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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Tuning Shinkarev’s Bicycle: Separating the Parallel Cycles of Photosystem II Using Empirical Wavelet Transform

The oxygen-evolving complex (OEC) of Photosystem II (PSII) catalyzes light-driven water oxidation, a process necessary to sustain Earth’s atmospheric oxygen. Oxygen yields measured during single-turnover flash sequences exhibit period-four oscillations, which form the basis of the Joliot–Kok (S-state) model. However, when the oscillations of other processes contribute to the measured oxygen yield, fitting methods can conflate these signals and distort estimates of inefficiencies and initial S-state populations. To address this, we applied the empirical wavelet transform (EWT) as a model-independent method to separate overlapping oscillators and capture damping dynamics that are not well represented in Fourier analysis. We tested this framework on polarographic flash-oxygen traces from both our Synechocystis sp. PCC 6803 thylakoid membrane preparations and archival datasets on Chlorella and isolated chloroplasts. EWT consistently resolves the expected period-four component alongside a distinct binary oscillation. Simulations suggest that fitting this isolated period-four signal recovers VZAD parameters more accurately than analysis of raw traces, yielding different estimates for S-state distributions and transition probabilities. Notably, this binary oscillation aligns closely with semiquinone dynamics predicted solely from period-four fit parameters. These findings indicate that EWT can effectively distinguish complex signals in oxygen evolution, offering a framework potentially applicable to other spectroscopic probes of the S-state cycle.

Ferrari, Nicholas [Louisiana State Univ., Baton Ro↗
DOE OSTI2023

Airborne flux measurements of ammonia over the southern Great Plains using chemical ionization mass spectrometry

Abstract. Ammonia (NH3) is an abundant trace gas in the atmosphere and an important player in atmospheric chemistry, aerosol formation and the atmosphere–surface exchange of nitrogen. The accurate determination of NH3 emission rates remains a challenge, partly due to the propensity of NH3 to interact with instrument surfaces, leading to high detection limits and slow response times. In this paper, we present a new method for quantifying ambient NH3, using chemical ionization mass spectrometry (CIMS) with deuterated benzene cations as reagents. The setup aimed at limiting sample–surface interactions and achieved a 1σ precision of 10–20 pptv and an immediate 1/e response rate of < 0.4 s, which compares favorably to the existing state of the art. The sensitivity exhibited an inverse humidity dependence, in particular in relatively dry conditions. Background of up to 10 % of the total signal required consideration as well, as it responded on the order of a few minutes. To showcase the method's capabilities, we quantified NH3 mixing ratios from measurements obtained during deployment on a Gulfstream I aircraft during the HI-SCALE (Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems) field campaign in rural Oklahoma during May 2016. Typical mixing ratios were 1–10 parts per billion by volume (ppbv) for the boundary layer and 0.1–1 ppbv in the lower free troposphere. Sharp plumes of up to tens of ppbv of NH3 were encountered as well. We identified two of their sources as a large fertilizer plant and a cattle farm, and our mixing ratio measurements yielded upper bounds of 350 ± 50 and 0.6 kg NH3 h−1 for their respective momentary source rates. The fast response of the CIMS also allowed us to derive vertical NH3 fluxes within the turbulent boundary layer via eddy covariance, for which we chiefly used the continuous wavelet transform technique. As expected for a region dominated by agriculture, we observed predominantly upward fluxes, implying net NH3 emissions from the surface. The corresponding analysis focused on the most suitable flight, which contained two straight-and-level legs at ∼ 300 m above ground. We derived NH3 fluxes between 1 and 11 mol km−2 h−1 for these legs, at an effective spatial resolution of 1–2 km. The analysis demonstrated how flux measurements benefit from suitably arranged flight tracks with sufficiently long straight-and-level legs, and it explores the detrimental effect of measurement discontinuities. Following flux footprint estimations, comparison to the NH3 area emissions inventory provided by the U.S. Environmental Protection Agency indicated overall agreement but also the absence of some sources, for instance the identified cattle farm. Our study concludes that high-precision CIMS measurements are a powerful tool for in situ measurements of ambient NH3 mixing ratios, and even allow for the airborne mapping of the air–surface exchange of NH3.

