This PDF file contains the front matter associated with SPIE Proceedings Volume 11914, including the Title Page, Copyright information, and Table of Contents.

Here we present implementations snapshot–image reorganization/mapping techniques including IMS (Imaging Mapping Spectrometry), and high density fiber image processors. These instruments are compact, non-scanning / high speed and provide high light throughput. The general principle of these methods is based on mapping areas (pixel, lines) of the object/scene on a large format CCD/CMOS to create void regions for spectral spread and simultaneous 3D+ data acquisition. This allows for a rapid detection and/or for increased SNR. High light throughput allows imaging at ultra-short acquisitions (entire cubes) down to microsecond levels. Our newest implementations include gas detection, environmental imaging and compact UAV applications. This presentation describes advantages of IMS, and fiber based systems and compares them to other snapshot techniques (computational methods). Examples of remote sensing results are presented (vegetation/crops assessment).

In this talk, we demonstrate a black phosphorus/molybdenum ditelluride heterostructure photodetector, integrated with a silicon-on insulator (SOI) waveguide. The performed heterostructure photodetector exhibits high responsivity in the midinfrared spectral region. Moreover, our developed hybrid detector can exhibit ultrafast rise and fall time. Such features suggest its usefulness to real-time on-chip sensing applications.

Quantum technologies containing key GaN laser components will enable a new generation of precision sensors, optical atomic clocks and secure communication systems for many applications such as next generation navigation, gravity mapping and timing since the AlGaInN material system allows for laser diodes to be fabricated over a wide range of wavelengths from the U.V. to the visible. We report our latest results on a range of AlGaInN diode-lasers targeted to meet the linewidth, wavelength and power requirements suitable for quantum sensors such as optical clocks and cold-atom interferometry systems. This includes the [5s²S_1/2-5p²P_1/2] cooling transition in strontium⁺ ion optical clocks at 422 nm, the [5s₂¹S₀-5p¹P₁] cooling transition in neutral strontium clocks at 461 nm and the [5s²s_1/2 – 6p²P_3/2] transition in rubidium at 420 nm. Several approaches are taken to achieve the required linewidth, wavelength and power, including an extended cavity laser diode (ECLD) system and an on-chip grating, distributed feedback (DFB) GaN laser diode.

We experimentally demonstrate a fiber optic distributed strain sensor based on slope assisted Brillouin optical time domain analysis (SA-BOTDA) using virtual Brillouin gain spectrum (BGS) generated by multi-frequency light. Generally, strain along an optical fiber can be measured by using Brillouin scattering, because BGS has a linear dependence on the fiber strain. While conventional Brillouin fiber optic strain sensors sweep the probe light frequency to measure the BGS, it limits the measurement time and requires precise control of probe frequency. In contrast, the recently proposed method of SA-BOTDA measures the strain in real time from the power change of fixed frequency probe light that has been amplified by Brillouin amplification. However, because the conventional SA-BOTDA has a nonlinear response to the strain, it has to compensate the nonlinearity or only use BGS with relatively narrow range of linear slope region. In the last study, we proposed virtual BGS that has a relatively wide linear slope region and confirmed the principle. In this paper, we experimentally generate virtual BGS that has more than 100 MHz of linearslope region by using 12-GHz spacing five-frequency pump and probe. The strain distribution along the fiber is successfully measured in real time.

We propose temperature independent fiber bending sensor for catheters, which uses a multicore fiber with a Bragg grating inscribed in each core and spectral measurement technique based on intensity correlation of optical signals based on two-photon absorption process in a silicon avalanche photodiode. Although fiber Bragg gratings are generally sensitive to temperature change, the effect can be suppressed by processing the spectral data of reflected light from all the Bragg gratings, which was confirmed by the proof-of-concept experiment, where we successfully measured the same curvature for different temperature of 20, 25, 30, 35 and 40 degrees Celsius.

In this work, we propose a low-coherence Doppler lidar (LCDL) to measure particle velocity at near-surface atmosphere. To measure particle velocity information at near-surface atmosphere, it is necessary to conduct the measurement at high frequency and resolution. Low coherence light source can satisfy this high-resolution criterion. We discuss the system efficiency to detect atmospheric echoes by theoretical analysis. In the particle velocity experiment, Doppler shifted frequency is about 5 MHz and the particle velocity calculated from the peak Doppler signal is 2.7 m/s, while minimum and maximum velocity are 1.85 and 2.92 m/s, respectively. Anemometer result shows good agreement with LCDL results.

In this study, LED based mini-lidar to install into Mars rover has been developed. Its aim is to capture the dynamics of dust twister called “Dust Devil”. For Mars rover installation, there are large restrictions of 10cm3 optics size, 1kg weight, 1W power consumption, etc. We have developed its concept model, and conducted the demonstration experiment. The LED mini-lidar is equipped with a high speed photon counter, of which data sampling interval is 1ns, corresponding to a range bin of 0.15m, and a high repetition pulsed LED beam, of which repetition frequency is greater than 500kHz. Transmitting optical pulse power was <10nJ/10ns (= 1W). For summation time of greater than 0.2s, surface atmosphere activity in the order of seconds can be visualized with this high resolution. The dynamics measurement of an artificial twister was successfully conducted with this mini-lidar. In this report, the quantitative measurement method to distinguish dust samples in relation to Zirconia beads measurements is proposed. The relationship between the shaking off dust weight and its scattering cross section is obtained. The dust chamber experiment was conducted with several kinds of dust particles, too. These lidar echo results were compared with transmissometer, and they were well coincided due to the concentration change. The nature fog observation was conducted with this mini-lidar, too. The liquid water content was estimated under the consideration of the result of the quantitative measurement.

