and the traveling waves velocity were measured at multiple points. Then, the wave velocities were calculated by calculating the signal phase difference between the different measured positions. The viscoelastic property of the latex pipe was used to calculate the Lamb wave velocity by using a Lamb wave dispersion model. In conclusion, we propose a novel method to measure the viscoelastic properties of arterial walls in-vitro with high accuracy. Simulated experiments were performed to validate the proposed method. Our new proposed method has the potential to more accurately diagnose cardiovascular diseases for both home health care and for clinical medicine.

We present a study on the use of state-of-the-art distributed sensing systems to extract temperature and vibration information from existing single-mode, optical fibre infrastructure in Cyprus (~25-year-old installation); as a means of optical fibre distributed sensing. In this study, we have focused on the underground optical fibres of the Electricity Authority Cyprus (EAC). The optical fibres have been selected to be collocated with existing underground power distribution cables that are sited in and around Nicosia, for the purpose of monitoring power cable joints that are prone to failure, along with general monitoring for unusual behaviour and potential cable fault conditions. Three state-of-the-art distributed sensing systems have been deployed to run in parallel, on the same optical cable branch and all optimised for use with single-mode fibre. The underground power cables were monitored using a fast Brillouin optical time-domain reflectometer (BOTDR, with temperature and vibration measurement capability), a Distributed Temperature Sensor (Silixa, single-mode DTS) and an intelligent Distributed Acoustic Sensor (Silixa, iDAS). The results include calibration methods performed under controlled laboratory conditions for the fibres under test, prior to field deployment. The field data, recovers both temperature and vibration measurements over a 3-month period the results of which will be presented.

In LiDAR system, atmospheric backscatter is one kind of important background radiation noise for target detection. When the intensity of atmospheric backscatter signal received by LiDAR system exceeds the detection threshold, the system will make a false alarm. To reduce the atmospheric backscatter interference efficiently, it is necessary to evaluate the importance of influencing factors of atmospheric backscatter. The intensity of atmospheric backscatter noise is mainly related to the parameters of transmitter, receiver, and atmospheric transport properties. We choose nine feature parameters in this study: transmitter features (including Wavelength (λ), Pulse Energy (E), and Divergence (D)), receiver features (including Detection Threshold (ITh), Field of View (FOV), and Responsiveness (Ri)), and atmospheric features (including Visibility (V), Asymmetric Factor (g), and Extinction-to-Backscatter Ratio (EBR)). Based on Mie scattering theory, we establish a LiDAR system atmospheric backscatter impact model with Monte Carlo method and set the false alarm rate as the indicator to evaluate the impact of atmospheric backscatter noise to LiDAR system, and next we assess the importance of these nine selected features by three feature selection methods (F-test, Neighborhood Component Analysis (NCA), and Bagging). The evaluation results prove that these three feature selection methods can successfully access the importance of thesee nine features. Although the importance values of the nine features evaluated by these three methods are not exactly all the same, the features belonged to the first-level, second-level, and third-level are consistent. The most important three features are ITh, g and V, which means atmospheric features are relatively important compared to the features of transmitter and receiver. The feature importance evaluation results can qualitatively provide guidance for LiDAR System design to avoid atmospheric backscatter effect and help improve the performance of LiDAR System.

We present a study on the application of machine learning to optical fibre distributed sensing, with data recovered using a state-of-the-art, commercial BOTDR distributed sensing system, to extract temperature information from single-mode optical fibre over a 40-km distance. The application is for power line monitoring of underground cables that are collocated with optical fibres that form part of the Electricity Authority of Cyprus’ island wide power distribution networks. The existing optical fibre infrastructure acts as the sensing element, monitoring temperature changes when in close proximity to the power lines. The initial training measurements for the machine learning algorithm were recorded in a laboratory setting using temperature and humidity-controlled elements, with sections of fibre spliced to underground fibre cables subjected to temperature excursions. A machine learning approach was implemented for the prediction task of finding points that are likely to get damaged, mimicking the behavior of power cable joints that are prone to failure, along with general monitoring for unusual behavior and potential cable fault conditions; the task is a binary classification one. Labels “0/1” were assigned to the BOTDR measurements, with “1” corresponding to data points in space and time for which the signal showcased a problematic scenario, such as the collocated fibre’s temperature rising to dangerously high values, and “0” to the rest. The algorithm’s base is a variation of the state-of-the-art transformer architecture, which depends solely on attention mechanisms. The training was undertaken on the laboratory data and re-training is done periodically with new field measurements. The completion of the training phase shows the potential of the algorithm to predict spatiotemporally problematic points, using the temperature measurements of the collocated fibre; this will be extended to BOTDR data taken in the field.

This study investigates a fiber-optic biosensor based on a tilted fiber Bragg grating (TFBG) partially-coated with a thin gold film on its axis and around its cross-section. The effects induced in the TFBG transmitted amplitude spectrum were analyzed for different input light directions for surrounding refractive index (SRI) changes in the range 1.3356 – 1.3370. Partially-coated gratings present the potential ability to sense both volume and surface refractive index changes, which is interesting in biosensing to enhance the signal-to-noise ratio. The gold film was bio-functionalized by human epidermal growth factor receptor (HER2) aptamers using thiol chemistry. The detection of HER2 proteins (a relevant cancer biomarker) at 10-9 g/mL, 10-8 g/mL and 10-6 g/mL demonstrated the advantage to identify environmental perturbations through the bare area of the TFBG. The non-specific drifts that could exist in samples are eliminated and a wavelength shift only related to the surface modification is obtained.