Analysis Of Spectral Features Research Articles

Innovative approaches to English pronunciation instruction in ESL contexts: integration of multi-sensor detection and advanced algorithmic feedback

IntroductionTeaching English pronunciation in an English as a Second Language (ESL) context involves tailored strategies to help learners accurately produce sounds, intonation, and rhythm.MethodsThis study presents an innovative method utilizing advanced technology and algorithms to enhance pronunciation accuracy, fluency, and completeness. The approach employs multi-sensor detection methods for precise data collection, preprocessing techniques such as pre-emphasis, normalization, framing, windowing, and endpoint detection to ensure high-quality speech signals. Feature extraction focuses on key attributes of pronunciation, which are then fused through a feedback neural network for comprehensive evaluation. The experiment involved 100 college students, including 50 male and 50 female students, to test their English pronunciation.ResultsEmpirical results demonstrate significant improvements over existing methods. The proposed method achieved a teaching evaluation accuracy of 99.3%, compared to 68.9% and 77.8% for other referenced methods. Additionally, students showed higher levels of fluency, with most achieving a level of 4 or above, whereas traditional methods resulted in lower fluency levels. Spectral feature analysis indicated that the amplitude of speech signals obtained using the proposed method closely matched the original signals, unlike the discrepancies found in previous methods.DiscussionThese findings highlight the effectiveness of the proposed method, showcasing its ability to improve pronunciation accuracy and fluency. The integration of multi-sensor detection and neural network evaluation provides precise results, outperforming traditional approaches.

Boosting adversarial example detection via local histogram equalization and spectral feature analysis

Boosting adversarial example detection via local histogram equalization and spectral feature analysis

Unlocking the Secrets of Corn: Physiological Responses and Rapid Forecasting in Varied Drought Stress Environments

Understanding the intricate relationship between drought stress and corn yield is crucial for ensuring food security and sustainable agriculture in the face of climate change. This study investigates the subtle effects of drought stress on corn physiological, morphological, and spectral characteristics at different growth stages, in order to construct a new drought index to characterize drought characteristics, so as to provide valuable insights for maize recovery mechanism and yield prediction. Specific conclusions are as follows. Firstly, the impact of drought stress on corn growth and development shows a gradient effect, with the most significant effects observed during the elongation stage and tasseling stage. Notably, Soil and Plant Analyzer Development (SPAD) and Leaf Area Index (LAI) are significantly affected during the silking stage, while plant height and stem width remain relatively unaffected. Secondly, spectral feature analysis reveals that, from the elongation stage to the silking stage, canopy reflectance exhibits peak–valley variations. Drought severity correlates positively with reflectance in the visible and shortwave infrared bands and negatively with reflectance in the near-infrared band. Canopy spectra during the silking stage are more affected by moderate and severe drought stress. Thirdly, LAI shows a significant positive correlation with yield, indicating its reliability in explaining yield variations. Finally, the yield-related drought index (YI) constructed based on Convolutional Neural Network (CNN), Random Forest (RF) and Multiple Linear Regression (MLR) methods has a good effect on revealing drought characteristics (R = 0.9332, p < 0.001). This study underscores the importance of understanding corn responses to drought stress at various growth stages for effective yield prediction and agricultural management strategies.

EEG biomarkers in Alzheimer’s and prodromal Alzheimer’s: a comprehensive analysis of spectral and connectivity features

