This PDF file contains the front matter associated with SPIE Proceedings Volume 13083, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

In this talk, we will provide an overview of Sony's cutting-edge sensors that offer special functionalities. We will explore the exciting possibilities that arise when they are combined with advanced AI and signal-processing techniques. Our talk will showcase the latest research and development updates from Sony, highlighting the diverse range of applications that can be achieved through this powerful synergy.

In recent years, AI technology using Neural Network (NN) has made remarkable progress and is used for highly accurate classification, object detection, and anomaly detection in sensing. The difficulties with high-accuracy NN are the long processing time and high-power consumption. As one solution, an optical neural network (ONN), which realizes NN by diffraction and propagation of light, has attracted attention as an implementation method with ultra-high speed and low power consumption. Although many of the prior studies on ONN are related to classification, ONN has the potential to be applied to various tasks. As one example, the use of ONN has the possibility of ultra-fast object detection. In this study, simulations and experiments were conducted to verify the possibility of detection by ONN. Metal nuts were selected as the detection targets as a representative example of mass-produced industrial parts. In the experiment, SLM was used to implement the data input layer as phase input and the trained diffraction layer. First, the case of a single detection target in the input data was demonstrated. The precision for the 551-input data was 96.4 % in the experiment. In the data that could be detected correctly, the root mean square error between the inferred and correct positions was 2.2 % of the metal nut size. Next, another experiment has confirmed that ONN can detect multiple targets accurately. In addition, we examined ONN that uses light transmitted through the sample and found that the inference process finished within 4.17 msec (the response time of the CMOS of this setup). The results show that ONN can accurately and rapidly detect objects.

Quantum computers are expected to be the next-generation computers in various fields. Quantum computers are different from classical computers, which store the current binary information of 0 and 1. Quantum computers can use quantum superposition, which can hold 0 to 1 probability as continuous quantities in one qubit. Therefore, existing algorithms are not necessarily efficient computational algorithms on quantum computers. GPU-based quantum simulations, such as cuQuantum, have recently been released to develop quantum algorithms. This paper focuses on quantum image processing, presents how far quantum image processing can be efficiently described today and verifies on GPUs that multiple image processing can be described using cuQuantum.

The optical-rotation-encoded snapshot multispectral imager combines an optical rotator and polarizer with a color polarization camera, allowing up to 12 band images to be reconstructed in a single snapshot. This approach provides a compact system for spectral imaging but requires computational reconstruction in order to estimate scene spectra. After reviewing the system architecture, we show experimental results and discuss performance. This system can be used as a simple platform for testing ideas of how we should analyze computational systems: their robustness, accuracy, and reliability.

Acquiring a hyperspectral image in a single frame enables the acquisition of fast phenomena (relatively to the whole acquisition time). One way to achieve such a snapshot configuration is to combine an interferometer and a lenslet array. A hyperspectral instrument using a birefringent interferometer operating in the visible/near-infrared wavelength band [500 nm: 850 nm] is presented. It has the advantages of being compact as the interferometer is placed close to the focal plane array. Additionally, we use a 4-directional wire grid polarizer array integrated to the focal plane array: such a device enables the measurement of the two complementary interferograms and of a fringe-free image in a single acquisition, which facilitates the spectral image reconstruction.

The polarization of light is one of the fundamental concepts used in fiber optic sensors. Combination of polarization measurement and interferometry seems to be promising concept of future fiber optic-based sensors. We have developed a cheap and easy concept of fiber optic precise length measurement which is needed for construction of fiber ring resonators used as the light source for this combined type of sensors. This paper presents a detailed description of the measurement setup, calibration and measurement.

Montana and similar regions contain numerous rivers and lakes that are too small to be spatially resolved by satellites that provide water quality estimates. Unoccupied Aerial Vehicles (UAVs) can be used to obtain such data with much higher spatial and temporal resolution. Water properties are traditionally retrieved from passively measured spectral radiance, but polarization has been shown to improve retrievals of the attenuation-to-absorption ratio to enable calculation of the scattering coefficient for in-water particulate matter. This feeds into improved retrievals of other parameters such as the bulk refractive index and particle size distribution. This presentation will describe experiments conducted to develop a data set for water remote sensing using combined UAV-based hyperspectral and polarization cameras supplemented with in-situ sampling at Flathead Lake in northwestern Montana and the results of preliminary data analysis. A symbolic regression model was used to derive two equations: one relating DoLP, AoP, and the linear Stokes parameters at wavelengths of 440 nm, 550 nm and 660 nm, to chlorophyll-a content, and one relating the same data to the attenuation-to-absorption ratio for 440 nm, 550 nm and 660 nm. Symbolic regression is a machine learning algorithm where the inputs are vectors and the output is an analytic expression, typically chosen by a genetic algorithm. An advantage of this approach is that the explainability of a simple equation can be combined with the accuracy of less explainable models, such as the genetic algorithm.

The study focuses on flood susceptibility in the Nam Ngum River Basin, Lao PDR, an area prone to annual flooding due to monsoons and rainstorms. Flooding in this region significantly threatens human life, causes economic losses, and damages communities and agriculture. The study employs advanced remote sensing and machine learning techniques, including Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN), to address these issues and create detailed flood susceptibility maps. The machine learning models used historical flood data, Sentinel-1 SAR imagery from 2018 to 2020, and open-source flood data for training and validation. Eleven flood factors were considered. With 776 samples, 70% were trained, and 30% tested the model. Flood susceptibility map accuracy is assessed using statistical techniques such as multicollinearity, Kappa index, and Area Under the curve of Receiver Operating Characteristics (AUROC). The generated flood susceptibility map is used to analyze the possible effect on the different land use/land cover classes and populations. RF outperforms SVM and ANN, achieving higher accuracy based on Receiver Operating Characteristics. The resulting flood susceptibility map reveals that 25-44% of the basin area is highly susceptible, predominantly in low-elevation and low-slope regions. Likewise, 85 to 90% of the people are highly vulnerable to flooding within 260 to 280 km² of built-up area. The study proposes a new approach to using machine learning and readily available remote sensing data for flood susceptibility mapping. The findings of this study provide essential insights for policymakers, aiding in disaster risk reduction and facilitating sustainable development planning in Lao PDR.

