This PDF file contains the front matter associated with SPIE Proceedings Volume 12327, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Global wind profiling is very important to improve the initial conditions for numerical weather prediction and meteorological studies. Current space-based passive sensors and micro sensors have large coverage and high temporal resolution but low vertical resolution. A Doppler Wind Lidar is a useful remote sensing technique for measuring line-of-sight (LOS) wind speed under clear-sky and/or thin-cloudy conditions, and it provides wind profiles with good vertical and temporal resolutions. We will present activities on future space-based lidar in Japan and 2-μm key technology meeting requirements for the future space-based coherent Doppler Wind Lidar.

OPO technique has the advantage of producing pulsed mid-infrared radiation with broad wavelength coverage which makes up for the absence laser emission in certain band of frequencies. The 2.6 micrometer band lasers have many potential applications in fields such as industrial pollution sources monitoring [1,2].Moreover, KTA crystal features large non-linear optical and electro-optical coefficients and low crystal loss in the wavelength range 2.6-3.2 μm; high nonlinear coefficient; high optical damage threshold; low temperature sensitivity. KTA is also non-hygroscopic, and offers the possibility of critical phase matching with a high value of dλ/dθ which results in a narrow free running linewidth [3,4]. It has found wide applications in optical parametric oscillation/ amplification (OPO/OPA), and electro-optical Qswitching involving MIR wavelengths [5]. In this paper, we report on an intra-cavity pumped self-cascaded OPO-based on the NCPM KTA-OPO design [6,7] for the generation of a 2.59 μm high laser peak power.

Polymer waveguide optical phased array (OPA) beam scanners can achieve stable beam steering with low driving power owing to the large TO coefficient and low thermal conductivity of the polymer material. However, polymeric OPAs with low refractive index contrast exhibit narrow field-of-view (FOV) because of the wide pitch of the output waveguide channels compared to their counterparts of Si and silicon nitride (SiN) photonic integrated circuit platforms. Meanwhile, SiN waveguides offer stronger mode confinement with smaller waveguide core sizes than polymer waveguides, and they can handle high optical power without nonlinear effects. Through monolithic integration of polymer and SiN waveguides using adiabatic transition tapers, a high-performance phase modulator (PM) is achieved for the purpose of demonstrating an OPA device with low power consumption, wide scanning angle, and fast response.

With the advancement of recent technologies, various methods have evolved in response to the rising demand for 2-D/3- D mapping in Geographical Information Systems (GIS). Although there are different traditional methods for 3-D mapping, they are time-consuming, provide low accuracy, and involve complex processes. Therefore, point cloud data technology based on laser and photo scanning is gaining popularity in 3-D mapping data on construction sites. Preparing a 3-D map with more details requires a high-quality laser scan with greater accuracy. The purpose of this study is to prepare the 3-D map and assess and compare the horizontal and height accuracy of point, line, or polygon features using a Total Station (TS)/Smart Station (SS), Differential Global Positioning System (DGPS), and Terrestrial Laser Scanner (TLS). Furthermore, the accuracy provided by the tools used has been assessed for 2-D/ 3-D mapping at large scales, such as 1:500 or 1:1000. The coordinates for the polygon feature, line feature, and point feature were measured using DGPS, Smart Station, and TLS. The comparative planimetric and height accuracy of georeferenced point cloud data collected using TLS has been compared with SS and DGPS data collected from the field survey. The outcome of the present study demonstrated that the horizontal and vertical accuracy of 1 mm and 33 cm for horizontal and vertical had been achieved using a terrestrial laser scanner compared with the smart station, while comparing smart station with differential global positioning system, the observed differences is 1 mm in horizontal and maximum of 12.0 m in vertical measurements. Finally, the overall expended time on the reference network using SS consumed more data acquisition and post-processing time i.e., about 2 hours 30 minutes, than other advanced instruments, such as DGPS and TLS.

Trace acetone contained in the human-derived vapor is a potential indicator of lipid metabolisms. Exhaled breath and skin gas contain various kinds of volatile chemicals. Thus, major analytical systems implement chromatographical separation techniques to measure only target chemicals without interference. Such systems are suitable for clinical lab analysis but not for monitoring personal health status. Optical biosensors are suitable for measuring human-derived vapor in a real-time manner because of their high selectivity and sensitivity. Here we report an acetone vapor imaging technique based on secondary alcohol dehydrogenase (S-ADH) immobilized on a laser-patterned hydrophilic polytetrafluoroethylene (PTFE) mesh. The S-ADH catalyzes the reduction of acetone to 2-propanol with concurrent oxidation of coenzyme nicotinamide adenine dinucleotide (NADH) to NAD+. The NADH can be excited by 340 nm of ultraviolet light and emits visible fluorescence at around 490 nm. Acetone concentration can be transduced to a decrement in fluorescence intensity by combining the NADH fluorescence property and S-ADH reaction. S-ADH was immobilized on a breathable hydrophilic PTFE mesh fabricated with laser patterning. In the experiment, fluorescence intensity distribution changing by application of acetone vapor to the S-ADH immobilized mesh was captured by the camera. Fluorescence intensity rapidly decreased at the application point. The decrement was correlated to acetone vapor concentration. The high selectivity against acetone was also observed at typical concentrations and components of volatiles in human breath. Those characteristics will enable the developed technique to measure acetone in human samples in the future.

We review how optofluidic hollow-core photonic crystal fibres (HC-PCF) can be integrated with in-line microfluidic circuits to achieve sensitive, label-free detection of proteins at low concentrations.[1] Proteins are the key building blocks of life, and sensing proteins is a challenging problem encountered across disciplines, from the study of fundamental interactions in biophysics to clinical applications in medicine. As nanometer-sized objects of low refractive index contrast, they are difficult to detect but have to be quantified to high precision. Because of their biological origin, they are also resource-intensive to synthesise or extract, requiring sensors to be capable of detecting small sample volumes at low concentrations. A key development in studying proteins came with the development of bright fluorescent markers known as labels. They revolutionised the field by adding fluorophores to the proteins either through genetic engineering or by chemical staining. However, increasing evidence shows that these labels alter protein behaviour, necessitating new sensing technologies. Hollow-core fibre-based sensors confine light into sub-microliter volumes while achieving high light-matter interaction over long path lengths. This enables them to achieve higher sensitivity compared to conventional non-waveguiding geometries. Here, we discuss our recent advances in realising this system experimentally and highlight future directions.

Transition-edge sensors (TESs) have found a wide range of applications in both space- and land-based astronomical photon measurement and are being used in the search for dark matter and neutrino mass measurements. A fundamental aspect of TES physics that has not been investigated is the sensitivity of TESs to strong DC electric fields (10 kVm−1 and above). Understanding the resilience of TESs to DC electric fields is essential when considering their use as charged particle spectrometers, a field in which TESs could have an enormous impact. Techniques such as x-ray photoelectron spectroscopy produce a high number of low-energy electrons that are not of interest and can be screened from the detector using electrostatic deflection. The use of strong electric fields could also provide a mass-efficient route to prevent secondary electron measurements arising from cosmic radiation in space-based TES applications. Integrating electron optics into the TES membrane provides an elegant and compact means to control the interaction between charged particles and the sensor, whether by screening unwanted particles or enhancing the particle absorption efficiency but implementing such techniques requires understanding the sensitivity of the TES to the resulting electric fields. In this work, we applied a uniform DC electric field across a Mo/Au TES using a parallel pair of flat electrodes positioned above and below the TES. The electric field in the vicinity of the TES was enhanced by the presence of silicon backing plate directly beneath the TES. Using this arrangement, we were able to apply of electric fields up to 90 kVm−1 across the TES. We observed no electric field sensitivity at any field strength demonstrating the capability to use TESs in environments of strong electric fields.

