This PDF file contains the front matter associated with SPIE Proceedings Volume 11525, including the Title Page, Copyright information, and Table of Contents.

The Road to Vehicle Automation: A Toyota Guardian Approach

Graphene is a promising material for next-generation high-performance photodetectors because of its fast response, broadband photodetection (from the ultraviolet region to the terahertz region), mechanical and chemical stability, flexibility, and low manufacturing cost. We developed high-responsivity graphene infrared (IR) photodetectors based on field-effect transistors (FETs) with photogating that operate in the middle- or long-wavelength IR (MWIR or LWIR) region. The photogating effect is induced by a photosensitizer located in the vicinity of graphene. The photosensitizer generates a voltage change via incident light, which modulates the carrier density of graphene and produces an extremely large differential output current. This effect, unique to graphene, is attributed to its high carrier mobility and single-atom thickness. As photosensitizers, InSb and LiNO3 were used for the MWIR and LWIR regions, respectively. However, graphene FET-based structures inevitably produce a large dark current because graphene has no bandgap. This degenerates photodetector performance and prevents the construction of IR image sensors using conventional readout integrated circuits. To overcome this problem, a graphene/InSb heterostructure is proposed. It exhibits both a low dark current and a high responsivity by amplifying injected photocarriers via photogating in the MWIR region. These results can be applied to other wavelength regions and could contribute to the development of high-performance graphene-based IR image sensors and next-generation optoelectronic devices.

Distance measurement using two-photon absorption (TPA) process in a Si-avalanche photodiode (Si-APD) can provide a wide measurement range and high precision. The principle is based on a characteristic of TPA photocurrent that is proportional to the mean square of the optical intensity. Thanks to this square characteristic, the intensity correlation between the probe and reference lights sinusoidally modulated at the same frequency is obtained without using a complicated electrical circuit, and then the distance to the target is measured. The TPA-based distance measurement can also be applied to a multi-point fiber Bragg grating (FBG) sensor and a multicore FBG curvature sensor, where multiple FBGs with the same reflection spectrum is discriminated from the distance to each FBG. In these applications, FBGs with low reflectance should be used to suppress the interference noise due to multiple reflection between the FBGs. However, it also weakens the reflected light from the FBG and deteriorates the signal-to-noise ratio (SNR) of the detected signal. This problem cannot be solved by simply increasing the probe and pump power, because it can damage the optical components. In this study, we propose using pulsed reference light with a high peak power and synchronously sampling the signal with the pulsed light. The principle is confirmed by a distance measurement of a 5-km-long optical fiber.

Brillouin fiber optic sensing has been attracting much attention as one of the best ways of monitoring the temperature and/or strain distribution of large structures, such as bridges or pipelines. To detect abnormal sections of such structures at an earlier stage, improvement of measurement sensitivity is required. In the standard Brillouin fiber optic sensing, sensitivity given by the temperature or strain coefficient of Brillouin frequency shift (BFS) is about 1 MHz/℃ or 0.05 MHz/με, respectively. In this study, we introduce a new method utilizing two-frequency pump and probe light to enlarge these coefficients without using special fibers. In this method, Brillouin gain spectrum is measured by sweeping the two frequencies of probe light in the opposite directions, where the measured spectrum has two peaks. The separation between the two peaks linearly changes with BFS, and so it has a linear relation to temperature and strain of an optical fiber. Since the changing rate of the separation between the two peaks is twice as large as that of BFS, the temperature or strain sensitivity is doubled to 2 MHz/℃ or 0.1 MHz/με. The enhanced sensitivity was experimentally confirmed in the proof-of-concept experiment.

Satellite Remote-Sensing has been successfully applied for detection of natural-hazards, (e.g. seismic events, landslides and subsidence) and transport infrastructure monitoring over the last few years. Persistent Scatterer SAR Interferometry (PSI), is a satellite remote sensing technique able to measure ground displacements over the time. More specifically, the PSI technique is an evolution of the DInSAR technique and it is based on a statistical multi-temporal differential interferogram analysis. This allows to determine coherent stable-pixels over a data-stack of SAR images, in order to identify potential ground displacements. This study aims at demonstrating the potential of the PSI technique as an innovative health-monitoring methodology for the structural integrity of bridges. For this purpose, X‐Band COSMOSkyMed images provided by the Italian Space Agency (ASI) were acquired and processed in order to detect structural displacements of the Rochester Bridge in Rochester, UK. Outcomes of this investigation outlined the presence of various PSs over the inspected bridge, which were proven useful to achieve a more comprehensive monitoring methodology and to assess the structural integrity of the bridge. This research paves the way for the development of a novel interpretation approach relying on the integration between remote-sensing technologies and on-site surveys to improve upon current maintenance strategies for bridges and transport assets.

Inkjet-printed surface enhanced Raman spectroscopy (SERS) sensors are fabricated on cellulose based paper or fabric substrates. These flexible sensors provides basic point-of-sampling advantages that is particularly useful in field applications. Due to the heterogeneous loading of nanoparticles on the substrate, SERS intensities inevitably vary across the active area of the printed sensor. This paper will discuss the use of receiver operating characteristics (ROC) for the analysis of inkjet-printed SERS sensors. The aim is to provide an alternative measurand to the SERS enhancement factor that can be used to compare different types of SERS substrates. We have developed statistical analysis from multiple data sets obtained from sensors exposed to both analyte and control to determine the probability of positive detection (PD) at various analyte concentration. This dependence describes the ROC of the sensor and also provides confidence level associated with a given detection limit. We propose this methodology for the evaluation of SERS sensors to enable their field applications.

A method for measurement range extension without deteriorating the accuracy is proposed on real-time laser displacement measurement based on intensity correlation with phase-modulated signal that we have been investigating for years. The proposed method uses the phase sign of the fundamental and second harmonic frequency components of the detected signal, where these components are expressed by sinusoidal and cosinusoidal functions. In principle, any displacement is measured from the combination of the amplitudes and phase signs of the detected signal components. We performed proof-of-concept experiments of displacement measurement by using a precision motorized stage and successfully confirmed the principle.

Food spoilage and adulteration are rising challenges in the global food supply chain. These food frauds are another major cause of health anomaly across the world. Thankfully, they are being investigated and significantly reported nowadays with advanced tools and mechanisms. Moreover, with getting reported, these unethical practices are being curbed with the development of innovative tools and techniques. Typical food substances prone to such malpractices are now extensively studied, characterized, and reported for potential innate or externally mediated strategies to ensure food safety for all. Development in the field of biosensors and other sensing techniques have been a great support in this cause. With the help of latest techniques, new markers are being investigated and reported frequently. In this study, we have investigated riboflavin as an integral constituent of milk to be utilized as an internal biomarker for milk quality assessment. Since riboflavin is fluorescently active and it gets affected by adulterants such as urea; this study aims to explore and establish riboflavin bio-sensing as a standard for quality monitoring technique in milk.

In this paper, a fiber optic based acoustic sensing system (FOTAS) is presented. Utilizing such cables as hundreds of acoustic movement detectors has become a novel approach for many security applications. Developed for securing critical infrastructures via utilizing both buried and fence-mounted cables, phase OTDR system FOTAS’s requirements, design, development, test and analysis modules are outlined. The detected movement type, location, magnitude are conveyed to the user via a user-friendly interface. Associated hardware, software, and utilized fiber optic network characteristics are presented. Detailed information about developed novel signal processing and artificial intelligencebased movement detection studies are provided. Real world data indicate that preliminary threat detection performance is more than 90%.

This paper proposes a new, low cost, and high sensitivity smart pipeline inspection gauge (smart PIG) that integrates different in-line inspection (ILI) techniques to inspect the pipeline defects such as dents, metal loss or corrosions. The proposed ILI techniques include: (1) optical sensor (based on photoconductivity); and (2) bimorph sensor (based on piezoelectricity). Laboratory tests are conducted on various types of defects such as convexities (e.g., deposit corrosion) and metal losses (e.g., cavity corrosion) to demonstrate the validity of the proposed techniques. Furthermore, the conventional magnetic flux leakage (MFL) technique is developed to verify the effectiveness of the proposed ILI techniques. The test results show that the proposed ILI techniques can effectively identify the defect position. It is envisioned that the developed smart PIG has significant potential for real-time inspection of the long-buried pipeline.

We report on our latest progress on the ongoing development of a compact optical frequency reference for CubeSat missions in LEO. This reference will be based on Doppler free laser spectroscopy of rubidium vapor. Our simplified solution is based on frequency modulation spectroscopy using a customized, shielded 5 mm long vapor cell and a monolithic distributed feedback (DFB) laser diode on a mesoscopic breadboard setup. The electro-optical system features miniaturized optics as well as a polarization maintaining fiber coupling inside a housing with dimensions of 70 mm × 26 mm × 19 mm (38 cm³). The latter will be installed on a 90 mm × 80mm electronics board for full functionality of the system. This way, our reference constitutes a key technology for future compact, simple and robust atomic quantum technologies for nanosatellite 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 [5s^{2 1}S₀-5p¹P₁] 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.

Bend-induced loss in microbending fiber-optic sensor has proved to be an effective one for the direct and indirect measurement of various physical parameters. In this research, a novel and highly sensitive microbend sensor has been explored by launching a zero order Bessel-Gauss beam inside a waveguide arrangement having a No-core fiber bonded amidst two special higher-order mode supporting fibers. By harnessing the special characteristics of the Bessel-Gauss beam, pairing of manifold high-order modes has been affirmed inside the sensor structure. The captivating feature of such sensor is that it defies the conventional wisdom and significantly improves the sensitivity without any intricate fabrication techniques like in tapering, bending etc. To our knowledge, such realization of Bessel-Gauss beam-shined microbend sensor has not been reported earlier in any of the contemporary literature. In support of our theoretical analysis; a Beam propagation method is employed in OptiBPM software (Optiwave Systems Inc.) to envisage the full transmission spectrum of the waveguide. For different bend radii, the sensor response has been numerically investigated and it is anticipated that the sensitivity is expected to be enhanced by a gentle reduction in the bend radius. With the presence of six microbends, the proposed sensor manifests an average bend sensitivity of 2.8 dB/mm which is 3.2 times superior to the classical microbend sensing configuration. Due to such superior sensing performance, the present paradigm paves the way for many potential applications, like damage detection of various engineering structures, and measurement of different physical parameters like temperature and pressure.