BackgroundBiomarkers of Alzheimer’s disease (AD) and mild cognitive impairment (MCI, or prodromal AD) are highly significant for early diagnosis, clinical trials and treatment outcome evaluations. Electroencephalography (EEG), being noninvasive and easily accessible, has recently been the center of focus. However, a comprehensive understanding of EEG in dementia is still needed. A primary objective of this study is to investigate which of the many EEG characteristics could effectively differentiate between individuals with AD or prodromal AD and healthy individuals.MethodsWe collected resting state EEG data from individuals with AD, prodromal AD, and normal cognition. Two distinct preprocessing pipelines were employed to study the reliability of the extracted measures across different datasets. We extracted 41 different EEG features. We have also developed a stand-alone software application package, Feature Analyzer, as a comprehensive toolbox for EEG analysis. This tool allows users to extract 41 EEG features spanning various domains, including complexity measures, wavelet features, spectral power ratios, and entropy measures. We performed statistical tests to investigate the differences in AD or prodromal AD from age-matched cognitively normal individuals based on the extracted EEG features, power spectral density (PSD), and EEG functional connectivity.ResultsSpectral power ratio measures such as theta/alpha and theta/beta power ratios showed significant differences between cognitively normal and AD individuals. Theta power was higher in AD, suggesting a slowing of oscillations in AD; however, the functional connectivity of the theta band was decreased in AD individuals. In contrast, we observed increased gamma/alpha power ratio, gamma power, and gamma functional connectivity in prodromal AD. Entropy and complexity measures after correcting for multiple electrode comparisons did not show differences in AD or prodromal AD groups. We thus catalogued AD and prodromal AD-specific EEG features.ConclusionsOur findings reveal that the changes in power and connectivity in certain frequency bands of EEG differ in prodromal AD and AD. The spectral power, power ratios, and the functional connectivity of theta and gamma could be biomarkers for diagnosis of AD and prodromal AD, measure the treatment outcome, and possibly a target for brain stimulation.

Integrating sentinel-2a imagery, DEM data, and spectral feature analysis for landslide detection via fully convolutional networks

Integrating sentinel-2a imagery, DEM data, and spectral feature analysis for landslide detection via fully convolutional networks

On-orbit spectral characteristics analysis of the greenhouse gases monitoring instrument: Spectral wavelength stability and instrumental line shape stability

The Greenhouse Gases Monitoring Instrument (GMI) is a carbon satellite payload developed based on the principle of spatial heterodyne spectroscopy technology, which is specifically designed for the global analysis of greenhouse gases (CO2 and CH4) “sources” and “sinks”. Due to the low concentration and minimal gradient variations of CO2 and CH4 in the atmosphere, higher precision is required for their retrieval. Vibrations during satellite launch and harsh space environments during on-orbit operation may cause changes in the performance of various payload components, resulting in decreased quality of spectrum products and retrieval precision. This paper proposes a set of on-orbit spectral characteristic evaluation methods tailored for the GMI to monitor the quality of its spectrum. It also develops an adaptive blind pixel detection and correction algorithm specifically for the GMI and optimizes the interferogram processing flow algorithm. On-orbit spectral calibration is performed, and methods to evaluate spectral characteristic parameters have been established. The analysis focuses on the variations of the spectral characteristics in the CO2-1 bands (1.575 μm) of the GMI during one-year of on-orbit calibration spectrum. The initial spectral wavenumber offset is 0.133 cm−1 after entering orbit, and the average spectral wavenumber offset is 0.0313 cm−1 during stable operation. During the one-year period, the maximum variation in the instrumental line shape function of the GMI is 0.006 cm−1. The observed spectrum obtained in nadir observation mode, underwent to accuracy verification. The results demonstrate consistency between the spectral peak wavenumbers and the relative trend changes of the observed and theoretical spectrum, with an average relative residual of 0.987%.

Mass Spectral Feature Analysis of Ubiquitylated Peptides Provides Insights into Probing the Dark Ubiquitylome.

Ubiquitylation is a structurally and functionally diverse post-translational modification that involves the covalent attachment of the small protein ubiquitin to other protein substrates. Trypsin-based proteomics is the most common approach for globally identifying ubiquitylation sites. However, we estimate that such methods are unable to detect ∼40% of ubiquitylation sites in the human proteome, i.e., "the dark ubiquitylome", including many important for human health and disease. In this meta-analysis of three large ubiquitylomic data sets, we performed a series of bioinformatic analyses to assess experimental features that could aid in uniquely identifying site-specific ubiquitylation events. Spectral predictions from Prosit were compared to experimental spectra of tryptic ubiquitylated peptides, revealing previously uncharacterized fragmentation of the diGly scar. Analysis of the LysC-derived ubiquitylated peptides reveals systematic, multidimensional peptide fragmentation, including diagnostic b-ions from fragmentation of the LysC ubiquitin scar. Comprehensively, these findings provide diagnostic spectral signatures of modification events that could be applied to new analysis methods for nontryptic ubiquitylomics.