This research shows a prototype for crowd location and counting for earthquakes based on deep learning and the infrastructure of a state-of-the-art 5G standalone network deployed at the Universidad de Concepcion, Chile. The system uses an 8 MP panoramic network camera to capture real-time crowd images, which are sent to a Deep Learning Server (DLS) over the 5G network. The camera provides visible color images, and its sensor technology can provide color images even at night. The DLS uses frames from the video feed and generates Focal Inverse Distance Transform (FIDT) maps, in which the counting and location of people are carried out. In particular, the FIDT maps are generated from the crowd images using a deep-learning model composed of two cascaded autoencoders. The 5G technology allows the system to transfer data from the camera to DLS at high speed, an essential feature for a system that will help authorities make critical decisions during natural disasters. Under this scenario, and considering that the number of rescuers is usually limited, our system enables a better distribution of them among several crowded places by instantly knowing the number of people at any time of the day or night.

In this work, a deep convolutional neural network is proposed to improve the registration of microtopographic data. For this purpose, different mechanical surfaces were optically measured using a confocal laser scanning microscope. A wide range of surfaces with different materials, processing methods, and topographic properties, such as isotropy and anisotropy or stochastic and deterministic features, are included. Training and testing datasets with known homographies are generated from these measurements by cropping a fixed and moving image patch from each topography and then randomly perturbing the latter. A pseudo-siamese network architecture based on the VGG Net is then used to predict these homographies. The network is trained with a supervised learning approach where the Euclidean distance between the predicted and the ground truth gives the loss function. The 4-point homography parameterization is used to improve the loss convergence. Furthermore, different amounts of image noise are added to enhance the prediction’s robustness and prevent overfitting. The effectiveness of the proposed method is evaluated through different experiments. First, the network performance is compared to intensity-based and feature-based conventional registration algorithms regarding the resulting error, the noise-robustness, and the processing speed. In addition, images from the Microsoft Common Objects in Context (COCO) dataset are used to verify the network’s generalization capability to new image types and contents. The results show that the learning-based approach offers much higher robustness regarding image noise and a much lower processing time. In contrast, conventional algorithms have a smaller registration error without image noise.

In recent years, sensing and imaging have significantly progressed due to AI algorithms such as Neural Network (NN). The main issues of applying NNs to information processing are the limited processing speed and high energy consumption of electronic processors. Optical Neural Network (ONN), which utilizes diffraction and propagation of light for processing, is an intriguing implementation of an ultra-fast and low-energy-consuming NN. However, previous studies of ONN are mainly on simulations due to the experimental difficulty of processing more than hundreds of input data. In hardware implementations, the performance or the classification accuracy of ONNs can be reduced by the noise and the displacements. Therefore, not only must the ONN achieve high theoretical accuracy, but it must also be robust to these experimental errors. In this study, the classification of 1,000 MNIST input data (100 data for each of 10 classes) was realized experimentally as well as theoretically, taking advantage of our novel setup with a variable spatial light modulator (SLM). With our experimental configuration, we investigated the classification accuracy with several loss functions for the ONN training. The inference accuracy of the MNIST classification task was up to 97% in the simulation and ~95% in the experiment by softmax-cross-entropy (SCE) loss function. Also, the classification accuracy of 98% for a Surface crack classification and 93% for a Pollen classification was achieved experimentally. These results show that SCE can realize high-accuracy classification in the ONN implementation. Our results revealed the high application capability of the optical neural network for practical sensing tasks.

Leaf disease in rubber leaves causes a significant effect on latex production, especially Pestalotiopsis sp. This disease has caused a massive leaf fall in many plantations in Indonesia. Hyperspectral-based analysis can identify the difference in spectral leaf due to disease. The site location is Sembawa Rubber Research Institute in Sembawa, Palembang, Indonesia. Further, this study also compared other fungi leaf diseases, namely Oidium sp., Collectotrichum sp., and Corrynesspora sp. The leaf spectral reflectance was measured using Ocean Optics USB 2000+ UV-VIS Spectrometer, which measured the spectral response from 350-850 nm. This study aims at (a) defining the spectral signature of rubber leaf infected by leaf fall disease, b) analyzing whether the spectral response of infected leaf can be distinguished, and (c) defining the most useful wavelengths for discriminating spectral responses. The methodology used in this study is spectral response curve, principal components, and partial least square discriminant analyses. As a result, the average spectra indicated differences in color-infected leaves in the chlorophyll-associated wavelengths 490-589, 681–685, 718-752, and 839-875 nm. At the same time, the identification of disease type based on the spectral response found that it is not separable due to high similarity in the spectral curve.

Harmful and nuisance algal blooms are becoming a greater concern to public health, riparian ecosystems, and recreational uses of inland waterways. Algal bloom proliferation has increased in the Upper Clark Fork River in western Montana, USA, due to a combination of warming water temperatures, naturally high phosphorus levels, and an influx of contaminants through anthropogenic nitrogen enrichment along its banks. To improve understanding of bloom dynamics, such as algal biomass, a UAV-based hyperspectral imaging system was deployed to monitor several locations along the Upper Clark Fork River. Image data were collected across the spectral range of 400 - 1000 nm with 2.1 nm spectral resolution during two field sampling campaigns in 2021. Included are methods to estimate chlorophyll a standing crops using regression analysis of salient wavelength bands, before and after separating the pigments according to growth form. Estimates of total chlorophyll a standing crops generated through a brute-force analysis are compared to in-situ data, resulting in a maximum rsquared of 0.62 for estimating filamentous plus epiphytic chlorophyll a. Estimates of total and epilithic pigment standing crops are also included. The salient wavelengths bands used to estimate these pigments were then used as the basis for creating a low-cost imaging system for identifying algal blooms.

We investigated the oxygenated hemoglobin concentration (C_HbO), deoxygenated hemoglobin concentration (C_HbR), methemoglobin concentration (C_metHb), total hemoglobin concentration (C_HbT), tissue oxygen saturation (S_tO₂) and methemoglobin saturation (StMet) in rat burn wound model using a diffuse reflectance spectral imaging (DRSI) method. Superficial dermal burns (SDBs), deep dermal burns (DDBs), and deep burns (DBs) were induced in rat dorsal skin by exposing the skin of approximately 20% of the total body surface area (4×10 cm2) to water maintained at 70, 78, and 98 °C for 10 second, respectively, using a Walker–Mason template. A set of spectral images for dorsal regions were acquired pre-burn and immediately post burn under anesthesia. Subsequent measurements were acquired at 24, 48 and 72 hours after injury. We found that the time courses of C_HbO, C_HbR, C_metHb, C_HbT, StO₂ and StMet have different trends depending on the depth of burn wounds. The results in this study demonstrated the feasibility of our proposed DRSI method for in vivo monitoring of hemoglobin derivatives in rat burn wound model to differentiate the burn depth groups in rats.

Multispectral imaging involves capturing the same scene at different wavelengths using various narrowband filters stacked or integrated into digital camera sensors. This technology makes it possible to extract the additional information that a human eye or conventional camera fails to capture and thus has important applications in object identification, precision agriculture, and medicine. Multispectral imaging in visible wavelengths is readily possible due to the availability of digital imaging sensors and existing narrowband filter designs like metal-dielectric-metal films, dielectric films, or Fabry-Perot cavities [1-2]. Multispectral imaging in thermal longwave infrared (LWIR) wavelengths of 8-14 μm range has more advanced applications as they can see through fire, detect various gases, and investigate materials non-destructively through thermal signatures. However, conventional thermal image sensors can image in a single spectral band only. Thermal multispectral imaging is hindered by traditional filter technology where many layers of different materials are required for obtaining various spectral bands and limited wavelength tunability. On-chip integration of the infrared filters on the thermal image sensors to build a compact multispectral thermal camera is still an emerging area [3-5]. In the current work, we design and demonstrate a low-cost single sensor-based multispectral thermal sensor system composed of copper-based plasmonic imaging filter mosaic (multiple spectral filters are fabricated on a single substrate using only one lithography step and two deposition steps) integrated into an uncooled monochrome thermal sensor. The proposed work is mass-fabricable, scalable, and integrable, thereby leveraging next-generation LWIR thermal snapshot multi- and hyperspectral imaging.

