Multiband Model Research Articles

Assessing the feasibility of mapping channel bathymetry of long river corridors from hyperspectral data across a range of flow conditions

ABSTRACT Hyperspectral imagery is a promising tool for bathymetric retrieval because that might be more robust than traditional imagery. However, in fluvial sciences, this approach has only been applied to short reaches for which sparse bathymetric information is available and studies extrapolating such models to longer reaches are lacking. In this paper, we use linear regression and random forest (RF) regression to derive bathymetric estimates from hyperspectral data acquired under two different flow conditions: an unmanned aerial vehicle (UAV) acquisition at 27 m3.s−1 (base flow) and an airplane acquisition at 127 m3.s−1 (mean flow) for reaches of 0.35 km and 20 km, respectively. Using a 2D bathymetric model based on a green LiDAR acquisition, we generate calibration and validation data for the 20 km reach and the two flow conditions. First, we show that the airplane dataset is able to retrieve base flow bathymetry with a similar accuracy to the UAV acquisition (5–10 cm) despite having been acquired at a higher discharge. Then, we show that we can extrapolate bathymetric models calibrated on a smaller reach to the 20 km study reach with a loss in accuracy compared to an RF model calibrated on the study reach (20–30 cm vs. 5–10 cm). By analysing the errors from our models, we show that the accuracy of the extrapolated models dropped due to an underprediction of deep pool areas and changes in the optical properties of the water column and substratum across the study reach, with some spectral bands performing more poorly. The better performance of the random forest calibrated on the study reach led us to suggest using multi-band machine learning models when attempting bathymetric predictions over long river corridors, and highlights the need to cover the range of environmental conditions in the target reach when acquiring field data.

Score-matching neural networks for improved multi-band source separation

We present the implementation of a score-matching neural network that represents a data-driven prior for non-parametric galaxy morphologies. The gradients of this prior can be incorporated in the optimization of galaxy models to aid with tasks like deconvolution, inpainting or source separation. We demonstrate this approach with modification of the multi-band modeling framework scarlet that is currently employed as deblending method in the pipelines of the HyperSuprimeCam survey and the Rubin Observatory. The addition of the prior avoids the requirement of non-differentiable constraints, which can lead to convergence failures we discovered in scarlet. We present the architecture and training details of our score-matching neural network and show with simulated Rubin-like observations that using a data-driven prior outperforms the baseline scarlet method in accuracy of total flux and morphology estimates, while maintaining excellent performance for colors. We also demonstrate significant improvements in the robustness to inaccurate initializations. The trained score models used for this analysis are publicly available at https://github.com/SampsonML/galaxygrad.

Variational approach for the two-body problem in a multiband extended-Hubbard model

Considering a spin-up and a spin-down fermion in a generic tight-binding lattice with a multi-site basis, we investigate the two-body problem using a multiband extended-Hubbard model with finite-ranged hopping and interaction parameters. We derive a linear eigenvalue problem for the entire two-body spectrum, alongside a nonlinear eigenvalue problem for the bound states in the form of a self-consistency equation. Our results, based on an exact variational approach, suggest potential applications across various lattice geometries. As an illustration, we apply them to the linear-chain model and show that the resultant spin singlet and triplet bound states align well with the existing literature.

Multi-band image synchronous fusion model based on task-interdependency

Synchronous multi-band image fusion is a challenging, yet urgent task in the development of high-precision detection systems. This study proposes a novel method for synchronous fusion modeling of multi-band images based on task-interdependency. In the proposed method, the task of image fusion is divided into two mutually exclusive sub-tasks that produce bright thermal targets and obtain precise textural details. First, two generators with different network structures and several discriminators produce a preliminary fused image. Second, an image fusion strategy is defined using a model- and data-driven theory to obtain fused images. Then, each discriminator classifies the fused image and source images of each band to force the generators to produce the desired results. A novel loss function is constructed to enhance the fused effect by selecting the most significant gradient loss and loss of brightness. Finally, the network is trained based on a multi-generative adversarial framework.The trained generators can be used individually or jointly as a model for fusing multiple images. We verified our method with several datasets and determined that it outperforms other current methods.