The definition of the coastal edge that defines the outline of the coastline, is one of the main tasks in the drafting of digital maps. The solution to this problem is extremely important for maritime navigation. It is important to constantly monitor the outlines of coastlines in connection with natural disasters that can change the coastal edge, such as landslides, tsunami, ice melting, etc. This, in turn, can lead to dangerous consequences for vessels carrying out civil transportation and humanitarian assistance in waters affected by natural disasters. The solution to this problem is of particular importance for archipelago countries such as Indonesia, England, etc The most common methods for determining the coastline are radar-optical methods using opticalradar systems located on-board unmanned small aerial vehicles flying according to predetermined programs. This makes it possible to determine the outlines of the coastline in an automatic (automated) mode, in fact, not depending on weather conditions, and to receive operational information in realtime, practically without time delays The paper considers and analyzes the algorithm for determining the coastal edge using smallsized radar stations located on small unmanned aerial vehicles. This allows you to fully automate the monitoring process. Depending on the characteristics of the on-board equipment and its operating modes, the accuracy of determining the land-sea edge can reach one and a half meters.

The construction of mathematical models of micromechanical RF switches, taking into account their design features, the influence of technological factors, as well as the correct choice of materials, contributes to the most accurate design and creation of a switch with specified characteristics. The earlier analysis and study of the technological process of creating a MEMS switch made it possible to determine the groups of factors that have a significant impact on the electrophysical and frequency characteristics. Within the framework of assessing the influence of technological factors on their electrophysical and frequency properties, the influence of such technological parameters as the etching time of various layers and the processing temperature was established. The thickness of the dielectric layer and the membrane and the thickness of the gap between the membrane and the dielectric layer were set as design parameters. Also, a model of a MEMS switch was built, taking into account the influence of design and technological parameters on the characteristics of the switch in the on and off state. Aluminum was used as materials for conducting lines and electrodes, and silicon oxide was used as a dielectric layer. Measurements of the characteristics of the manufactured switches showed a low capacity ratio of the MEMS switch. As a result, it was decided to research and select the most suitable materials for this switch. This paper proposes a methodology for the selection of materials used in the design of an RF MEMS switch. The switches have been redesigned with new materials. Significantly better results were obtained by measuring the switches.

Crop yield prediction is one of the tasks of Precision Agriculture that can be automated based on multi-source periodic observations of the fields. We tackle the yield prediction problem using a Convolutional Neural Network (CNN) trained on data that combines radar satellite imagery and on-ground information. We present a CNN architecture called Hyper3DNetReg that takes in a multi-channel input image and outputs a two-dimensional raster, where each pixel represents the predicted yield value of the corresponding input pixel. We utilize radar data acquired from the Sentinel-1 satellites, while the on-ground data correspond to a set of six raster features: nitrogen rate applied, precipitation, slope, elevation, topographic position index (TPI), and aspect. We use data collected during the early stage of the winter wheat growing season (March) to predict yield values during the harvest season (August). We present experiments over four fields of winter wheat and show that our proposed methodology yields better results than five compared methods, including multiple linear regression, an ensemble of feedforward networks using AdaBoost, a stacked autoencoder, and two other CNN architectures.

In wireless communication systems, a received signal is corrupted by various means, such as noise, multi-path fading, and defects in hardware. To properly demodulate the signal and recover information, complex systems are used. This typically consists of a series of filtering, corrections, timing recovery, and finally demodulation. Furthermore, the approaches for each stage are application specific. Deep learning (DL) can be applied to create an automatic demodulator, independent of modulation type, with no preprocessing, replacing the complex traditional system. However, these systems can only handle scenarios that are incorporated at the initial training stage. If new modulation types are encountered, the system must be re-trained to adapt. Traditional DL systems require the entire original dataset to retain old information, which increases storage requirements and training time. To increase adaptability, we incorporate incremental learning (IL) into a DL demodulator. Incremental learning attempts to overcome these issues, allowing a system to train on only new information. We apply IL to learn to demodulate new modulation types, not initially introduced to this system. We demonstrate this system in the field through the use of software defined radio. The system is subjected to unknown modulation types, and shown to adapt in real-time and over-the-air in an unsupervised environment.