A low-cost multispectral imager is described for routine monitoring of Cladophora nuisance algae and bluegreen algae in narrow rivers that are not spatially resolved by satellites. The goal is to identify algal blooms and estimate the chlorophyll a (chl a) and phycocyanin content from a network of low-cost imagers that can be mounted on bridges, trees, or other convenient objects at key river locations. The preliminary design uses Raspberry Pi cameras and computers with bandpass filters at 568, 671, 700, and 825 nm, based on data gathered with an airborne hyperspectral imager on the Upper Clark Fork River in southwestern Montana USA. This paper summarizes the initial design, calibration measurements, and preliminary reflectance data.

Hyperspectral imaging (HSI) is currently more and more utilized in waste recycling industry for both sensor-based sorting and quality control applications. Plastic waste is one of the flow streams in which HSI is particularly effective, due to its high capability of polymer identification in the near and short-wave infrared range, allowing to achieve high purity recycled plastic products and, therefore, secondary raw materials characterized by high quality. The aim of this work was to evaluate the potential of HSI-based data fusion, to achieve simultaneous identification of post-consumer plastic packaging flakes by polymer and color. Five different polymers among those commonly used for plastic packaging, i.e., polystyrene (PS), polyethylene terephthalate (PET), expanded polystyrene (EPS), polyethylene (PE), and polypropylene (PP), subdivided in 6 different color classes (orange, red, transparent, green, blue and white) were investigated. Two different HSI devices were used to perform the polymer and color identification, operating in the short-wave infrared range (1000-2500 nm) and in the visible range (400-750 nm), respectively. A hierarchical classification model based on partial least square - discriminant analysis (PLS-DA) was built in order to obtain a highlevel efficiency in prediction for all classes. The performances of the model were evaluated in terms of sensitivity, specificity, precision and F1 score. The obtained results were very promising, showing how HSI coupled with data fusion can be utilized as a non-invasive, fast and efficient tool to obtain high-quality recycled plastics, optimizing the industrial plastic recycling process.

The real-time power information from the power plants and substations are required for smart grid infrastructure. However, the conventional current sensors are vulnerable to external electromagnetic interference (EMI) and surge current. Therefore, optical current sensors have received high attention for their advantages over the conventional system. Optical current sensors have various merits of insulating sensor materials, linear response over wide current range, and light weight for easy installment. Furthermore, they can be produced in small footprints and are immune to the external EMI, and they can be used in high power capacity. The only disadvantage of optical current sensor is the complicated interferometry structure, which could be resolved by adopting integrated optics. In this work, we designed and fabricated a polymeric integrated-optic device for producing the polarization rotated reflection interferometry (PRRI). By adopting integrated optics, the PRRI could be fabricated by a simple process and is advantageous for mass production. The polymeric waveguide device consists of phase modulators, TE-pass polarizers, directional couplers, and polarization converters integrated on a single chip. It was fabricated through a conventional semiconductor fabrication process, such as photolithography, spin-coating, UV-curing, etc. Finally, the fabricated polymeric integrated optic current sensor exhibited excellent linearity, fast response time, and high accuracy satisfying the 0.2 accuracy class (IEC 60044-8).

Measurement of volatile organic compounds (VOCs) released from a human body holds great potential for non-invasive and convenient assessment of metabolisms or disease screening. Here, we introduce a biochemical monitoring system for external ear-derived ethanol vapor. The monitoring system consisted of an over-ear gas collection cell and a biochemical gas sensor (bio-sniffer) for ethanol. The bio-sniffer for ethanol was composed of a bifurcated optical fiber, which was connected to a UV-LED (λ = 340 nm), a photomultiplier tube, and an alcohol dehydrogenase (ADH) immobilized membrane. Gaseous ethanol was oxidized through an ADH-catalyzed reaction with the reduction of co-enzyme, β- nicotinamide adenine dinucleotide (NAD). Ethanol was finally measured by detecting the autofluorescence of a product of this reaction, a reduced form of NAD (NADH), at the wavelength of 490 nm. Characterization of the monitoring system demonstrated the high sensitivity with a broad dynamic range (26 ppb – 554 ppm), high selectivity to ethanol, and capability of continuous measurement, which allowed us to monitor external ear-derived ethanol vapor after drinking alcohol. The experiment revealed that there was little interference from sweat in sensor signals at the external ear and a similar temporal change of ethanol concentration to that of breath. These features indicate the suitability of external ears for non-invasive blood VOCs monitoring and the high degree of usefulness of the developed monitoring system.

The growing demand for biocompatible and biodegradable sensing devices emerges to fulfill applications in medicine, tissue engineering, and environmental monitoring. Nowadays, optical waveguides conceived with unconventional materials (like silk, cellulose, green polymers, and hydrogels) have replaced silica and polymer-based devices in optogenetics, phototherapy, and intra-body assessment. However, most biodegradable optical materials rely on expensive resources and intricate processing. Agar is an edible, soft, and renewable alternative presenting singular features: gelation at low temperatures, thermal reversibility, moldability, and transparency. Furthermore, one may enhance the optical and mechanical properties of the agar samples by choosing the chemical composition. This work proposes the design, characterization, and application of agar-based devices for optical sensing. Firstly, melt solutions comprising food-grade agar and water undergo solidification inside molds to create standard and structured optical fibers and waveguides. Besides, adding glycerol improves mechanical strength and stability, reduces optical losses, and provides reliable refractive index control. Subsequently, experiments evaluate the optic response of agar devices to mechanical, thermal, electrical, and chemical stimuli. Illumination with a visible laser creates speckle fields susceptible to mode coupling and phase deviation effects. Therefore, one may analyze these speckle patterns with image processing techniques to detect subtle changes in the output light and retrieve the measured parameters with high sensitivity through a straightforward camerabased setup. The agar optical waveguides provide new perspectives for physical and biochemical sensing based on an edible and biodegradable material for intra-body applications and environmental monitoring.

We report a silicon nitride (Si3N4) Mach-Zehnder interferometer (MZI) waveguide for the evanescent-wave detection of carcinogenic volatile organic compound (VOC) pyridine vapor for environment safety. The Si3N4 MZI waveguide is coated with cross-linked dipolar dyes as sensitive cladding for specific pyridine sensing. Waveguide engineering in virtue of evanescent field, cladding thickness and group refractive index regulation is carried out. The sensor chip with width/height of 2μm/200 nm is fabricated by photolithography/developing and ICP etching. In pyridine sensing, the MZI sensor shows the high sensitivity of 19 pm/ppm and the lowest detection limit of 1.5 ppm and fast response of 60s.

Microplastic pollution has become prevalent due to industrialization and the wide use of plastic. In this study, the individual and combined effects of polyethylene microplastic with Zn on lentil seed germination were monitored using Biospeckle Optical Coherence Tomography (bOCT), a technique established by authors to visualize the internal activity of biological objects. The results imply that both 100 mg L^-1 heavy metal Zn or polyethylene microplastics (PEMPs) alone had a strong inhibitory effect on lentils. However, in combination with PEMPs, the Zn toxicity on lentils was reduced due to the reduction of Zn uptake by the seedlings through the interaction of Zn and MPs. These findings suggest that PEMPs could be able to mitigate the effect on the bioavailability and toxicity of Zn on lentils.

