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This study introduces electro-optical (EO) sensors (TurbNet sensors) that utilize a remote laser beacon (either coherent or incoherent) and an optical receiver with CCD camera and embedded edge AI computer (Jetson Xavier Nx) for in situ evaluation of the path-averaged atmospheric turbulence refractive index structure parameter Cn2 at a high temporal rate. Evaluation of Cn2 values was performed using deep neural network (DNN)-based real-time processing of short-exposure laser-beacon light intensity scintillation patterns (images) captured by a TurbNet sensor optical receiver. Several pre-trained DNN models were loaded onto the AI computer and used for TurbNet sensor performance evaluation in a set of atmospheric propagation inference trials under diverse turbulence and meteorological conditions. DNN model training, validation, and testing were performed using datasets comprised of a large number of instances of scintillation frames and corresponding reference (“true”) Cn2 values that were measured side-by-side with a commercial scintillometer (BLS 2000). Generation of datasets and inference trials was performed at the University of Dayton’s (UD) 7-km atmospheric propagation test range. The results demonstrated a 70–90% correlation between Cn2 values obtained with the TurbNet sensors and those measured side-by-side with the scintillometer.

Composite insulators for high-voltage overhead lines have better performances and are lighter than traditional designs, especially in heavily polluted areas. However, since it is a relatively recent technology, reliable methods to perform live-line diagnostics are still under development, especially with regard to internal defects, which provide few external symptoms. Thermal cameras can be employed, but their use is not always straightforward as the sun radiation can hide the thermal footprint of internal degenerative effects. In this work, an optical E-field sensor has been used to diagnose the internal defects of a set of composite insulators (bandwidth 200 mHz–50 MHz, min. detectable E-field 100 V/m). Moreover, a modelling activity using finite elements has been carried out to identify the possible nature of the defects by comparing experimental E-field profiles with those simulated assuming a specific defect geometry. The results show that the sensor can detect the presence of an internal defect, since its presence distorts the E-field profile when compared to the profile of a sound insulator. Moreover, the measured E-field profiles are compatible with the corresponding simulated ones when a conductive defect is considered. However, it was observed that a defect whose conductivity is not at least two orders of magnitude greater than the conductivity of the surroundings remains undetected.

This paper presents results based on direct experimental measurements of tangential (Et) and normal (En) E-field components along the stress grading system (SGS) of a real stator bar (Roebel type) for different AC 60 Hz applied voltages. These measurements were made with a new electro-optic system allowing for the study of both spatial distributions of two E-field components along the bar and their temporal evolution at critical points. The results obtained allowed us to calculate the correlation between the distribution of En and Et along the SGS. In particular, it was demonstrated that the En distribution presents a characteristic minimum, which can be used to identify the zone of partial discharge inception. Moreover, it was possible to observe an enlargement of the Et component distribution followed by a saturation in magnitude with the applied voltage increase. Moreover, the results have demonstrated that the waveform of the En component is mostly affected by the SG material used, producing a greater distortion in its waveform than those obtained for the Et component. The more significant distortion was obtained at the end of the outer corona protection (OCP) material, corresponding to the first maximum of the En component and characterized by the appearance of a third harmonic of large amplitude.

We have developed a real-time terahertz time-domain polarization analyzer by using 80-MHz repetition-rate femtosecond laser pulses. Our technique is based on the spinning electro-optic sensor method, which we recently proposed and demonstrated by using a regenerative amplifier laser system; here we improve the detection scheme in order to be able to use it with a femtosecond laser oscillator with laser pulses of a much higher repetition rate. This improvement brings great advantages for realizing broadband, compact and stable real-time terahertz time-domain polarization measurement systems for scientific and industrial applications.

The quantum noise of light, attributed to the random arrival time of photons from a coherent light source, fundamentally limits optical phase sensors. An engineered source of squeezed states suppresses this noise and allows phase detection sensitivity beyond the quantum noise limit (QNL). We need ways to use quantum light within deployable quantum sensors. Here we present a photonic integrated circuit in thin-film lithium niobate that meets these requirements. We use the second-order nonlinearity to produce a squeezed state at the same frequency as the pump light and realize circuit control and sensing with electro-optics. Using 26.2 milliwatts of optical power, we measure (2.7 ± 0.2)% squeezing and apply it to increase the signal-to-noise ratio of phase measurement. We anticipate that photonic systems like this, which operate with low power and integrate all of the needed functionality on a single die, will open new opportunities for quantum optical sensing. Squeezed light allows for quantum-enhanced, sub-shot-noise sensing, but its generation and use on a chip has so far remained elusive. Here, the authors fill this gap by demonstrating a thin-film lithium-niobate-based integrated quantum optical sensor, which beats shot-noise-limited SNR by ~ 4%.