Multimodal, microspectroscopic speciation of legacy phosphorus in two US mid‐Atlantic agricultural soils

AbstractTo understand phosphorus (P) mobility in agricultural soils and its potential environmental risk, it is essential to directly measure solid phase P speciation. Often, bulk P K‐edge X‐ray absorption near edge structure (XANES) spectroscopy followed by linear combination fitting (LCF) is utilized to determine the solid P phases in soil. However, this method may limit results to only a few major phases. Additionally, XANES spectra for different P species may have very similar features, leading to an over‐ or underestimate of their contribution to LCF. Here, an improved P speciation by pairing multimodal microbeam‐X‐ray fluorescence (µ‐XRF) mapping coupled with µ‐XANES (microbeam‐X‐ray absorption near edge structure) analysis to directly speciate major and minor P phases on the micron scale is provided. We combined maps of both tender (P, sulfur, aluminum, and silicon) and hard energy (calcium, iron [Fe], and manganese) elements to evaluate the elemental co‐locations with P. To better account for uncertainty assigning XANES peaks to individual compounds, a more quantitative fingerprinting by “spectral feature analysis” was completed. With this analysis, an R‐factor is reported for the fit. These results were compared to traditional LCF. Pre‐edge fitting results revealed the presence of a two‐component pre‐edge feature for phosphate adsorbed to ferrihydrite. Additionally, phytate co‐precipitated with ferrihydrite (Phytate‐Fe‐Cop) had a pre‐edge feature, indicating direct association with Fe. Lastly, a unique P species associated with manganese oxide was identified in the soil via multimodal mapping and µ‐XANES. These results allow for better prediction of P dissolution and mobility.

Quantitative analysis of spectral properties and composition of primitive achondrites (acapulcoites, lodranites and winonaites)

The establishment of robust meteorite-asteroid links has been a major focus of planetary exploration, and a major driver of asteroid sample return missions. Reflectance spectroscopy has been shown to be a powerful tool for this purpose. For the meteorites dominated by silicate minerals, quantitative analysis of spectral absorption features caused by the Fe2+-bearing minerals (mainly olivine and pyroxene) is a common method to determine mafic silicate mineralogy and end member abundances, and establish the relationship between them and possible parent bodies. In this study, the reflectance spectra of 22 primitive achondrites (acapulcoites, lodranites and winonaites) from NASA RELAB database were analyzed to determine their positions in the plot of the band area ratio (BAR) and 1 μm band center (Band I center). We found that Band I center and BAR of acapulcoites and lodranites are in roughly the same range. Acapulcoite-lodranite partially overlap with the field of H chondrites in the plot of the BAR and Band I center. This overlap means that spectral calibrations (also referred to as mineralogical formulas) based on the two types of meteorites needs to be applied with caution. The 2 μm band center of acapulcoite–lodranite is significantly lower than that of H chondrites, which is consistent with the conclusion of previous studies and provides a means to separate these two groups. In addition, the choice of spectral parameter analysis techniques may be a potential error source in similar studies. We provide generalized spectral fields of primitive achondrites in the plot of the BAR and Band I center derived from two widely used technologies.

Machine-learning classification with additivity and diverse multifractal pathways in multiplicativity

Evidence of multifractal structures has spread to a wider set of physiological time series supporting the intricate interplay of biological and psychological functioning. These dynamics manifest as random multiplicative cascades, embodying nonlinear relationships characterized by recurring division, branching, and aggregation processes implicating noise across successive generations. This investigation focuses on how well the diversity of multifractal properties can be specific to the type of cascade relationship between generation (i.e., multiplicative, additive, or a mixture) as well as to the type of noise (i.e., including additive white Gaussian noise, fractional Gaussian noise, and various amalgamations) among 15 distinct types of binomial cascade processes. Cross-correlation analysis of multifractal spectral features confirms that these features capture nuanced aspects of cascading processes with minimal redundancy. Principal component analysis using 13 distinct multifractal spectral features shows that different cascade processes can manifest multifractal evidence of nonlinearity for distinct reasons. This transparency of multifractal spectral features to underlying cascade dynamics becomes less amenable to machine-learning strategies. Fully connected neural networks struggled to classify the 15 distinct types of cascade processes based on the respective multifractal spectral features (45.5% accuracy) yet demonstrated improved accuracy when addressing single categories of cross-generation relationships, that is, additive (91.6%), multiplicative (75.4%), or additomultiplicative (70.6%). While traditional principal component analysis reveals distinct loadings attributed to individual noise processes, multiplicative relationships between generations effectively make the constituent noise processes less discernible to neural networks. Neural networks may lack sufficient hierarchical depth required to effectively distinguish among nonadditive cascading processes, recommending either elaborating multifractal geometry or using alternate architectures for machine-learning classification of cascades with multiplicative relationships. Published by the American Physical Society 2024