Volatile organic compounds in exhaled breath and transdermal gas reflect biochemical information such as conditions of diseases and activities metabolisms. In the laboratories, such volatile markers were measured by large analytical systems like gas chromatography-mass spectrometry. If a sensor for volatile markers could be developed that could be used as easily as a wearable device, it could provide early screening for signs of disease and metabolic abnormalities. We have been developing optical biosensors for volatile markers based on nicotinamide adenine dinucleotide (NADH)-dependent enzymes. The NADH has a fluorescence (excitation 340 nm, emission 490 nm) and can be utilized to monitor enzyme reactions. Various kinds of volatile markers including ethanol, acetaldehyde, acetone, 2-propanol and etc could be measured by this technique. Gas imaging was also possible by employing fluorescence imaging optics. Here we introduce gas sensors and imaging systems using a bio-fluorometric technique for human volatile marker sensing.

This conference presentation was prepared for the Future Sensing Technologies conference.

Mid-infrared tuneable diode laser absorption spectroscopy (TDLAS) realized with photonic integrated circuits (PICs) has the potential to create small and portable sensing devices with exceptional sensitivity. Here we present a photonic crystal (PhC) W5 waveguide-based sensor, which provides stronger interaction between the light and the analyte than a free space beam, as well as low scattering and material absorption losses. We demonstrate its performance by analysing the transmission spectra of two CO2 isotopes at 4345 nm.

Yielding on the intimate link between losses and polarization of the evanescent profile in optical waveguide via complex wave-vector polarization-loss locking phenomena was discussed in the context of spin orbit interactions of light. Inspired by this, we conducted an investigation into a novel method to achieve improved transmission of light across optically opaque materials showing Helmholtz equation solution for complex values introducing a passive metasurface placed on the boundaries of the material, affecting transmission coefficients for phase and amplitude simultaneously. This approach resulted in a non-monotonic beam penetrating inside the lossy material, enhancing its intensity while reducing its amplitude near the interface. Such proposal have interesting applications in various scenarios, including acoustic isolation, sensing technologies via grid metasurface matching, improved telecommunications transmission in homogeneous materials, or enhanced laser resilience in biological tissues for subcutaneous imaging or treatment. Furthermore, our theoretical approach paves the way for potential realization in similar physical systems, ranging from near-zero-index materials to purely plasmonic interfaces, offering a comprehensive overview of the formalism.

This paper investigates the change in optical performance in a multi-element lens objective from a series production automotive camera, due to elevated temperature. The change in optical performance is evaluated by a coupled thermomechanical and optical finite element (FE) simulation. The change in the overall geometry of the objective at 85°C is calculated by thermo-mechanical simulation and caused by the expansion of individual elements. In addition, the change in the surface geometry of the lenses is obtained after smoothening the distorted FE mesh. Optical simulations are performed afterward on the deformed objective to evaluate the optical parameters including MTF (modulation transfer function) at 60 line pairs per millimeter (lp/mm). A shift of the best focus position of the lens is observed. Simulated MTF values are validated by measuring the MTF at 60 lp/mm of 10 lens samples by heating it in-situ on the measurement system. It is seen that change in MTF is about 10 percent when the lens is heated from room temperature to 85°C. Coupled optical simulations indicate the presence of astigmatism and coma, implying the asymmetric change in lens geometry at elevated temperature and tilt in one or more lens elements.

State-of-the-art optically transparent antennas used for shared RF/EO/IR apertures typically utilize mesh conductors to simultaneously achieve high optical transmission and low RF resistivity compared to that of transparent conducting oxides (TCOs). Unlike TCOs, however, mesh conductors need to be patterned in a geometric shape – most commonly a square or rectangular periodic grid. As a result, this grid can introduce a diffraction pattern which can degrade the imaging quality of the optical system. It is shown that transmission decreases according to the geometric area covered by the mesh at normal incidence and that transmitted energy is pushed into higher diffractive orders, as expected. These effects are ultimately summarized as a decrease in the signal-to-noise and signal-to-background ratios. Performance tradeoffs in varying the line width and line spacing of a rectangular grid are shown whereas the smaller the line width and spacing, the smaller effects there are from the grid. Furthermore, methods to reduce diffractive effects –improving imaging quality - in structures by randomizing the substructure in which light interacts are presented. Randomizing tiled mesh substructures reduces periodicity to generate ultra-uniform diffraction, while maximizing conductivity of the structure. Increased randomization of a meshed aperture allows for a reduction in higher diffraction orders by 99%. By leveraging the results of this study, shared RF/EO/IR apertures can maximize their performance across the electromagnetic spectrum.

This paper analyzes the method of measuring optical power with an emphasis on the known methods of measuring the power of polarized light propagating through an optical fiber by two polarization axes. The method relies on monitoring the change in the behavior of the sensor fiber parameters when the temperature conditions in its surroundings change. This work deals with the possibilities of measuring the optical power for each propagation axis separately. Simultaneous measurement of the instantaneous state of polarization and a very accurate measurement of the wavelength makes it possible to obtain the necessary information about nonlinear phenomena in the optical fiber. The design of various connections that were selected for implementation in the laboratory is described in detail. The practical implementation includes a set of measurements under different conditions in which a source of temperature change was applied and the response of the parameters to these changes was monitored. The results were measured with a polarimeter and a power meter and are presented in graphs and summarized in the discussion.

In polarimetric imaging, degree and angle of linear polarization (DoLP and AoLP, respectively) are computed from ratios of Stokes parameters. In snapshot imagers, however, DoLP and AoLP are degraded by inherent mismatches between the spatial bandwidth supports of S₀, S₁, and S₂ parameters reconstructed by demosaicking from microgrid polarizer array (MPA)-sampled data. To overcome this shortcoming, we rigorously show that log-MPA-sampled data approximately decouples DoLP and AoLP from the intensity component (S₀) in the spatial Fourier domain. Based on this analysis, we propose an alternative demosaicking strategy aimed at estimating DoLP and AoLP directly from MPA-sampled data. Our method bypasses Stokes parameter estimation, alleviating the spatial bandwidth mismatch problems altogether and reducing the computational complexity. We experimentally verify the superior DoLP and AoLP reconstructions of the proposed log-MPA demosaicking compared to the conventional Stokes parameter demosaicking approach in simulation. We simulated the conventional 2 × 2 MPA patterns as well as the more recently introduced 2 × 4 MPA patterns, and report quantitative (mean squared error, structural similarity index, and polarization angular error) and qualitative results. We also provide a closed-form approximation error analysis on the log-MPA-sampled data to demonstrate that the approximation error is negligible for real practical applications.

Fourier-domain filtering methods have been developed for monochrome polarization cameras and for Bayer-filter RGB color cameras, but not yet for color polarization cameras. We review the Fourier-domain filtering theory for the first two cameras and then show how to generalize it to the more complex case of color polarization camera images.

Constant global monitoring of the air quality requires coverage of the earth by satellites with spectro-polarimetric capabilities. To make this financially viable, miniaturization is essential. Modern techniques enable us to directly pattern spectral and polarimetric filters on detector chips, varying their properties pixel-to-pixel. Each pixel contains unique information and by combining the information of neighboring pixels through a reconstruction algorithm, the spectro-polarimetric data of the earth can be approximated. We will present a design framework that simultaneously optimizes the properties of the filters and the parameters of the reconstruction algorithm to get the most accurate representation of the spectro-polarimetric data.

