Spectral Separation Research Articles

Analysis of serum metabolism in premature infants before and after feeding using GC-MS and the relationship with necrotizing enterocolitis.

Preterm birth and enteral feeding are two main factors leading to necrotizing enterocolitis (NEC). The metabolomics of preterm infants before and after feeding can provide a basis for the prediction of NEC. Using the method of cross-sectional study, the mode was established with the serum samples of 19 premature infants at birth and after feeding as the control group. The serum was analyzed using GC-MS. Chemometric analysis includes principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis. Spectral separation of serum metabolites occurred in premature infants before and after feeding. The levels of xylose, d-talose, phosphoglycolic acid, maleimide, l-gulonolactone, maleic acid, β-hydroxypyruvate, itaconic acid, and pantothenic acid in the serum of premature infants after feeding were significant in both multidimensional and single-dimensional modes (variable importance in projection >2, P < 0.01). There was a moderate correlation between total bilirubin and l-gulonolactone and β-hydroxypyruvate (0.8 > r > 0.5). Maleimide, maleic acid, and itaconic acid have diagnostic value (area under the curve >0.9). The results indicated that serum metabolism of preterm infants changes significantly after feeding. Some metabolites have potential value in predicting NEC.

Assessing beech bark-diseased forest canopies over landscapes using high resolution open-source imagery in an ecological framework

Locating and tracking outbreaks of pests and pathogens enables forest ecologists and managers to assess impact or develop interventions. Beech bark disease (BBD) is actively affecting eastern US forests, including within the upper Great Lakes region. Our goal was to develop and apply an open-source framework for identification of BBD-affected tree crowns and for assessment of their spatial–temporal distribution over heterogeneous forested landscapes. We used the freely available USDA NAIP high spatial resolution aerial imagery, developed the identification algorithm in the open-source R environment, and integrated these within several open-data ecological classifications. The study landscape was the uplands of the ∼ 4200-ha University of Michigan Biological Station (UMBS) in northern Lower Michigan. Our objectives were to: 1) collect field data to characterize BBD on the landscape and to train and test remote sensing methods; 2) compile mapped ecological classifications; 3) explore NAIP spectral separability of BBD-affected crowns from other types of interest; 4) develop and apply a mostly-automated segmentation algorithm for identification of BBD-affected tree crowns; 5) assess mapped results over the entire landscape and within ecological classification strata; and 6) suggest an ecologically based framework for extensibility beyond the study landscape. Of a sample of visually interpreted yellowed tree crowns on NAIP at Training Sites, 99% were field-verified as beech with Neonectria. Spectral explorations confirmed affected (yellow) beech crowns had consistently higher reflection in NAIP red and green bands and were distinct from healthy beech and aspen undergoing age-related mortality; there was some spectral confusion with yellowed herbaceous areas. The segmentation algorithm was accurate to 82 %, and over the 2012–2018 biennial time series mapped > 9,700 BBD-affected canopies (single or interspersed beech tree crowns). Optimized Getis-Ord Gi* showed significant clustered distributions of BBD hotspots and coldspots on the landscape at each date. Ecological classifications presented strata statistically associated with the greatest density of mapped BBD-affected canopies and provide a framework for extensibility. This integrated approach may provide an accessible option for forest managers to map and assess hotspots of canopy-level BBD symptoms over heterogeneous landscapes and over time.

Developing a sub-meter phenological spectral feature for mapping poplars and willows in urban environment