Distribution of Sports Load by the Smart Photovoltaic Cell Switching System

The article presents a summary of the new energy scenario, based on distributed generation and on the integration into the electricity networks of the generators of small and medium power. They are based on clean technologies and renewable energy, including cogeneration, micro-generation with renewables (non fossil energy sources, such as wind, solar, geothermal, tidal, hydroelectric, biomass), energy storage systems, energy efficiency techniques, and flexibility and demand responsibility techniques. The perspective shows a new system, where thousands or millions of users are their own generators, becoming producers and consumers of electricity. All these generators will be interconnected through an electricity network totally interactive and intelligent. For this new environment a sophisticated control and communications technologies are required to ensure the correct operation of the electricity networks, the creation of new models of power distribution, and advanced technology development of energy storage, power electronics and superconductor devices. The main technology that can lead to this new revolution, equivalent to the technology of silicon, which is revolutionizing the conventional limits of electricity and electronics, is nanotechnology, as well as its applications in the development of new nanostructured materials. The new energy systems will present smaller scale, will be easier to build and dismantle, decentralized, renewable, with localized and decentralized energy production.

Spectral Characteristics Analysis of Images Matrix Masking Results

The article describes the results of a computational experiment to assess the capabilities of extracting useful information if an image masked by quasi-orthogonal matrices sent over an open channel became available to a third party. Hadamard and Mersenne matrices of symmetric and cyclic structure are considered. The results confirm the data that images masked by small-sized matrix leaves edges of the original image on the masked image. However, with an increase in the size of the masking matrix, all considered in the article matrices reliably hides the original image during visual analysis. Masking by symmetric Mersenne-Walsh matrices and cyclic Mersenne matrices based on modified M-sequences provides better spectral secrecy of masked images in comparison with Hadamard matrices. Mersenne matrices of cyclic structure, with equal sizes of the image and the masking matrix, bring the phase spectrum of the masked image to a form close in spectrum to uniform noise, which makes their use more preferable based on the considerations that the human visual system is extremely sensitive to phase-frequency distortions of the visual information.

Optical characteristics of dissolved organic matter in relation to phytoplankton in Lake Erhai, China

Dissolved organic matter (DOM), as the primary form of organic carbon in lakes, is crucial for the lake carbon cycling. As an important component of lake ecosystems, phytoplankton plays a crucial role in carbon fixation. However, we are not yet clear about the contribution of phytoplankton composition to DOM. To clarify this issue, we conducted we conducted continuous monitoring for 6 months in Lake Erhai, a plateau lake in China. Through quantitative detection of phytoplankton and spectral characteristic analysis, four fluorescence components were identified: tryptophan-like (C1), tyrosine-like (C2), fulvic-like (C3), and humic-like (C4). The Fluorescence Index (FI) ranged from 1.70 to 2.54, and the Biogenic Index (BIX) was above 0.8, indicating the DOM in Lake Erhai’s overlying water was predominantly autochthonous. Cyanophyta prevailed in September and November, while Chlorophyta dominated in other months. Phytoplankton diversity was significantly related to DOM concentration that may be related to the difference in the DOM contribution of different phytoplankton. The effect of phytoplankton biomass on C3 and C4 was prominent. Whereas, phytoplankton did not stimulate C1 and C2 significantly, indicating that protein-like may originate from terrestrial input and microbial activity. The composition of phytoplankton significantly affects the DOM composition in Lake Erhai. Therefore, the autochthonous DOM production by phytoplankton warrants further consideration in plateau lake systems.