Digital screen time has increased as a result of the COVID-19 pandemic. Visual fatigue is a common problem that can arise due to prolonged visual work or exposure to certain environmental factors. The objective measurement of visual fatigue is important for assessing the severity of the problem and developing effective interventions for the same. It is crucial that the visual fatigue level of those who use digital displays is monitored and managed on a regular basis in order to keep their visual fatigue under control. An objective method for measuring visual fatigue that links changes in visual features to changes in visual fatigue has been suggested as a part of this research work. The effect of environmental factors like change in lighting and ambient temperature on visual fatigue has also been studied. Readings from a standard visual fatigue test have been used for the research study. Going further, the proposed method for visual fatigue measurement and the sensor network for monitoring environmental factors can be adapted for integration with wearable devices.

Social infrastructures are rapidly aging, and there are concerns about the increasing cost and effort required for maintenance and management. Deflection measurement is critical in evaluating the integrity of bridges as transportation infrastructure. The sampling moiré method was developed to accurately measure the displacement of structures by capturing the regular patterns (i.e., moiré markers) attached to the structures with a digital camera. The conventional approach rigidly attaches a camera to a tripod or a fixed point. However, finding a place to photograph bridges over the sea or mountains can be challenging in real scenarios. In recent years, camera-equipped drones have rapidly become an inspection technology for bridges and other transportation infrastructure. Here, we believe drone cameras open a new door for unconstrained bridge vision inspection using the moiré phase analysis method. Therefore, we are striving to develop a novel displacement measurement method that can measure the deflection of bridges by drone aerial photography. We measured the vertical displacement of a target with a 50-mm-pitch grid pattern in the laboratory to verify its effectiveness. The newly developed measurement technology alleviates the restriction that the camera must be fixed and enables the measurement of the deflection of a more significant number of bridges under various real situations.

Generating accurate 3D models of the city or a region has become a key important step to plan any further development or modifications from the current plan. Close-range remote sensing technologies have been very efficient in generating accurate 3D models of the regions with very detailed information. Unmanned Aerial Vehicle (UAV) is one such technology that is widely used due to their cost-effectiveness, time efficiency, and for various other reasons. In this research, the quality of the Digital Surface Model (DSM) produced through the photogrammetric software process is tried to enhance in some parts of the datasets. The DSM is generated from the pointcloud generated from Structure from Motion (SfM). In dense urban regions, the overlap of the UAV images is not sufficient or incapable to generate ample points in the point cloud at some places in the dataset, due to which the DSM generated is not that accurate. The point cloud is enhanced using an interpolation technique at those regions. From which the DSM generated is enhanced. Qualitative analysis is also done between the DSM obtained through the photogrammetric software process and the DSM enhanced through the proposed methodology. This low-cost approach can be implemented while creating digital twin models.

We propose a super resolution method based on coded aperture imaging using two cameras with relatively different spatial positioning. A digital micromirror device (DMD) is used to encode the object and its reflected lights are acquired by two cameras simultaneously. The spatial displacement of each camera relative to the coded aperture allows reconstruction of super-resolution images with a resolution higher than that of the coded aperture at high speed. We compared and verified the proposed method with the case of using one camera.

Infrared thermography using a focused (spot or line) beam has proved to be effective for detection of surface breaking cracks on planar samples. In this work, we use the same principle, but applied to complex shaped components, like a rail section, a gear, and a gas turbine blade. We use a six-axis robot arm to move the sample in front of our thermographic setup. Several scanning paths and thermographic parameters are explored: scanning speed, density of points in each scanning slice, laser power and camera frame-rate. Additionally, we explore semi-automatic evaluation algorithms for crack detection, as well as 2D-to-3D registration of the found indications.

Due to the trade-off between spatial resolution of the imagery and satellite revisit times, the research topics using multiresolution remote sensing data have attracted much attention. In recent years, the techniques for improving the visibility of multi-resolution imagery have been proposed, including pan-sharpening and super-resolution. However, there are relatively few studies on the techniques of improving the model performance on low-resolution imagery by referring to detailed information from the high-resolution imagery during training time. To tackle this type of task, domain adaptation has been proposed in the field of computer vision to adapt a model trained on one dataset to another with different properties. Yet, domain adaptation for multi-resolution data is difficult due to the scale variation in addition to differences from the camera sensors. In this study, we propose a new approach for multi-resolution modeling that combines the major techniques, semi-supervised domain adaptation (SSDA) and multiple instance learning (MIL). Under the MIL framework, a large scene can be regarded as a bag of instances (e.g., image patches), and information from different receptive field sizes can be exploited. We conducted experiments on a dataset of Japanese oak wilt, which is known to have severe forest damage with two different optical satellite imagery, SPOT6&7 satellite imageries (1.5m) and Pleiades-1B satellite imagery (0.5m). The proposed method improves the discrimination accuracy of the low-resolution model compared to the standard SSDA technique. This obtained result reveals the potential usefulness of MIL for effective multi-resolution modeling.

The use of chemical warfare agents (CWAs) remains a real threat to public life, in order to safeguard and protect lives. Continues research and investment need to make into early warning systems. Within the scientific literature several sensors have been produced for the detection of CWAs and organophosphorus compounds with varying sensitivities and selectivity. Most sensors developed are unable to detect below the minimum risk level, rendering them ineffective for early warning systems. The use of molecularly imprinted polymers (MIPs) to increase the sensitivity of the sensor is a possible solution to this problem. For an MIP to be successful, the correct monomer must be selected that is complementary to the chosen template molecule. This study utilises computational chemistry for the identification of potential monomers complementary to the chosen template, for the fabrication of an MIP. Dimethyl methylphosphonate (DMMP) was used as the template for the MIP as the compound is a mimic for Sarin and is used heavily within the literature for the design of chemical warfare sensors. By investigating the polymerisation capabilities and interactions with the template, a suitable candidate can be selected without wasting time and resources. The chemical structures of the monomers and template were imported into Spartan and converted into a 3D model. Density functional theory (DFT) with the B3LYP/6-31G level was used to calculate the electronic binding energies with a 1 to 3 ratio of Chitosan to DMMP proving to be the most efficient option.

We are developing optical parts made of infrared-transmitting glass, which can be used in various fields of science and in other applications. Herein, spectral dispersers were fabricated from FI-02, which is developed by Nippon Electric Glass Co., Ltd. The radiation test showed that FI-02 is resistant to radiation. Further, a reflection coating to produce an immersion grating made of FI-02 was tested. We designed, manufactured, and evaluated a prototype grating made of FI- 02. The dimensions of the prototype grating were 30 mm × 20 mm, and the pitch of the steps of the grating was 0.217 mm. We observed the microscopic structure of the steps of the prototype grating. The surface roughness values of the wide and narrow surfaces of the steps were 0.084 μm rms (0.070 μm Ra) and 0.082 μm rms (0.064 μm Ra), respectively. The radius of curvature of the edge of the step was also quantified.

In this paper, we present an electronic nose ammonia gas monitoring via IoT system for Chlorella sp. cultivation. The MQ–137 gas sensor module is selected as the primary sensor for measuring ammonia gas concentration. The pH sensor module is the sensor for measuring the pH levels in Chlorella sp. ponds. The MQ–137 gas sensors are calibrated with a known concentration of ammonia gas in the calibration box. Calibration conditions are set corresponding to low concentration of ammonia gas produced by Chlorella sp. cultivation. The pH sensor module is calibrated against a standard pH buffer. The calibration data obtained from the calibration method is input into computer programming and processed by the ESP8266 microprocessor. After calibration, the MQ–137 gas sensor and pH sensor modules are used to measure the ammonia gas concentration and pH levels in the Chlorella sp. ponds. Chlorella sp. is cultivated under three conditions of light intensity, natural light with an average light intensity of 1521 Lux, and artificial light with a light intensity of 1000 Lux and 2000 Lux, respectively. The ammonia gas concentration and pH levels of Chlorella sp. cultivation are transferred to the cloud system and displayed via the IoT system. And the system can also send a notification to a smartphone when ammonia gas concentration and the pH levels reach the specified value.