Inspired by the concept of quantum entangled radars, the extraordinary and utterly innovative concept of no-detectors microscope-imaging by the implementation of entangled quantum particles such as electrons as the probe of the microscope is touched on. Instead of simultaneous measurement of two entangled particles, we propose repetitive measuring idler electrons in two conditions; when the object does not reflect the signal that means there is an entanglement exists. Otherwise, when entanglement is broken or degraded, it means a reflection or absorption of the signal electron. It is showed that the repetitive measurement of entanglement among a sequence of idlers in the two above mentioned conditions approaches different values. When entanglement measurement showed the collapse of entanglement, it concludes the hitting of the probe particle to the object has happened. The number of broken entanglement between idle electrons is considered proportional to the number of detected electrons by the detector and imaging is accomplished by this assumption. For a 3D visualization of the object, a curve fitting algorithm available in the field of Computer Science is applied. The fitted curve illustrates an approximate surface of the object.

We introduce a simulator of charge transport in fully-depleted, thick CCDs that include Coulomb repulsion between carriers. The calculation of this long-range interaction is highly intensive computationally, and only a few thousands of carriers can be simulated in reasonable times using regular CPUs. G-CoReCCD takes advantage of the high number of multiprocessors available in a graphical processing unit (GPU) to parallelize the operations and thus achieve a massive speedup. We can simulate the path inside the CCD bulk for up to hundreds of thousands of carriers in only a few hours using modern GPUs.

We have studied how the light-matter colocalization effect, which is the overlap effect between target and electric field distribution, affects the sensitivity of nanogap-based surface plasmon resonance (SPR) sensors. The SPR characteristics of the nanogap structure were calculated using a rigorous coupled-wave analysis program with DNA immobilization and hybridization cases. The colocalized shift (COS) and its relative value, relative optical signature (ROS), were defined to explain the capability change according to the gap size (g) along with the momentum matching condition of the nanogap. The maximum sensitivity, defined as the COS value compared to the changed refractive index, was 514.8 deg / (μm * RIU), which appeared at a relatively large interval (g = 17nm). When increasing the nanogap size, the ROS and reflectance also increased. However, the trend reversed, and the decrease in ROS reached a negative range at a 9 nm gap or more. This ROS is due to damping in the multiple localized surface plasmon polariton mode, and the strong field confinement inside the nanogap induces a negative shift. The zero-shift, the most avoidable when designing an SPR sensor, and near-field distribution with plasmon mode, is observed across different gap size and period pairs. The effect of colocalization inside the nanogap, as identified through various metrics, promises a highly sensitive detection potential for biological applications.

USDA researchers in Beltsville, Maryland, have developed various spectral imaging technologies for applications in food safety and quality evaluations of agricultural commodities. We pioneered the online implementation of hyperspectral line-scan imaging technologies in a food processing environment, such as wholesomeness inspection of broiler chickens, and online safety and quality inspection of fresh produce. In addition, a macro-scale Raman chemical imaging system was developed to implement high-throughput Raman imaging using a 785-nm line-laser including a rapid line-scan system. The macro Raman imaging technologies have been used to authenticate powdered food ingredients and to detect chemical adulterants in powdered foods. The advancement of the above spectral imaging technologies is presented.

In this talk, Abbie will present an overview of imaging through degraded environments such as turbid water and dense fog using range-gated digital holography. Digital holography provides the unique ability to separate out ballistic photons in a scattering and turbid environment through range-gating, coherence gating and polarization gating. The complex fields of the detected holograms and then post-processed using coherent averaging, incoherent averaging and singular value decomposition techniques to improve the SNR and reconstruct imagery in extremely challenging imaging conditions.

As Autonomous Vehicles (AVs) progress to become more prevalent on the roads, there is an increased focus on reliability of the underlying sensor technology supporting the decisions made by the AI. Continuous calibration of visual sensors, such as LiDAR and camera are essential for the commercial development and societal acceptance of fully autonomous vehicles (AVs). As we move towards full autonomy it is reasonable to demand that sensor tolerances should be minimized. The traditional methods that are used today are time consuming, require extensive setup and configuration, and as a result are prone to errors due to the reliance on human intervention. Furthermore, there is the risk of sensor errors creeping into the system due to the one-time nature of calibration, and the environmental factors that can impact sensors during the operation of an autonomous vehicle. Recently, various target-less calibration methods have been proposed involving traditional feature picking as well as using deep learning-based approaches but still require initial calibration parameters in order to be effective. Our method need not rely on getting initial calibration parameters from visual frames or using ground truth values. This approach is more versatile for calibration in AVs and subsequently improves the reliability when it comes to navigating and decision making on the road. We propose an improvement on our previously reported robust continuous calibration approach that uses one or many objects identified in the visual frames and calibrating one visual sensor with respect to the other without relying on the initial calibration parameters or ground truth values. Our approach extracts the object point cloud data (PCD), downsamples and cost-optimizes the extracted feature points to compute the extrinsic calibration parameters. Without initial calibration parameters, our method uses multiple frames to calibrate a sensor with respect to any other sensor provided that PCD can be generated or derived from the sensors. Our approach performs significantly better with no initial calibration values provided and thus can be applied continuously to calibrate any sensors that become mis-calibrated during the operation of the AV. We have tested our method on the publicly available KITTI dataset and are benchmarking our results against state-of-the-art methodologies. Our goal is to automate the process of object detection and point cloud extraction and test the speed and accuracy.

Accurate and complete rail extraction from mobile laser scanning (MLS) data is currently a fundamental and challenging problem for its application on the railway. By using the track knowledge, a signed cylindrical neighborhood difference is defined as the rail descriptor and then proposed a new rail extraction algorithm from MLS data. It can extract accurate, continuous, and complete railhead, which is most critical for the rail geometric parameter and centerline, of the entire railway. Moreover, it can successfully extract the railhead of the main-line, including the curve section with different superelevation, and turnout. A 3-km long trunk railway, including main-line and turnout, straight line and curve line, located in the southwest of China is selected to test the performance of the proposed rail extraction algorithm. The experimental results show that the proposed algorithm can correctly extract the railhead of the whole railway, with an overall accuracy (F-measure) of 88.73%. Its accuracy is improved by 42.68% compared with the rail extraction algorithm based on spherical neighborhood difference.

Based on IMBR algorithm, this paper proposes an algorithm for building contour extraction which is suitable for different scale digital line graphic (DLG). The new algorithm solves the tooth contour that may occur when IMBR algorithm is used to extract building contour. Firstly, a plane extraction algorithm based on region growth is used to extract the point cloud of the building roof. The algorithm introduces graded seed points and neighborhood-constrained growth criteria to avoid dividing the tree point cloud into building planes. Then, a Foot Point Correction algorithm is proposed to avoid the tooth contour in IMBR algorithm. The experimental results show that the Correctness and completeness of extracting building roof point cloud are above 97%. There is no tooth contour in the extracted building contour, which can meet the requirements of generating DLG with different scales.

Navigating through fog plays a vital part in many remote sensing tasks. In this paper, we propose an Expectation- Maximization (EM) algorithm for fitting a mixture of lognormal and Gaussian distributions to the probability distributions of photon returns for each pixel of a 32x32 Single Photon Avalanche Diode (SPAD) array image. The distance range of the target can be determined from the probability distribution of photon returns by modeling the peak produced due to fog scattering with a lognormal distribution while the peak produced by the target is modeled by a Gaussian distribution. In order to validate the algorithm, 32x32 SPAD array images of simple shapes (triangle, circle and square) are imaged at visibilities down to 50.8m through the fog in an indoor tunnel. Several aspects of the algorithm performance are then assessed. It is found that the algorithm can reconstruct and distinguish different shapes for all of our experimental fog conditions. Classification of shapes using only the total area of the shape is found to be 100% accurate for our tested fog conditions. However, it is found that the accuracy of the distance range of the target using the estimated model is poor. Therefore, future work will investigate a better statistical mixture model and estimation method.

We present a novel method to measure the range of an object in a lab setting. This is realised by utilizing the phase insensitive cross-correlation analysis of the probe and reference photons generated in a spontaneous parametric down-conversion process. In our technique, the comparative measurement of time-correlated photons after one is transmitted to the target direction and the copy of it retained at the lab allows to single out the signal from the noise lead to sense, range and imaging the sample.

Performance of Surveillance Systems specially in terms of Detection and Recognitions completely dominated by environmental conditions as well as depends on number of parameters like range, target size, target type etc. Accordingly use of range gating technology seems inevitable. Fast Detection and Recognitions is playing a key role in today advance technological warfare scenario. As one with faster detection system will ultimately have an edge in decision and strategy making. Numerous experiments were going on in fields of Active range gating for optimizing transmitter in terms of Illuminator’s power, divergence, receiver’s gating for different environmental situations. As a result Range gating technology is maturing day by day. Although still for specific environmental scenario user has to manually adjust the Transmitters and Receivers performance parameters which is a somewhat trial and error type and time taking process. To outwit this redundancy, algorithm based Range gating system is presented which automatically suggests Transmitter power for specific scenario. Here algorithm based mathematical model of the range gating technology is simulated by using MATLAB as well as open source software Scilab.