Current trends of environmental awareness combined with a focus on personal fitness and health, motivates many people to switch from cars and public transport to micromobility solutions, namely bicycles, electric bicycles, cargo bikes, or scooters. To accommodate urban planning for these changes, cities, and communities need to know how many (micromobility) vehicles are on the road. In this work, we design a concept for a compact, mobile, and energy-efficient system to classify and count micromobility vehicles. We aim for a battery-powered system, which can be installed in a short time by a single person and is non-invasive for the ground or traffic. For this purpose, we investigate a system architecture consisting of uncooled long-wave infrared (LWIR) imaging sensors and a neuromorphic co-processor. This processor can perform classifications in a fixed time of 16,2μs without requiring additional hardware for learning. It can adapt its knowledge base to new environments without external updates. Our system is aimed to achieve adequate accuracy only using a low-resolution sensor and moderate processing power. We will show that an object representation of only 256 Bytes is sufficient to achieve a four-class classification accuracy higher than 80%, with an overall power consumption of less than 7mW. We estimate a total operating time of at least two weeks using a common 12V 17Ah lead battery.

Wildfires burn millions of hectares of land every year globally. Most of them are caused by humans, while only 10-15% occur naturally due to the climate change. The hotter weather dries out forests and plants, making them more prone to fire. The “frontline wildfire defense” has fully utilized satellite imagery to monitor, map, and control the fire spread and damage. However, there are three major challenges of using traditional satellite data: (1) the spatial resolution, (2) the temporal resolution, and (3) the downlink and analyzing data on the ground. In recent technology, the satellites are developed into small-size CubeSats that supporting the resolution issues. By exploiting the deep learning (DL) technique, the CubeSat can become sufficiently “intelligent” to detect wildfire events. This paper discusses a potential approach for implementing a Convolution Neural Network (CNN) onboard a CubeSat to sense wildfire. The DL model has been tested on the Camera Controller Board (CCB) embedded with Raspberry Pi Compute Module (RPi CM3+), that interfacing with the imaging mission of a 6U CubeSat named KITSUNE. In addition, the space environment test of radiation Total Ionizing Dose (TID) with functional tests of the board has been discussed. The results have shown no anomaly observed on the RPi while the DL model achieved a 94% overall accuracy with 16 minutes of learning time and 32 seconds of classification time. Hence, the state-of-art processing images onboard CubeSat will improve the valuable downlink data as the limited time window passes through the ground station.

In this work we introduce an algorithm based on the well-known Canny approach for effectual crack detection in thermographic films obtained using flying spot thermography or flying line thermography. A detailed description of the proposed algorithm is presented and discussed. Experimental measurements are performed on a metallic component of curved shape which contains real defects, that is, surface breaking cracks due to industrial use. The specimen is tested using flying line thermography. The results obtained with the proposed algorithm are additionally compared with a previously established algorithm for flying spot thermography. It is shown that the proposed algorithm can be used in automated systems for thermographic non-destructive testing.

As a foremost resource, water has a wide-ranging effect on numerous economic activities including agriculture, manufacturing, households etc. and services such as hydroelectricity generation, recreation, and amenity. Water is also very significant for the urban ecological community. Precise and systematic location of water resources is important for urban administration. Extraction of water resources and its management is extremely vital in a variety of remote sensing applications such as estimation of availability of water, flood, and drought prediction etc. This research work implements and investigates the applicability of the popular deep learning architectures such as U-Net and Encoder-Decoder for extraction of water features from sentinel-2 satellite Images of Delhi-NCR region, India. The performance of both the models have also been evaluated by calculating F1-score as 82.70%, and 89.41% respectively. In general, the proposed deep learning approach is relevant to precise water planning with improved spatial goals and exactness, which conceivably encourages effective management of water resources.

Photoacoustic microscopy suffers from limited depth-of-field due to the strong focus of laser beam, which implies that only some part of the imaging result is clear. Such shortage limitsthe further application of this powerful imaging technique, which is the problem we hope to address. In this work, we consider to solve the target problem through information fusion method. By fusing multi-focus 3D photoacoustic images, a large volumetric and high resolution photoacoustic microscopy can be obtained. However, the task of fusing photoacoustic signal is different from general 2-D multi-focus image fusion problem. The core challenge for our work is the fusion of different 3D photoacoustic volumes. We simplify the task as 2D problem, which is achieved by slicing the 3D data to 2D and reconstruction of the 2D slices. We propose a 3D fusion method which involves proper data preprocessing, slicing of the 3D data, fusion of slices and 3D reconstruction. Experiment results verify that the fused data shows larger depth of field and contains more useful information, which supports our thought of extending depth-of-field through information fusion method. To further demonstrate the superior of deep-learning (DL) method, a few non-DL 2D algorithms are selected for a comparison study based on objective assessments to show the generalization ability of convolutional neural network based image fusion algorithm.

The uniqueness and complexity of geological settings create challenges for hyperspectral sensing applications in mineral exploration. The optimization of an airborne hyperspectral survey design is a necessary step providing constraints on spectral signatures of the target, sensors used, and best flight parameters. Herein, we present a versatile software tool called HYSIMU that can be used to simulate different flight/sensor scenarios using mineral reflectance spectra from the USGS spectral library and find the most optimum survey design for a specific surface target. Random or selectable mineral reflectance spectra were assigned to synthetic random fields to create mineral distributions on synthetic digital elevation models with added spatial noise, spectral noise, and sun shadow effect. Several flights were simulated with various flight parameters such as altitude (10-200 meters), speed (1-100 m/s), and sun elevation (0, 45, 90 degrees). Several target scenarios with different levels of complexity and a 224-band hyperspectral sensor were simulated. The synthetic hyperspectral data generated from each flight and scenario were classified using ENVI. A sensitivity study was done by comparing the classification maps obtained and the ground truth using four different methods: Hausdorff Distance, Structural Similarity Index, Equal Pixel Percentage, and Correlation Coefficient. The results show a dependency of the classification maps on altitude (best for 10-50 meters) and slow flight speeds (best for 1-10 m/s) while the sun elevations did not cause any observed change in this altitude range. The results show that this toolkit can simulate any type of mineral exploration target from various airborne platforms and different flight parameters towards optimization of hyperspectral surveys.