Deformation monitoring of dam infrastructure and slopes surrounding the reservoir is essential for the maintenance and sustainable operation of the dam. Interferometric Synthetic Aperture Radar (InSAR) based monitoring is a promising remote sensing geospatial technology for effective monitoring of dam structures and reservoir rims with an accurate measurement of displacement at mm-level accuracy. In this study, the SBAS-InSAR method was used for data processing, and monitoring deformations at a dam and its reservoir rim, located in the Indian Himalayas. We found several sites around the reservoir having a noticeable mean line of sight deformation rate of 30 mm/yr to -30 mm/yr for ascending and +45 to -45 mm/yr for descending orbit. Results demonstrate a trend of deformation at the dam body and cumulative Line of Sight (LOS) deformation values reach up to -40 to +40 mm.

The recycling of space debris is being prioritized more and more by international space agencies as a critical waste reduction method enabling the reuse of materials that would otherwise be wasted. Space missions typically generate various types of waste, including metals, nonmetals (such as plastics, foams, and packaging materials), and liquids (such as water, beverages, and chemicals). Reusing and/or recycling offers a sensible compromise between monetary gain and technological advancement. The potential for implementing the entirety of the recycling process and/or a portion of it directly in the space environment can be a fascinating and practical possibility. For the proper implementation of a recycling operation for materials and/or for the selection of production equipment (such as shredders, classifiers, separators, extruders, etc.) to be used in space, material identification and categorization are crucial. In this context, the possibility to use a sustainable low-cost strategy based on HyperSpectral Imaging (HSI) approaches was explored. In more detail, the possibility to classify plastic space debris by HSI sensors working in the Near InfraRed (NIR) range to recognize the presence of different polymer type for sorting/classification purposes was thus investigated in this study. The achieved results are very promising for the further development of the research activities, showing as the adopted HSI approach can be profitably utilized to identify, recognize, and classify the different material categories. This study was carried out in the framework of “Hyperspectral based sensing architectures for resource circularity project” belonging to the Spoke 5 - Closed-loop, sustainable, inclusive factories and processes of the Extended Partnership "MADE IN ITALY CIRCOLARE E SOSTENIBILE” (MICS) of the Italian PNRR.

The monitoring of water vapor dynamics and evapotranspiration provides feedback for vegetation and crop water requirements. Many current evapotranspiration techniques heavily relay on satellite imagery, temperature distribution and incorporated into estimation models. The work presented here is based on a direct water vapor change monitoring using snapshot SWIR and NIR spectrometers utilizing custom fiber bundles. These bundles have dense input and sparse output creating void spaces for spectral information and allow recording of spatial-spectral data cube information in parallel at the CMOS/CCD cameras. SWIR / NIR snapshot spectrometers were packaged and ruggedized for field imaging. The number of spatial samples is adaptable within 8000 ~ 35,000 range and spectral sampling 20-50 values. SWIR system allowed 1050-1300 nm spectral range while NIR 650-1000nm, applying 1130nm and 940nm absorption windows respectively. We performed and present series of experiments including controlled reference measurements and field tests.

Although fire events inside nuclear power plants (NPPs) are infrequent, when they occur, they can affect the safe operation of the plant if there is not sufficient protection addressing the risk. As mitigation for fire events, NPPs have comprehensive fire protection systems intended to reduce the likelihood of a fire event and the associated consequences. An electrical arcing fault involving components made of aluminum is one such hazard that could lead to a significant consequence. Because the original evaluation of High-Energy Arcing Faults (HEAF) was performed on components made of copper, there is an interest in understanding the effects of aluminum in these incidents. The Nuclear Regulatory Commission (NRC) has led a series of HEAF experiments at a facility near Philadelphia, PA, in conjunction with the National Institute of Standards and Technology (NIST), European and Japanese partners, and Sandia National Laboratories (SNL). To capture a range of different HEAF events, Sandia has provided high-speed visible and IR videography from multiple angles during this series of experiments. One of the data products provided by Sandia is the combination and synchronization of infrared and visible data from the multiple cameras used in the tests. This multispectral fusion of information (visible, MWIR, and LWIR) allows the customer to visualize the tests and understand when different events happen in the two to four second duration of a test. The presentation will dissect three experiments and describe the different events occurring during their duration. The presentation will compare the behavior of equipment that contains aluminum components versus the ones containing copper or steel. Finally, data from a switchgear experiment will be presented to complement the bus duct data.

Camera calibration is a fundamental technology for image measurement. Calibration using 2D planar patterns is the de facto standard and is performed by displaying and detecting patterns such as a circle pattern or a checkerboard pattern, and calibrating based on the correspondence between the coordinates of the patterns. Recently, the patterns are often displayed on a Liquid Crystal Display (LCD). While many previous papers have focused on the detection method, this paper focuses on the display method. This is because LCDs have coarser dots per inch than printing devices. In this paper, we propose a method for drawing circular patterns on LCD that accurately and fast detects feature points based on the Gauss circle problem. Experimental results show that the proposed method is four times more accurate and 2.5 times faster than previous drawing methods.