An accurate supervised classification technique requires a large training database with an equal number of samples in each category. However, in practice, data class imbalance is naturally inherent in detection and identification tasks. In an extreme case, one category of data has a majority of training samples (positive class), causing over-classifying. In these circumstances, the negative classes are either absent, poorly sampled or not well defined. Deep one-class classifiers are artificial neural networks developed to overfit the positive class samples. This unique situation constrains the network model to be trained data features just with the knowledge of the positive class. One well-known application of one-class classifiers is for anomaly detection problem, where the model stands out outliers. In this study, we proposed using a one-dimensional CNN model for anomaly detection of Surface-Enhanced Raman Spectra applicable for Portable Raman Spectrometer in field investigations.

In recent years, unmanned aircraft systems (UASs) are frequently used in many different applications of photogrammetry such as building damage monitoring, archaeological mapping and vegetation monitoring. In this paper, a new vertical take-off and landing fixed-wing UAS is proposed to robust photogrammetry missions, called SAMA-VTOL. In this study, the capability of SAMA-VTOL is investigated for generating orthophoto. The major stages are including designing, building and experimental scenario. First, a brief description of design and build is introduced. Next, an experiment was done to generate accurate orthophoto with minimum ground control points requirements. The processing step, which includes automatic aerial triangulation with camera calibration and model generation. In this regard, the Pix4Dmapper software was used to orientate the images, produce point clouds, creating digital surface model and generating orthophoto mosaic. Experimental results based on the test area covering 26.3 hectares indicate that our SAMA-VTOL performs well in the orthophoto mosaic task.

We are all well aware of the fact that heavy rainfall is one of the major sources of causing disasters. Be it flood or drought, landslide or erosion rainfall is the prime factor to be causing these calamities. Timely prediction of rainfall is important to be ready for facing upcoming disasters. In this research work, an attempt has been made to predict daily rainfall from satellite observation for disaster management using Artificial Intelligence (AI). A multi-stacked LSTM based model has been used for prediction of daily rainfall. The model uses 40 years of dataset provided by the National Aeronautics and Space Administration (NASA) / Goddard Space Flight Center through MERRA-2 portal. The dataset belongs to Yamuna Nagar district in Haryana, India during the period 1980 - 2020 for the training purpose. The outcome of the research proves that LSTM based neural networks are better alternative to forecast general weather conditions when compared with traditional methods. The outcome of the model can further be improved by including more parameters and better hyper parameter tuning.

In this paper, an efficient image-free target detection and classification framework for single-pixel imaging (SPI) is presented. The proposed method captures target information by sampling it with very few patterns (at 1-10% sampling rate), and employs signal-processing based feature extraction coupled with radial basis function neural network (RBF-NN) for accurate target classification. The proposed method can replace existing deep learning (DL) based target detection and classification methods because of its high-speed, accuracy and simple shallow design.

The paper presents a method for optical detection drones using the YOLO v4 neural network. Recognition performs simultaneously in the visible (Vis) and long-wave infrared (LWIR) ranges. The results of UAV detection on various types of urban background environment at day and night conditions, as well as at different distances from cameras, are presented. An algorithm for detecting of unmanned vehicles in the video cameras field of view of the Vis and LWIR ranges is described. This algorithm takes as input the outputs of two neural networks that recognize the drone in two ranges and estimates the probability of detection. Its shown that the YOLO v4 neural network recognizes unmanned objects on various background substrates with a minimum temperature difference of 0.4 degrees on the NEC 2640 thermal imager.

Hyperspectral band selection is the process of selecting an optimal set of narrow wavelength bands from a large number over a broad range, typically for one of two purposes: hyperspectral reconstruction or classification. The former seeks to condense the information content of the full resolution spectrum so that the spectrum may be reconstructed from a relatively small subset of wavelength bands. The latter seeks to enable classification based on features contained within this small subset. In this paper, we introduce a new approach for automated band selection based on analysis of the weight planes from a trained self-organizing map. We refer to this approach as the self-organizing map weight plane distance (SOM WPD) method. We evaluate its benefits by using it to select optimal visible/near infrared (VNIR) and fluorescence wavelength bands from a recent fish fraud study where hyperspectral imaging was used to identify the true species of fish fillets. We apply four common machine learning classifiers to perform this species classification and compare the results to those obtained using a genetic algorithm-based method. This latter method optimized band selection for hyperspectral reconstruction, and these same bands were in turn used for classification. The findings presented in this paper show great promise for the SOM WPD which produced higher classification accuracy with two of the four classifiers for the VNIR data and with all four classifiers for the fluorescence data as compared with the genetic algorithm-based method.

Improving the quality of recovery and recycling of post-consumer plastic packaging is certainly one of the objectives of the circular economy. Currently, these secondary raw materials still represent only a small part of the materials used in the EU compared to primary ones. Therefore, it is necessary to improve the selection, the separation and recovery techniques with the aim to increase the quantity and quality of these materials put in the market. For these reasons, hyperspectral imaging (HSI) techniques represent a great solution for the characterization, the classification and quality check of different secondary raw materials in several industrial sectors. The present study proposes an efficient characterization of the most used polyolefins, polyethylene (PE) and polypropylene (PP), derived from post-consumer plastic packaging, based on the type of polymer and color, through HSI analysis and the implementation of classification models. Two different HSI acquisition tools were used, working in the short-wave infrared range (1000-2500 nm), to determinate the polymer, and in the visible range (400-700 nm) for the identification by color. In addition, the data processing and the chemometric techniques, used for the development of the classification strategies, have been performed with the PLS_toolbox (Eigenvector Research, Inc.) in Matlab (The MathWorks, Inc.) environment. The obtained results proved the correct identification of the polymer and the color of the investigated plastic flakes, confirming the sustainability and great potential of the HSI analysis techniques, which can be implemented to improve the quality of the plastic materials produced in the recycling plants of polyolefins.

Automated crop monitoring techniques help in better management of growing conditions in order to improve quality and yield, and to reduce the impact on the environment. Various stages of a crop in its life cycle is noticeable with a change in its leaf color. The yellowing of leaf is considered as an important quality defect in green leafy vegetables. Yellowing is initiated at the end of maturity stage and continues in the senescence stage. Most of the methods used for monitoring leaf quality requires the detachment of the leaf from the plant, which is destructive in nature. Among the several nondestructive techniques available, hyperspectral imaging (HSI) modality offers the possibility to address this problem by monitoring the reflection spectrum of the leaf in situ. When the freshness of the leaf reduces, the chlorophyll content in the leaf decreases. This results in an increase in its reflectance in the visible region as the absorption of light by chlorophyll reduces. Hence, the reflection spectrum can be used as a measure for the freshness of the leaf. However, monitoring large areas usually require translation of the whole imaging system or removal of the leaf from the plant. In this context, we propose to use a flexible probe-based HSI system to mitigate these issues. We demonstrate the adoption of a probe based HSI modality to enable in situ live plant monitoring. The classification of the leaves from the HSI data is performed using principal component analysis (PCA) technique.

In this work, quantum cascade laser (QCL) mid-infrared (MIR) reflectance spectroscopy is used to discriminate silicate and carbonate minerals in a standoff measurement setting. The tunable external cavity QCL source that was used allows measurements from 5.2 μm to 13.4 μm wavelength, where the fundamental vibrational bands of silicates and carbonates are observed. Spectra measured from a half-core sample were analyzed using multivariate analysis to extract and identify the end-member spectra from the mixtures. The end-member spectra were compared and validated using the ASTER database spectra and the spectra measured on reference samples with the same QCL MIR reflectance spectroscopy setup. Spectra of minerals commonly found in the mining industry were compared: quartz, microcline, albite, chlorite, muscovite, biotite, calcite and dolomite. MIR reflectance spectroscopy using compact QCL sources allow rapid spectral measurements at standoff distances and high spatial resolution. All these advantages show the potential of QCL MIR reflectance spectroscopy for in-the-field mining applications.

This article suggest new methods for measuring PM1.0 using extinction and others methods with assumption of constant aerosol mass density and bimodal size distribution. In this assumption, we have 6 independent parameters to describe full aerosol particle size. At first, we investigate the sensitivity of these 6 parameters to the extinctions, PM10, PM2.5, extinction coefficients. And we have found that there are only 4 independent parameters(total volumes and maximum sizes) which effects to the measureable quantities such as aerosol scattering extinctions and PM(particular matter). Other parameters such as the widths of particle size are not so important. From sensitivity analysis, we can conclude that we need only 4 main parameters to completely describe aerosol particle size distribution such as central size of fine particle and coarse particle, total volume of fine and coarse particle. If we use the aerosol information such as PM10 and PM2.5 which are measured by commercial methods, we only need two other information to retrieve two mode aerosol size and volume. The easy and allowable candidates are aerosol extinction coefficients at different at least two wavelengths. To find the retrieval characteristics, we have assumed aerosol size distribution and calculated PM10, PM2.5, and two extinctions at two wavelengths. Using simplex inversion algorithms, we found that fine particle size can be retrieved more precisely than coarse particle size. Finally we have discussed the reason of these retrieval characteristic and suggested the methods how to retrieving all information about aerosol.

Remote sensing has provided some water quality data that can be used to assess the suitability of water quality for cultivation. It is the main approach used to map seaweed farming. The aims of this study were to map the seaweed farming potential of the study area and calculate the amount of plastic waste likely to be produced from the use of plastic buoys in seaweed aquaculture. SPOT 6 and Sentinel 2 provide high and moderate resolution satellite imagery that can be used as primary data to map seaweed farming at fine and broad scales. ArcMap application was applied to analyse the vector data of water quality. Weighting and scoring were carried out to give value to the parameters for culture suitability. The plastic waste used as a buoy during cultivation is calculated based on primary data from field surveys. The results suggest that the simple analysis of high and moderate spatial resolution imagery with digitation on screen method can delineate seaweed farming areas efficiently. Furthermore, this method could be applied in the future to monitor the extent of seaweed farms and to predict the amount of plastic waste produced by seaweed farming activities.