Digital cancellation of multi-band passive inter-modulation based on Wiener-Hammerstein model

Utilizing multi-band and multi-carrier techniques enhances throughput and capacity in Long-Term Evolution (LTE)-Advanced and 5G New Radio (NR) mobile networks. However, these techniques introduce Passive Inter-Modulation (PIM) interference in Frequency-Division Duplexing (FDD) systems. In this paper, a novel multi-band Wiener-Hammerstein model is presented to digitally reconstruct PIM interference signals, thereby achieving effective PIM Cancellation (PIMC) in multi-band scenarios. In the model, transmitted signals are independently processed to simulate Inter-Modulation Distortions (IMDs) and Cross-Modulation Distortions (CMDs). Furthermore, the Finite Impulse Response (FIR) filter, basis function generation, and B-spline function are applied for precise PIM product estimation and generation in multi-band scenarios. Simulations involving 4 carrier components from diverse NR frequency bands at varying transmitting powers validate the feasibility of the model for multi-band PIMC, achieving up to 19 dB in PIMC performance. Compared to other models, this approach offers superior PIMC performance, exceeding them by more than 5 dB in high transmitting power scenarios. Additionally, its lower sampling rate requirement reduces the hardware complexity associated with implementing multi-band PIMC.

Continental-scale mapping of soil pH with SAR-optical fusion based on long-term earth observation data in google earth engine

The advent of cloud computing platforms (e.g., Google Earth Engine (GEE)) and the massive amounts of optical and radar Earth Observation (EO) data hosted by these platforms present new opportunities for mapping soil pH at large scales. However, existing studies generally lack consensus on the effects of satellite and radar sensor parameters on GEE-based soil pH prediction models. In this study, we assessed the suitability of long-term radar (C-band Sentinel-1 and L-band PALSAR-1/2) and optical (Sentinel-2) EO data on GEE for the digital mapping of soil pH on a continental (Europe) scale and determined the most appropriate radar sensor parameters. Thirteen scenarios with different data configurations were simulated and combined with the 2018 LUCAS soil database and two machine learners (boosted regression trees and extreme gradient boosting) to develop soil prediction models. Results showed that the selection of modeling techniques, satellite sensors and radar system parameters largely affected the model output. Models involving a single polarization mode of PALSAR-1/2 data performed the worst (RPD = 1.24). Models based on Sentinel-1 data performed better than those built using PALSAR-1/2 data. The model performance was improved when a model involved more polarization bands, orbital directions, and band frequencies. The multiband model built using the two radar datasets achieved a comparable accuracy to the model based on optical data. Moreover, the model that fused radar-optical data achieved better results, with RPD values of 1.56 and 1.46 for the models with and without radar data, respectively; its performance was comparable to that of models built with commonly used variables (topography and climate). The analysis of importance indicated that long-term optical and radar EO data on GEE were important in our model. The modelling of soil pH at the continental scale largely benefits from GEE. The predicted maps exhibited strong spatial heterogeneity among different biogeographic regions, with similar spatial patterns under different modelling scenarios.

A synchronization-constraints-based dual bands method of traffic signal optimization

This paper presents a synchronization-constraints-based dual bands method of traffic signal optimization for multiple traffic flows. The synchronization coefficient, traffic volume ratio, synchronization benefit, and their thresholds of traffic flow pair are proposed to select synchronized traffic flow pairs for progression. Then, progression bands of multiple traffic flows can be achieved by merging time windows, combining equivalent phase, and optimizing benefits evaluation index of the synchronized traffic flow pair. The proposed method adopts step-by-step optimization processing, and the combinatorial optimization of signal cycle, phase sequence, and offset is decomposed by performing synchronization and progression. A real-world case is presented to illustrate the application of the proposed method with VISSIM simulations. The results show that compared with the MULTIBAND model and TRANSYT, this method significantly decreased the average delay and average stops along the arterial of the entire traffic system composed of buses and cars.

Depression assessment using integrated multi-featured EEG bands deep neural network models: Leveraging ensemble learning techniques

Mental Status Assessment (MSA) holds significant importance in psychiatry. In recent years, several studies have leveraged Electroencephalogram (EEG) technology to gauge an individual's mental state or level of depression. This study introduces a novel multi-tier ensemble learning approach to integrate multiple EEG bands for conducting mental state or depression assessments. Initially, the EEG signal is divided into eight sub-bands, and then a Long Short-Term Memory (LSTM)-based Deep Neural Network (DNN) model is trained for each band. Subsequently, the integration of multi-band EEG frequency models and the evaluation of mental state or depression level are facilitated through a two-tier ensemble learning approach based on Multiple Linear Regression (MLR). The authors conducted numerous experiments to validate the performance of the proposed method under different evaluation metrics. For clarity and conciseness, the research employs the simplest commercialized one-channel EEG sensor, positioned at FP1, to collect data from 57 subjects (49 depressed and 18 healthy subjects). The obtained results, including an accuracy of 0.897, F1-score of 0.921, precision of 0.935, negative predictive value of 0.829, recall of 0.908, specificity of 0.875, and AUC of 0.8917, provide evidence of the superior performance of the proposed method compared to other ensemble learning techniques. This method not only proves effective but also holds the potential to significantly enhance the accuracy of depression assessment.