Anti-reflective coating on ophthalmic lenses is an essential process for glare reduction. After coating, lenses must be individually inspected to obtain the highest quality control. The ISO 8980-4 method recommends that the spectral reflectance measurement of the sample’s top surface require the sample’s back surface to be frosted or painted matte black, a destructive process. Lens manufacturers often employ a manual inspection technique, observing color appearance and brightness reflected from a lens sample compared to a reference sample by the naked eye of well-trained technicians. With many lenses to be inspected daily, human errors are unavoidable. In this work, we have developed a new non-destructive optical technique that can be used to measure the spectral reflectance of the lenses based on a modified spectral confocal microscopy. The critical procedure of this technique is that the confocal probe must be placed at the confocal peak of the reference standard and the confocal peak of the lens sample. Compared to a spectrophotometric method, a mean relative reflectance error of 0.042 %R in the 400-780 nm spectral range can be archived using the proposed confocal technique, provided that the confocal probe is placed within ± 5 μm from the peaks of the confocal signal. The proposed confocal technique could be utilized non-destructively and routinely for spectral reflectance measurement of the coating quality of ophthalmic lenses and other transparent optical elements.

Laser Ultrasonic Testing (LUT) is a noncontact and nondestructive method for inspecting internal and surface defects of materials. Ultrasonic waves are generated by pulsed lasers, reflected or scattered by defects, and detected by an optical interferometer through surface displacements. One of the most significant challenges of LUT is improving the sensitivity of the interferometer. In this paper, we report the development of a highly sensitive interferometer and its application in detecting minute defects within the internal structure and on the surface of metals. The interferometer utilizes a fiber-optic Sagnac configuration, which incorporates a loop of polarization-maintaining fiber components. We enhanced the sensitivity not only by amplifying the optical source power to increase the signal-to-shot-noise ratio, but also by eliminating stray light in the optical head to minimize beat noise. The resulting sensitivity, evaluated by noise-equivalent surface displacement, is 1.1×10⁻⁶ nm/ √ Hz, which is nearly half of the best sensitivity achieved by commercial industrial interferometers currently used for ultrasonic measurement. We successfully applied the interferometer to two specific cases: the detection of artificial line defects with a diameter of 100 μm engraved on the backside of a 10-mm-thick SS400 steel, and the detection of surface cracks with a width of 0.5 μm and depth of 10 μm. These results demonstrate the potential of our LUT system in detecting minute defects.

Solid state beam steering is the predominate technique for Light Detection and Ranging (LIDAR). In the field of solid state beam steering devices, the commercial mass production device liquid crystal on silicon spatial light modulator (LCOS-SLM) for beam steering are attracting a lot of engineer attention but beam steering efficiency and speed are limited by the optical diffraction principals according to characteristic of SLM. Although ample studies have been conducted on the concept of beam steering by commercial LCoS-SLM and analyze the performance of beam steering, no work has succeeded in realization of SLM beam steering techniques for LIDAR range detection with Field of View (FOV) larger than 5° and distance detection ⪆10m. We present a prototype and methods of receiving rays reflected back from object and detected by Avalanche Photodiode (APD) with 1mm receive area under 1550nm pulse fiber laser source with 10ns and 100 khz repetition rate. The Fourier diffraction principle is employed to obtain the phase pattern on SLM for beam steering. Beam steering efficiency includes beam profile and coordinates after spanning angle optical system is analyzed. The performance and analysis of solid state SLM beam steering for LIDAR are explored.

In the last decades, advanced sensors play increasingly important role in modern vehicles. The first step towards fully autonomous driving were Advanced Driver Assistance Systems (ADAS), where smart sensors played a crucial role. For ADAS applications, RGB cameras, radars and ultrasonic sensors were used. In order to achieve higher levels of automated driving, more advanced sensors are required in order to provide better accuracy at higher distances, necessary for highway applications. Moreover, it is necessary to achieve improved robustness in challenging atmospheric and illumination conditions. In order to achieve these goals, different types of sensor were proposed, like radar, thermal cameras and lidar. However, to achieve the required angular resolution for highway operation at distances up to 250m, FMCW lidar proved to be the most promising solution. In this paper we will present methods for advanced processing of FMCW lidar signals, noise reduction and temporal filtering. The first component of the proposed work is enhanced demodulation processing, which improves the accuracy of lidar, in depth resolution, based on neural networks. This processing step includes improved beat frequency detection and noise reduction. To further improve the robustness of the result, we perform temporal filtering of the point clouds. This results in enhanced temporal stability of the point clouds and reduced uncertainty of the object position. We compare the results of the proposed method with the recent state of the art in terms of objective quality measures and prove its effectiveness.

This study thoroughly examines how Laguerre-Gaussian (LG) superposition beam is attenuated by rainfall in free space, through Monte-Carlo simulations with both geometrical optics and Mie scattering theories. Unexplored in atmospheric optics, the research encompasses rainfall rates (5-100 mm/h) and a propagation distance (100 m) at wavelengths of 1064 nm. The superposition of LG modes of zero radial, but 1 and -4 azimuthal order beam exhibited 85.7% unimpeded photon transmission through moderate rainfall for 100 m by geometrical optics theory and 86.6 % by Mie scattering theory. The study develops intensity and phase profiles, emphasizing performance advantages over traditional methods in precipitation. Its outcomes are crucial for improving optical communication systems in challenging atmospheric conditions, bridging a significant gap. Empirical evidence supports LG beams' efficacy in environmental challenges, with implications for environmental monitoring, navigation, remote sensing, and a foundational framework for diverse real-world applications.