The complex relationship of time and spatial variations of the electromagnetic field with physical and biogeochemical processes determines the interest of the atmospheric electricity monitoring as a special factor that is an integral characteristic of the state of the atmosphere. The growing of anthropogenic factors role in the formation of electric fields near the earth's surface leads to the need of continuous measurements of the atmospheric electric field and further analysis of the data obtained to identify global, regional and local mechanisms of the global electric circuit. The data of local atmospheric electric field measurements is investigated. The electric field was measured using EFM 550 network sensors installed in the region as part of a thunderstorm monitoring system. Along with the study of periodic (quasi-stationary) components of the atmospheric electric field, the interest of non-stationary processes in disturbed conditions under various meteorological phenomena is increased. Comprehensive monitoring fulfilled at the Nalchik city point for eight years, allowed to establish a number of characteristic features of the behavior of the atmospheric electric field in the surface layer. To analyze time series, the time series decomposition technique was used. The set of graphs obtained for the components of the series is compared with those obtained as a result of standard processing of observations.

Hyperspectral imagers collect images at a very high spectral resolution in a contiguous region of the electromagnetic spectrum. These signals contain spectral, spatial and, potentially, temporal information about the scene of interest. Different mathematical and physics-based approaches can be used to model these signals providing different insights that can be explored for signal exploitation. In this talk, we will discuss different approaches to model hyperspectral imagery, the image processing algorithms they motivate, and their application in different domains.

Recent developments in the Infrared detector technology lead to imaging in multiple wave bands (MWIR and SWIR). It demands to development of 1-5 μm Common optics for the integration of SWIR, MWIR, Non Eye safe LRF and Eye safe LRF spectral bands against traditional optics. This common optics module coupled with the detector can meet the demands of 1-5 μm Multi spectral imaging which can improve the visibility in foggy weather, night and bad weather conditions and Multi-function applications like Thermal Imager, Low light imager, Laser Range Finder and Laser Designator. We present a design of a passive athermalized Wide Field of View (WFoV) Objective lens operating in 1-5 micron spectral band for a 1Kx1K format, 10 micron pixel pitch cooled detector. This lens has a 45 deg FoV and operates at F/4. The lens is passively compensated from -30 deg C to +50 deg C to work in all thermal environments.

The Skipper-CCDs, a special type of charge-coupled device (CCD) sensor that features sub-electron readout noise levels, was proposed decades ago. However, only in recent years it has been possible to develop large size Skipper-CCDs ensuring stable operation. Their extreme low noise operation makes them suitable for experiments that require low thresholds and high energy resolution, such as dark matter and neutrino interactions detection, and more recently quantum-imaging and astronomy. New experiments are planning to use kilograms of active silicon from Skipper-CCDs as sensitive mass. In this way, they can achieve extremely low detection thresholds and a high probability of particle interaction. However, this approach needs arrays of thousands of Skipper- CCDs operating at the same time imposing challenging requirements. Also, introduction of this technology in astronomy and quantum-imaging applications requires a large number of channels per sensor to speed up the readout. The front-end needs to be redesigned from scratch: it must achieve low noise performance, be simple for easy integration and allow the routing of thousands of channels out of the sensors with minimal connections. This paper presents a detailed analysis of options for the front-end electronics and their noise performance. It describes a novel way of using a dual-slope integrator with minimal components to pile up the charge of consecutive readouts of the same pixel in a concept that we call a multi-slope integrator. This reduces drastically the output bandwidth, simplifying the wiring and the warm electronics. These proposals will allow the generation of new scientific instruments based on Skippers-CCD arrays.

In this talk, we will demonstrate a heterostructure that consists of two graphene layers, a bulk BP and a 18 nm-thick hBN. The maximum responsivity reaches 1.43 A/W (corresponding to EQE of 52%) at wavelength of 3.4 μm, and the overall efficiency, from visible to MIR, can exceed 20%. Additionally, the performed photodetector can exhibit ultrafast rise and fall times (< 2 ns), and the switching behavior of photodetector is highly reproducible as confirmed by the eye diagram measurement with the data rate at 250 Mbits/s. Finally, we examine the capability of MIR imaging of our detector. The acquired images exhibit high sharpness and contrast even under low light intensity illumination and fast scanning conditions.

The stray light analysis is always carried out after the optical system design is completed. However, for the off-axis system with 3 mirrors, if the space between the mirrors is not enough to place baffles, the optical system must be redesigned. To avoid this, a new design method for 3-mirror off-axis optical system with the consideration of stray light analysis based on vector aberration theory is presented. At the initial stage of the optical design, the correction of varied aberrations, such as spherical aberration, coma, astigmatism, etc., and the reservation of enough space for different baffles by decentering and tilting mirrors are carried out at the same time. Considering that in order to get enough reserved space, the decenters and tilts of some mirrors may be not a small amount, and the third-order aberration theory must have large deviations in guiding the design of off-axis optical system with 3 mirrors. For the convenience and efficiency of aberration correction, the vector aberration theory is also applied to the design to make sure the decenters and the tilts of mirrors will introduce minimum interference to image quality. Based on the vector aberration theory, we can obtain the initial results by solving the aberration equations with proper constrains. The initial structure can be served as the starting point for optimization design. At the end of the paper, a design example is illustrated and its stray light analysis is also finished in the software Fred, which a professional stray light analysis software. And the design and analysis results show that the optical system has a very high imaging quality and a good ability of suppression of stray light.

Acousto-optic tunable filters (AOTF) enable high-performance imaging spectral photometry used in astronomy, remote sensing, biomedical imaging, and security. An essential feature of AOTFs is polarimetric capability owing to birefringence of crystals used in their design. A spectral polarimeter captures two orthogonal polarizations simultaneously. However, AOTF’s two-dimensional transfer function in polarimetric mode is asymmetric. We demonstrate using chirped driving RF signals to obtain uniform transfer function for two orthogonally polarized Bragg images. This technique can be also used for adjusting the spectral transmission of an AOTF supporting up to 10-fold extension of the passband.

THz-waves can be characterized with SI-traceability from the comparison between precision measurements with cold trapped HD+ ions and accurate predictions of the molecular ion theory. A THz-wave off-resonantly coupled to the rotational transition at 1.3 THz can be detected via the measurement of the lightshift induced on a Zeeman component of the two-photon rovibrational transition at 55.9 THz. A set of six lightshift measurements for two orientations of the magnetic field in the ion trap can be converted into the amplitudes and the phases of the THz electric field components in the orthogonal laboratory frame.

The phenomenon of colored shadows were first published by Goethe in 1810. These colored shadows can be displayed stereoscopically through a pair of colored glasses. Furthermore, the stereoscopic shadow images can also be used to perform optical measurements. We present the principles of the measurements, particularly for measuring the threedimensional position of an object. The limits of the measurement method, in terms of its sensitivity, resolution, and sources of errors, are described. When the size of the measured object becomes comparable with the light wavelength, and when the light intensity becomes smaller and the photon nature of the light needs to be taken into account, the geometrical analysis of the measurement method needs to be replaced first with the wave analysis and then with the photon analysis. Our measurement of position can be extended into measurements of displacement, velocity, acceleration, shape, and orientation.

Light field (LF) imaging provides rich spatial and angular information, but is problematic in low-light environment as the images suffer from low contrast and visibility. In this paper, we present a learning-based method to enhance low-light LF images. A high-dimensional convolutional neural network (CNN) is introduced to extract the spatio-angular features from the LF. The network operates directly on the four-dimensional LF data rather than on individual sub-aperture images, avoiding the loss of geometric information. Color compensation is then performed on the enhanced LF images coming from the high-dimensional CNN to reduce color distortion. The experimental results show that the proposed method achieves noticeable improvement compared with state-of-the-art low-light image restoration techniques in both visual inspection and objective assessments.

In this work, the novel imaging algorithm of synthetic aperture radar (SAR) system to be used in near-range based on fractional Fourier transform is presented. The algorithm projects the target reflectivity function onto pseudopolar coordinates and focuses along the short time through the fractional Fourier transform. The discrete format procedure for imaging near-range target is also introduced in this paper. The numerical simulation results show that the algorithm is more suitable for the high-precision synthetic aperture radar imaging in the near field.

The monitoring and conservation of natural assets are nowadays increasingly important, as the emergence of unknown pathogens endangers tree survival. In this respect, root systems are affected by fungal infections that cause root rot and ultimately contribute to the death of trees. Such diseases can quickly spread to surrounding trees and affect wider areas. Since these decays do not usually show visible symptoms, early identification is key to the protection of trees. Within this framework, non-destructive testing (NDT) methods are becoming increasingly popular, as they are faster and more flexible than destructive methods. Specifically, ground penetrating radar (GPR) is emerging as an accurate geophysical tool for tree roots mapping. Recent research has focused on the implementation of automated algorithms for root mapping in a 3D environment and the investigation of root mass density. This research aims to investigate the changes occurring in the frequency spectrum of the GPR signal during root system surveys. To this extent, advanced signal processing techniques (both in time and frequency domain) are applied, to eliminate noise-related information and the disturbance induced by the presence of other features (i.e. pavement layers or underground utilities). The proposed processing framework can be applied for expeditious analyses or on trees difficult to access, where more comprehensive survey methods are not applicable. The results of the application of this methodology to a real-case scenario showed the potential of the applied procedure and will lead to the development of a new approach for investigating root systems using GPR.

Recently, indoor activity monitoring of human beings has gained more and more relevance. In particular, the determination of the spatial and temporal context of a user is of utter importance in many applications like monitoring or safety. In this paper, we present a framework that can identify what, where and how long a user is performing a certain activity by the utilization of a low cost and low complex system. Our system only comprises of a single inertial measurement unit and a single RGB sensitive photodiode, with no prerequisite for infrastructural modifications. By using independent decision trees, also the training effort can be kept minimal. Additionally, we verify experimentally the optimal set of features to be used for the framework. Overall, the achieved results are above 90 % in correct determinations of the room the user is in, the activity the user is performing and in which direction the activity is undertaken.