We present a comparative study of optical designs of ultraviolet (UV) spectrometers onboard spaceborne telescopes. Such spectrometers provide unique findings for the space sciences that cannot be obtained via ground-based observation due to the atmospheric opacity in this wavelength region. Instruments for observing in the UV wavelength range should have as few reflective surfaces as possible to maintain a high throughput. For instance, the Hisaki spectrometer uses simple optics, consisting of only one toroidal grating. However, in this case, the spot qualities produced by optics onto detectors have room for improvement. In this study, we present several optical designs with improved spot qualities by adding one or a few reflective surfaces, and compare them. We also discuss optical designs that is potentially useful for the proposed Life-environmentology, Astronomy, and Planetary Ultraviolet Telescope Assembly (LAPYUTA) mission.

We present a comparative study of methods with a table summarizing them in searching extraterrestrial life. This work represents an update and improvement of our previous publication. In these works, microorganisms are supposed to be the object of extraterrestrial life exploration. We selected several characteristics and molecules which can be detected and analyzed in searching for extraterrestrial life. We also evaluated the search technique which targets those characteristics and molecules from several points of view. In this revision, “Generality for terrestrial microorganism” and “Source of false positives” were resolved into sub-issues and described in detail. We also introduced new issues, i.e., “Possible meaning of negative detection” and “Science objectives other than life search.” We classified life in space into three types: Earth life, Earth-independent life (life that emerged from non-life independently of Earth life), and Earth-kin life (extraterrestrial life sharing a common ancestor life with Earth life). All the methods which are effective in detecting Earth life are potentially useful for the detection of Earth-kin life. In contrast, their applicability is not guaranteed for Earth-independent life. The three major conclusions of the previous article were re-evaluated and confirmed to remain unchanged: (i) there is no realistic single detection method which can conclude on the discovery of life independent of Earth, (ii) there is no single best method which is superior to the other methods in all respects, and (iii) there is no single method which can distinguish between Earth life and life independent of Earth. Subsequently, we discussed the combination of the detection methods and dividing extraterrestrial microorganism search into multiple steps: the survey and- detection step and the analysis-and-conclusion step. We also emphasized the importance of imaging; even with not enough resolution for observing the detailed morphology of a microbial cell, it is highly worthwhile to distinguish whether the detected molecules are concentrated at micron size or diffused thinly. Of course, high-resolution imaging provides information with regard to the morphology of microorganisms. Accordingly, imaging is necessary for both the survey-and-detection and the analysis-and-conclusion steps. To realize a space exploration mission or instrument for extraterrestrial life search, not only in scientific but programmatic sense, it is important to design it so that scientific results can be obtained even if no life is found. The studies included in this article primarily supposed Mars as a target; however, they are valid for a wide variety of planets of the solar system, as well as their moons and small objects.

We describe a cell culture experiment utilizing Mars soil simulant and yeast as a study for extraterrestrial-microorganism detection in future. The following conditions were combined to create different sample configurations: solid medium (agar plate) or liquid medium, with the Mars soil simulant (100 mg) or without, and various yeast cell densities (30 μL of a suspension of yeast prepared to achieve dilution rates of 10⁻², 10⁻³, 10⁻⁴, and 10⁻⁵ from the original suspension). For redundancy, two samples were created for each setting. On each agar plate, colonies of yeast cells were visible. In contrast, only samples created with a dilution rate of 10⁻² and 10⁻³ were proven to show the presence of yeast proliferating in liquid media. The agar plates were observed via imaging using a digital camera. Colony counting was conducted using functions of the ImageJ software; the yeast density in the original suspension was estimated to be 2.5 × 10⁷ colony forming units (CFU)/mL. Based on the results of cell culture on the agar plates, the detection limit that was experimentally demonstrated in this work amounted to 7.5 CFU/100 mg of soil, which corresponded to 75 CFU/g of soil. Thus, we confirmed that the Mars soil simulant reduced the number of colonies to one-third of that detected without the Mars soil simulant. In the discussion, we pointed out that the method that used the solid media has potential to provide high sensitivity using a simple, compact, and lightweight hardware. Microscope with no optics is also discussed. The lower limit of detection obtained in this study was compared with those obtained using other methods. We compared and discussed extraterrestrial life search based on (1) in situ experiments by unmanned missions, (2) analysis of samples returned to Earth, and (3) dedicated in situ analyses by manned bases located on extraterrestrial sites in the future. The perspectives discussed herein are applicable not only to the samples from Mars but also to the samples from other solar system bodies. Identifying cell culture conditions for these applications is a critical difficulty. Notably, it is a possible scenario that cell culture (although challenging) will become necessary to obtain clear evidence of extraterrestrial life. We also note that in cases where there are multiple species of microorganisms, it can happen that microorganisms that fit the culture conditions used in an experiment are selectively cultured and detected, even if the culture conditions for each species are not fully discovered. It is worthwhile to conduct exploration in advance designed with the intention of determining culture conditions with the plan to culture microorganisms in the future.

Network traffic has increased substantially due to the introduction of advanced network-enabled applications and devices. The introduction of software defined networks (SDNs) and machine learning (ML) has empowered optimizing network operations and network traffic monitoring, resulting in improved complex traffic operations and security with faster malicious intention detections. This paper focuses on network traffic data collection systems, and the data is evaluated using a survey of ML algorithms, depending on the data type (tabular or image). Adhering to system architecture best practices including a decoupled design to integrate with existing network monitoring infrastructures and cybersecurity standards; and online and offline data collection via packet capture (PCAP) standards. For packet based network traffic data analysis, we convert captured data into images and feed into a convolutional neural network to classify the data based on requirements. For statistical based network traffic data analysis, we apply feature engineering on tabular data and feed into various ML systems to classify based on requirements. Finally, We show that the same ML algorithm outperforms publicly available datasets using our collection method.

In this paper, we propose a Refractive index Fiber-Optic SPR sensor with a D-shaped structure. Due to numerous advantages such as miniaturized size, fast response, and immunity to electromagnetic interference, optical fiber has been comprehensively used for different sensing applications. It consists of a multi-mode sensor with a bi-metallic thin film structure with Ag and AlSb as the sensing layer. The refractive index (RI) of the testing solution gets incremented from 1.37 till 1.40. The refractive index sensitivity gets varied non-linearly from 15,400 to 30,800 nm/RIU in the NIR region. Fabrication of the sensor is easier and thereby it is suitable for any sensing environment.

There is an increasing need for the development of instrumentation and approaches for fast identification for novel psychoactive substances (NPS). Most NPS have an ambiguous legal status and are often sold in the form of healthy herbs to the public. Up until December 2021, synthetic cannabinoids (SC) constituted 29% of the known NPS, according to the United Nations office on drugs and crime. Various instrumentation and methods are well-known to identify these drugs, however they are mainly benchtop instruments difficult to be taken to the crime scene. The increasing use of molecularly imprinted polymers (MIPs) in forensic science and the need for portable instruments have been paired in previous research from our group to design small sensors for identification of different molecules of forensic interest. Computational calculations are produced to study the electronic binding energies between polymer and template (NPS). This study was developed by using the density functional theory (DFT) with the B3LYP/6-31G level to calculate the electronic binding energies. The template used in the calculations was the synthetic cannabinoid Apinaca (AKB-48). Carbazole was the monomer found to be the best for the synthesis of the MIP. Our present work presents the computational design of the polymers and the results achieved, as a first step in the fabrication of the sensor.