Poplar and willow catkins (PWCs) have caused severe impacts on human health and environmental quality, and accurate poplars and willows (PaWs) mapping with remote sensing is essential to monitor and manage the PWCs. However, two major issues have constrained the urban tree species (e.g. PaWs) identification: (1) the urban tree landscapes are highly fragmented and susceptible to the existence of mixed pixels in the remote sensing imagery; (2) the tree species in urban environment are diverse with high spectral similarity. To this end, this study developed a sub-meter phenological spectral feature (Spsf) with multi-scale and multi-temporal remote sensing imagery for monitoring PaWs at the tree species level.Spsf includes three steps: (1) exploring three key phenological periods of PaWs (leafless period, greenleaf period, and senescence period); (2) selecting one or three spectral indexes to characterize each phenological period; (3) stacking the spectral vegetation indexes from Sentinel-2 SR imagery and freely available sub-meter (0.8 m) Google Earth imagery together. Subsequently, Spsf was taken as the input data to train the deep learning DeepLabv3 + model for predicting the PaWs distribution. The Beijing Plain was chosen as the study area, where the distribution of PaWs was extensive and fragmented. Compared with the field survey reference data, the derived PaWs map achieved the overall accuracy higher than 92 % and the Kappa coefficient of 0.83. The Spsf integrated rich spatial information from sub-meter imagery and phenological spectral information from Sentinel-2 imagery, which may alleviate the impacts of mixed pixels and enhance the spectral separability between PaWs and other tree species effectively. The proposed Spsf-based method provides a new paradigm for sub-meter tree species mapping with multi-source free remote sensing data. The PaWs map can serve as reference data for the relevant departments to monitor and manage the PWCs.

Solar Energy Management Using a V-Groove: An Approach Based on a Multiple Optical Path Algorithm

Angular and spectral separations of thermal radiation have become a key challenge in solar concentration or thermal management of sources radiating at extremely high or low temperatures. Reflections obtained from electromagnetic theory in an open cavity geometry increase the emission and absorption compared to a flat surface due to the cavity effect. In this paper, in order to obtain the directional emission of geometric surfaces (V-Grooves) using ray tracing and studying the propagation of light, a new algorithm is developed. The numerical simulations take into account the materials properties of both facets of the V-shape, thus simulating an original asymmetric and a multilayer V-shape and providing a very interesting directive thermal emission behavior. We evaluated the emission behavior from the reflection and emission coefficients of different rays at different angles for different parameters (materials properties, wavelength, and geometry). The simulations of a V-groove showed that due to the different reflections occurring inside an aluminum V-cavity with an aperture angle of 28°, the emissivity was well enhanced by 86% in the normal direction compared to a flat surface made of the same material. Moreover, using the original asymmetric opposite-sided materials (Al and Si) in a V- groove, it was possible to separate and control the emission by focusing the radiation or directing different spectral bands in different directions.

Auditory stream segregation of amplitude-modulated narrowband noise in cochlear implant users and individuals with normal hearing.

Voluntary stream segregation was investigated in cochlear implant (CI) users and normal-hearing (NH) listeners using a segregation-promoting objective approach which evaluated the role of spectral and amplitude-modulation (AM) rate separations on stream segregation and its build-up. Sequences of 9 or 3 pairs of A and B narrowband noise (NBN) bursts were presented which differed in either center frequency of the noise band, the AM-rate, or both. In some sequences (delayed sequences), the last B burst was delayed by 35 ms from their otherwise-steady temporal position. In the other sequences (no-delay sequences), the last B bursts were temporally advanced from 0 to 10 ms. A single interval yes/no procedure was utilized to measure participants’ sensitivity () in identifying delayed vs. no-delay sequences. A higher value showed the higher ability to segregate the A and B subsequences. For NH listeners, performance improved with each spectral separation. However, for CI users, performance was only significantly better for the condition with the largest spectral separation. Additionally, performance was significantly poorer for the largest AM-rate separation than for the condition with no AM-rate separation for both groups. The significant effect of sequence duration in both groups indicated that listeners made more improvement with lengthening the duration of stimulus sequences, supporting the build-up effect. The results of this study suggest that CI users are less able than NH listeners to segregate NBN bursts into different auditory streams when they are moderately separated in the spectral domain. Contrary to our hypothesis, our results indicate that AM-rate separation may interfere with the segregation of streams of NBN. Additionally, our results add evidence to the literature that CI users build up stream segregation at a rate comparable to NH listeners, when the inter-stream spectral separations are adequately large.

Kiwi Plant Canker Diagnosis Using Hyperspectral Signal Processing and Machine Learning: Detecting Symptoms Caused by Pseudomonas syringae pv. actinidiae.