For the organization of lightning protection measures, data on the nature of the distribution of lightning discharges, their number and current value over the territory are required. To this end, lightning direction finding systems are being developed and implemented in many countries around the world. Lightning direction finding technology significantly reduces the risk of lightning strikes, and also allows you to take into account thunderstorm activity in the design and placement of buildings and structures. It is aimed at minimizing serious disruptions in the power industry, mass accidents and damage to power lines, preventing the failure of sensitive electronics and communication networks, and so on, which, in turn, determines its high economic efficiency. This article discusses issues related to clustering and visual representation of long-term data on thunderstorm processes in the North Caucasus in 2009-2019. Information about lightning discharges was obtained on the basis OF ls8000 data from Vaisala using a network of lightning recorders at the geophysical situation monitoring center. The article presents a picture of the spatial and temporal distribution of thunderstorm clusters for the North Caucasus. This makes it possible to trace the dynamics of thunderstorm centers, the spatial position of thunderstorms, the characteristics of discharges per unit area and time, to determine the characteristic development trends, and to identify the main factors that affect the obtained characteristics of the spatial and temporal distributions of lightning activity.

Ensuring the actual presence of a genuine legitimate trait as opposed to a fake self-manufactured synthetic is a major problem in bio-metric authentication. The proposed system's objective is to improve the reliability of bio metric recognition systems through the use of image quality evaluation. The proposed technique uses general image quality features derived from a single image to distinguish between legitimate and impostor samples, making it optimal for applications with a very low degree of complexity. In the proposed method, we are using publicly available ATVSFir_ DB dataset of iris which makes it highly competitive. We have also tested the algorithm on self-generated dataset for authenticity and rigorous testing purposes. The results acquired from the experimental phase were satisfying and authentic. The proposed method is able to achieve an averaged accuracy of 99.1% for the ATVS-Fir_DB dataset and 99.9% for the self-generated dataset.

With the emergence of dual band colour (RGB) and Near Infrared (NIR) cameras, the attributes of both spectral regions can be combined through an image fusion process to enhance the imagery presented to an operator. In this paper, techniques for pixel-level image fusion of RGB and NIR (RGBN) are investigated. An RGBN fusion scheme is presented that employs a Colour Weight Map (CWM) to control colour distortion and enhance the visibility of scene features of interest. The CWM uses a Vegetation Index (VI) which is widely used by the remote sensing community for the analysis of crop health and other land resource applications. The RGBN fusion design is a simple arithmetic solution that is modified version of the Hue Saturation Intensity (HSI) and Brovey Transforms. Specifically, the CWM is used to adjust the NIR image prior to image fusion to reduce colour distortion effects. The fusion scheme can be readily implemented in hardware and can operate at high camera frame rates. Performance of the processing scheme is evaluated both qualitatively and quantitatively and a comparison with a more complex image pyramid fusion approach is also made. Several image metrics are used to examine both the colour distortions and retention of detail across a range of different image sets. The results from visual inspection and metric analysis are discussed and shown to be well correlated which is particularly important for those fusion schemes where the imagery is displayed directly to an observer.

Increased leading edge roughness (LER) is one of the main causes for wind turbine blade performance degradation. To ensure a consistently high energy output, the surface erosion of wind turbine blades, needs to be monitored regularly, so preventive measurements can be done. Capturing 3D surface data is becoming a more and more popular way to extract and quantify roughness on a micro level. In this paper we want to test the possibility of using Structure from Motion (SfM) 3D reconstructions for extracting surface roughness information from wind turbine blades. We test various capturing scenarios with varying horizontal and vertical image overlap, as well as varying distances to the blade, using a real blade in outdoor conditions. We analyze the quality of the reconstructions and provide a benchmark, as well as guidelines to what are the best possible capturing conditions for ensuring a high quality and noise free 3D surface results. All data from the experiments is made publicly accessible.

The bundengan is an endangered musical instrument from Indonesia. To support its conservation, a non-contact method is needed to characterize the shape of its resonator. Here we report a low-cost and portable setup, which uses only one camera and one grid ruler, that allows for quantitative imaging of the resonator's shape directly in the remote rural areas where the musical instrument can be found. To perform the measurement, the bundengan needs to be positioned such that the camera can see the whole grid of its woven bamboo lattice. Two digital images are then taken from two different positions. Using a mathematical model of the setup, we determine the three-dimensional positions of all the points in the bamboo grid. These points are then used to estimate the resonator's shape. With this setup, we have been able to characterize the resonator's shape from one bundengan.

This work aims at the detection and classification of Distributed Acoustic Sensor (DAS) acquired acoustic signals. We obtained the data by probing an optical fiber with light pulses and gauging the Rayleigh backscatter. Said data contains four different classes; Walking, Shovel and Pick digging as well as Hammer hitting. We first proceed by detecting the event and its location along the fiber and extracting it from the random noise using Spiked Random Matrix Theory (RMT) models, namely Marchenko-Pastur (MP) and Tracy-Widom (TW) distributions. We then label the datasets accordingly and proceed with the classification process using machine learning algorithms. For this, we test and evaluate Convolutional Neural Networks (CNN), which has been proven to provide high accuracies in similar studies, taking the spectrograms of the signals as our network’s input. We conclude by providing the performance of our CNN architecture and propose a few options to further improve the performance of the model.

Modern sensors produce increasingly high volume of data that requires efficient and reliable statistical methods for information processing. We consider frequent problems of information processing which can be cast into the framework of parameter estimation and multihypothesis testing. We propose a unified approach for statistical inference of information processing by introducing the inclusion principle, confidence process, unimodal likelihood estimator, and time-uniform concentration inequalities. Our methods attempt to make decision based on observing data in an adaptive and sequential way so that the decision can be made as quick as possible, while the probability of committing mistakes is acceptably small.

Interval estimation of data parameters is a frequent task of information processing for sensor systems. Classical parameter estimation methods for information processing suffer from the drawbacks of inaccuracy or conservatism. In this article, we propose a general method for constructing confidence regions for parameters of data. Moreover, we develop computable expressions on the minimum coverage probability of random intervals, which allows for a bisection coverage tuning method for constructing confidence intervals for parameters of various types of data. The proposed theory and algorithms can be applied to relevant tasks such as pattern classification, data fusion, target recognition and tracking.

We demonstrate an alternative concept of laser wavelength conversion to the NIR – SWIR range with simultaneous multiple spectral broadening based on transient stimulated Raman chirped pulse amplification (TSRCPA) and pulse compression to fs pulsewidth for the sub-TW class lasers, with possible application in the next generation LIDARs. Transform limited pulses of a Yb:YAG laser with a duration of ~1.2 ps were used to pump a multistage TSRCPA based on KGW crystals seeded with 1100 – 2500 nm supercontinuum. A 16-times spectral broadening was shown after TSRCPA compared to pump pulses, conversion efficiency 55%, and compression to ~145 fs. This concept, extended for gases and diamond with larger Stokes shifts, allows further exploration of SWIR range, especially for the 2nd Stokes.

Present scenario is highly competitive and meeting customer requirements in all aspects is becoming quite challenging since the product needs to withstand harsh environmental conditions, long life and low maintenance cost, etc. Hence the design of such systems is challenging since these requirements are opposing with each other and will contradict in costing aspect. Being a key assembly, “Long range IR zoom lens” plays vital role in overall life of Thermal Imager. This is because, in traditional design approach, zoom lens is having number of Optics that required to be traversed along the Central Axis for required optical performances. These Lenses are bonded to Mechanical Mounts (with adhesive) which are guided in Main Housing. The desired air spaces between the Lenses get maintained by the rotation of Mechanical Cam Barrel. The scope of this paper is to present the design enhancement of mechanical interfaces that will be going to contribute overall lifetime enhancement of IR Continuous Zoom Lens Module and also emphasizes the comparison between traditional and enhanced design approaches that are being calculated through Analytical and through Mechanical Structural Analysis (a tool that uses Finite Element Methodologies for the calculation of Mechanical changes between the interfacing Components). This paper will be having a detailed comparison between traditional and enhanced design approaches and thereby conclusion will be drawn that the enhanced design approach is highly recommended over the other.

Diabetic retinopathy is one of the leading causes of blindness among the working age population world-wide due to late detection and intervention. This study pilots the application of deep learning models to automatically diagnose the occurrence and severity of diabetic retinopathy. With color fundus photography as input, this study tested the performance of transfer learning based on the most recent architectures of Convolutional Neural Network (CNN) models, the EfficientNets, claimed to be superior than many current well-performing network architectures.

Infrared thermography (IRT) has gained significant popularity in biomedical instrumentation. One of its important applications is the contactless monitoring of the human respiration rate (RR). This paper reports application of IRT for human RR monitoring using a passive thermography approach. The variation in nostril temperature during breathing is observed using a thermal camera, and the breathing signals (BS) are obtained. The BS have a low signal to noise ratio (SNR), which is improved by using the Butterworth filter. The signals are monitored under different breathing and ambient conditions, and the heat exchange mechanisms occurring under these scenarios are discussed in detail. Further, the emissivity value of the human volunteers of different complexion and skin types is analyzed. The mean emissivity value obtained in the study is 0.98.