58 GEOSCIENCES↗
DOE OSTI2022

Airborne flux measurements of ammonia over the Southern Great Plains using chemical ionization mass spectrometry

Ammonia (NH 3 ) is an abundant trace gas in the atmosphere and an important player in atmospheric chemistry, aerosol formation and the atmosphere-surface exchange of nitrogen. It is recognized as a major source of aerosol pollution, and it may limit the formation of cloud nuclei in remote or cold parts of the atmosphere. For soil and plants, NH 3 -mediated nitrogen can act as a harmful pollutant or as a desirable nutrient, mostly in natural and agricultural settings, respectively. Agriculture is also the main source of atmospheric NH 3 via volatilization from fertilizers and manure processing in livestock farming. The accurate determination of NH 3 emission rates remains a challenge, partly due to the propensity of NH 3 to interact with instrument surfaces leading to high detection limits and slow response times. In this paper, we present a new method for quantifying ambient NH 3 , using chemical ionization mass spectrometry (CIMS) with deuterated benzene cations as reagents. The setup aimed at limiting sample-surface interactions and achieved a 1-σ precision of 10–20 pptv and an immediate 1/e response rate < 0.4 s, which compares favorably to the existing state of the art. The sensitivity exhibited an inverse humidity dependence, in particular in relatively dry conditions. Background of up to 10 % of the total signal required consideration as well, as it responded on the order of a few minutes. To showcase the method’s capabilities, we quantified NH3 mixing ratios from measurements obtained during deployment on a Gulfstream I aircraft during the HI-SCALE (Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems) field campaign in rural Oklahoma during May 2016. Typical mixing ratios were 1–10 parts per billion by volume (ppbv) for the boundary layer and 0.1–1 ppbv in the lower free troposphere. Sharp plumes of up to 10s of ppbv of NH3 were encountered as well. We identified two of their sources as a large fertilizer plant and a cattle farm, and our mixing ratio measurements yielded upper bounds of 350 ± 50 and 0.6 kg NH 3 h –1 for their respective momentary source rates. The fast response of the CIMS also allowed us to derive vertical NH 3 fluxes within the turbulent boundary layer via eddy covariance, for which we chiefly used the continuous wavelet transform technique. As expected for a region dominated by agriculture, we observed predominantly upward fluxes, implying net NH 3 emissions from surface. The corresponding analysis focused on the most suitable flight, which contained two straight-and-level legs at ~300 m above ground. We derived NH 3 fluxes between –4 and 18 mol km –2 h –1 for these legs, at an effective spatial resolution of 1–2 km. The analysis demonstrated how flux measurements benefit from suitably arranged flight tracks with sufficiently long straight-and-level legs, and explores the detrimental effect of measurement discontinuities. Following flux footprint estimations, comparison to the NH 3 area emissions inventory provided by the US Environmental Protection Agency indicated overall agreement, but also the absence of some sources, for instance the identified cattle farm. Our study concludes that high-precision CIMS measurements are a powerful tool for in-situ measurements of ambient NH 3 mixing ratios, and even allow for the airborne mapping of the air-surface exchange of NH 3 .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Robustness of the Galactic Center excess morphology against masking

The Galactic Center excess (GCE) remains an enduring mystery, with leading explanations being annihilating dark matter or an unresolved population of millisecond pulsars. Analyzing the morphology of the GCE provides critical clues to identify its exact origin. We investigate the robustness of the inferred GCE morphology against the effects of masking, an important step in the analysis where the gamma-ray emission from point sources and the galactic disk are excluded. Using different masks constructed from Fermi point source catalogs and a wavelet method, we find that the GCE morphology, particularly its ellipticity and cuspiness, is relatively independent of the choice of mask for energies above 2–3 GeV. The GCE morphology systematically favors an approximately spherical shape, as expected for dark matter annihilation. Compared to various stellar bulge profiles, a spherical dark matter annihilation profile better fits the data across different masks and galactic diffuse emission backgrounds, except for the stellar bulge profile which provides a similar fit to the data. Modeling the GCE with two components, one from dark matter annihilation and one tracing the Coleman bulge, we find this two-component model outperforms any single component or combinations of dark matter annihilation and other stellar bulge profiles. Uncertainty remains about the exact fraction contributed by each component across different background models and masks. Furthermore, when the Coleman bulge dominates, its corresponding spectrum lacks characteristics typically associated with millisecond pulsars, suggesting that it mostly models the emission from other sources instead of the GCE that is still present and spherically symmetric.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Limits of Detection for EXAFS Characterization of Heterogeneous Single-Atom Catalysts