Pseudomonas syringae pv. actinidiae (Psa) has been responsible for numerous epidemics of bacterial canker of kiwi (BCK), resulting in high losses in kiwi production worldwide. Current diagnostic approaches for this disease usually depend on visible signs of the infection (disease symptoms) to be present. Since these symptoms frequently manifest themselves in the middle to late stages of the infection process, the effectiveness of phytosanitary measures can be compromised. Hyperspectral spectroscopy has the potential to be an effective, non-invasive, rapid, cost-effective, high-throughput approach for improving BCK diagnostics. This study aimed to investigate the potential of hyperspectral UV–VIS reflectance for in-situ, non-destructive discrimination of bacterial canker on kiwi leaves. Spectral reflectance (325–1075 nm) of twenty plants were obtained with a handheld spectroradiometer in two commercial kiwi orchards located in Portugal, for 15 weeks, totaling 504 spectral measurements. Several modeling approaches based on continuous hyperspectral data or specific wavelengths, chosen by different feature selection algorithms, were tested to discriminate BCK on leaves. Spectral separability of asymptomatic and symptomatic leaves was observed in all multi-variate and machine learning models, including the FDA, GLM, PLS, and SVM methods. The combination of a stepwise forward variable selection approach using a support vector machine algorithm with a radial kernel and class weights was selected as the final model. Its overall accuracy was 85%, with a 0.70 kappa score and 0.84 F-measure. These results were coherent with leaves classified as asymptomatic or symptomatic by visual inspection. Overall, the findings herein reported support the implementation of spectral point measurements acquired in situ for crop disease diagnosis.

Image segmentation and separation of spectrally similar dyes in fluorescence microscopy by dynamic mode decomposition of photobleaching kinetics

BackgroundImage segmentation in fluorescence microscopy is often based on spectral separation of fluorescent probes (color-based segmentation) or on significant intensity differences in individual image regions (intensity-based segmentation). These approaches fail, if dye fluorescence shows large spectral overlap with other employed probes or with strong cellular autofluorescence.ResultsHere, a novel model-free approach is presented which determines bleaching characteristics based on dynamic mode decomposition (DMD) and uses the inferred photobleaching kinetics to distinguish different probes or dye molecules from autofluorescence. DMD is a data-driven computational method for detecting and quantifying dynamic events in complex spatiotemporal data. Here, DMD is first used on synthetic image data and thereafter used to determine photobleaching characteristics of a fluorescent sterol probe, dehydroergosterol (DHE), compared to that of cellular autofluorescence in the nematode Caenorhabditis elegans. It is shown that decomposition of those dynamic modes allows for separating probe from autofluorescence without invoking a particular model for the bleaching process. In a second application, DMD of dye-specific photobleaching is used to separate two green-fluorescent dyes, an NBD-tagged sphingolipid and Alexa488-transferrin, thereby assigning them to different cellular compartments.ConclusionsData-based decomposition of dynamic modes can be employed to analyze spatially varying photobleaching of fluorescent probes in cells and tissues for spatial and temporal image segmentation, discrimination of probe from autofluorescence and image denoising. The new method should find wide application in analysis of dynamic fluorescence imaging data.

Multi-temporal spectral reflectance of tropical savanna understorey species and implications for hyperspectral remote sensing

• Spectra of savanna understorey vegetation species over phenological seasons. • Separability of functional groups, and species, depended on season. • Informs species separation and timing for drone-based hyperspectral captures. • VNIR drone spectral range excludes foliar traits such as cellular water absorption. The relationship between species phenology and spectral separability is essential to determine the optimal remote sensing sampling period to maximise spectral separability of vegetation species. However, this relationship in many native grasses, introduced grasses and herbs, shrubs, and vine species in tropical savannas is unknown. We measured the in-situ hyperspectral response of monospecific vegetation stands of several understorey species representing different functional groups over phenological stages (throughout dry and wet seasons) using a non-imaging spectrometer. We present a spectral library of both native and introduced species of a tropical savanna environment. We analysed the data using continuum removal to highlight absorption features. Most understorey species displayed a photosynthetic spectral response with increased greenness at the end of the wet season that progressively declined as vegetation dried out. For some species, there were seasonally dependent differences in absorption features with spectral differences between the late wet and early dry season, and late dry and early wet seasons. We resampled the data to the spectral range typical of drone-mounted hyperspectral sensors (i.e., 150 bands between 400 and 1000 nm), which omits the water absorption features of the SWIR. These findings suggest an ideal sampling period for measuring outdoor canopy reflectance of understorey species, which will promote methodological improvements of hyperspectral data capture. The use of a VNIR only hyperspectral sensor will exclude the important regions of the spectrum for plant spectral identification including non-pigment bands for water, nitrogen and cellulose. The results have implications for determining the completeness of rehabilitation assessing reestablishment among indigenous species across mine sites under rehabilitation.