Ultrasonic transducer is a sensor that realizes the mutual conversion of ultrasonic and electrical signals, and it is widely used in quality inspection, biomedical imaging and other fields. Commonly used ultrasonic transducers have a small detection range and low sensitivity due to the diffraction of sound waves. Focused transducers are used to improve detection sensitivity. Unfortunately, focused transducers have narrow depth of field. Here, we developed a Bessel ultrasonic transducer for large depth of field by using conical acoustic lens. An acoustic lens is attached to a unfocused ultrasonic. And the acoustic lens is a cuboid prism with a concave cone on the bottom, made of fused silica. Similar to an axicon that can generate a Bessel beam, the Bessel ultrasonic transducer can produce nondiffracting Bessel ultrasonic beams. Therefore, extended depth of field with uniformly high resolution and high detection sensitivity can be obtained. We used COMSOL to simulate the transmission of ultrasonic field of the designed conical acoustic lens, and compare it with the spherical focused ultrasonic transducer. The results show that the depth of field of the Bessel ultrasonic transducer is about 8 times that of the conventional spherical focused ultrasonic transducer. And the depth of field of the Bessel ultrasonic transducer can be further adjusted by adjusting the cone angle of the conical acoustic lens. The Bessel ultrasonic transducer will help improve the capabilities of the ultrasound probe and expand its application range. For example, an ultrasonic probe with a large depth of field will expand the imaging depth of photoacoustic microscopy and enhance its ability in non-destructive testing.

Infrared and visible image fusion aims to obtain an integrated image which contains more recognizable in- formation. To attain this object, an effective infrared and visible image fusion algorithm through multi-level co-occurrence filtering is proposed in this paper. Firstly, the input images are decomposed into three layers through a co-occurrence filtering decomposition model. Secondly, a gradient-domain-based pulse-coupled neural network (PCNN) fusion strategy is applied in the three layers. Finally, the fused image is reconstructed by the three fused layers. Experiments show that the proposed algorithm outperform most state-of-the-art algorithms in both qualitative and quantitative measures.

Despite the relatively high implementation costs and the complexity of the system, Frequency-modulated Continuouswave Light Detection and Ranging (FMCW LIDAR) has attracted special attention as the next generation LIDAR because generally existing LIDARs using pulse or amplitude modulation have limitation for high-resolution and interference phenomenon. Bandwidth and its linearity of the optical wavelength modulation in FMCW LIDAR have remarkable effects on spatial and range resolution including detection range. Hence, most FMCW LIDAR systems need to linearize the optical frequency sweep for best performance. However, since the linearization techniques normally require high-cost and complex systems, its adoption can be restricted in some application fields where low-cost and simple architecture are important. In this paper, measurement and analysis results for high-resolution of low-cost FMCW LIDAR using non-linear sweep characteristics of different lasers were presented. DFB laser and VCSEL of wavelengths near 1550 nm were utilized in these experiments, and the optical frequency sweep and beat frequency characteristics of each laser were analyzed in detail. The problem of low spectral resolution that occurs due to sweep nonlinearity was improved by the partial-waveform technique. Furthermore, by means of the beat frequency distribution which was obtained from repeat measurement and spline interpolation, the detailed analysis of beat frequency stability and distance resolution for various modulation frequencies were presented. Finally, using the multi-peak-averaging (MPA) method which efficiently utilizes high modulation frequency to improve the distance resolution, it is possible to achieve um-level range detection accuracy in the implemented FMCW system based on the uncompensated FMCW sources.

Liquid crystal display (LCD) is one of the optical devices that we see in our daily life. Its functionality is designed to operate in an amplitude modulation with up to a 32-bit resolution. As it is a low-cost spatial light modulator, this paper shows that two-dimensional (2-D) phase modulator can be obtained from this device. Experimental proof of concept using an off-the-shelf 60-USD 800x480-pixel LCD, a 633-nm wavelength laser diode, and a wavefront sensor shows that a linear 2-D phase modulator is obtained, offering a maximum 0.76-wave phase shift at a maximum grayscale value of 255. Varying digital bit control values from 0 to 255 leads to a very low optical power fluctuation of 1.53%.

Lightfield imaging systems were brought to market for consumer and professional media recording. But so far, this technology is less known for applications in the industrial space. The unique optical concept developed by K Lens allows to capture multiple perspectives of a scene with a single exposure as regular colour images on the camera sensor. The current design is tailored to capture 9 different perspectives. Effectively, such a lens turns any camera without any modification of the camera into a 3D camera. It even makes it possible to record 3D video and instantaneously view it in 3D. This technology has unique benefits specifically for applications requiring the inspection of small and deep structures. For example, capturing 3D information of small pins in electric plugs with high walls is possible with such an optical system. The optical concept can be adapted to different measurement volumes. An optimization routine has been developed to identify if there is a solution for a certain application and which performance parameter could be achieved. Two different types of depth estimation algorithms have been developed which utilize the multiple perspectives. While the optical flow based classical approach allows for manual optimization the machine learning (CNN-based) approach appears to be more robust. The paper outlines the technical principles of the optical concept and points out application areas where such an optical concept has unique advantages. It also gives an indication of the achievable performance in terms of depth resolution based on a mathematical simulation model as well as from measurements using a first prototype. Some examples from real objects conclude the paper.

Infrared systems operating in mid wave infrared band (MWIR) are using in a wide variety of coastal and border surveillance applications. This paper discloses about the need of triple FOV MWIR system for surveillance applications. Initial calculation to determine the first order parameters of Narrow FOV, Middle FOV and Wide FOV positions was discussed. Mechanical compensation approach along with the axial motion of lenses is used to obtain the triple FOVs. Configuration constraints are mentioned, limitations of substitute approaches explored, and characteristics of the chosen design are presented. Limited optical lenses in the optical module have an advantage to enhance the transmission of the system to achieve better ranges. To get better ranges the triple FOV systems is designed using only four lenses. Design of compact Triple FOV MWIR lens module demands at least four lens groups. To get the systems in compact form fit, for each group only one lens element has been used. This paper also discloses the optical performance analysis parameters aberrations curves and polychromatic modulation transfer function plots at all fields of views. The effect of lens performance by the rate of change of temperature in terms of various seasons was analyzed and active thermal compensation method was discussed. The design and experiment results indicate that image quality of the optical system is acceptable performance in all the fields of view and system should be applied in many civilian and military applications.

In this article, we analyze the possibilities of non-contact confinement of micron-sized absorbing particles using optical traps based on a spatially structured beam. The proposed method utilizes multi-beam interference with controllable parameters including tilt angles, waists and a number of superposed beams. The effectiveness of trapping and its selectivity to variable masses and shapes of particles is determined by axial symmetry of spatial intensity distribution at the trapping area. In addition to implementing non-contact trapping, optical visualization was performed with axial digital holographic microscopy in-situ. This method contributes to the accurate localization of studied specimens inside the cuvette.

In this paper, we present the results of the propagational dynamics of vortex beams array in the scope of their possible applications for non-contact robust and precision optical surface profilometry with nanoscale resolution. The result of coaxial superposition of the reference array with the object array of oppositely charged vortex beams represents a dynamically changing intensity distribution. The nature of this changes, namely, rotational effects of "two-petal” beams, allows to determine directly the phase delay between the beams which is introduced by the test object. We have analyzed the process of creating an array of singular beams and the features of experimental setup for detecting of reflecting and transmitting objects.

Photoacoustic imaging is a promising technique that combines optical contrast with ultrasonic detection to map the distribution of the absorbing pigments in biological tissues. Optical-resolution photoacoustic microscopy (OR-PAM) is an important branch of PAM that can achieve spatial resolutions at the submicron level. In the OR-PAM, the laser light is tightly focused into the sample to achieve sharp excitation. However, in OR-PAM, the depth of field (DoF) is quite limited, for it is determined by the optical focus. The short DoF hinders OR-PAM from high-quality three-dimensional volumetric imaging or acquiring dynamic information in depth direction. Here, we developed a fast focus-scanning optical-resolution photoacoustic microscopy with extended depth of field by using tunable acoustic gradient lens (TAG). The TAG lens was designed to continuously changing the focal plane of OR-PAM by modulating its refractive power with fast-changing ultrasonic standing wave. A function generator generates a sinusoidal signal to drive the TAG lens at an eigenmode. The focusing power of the TAG lens will exhibit a sinusoidal oscillation at the frequency of the driving signal. By using home-made synchronization circuit to synchronize the laser pulse with the three vibration states of TAG lens in turn, the focal plane shifts periodically with each step in a B scan. In our experiments, the axial scanning time is as low as 1 ms, which is at least an order faster than any other axial scanning method in OR-PAM, to the best of our knowledge. Faster focus scanning could be achieved as long as we have a laser with higher repetition. The performance was shown by imaging a tungsten wire. We achieved a DoF of about 750 μm, which is three times of that of OR-PAM with single fixed focus. The head of a zebrafish was also imaged to further demonstrate the feasibility of our method. Our system could be used for various applications, including the in vivo imaging of non-flat thick biological tissues and samples subject to breathing movements.

Photoacoustic imaging is a promising technique that combines optical contrast with ultrasonic detection to map the distribution of the absorbing pigments in biological tissues. Photoacoustic microscopy with large depth of focus is significant to the biomedical research. The conventional optical-resolution photoacoustic microscope (OR-PAM) suffers from limited depth of field (DoF) since the employed focused Gaussian beam only has a narrow depth range in focus, little details in depth direction can be revealed. Here, we developed a large volumetric optical-resolution photoacoustic microscopy based on contourlet transform fusion rules. Using a self-made optical-resolution photoacoustic microscope to obtain source images of the same sample with different focus. Firstly, the contourlet transform is used to decompose the source images A and B in J-layers to obtain subband coefficients of different scales and directions. Secondly, The PCA fusion rule is used for the low-pass subband coefficients of the source images A and B, and the SSIM fusion rule is used for the bandpass direction subband coefficients to obtain the contourlet coefficient of the fused image. Finally, performing contourlet inverse transformation on the fused coefficients to produce the final fused image. Simulation was performed to test the performance of our method, different focused images were used to verify the feasibility of the method. Performance of our method was analyzed by calculating Entropy, Average gradient, Mean Square Error (MSE) and Edge strength. The result of simulation shown that this method can extend the depth of field of PAM two times without the sacrifice of lateral resolution. And the in vivo imaging of zebra fish further demonstrates the feasibility of our method.