In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques (i.e., blending LIDAR with existing electro-optical sensors) offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms (handheld, ground-based, airborne, ground/aerial robotic vehicles or satellites), these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field.

Atmospheric effects have a significant impact on the performance of airborne and space laser systems. Traditional models used to predict propagation effects rely heavily on simplified assumptions of the atmospheric properties and their interactions with laser systems. In the engineering domain, these models need to be continually improved in order to develop tools that can predict laser beam propagation with high accuracy and for a wide range of practical applications such as LIDAR (light detection and ranging), free-space optical communications, remote sensing, etc. The underlying causes of laser beam attenuation in the atmosphere are examined in this paper, with a focus on the dominant linear effects: absorption, scattering, turbulence, and non-linear thermal effects such as blooming, kinetic cooling, and bleaching. These phenomena are quantitatively analyzed, highlighting the implications of the various assumptions made in current modeling approaches. Absorption and scattering, as the dominant causes of attenuation, are generally well captured in existing models and tools, but the impacts of non-linear phenomena are typically not well described as they tend to be application specific. Atmospheric radiative transfer codes, such as MODTRAN, ARTS, etc., and the associated spectral databases, such as HITRAN, are the existing tools that implement state-of-the-art models to quantify the total propagative effects on laser systems. These tools are widely used to analyze system performance, both for design and test/evaluation purposes. However, present day atmospheric radiative transfer codes make several assumptions that reduce accuracy in favor of faster processing. In this paper, the atmospheric radiative transfer models are reviewed highlighting the associated methodologies, assumptions, and limitations. Empirical models are found to offer a robust analysis of atmospheric propagation, which is particularly well-suited for design, development, test and evaluation (DDT&E) purposes. As such, empirical, semi-empirical, and ensemble methodologies are recommended to complement and augment the existing atmospheric radiative transfer codes. There is scope to evolve the numerical codes and empirical approaches to better suit aerospace applications, where fast analysis is required over a range of slant paths, incidence angles, altitudes, and atmospheric conditions, which are not exhaustively captured in current performance assessment methods.

Two-dimensional (2D) imaging probes, such as the 2D stereo (2D-S) probe and the cloud imaging probe (CIP), are routinely used to provide in situ measurements of cloud particle properties. The basic measurement is shadowgraphs of water drops and ice particles from which particle size distributions, projected particle area, and mass concentrations are determined. These data permeate data archives of domestic and foreign government agencies, universities, and the private sector. This paper provides results from laboratory tests and flight tests on a Learjet research aircraft that give new insights into the performance of the 2D imaging probes, and how their performance may have impacted measurements collected in data archives. The laboratory tests are conducted with the aid of two devices: 1) a droplet generator that provides known concentrations of water drops from 15 to 65 µ m ± 1 µ m that can be positioned in the probe’s sample volume with 10- µ m precision; and 2) a motorized spinning platform that supports transparent disks with small opaque features (i.e., a “spinning disk”), which replicates the effect of particles transecting the probe’s sample volume at translational speeds up to 190 m s −1 . The flight tests were conducted with a Learjet research aircraft that collected cloud particle data at true airspeeds from 99 to 170 m s −1 . The results provide new insights into how probe optics, time response, and data throughput of the 2D-S and CIP electro-optics impact the measurements of cloud particles. The results, summarized in the conclusions, suggest how archived data are impacted.

For many spacecraft to accomplish their mission goals there is a need to have an accurate control of their spatial orientation (attitude), so that instruments aboard the spacecraft, such as remote sensing cameras, telescopes, and other scientific payload be correctly pointed to their targets. For that purpose, many spacecraft employ sophisticated attitude control systems, fed by a variety of attitude sensors: sun sensors, horizon sensors, gyroscopes, star trackers, etc. Among the attitude sensors capable of providing an absolute attitude measurement, star trackers are considered the most accurate. This work presents an overview of the test and calibration infrastructure built for an autonomous star tracker (AST) being developed by the Electro-Optics group of INPE, result of an effort to increase the competence of the country in attitude control systems.

Enhanced functionality of electro-optic devices by implementing piezoelectric micro fibers into their construction is proposed. Lanthanum-modified lead zirconate titanate (PLZT) ceramics are known to exhibit high light transparency, desirable electro-optic properties and fast response. In this study PLZT fibers with a diameter of around 300 microns were produced by a thermoplastic processing method and their light-induced impedance and piezoelectric coefficient were investigated at relatively low light intensity (below 50 mW/cm2). The authors experimentally proved higher performance of light controlled microfiber transducers in comparison to their bulk form. The advantage of the high surface area to volume ratio is shown to be an excellent technique to design high quality light sensors by using fibrous materials. The UV absorption induced change in elastic constants of 3% and 4% for the piezoelectric coefficient d33.