Synergistic Combination of Charge Carriers and Energy-Transfer Processes in Plasmonic Photocatalysis.

Important efforts are currently under way in order to develop further the nascent field of plasmonic photocatalysis, striving for improved efficiencies and selectivities. A significant fraction of such efforts has been focused on distinguishing, understanding, and enhancing specific energy-transfer mechanisms from plasmonic nanostructures to their environment. Herein, we report a synthetic strategy that combines two of the main physical mechanisms driving plasmonic photocatalysis into an engineered system by rationally combining the photochemical features of energetic charge carriers and the electromagnetic field enhancement inherent to the plasmonic excitation. We do so by creating hybrid photocatalysts that integrate multiple plasmonic resonators in a single entity, controlling their joint contribution through spectral separation and differential surface functionalization. This strategy allows us to create complex hybrids with improved photosensitization capabilities, thanks to the synergistic combination of two photosensitization mechanisms. Our results show that the hot electron injection can be combined with an energy-transfer process mediated by the near-field interaction, leading to a significant increase in the final photocatalytic response of the material and moving the field of plasmonic photocatalysis closer to energy-efficient applications. Furthermore, our multimodal hybrids offer a test system to probe the properties of the two targeted mechanisms in energy-related applications such as the photocatalytic generation of hydrogen and open the door to wavelength-selective photocatalysis and novel tandem reactions.

Amplified Spontaneous Emission from Zwitterionic Excited-State Intramolecular Proton Transfer.

The unique four-level photocycle characteristics of excited-state intramolecular proton transfer (ESIPT) materials enable population inversion and large spectral separation between absorption and emission through their respective enol and keto forms. This leads to minimal or no self-absorption losses, a favorable feature in acting as an optical gain medium. While conventional ESIPT materials with an enol-keto tautomerism process are widely known, zwitterionic ESIPT materials, particularly those with high photoluminescence, are scarce. Facilitated by the synthesis and characterization of a new family of 2-hydroxyphenyl benzothiazole (HBT) with fluorene substituents, HBT-Fl1 and HBT-Fl2, we herein report the first efficient zwitterionic ESIPT lasing material (HBT-Fl2). The zwitterionic ESIPT HBT-Fl2 not only shows a remarkably low solid-state amplified spontaneous emission (ASE) threshold of 5.3 μJ/cm2 with an ASE peak at 609 nm but also exhibits high ASE photostability. Coupled with its substantially large Stokes shift (≈236 nm ≈10,390 cm-1) and an extremely small overlap of excited-state absorption with ASE emission, comprehensive density functional theory (DFT) and time-dependent DFT studies reveal the zwitterionic characteristics of HBT-Fl2. In opposition to conventional ESIPT with π-delocalized tautomerism as observed in analogue HBT-Fl1 and parent HBT, HBT-Fl2 instead shows charge redistribution in the proton transfer through the fluorene conjugation. This structural motif provides a design tactic in the innovation of new zwitterionic ESIPT materials for efficient light amplification in red and longer-wavelength emission.

Laser-induced fluorescence of PO-photofragments of dimethyl methylphosphonate.

The paper presents the results of calculating the absorption spectrum of a phosphorus monoxide (PO) molecule corresponding to the A2Σ+(v'=0)-X2Π(v''=0) transition. The efficiency of excitation of the PO molecule is estimated as a function of the spectral parameters of the laser radiation. The positions of the fluorescence bands of PO are calculated. It is shown that the use of excitation wavelengths near the bandheads of the (P22+Q12) and P12 branches of the A2Σ+(v'=0)-X2Π3/2(v''=0) band provides spectral separation of the (0, 1) γ-band of the LIF of PO and vibrational-rotational Raman spectrum of atmospheric oxygen. The spectral responses of dimethyl methylphosphonate vapor in air under the action of KrF-laser radiation at a wavelength of 247.78 nm have been experimentally studied. In the wavelength range 252-260 nm, the (0, 1) γ-band of the LIF of PO-fragments and the vibrational-rotational band of spontaneous Raman scattering on oxygen molecules are unambiguously interpreted. It is shown that the results of calculations of the shape and position of the fluorescence spectra are in good agreement with the experimental data.

