Hyperspectral images and optical signatures acquired by NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) from multiple locations are examined. The spectral responses obtained are used to promote the development of physics-based models for these signatures with future application in improvements in hyperspectral sensing.

Contemporary communication systems demand continual improvements in power efficiency, motivating the operation of their power amplifiers in increasingly nonlinear regions. To mitigate the associated waveform distortion, a linearization technique is often implemented. While linearization is typically performed on transmit, it is also possible to perform at the receiver as a Digital Post-Distortion (DPoD) correction (N. Naraharisetti, et al., “Efficient Least-Squares 2-D-Cubic Spline for Concurrent Dual-Band Systems,” in IEEE Transactions on Microwave Theory and Techniques, vol. 63, no. 7, pp. 2199–2210, July 2015). Previous results have shown success in the use of DPoD techniques for waveforms that have non-constant amplitudes. However existing DPoD techniques are inapplicable for correcting any distortions in constant amplitude waveforms such as Binary Phase-Shift Keying (BPSK).

We evaluated the capability of near-infrared (NIR) transflectance spectroscopy coupled with multivariate analysis to develop predictive regression models that quantify taste indicators, i.e., soluble solids, titratable acidity (TA), citric acid, sucrose, glucose, and fructose, in orange juice (sample size = 123). The NIR spectra were collected in transflectance mode using a handheld scanner with a series of reflectors that provide three distinct pathlengths (0.50 mm, 0.80 mm, and 2 mm) across the juice samples. Reference data were collected using automatic titrators for TA, refractometer for Brix, and high-performance liquid chromatography (HPLC) for organic acids and sugars. Orange juice samples represented a diverse genetic pool of Hamlin and Valencia varieties harvested at different maturity stages providing a unique range of concentration for each parameter (Brix, 4.3–12.3%, and TA 0.8–3.5 g citric acid/L). Pattern recognition analysis correlated the spectral data to reference values using partial least square regression. Results showed better performance using 0.50 mm and 0.80 mm pathlengths, R pre ≥ 0.82, root mean square error of prediction (RMSEP) range 0.14–6.87, residual prediction deviation (RPD) range 1.2–8.3, residual error rate (RER) range 2.7–30 compared to the 2 mm pathlength (R pre ≥ 0.72, RMSEP range 0.27–4.47, RPD range 1.2–1.7, RER range 5.2–7.8). We demonstrated that a field deployable NIR scanner can provide reliable prediction using a transflectance approach using algorithms that were synchronized for cloud computing using R programming, providing easy accessibility for analysis. This technology offers orange breeders and growers an affordable, rapid (10 s), and accurate solution for in-field, real-time monitoring of taste indicators in orange juice, which can help expedite critical decision-making processes in the field (harvest timing, process optimization, and aid in breeding research).

The use of Hagfors' law for calculating the Bidirectional Reflectance Distribution Function (BRDF) and emissivity of exponentially correlated rough surfaces under the first order small slope approximation is described. Sample results are shown to illustrate the effect of surface roughness, permittivity, and polarization under this model.

Quasi-identical power amplifiers (PA) can be differentiated through the unique non-linearities that are inherent to each individual PA. It was recently demonstrated that digital post distortion (DPoD) applied to the measured output data facilitates this PA identification. In this paper, a generalized cubic spline basis (GCSB) with selective deep memory is used to perform an enhanced DPoD. It is experimentally verified that the use of deep memory in the GCSB model can not only increase the performance of DPoD but also greatly magnify the differences observed in the normalized mean squared error (NMSE) of the linearized PA output signal relative to the reference input signal. This new technique can thus be used to reliably differentiate between two quasi-identical power amplifiers from the same device's manufacturer.

Efficient use of the limited bandwidth available is a persistent struggle with communication systems. The ever-increasing demand for spectral efficiency has put more stress on linearity of power amplifiers, and as such a linearization technique is often required. An often-utilized technique is Digital Pre-Distortion (DPD), which modifies the waveform prior to input to the amplifier in order to obtain a more linear output. Another possible avenue is Digital Post-Distortion (DPoD), in which the output waveform is corrected at a receiver to remove distortion effects.

