Multi-band Analysis Research Articles

FBANet: An Effective Data Mining Method for Food Olfactory EEG Recognition.

At present, the sensory evaluation of food mostly depends on artificial sensory evaluation and machine perception, but artificial sensory evaluation is greatly interfered with by subjective factors, and machine perception is difficult to reflect human feelings. In this article, a frequency band attention network (FBANet) for olfactory electroencephalogram (EEG) was proposed to distinguish the difference in food odor. First, the olfactory EEG evoked experiment was designed to collect the olfactory EEG, and the preprocessing of olfactory EEG, such as frequency division, was completed. Second, the FBANet consisted of frequency band feature mining and frequency band feature self-attention, in which frequency band feature mining can effectively mine multiband features of olfactory EEG with different scales, and frequency band feature self-attention can integrate the extracted multiband features and realize classification. Finally, compared with other advanced models, the performance of the FBANet was evaluated. The results show that FBANet was better than the state-of-the-art techniques. In conclusion, FBANet effectively mined the olfactory EEG data information and distinguished the differences between the eight food odors, which proposed a new idea for food sensory evaluation based on multiband olfactory EEG analysis.

Multi-Band Optical Variability of Blazar 1ES 2344+514 on Diverse Time-Scales

This study presents the results of multi-band observations from 2022 to 2024 and Zwicky Transient Facility (ZTF) observations from 2018 to 2023, examining the flux variability of the blazar 1ES 2344+514 on diverse time-scales in the optical bands. The blazar has mild short-term variability (STV) and long-term variability (LTV), with small amplitudes of $\sim0.7$ mag and $0.4$ mag for the host subtracted- and included-light curves, respectively. The power-enhanced F-test and the nested Analysis of Variance (ANOVA) statistical tests of the six intra-day light curves show that the blazar has no minute-scale variability. The multiband color behavior analysis revealed a moderate redder-when-brighter (RWB) trend on intra-day time scales, while the LTV shows no detectable color behavior. We found a strong correlation between the ZTF optical light curves without any time lag, but no detectable correlations for the optical band emissions. From our periodicity searches using WWZ and LS methods, three significant quasi-periodic oscillation (QPO) signals in the ZTF light curves are found at about 1.02, 1.3, and 2.85 years. The observational results indicate that the blazar 1ES 2344+514 has a complex variability while emphasizing the need for future observations to unravel its underlying mechanisms.

Multiband Analysis of Strong Gravitationally Lensed Post-blue Nugget Candidates from the Kilo-degree Survey

During the early stages of galaxy evolution, a significant fraction of galaxies undergo a transitional phase between the “blue nugget” systems, which arise from the compaction of large, active star-forming disks, and the “red nuggets,” which are red and passive compact galaxies. These objects are typically only observable with space telescopes, and detailed studies of their size, mass, and stellar population parameters have been conducted on relatively small samples. Strong gravitational lensing can offer a new opportunity to study them in detail, even with ground-based observations. In this study, we present the first six bona fide samples of strongly lensed post-blue nugget (pBN) galaxies, which were discovered in the Kilo Degree Survey. By using the lensing-magnified luminosity from optical and near-infrared bands, we have derived robust structural and stellar population properties of the multiple images of the background sources. The pBN galaxies have very small sizes of R eff < 1.3 kpc, high mass density inside 1 kpc of log(Σ1/M⊙kpc−2)>9.3 , and low specific star formation rates of log(sSFRGyr-1)≲0 , The size–mass and Σ1–mass relations of this sample are consistent with those of the red nuggets, while their sSFR is close to the lower end of compact star-forming blue nugget systems at the same redshift, suggesting a clear evolutionary link between them.