A Study for Design of Lightweight Dense Connection Network on Hyperspectral Image Classification

The characteristics of hyperspectral remote sensing images such as inconspicuous feature representativeness, single feature level, and complex information content, can lead to unstable classification results. We propose a lightweight dense network model that injects channel attention in the form of dense connections between network layers (DSE-DN) for the classification of hyperspectral images. In the DSE-DN network, principal component analysis (PCA) is applied to reduce redundancy in the hyperspectral images. Subsequently, a densely connected network is constructed, incorporating channel attention mechanisms through dense connections to enhance the analysis of spectral image features. Finally, the processed hyperspectral images are classified using a fully interconnected layer. We assess two classical hyperspectral datasets and construct 2DCNN, 3DCNN, ResNet, and the network that injects channel attention layer by layer to compare with DSE-DN. The experimental results indicate the utility of the DSE-DN network in hyperspectral image classification and its superiority over other networks.

Synergistic spectral and spatial feature analysis with transformer and convolution networks for hyperspectral image classification

Synergistic spectral and spatial feature analysis with transformer and convolution networks for hyperspectral image classification

ALMA detection of CO rotational line emission in red supergiant stars of the massive young star cluster RSGC1

Context. The fate of stars largely depends on the amount of mass lost during the end stages of evolution. For single stars with an initial mass between ∼8–30 M⊙, most mass is lost during the red supergiant (RSG) phase, when stellar winds deplete the H-rich envelope. However, the RSG mass-loss rate (Ṁ) is poorly understood theoretically, and so stellar evolution models rely on empirically derived mass-loss rate prescriptions. However, it has been shown that these empirical relations differ largely, with differences up to 2 orders of magnitude. Aims. We aim to derive a new mass-loss rate prescription for RSGs that is not afflicted with some uncertainties inherent in preceding studies. Methods. We have observed CO rotational line emission towards a sample of RSGs in the open cluster RSGC1 that all are of a similar initial mass. The ALMA CO(2–1) line detections allowed us to retrieve the gas mass-loss rates (ṀCO). In contrast to mass-loss rates derived from the analysis of dust spectral features (ṀSED), the data allowed us a direct determination of the wind velocity and no uncertain dust-to-gas correction factor was needed. Results. Five RSGs in RSGC1 have been detected in CO(2–1). The retrieved ṀCO values are systematically lower than ṀSED. Although only five RSGs in RSGC1 have been detected, the data allow us to propose a new mass-loss rate relation for M-type red supergiants with effective temperatures between ∼3200 and 3800 K that is dependent on the luminosity and initial mass, and that is valid during the phase where nuclear burning determines the evolution along the RSG branch. The new mass-loss rate relation is based on the new ṀCO values for the RSGs in RSGC1 and on prior ṀSED values for RSGs in four clusters, including RSGC1. The new Ṁ-prescription yields a good prediction for the mass-loss rate of some well-known Galactic RSGs that are observed in multiple CO rotational lines, including α Ori, μ Cep and VX Sgr. Moreover, there are indications that a stronger, potentially eruptive, mass-loss process is occurring during some fraction of the RSG lifetime, suggesting that RSGs might experience a phase change in mass loss leading to the wind mass-loss rate dominating the RSG evolution at that stage. Conclusions. Implementing a lower mass-loss rate in evolution codes for massive stars has important consequences as to the nature of their end-state. A reduction of the RSG mass-loss rate implies that quiescent RSG mass loss is not enough to strip a single star’s hydrogen-rich envelope. Upon core collapse such single stars would explode as RSGs. Mass-loss rates of order ∼6 times higher would be needed to strip the H-rich envelope and produce a Wolf-Rayet star while evolving back to the blue side of the Hertzsprung–Russell diagram. Future observations of a larger sample of RSGs in open clusters should allow a more stringent determination of the ṀCO–luminosity relation and a sharper diagnostic as to when the phase change in mass loss is occurring.