Quasi-identical power amplifiers can be differentiated through the unique non-linearities that are inherent to each individual power amplifier (PA). The use of error vector magnitude (EVM) and/or normalized mean squared error (NMSE) metrics relative to the reference input signal has been recently proposed as a metric. In this paper it is experimentally verified that first linearizing the PA outputs using the same digital post distortion (DPoD) technique to correct for the non-linearities of the reference PA, magnifies the differences observed in the EVM and NMSE metrics. As the signal-to-noise ratio (SNR) of the distorted signal output increases, the differences between the EVM/NMSE measurements of the two Pas also increases. At a high enough SNR, two quasi-identical Pas can be reliably differentiated using the EVM/NMSE metrics once DPoD has been applied.

A novel approach for rapid (15s) detection and quantification of predominant cannabinoids in hemp was developed using Fourier-transformed near-infrared spectroscopy (FT-NIR), enabling real-time and field-based applications. Hemp samples (n = 91) were obtained from certified online vendors, the OARDC Weed Lab, and a local Ohio farm. Reference data of major cannabinoids content were determined by uHPLC-MS/MS. Spectral data were collected by a miniaturized, battery-operated FT-NIR instrument, and combined with the reference data to generate partial least squares regression (PLSR) models. uHPLC-MS/MS analysis showed two samples had over 0.36% of delta 9-tetrahydrocannabinol (delta(9)-THC), and 64% (32 out of 50) of online-bought hemp samples were not in compliance with their total cannabidiol (CBD) content declaration. PLSR prediction models showed excellent correlation (Rpre = 0.91-0.95) and a low standard error of prediction (SEP = 0.02-0.61%). This method could be used as an alternative to traditional methods for in-situ assessment of hemp quality.

We aimed at developing fast and accurate predictive algorithms to quantify trans-fat, conjugated linoleic acid (CLA), and other fatty acids using portable and handheld infrared sensors for butter and margarine products. Butter (n = 21) and margarine (n = 15) samples were collected from local grocery stores in Lima, Peru. Their fatty acid content was determined by gas chromatography fatty acid methyl ester analysis (GC-FAME). Infrared spectra were collected using portable (five-reflections) and handheld (single-reflection) infrared (FTIR-ATR) spectrometers and a palm-sized Near-Infrared (FT-NIR) sensor. None of the margarine samples, except those made with partially hydrodenated oils (PHOs), contained trans-fat, and the trans-fat levels in the butters ranged from 0.24 to 0.62 g trans-fat/serving. Partial least squares regression models showed strong correlation (RPre >= 0.91), low standard error of prediction (SEP <= 2.62), and high predictive performance based on the ratio of prediction to deviation (RPD: 1.4-15.1) and Ratio of Error Range (RER: 5.7-56.9). Quantification and classification models obtained with the five-reflections FTIR-ATR system exhibited best performances in terms of RPre, SEP, RPD, and RER, followed by the single-reflection FTIR-ATR and FT-NIR systems, respectively. Portable and handheld devices therefore can provide real-time and in situ results to the fat/oil and dairy industry and regulatory agencies for actionable decisions.

This study evaluated the performance of low-cost, real-time, and field-deployable spectroscopic instruments operating at near-infrared (NIR) and mid-infrared (MIR) wavelengths for measuring quality traits (8-glucan, starch, protein, and lipid) of oats to support breeding selection. Samples were kindly provided by PepsiCo R&D (n = 150) as oat groats. A handheld FT-NIR sensor (1350-2560 nm) measured spectra of ground and intact oat samples, while a portable FT-IR spectrometer (4000-650 cm -1) measured ground samples only. Several laboratory reference methods were used to measure 8-glucan, starch, protein, and lipid composition to develop spectroscopic analysis models based on Partial Least Squares Regression (PLSR). Best model performance was obtained from NIR spectra of ground groats, with standard error of prediction (SEP) for 8-glucan, starch, protein, and lipid of 0.2%, 1.0%, 0.6%, and 0.3%, respectively. PLSR models for the MIR spectra exhibited similar predictive accuracy. The performance of these PLSR models either matched or outperformed NIR techniques reported in the literature using portable and benchtop systems. Therefore, novel miniaturized NIR sensors can provide breeders with a rapid method (15 s) to screen for unique traits in the field with equivalent reliability and sensitivity as benchtop systems.