Nighttime clouds are detected using a camera without a NIR-cut filter by exploring the pixel value distribution in each red, green, and blue (RGB) color space from clear and 100% cloudy sky images. The removal of the NIR-cut filter enhances the pixel values of each color space due to the additional NIR signals. Cloud pixels can be separated from clear sky pixels by only using each color space and applying the appropriate threshold. Exploring different cloud detection methods is vital in our ongoing research activity collecting clear and cloudy sky images in other countries using this camera.

Active thermography as a nondestructive testing modality suffers greatly from the limitations imposed by the diffusive nature of heat conduction in solids. As a rule of thumb, the detection and resolution of internal defects/inhomogeneities is limited to a defect depth to defect size ratio greater than or equal to one. Earlier, we demonstrated that this classical limit can be overcome for 1D and 2D defect geometries by using photothermal laser-scanning super resolution. In this work we report a new experimental approach using 2D spatially structured illumination patterns in conjunction with compressed sensing and computational imaging methods to significantly decrease the experimental complexity and make the method viable for investigating larger regions of interest.

Optical projection tomography (OPT) is a tool used for three-dimensional imaging of millimeter-scale biological samples. For higher image quality, new methods will need to be researched for OPT imaging systems. To make full use of the advantages of light polarization, an OPT image system with a polarization device was built, which can provide polarized projection data. The optimum polarization angle of the polarization device was acquired by experiments. At the optimum polarization angle, the high quality polarized projection data of samples were obtained, the reconstructed tomographic images got more details of samples and the influence of the stray light was eliminated. FSRCNN（Fast Super-Resolution Convolutional Neural Network）based on deep learning was applied for the SR reconstruction of tomographic images. SR tomographic images were assessed by the metrics of image quality and subjective observation. The outline and details of the samples were considerably represented in three-dimensional images reconstructed by SR tomographic images. So, polarization technology and FSRCNN can complement the performance of OPT imaging systems, and enhance imaging ability in the micron range.

Photoacoustic imaging technology is an emerging functional imaging method in the field of biomedical applications. It combines the light absorption characteristics of tissues and ultrasonic detection, and has the advantages of strong contrast, high sensitivity, and deep imaging depth. Light absorption and heat transfer are important processes of photoacoustic effect. Therefore, this article uses the finite element software COMSOL Multiphysics to study the photothermal effect of the interaction between laser pulses and mouse brain tissue. In COMSOL, a laser with a pulse width of 5ns is used as the excitation light source. The coefficient form partial differential equation module, the biological heat transfer module, etc. are used to simulate the light transmission and light-to-heat conversion process in the photoacoustic imaging of rat brain tissue. From this we can explore the photothermal effect produced when the laser interacts with biological tissues.

We propose a measurement method of curvature radius and shell thickness of laser fusion capsule by combining computer vision and virtual laser differential confocal technology. We use a CCD to photograph the surface of the capsule, to obtain the subpixel coordinates of the contour edge of the capsule. The qualified capsule can be quickly selected by using the least square fitting algorithm to fit the outer curvature radius. We then use the virtual laser differential confocal detection device to measure the position of the vertex on the outer surface of the capsule, the vertex on the inner surface of the capsule and the center of the capsule. Based on the characteristic that the zero-crossing of the laser differential confocal curve accurately corresponds to the focus of the objective lens, the outer and inner curvature radius and shell thickness of the capsule are measured precisely. The experimental results show that the repeatability of measurement of the outer curvature radius, inner curvature radius and shell thickness are 23 nm, 41 nm and 41 nm, respectively. Compared with the existing measurement methods, the capsule whose curvature radius and shell thickness are qualified can be accurately and quickly selected from the batch capsule using the method, which provides a technical support to the quality characterization of the laser fusion capsule.

In AR and VR devices, freeform surfaces are widely used to improve system performance. The manufacture of freeform surfaces is limited to the measurement. In order to guide the manufacturing process, we have proposed a real-time interferometric measurement system. In the system, an accurate, automatic and fast description method is needed to describe complex freeform surface. In order to improve this situation, a description method with automatically configurable Gaussian radial basis function (AC-GRBF) has been proposed. The key parameters of AC-GRBF, the number of subapertures N, coefficient A and the base number of GRBFs affect the fitting accuracy and speed, and they are analyzed by numerical simulation in the paper. The analysis in this paper can provide reference for the description method of GRBF, especially AC-GRBF, and the description of complex freeform surfaces in the design.