Impact of image acquisition lag-time on monitoring short-term postfire spectral dynamics in tropical savannas: the Campos Amazônicos Fire Experiment

The use of optical remote sensing to monitor short-term postfire dynamics in rapidly recovering ecosystems is a complex task, requiring strong spectral separability and high temporal resolution. Thus, characterizing the effects of temporal lag in image acquisitions in relation to fire occurrence is of fundamental importance to improve our understanding of how fire-related environmental and ecological processes are connected to multitemporal spectral information. We used a harmonized series of Sentinel-2A/2B and Landsat-7/8 images paired with a fire experiment specifically designed for remote sensing assessment to evaluate short-term spectral responses of fire in tropical savannas. Our experimental design included strategic geolocation of experimental plots and separate prescribed burnings during the early- and mid-dry seasons. The M-statistic was used to assess spectral separability between burned and control treatments, and the temporal stability was assessed using fire scar latency, a custom statistic adapted to capture scar spectral responses over time. Finally, linear regressions were fitted to explore the association between consumed fuel load and spectral indices. The variation of normalized burn ratio and mid-infrared bispectral index indices stood out with the highest separability (M-statistic > 3) in both treatments and moderate-high association with consumed fuel load (R2 > 0.54). We observed that burned areas spectral sensitivity depends not only on timing of image acquisition but also on fire season and their interactions with land surface phenology. Our characterization provides a solid benchmark for future experimental assessments of fire spectral responses and provides new insights for improving fire monitoring algorithms.

Remote Sensing of Coastal Vegetation Phenology in a Cold Temperate Intertidal System: Implications for Classification of Coastal Habitats

Intertidal vegetation provides important ecological functions, such as food and shelter for wildlife and ecological services with increased coastline protection from erosion. In cold temperate and subarctic environments, the short growing season has a significant impact on the phenological response of the different vegetation types, which must be considered for their mapping using satellite remote sensing technologies. This study focuses on the effect of the phenology of vegetation in the intertidal ecosystems on remote sensing outputs. The studied sites were dominated by eelgrass (Zostera marina L.), saltmarsh cordgrass (Spartina alterniflora), creeping saltbush (Atriplex prostrata), macroalgae (Ascophyllum nodosum, and Fucus vesiculosus) attached to scattered boulders. In situ data were collected on ten occasions from May through October 2019 and included biophysical properties (e.g., leaf area index) and hyperspectral reflectance spectra (Rrs(λ)). The results indicate that even when substantial vegetation growth is observed, the variation in Rrs(λ) is not significant at the beginning of the growing season, limiting the spectral separability using multispectral imagery. The spectral separability between vegetation types was maximum at the beginning of the season (early June) when the vegetation had not reached its maximum growth. Seasonal time series of the normalized difference vegetation index (NDVI) values were derived from multispectral sensors (Sentinel-2 multispectral instrument (MSI) and PlanetScope) and were validated using in situ-derived NDVI. The results indicate that the phenology of intertidal vegetation can be monitored by satellite if the number of observations obtained at a low tide is sufficient, which helps to discriminate plant species and, therefore, the mapping of vegetation. The optimal period for vegetation mapping was September for the study area.

Impact of computed tomography metal artifact reduction protocol on periprosthetic tissue characterization after total hip arthroplasty:A cadaveric study.