We developed a “Metal-Molecule-Metal Nanoparticle” (MMNP) configuration on a fiber-optic SPR sensor system in the present work. For this, a gold-coated optical fiber (with an exposed core in the central portion) was used, and this gold-coated surface was first functionalized with the Self-Assembled Monolayer (SAM) of 4-aminothiophenol (4-ATP) molecules. The 4-ATP SAMs were functionalized with biotin molecules through EDC/NHS coupling agents. Separately, citrate-capped gold nanoparticles (~20 nm) were synthesized and subsequently functionalized with streptavidin protein, which exhibits exceptional binding affinity towards biotin molecules. To establish the MMNP configuration, streptavidin-functionalized gold nanoparticles were flown over a biotin-functionalized SPR sensor surface. The Raman signal of 4-ATP molecules in the SPR sensor region was collected before and after interaction with streptavidin-functionalized gold nanoparticles. The Raman signals of the sensor region exhibited huge enhancement after interaction with gold nanoparticles, confirming the creation of the hypothesized MMNP configuration on successful biomolecular interaction between streptavidin-functionalized gold nanoparticles and biotin molecules on the SPR sensor surface. Concomitant with the Raman signal enhancement, a marked shift (~ 3-fold) in the SPR peak is also observed when interacting with the gold nanoparticles.

Whiskers (vibrissae) are functional hairs connected to a mechanically sensitive base and devoted to tactile surveillance by contact or approximation. Nowadays, bioinspired robots equipped with electronic, piezoelectric, or magnetic whisker-like transducers are available for 3D localization, texture recognition, and shape retrieval. However, these sensors demand a multi-axis response for decoding the magnitude and direction of applied stimuli, making the interrogation hardware intricate with excessive measurement channels. Therefore, we propose a biocompatible whisker device based on an optical fiber specklegram sensor, wherein the transducer comprises a flexible, conical structure made of glycerol-agar gel. The whisker foundation embeds a short single-mode fiber section spliced between multimode waveguides to create a hetero-core structure responsive to the vibrissa bending. Thus, we excite the optical fiber with a 633 nm laser and analyze the output speckle field through image correlation techniques. Stimulation with transverse displacements revealed sensitivity and resolution of 0.09 mm⁻¹ and 0.62 mm, respectively. Furthermore, one may calibrate the correlation algorithm with additional reference speckle images to retrieve the bending magnitude and direction with a single optical channel, avoiding wavelength-based demodulation schemes. We anticipate applications of such compact, biocompatible whisker probes in tactile surveillance regarding biomedical and human-robot interaction setups.

Assessing lower limb motions is crucial for several applications in biomechanics and human-computer interaction. Nowadays, the displacement of lower limb joints is retrievable through piezoresistive strain gauges, magnetic sensors, and inertial measurement units, whereas non-invasive alternatives comprise surface electromyography (sEMG), brain-computer interfaces, and optical tracking. However, most prevailing technologies demand expensive, intricate hardware with multiple channels and rely on burdensome signal/video processing. Therefore, this paper presents an optical fiber sensor for assessing lower limb motions employing the Force Myography (FMG) technique. FMG predicts user movements and intentions from the radial pressures exerted by leg muscles, providing an intuitive force-level response and demanding fewer channels than the sEMG. Besides, fiber sensors are immune to variations of skin impedance and electromagnetic noise, circumventing the drawbacks of widespread force-sensing resistors and capacitive devices. In this work, a microbending optomechanical transducer converts muscle stimuli into light-intensity modulation, generating FMG waveforms that carry information about performed postures or movements. Firstly, experiments evaluated the average intensity versus knee joint displacements (0 to 120°) by attaching the optical transducers to the vastus intermedius and gastrocnemius/soleus muscles, achieving an average resolution of 5.4°. Subsequently, we measured the sensor response to different movements to characterize and classify the performed actions according to their waveforms. The results demonstrate the sensor’s feasibility for detecting lower limb motions based on a straightforward and non-invasive setup, motivating further applications in robotic exoskeletons for walking assistance and rehabilitation.

We have established an advanced nonlinear multimodal imaging system, integrating Stimulated Raman Scattering (SRS), multiphoton fluorescence (MPF), and Second Harmonic Generation (SHG), to investigate the intricate relationships between metabolic activities and metabolite distribution in cells and tissues. In addition, the innovative Adam-based Pointillism Deconvolution (A-PoD) and Correlation Coefficient Mapping (CoCoMap) algorithms have been designed, enabling a deeper insight into the simultaneous recording and analysis of correlation between various metabolic processes within super-resolved images of nanoscale Regions of Interest (ROIs). To precisely pinpoint signals originating from specific subcellular organelles, the state-of-the-art Multi-SRS reference matching (Multi-SRM) clustering algorithm was introduced. This technique holds the potential of enhancing the detection and prognosis of diseases, assessing therapeutic outcomes, and deepening the understanding of the aging process and biomedical science.

Experimentation and research on safe and autonomous landing selection pipelines of UAVs are critical for their widespread deployment. Most of the methods of landing site selection are concentrated on control system-based methods that rely on feedback from sensors. For vision-based methods, extracting different surface parameters from images is vital of surface inclination is considered in this work. In this paper, a novel dataset consisting of images with different inclination label is prepared and tested with an end-to-end CNN based architecture. This LandUAVSafe dataset consists of RGB images with different inclination angles as ground truth labels. The dataset is created on ROS Gazebo using Iris UAV to capture different surfaces at several inclination angles at different heights. Three different CNN based architecture for inclination estimation based on Faster-RCNN, YOLOv3, and YOLOv8 has been experimented for classifying inclination between 0, 15, 30, 45 and 60 degrees. The experimental results depict significant improvement with the YOLOv8-based architecture.