Single-atom catalysts (SACs), consisting of individual metal atoms dispersed on a support, attract attention due to their unique reactivity, efficient use of precious metals, and precise chemical tunability. Characterization of the metal species is crucial to substantiate structure–function relationships. Authors often use—and referees often require—X-ray absorption spectroscopy (XAS) data to prove the absence of clustered metal (or metal oxide) structures after pre-treatment and under in situ or operando conditions. However, there has been no critical assessment of the limitations of XAS in substantiating such conclusive statements, which is particularly important given the potential outsized influence of minority catalyst structures in dictating catalytic activity. Here, in this article, we quantitatively assess the detection limits of XAS to identify metal (or metal oxide) clusters in samples containing predominantly single atoms by modeling the extended X-ray absorption fine structure (EXAFS) of mixtures of structures. We identified that a significant fraction of clusters can coexist with SAC active sites (e.g., ~10% metallic Pt or ~40% oxidized Pt clusters in Pt/CeO 2 SACs), while eluding detection via EXAFS with any statistical significance. To generalize these conclusions, a descriptor-based screening of bulk metal oxides using a continuous Cauchy wavelet transform was proposed that suggests certain materials for which differentiating atomically dispersed metal species and metal oxide clusters would be infeasible by EXAFS (e.g., ReO x ). Based on this analysis, we suggest best practices for the study of SACs using EXAFS and provide recommendations to ensure that conclusions do not outpace the evidence used to support them. In this rapidly expanding research area, rigorous characterization will lead to greater understanding of the behavior of SACs and ultimately improved catalytic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

SPARR: Spiking/Processing Array for Wide Dynamic Range and High Resolution Photonic Sensing

The Spiking/Processing Array (spARR) is a novel photonic focal plane that uses pixels which generate electronic spikes autonomously and without a clock. These spikes feed into a network of digital asynchronous processing elements or DAPES. By building a useful assemblage of DAPES, and connecting them together in the correct way, sophisticated signal processing can be accomplished within the focal plane. Autonomous self-resetting pixels (AsP) enable SPARR to generate electronic response with very small signals--as little as a single photon in the case of Geiger mode avalanche photodiodes to as few as several hundred photons for in-cmos photodetectors. These spiking pixels enable fast detector response, but do not draw as much continuous power as synchronous clocked designs. The spikes emitted by the pixels all have the same magnitude, the information from the scene is effectively encoded into the rate of spikes and the time at which the spike is emitted. The spiking pixels, having converted incident light into electronic spikes, supply the spikes to a network of digital asynchronous processors. These are small state machines which respond to the spikes arriving at their input ports by either remaining unchanged or updating their internal state and possibly emitting a spike on one or more output ports. We show a design that accomplishes the sophisticated signal processing of a Haar spatial wavelet transform with spatial-spectral whitening. We furthermore show how this design results in a data streams which support imaging and transient optical source detection. Two simulators support this analysis: SPICE and sparrow. The CMOS SPICE simulator Cadence provides accurate CMOs design with accounting for effects of circuit parasitics throughout layout, accurate timing, and accurate energy consumption estimates. To more rapidly assess larger networks with more pixels, sparrow is a custom discrete event simulator that supports the non-homogeneous Poisson processes that lie behind photoelectric interaction. Sparrow is a photon-exact simulator that nevertheless performs SPARR system simulator for large-scale systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

PySolate : A Python‐Based Thresholding Tool to Denoise or Designal Seismic Waveforms Based on the Continuous Wavelet Transform

PySolate is a Python‐based toolset that implements the continuous wavelet transform and nonlinear thresholding operations to denoise or designal seismic data, following Langston and Mousavi (2019). This filtering approach can remove microseismic noise to isolate intermediate‐period seismic signals that are key to enabling full‐waveform modeling and analysis of smaller‐magnitude regional events. This approach is best for the application to signals with frequency or time separation of signal and noise, in contrast to Fourier analysis, which is effective when signal and noise are separated in frequency. We demonstrate the Python toolset using the six announced Democratic People’s Republic of Korea declared nuclear tests, showing the effectiveness of isolating the seismic signal compared to standard bandpass filtering. In conclusion, we also demonstrate the ease of using the toolset with any Python processing tools.