Due to the demand for increasingly large format focal plane arrays, smaller and smaller pixels are required for high resolution imaging. A promising technique for backside illuminated devices is self-aligned etching of the mesas, or using the metal contact pad as the etch mask. In this work, we report on the self-aligned etching of two Type-II superlattice materials and some of their constituent material components to create pixels with subwavelength dimensions in a longwave infrared detector. Palladium was used as the primary mask material to prevent the exposure of the gold contacts to the etch plasma. The inductively coupled plasma conditions were varied, including varying the etch gas composition through different ratios of BCl3 and Cl-2, and the etch rate and sidewall angle were measured. Using a mixture of BCl3 and Cl-2 produced higher etch rates at room temperature than previously reported results at high temperatures with similar sidewall angles, thus reducing undesired diffusion of the device stack layers.

NASA’s New Observing Strategies (NOS) thrust provides a framework for identifying technology advances needed to exploit newly available observational capabilities, including high-quality instruments on constellations of SmallSats and CubeSats, that enable measurement of phenomena that could not be studied using previously available techniques. Satellite developers and operators require software tools to simulate new technologies and validate new mission concepts that can incorporate a dynamic set of observing assets with various instruments located at different vantage points. These new mission concepts include many more design variables than traditional missions, requiring tools to facilitate trade analysis and concept validation in an iterative fashion, similar to an Observing System Simulation Experiment (OSSE) framework. Several recent projects address design and operational trades by designing software packages such as the Trade-space Analysis Tools for Constellations (TAT-C) co-developed by Stevens Institute of Technology, the Simulation Toolset for Adaptive Remote Sensing (STARS) developed by The Ohio State University, and the Virtual Constellation Engine (VCE) developed by the University of Southern California. Each tool has different but complementary capabilities and can be run independently. However, linking capabilities using modern web-based service application programming interfaces (APIs) contributes to a powerful modeling ecosystem for the Earth Science community with well-defined interfaces that facilitate interoperability with existing mission planning tools. This presentation will describe the capabilities of each individual software tool as well as recent efforts to integrate their capabilities to evaluate and mature constellation mission concepts as part of the NOS thrust.

In an infrared photodetector, noise current (dark current) is generated throughout the volume of the detector. Reducing the volume will reduce dark current, but the corresponding smaller area will also reduce the received signal. By using a separate antenna to receive light, one can reduce the detector area without reducing the signal, thereby increasing its signal-to-noise ratio (SNR). Here, we present a dielectric resonator antenna (DRA)-coupled infrared photodetector. Its performance is compared to a conventional resonant cavity-enhanced slab detector. The noise equivalent power (NEP) is used as a figure of merit for the comparison. Formulas for the NEP are derived for both cases. A pBp photodiode detector is assumed in the comparison. The active region of the photodiode is an InAs/GaSb Type II Superlattice (T2SL). A Genetic Algorithm is used to optimize the dimensions of the detector and the DRA to achieve the smallest NEP. The result is a photodetector that converts over 85% of the incident light into carriers with a volume reduced by 95%. This optimal geometry leads to an NEP reduced by 6.02 dB over that of the conventional resonant cavity-enhanced slab detector.

This research demonstrates simultaneous predictions of individual and total sugars in breakfast cereals using a novel, handheld near-infrared (NIR) spectroscopic sensor. This miniaturized, battery-operated unit based on Fourier Transform (FT)-NIR was used to collect spectra from both ground and intact breakfast cereal samples, followed by real-time wireless data transfer to a commercial tablet for chemometric processing. A total of 164 breakfast cereal samples (60 store-bought and 104 provided by a snack food company) were tested. Reference analysis for the individual (sucrose, glucose, and fructose) and total sugar contents used high-performance liquid chromatography (HPLC). Chemometric prediction models were generated using partial least square regression (PLSR) by combining the HPLC reference analysis data and FT-NIR spectra, and associated calibration models were externally validated through an independent data set. These multivariate models showed excellent correlation (Rpre ≥ 0.93) and low standard error of prediction (SEP ≤ 2.4 g/100 g) between the predicted and the measured sugar values. Analysis results from the FT-NIR data, confirmed by the reference techniques, showed that eight store-bought cereal samples out of 60 (13%) were not compliant with the total sugar content declaration. The results suggest that the FT-NIR prototype can provide reliable analysis for the snack food manufacturers for on-site analysis.