The attenuation of backscattered light through human tissue makes it difficult to measure the interferometric signal with high accuracy. We present a new method that combines the originally developed Time Domain-OCT (TD-OCT) technique with Ghost Imaging (GI) technique to detect targets at a certain depth in the scattering medium. Our method's first result shows that sample images can be reconstructed through a homogeneous mixture of 0.5% milk solution (1 cm thickness) scattering medium. Theoretical calculations show that it is possible to reconstruct images in higher milk solutions (about 20%) with optical properties close to human tissues.

Ultra-throw projector has been widely used in the projection application of large-FOV and short projection distance. However, the projection imaging distortion problem cannot be neglected due to the uncertain factor, such as the unsmooth factor of projection surface. In this paper, we proposed the image predistortion method based on radial basis function (RBF) image warping method to correct the projection distortion due to the unsmooth or wrinkled projection surface. The RBF image predistortion method and chief ray tracing method are used to realize a high-precision image predistortion. In addition, the distortion effect caused by the wrinkled projection surface with different depths are simulated. The distortion correction effects are also verified to less than 1% by the ray-tracing simulations.

Optically transparent antennas integrated within an optical aperture enable unique opportunities for shared aperture radio frequency (RF)/electro-optical(EO)/infra-red (IR) sensing and communication. While optical transmission and RF efficiency are critical performance metrics for most applications, those applications with optical imaging requirements necessitate additional imaging quality considerations. This work will present initial investigations into the effects of micromesh antenna elements utilized in a common aperture. Simulated imaging performance as a function of micro-mesh design indicates that as the mesh fill factor increases, the imaging quality as well as the optical transmission decrease. Fill factor increase has also been previously associated with increased RF performance. Specifically, for a 0.5 inch diameter optical aperture with a fill factor of 0.05, a 5% reduction in peak point spread function (PSF) and a 13% reduction in contrast in group-2 of the USAF-1951 resolution chart is presented. The obstruction from the solid topology causes a significant 75% reduction in the PSF. This degradation can be used as an additional metric to evaluate and optimize a hybrid aperture.

There is a need for enhanced ability in the nuclear detectors operated from a broader spectrum of long-stand-off-range aerospace platforms. A technique to transform the high-energy gamma photons into relatively low RF signal has been investigated and reported here. The foundation for the detection is the process of terrestrial, littoral and tropospheric radiolysis of the ubiquitous water molecule, that has been established theoretically in our work as a source modeling exercise. Radio images created using this method shall be of tremendous import in the detection, classification and quantification of nuclear energetic events occurring over varying spatial and temporal scales.

This article proposes a method for finding the focal length for determining the coordinates of the cutting edge on the toroidal section of the end producing surface, formed on the basis of the functional relationships between the parameters of the focal length and the cutting tool design parameters. Recognition of focal zones is carried out using an algorithm for analyzing the minimum transitions of color intensity in the focal zone based on a statistical analysis of differences in the levels of shades of gray in the image, which makes it possible to determine not only the focal point, but also the focusing range when measuring the edge with the original curvature determined by the radius of the cutter and the radius of the arc of the toroidal section. Experimental results, obtained on a measuring machine with a CCD matrix camera of reflected light with 400x magnification and 8: 1 telecentric optics, show that, in comparison with the existing algorithms, the new method allows increasing the recognition accuracy of the focal zone up to quality over a wide focal range. The proposed method allows for the rational search for the focal length when measuring the cutting edge that greatly reduces the number of calculations.

We propose a method for detecting the boundaries of the cutting edge and flank surface of helical drills.To implement the proposed method, the drill is installed in a rotating holder on the coordinate measuring machine and the rotation around the axis and simultaneous recording of video from the transmitted light camera to create a sample of images, the processing of which determines the profile boundary in the axial section of the drill. To evaluate the accuracy of this method, comparisons of the data obtained with the algorithm and the measuring machine are given. To find the gradient drop zones, the Canny's method was improved with discrete smoothing processing of a sequential set of images while changing the angular position of the drill. In this case, the new method is relevant for real-time image processing with a specified accuracy of up to 5 µm and 30 minutes for linear and angular measurements.

We introduced a new method called divided-aperture dual-differential confocal microscopy (DADDCM), which delivered large sensing measurement range and high axial focusing capability for profile measurement. There are three virtual pinholes, one is on the optical axis and the other two are placed either side of the optical axis. The signal from each off-axis will be processed with the on-axis one, and the processed signals are added up to acquire the axial intensity response curve with large linear sensing range. So, it can realize the large-scale non-axial fast sensing scanning with an axial focusing capability of ~2 nm and an improved linear sensing range up to 2.1 times that of divided-aperture differential confocal microscopy (DADCM). Benefiting from this large linear sensing range, a non-axial scanning imaging detection of microstructures is implemented, which leads to a high scanning speed. This method provides a new high precision and fast measurement method for the three-dimensional morphology of microstructure.