Post communication modulation by implementation of polarization entangled photons, or post transmission communication is a novel method by in which raw data photons is transmitted to the receiver and transmitter from an entangled photon source through two distinct polarization maintaining optical fiber. A pair of photons is generated in entangled photons source, one of twin photons e.g. |V> is sent to the transmitter and its counterpart e.g. |H> is sent to the receiver through the two links of polarization-maintaining optical fibers, having exactly the same length to guarantee reaching of photons to transmitter and receiver exactly at the same time. When one of entangled photons is ready to be polarization modulated in the transmitter, its twin photon is ready in the receiver to be read. We know the polarization of each one of twin entangled photons transmitted to receiver and transmitter, because the polarization is maintained through the fiber and by tuning and checking some photons in receiver and transmitter we can find the normal incidence polarization. When polarization of one of the photons in the transmitter is changed, this causes changing of photon counterpart in the receiver at zero-time delay. This makes zero-time communication possible ignoring transmission delay of optical fiber classical link. On the other hand, entangled photon enhancer is proposed here, because due to no-cloning theorem, it is impossible to use optical amplifier to amplify entangled photons.

In contrast to high-frequency electrical clock pulses of microprocessors, the shorter optical pulse and the higher rate of optical pulses means faster optical computation and communication applications. This paper provides several new methods for generating short optical pulses having a high repeating rate These methods are categorized into two areas; generating methods for higher bit rates and generating methods for shorter duration pulses. These methods may go beyond current borders of optical bit rates and ultrashort optical pulses but subject to more future lab investigations.

We conducted detailed in-lab tests on Balor 17F sCMOS camera, the latest model of Andor, for its performances, including gain, quantum efficiency, linearity, read noise, dark current, etc. We also evaluated the camera in terms of functionality considering the needs of astronomical observation. According to the tests, the camera’s performance is consistent with the official specifications, and to use the camera properly, liquid cooling is mandatory. The dark current in different areas of the sensor varies greatly. We also find that the sensor has a strong response at the short end of optical band. There are still some controversial issues in the camera to be further explained.

The study of the interaction between laser and brain tissue has important theoretical and practical significance for brain imaging. A two-dimensional simulation model that studies the propagation of light and heat transfer in brain tissue based on finite element has been developed by using the commercial finite element simulation software COMSOL Multiphysics. In this study, the model consists of three parts of 1) a layer of water on the surface of the brain, 2) brain tissue and 3) short pulsed laser source (the wavelength is 840nm). The laser point source is located in the middle of the layer of water above the brain tissue and irradiates the brain tissue. The propagation of light in brain tissue was simulated by solving the diffusion equation. And the temperature changes of gray matter and blood vessels were achieved by solving the biological heat transfer equation. The simulation results show that the light energy in the brain tissue decreases exponentially with the increase of penetration depth. Since the cerebral blood vessels have a stronger absorption on light compared with the surrounding tissues, the remaining light energy of the blood vessels in the cerebral cortex is ~ 85.8% of the remaining light energy in the surrounding gray matter. In the process of biological heat transfer, due to more light deposition in blood vessels, the temperature of blood vessels is 0.15 K higher than that of gray matter, and the temperature of gray matter hardly changes. This research is helpful to understand the propagation of light in the brain and the interaction between them, and has certain theoretical guiding for the optical imaging of the brain.

The brain is composed of the cerebrum, cerebellum, diencephalon and brainstem. The cerebrum is the superlative part of the central nervous system and also the main part of the brain. There are differences and similarities between humans and mouse. The study of mouse brain model is helpful to understand the process in clinical trials and also has reference significance for the study of human brain. Therefore, the study of mouse brain is particularly important. As the skull has a large scattering effect on light, it is difficult for us to image the brain through the skull directly. Therefore, we often use methods such as optical clearing or thin skull to reduce or remove the influence of the skull on imaging. In this paper, the transmission of photons in mouse brain was studied using Monte Carlo method. In the study of photon trajectories, the photon distribution without intact skull went farther in both longitudinal and transverse directions compared with that of with intact skull. In terms of the optical absorption density and fluence rate. On the condition of with intact skull, the distribution of optical absorption density and fluence rate was fusiform and rounder on the whole. The radial distribution range of optical absorption density and fluence rate was 0.25 cm, which was approximately 2.5 times of that of with intact skull. In the depth direction, due to the strong scattering and absorption of the scalp and skull, the optical absorption density dropped sharply from 0.890 cm^-1 to 0.415 cm^-1. When the photons arrived at the gray matter layer, only a few photons were reserved. Due to the strong absorption and scattering effect of the gray matter layer, only a few photons left, the optical absorption density increased from 0.415 cm^-1 to 0.592 cm^-1, and then decreased again. When the depth was 1.35 cm, the optical absorption density dropped to 0 cm^-1. After removing the skull, due to the weak absorption and scattering effect of normal saline and cerebrospinal fluid, the optical absorption density was low (0.119 cm^-1) and dropped slowly. When the photons arrived at the gray matter layer, most of the photons were reserved. Due to the strong absorption and scattering effect of the gray matter layer, the optical absorption density increased from 0.117 cm^-1 to 0.812 cm^-1, then the optical absorption density decreased to 0 cm^-1 at a depth of 1.35 cm. The distribution of radiant fluence rate is similar to that of optical absorption density. This study will provide reference and theoretical guidance for the optical imaging of mouse brain and the study of the mouse and human brain.

To rapidly and accurately distinguish the real and fake bloods, a photoacoustic detection system was established based on an optical parameters oscillator (OPO) pulsed laser and a focused ultrasonic transducer in this paper. The time-resolved photoacoustic signals of ten cow bloods and ten props bloods were captured. The amplitudes and profiles of photoacoustic signals for real and fake bloods were different. In addition, the photoacoustic peak-to-peak values of all blood samples were obtained from 700nm to 1064nm, and used as the train samples. The photoacoustic peak-to-peak values were firstly standardized and the data dimensions were reduced by using principle component analysis (PCA) method. Different principle components were chosen according to the cumulative contribution rate of characteristic values for the standardized photoacoustic peak-to-peak data matrix. Then, the scores of different principle components for ten cow bloods and ten props bloods were obtained and used as the train data. Five cow bloods and five props bloods were test. The linear discrimination analysis (LDA) algorithm was used to distinguish the real and fake bloods. The test results show that, based on the score data for two principle components by using PCA, the correct rate (CR) of real and fake bloods was improved from 80% to 100%. Results show that the LDA coupled with PCA, i.e.,PCA-LDA, has good distinction effect for the real and fake bloods.

Photoacoustic imaging technology is a new imaging technology has emerged in recent years, which combines the advantages of optical imaging and acoustic imaging, optical imaging and acoustic imaging have their own advantages, optical imaging has rich contrast , acoustic imaging has high resolution and high penetration. Photoacoustic imaging has been widely used in biomedical fields, such as brain imaging, tumor detection. As an important branch of photoacoustic imaging technology, optical-resolution photoacoustic microscopy can be applied from cells to tissues. However, the traditional optical-resolution photoacoustic microscopy usually uses a tight focused gaussian beam, the depth-of-field is small, and it is difficult to achieve rapid large volumetric imaging. In order to overcome this issue, researchers have proposed many solutions, one of which is to replace gaussian beams with non-diffracted beams. Bessel beam, as a nondiffracted beam, has a large depth of field. We applied it to optical -resolution photoacoustic microscopy. In reality, the cost of building such a Bessel-beam photoacoustic microscopy is relatively high and the system is complicated. Therefore, a virtual simulation technology is urgently needed for simulation research. Here, we proposed a virtual simulation platform of Bessel-beam photoacoustic microscopy based on k-Wave toolbox. The platform generates Bessel beam in the focus area of the objective lens by the circular slit, and the Bessel beam irradiates on the sample to generate the initial photoacoustic pressure field in a wide depth range. The K-Wave toolbox is used to simulate the propagation of photoacoustic fields, record photoacoustic signals and image reconstruction. The system can image a vascular network. The three-dimensional imaging of vascular network further verifies the large depth of field of Bessel-beam photoacoustic microscopy simulation platform. The establishment of the simulation platform is of great significance to the theoretical research of Bessel-beam photoacoustic microscopy and its application in biomedicine.

Photoacoustic imaging is becoming a very promising tool for the research of living organisms. It combines the high contrast of optical imaging and the high resolution of acoustic imaging to realize the imaging of absorption clusters in biological tissues. Since the scattering of ultrasound signals in biological tissues is 2-3 orders of magnitude weaker than the scattering of light in biological tissues, the endogenous absorption difference of tissues is directly used in the imaging process, so photoacoustic imaging has the advantages of deep imaging depth and non-destructive. As an important branch of photoacoustic imaging, photoacoustic microscopy can provide micron-level or even sub-micron-level imaging resolution, which is of great significance for biological research such as blood vessel detection. Since the lateral resolution of the photoacoustic microscopy imaging system depends on the focus of the laser, a higher resolution can be obtained by increasing the numerical aperture of the condenser objective. However, a large numerical aperture usually means a shorter working distance and makes the entire imaging system very sensitive to small optical defects. Therefore, the improvement of resolution through this method will be limited in practical applications. This paper implements a method of using iterative deconvolution to obtain a high-resolution photoacoustic image of the brain. The focal spot of the photoacoustic microscopy is measured to obtain the lateral PSF (point spread function) of the system. Making the measured PSF as the initial system PSF to perform Lucy- Richardson (LR) deconvolution. The image resolution of cerebral vasculature obtained by this method is higher. The full width at half maximum (FWHM) of width of the same cerebral capillaries before and after deconvolution are 7 μm and 3.6 μm, respectively, and the image definition is increased by about 1.9 times. Experiments show that this method can further improve the clarity of photoacoustic images of cerebral capillaries, which lays the foundation for further research on brain imaging.