The VIIRS Day-Night Band (DNB), with a spectral bandwidth of 500-900 nm, collects data at three different gain stages: low, mid, and high gain (LG, MG, and HG), covering a very large dynamic range and capable of sensing reflected light from space during both day and nighttime. This paper describes the use of stars for VIIRS DNB HG calibration, discusses some of the challenges and limits to this approach, and demonstrates various strategies and techniques developed for its calibration stability monitoring and results of calibration inter-comparisons among three VIIRS instruments currently operated on the S-NPP, NOAA-20, and newly launched NOAA-21.

This paper presents a groundbreaking development in the field of organic photodetectors, introducing a novel dual-mode photodetector with exceptional thickness capabilities. The technology is achieved through the innovative use of multilayer blade coating techniques, enabling the seamless transition between narrowband and broadband modes through the application of bias. This pioneering device, termed the "Bias Switchable Narrowband-Broadband Near-Infrared (NIR) Organic Photodetector," is manufactured in ambient conditions and offers several distinct advantages, including dual-mode operation, halogen-free solvent usage, ambient fabrication conditions, and scalability in the manufacturing process.

Silicon Ring Resonators (RR) are currently being assessed by several national metrology organizations as thermometers for use in calibration laboratories and in high-accuracy commercial applications. In this paper, we summarize the results of one such assessment carried out at the National Research Council of Canada (NRC). The prototype of silicon RR thermometer (developed at NRC) was evaluated in the stirred liquid bath between 23 °C and 80 °C over the period of several years in order to get the full uncertainty budget. The combined 10-mK standard uncertainty for our RR thermometer is not only identical to the repeatability reported previously for an unpackaged RR but it also includes a contribution due to long-term drift of RR thermometer estimated over two consecutive 11-month periods. We also report the results of our on-going efforts to reduce the long-term drift by using the controlled gas atmosphere inside RR thermometer and discuss the ultimate accuracy achievable with our current setup.

The Short-Wave Infrared (SWIR) spectrum reveals distinct optical properties of many materials which considerably differ from their properties in the visible spectrum, creating unique imaging opportunities. SWIR imaging is usually done with InGaAs focal plane arrays which due to their low-volume manufacturing and high price remain inaccessible for many applications. Image sensors based on Colloidal Quantum Dot (CQD) photodiodes recently emerged as alternative that has a potential to enable high-volume manufacturing of SWIR image sensors. Multiple images in distinct SWIR bands often need to be captured, but due to the unique properties of CQD photodiodes, it is challenging to directly integrate optical filters on top of them for the purposes of spectral imaging. Here, we propose a strategy to overcome these challenges by integrating CQD photodiodes on top of optical metasurfaces. Patterned silicon nanostructures enable detection of spectrally and polarization sensitive SWIR light. The proposed device stack is compatible with thin-film processing and demonstrates a path towards affordable spectral imaging in SWIR which can impact many industrial, scientific and consumer domains.

The monitoring of mechanical strain is essential in developing new materials, designing mechanical components and structural health monitoring. In applications where contactless measurement is required, a novel method is needed to allow for absolute and long-term measurements. We discuss a measuring principle based on diffractive nanostructures featuring these advantages. For the measurement, periodic nanostructures are applied to a component, illuminated with a defined light source and the resulting color impression is monitored. The relationship between the stretched geometry of the nanostructure and diffraction spectra allows to quantify the component’s strain. We present a guideline for the design of industrial applicable and sensitivity-optimized nanostructures and discuss the advantages in different application scenarios.

In this work, we propose a miniature and high-performance MXene-based fibre optic Fabry-Perot Interferometer (FPI) for the detection of heavy metal (mercury) in the water. Light from fibre tips (FP cavity) induce interference through partial reflection at interfaces. The systematic investigations have been conducted for the free spectral range against the FP cavity length, the effect of finesse on the surface reflectance of FP cavity surface using thin film gold-coated fibre tips of various thickness in nanometre scales. The FP fibre tip deposited with MXene nanolayer will be developed to detect Hg²⁺ in water by inducing a strong response through the adsorption and reduction of Hg²⁺ to Hg⁺ with MXene nanomaterials. The nanomaterial integrated FPI sensing scheme could be an alternative for food, water quality, and environmental monitoring.

Chiral sensing is essential for comprehending biological processes. Our study emphasizes the use of Metallic Helical structures (MHCs) in Surface-Enhanced Raman Spectroscopy (SERS). The double helices, fabricated by TPL method reveal exhibit high SERS response (approximately 10¹⁴ EF) in simulation. This facilitates the detection of both chiral and non-chiral molecules.

Surface and ground air temperatures are one of the variables that best distinguish and characterize the specific climate in urbanized spaces. Over the years, research has shown that urbanized spaces have experienced persistently higher temperatures, which is defined as the urban heat island effect (Urban Heat Island-UHI). Wind turbines and solar panels are two of the main types of renewable energy sources used in Bulgaria. The presence of too many different facilities related to renewable energy sources often has an impact, but sometimes this impact can be negative for specific territories even if they are not highly urbanized, such as the selected territory in Western Pontic steppes, North-Еast Planning Region (BG33). The study covers examples from the planning region defined in the Law on Regional Development of the Republic of Bulgaria under Art. 11, which will support the Integrated Territorial Strategy for the Development of NUTS 2 planning district. These are territories whose selection is determined by the fact that they have extremely high economic and ecological importance for monitoring the normal course of natural processes, disasters and consequences of sudden changes. The aim of the research is to create a methodology for monitoring through a complex approach, to be used by experts and non-experts, in order to make decisions for the management of the territories occupied with renewable energy sources. Different indicators and indices from the optical range, such as Normalized Differential Greenness Index (NDGI), Tasseled Cap Transformation (TCT), Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST), were used for the different groups of objects. The spectral reflectance characteristics of natural and anthropogenic objects have been used to classify temperatures. Open data, data from the National Spatial Data Portal (Inspire). Orthophoto and aircraft images from 100 years ago were used for the needs of the methodology.