In this paper, we propose a method for single-pixel 2D/3D imaging to be applied in hyperspectral cameras operating in outdoor applications in unmanned aerial vehicles (UAVs). In rain rich environments, if camera systems operating in the visible range are used, alongside the atmospheric absorption that inevitably takes place, additional interactions occur between the light to be detected and the raindrops that produce Rayleigh and other scattering effects, thus limiting the visibility of the imagers mainly due to induced limitations in what radiation propagation is concerned. To solve this problem, we follow the approach that consists in using InGaAs based single photodetectors or line sensors that operate in the near-infrared (NIR) part of the spectra, in the wavelength range between 905 nm and 1600 nm. Due to the high costs of the few commercially available InGaAs based image sensors, we propose implementing a vision system based on the principle of single-pixel imaging (SPI) using active NIR illumination as a low-cost solution for image sensors in low-light scattering media. The SPI approach allows for the reconstruction of images from the electrical response signals of single InGaAs photodetectors to impinging active NIR illumination patterns created by an array of NIR LEDs. The latter is enabled by applying compression sensing (CS) algorithms. In this work, we aim at improving the quality of the reconstructed 2D images, defining the SSIM > 0.6 and RMSE<0.3 in rain rich scenarios as goal specifications. This SPI approach is additionally used to generate 3D images by applying Shape-From-Shading (SPS) and improving mesh 3D algorithms. By projecting Hadamard active illumination patterns analyzed in different regions of the reconstructed image, we enable scanning Spiral and Hilbert traces for image reconstruction to improve the SSIM level and reduce RMSE level of the reconstructed 2D/3D image obtained in the presence of rain. For testing purposes, we developed a controlled characterization system that enables rain simulation to evaluate the quality of the reconstructed 2D/3D single-pixel images.

Under the Japanese Cross Strategic Innovation Promotion Program (SIP), studies are conducted to perform very short-term predictions of local torrential rains based on a new multi-parameter phased-array weather radar (MP-PAWR) and deep neural networks (DNNs). The association of the two methods is expected to overcome the limitations of the conventional rains observation systems and numerical models that are not well suited to handle the rapid non-linear processes inherent in heavy convective rains. The unique spatio-temporal resolution of the observations allows us to train supervised DNNs to extrapolate the fast evolution of 3D convective cells. We compared two DNNs (CLM3D and CGRU3D) designed to fully exploit the information in the vertical dimension. Both methods use new techniques involving spatial convolutions in temporal recurrent iterations such as Long Short-Term Memory (LSTM) or Gated Recurrent Units (GRU) ones. The core of CLM3D is a stack of convLTSM2D layers, each of which is applied to a single altitude. CGRU3D uses a multilayer encoderdecoder with convGRU3D layers, each layer is associated with a size of 3D spatial features. Forecasts with a lead-time of 10 min at an altitude of 600 m with a horizontal resolution of about 500 m are compared. The models are tested with different types of heavy precipitation: localized short-lived rains on July 24, 2018 and wide-spread ones on the 29 of the same month. The models are evaluated with respect to a 3D linear advection nowcast model (OF3D) and a persistent one. We found that the DNN and OF3D models perform better on July 24 with similar scores that are significantly higher than those of the persistent model. Considering all rain events, critical success indexes (CSI) of 0.62, 0.53, 0.55 are found for CGRU3D, CLM3D and OF3D, respectively, and 0.43 for the persistent model. Regarding only heavy precipitation, the CSIs show a great variability between 0 and 0.4 on the predictions made that day. These results clearly illustrate the great challenge of nowcasting heavy precipitation. On July 29, none of the models have significantly higher scores than those obtained with the persistent nowcast. The interesting result of this study is that the two DNNs show similar nowcasting skills whatever the intensity and the type of rain, and this despite their architectures and training strategies being different. This may indicate that optimizing the tunning of the hyperpameters and the training dataset could not bring significant improvements and, the key, could be by feeding the models with more comprehensive information on the atmospheric state.

Thermal imaging can be used to characterize heated objects in numerous applications, including security and surveillance, where it is vital to locate an object within a 3D space. This can be achieved with stereoscopic vision, where optical sensors are used to construct a 3D image of the space it is looking at. In order to enhance computer vision research in the thermal modality, stereo thermal-infrared camera calibration must be precise and effective. This research presents a calibration board for thermal stereo vision systems that allows for visual thermal contrast and corner detection within the pattern. In an unstructured and dynamic environment, this thermal stereo vision provides a 3D volumetric measurement for depth estimation of the whole scene within the optical sensors’ combined field of view. Experiments are carried out in the lab, with a mean calibration re-projection error of 0.4 pixel and it is estimated the accuracy of 97.26% at a distance of around 6m for 3D depth measurement.

Currently, there is a significant increase in the number of violations of the airspace by small aircrafts in civilian protected areas, such as airports, crowded areas, industrial enterprises, etc. In this regard, the task of detecting, determining the coordinates and tracking air objects crossing the guarded perimeter and used both for video filming and for the delivery of goods becomes especially urgent. Often used locating tools located on the territory have blind spots, or observation of objects is difficult, which is mainly observed in dense urban development. The paper presents the advantages and disadvantages of radar and optical monitoring systems allowing for real-time observation. The problem of processing frames of the video stream of optical and radar images for the purpose of tracking, as well as extrapolating the coordinates of the position of unmanned aerial vehicles in the event of a temporary loss of visibility, including full shading, partial shading or other types of natural interference. To solve this problem, a method is proposed based on the use of a set of adaptive filters tuned to various types of motion of objects in order to predict their position.