Metal artifact reduction (MAR) has improved computed tomography (CT) imaging of total hip arthroplasty (THA) but the assessment of osteolysis and implant to bone contact relies on the accurate depiction of bone defects, cancellous bone, and cement. This study evaluates the impact of available single and dual-energy protocols on periprosthetic tissue characterization in a cadaveric phantom. Bilateral THA was performed on a fresh frozen cadaveric pelvis with simulated osteolytic cavities. CT acquisitions with projection-basedMAR and noise equivalence were performed using single energy 140 kVp, single energy 150 kVp with 0.6 mm tin filtration, and dual-energy at 100/150 kVp with 0.6 mm tin filtration, from which simulated energies were extracted. Image subtraction, segmentation, region of interest histograms, and line profiles were used to characterize tissue density and separation. Tissue densities were heavily dependent on the energy profile of the protocol. Cancellous bone ranged from 182to 45 HU and cement from 1012to 131 HU using 140 kVp compared to dual-energy with weighted high energy tube, respectively. Spectral separation between cancellous bone, osteolytic defect, and cement was reduced for all protocols compared with 140 kVp. Spectral overlap was most severe using dual-energy with heavily weighted high-energy tubes. Dual-energy algorithms reduced trabecular contrast within the cancellous bone and cortical edge response. Althoughthe dual-energy acquisition has been proposed as an additive to projection-based MAR techniques in THA, reduced density and contrast in clinically relevant periprosthetic tissue compared to 140 kVp single energy may limit its use in characterizing periprosthetic tissues.

Investigation on the self-starting Mamyshev oscillator with low threshold

We have demonstrated a mode-locked Mamyshev oscillator based on a single-mode-fibre configuration with low threshold. The influences of spectral filters and the pump power on the output laser characteristics have been investigated. In the case with spectral-filter central wavelength far away from the peak of gain spectrum, the laser pulses have deviated slightly from similariton evolution with gain-shaping under high pump power. Moreover, self-starting mode-locking operation has been realized when the pump power was only 520 mW, which might benefit from the appropriate polarization conditions and small spectral filter separation. The laser can generate 1.89 nJ ultra-short pulses with 20 dB spectral width of 54.6 nm. The pulse duration can be compressed externally to 64.69 fs with the peak power of 21.3 kW.

Spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A field-, laboratory-, and satellite-based approach to identifying cyanobacteria genera from remotely sensed data

Algal blooms around the world are increasing in frequency and severity, often with the possibility of adverse effects on human and ecosystem health. The health and economic impacts associated with harmful algal blooms, or HABs, provide compelling rationale for developing new methods for monitoring these events via remote sensing. Although concentrations of chlorophyll-a and key pigments like phycocyanin are routinely estimated from satellite images and used to infer algal or cyanobacterial cell counts, current methods are unable to provide information on the taxonomic composition of a bloom. This study introduced a new approach capable of differentiating among genera based on their reflectance characteristics: Spectral Mixture Analysis for Surveillance of HABs, or SMASH. The foundation of SMASH is a multiple endmember spectral mixture analysis (MESMA) algorithm that takes a library of cyanobacteria endmembers and a hyperspectral image as input and estimates the fractional abundance of each genus, plus water, on a per-pixel basis. Importantly, we assume that the water column consists of only pure water and cyanobacteria, implying that our linear spectral unmixing models do not account for other optically active constituents such as suspended sediment and colored dissolved organic matter (CDOM). We used reflectance spectra for 12 genera measured under a microscope to populate an algal spectral library and applied the SMASH workflow to satellite images from four waterbodies across the United States. Normalized spectral separability scores indicated that the 12 genera were distinct from one another and the MESMA algorithm reproduced known input fractions for simulated mixtures that included all pairwise combinations of genera and water. We used Upper Klamath Lake as an example to illustrate data products generated via SMASH: maps of the normalized difference chlorophyll index and cyanobacterial index, a MESMA-based classification of algal genera, fraction images for each endmember, and a root mean square error (RMSE) image that summarizes uncertainty. For Upper Klamath Lake, these outputs highlighted a complex algal bloom featuring several genera, primarily Aphanizomenon, and intricate spatial patterns associated with gyres. The maximum RMSE constraint imposed on the MESMA algorithm provided a means of avoiding false positive detection of genera not present in a waterbody but must not be set so low as to leave much of an image unclassified in cases where genera included in the library are present. Comparison of endmember fractions with relative biovolumes calculated from field samples indicated that taxonomic information from SMASH was consistent with field observations. For example, the algorithm successfully identified Microcystis in Owasco Lake but avoided misclassifying Asterionella, a genus not yet included in our library, in Detroit Lake. This proof-of-concept investigation demonstrates the potential of SMASH to enhance our understanding of algal blooms, particularly with respect to their spatial and temporal dynamics.

Temperature measurement of cloud or haze layers based on Raman rotational and vibrational spectra.