A dual-axis numerical control rotary table is designed in this article to calibrate a dual-axis tilt sensor. The device comprises a swing axis and a rotating axis, which are used to produce tilts in two perpendicular directions. To achieve precise control of the motion mechanism and enhance positioning accuracy, the time-grating angular displacement sensors have been adopted as the feedback and measurement components in both axes of the device. The experimental results indicate that the error of the swing axis of this calibration device is ±1.5 arcsec, while the error of the rotation axis is ±1.0 arcsec, with a repeatability of less than 0.5 arcsec, which is suitable for the calibration of the majority of dual-axis tilt sensors.

In this paper, a full geometric information measuring model of 3D gear was developed based on laser scanning techniques. In order to generate a optimum sampling path, a measuring cost function was proposed by considered of the relationship among the sampling interval, sampling point clouds, and measurement precision of key features. On the other hand, 3D tooth surface measurement error was described by quantitative point cloud data. A commercial 3D gear measuring system was also introduced, which has a degrees of laser scanner and self-developed special analysis software. Finally, a series of experiments on typical cylindrical gears (base circle diameter from 100 mm to 500 mm) were performed to demonstrate this automated path planning technique, measurement cost, and the full geometric measurement accuracy.

For military applications, recognizing the targets with a good accuracy is a vital skill. In the literature there are many machine learning-based works on target recognition in visible spectrum, since there are massive RGB datasets. However, it is very important to have the same capability in infrared spectrum for military applications. Because of that reason DSIAC database, which has both visible and infrared images of the same targets is introduced first. Then a straightforward and efficient transfer learning-based ATR algorithm is proposed. Each step of the transfer learning process is explained in detail. The proposed transfer learning algorithm is tested with many challenging scenarios of DSIAC database. We extract valuable results how the ATR performance depends on range, wavelength and time changes. We also test ATR capability of our proposed model against extensive data. At the end we achieve very satisfactory accuracy scores thanks to the power of transfer learning.

In developing countries such as Southeast Asia, there are many areas where urbanization is progressing. In developing countries, detailed data on land use and other matters are often insufficient, making it difficult to understand the status of urbanization, the effects of projects, and the extent of their impact. First, in order to evaluate the effectiveness and accuracy of the urban area extraction method targeting Tokyo and its surrounding areas, we considered land cover classification processing using clustering using optical observation information from Landsat satellites and polarization information from Sentinel. did. The results showed that the accuracy of urban area extraction was improved by using optical satellites and SAR satellites in combination. Subsequently, this method was applied to areas with social infrastructure development in developing countries in Southeast Asia, and the state of urbanization was investigated in detail, and characteristics such as the state of urbanization and the scope of influence associated with social infrastructure development were effectively evaluated.

COVID-19 rapidly spread worldwide in 2020, leading to a pandemic. In Japan, measures like stay-at-home requests and remote work were implemented, shifting towards an internet-based lifestyle. Strict restrictions were imposed on high-risk industries such as restaurants, resulting in a 10-15% GDP decrease and significant societal and economic impacts. Nighttime light is known to correlate with various social and economic indicators. This observational information is expected to be used in the future, as it will be possible to easily estimate economic indicators from satellite night light observations in areas where statistical information research and maintenance is insufficient. This study utilized nighttime observation images from the Suomi-NPP satellite, which has improved spatial resolution and observation range compared to conventional night light observation satellite. Using these images observation information, the social situation and living environment of Tokyo's 23 wards were investigated, and changes in people's lives under COVID-19 were considered. Time-series changes in nighttime light distribution before, during, and after the COVID-19 outbreak were investigated. Different trends in the distribution of night light were observed in each of Tokyo's 23 wards. These trends were analyzed by overlaying them with various spatial information, such as land use and transportation networks like railways and highways. As a result, in commercial areas with large bustling commercial districts, radiance decreased, while in areas with a high concentration of residences, radiance increased. From this study, trends in changes in radiance values related to land use and transportation infrastructure were considered.

Contact-free measurement devices known as Terrestrial Laser Scanners (TLS) capture dense point clouds of objects or sceneries by obtaining the coordinates and intensity value of each individual point. The point clouds are noisy and dispersed. By converting "data" to "information", a mathematical surface approximation may effectively decrease data storage and organize point clouds without requiring direct manipulation of the data. Uses include conducting stringent statistical testing for deformation analysis in the context of monitoring landslides. Classification and segmentation algorithms may recognize and eliminate non-uniform features like trees and shrubs to provide a smooth and precise mathematical surface of the ground by reaching an ideal approximation. In order to lead the reader through the current techniques, we provide a comparison of approaches for classifying TLS point clouds. In addition to the conventional point cloud filtering techniques, we will examine machine learning classification algorithms that rely on the manual extraction of point cloud features and PointNet++, a deep learning strategy that uses automated feature extraction.

Hyperspectral analysis is an important remote sensing tool. Corresponding spectrometers require accurate calibration in optical laboratories. Non-linearity and temperature dependence are important factors that degrade the calibration quality. In this paper, we report on the investigation of the non-linearity of a temperature stabilized spectroradiometer HR-1024i from Spectra Vista Corporation (SVC). The spectroradiometer is applied as transfer spectroradiometer for the radiometric calibration of air- and spaceborne imaging spectrometers. Results show signal dependent non-linearity. The non-linearity of the spectrometer’s VNIR detector in spectral range 450 − 980 nm shows a strong spectral channel dependence with maximal radiometric error of 2%. The non-linearity of the SWIR-2 detector in spectral range 1896 − 2510 nm could not be evaluated precisely due to the low signal level. This impact of non-linearity requires correction in post-data processing.