Asia↗
DOE OSTI2021

Scale-dependent spatial variabilities of hydrological exchange flows and transit time in a large regulated river

Hydrological exchange flows (HEF) across the river-aquifer interface and the associated residence time of river water in the aquifer have important implications for contaminant plume migration and biogeochemical processes in the river corridor. HEFs and residence time are influenced by both subsurface physical features and hydrologic forcing related to the transport process, which can exhibit complex spatial and temporal variations. In this study, we used a massively parallel subsurface flow model and a particle-tracking model to study the influences of different control factors on spatial variability of HEFs and residence time distributions (RTD) in the Hanford Reach of the Columbia River in Washington State. A total number of 100M particles were randomly injected in time and space and then tracked in a model domain that covers a 51-km 2 area (15.1M model cells). We used hourly river stages and groundwater levels to drive the model to provide dynamic velocity fields for the particle tracking in the simulation period that was longer than 2 years. The groundwater flow simulation and particle-tracking results provide the first comprehensive assessment of the spatial distribution of HEFs and residence time in large complex river corridors. Overall, our results show that the aquifer hydrogeological structure has the strongest correlation with the extent and magnitude of exchange flux. The residence time exhibits complex patterns that are impacted by all the river geomorphologic, hydrodynamic, and hydrogeologic factors and are strongly correlated with the downwelling ratio of exchange flux. The new insights gained through this study can be used to support the development of reduced-order models of HEFs and RTDs for large complex river systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Deep neural operators can predict the real-time response of floating offshore structures under irregular waves

The use of neural operators in a digital twin model of an offshore floating structure holds the potential for a significant shift in the prediction of structural responses and health monitoring, offering valuable real-time control insights. In this work, we investigate the effectiveness of three neural operators, namely the deep operator network (DeepONet), the Fourier neural operator (FNO), and the Wavelet neural operator (WNO), to accurately capture the responses of a floating structure under six different sea state codes (3 − 8) based on the wave characteristics described by the World Meteorological Organization (WMO). To further enhance the accuracy of the vanilla architecture of the neural operators, novel extensions, such as wavelet-DeepONet and self-adaptive WNO, are proposed in this paper. The results demonstrate that these high-precision neural operators can deliver structural responses more efficiently, up to two orders of magnitude faster than a dynamic analysis using conventional numerical solvers. Additionally, compared to gated recurrent units (GRUs), a commonly used recurrent neural network for time-series estimation, neural operators are both more accurate and efficient, especially in situations with limited data availability. Taken together, our study shows that FNO outperforms all other operators for approximating the mapping of one input functional space to the output space as well as for responses that have small bandwidth of the frequency spectrum. Conversely, DeepONet, with historical states, proves most accurate in learning the mapping of multiple input functions to the output space and capturing responses within a broad frequency spectrum.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Waveform retrieval for ultrafast applications based on convolutional neural networks

Electric field waveforms of light carry rich information about dynamical events on a broad range of timescales. The insight that can be reached from their analysis, however, depends on the accuracy of retrieval from noisy data. In this article, we present a novel approach for waveform retrieval based on supervised deep learning. We demonstrate the performance of our model by comparison with conventional denoising approaches, including wavelet transform and Wiener filtering. The model leverages the enhanced precision obtained from the nonlinearity of deep learning. The results open a path toward an improved understanding of physical and chemical phenomena in field-resolved spectroscopy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Disentangling the Effects of Temperature, Moisture, and Substrate Availability on Soil CO 2 Efflux

Soil respiration ($R_s$), the largest carbon emission flux in ecosystems, is usually modeled as an empirically parameterized function of temperature, and sometimes water availability. The likely contribution by other factors, such as carbohydrate substrate supply from photosynthesis, has been recognized, but modeling capacity to use this information is limited. Wavelet transformations of the residuals of a seasonal Q 10 temperature response model indicated structure at different temporal scales. In this work, we hypothesize that this indicates the lack of explicit representation of relevant processes in the models. Using cross-spectral analysis, we found that time series of photosynthetically active radiation explained most of the diurnal variation, temperature, explained variability at multiple time scales (diurnal-synoptic), whereas volumetric soil water content correlated with variability in Rs at scales 15-30 days. The results suggest that the time domains of influence for different driving variables of $R_s$ are discrete, and largely nonoverlapping, and represent functional relationships between soil biological activity and its constraints. Analysis of phase angles showed that $R_s$ lagged photosynthetically active radiation by 1.5-3.0 hr. As this time lag was the same in both young and mature trees, with more than fivefold difference in transport distances, we hypothesize that this finding adds to the body of literature that support the pressure-concentration-wave model of carbohydrate availability in plants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Towards automated and real-time multi-object detection of anguilliform fishes from sonar data using YOLOv8 deep learning algorithm