In this presentation we describe the application of a previously developed technique that is now being used to correct the daytime polarization calibration of the CALIPSO lidar. The technique leverages the fact that the solar radiation background signals from dense cirrus clouds are largely unpolarized due to the internal multiple reflections within the non-spherical ice particles and the multiple scattering that occurs among these particles. Therefore, the ratio of polarization components of the cirrus background signals provides a good estimate for the polarization gain ratio (PGR) of the lidar. However, in the visible and ultraviolet regime, the molecular contribution is too large to be ignored, and thus corrections must be applied to account for the highly polarizing characteristics of the molecular scattering. This presentation describes the theory and implementation of the molecular scattering correction.

Image Mapping Spectrometry (IMS) is a compact snapshot hyperspectral imaging technology. However, the image mapper used in the IMS causes degradation of the reconstructed spectral datacube, such as, low spatial resolution, missing areas and stripe artifacts. In this paper, we propose an end-to-end deep learning method to jointly inpainting and super resolution the restored spectral images of the IMS. The method includes an image inpainting network, which is designed to correct the nonuniform intensity and missing data, and an image super resolution network, which aims to enhance the spatial resolution of images. In addition, a local nonuniformity correction method is proposed to preprocess the IMS images. Simulation and experimental results demonstrate the effectiveness of the proposed method.

Recent developments in the field of Geographic Object-Based Image Analysis (GeoBIA) have been utilized for the Automation of Landslide detection. In this study, we have tried to develop a semi-automated detection methodology applying concepts of GeoBIA on High-Resolution Earth Observation imagery acquired from an Uncrewed Aerial Systems also referred to as Uncrewed Aerial Vehicles (UAV). The study area is in the Himalayan state of Uttarakhand, India. The UAV was flown over a landslide site. The UAV data was processed for deriving photogrammetric products (Digital Elevation Model and Orthomosaic). The methodology implements the segmentation and classification of UAV images using Multi-otsu thresholding method and machine learning algorithm of random forest. It incorporates spectral (RGB), textural (GLCM entropy and GLCM angular second moment), morphological (sky view factor), and topographical (elevation, slope, curvature) features derived from UAV photogrammetric products. When determining landslide locations is of utmost concern, this system's ability to detect landslides quickly and effectively gives it a viable alternative to manual procedures for landslide mapping across wide areas. The developed system was able to detect 86% of the total landslide area.

Raman spectroscopy is a spectroscopic method which indirectly measures molecular vibration and can identify molecules by obtaining their unique vibrational fingerprints. Raman spectroscopy has the advantage of non-invasive and label-free detection, but it has the problem of low sensitivity due to the very weak intensity of Raman scattered light. In order to make Raman spectroscopy high sensitivity, surface enhanced Raman scattering (SERS) induced via metallic nanostructures has been widely studied. SERS is attracting attention in various fields of analysis such as bioanalysis, material analysis, environmental analysis, and food analysis because of its high sensitivity and selectivity. However, SERS spectra of adsorbed molecules with similar structures have very similar shapes, making it difficult to discriminate molecules from their spectra. Especially, it is even more difficult to determine the mixing ratio of mixtures of those molecules. Thus, we focused on the use of machine learning to determine the mixing ratio of similar structural molecules on gold nanostructure. In this study, we fabricated gold nanostructures by depositing gold on a cyclo-olefin polymer (COP) film with periodical nanostructure. Then, self-assembled monolayer (SAM) of mixed benzene thiol derivatives, as a model of surface adsorbed molecules, were prepared on the surface of gold nanostructure, and measured SERS spectra. We examined several machine learning models that can accurately determine mixing ratios from the obtained spectra. As a result, we succeeded in determining the mixing ratios of molecules with approximately 99% accuracy, with multilayer perceptron (MLP) model being the most accurate.

Application of laser displacement measurement is expected in various fields such as manufacturing, medical care, and inter-vehicle distance measurement. In this paper, we demonstrate real-time shape measurement using a laser displacement measurement system based on intensity correlation with phase modulation signals, which we have been studying for years. The intensity-correlation-based laser displacement measurement system measures the intensity correlation between the probe light signal and the phase-modulated electrical reference signal by using an optical modulator. The displacement of an object is measured in real time from a simple calculation using the fundamental and second harmonic frequency components of the intensity correlation signal. The measurement range can be freely adjusted by the modulation frequency applied to the probe and local light. In the previous study, we also developed a method to extend the measurement range without deteriorating the measurement accuracy. Furthermore, the proposed system does not need sophisticated highfrequency circuits, because multiplication operation necessary for intensity correlation measurement is performed optically by using an optical modulator. In this study, we have developed an experimental setup for scanning the probe laser beam on the sample and combined it with the laser displacement measurement system. In the experiment, we used a sample with a stepwise profile composed of aluminum mirrors and successfully measured the shape of flat surfaces and steps within an error range of the order of 10^-5 m.

Nowadays nanostructures are in demand in various fields from biomedicine to green energy. Photoluminescence (PL) spectral measurements are a powerful tool to study nanomaterials unique physical and optical properties. Most modern spectral approaches are associated with the study of a sample on a substrate or in colloidal solution. In turn, we propose a technique for studying the luminescence of a single object levitating in a quadrupole Paul trap. To verify the technique, we investigate PL spectra of individual trapped charged microcluster of CdSe/ZnS quantum dots. The results obtained open prospects of optical research on single particles isolated from the environment.

This paper demonstrates the different modelling of detection for lidar by using continuous wave and pump laser configurations as well as the signals received performance of the set test range. The configurations are developed by three main subsystems namely the transmission part that is consists of laser source, modulator, amplifier, oscillator, the target, and the receiver part that is constructed of photodetector, amplifier, filter, and signal processing. The distances tested in the simulation focuses on several ranges that is less than 1 kilometer and the wavelength used is 1550 nm. The signals obtained from both configuration modellings were analyzed and compared in terms of the tests delay, distance measurement of the target range from 20 m till 1000 m, frequency and the received signal power. Observations and analysis from the simulation results show the differences of signal pulses obtained when using continuous wave and laser pulse as well as the difference of received signal power for both configurations. Analysis shows that for lidar distance measurement, range of target also affect the received signal power where the laser pulses show detection of longer range as compared to continuous wave although for shorter distances, continuous wave displayed averaged good performance in terms of received signal power. This work will be the basis of further investigations of light detection and ranging for optimization purpose in terms of laser beam shape, fiber medium, polarization effect, optimum distance, and particle detection.

In this study, U-shaped fiber optic sensors are fabricated and analyzed to measure the sensitivity of the developed sensor and optimized the detection of the refractive index (RI) of a given liquid. Identifying the authenticity of the RI is very important in food processing, chemical, liquid security and pharmacy. In this research, three types of sensors with different curvature radii (3 mm, 4 mm, and 5 mm) and different angles (30°C and 60°C) with 60 cm length of polymer fibers have been developed to characterize and analyze which type of sensor that will give optimal reading. This sensor is expected to be used for future studies such as in bioengineering, food and liquid security and chemical detection. Each sensor is tested with several types of liquids that have different densities. The development of this RI sensor is also intended to detect a suitable temperature for RI of a liquid that is between 20°C - 55°C. The research analyzed the RI sensitivity using impurity-free liquid (mineral water) and non-impurity liquid (saline water and used cooking oil). The result is measured and collected using Optical Power Meter and a 6500-input light source. The selection of this U-shaped sensor is due to the robustness of this sensor in various environments, high sensitivity, and its simple construction. This work aims to produce a low-cost and highly optimal U-shaped sensor for detecting and measuring the RI of a liquid impurity and security in any environment.