Measurement-based Multi-band Clutter Loss Modeling for Non-terrestrial Network

Measurement-based Multi-band Clutter Loss Modeling for Non-terrestrial Network

Electronic structures and quantum capacitance of twisted bilayer graphene with defects based on three-band tight-binding model.

Twisted bilayer graphene (tBLG) with C vacancies would greatly improve the density of states (DOS) around the Fermi level (EF) and quantum capacitance; however, the single-band tight-binding model only considering pz orbitals cannot accurately capture the low-energy physics of tBLG with C vacancies. In this work, a three-band tight-binding model containing three p orbitals of C atoms is proposed to explore the modulation mechanism of C vacancies on the DOS and quantum capacitance of tBLG. We first obtain the hopping integral parameters of the three-band tight-binding model, and then explore the electronic structures and the quantum capacitance of tBLG at a twisting angle of θ = 1.47° under different C vacancy concentrations. The impurity states contributed by C atoms with dangling bonds located around the EF and the interlayer hopping interaction could induce band splitting of the impurity states. Therefore, compared with the quantum capacitance of pristine tBLG (∼18.82 μF cm-2) at zero bias, the quantum capacitance is improved to ∼172.76 μF cm-2 at zero bias, and the working window with relatively large quantum capacitance in the low-voltage range is broadened in tBLG with C vacancies due to the enhanced DOS around the EF. Moreover, the quantum capacitance of tBLG is further increased at zero bias with an increase of the C vacancy concentration induced by more impurity states. These findings not only provide a suitable multi-band tight-binding model to describe tBLG with C vacancies but also offer theoretical insight for designing electrode candidates for low-power consumption devices with improved quantum capacitance.

To the Theory of Dimensional Quantization in Narrow-Gap Crystals

This article discusses studies of size quantization phenomena in zero-, one-, and two-dimensional semiconductor structures. The main attention is paid to the mechanisms of photon-kinetic effects in these structures. Despite many studies of the physical properties of low-dimensional systems of current carriers, the size quantization of energy spectra in narrow-gap semiconductors and the associated photonic-kinetic effects are still insufficiently studied. Therefore, this study focuses on the quantum mechanical study of size quantization in certain cases using Kane's multiband model. The insolvability of the 8×8 matrix Schrödinger equation in the Kane model for a potential well of arbitrary shape is analyzed. The dependence of the energy spectrum on the two-dimensional wave vector is studied for various cases. In particular, the energy spectra for InSb and GaAs semiconductors are considered, depending on the band parameters and the size of the potential well. Conclusions are presented on the analysis of various cases of size quantization in narrow-gap crystals with cubic or tetrahedral symmetry in the three-band approximation. It is shown that the energy spectrum corresponds to a set of size-quantized levels that depend on the Rabi parameter, band gap, and well size. The size-quantized energy spectra of electrons and holes in InSb and GaAs semiconductors are analyzed in a multiband model.

Effect of atmospheric corrections on shallow sea bathymetric mapping using gaofen-2 imagery: a case study in Lingyang Reef, South China Sea

Satellite-derived bathymetry (SDB) with high spatial resolution effectively maps detailed information about shallow sea depths. Properly selecting atmospheric correction (AC) methods is crucial to obtaining precise bathymetric information from satellite data. In this work, three different AC methods (FLAASH, 6S, and DOS) were applied to GaoFen-2 imagery in Lingyang Reef, South China Sea. Water depth retrieval models, such as single-band, multiband, and band ratio models, were established by 470 points of in-situ water depth data and used for evaluating the model performances. Additionally, the optimal model was applied to depth inversion. The results show that the multiband model based on four bands performs well in this study area with R2 =0.736. The choice of AC methods significantly affects SDB, although acceptable results can be derived without these methods. Specifically, the DOS method has the highest inversion accuracy, with a mean relative error and root mean square error of 14.05% and 3.31 m, respectively. Furthermore, the inversion accuracy in various depth ranges may be primarily influenced by suspended sediment concentration and bottom-type uniformity. This study provides a valuable reference for selecting AC approaches and inversion models in high-spatial-resolution SDB in shallow seas.