Pure rotational Raman lidar is often used for atmospheric temperature profile measurements. However, high elastic scattering suppression ratios (>107) are required for temperature measurement in clouds and haze, which imposes stringent requirements on spectral separation techniques. To solve this problem, a lidar measurement technique based on vibrational and rotational Raman spectra is proposed. Using nitrogen vibrational and rotational Raman scattering to obtain temperature profiles under strong elastic scattering, combined with the dual-rotational Raman temperature measurements under weak elastic scattering, a vertical distribution of atmospheric temperature including cloud and haze layers, can be obtained. The feasibility of the method was verified by numerical simulation. The Raman lidar for temperature measurements was established in Xi'an University of Technology, and the obtained temperature results show good agreement with the radiosonde measurements. The proposed method combines the high sensitivity of the dual-rotational Raman method and the high Mie-scattering suppression of the vibrational Raman method, thus further improving the adaptability of Raman lidar to cloudy and hazy air conditions and supporting atmospheric and cloud physics research.

Randomized Spectral Separation Coefficient for Short Code Acquisition Performance Evaluation

Reliable signal acquisition with low computational complexity is an important design objective for the evolution of global navigation satellite systems (GNSS). Most GNSS signals consist of long pseudorandom noise (PRN) codes whose acquisition is expensive in terms of memory, computation time, and energy. As these resources are particularly scarce in the emerging mass-market user segment, cyclostationary pilot signals with short PRN codes are an attractive option to keep the number of acquisition search bins low. However, reducing the code length degrades the acquisition performance, as multiple access interference (MAI) becomes more pronounced and can lead to an increased false alarm rate. We demonstrate that, quite different from stationary MAI, cyclostationary MAI does not affect each bin of the search space uniformly, and is therefore not easily modeled with the well-known spectral separation coefficient (SSC). We propose a new randomized SSC (SSC-R) based on code/Doppler interference functions, which can be used for simple and accurate acquisition performance evaluation. As an application example, we demonstrate how the SSC-R can be utilized in signal design to minimize the PRN code length under an acquisition performance constraint. We conclude that feasible PRN code lengths for GNSS can be on the order of 300-700.

Resolution Limit in Infrared Chemical Imaging.

Chemical imaging combines the spatial specificity of optical microscopy with the spectral selectivity of vibrational spectroscopy. Mid-infrared (IR) absorption imaging instruments are now able to capture high-quality spectra with microscopic spatial detail, but the limits of their ability to resolve spatial and spectral objects remain less understood. In particular, the sensitivity of measurements to chemical and spatial changes and rules for optical design have been presented, but the influence of spectral information on spatial sensitivity is as yet relatively unexplored. We report an information theory-based approach to quantify the spatial localization capability of spectral data in chemical imaging. We explicitly consider the joint effects of the signal-to-noise ratio and spectral separation that have significance in experimental settings to derive resolution limits in IR spectroscopic imaging.

Lateral spectrum splitting system with perovskite photovoltaic cells

We examine the potential of a multijunction spectrum-splitting photovoltaic (PV) solar energy system with perovskite PV cells. Spectrum splitting allows combinations of different energy band gap PV cells that are laterally separated and avoids the complications of fabricating tandem stack architectures. Volume holographic optical elements have been shown to be effective for the spectrum-splitting operation and can be incorporated into compact module packages. However, one of the remaining issues for spectrum splitting systems is the availability of low-cost wide band gap and intermediate band gap cells that are required for realizing high overall conversion efficiency. Perovskite PV cells have been fabricated with a wide range of band gap energies that potentially satisfy the requirements for multijunction spectrum-splitting systems. A spectrum-splitting system is evaluated for a combination of perovskite PV cells with energy band gaps of 2.30, 1.63, and 1.25 eV and with conversion efficiencies of 10.4%, 21.6%, and 20.4%, respectively, which have been demonstrated experimentally in the literature. First, the design of a cascaded volume holographic lens for spectral separation in three spectral bands is presented. Second, a rigorous coupled wave model is developed for computing the diffraction efficiency of a cascaded hologram. The model accounts for cross-coupling between higher diffraction orders in the upper and lower holograms, which previous models have not accounted for but is included here with the experimental verification. Lastly, the optical losses in the system are analyzed and the hypothetical power conversion efficiency is calculated to be 26.7%.