Laser beam propagation in highly scattering media has attracted much attention for optical sensing fields because the propagation of the light is often limited to the near-surface regions of the media. To increase the sensing capability in such scattering conditions, we focus on the propagation property of an annular beam in a highly scattering media. In our previous work, the non-diffractive beam was generated by propagating an annular beam in a scattering medium of colloidal suspension (diluted milk) up to the concentration of 22%, which is near the human tissue. In this study, the propagation property of a partially blocked annular beam using a fan-like obstacle with different apex angles is investigated to realize object detection in highly scattering media such as human tissue, fog, or cloud. Simulation results show that non-diffractive beams can be generated when partially blocked annular beam with blocking angle from 0° to 30o is propagated in free space. The propagation experiment of partially blocked annular beam in scattering media shows that the center peak intensity in scattered light is detected when the apex angle is set up to 30° . By measuring scattered light at different receiving distances, the experiment results show that the center peak is kept at a certain distance away from the optical cell. This result verifies that the central peak intensity of this scattered light is a non-diffractive beam.

Nanostructured Ti-TiN coatings are one of the most common types of wear-resistant and corrosion-resistant coatings of machine parts and cutting tools. Multicomponent nanostructured coatings were deposited on AISI 318 substrates by vacuum-arc spraying by ion flux ratio for 30-45 seconds with formation of three types of Ti-TiN coatings: three-layer coating, multilayer coating and nanostructured coating. We propose to use the technology of Ti-TiN multilayer coating based on surface modification of the sample by bombarding with a high-current discharge using a “PINK” plasma generator, heating the substrate with ions of cathode material and obtaining cathode material condensation on targets followed by formation of multilayer coating. Target bombarding with an additional ion flux creates active nucleation centers where adsorption occurs and a fine-grained structure is formed. Ion bombardment significantly affects the physical and chemical properties of the sample surface. A detailed research of the change in the relationship of microhardness and adhesion strength of the coating depending on the type of coating of the sample was conducted, and corrosion resistance and wear resistance were investigated by high-precision signal processing from an automatic friction tribometer at a speed of 127 rpm for 60 minutes. As a result of data processing of measuring instruments during control of tribological and physical-mechanical characteristics it was established that formation of nanostructured coatings Ti-TiN with smaller thickness of Ti-TiN layer (30-100 nm) with submicrocrystalline structure allows to improve significantly physicalmechanical characteristics of samples.

As a functional imaging technique, photoacoustic imaging has the ability to produce high contrast and high resolution image. However, photoacoustic microscopy shows the disadvantage of limited depth of field, due to the strong focusing process. Only some part of the whole image is clear while some other information might be lost or misleading. To achieve a large volumetric and high resolution photoacoustic, we propose a depth-of-field expansion motivated multi-focus photoacoustic microscopy image fusion algorithm which fuses the information from imaging of different focal positions into single one to obtain all-in-focus image. To achieve this goal, the volumes obtained at different focus positions are sliced into 2D and the 2D image fusion based on cross bilateral filter is performed for correspond slices. Finally, 3D reconstruction can be performed on these fused slices. Experiment results show that our method can produce a 3D fusion result which maintains useful photoacoustic signal information for further analysis and visualization.

There are certain laws that govern the propagation of light in brain tissue and these laws can be used to better understand and study the brain. Since the skull has a significant influence on the propagation of light in the brain tissue, it is imperative to study the effects of the skull on the photothermal effects of light incident on the tissue. Different biological tissues have different optical and thermal properties, and this study applied the Monte Carlo method to simulate the propagation path of a large number of photons within the brain tissue, and FEM (Finite Element Method) to calculate the absorption of energy by each unit of light. MCmatlab is an open-source MATLAB program based on the Monte Carlo method and FEM. MCmatlab is capable of simulating the propagation of photons in different tissues and calculating the energy absorption of those tissues. In addition, it can generate 3D model diagrams to visualize the results. In this study, MCmatlab was used to construct the photothermal model of mouse brain. We examined the effects of incident light on the mouse brain with and without the skull to determine the effect of the skull on the photothermal effects on the mouse brain.

The k-wave toolbox is used to construct a compressed-sensing-based photoacoustic imaging model. And the image is reconstructed by the Jacques Hadamard Observation Matrix and the two reconstruction algorithms (OMP and ROMP). The restored image contains the main information of the original image from the visual and PSNR value, it means that the original image can be restored by the combination of Compressed Sensing theory and suitable de-noising method. Compared with Nyquist's sampling method, the amount of data collected by our compressed sensing theory is greatly reduced, which saves resources and space to a great extent. This theory has a great advantage for the photoacoustic imaging of big data, and also can provide the convenience of time for the following image analysis. By the way, it's been experimentally determined that the ROMP reconstruction algorithm has a better reconstruction effect than OMP reconstruction algorithm does.