Photoacoustic tomography is a new medical imaging technology with the advantages of high resolution, high contrast and high penetration depth. There are three common photoacoustic imaging methods in practical applications: photoacoustic microscopic imaging (PAM), photoacoustic computed tomography (PACT), photoacoustic tomography (PAE). As an important branch of photoacoustic imaging, photoacoustic microimaging combines high contrast of optical imaging with high resolution of ultrasonic imaging. In photoacoustic microimaging system, acoustooptic coupling prism is a very important component, which is usually composed of irregular prism and spherical concave acoustic lens at the bottom. Its function is to carry out optical transmission and ultrasonic detection. The ultrasonic depth of field of spherical concave acoustic lens is limited. In order to overcome this defect, researchers propose to use conical concave acoustic lens to produce Bessel sound beam to realize large depth of field ultrasonic detection. But the conical concave acoustic lens affects laser focusing and imaging. In order to solve this problem, we propose an optimization method to eliminate the influence of conical concave acoustic lens on beam transmission. A calibration mirror is added to the acoustooptic coupling prism with conical concave acoustic lens at the bottom, and the deterioration of the cone concave acoustic lens to the beam transmission is eliminated by optimizing the surface shape and thickness of the calibration mirror by Zemax. The optimization effect is evaluated by analyzing the spot. The simulation results show that the optimization method can eliminate the influence of the conical concave acoustic lens on the beam transmission, make the focal point and the focal point keep the coaxial focus, and improve the detection efficiency of the photoacoustic signal. This work is of theoretical significance for the systematic study of large depth of field photoacoustic microimaging.

Photoacoustic imaging is a new imaging technology in recent years, which combines the advantages of high resolution and rich contrast of optical imaging with the advantages of high penetration depth of acoustic imaging. Photoacoustic imaging has been widely used in biomedical fields, such as brain imaging, tumor detection and so on. The signal-tonoise ratio (SNR) of image signals in photoacoustic imaging is generally low due to the limitation of laser pulse energy, electromagnetic interference in the external environment and system noise. In order to solve the problem of low SNR of photoacoustic images, we use feedforward denoising convolutional neural network to further process the obtained images, so as to obtain higher SNR images and improve image quality. We use Python language to manage the referenced Python external library through Anaconda, and build a feedforward noise-reducing convolutional neural network on Pycharm platform. We first processed and segmated a training set containing 400 images, and then used it for network training. Finally, we tested it with a series of cerebrovascular photoacoustic microscopy images. The results show that the peak signal-to-noise ratio (PSNR) of the image increases significantly before and after denoising. The experimental results verify that the feed-forward noise reduction convolutional neural network can effectively improve the quality of photoacoustic microscopic images, which provides a good foundation for the subsequent biomedical research.

As an emerging nondestructive imaging technology recently, Photoacoustic imaging (PAI), which is based on Photoacoustic effect, combines the advantages: the high resolution and contrast of optical imaging and the high penetration depth of acoustic imaging. Thereinto, as a branch of Photoacoustic imaging, Photoacoustic microimaging inherited the advantages of Photoacoustic imaging. The unique focusing mode of Photoacoustic microimaging can meet the requirements of higher resolution in biological imaging, thus, it gained extensive applications in medical science field. However, on account of using high numerical aperture objective lens strongly focus on Gaussian beam, traditional Photoacoustic microimaging system has shallow depth of imaging field, and its transverse resolution and signal-to-noise ratio deteriorate rapidly outside the focal point, limiting the velocity of large volume imaging. Owing to solve these problems, in this paper, we build a simulation platform for Airy beam photoacoustic microscopy based on K-Wave simulation toolbox. This platform uses Airy beam to inspire initial Photoacoustic signal in large volume and K-Wave simulation toolbox to simulate the propagation, recording and reconstruction process of Photoacoustic signal. As Nondiffraction beam, Airy beam features the capacity of large depth of field, thus, its application could reach the requirement of large depth of field imaging of Photoacoustic microscopy system. Measuring the performances of the constructed Photoacoustic microscopy system, we constructed three-dimensional imaging of the blood vessel. By simulating A-Scan, B-Scan and C-Scan, we measured the performances of this system, such as axial resolution, transverse resolution and depth of field. Meanwhile, the three-dimensional imaging of the vertically tilted fiber also verified the three-dimensional imaging capability of the Airy beam photoacoustic microscopy simulation platform. The establishment of the simulation platform has a significance for the theoretical research of photoacoustic microscopy and its application in biomedicine.

Photoacoustic imaging is a high-resolution and high-contrast technique, which combines optical contrast with ultrasonic detection to map the distribution of the absorbing pigments in biological tissues. As an important branch of photoacoustic imaging, optical-resolution photoacoustic microscopy (OR-PAM) suffers from narrow depth-of-field (DoF), since the lateral resolution is determined by tight optical focusing. The small DoF will prevent OR-PAM to achieve large volumetric imaging. Here, we developed a parallel multifocus optical-resolution photoacoustic microscope with large depth-of-field based on a tunable acoustic gradient lens (TAG) and fiber delay network. The TAG lens is used to high -speed focus-shift. And a fiber delay network consists of three optical fibers with different lengths is used to split a single laser pulse into three sub-pulses with different delay time. A function generator generates a sinusoidal signal to drive the TAG lens at an eigenmode. The focusing power of the TAG lens will exhibit a sinusoidal oscillation at the frequency of the driving signal. Then, the three sub-pulses synchronizes with three vibration states of the TAG lens, respectively. Finally, we can obtain three focuses with different depth in one A-line data acquisition to improve the DoF. The DoF we measured by a vertically tilted carbon fiber is eatimated to larger than 775 μm, which is ~ three times of that of single-focus PAM. The large DoF of large volumetric PAM was also verified by imaging a tungsten wire network. This system can achieve rapid and large-scale monitoring of physiological activities, which could expand the application of OR-PAM in biomedical researches.

Surface Plasmon Resonance (SPR), an established optical sensing mode for providing quantified surface, is applied here to evaluate its suitability to provide optical parameters from metal layer measurements in a synthetic marine environment. We investigated silver noble metal films exposed to standard saline solution. Silver layers exhibit both long-term durability, and linear temporal reflectivity change, recorded in SPR minimum angle and SPR curve shape. Optical sensor design was achieved using Fresnel’s optical theory for isotropic multi-layer media. We developed a datafitting routine, providing numerical real and imaginary permittivity, and thickness solutions for corroded surfaces.

We discuss a microwave planar evanescent waveguide sensor, based on a patented technology demonstrator (2016), for a range of potential UAV platform skin-embedded waveguide sensing applications such as: refractive index sensing, temperature sensing, or surface corrosion. Evanescent microwave sensors can probe the near surface region from millimetre to metric scales, with minimal direct radiation into the external environment, ideal for evading detection, and able of detecting various dielectric or metal materials adjacent to the surface. This paper examines evanescent planar sensor use as an environmental evanescent sensor to both detect and quantify metal or dielectrics without direct contact, measuring material loss from chosen waveguide materials, and waveguide surface cladding materials. We also present microwave transmission for a range of temperature sensor configurations, with good sensitivity across a broad range of temperature-related applications, including temperature hysteresis. [1].

Advanced optical stealth technology is increasingly important in the role of aircraft platforms whether manned (e.g. F117A) or unmanned (e.g. X47B Pegasus). Here we consider the concept validity of using low power passive reflective display elements rather than high power active projection to achieve the same result. This paper presents practical consideration towards minimal radiated power, and power requirements using adaptive passive methods. Prism-coupling as a means of probing thin liquid crystal layers to obtain information on optical tensor parameters is possible using the Kraetschmann-Raether configuration. Leaky liquid crystal cells composed of a glass/ITO/aligning layer/liquid crystal/aligning layer/ITO/glass multi-layer structure support guided modes. Optical reflectivity as a function of incident angle and applied voltage are compared with theory generated from a Fresnel matrix formalism. Reflectivities are then simulated across an aircraft platform to evaluate the method for low power consumption and minimal radiated optical radiation.

The yield of agricultural crops is directly related to the quality of the seed. One of the ways to improve the quality is to sort seeds by physical and optical properties (color, shade of color, shape, size and structure) using grain cleaning units (photoseparators or color sorters). Currently, two main approaches are used to create sorting algorithms: generalized statistical and neural networks. The benefits of a neural-based sorting approach include the absence of any restrictions on training series; there is no need to pre-examine the nature of the data and adjust sorting algorithm. Nowadays, neural networks are better than statistical methods, especially in the case of deep neural networks. The disadvantages of neural networks are in the high computational complexity of obtaining results and huge size of the training series, sufficient to achieve high accuracy in making reliable decisions. This work discusses the creation of a convolutional neural network architecture that allows dividing the input flow of wheat seeds into two classes: "good" and "bad" (with flaws in shape and color). The implementation of convolutional neural network on FPGA using the Xilinx System Generator for DSP and Matlab / Simulink package is also considered. The results of a convolutional neural network training, assessment of the accuracy of seed correct classification, and verification of the convolutional neural network hardware FPGAs implementation are presented.

The Earth orientation sensor (ERS) device installed on board the spacecraft (SC) is discussed. In this device related to the local vertical reference, the so-called circular scanning principle is implemented. The circular scanning principle implies the angular field being scanned in a circumferential direction, with the angular radius coinciding with the angular radius of the Earth, and the signal of the receiving device being detected by two phase-sensitive detectors, which generate signals that determine the deviation of the instrument's optical reference axis from the vertical in two mutually perpendicular planes. Provided a mathematical description of the kinematic model of the Earth orientation sensor (EOS). Performed the validation of the analytical description of the device by using the Matlab system and with a model-based approach. As a result, defined that for reliable recognition of four points of the Earth-Space border intersection, it is desirable that the time between transitions approaches to 125ms. Thus, to receive a substantial measurement base on Z axis the angle at the apex of the scanning cone should be reduced. Modeling has shown that the optimal angle at the apex of the scanning cone for an orbital altitude range of 200-2000 km is 55 degrees.