Eels (Anguilla spp.), including American eels (Anguilla rostrata), European eels (Anguilla anguilla), and Japanese eels (Anguilla japonica), are species of critical management and regulatory concern due to their vulnerability to various stressors during downstream migrations. Accurate and efficient detection of migrating eels can improve our understanding of fish behaviors and fish-hydraulic structure interactions, thereby facilitating the design, operation, and optimization of more effective downstream passage facilities from both biological and economic perspectives. However, a real-time, automated framework for detecting migrating eels in real-world applications is currently lacking. Leveraging imaging sonar as a reliable technology for fish passage monitoring, field data are acquired using imaging sonar and then converted to single sonar frames/images for subsequent analysis. In this study, a framework based on the You Only Look Once Version 8 (YOLOv8)-based convolutional neural network is proposed for multi-object detection of eels and non-eel fish using the sonar images after image subtraction and additional wavelet denoising. The results from both training and testing phases demonstrate that the framework's ability can successfully detect both eels and non-eel fish in preprocessed sonar images, achieving F1-scores and mAP@0.50 exceeding 0.84. Additionally, the incorporation of wavelet denoising during preprocessing slightly improve detection performance. Furthermore, the transferability of this framework from eel to lamprey detection is demonstrated to be feasible given the similar morphological characteristics of these two species. Overall, the proposed framework achieves accurate and efficient detection of migrating eels, providing reliable and real-time information that can help conserve vulnerable eel and eel-like populations.

Deep learning↗
DOE OSTI2024

Denoising Seismic Waveforms Using a Wavelet-Transform-Based Machine-Learning Method

Seismic waveform data recorded at stations can be thought of as a superposition of the signal from a source of interest and noise from other sources. Frequency‐based filtering methods for waveform denoising do not result in desired outcomes when the targeted signal and noise occupy similar frequency bands. Recently, denoising techniques based on deep‐learning convolutional neural networks (CNNs), in which a recorded waveform is decomposed into signal and noise components, have led to improved results. These CNN methods, which use short‐time Fourier transform representations of the time series, provide signal and noise masks for the input waveform. These masks are used to create denoised signal and designaled noise waveforms, respectively. However, advancements in the field of image denoising have shown the benefits of incorporating discrete wavelet transforms (DWTs) into CNN architectures to create multilevel wavelet CNN (MWCNN) models. The MWCNN model preserves the details of the input due to the good time–frequency localization of the DWT. In this report we use a data set of over 382,000 constructed seismograms recorded by the University of Utah Seismograph Stations network to compare the performance of CNN and MWCNN‐based denoising models. Evaluation of both models on constructed test data shows that the MWCNN model outperforms the CNN model in the ability to recover the ground‐truth signal component in terms of both waveform similarity and preservation of amplitude information. Model evaluation of real‐world data shows that both the CNN and MWCNN models outperform standard band‐pass filtering (BPF; average improvement in signal‐to‐noise ratio of 9.6 and 19.7 dB, respectively, with respect to BPF). Evaluation of continuous data suggests the MWCNN denoiser can improve both signal detection capabilities and phase arrival time estimates.

58 GEOSCIENCES↗
DOE OSTI2021

Evaluating Scalograms for Seismic Event Denoising

Denoising contaminated seismic signals for later processing is a fundamental problem in seismic signals analysis. The most straightforward denoising approach, using spectral filtering, is not effective when noise and seismic signal occupy the same frequency range. Neural network approaches have shown success denoising local signal when trained on short-time Fourier transform spectrograms (Zhu et al 2018; Tibi et al 2021). Scalograms, a wavelet-based transform, achieved ~15% better reconstruction as measured by dynamic time warping on a seismic waveform test set than spectrograms, suggesting their use as an alternative for denoising. We train a deep neural network on a scalogram dataset derived from waveforms recorded by the University of Utah Seismograph Stations network. We find that initial results are no better than a spectrogram approach, with additional overhead imposed by the significantly larger size of scalograms. A robust exploration of neural network hyperparameters and network architecture was not performed, which could be done in follow on work.