Dynamic Filter Application in Graph Convolutional Networks for Enhanced Spectral Feature Analysis and Class Discrimination in Medical Imaging

Dynamic Filter Application in Graph Convolutional Networks for Enhanced Spectral Feature Analysis and Class Discrimination in Medical Imaging

Discriminative feature analysis of dairy products based on machine learning algorithms and Raman spectroscopy

Discriminant analysis of similar food samples is an important aspect of achieving food quality control. The effective combination of Raman spectroscopy and machine learning algorithms has become an extremely attractive approach to develop intelligent discrimination techniques. Feature spectral analysis can help researchers gain a deeper understanding of the data patterns in food quality discrimination. Herein, this work takes the discrimination of three brands of dairy products as an example to investigate the Raman spectral feature based on the support vector machines (SVM), extreme learning machines (ELM) and convolutional neural network (CNN) algorithms. The results show that there are certain differences in the optimal spectral feature interval corresponding to different machine learning algorithms. Selecting the appropriate spectral feature interval can maintain high recognition accuracy and improve the computational efficiency of the algorithm. For example, the SVM algorithm has a recognition accuracy of 100% in the 890-980 cm−1, 1410-1500 cm−1 fusion spectral range, which takes about 200 s. The ELM algorithm also has a recognition accuracy of 100% in the 890-980 cm−1, 1410-1500 cm−1 fusion spectral range, which takes less than 0.3 s. The CNN algorithm has a recognition accuracy of 100% in the 890-980 cm−1, 1050-1180 cm−1, 1410-1500 cm−1 fusion spectral range, which takes about 80 s. In addition, by analyzing the distribution of spectral feature intervals based on Euclidean distance, the distribution of experimental samples based on feature spectra is visually displayed. Through the spectral feature analysis process of similar samples, a set of analysis strategies is provided to deeply reveal the data foundation of classification algorithms, which can provide reference for the analysis of relevant discriminative research patterns.

Development Research of an ADT-24 Multichannel Real-time Fluorescence PCR Detection System

This study describes a fluorescence detection system for purified ADT-24 nucleic acids and a PCR detection-integrated machine. The application background and structure of the fluorescence scanning system are described. We designed a confocal 4-channel fluorescence detection scanning head with monochromatic LEDs as the excitation light source, a multipixel photon counter (MPPC) as the optical path detection terminal, and a narrow-band filter and dichroic mirror as the main optical components. This paper discusses in detail the principle of the working system, including optical path selection and standards, optical component selection, channel spectral characteristic analysis, detector selection, performance evaluation, optical path simulation, and sensitivity verification. The design and use of this optical system are also summarized. Finally, a 4-fold dilution of mix reagent was used to test the detection effect of the fluorescence scanning head, and a scheme to control “temperature drift” and optimize performance was proposed. According to the verification results and subsequent live debugging, the debugged results achieved the expected design objectives.

A pilot study on non-invasive in situ detection of phytochemicals and plant endogenous status using fiber optic infrared spectroscopy

Traditional methods for assessing plant health often lack the necessary attributes for continuous and non-destructive monitoring. In this pilot study, we present a novel technique utilizing a customized fiber optic probe based on attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) with a contact force control unit for non-invasive and continuous plant health monitoring. We also developed a normalized difference mid-infrared reflectance index through statistical analysis of spectral features, enabling differentiation of drought and age conditions in plants. Our research aims to characterize phytochemicals and plant endogenous status optically, addressing the need for improved analytical measurement methods for in situ plant health assessment. The probe configuration was optimized with a triple-loop tip and a 3 N contact force, allowing sensitive measurements while minimizing leaf damage. By combining polycrystalline and chalcogenide fiber probes, a comprehensive wavenumber range analysis (4000–900 cm−1) was achieved. Results revealed significant variations in phytochemical composition among plant species, for example, red spinach with the highest polyphenolic content and green kale with the highest lignin content. Petioles displayed higher lignin and cellulose absorbance values compared to veins. The technique effectively monitored drought stress on potted green bok choy plants in situ, facilitating the quantification of changes in water content, antioxidant activity, lignin, and cellulose levels. This research represents the first demonstration of the potential of fiber optic ATR-FTIR probes for non-invasive and rapid plant health measurements, providing insights into plant health and advancements in quantitative monitoring for indoor farming practices, bioanalytical chemistry, and environmental sciences.