As a new imaging method with high optical contrast and large ultrasonic detection depth, photoacoustic imaging breaks through the barrier between resolution and imaging depth of traditional optical imaging technology, and obtains rapid development. As an important branch, photoacoustic microscopy (PAM) imaging technology inherits the advantages of photoacoustic imaging technology. Using optical focusing imaging mode, high contrast and high resolution biological tissue structure, molecular and functional imaging can be realized, which has potential application value in neuroscience, ophthalmology, vascular biology and dermatology. However, PAM relies on close optical focusing to achieve high resolution imaging effect. The traditional PAM usually relies on high NA objective lens for laser focusing, which limits the depth of field (DoF) size of the imaging system while obtaining high lateral resolution, so that PAM has limitations in three-dimensional imaging and obtaining information of different depth directions. Moreover, the sharp decline of spatial resolution and signal-to-noise ratio in the defocus area will also affect the accuracy of quantitative analysis of tissue morphology and structure. In order to solve this problem, this paper proposes a three-dimensional photoacoustic information fusion method based on principal component analysis fusion algorithm. Virtual photoacoustic microscopy platform was used to obtain three-dimensional vascular data with different focuses. The B-scan slices at the same position of three-dimensional photoacoustic data with different focuses are fused in turn. The fused slices are recombined to reconstruct high-resolution photoacoustic data with large depth of field. The results show that this method can achieve depth of field extension while maintaining the high resolution advantages of photoacoustic microscopy.

The task of image fragments extraction with objects of interest is not new. The choice of appropriate segmentation algorithm and its implementation in real time mode become a problem in systems operating with a high-speed video data stream. Connected components labeling is an important step in training samples images preparation for usage in neural networks. To ensure high speed and feasibility of image labeling algorithm for the FPGA, it is necessary to justify the choice of segmentation algorithms, considering the hardware capabilities of the platform. In this paper, we propose a modified one-pass image labeling algorithm for FPGAs, as well as its implementation using the Xilinx System Generator for DSP and the Matlab/Simulink package. As a hardware platform the FMC-200-A mezzanine is used to provide high-speed video data stream from the line camera TELEDYNE DALSA LA-CC-04K05B00-R to the ZYNQ Ultrascale + MPSoC ZCU106 evaluation kit board. The procedure of using hardware implemented SPI interface on the MPSoC ZCU106 board, which is to configure and control the FMC200-A module is described. Implementation of SPI interface is made by using Vivado and Vitis IDE. The labeling results of proposed algorithm on test images, as well as images obtained experimentally are presented.

The high quality of seed cleaning is one of the factors in increasing the yield of agricultural crops. To improve its quality, it is important to estimate average size of seeds for adjusting their feeding method into analysis zone of the optical seeds sorter (the color sorter). Optical methods for average seed size measuring have a significant drawback, which consists in usage of video camera, which lens must be periodically cleaned from settling dust. An alternative method for estimating size of crop seeds is the use of millimeter-wave radar. To assess the geometric characteristics of seeds, it is proposed to use adapted algorithms for sea surface remote sensing. The possibility of applying remote sensing algorithms to the seed layer is justified due to the similarity of geometric structure of the seed layer with a quasi-periodic surface, which is the sea surface. This article discusses an approach for seeds layer spatial model obtaining. The description of backscattered radar signal model is given. We carried out the modeling data analysis and a comparison of estimated average seed size with the geometric characteristics of the layer for different crops. Obtained model results allow us to conclude about the applicability of remote sensing algorithms for geometric characteristics of seeds estimation. Usage of radar data acquisition methods allows to use the available in market mmWave solutions to reduce the cost of measuring equipment for geometric characteristics of seeds for various crops.

Earthquakes create significant volumes of rubbles and waste, strongly impacting the environment and posing serious health risks. In the last decade, earthquake occurred in several Italian areas, in particular the last sequence (Amatrice – Norcia, 2016-2017), still clearly visible in terms of destruction in the epicentral area, produced about 3 million tons of waste, mainly composed of Construction and Demolition Waste (CDW). Post-earthquake building waste is composed of heterogeneous materials, making difficult their separation and recovery as secondary raw materials. CDW recycling and reuse is of fundamental importance because it reduces the increase of landfilling, avoiding non-renewable raw materials exploitation and favoring circular economy. In this work, a sensor-based approach to classify different typologies of tile samples coming from buildings damaged by the Amatrice earthquake is proposed and investigated. Attention was focused on tiles, one of the most recycled masonry aggregates (RMA). This study presents a methodology based on a combination of two analytical techniques, HyperSpectral Imaging (HSI), working in the Short-Wave InfraRed (SWIR) range (1000-2500 nm), and micro-X-ray Fluorescence (micro-XRF), in order to discriminate different tile compositions and to detect the presence of cement mortar on the surface of the samples. The obtained results represent an important starting point to develop and introduce innovative strategies finalized to design, implement and set up automatic recognition and classification procedures of inert CDW fractions.

We previously presented an optical design of a wideband spectrometer for science observations using space-borne telescopes; a multichannel configuration was adopted to cover a wide wavelength region from visible to mid-infrared and using the optical designs of each channel as independent spectrometers was also possible. In this study, we report some progress on works for the optical design of a spectrometer for space-borne telescopes that uses spectral dispersers made of infrared transmitting glass. We consider that infrared transmitting glass is a promising material for space-borne and ground-based optics for astrophysics, planetary science, astrobiology, and other fields.