58 GEOSCIENCES↗
DOE OSTI2023

Engineering Relaxor Behavior in (BaTiO 3 ) n /(SrTiO 3 ) n Superlattices

Complex-oxide superlattices provide a pathway to numerous emergent phenomena because of the juxtaposition of disparate properties and the strong interfacial interactions in these unit-cell-precise structures. This is particularly true in superlattices of ferroelectric and dielectric materials, wherein new forms of ferroelectricity, exotic dipolar textures, and distinctive domain structures can be produced. Here, in this study, relaxor-like behavior, typically associated with the chemical inhomogeneity and complexity of solid solutions, is observed in (BaTiO 3 ) n /(SrTiO 3 ) n (n = 4–20 unit cells) superlattices. Dielectric studies and subsequent Vogel–Fulcher analysis show significant frequency dispersion of the dielectric maximum across a range of periodicities, with enhanced dielectric constant and more robust relaxor behavior for smaller period n. Bond-valence molecular-dynamics simulations predict the relaxor-like behavior observed experimentally, and interpretations of the polar patterns via 2D discrete-wavelet transforms in shorter-period superlattices suggest that the relaxor behavior arises from shape variations of the dipolar configurations, in contrast to frozen antipolar stripe domains in longer-period superlattices (n = 16). Moreover, the size and shape of the dipolar configurations are tuned by superlattice periodicity, thus providing a definitive design strategy to use superlattice layering to create relaxor-like behavior which may expand the ability to control desired properties in these complex systems.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Data Integration in Poplar: ‘Omics Layers and Integration Strategies

Populus trichocarpa is an important biofuel feedstock that has been the target of extensive research and is emerging as a model organism for plants, especially woody perennials. This research has generated several large ‘omics datasets. However, only few studies in Populus have attempted to integrate various data types. This review will summarize various ‘omics data layers, focusing on their application in Populus species. Subsequently, network and signal processing techniques for the integration and analysis of these data types will be discussed, with particular reference to examples in Populus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Coarse-grain cluster analysis of tensors with application to climate biome identification

A tensor provides a concise way to codify the interdependence of complex data. Treating a tensor as a d-way array, each entry records the interaction between the different indices. Clustering provides a way to parse the complexity of the data into more readily understandable information. Clustering methods are heavily dependent on the algorithm of choice, as well as the chosen hyperparameters of the algorithm. However, their sensitivity to data scales is largely unknown. In this work, we apply the discrete wavelet transform to analyze the effects of coarse-graining on clustering tensor data. We are particularly interested in understanding how scale affects clustering of the Earth's climate system. The discrete wavelet transform allows classification of the Earth's climate across a multitude of spatial-temporal scales. The discrete wavelet transform is used to produce an ensemble of classification estimates, as opposed to a single classification. Each element of the ensemble is a clustering at a different spatial-temporal scale. Information theoretic approaches are used to identify important scale lengths in clustering the L15 Climate Datset. We also discover a sub-collection of the ensemble that spans the majority of the variance observed, allowing for efficient consensus clustering techniques that can be used to identify climate biomes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Multi-scale fusion algorithm of intensity and polarization-difference images based on edge information enhancement

To better integrate complementary and redundant information from different source images, improve the edge information, and facilitate the target detection. A multi-scale fusion algorithm of intensity and polarization-difference (PD) images based on edge information enhancement is proposed. Firstly, intensity images are obtained by the polarization information analysis method. PD images are obtained by the adaptive polarization-difference imaging approach based on the principle of minimum mutual information. Secondly, guided filter, affine transformations and Block-Matching and 3D filtering are embedded in visibility enhancement to improve the intensity and PD images. Thirdly, the two images are decomposed into high-frequency and low-frequency images by the dual-tree complex wavelet transform (DT-CWT). The high-frequency and low-frequency images are fused by the fusion rules based on edge detection and the regional variance matching degree respectively. Finally, the fusion image is obtained by the inverse DT-CWT. Experimental results demonstrate that fusion images of the proposed algorithm are significantly improved in information entropy, average gradient, and spatial frequency. Compared with the existing methods, it can achieve a better edge enhancement for images in a turbid medium.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