Host galaxy of low-luminosity compact sources

The term `active galactic nuclei' (AGN) is applied to a huge variety of objects, classified on the basis of their properties at different wavelengths. Peaked sources (PSs) represent a class of AGN at the first stage of evolution, characterised by a peaked radio spectrum. Among these radio sources, low-luminosity compact (LLC) sources can be identified as PSs accreting with a high Eddington rate, harbouring low-power jets, and hosting low-mass black holes. These properties are also shared by narrow-line Seyfert 1 galaxies (NLS1s). In 2016, LLCs were hypothesised to be the parent population of NLS1s with a flat radio spectrum (F-NLS1s), suggesting that the former may be the same objects as the latter but, instead, seen at a higher inclination. Based on radio luminosity functions and optical spectra analysis, ten LLCs were identified as valid F-NLS1s candidates. To account for the missing piece to the puzzle and verifying whether these LLCs could be hosted in late-type galaxies as NLS1s, we performed a photometric decomposition of their Pan-STARRS1 images in all five filters. We used the 2D fitting algorithm GALFIT for the single-band analysis and its extension GALFITM for the multi-band analysis. Considering that the morphological type and the structural parameters of the host may be dependent on the wavelength, we found six out of ten LLCs hosted in late-type galaxies, probably with pseudo-bulges, along with three point-like sources and one object of an uncertain classification. Although this study is based on a small sample, it represents the first morphological analysis of LLC host galaxies. These results confirm the trend observed in NLS1s, indicating late-type and disc-like host galaxies for LLCs and supporting the validity of the parent population scenario.

Euclid: Early Release Observations -- Unveiling the morphology of two Milky Way globular clusters out to their periphery

As part of the Early Release Observations (ERO) programme, we analysed deep, wide-field imaging from the VIS and NISP instruments of two Milky Way globular clusters (GCs), namely NGC 6254 (M10) and NGC 6397, to look for observational evidence of their dynamical interaction with the Milky Way. We searched for such an interaction in the form of structural and morphological features in the clusters' outermost regions, which would be suggestive of the development of tidal tails on scales larger than those sampled by the ERO data. From our multi-band photometric analysis, we obtained deep and well-behaved colour--magnitude diagrams that, in turn, enabled an accurate membership selection. The surface brightness profiles built from these samples of member stars are the deepest ever obtained for these two Milky Way GCs, reaching down to $ mag/arcsec$^2$, which is $ mag/arcsec$^2$ lower than before. The investigation of the two-dimensional density map of NGC 6254 reveals an elongated morphology of the cluster peripheries in the direction and with the amplitude predicted by $N$-body simulations of the cluster's dynamical evolution, at high statistical significance. We interpret this as strong evidence for the first detection of tidally induced morphological distortion around this cluster. The density map of NGC 6397 reveals a slightly elliptical morphology, in agreement with previous studies, which requires further investigation on larger scales to be properly interpreted. This ERO project thus demonstrates the power of in studying the outer regions of GCs at an unprecedented level of detail, thanks to the combination of the large field of view, high spatial resolution, and depth enabled by the telescope. Our results highlight the future survey as the ideal dataset for investigating GC tidal tails and stellar streams.

Evaluation of the responsiveness pattern to caffeine through a smart data-driven ECG non-linear multi-band analysis

This study aimed to explore more efficient ways of administering caffeine to the body by investigating the impact of caffeine on the modulation of the nervous system's activity through the analysis of electrocardiographic signals (ECG). An ECG non-linear multi-band analysis using Discrete Wavelet Transform (DWT) was employed to extract various features from healthy individuals exposed to different caffeine consumption methods: expresso coffee (EC), decaffeinated coffee (ED), Caffeine Oral Films (OF_caffeine), and placebo OF (OF_placebo). Non-linear feature distributions representing every ECG minute time series have been selected by PCA with different variance percentages to serve as inputs for 23 machine learning models in a leave-one-out cross-validation process for analyzing the behavior differences between ED/EC and OF_placebo/OF_caffeine groups, respectively, over time. The study generated 50-point accuracy curves per model, representing the discrimination power between groups throughout the 50 min. The best model accuracies for ED/EC varied between 30 and 70 %, (using the decision tree classifier) and OF_placebo/OF_caffeine ranged from 62 to 84 % (using Fine Gaussian). Notably, caffeine delivery through OFs demonstrated effective capacity compared to its placebo counterpart, as evidenced by significant differences in accuracy curves between OF_placebo/OF_caffeine. Caffeine delivery via OFs also exhibited rapid dissolution efficiency and controlled release rate over time, distinguishing it from EC. The study supports the potential of caffeine delivery through Caffeine OFs as a superior technology compared to traditional methods by means of ECG analysis. It highlights the efficiency of OFs in controlling the release of caffeine and underscores their promise for future caffeine delivery systems.