The Indian Himalayas are prone to rock-debris slope failure every year during the rainfall period that lasts through JJAS months. A large number of active landslides lie along the Mandakini catchment in the Upper Indian Himalayas, posing a continuous threat to people and the rural landscape. To address this challenge, we conducted multi-temporal UAV surveys over a period of one year to monitor slope displacement efficiently. Flight plans for different epochs generated ultra-high-resolution products, including Digital Elevation Models (DEMs), orthomosaics, and 3D dense point clouds. In this study, we utilized the Geomorphic Change Detection (GCD) plugin to analyze the areal, volumetric, and vertical depth average change between pre- and post-digital elevation models for the Sonprayag Slope. The results help identify erosion and deposition values of rock-debris, contributing to a better understanding of slope dynamics. UAV-derived products, combined with GCD analysis, offer promising insights for monitoring and managing landslide hazards in the region.

The study of unregulated and regulated (legal and illegal) landfills on the basis satellite and field data allows complex monitoring and analysis of waste sites. This approach combines high-resolution satellite imagery to identify and map landfills with detailed field observations to verify data and assess their condition. This provides up-to-date information on the location, volume and potential impact of landfills on the environment, which is critical for effective waste management and nature conservation. The study covers examples of different NUTS 2 planning areas (under the Regional Development and Improvement Act) such as South East (BG 41) and South Central (BG 42). The data generated is for a period of at least five years. Regulated landfills are of national importance and selected events from the territory of Bulgaria have been investigated and monitored through a complex approach based on satellite data, Unmanned Aerial Systems (UAS) and ground-based spectrometric equipment, a thermal camera and an Automatic recording weather station (AWG).The optical monitoring indices used are Normalized Difference Vegetation Index (NDVI), Tasseled Cap Transformation (TCT) and Normalized Differential Greenness Index (NDGI). The satellite data used are Sentinel-2MSI, Landsat 9 (OLI/TIRS), Sentinel 3 SLTRS and Sentinel -1 SAR. The study of landfills based on complex methods of remote sensing and validation of the results through ground data brings significant benefits to the administration, society and NGOs. It facilitates the identification and monitoring of illegal landfills and dumps, supports the planning of cleanup measures and pollution prevention. This improves waste management, protects the environment and ensures a healthier life for people, while reducing costs for society and administration in the long term.

The mapping of snow and snow avalanche dynamics is important for determining potential danger zones, planning protective measures and understanding of climate change. Year-round snow cover monitoring through land surveys is almost impossible in the Longyearbyen area, aerial photography surveys are also insufficient due to the specific conditions of sunshine and the lack of it during the winter season. Due to the presence of clouds the snow cover information is very limited by the optic satellite images. Microwave images have the advantage over visible and NIR techniques as they are sensitive to changes in surface moisture and thus provide useful information about changes in their physical states. The study evaluates the usefulness of C-band SAR images for data mining for wet snow and snow from other surfaces, but also uses optical indices and indicators. TCT (Tasseled Cap Transformation) was used as a moisture indicator, as well as NDVI (Normalized-Difference Vegetation Index), which was used to map snow as a normalized difference of two bands (one in the visible and one in the near-infrared or short-wave infrared parts of the spectrum).

Wildfires considerably disturb the structure and forest ecosystem functioning. The disturbances estimation as well as the extent of damage to the soil and vegetation soon after the fire is crucial information for planning of restoration efforts. Because of the financial resources needed for field work and the involvement of experts, remote aerospace methods and data are extensively employed in monitoring ecological research. The aim of this paper is to assess postfire forest disturbances and initial regrowth processes using the tasseled cap derived Direction Angle (DA). DA is an index introduced by the authors in previous research – the angle between the Greenness component from the TCT (tasseled cap transformation) and VIC (Vector of Instantaneous Condition). The proposed method is based on linear orthogonal transformation of multispectral satellite images and is characterized with higher accuracy compared to standard methodologies using vegetation indices. The higher accuracy of the methodology is based on the linear orthogonal transformation of multispectral satellite images (TCT), which increases the degree of identification of the three main components changing during fire – soil, vegetation, and moisture/water. The methodology proposed in this paper is characterized by high accuracy in assessing the recovery of undergrowth, that is difficult to differentiate using standard monitoring methodologies based on vegetation indices. The DA raster images show the direction of change of the green tasseled cap component (TCG) relative to the VIC, which allows to estimate the degree of recovery of the vegetation component for different moments of the study period. The variations observed in DA values illustrate the pattern of the green component at various points during the investigation period, enabling the assessment of disturbances and the monitoring of regrowth processes. The test area is located in the Middle Rhodopes, near the village of Hvoyna (Smolyan region), Bulgaria, where on 28/08/2023 a wildfire broke out. 1,500 decares have been burnt by the fire, including deciduous and coniferous forest. The wildfire affected 100-130 years old black pine (Pinus nigra) forests.

The following study presents results derived by the ongoing research project “Monitoring of the seasonal dynamics and stability of the snow cover in the mountain range of the Republic of Bulgaria for a period of ten years (2014-2024) based on Remote Sensing“. The Vitosha mountain region situated in Bulgaria (278 km²) was used as a test area for monitoring Snow Cover Extent (SCE) for an eight-year period (2016-2023). The results derived by the conducted study consist of SCE information which is crucial part of the calculation of Snow Water Equivalent (SWE), hydrological runoff modeling and flood forecasting and monitoring. In the present research the available cloudless optical data of Sentinel-2 MSI, generated and exported from Google Earth Engine (GEE), from October to May, for the period between 2016 and 2023 were used for snow cover monitoring purposes. Normalized Differential Snow Index (NDSI) and Snow Water Index (SWI) were calculated, and the resulting output raster datasets were post-processed and inspected additionally to obtain threshold classifications used for calculation of snow cover area distribution. Global Warming and Climate Change with its negative effects, such as frequent droughts, water scarcity in the summers, and less-snow winters, have a significant impact on agriculture, hydrology, forests, and ecology in Bulgaria. The results obtained in the study can be used and integrated for observations and studies of climate change at the local and regional level.