A deep perceptual framework for affective video tagging through multiband EEG signals modeling

A deep perceptual framework for affective video tagging through multiband EEG signals modeling

Applicability and limitations of cluster perturbation theory for Hubbard models

We present important use cases and limitations when considering results obtained from cluster perturbation theory (CPT). CPT combines the solutions of small individual clusters of an infinite lattice system with the Bloch theory of conventional band theory to provide an approximation for the Green’s function in the thermodynamic limit. To this end, we are investigating single-band and multi-band Hubbard models in 1D and 2D systems. A special interest is taken in the supposed pseudogap regime of the 2D square lattice at half-filling and intermediate interaction strength (U≤3t\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$U \\le 3t$$\\end{document}) as well as the metal–insulator transition. We point out that the finite-size level spacing of the cluster limits the resolution of spectral features within CPT. This restricts the investigation of asymptotic properties of the metal–insulator transition, as it would require much larger cluster sizes that are beyond computational capabilities.

Multiband seizure type classification based on 3D convolution with attention mechanisms

Electroencephalogram (EEG) signal contains important information about abnormal brain activity, which has become an important basis for epilepsy diagnosis. Recently, epilepsy EEG signal classification methods mainly extract features from the perspective of a single domain, which cannot effectively utilize the spatial domain information in EEG signals. The redundant information in EEG signals will affect the learning features with the increase of convolution layer and multi-domain features, resulting in inefficient learning and a lack of distinguishing features. To tackle these issues, we propose an end-to-end 3D convolutional multiband seizure-type classification model based on attention mechanisms. Specifically, to process preprocessed electroencephalogram (EEG) data, a multilevel wavelet decomposition is applied to obtain the joint distribution information in the two-dimensional time–frequency domain across multiple frequency bands. Subsequently, this information is transformed into three-dimensional spatial data based on the electrode configuration. Discriminative joint activity features in the time, frequency, and spatial domains are then extracted by a series of parallel 3D convolutional sub-networks, where 3D channels and spatial attention mechanisms improve the ability to learn critical global and local information. A multi-layer perceptron is finally implemented to integrate the extracted features and further map them to the classification results. Experimental results on the TUSZ dataset, the world’s largest publicly available seizure corpus, show that 3D-CBAMNet significantly outperforms the state-of-the-art methods, indicating effectiveness in the seizure type classification task.

Gain-loss-induced non-Abelian Bloch braids

Onsite gain-loss-induced topological braiding principle of non-Hermitian energy bands is theoretically formulated in multiband lattice models with Hermitian hopping amplitudes. Braid phase transition occurs when the gain-loss parameter is tuned across exceptional point degeneracy. Laboratory realizable effective-Hamiltonians are proposed to realize braid groups B2 and B3 of two and three bands, respectively. While B2 is trivially Abelian, the group B3 features non-Abelian braiding and energy permutation originating from the collective behavior of multiple exceptional points. Phase diagrams with respect to lattice parameters to realize braid group generators and their non-commutativity are shown. The proposed theory is conducive to synthesizing exceptional materials for applications in topological computation and information processing.

Extraction of the bare rock rate in counties of typical karst areas based on unmanned aerial vehicle images and satellite data, China

AbstractRocky desertification is a land degradation process. The bare rock rate (BRR) is an important index. Satellite‐based methods for BRR estimation can generate BRR datasets, but the spatial resolution and accuracy are low. Unmanned aerial vehicle (UAV) data can be used for BRR extraction with much higher resolution and higher accuracy, but the data are laborious and costly to collect. This study proposed a stepwise upscaling method employing the UAV‐extracted BRR as the ground truth to establish a multiband regression model and calibrated the BRR derived from 30‐m resolution LANDSAT 8 data, which was then used to calibrate 500‐m resolution moderate resolution imaging spectroradiometer (MODIS) data. We compared the results with the satellite‐based BRR and the results of UAV‐MODIS upscaling methods. The results showed that (1) the UAV provides high‐resolution data, and exposed rocks can be efficiently extracted from UAV images. This method could replace ground surveys. (2) The accuracy of the stepwise upscaling method was higher than that of the nonstepwise upscaling method in large areas. R2 increased by 64.9%, and the root mean square error (RMSE) decreased by 82.9%. (3) Compared with that of the satellite‐based BRR, the accuracy of the multiband regression model‐extracted BRR was higher. R2 increased by 21.5%, and the RMSE decreased by 62.2%. This study demonstrated that the stepwise upscaling method and multiband regression can be combined to extract high‐precision and large‐scale BRR data, which can be used to serve ecological engineering monitoring and ecological governance.