The Cubesat radiometer radio frequency interference technology validation mission (CubeRRT) was developed to demonstrate real-time onboard detection and filtering of radio frequency interference (RFI) for wide bandwidth microwave radiometers. CubeRRT's key technology is its radiometer digital backend (RDB) that is capable of measuring an instantaneous bandwidth of 1 GHz and of filtering the input signal into an estimated total power with and without RFI contributions. CubeRRT's onboard RFI processing capability dramatically reduces the volume of data that must be downlinked to the ground and eliminates the need for ground-based RFI processing. RFI detection is performed by resolving the input bandwidth into 128 frequency subchannels, with the kurtosis of each subchannel and the variations in power across frequency used to detect nonthermal contributions. RFI filtering is performed by removing corrupted frequency subchannels prior to the computation of the total channel power. The 1 GHz bandwidth input signals processed by the RDB are obtained from the payload's antenna (ANT) and radiometer front end (RFE) subsystems that are capable of tuning across RF center frequencies from 6 to 40 GHz. The CubeRRT payload was installed into a 6U spacecraft bus provided by Blue Canyon Technologies that provides spacecraft power, communications, data management, and navigation functions. The design, development, integration and test, and on-orbit operations of CubeRRT are described in this article. The spacecraft was delivered on March 22nd, 2018 for launch to the International Space Station (ISS) on May 21st, 2018. Since its deployment from the ISS on July 13th, 2018, the CubeRRT RDB has completed more than 5000 h of operation successfully, validating its robustness as an RFI processor. Although CubeRRT's RFE subsystem ceased operating on September 8th, 2018, causing the RDB input thereafter to consist only of internally generated noise, CubeRRT's key RDB technology continues to operate without issue and has demonstrated its capabilities as a valuable subsystem for future radiometry missions.

This study evaluates a novel handheld sensor technology coupled with pattern recognition to provide real-time screening of several soybean traits for breeders and farmers, namely protein and fat quality. We developed predictive regression models that can quantify soybean quality traits based on near-infrared (NIR) spectra acquired by a handheld instrument. This system has been utilized to measure crude protein, essential amino acids (lysine, threonine, methionine, tryptophan, and cysteine) composition, total fat, the profile of major fatty acids, and moisture content in soybeans (n = 107), and soy products including soy isolates, soy concentrates, and soy supplement drink powders (n = 15). Reference quantification of crude protein content used the Dumas combustion method (AOAC 992.23), and individual amino acids were determined using traditional protein hydrolysis (AOAC 982.30). Fat and moisture content were determined by Soxhlet (AOAC 945.16) and Karl Fischer methods, respectively, and fatty acid composition via gas chromatography-fatty acid methyl esterification. Predictive models were built and validated using ground soybean and soy products. Robust partial least square regression (PLSR) models predicted all measured quality parameters with high integrity of fit (RPre ≥ 0.92), low root mean square error of prediction (0.02–3.07%), and high predictive performance (RPD range 2.4–8.8, RER range 7.5–29.2). Our study demonstrated that a handheld NIR sensor can supplant expensive laboratory testing that can take weeks to produce results and provide soybean breeders and growers with a rapid, accurate, and non-destructive tool that can be used in the field for real-time analysis of soybeans to facilitate faster decision-making.

With the increasing use of resonant structures in optical devices, broadband optical characterization of the refractive index and extinction coefficient is necessary for accurate simulation and device design. For resonance-enhanced photodetectors, the complex refractive index is necessary to impedance match not only the resonator to air, minimizing the reflection, but also the resonator to the detector element, ensuring absorption occurs in the photodiode. To work towards better resonator-detector coupling, we present the complex refractive index for GaSb and an InAs/GaSb strained layer superlattice designed to be the absorber layer for a long-wave infrared photodetector. The optical properties were extracted using spectroscopic ellipsometry. Several modeling methods will be discussed for both the superlattice and the single-side polished bulk GaSb. Comparison to transmission and reflection values as well as absorption coefficients from literature provide additional confidence in the extraction process. Future work will incorporate these values into a resonance-enhanced photodetector.

This paper demonstrates how the fully adaptive radar framework can be applied to cloud profiling radars. A simulation based on the GEOS5 nature run dataset is introduced in which the cloud profiling radar continuously adapts its pulse repetition frequency (PRF) such that the unambiguous range is 1.2 times the cloud column height. This process maximizes the (PRF) which would in turn maximize the unambiguous velocity estimate.