Fluorescence (or Förster) Resonance Energy Transfer (FRET) describes the flow of energy from a donor fluorophore to an acceptor chromophore. Among the parameters in determining FRET efficiency is the donor–acceptor energy matching, given by the overlap of the donor fluorescence spectrum and the acceptor absorption spectrum (J value). Calculations of the J value typically rely on experimentally acquired spectra. When a fluorescence spectrum is converted from the wavelength (nm) scale to the wavenumber (cm^-1 ) scale, the Y-axis intensity needs to be corrected by the square of the wavelength (termed the λ² correction), because fluorescence spectra (but not absorption spectra) are collected with a fixed wavelength bandpass. The λ² correction causes the peak intensity of the short-wavelength (long-wavenumber) side of the fluorescence spectrum to decrease, or the peak intensity of the long-wavelength (short-wavenumber) side to increase. This issue has been known for ⪆60 years but the impact remains little appreciated. The relatively new availability of libraries of spectral data enabled assessment here of the λ² correction for various donor–acceptor pairs (i.e., donor fluorescence spectra and acceptor absorption spectra). The magnitude of error introduced upon omitting the λ² correction increases with the width of the spectra. A meta-analysis of recent literature reveals trends in usage of wavenumber or wavelength scales. While either scale can be used, errors in calculating the ostensibly simple J term are not uncommon. Best methods are articulated here. The software program PhotochemCAD 3, which incorporates the λ² correction for FRET calculations and also contains diverse spectral libraries, is freely downloadable at www.photochemcad.com.

We have investigated the optimization of computational parameters in fluorescence imaging based on the Transport of Intensity Equation (TIE). For the phase distribution acquired by Fourier-transformed TIE calculation, two computational parameters such as intensity thresholding and constant value in spatial frequency to avoid zero division should be determined to obtain the accurate phase distribution. This study aims to determine the optimal parameters under experimentation and evaluates parameter changes when noise exists. In this paper, we evaluate numerically the reconstructed phase error by changing two parameters when white noise exists.

This study explores the challenges facing silicon-based Photodiodes (PDs) in color discrimination due to their thickness requirement and light absorption issues, prompting the investigation of perovskite halide materials as alternatives. However, the susceptibility of perovskite materials to degradation from humidity and oxygen remains a significant obstacle. To address this, the study proposes a novel approach involving the incorporation of PMMA/sodium borate salt (Borax) additives to reduce trap densities and enhance crystal quality in perovskite layers. The findings reveal that these additives act as passivators for Pb2+ ions and promote cross-linking between perovskite grains, leading to improved performance and stability of blue-light-targeting PDs. Specifically, the optimized Borax amount yields a PD with remarkable responsivity, external quantum efficiency, and stability, showcasing a promising pathway for future commercialization of photodiodes.

Recently, supervised-learning-based Single Image Super-Resolution (SISR) methods have been more and more popular, owing to their breakthroughs in SR performance for High Resolution (HR) and Low Resolution (LR) image pairs. However, LR images used for model training and performance evaluation are usually down-sampled from HR images by the same method. Thus, the evaluation results may not be consistent to a large extent when the down-sampling kernel is different. The motivation of this paper is to evaluate the robustness of supervised-learning-based SISR methods against different down-sampling kernels and analyze the impact of down-sampling to the supervised training and SR performance evaluation. We use six kinds of down-sampling methods to construct LR images from the same HR images, and comprehensively evaluate eleven popular supervised-learning-based SISR methods including dictionary learning and deep learning. Experimental results show that the SR performance of supervised-learning-based methods is the best when the down-sampling methods of the training data and the test data are consistent. We also collect a publicly available Standard Resolution Target (SRT) image dataset to provide a quantitative basis for subjective evaluation on real data. Our insights may facilitate further development and better evaluation of learning-based SISR methods.

Maritime surveillance systems employing thermal imaging encounter numerous challenges, where image quality significantly affects their effective range of vision. Adverse weather conditions such as haze, fog, and smog can obscure thermal imaging scenes, complicating the detection, identification, and tracking of objects of interest. For instance, these systems must track moving ships from a considerable distance using thermal imaging, while adapting to dynamic backgrounds and various weather conditions. Image quality assessment, a crucial research area, evaluates the perceived quality of an image. Standards for quantifying images often align with human perception, adopting user-focused approaches that consider an observer's ability to perform specific tasks, as outlined in the Johnson criteria. However, in real-time maritime surveillance applications, these criteria may prove inadequate in capturing image properties. This study explores the general factors that measure the dynamic range of marine surveillance thermal images, along with specific challenges in interpreting images using various quality assessment parameters.

Unmanned Aerial Vehicles (UAVs) are gaining more popularity for various applications such as surveillance, monitoring and mapping. However, navigating UAVs in low-features areas or GPS-denied areas poses a significant challenge, as conventional GPS-based method for estimating velocity become ineffective. Optical flow algorithms have become a promising approach for UAV velocity estimation in such scenarios. This paper proposes a novel method for UAV velocity estimation for a vision-based navigation system in GPS-denied low-features environments. The proposed method is evaluated and compared to various optical flow methods considering computational efficiency, accuracy, and robustness when predicting UAV velocity. Understanding the strengths and limitations of these optical flow methods will certainly enable the development and implementation of UAV navigation systems in challenging environments.