Incorporation of neighborhood information improves performance of SDB models

Mapping of water depth in nearshore waters is important for safe navigation and other human uses of coastal areas. Optical satellite imagery offers a cost-efficient source of data for this purpose. Several methods have been developed for satellite-derived bathymetry (SDB), but the details of their implementation, as well as their relative performance and its dependence on the data and environmental context, is unclear. We compared five SDB models, calibrated and tested against airborne lidar data from southern Corsica. We used a blocked strategy to split data into calibration, validation, and test data, because randomly splitting the data resulted in spatial autocorrelation between these data sets and underestimation of model prediction errors. In general, the non-parametric random forest and neural network models outperformed the parametric multi-band and band-ratio models. The convolutional neural network (CNN) model performed the best, especially in relatively deep (10–20 m) areas, and it could be near-optimally tuned with only ∼1000 data points. However, developing this CNN model was an iterative trial-and-error process that took several months. Using the random forest model, we demonstrated that using information from more than two bands in the visible and near-infrared spectrum contributed to improving model performance, as did incorporation of information from the local neighborhood. We suggest that the use of more than two bands, and the inclusion of spatial contextual information in addition to the values of the individual pixel, should become standard practice in SDB.

Impact of land use on water quality in buffer zones at different scales in the Poyang Lake, middle reaches of the Yangtze River basin

The water quality of Poyang Lake (PYL) is significantly influenced by land use which is a crucial factor that can exhibit complex changes in the environment and can serve as an indicator of the intensity of human activity. Therefore, this study analyzed the spatial and temporal distribution characteristics of nutrients and investigated the effects of land-use factors on water quality in the PYL during 2016–2019. The main conclusions are as follows: (1) Although there was some variation in the accuracy of the water quality inversion models (random forest (RF), support vector machine (SVM), and multiple statistical regression models), they were homogeneous. In particular, ammonia nitrogen (NH3-N) concentration from band (B) 2 and B2–B10 regression model was more consistent with each other. In contrast, the overall concentration levels from the combined B9/(B2–B4) triple-band regression model were relatively low, with approximately 0.03 mg/L in most areas of PYL. (2) The optimal inversion method varied for different water quality parameters. For instance, RF obtained better inversion of total phosphorus (TP) and total nitrogen (TN), with the fitting coefficient (r2) of 0.78 and 0.81, respectively; SVM had higher accuracy in the inversion of permanganate index (CODMn), with r2 of approximately 0.61; the accuracy of multi-band combined regression model in the inversion of each water quality parameter was at a higher level. (3) The influence of land use on water quality at different scales of buffer zone was different. In general, the correlation between water quality parameters and land use was higher at large spatial scales (1000–5000 m) than at small spatial scales (100 m, 500 m). A common feature of all hydrological stations was the significant negative correlation between crops, buildings, and water quality at all buffer scales. This study is of great practical significance for promoting water environment management and water quality health in the PYL.

Extracting quantum-geometric effects from Ginzburg-Landau theory in a multiband Hubbard model

We first apply functional-integral approach to a multiband Hubbard model near the critical pairing temperature, and derive a generic effective action that is quartic in the fluctuations of the pairing order parameter. Then we consider time-reversal-symmetric systems with uniform (i.e., at both low-momentum and low-frequency) pairing fluctuations in a unit cell, and derive the corresponding time-dependent Ginzburg-Landau (TDGL) equation. In addition to the conventional intraband contribution that depends on the derivatives of the Bloch bands, we show that the kinetic coefficients of the TDGL equation have a geometric contribution that is controlled by both the quantum-metric tensor of the underlying Bloch states and their band-resolved quantum-metric tensors. Furthermore we show that thermodynamic properties such as London penetration depth, GL coherence length, GL parameter and upper critical magnetic field have an explicit dependence on quantum geometry.