The study of charged particles in radio-frequency (RF) traps is associated with optical response registration. The common issue of optical collection efficiency is caused by the FR trap geometry, which restricts its coupling with optical equipment. To overcome this issue we propose a transparent surface RF traps with Indium Tin Oxide (ITO) thin-film electrodes. ITO sputtering techniques are well known and support deposition on any optical surface. The development of full transparent RF trap is associated with ITO refractive index and optical band gap optimisation while maintaining high electrical conductivity. Here we studied the ITO thin film properties depending on post-annealing temperature. We used the optimal procedure to develop transparent surface RF trap. We demonstrate a stable localization of charged microspheres in the electric field of the developed trap. The proposed approach allows full optical access to trapped charged particle.

Phase-Sensitive Optical Time Domain Reflectometry (Φ-OTDR) is the most common implementation of a Distributed Acoustic Sensing (DAS) system and employs the observation of speckles resulting from Rayleigh Backscattering from coherent pulses in an optical fiber. Since they are sensitive to local disturbances altering the intensity and phase of light, perturbations induced by events cause changes in the speckle pattern whose precise measurement gives information on the amplitude and frequency of vibrations distributed along the fiber. Demodulation of the local phase change is key to the precise measurement of events since it is more linearly related to the strain applied to the fiber. One of the key issues in distributed sensing is that phase demodulation schemes usually require additional post-processing algorithm runs for each spatial location, which introduces delays, and hence reductions in dynamic sensing capability when scaled along the whole sensing distance. In this contribution, we analyze the impact of the post-processing in different phase demodulation techniques employing Phase-Generated Carrier (PGC) on the bandwidth of distributed feature extraction in a typical DAS system by quantifying the total computation time needed for a benchmark, 10-km sensing range at meter-scale spatial resolutions. We then design, implement, and analyze a signal processing scheme for phase extraction in Φ-OTDR enabling real-time dynamic measurements based on a Fast Hilbert Transform (FHT). Particular focus is given to the choice of this demodulation scheme for optimized bandwidth of distributed feature extraction using parallel processing of adjacent blocks in such a way that the overall throughput of spatially resolved concurrent demodulation allows dynamic vibration sensing at speeds relevant to most structural health monitoring applications.

We propose a flexible computational EDFA dynamic model for amplified modulated probes used in long-distance distributed sensors based on pulse coding, employing Rayleigh, Raman and Brillouin Scattering phenomena. The EDFA dynamics is studied numerically solving the differential equations of the reservoir model for Erbium ion concentrations. The model allows one to identify the optimal choice of gain parameters as well as the gain control mechanism needed for gain stabilization, minimizing both the code-words output power fluctuations and avoiding nonlinear effects which can degrade the distributed sensor performance when using pulse coding techniques such as Simplex, Golay and Cyclic- Simplex. The technique allows distributed sensors to be robust against fluctuations induced by long amplified code sequences, ensuring at the same time enough optical gain to enhance their performance, especially in terms of sensing distance, without overcoming the threshold of nonlinear propagation effects.

Distributed Acoustic Sensing (DAS) is a ubiquitous technique which enables concurrent, real-time measurement of fault or event-induced vibrations over long distances. Although there has been focused research in increasing the performance of DAS based on Phase-Sensitive Optical Time Domain Reflectometry (Φ-OTDR), the cost of conventional schemes remains high due to the complexity of the opto-electronic components in the sources used in the interrogator for high coherent Rayleigh scattering visibility, which rely on optical amplifiers designed for wideband telecom networks and multi-purpose waveform generators. However, probes in DAS use narrow linewidth lasers, whose fluctuations are well below the bandwidth of a single ITU grid and the driving waveforms can be generated by compact RF sources. In this contribution, we propose and experimentally demonstrate the design of a compact DAS interrogator using a miniaturized Erbium-Ytterbium-Doped Fiber Amplifier (EYDFA) commonly used in CATV networks together with an integrated Direct Digital Synthesis (DDS) module which can generate readily programmable waveform probes with a bandwidth of up to 1.4 GHz. The DDS module is suitable for use with any digital acquisition system for real-time acquisition of traces. Optical pulse probes generated with the DDS an a miniaturized EYDFA were used to obtain coherent Rayleigh backscattering traces with high SNR and interference visibility, allowing the measurement of a generic vibration at the end of a 10-km fiber. The proposed technique enables the simplification of DAS systems and paves the way toward their scalable development for wider use in among others environmental, seismic and structural health monitoring systems.

We propose a method to address the issue of handling the large amount of data involved in Distributed Acoustic Sensing (DAS) by designing and implementing a data storage system for a benchmark DAS scheme for performing continuous monitoring over a 100 km range at meter-scale spatial resolution. We employ the DynamoDB functionality of Amazon Web Services (AWS) which allows highly expandable storage capacity with latency of access of a few tens of milliseconds. In addition, the scalability of the DynamoDB-based data storage scheme is evaluated for linear and nonlinear variations of number of batches of access and a wide range of data sample sizes corresponding to sensing ranges of 1km-110 km. The results show latencies of 40 msec per batch of access with low standard deviations of a few milliseconds, and latency per sample decreases for increasing sample size paving the way toward the development of scalable, cloud-based data storage services integrating additional post-processing for more precise feature extraction. The technique greatly simplifies DAS data handling in key application areas requiring continuous, large-scale measurement schemes such as remote environmental & railways infrastructure monitoring and precision agriculture.

Image enhancement is an ongoing research problem that the community is addressing through the development of fusion algorithms. Such techniques typically involve the reconstruction of RGB images by removing environmental artifacts and enhancing desired features. Infrared imagery is also widely used to improve situational awareness in low visibility scenarios. Recently, learning-based approaches are used for fusion purposes to extract meaningful representations from images and capture latent features that could otherwise be inaccessible using conventional image processing algorithms. The inadequacies of RGB images in these algorithms’ pipelines are still obvious, despite the fact that the viability of RGB-Infrared image fusion has been thoroughly demonstrated in the literature. For example, RGB images often have artefacts like sudden changes in exposure or motion blur when the illumination changes or sudden changes in the scene. A novel imaging sensor operating in the visible light spectrum has been developed to address these issues. In this paper, we explore the cutting-edge paradigm of Neuromorphic Vision Sensors (NVS), a class of asynchronous analog imaging platforms that operate based on the change of pixel luminosity within a scene. When compared to frame-based counterparts, NVS enhances scene interpretation, processing time, reaction time, and power consumption. Deep-learning reconstruction networks are evaluated in this study to determine the applicability of existing state-of-the-art multi-modal image fusion techniques with the addition of NVS data rather than RGB data. As a benchmark, metrics such as signal-to-noise ratio (SNR) and pixel wise error are used.

Object detection is a critical task in computer vision, with applications ranging from robotics to surveillance. Traditional RGB-based methods often face challenges in low-light, high-speed, or high-dynamic-range scenarios, resulting in blurred or low-contrast images. In this paper, we present a novel algorithmic approach that fuses event data from event cameras with RGB images to improve object detection performance in real-time. Event cameras, unlike traditional frame-based cameras, provide high temporal resolution and dynamic range, capturing intensity changes asynchronously at the pixel level. Our method leverages the complementary strengths of event data and RGB images to reconstruct blurred images while retaining contrast information from the original data. We propose an algorithmic pipeline that first fuses event data and RGB images, followed by a reconstruction step to generate enhanced images suitable for object detection tasks. The pipeline does not rely on deep learning techniques, making it computationally efficient and well-suited for real-time applications. To validate the effectiveness of our approach, we compare its performance against the popular YOLO benchmarks for object detection tasks. Moreover, we assess real-time metrics to demonstrate the practicality of our method in time-sensitive applications.