Classification of Sleep Quality and Aging as a Function of Brain Complexity: A Multiband Non-Linear EEG Analysis.

Understanding and classifying brain states as a function of sleep quality and age has important implications for developing lifestyle-based interventions involving sleep hygiene. Current studies use an algorithm that captures non-linear features of brain complexity to differentiate awake electroencephalography (EEG) states, as a function of age and sleep quality. Fifty-eight participants were assessed using the Pittsburgh Sleep Quality Inventory (PSQI) and awake resting state EEG. Groups were formed based on age and sleep quality (younger adults n = 24, mean age = 24.7 years, SD = 3.43, good sleepers n = 11; older adults n = 34, mean age = 72.87; SD = 4.18, good sleepers n = 9). Ten non-linear features were extracted from multiband EEG analysis to feed several classifiers followed by a leave-one-out cross-validation. Brain state complexity accurately predicted (i) age in good sleepers, with 75% mean accuracy (across all channels) for lower frequencies (alpha, theta, and delta) and 95% accuracy at specific channels (temporal, parietal); and (ii) sleep quality in older groups with moderate accuracy (70 and 72%) across sub-bands with some regions showing greater differences. It also differentiated younger good sleepers from older poor sleepers with 85% mean accuracy across all sub-bands, and 92% at specific channels. Lower accuracy levels (<50%) were achieved in predicting sleep quality in younger adults. The algorithm discriminated older vs. younger groups excellently and could be used to explore intragroup differences in older adults to predict sleep intervention efficiency depending on their brain complexity.

FINKER: Frequency Identification through Nonparametric KErnel Regression in astronomical time series

Optimal frequency identification in astronomical datasets is crucial for variable star studies, exoplanet detection, and asteroseismology. Traditional period-finding methods often rely on specific parametric assumptions, employ binning procedures, or overlook the regression nature of the problem, limiting their applicability and precision. We introduce a universal, nonparametric kernel regression method for optimal frequency determination that is generalizable, efficient, and robust across various astronomical data types. FINKER uses nonparametric kernel regression on folded datasets at different frequencies, selecting the optimal frequency by minimising squared residuals. This technique inherently incorporates a weighting system that accounts for measurement uncertainties and facilitates multi-band data analysis. We evaluated our method's performance across a range of frequencies pertinent to diverse data types and compared it with an established period-finding algorithm, conditional entropy. The method demonstrates superior performance in accuracy and robustness compared to existing algorithms, requiring fewer observations to reliably identify significant frequencies. It exhibits resilience against noise and adapts well to datasets with varying complexity.

Techniques for Accelerometer Reading Processing on Railway Transport Using Wavelet Transform

Introduction. Safety issues of railway transport are inevitably connected with the condition of railway tracks and railway wheels. Various defects, such as irregularities of the railway track, may lead to emergencies and incidents. Therefore, it is important to measure and calculate short and impulse irregularities. A joint analysis of vibration acceleration signals is needed in order to study the types and size of railway track irregularities.Aim. Development of an algorithm for irregularity search based on accelerometer readings with the vertical measurement axis.Materials and methods. The research encompassed wavelet transformation and wavelet-based signal processing including discrete-time signal processing and continuous wavelet processing. In addition, time-frequency analysis based on Fourier transform and continuous wavelet scalogram was used. These methods provide for time-frequency localization for irregularity detection and measurement.Results. Algorithms for vibrational signal processing using the continuous and discrete-time wavelet transform are proposed. The results show that the discrete wavelet transform is effective for multiresolution and multiband analysis, and continuous wavelet transform and wavelet scalogram allows extraction of irregularities and determination of their parameters. The relative error for irregularity depth was improved by 18 %, and the absolute error for irregularity length determinations was reduced by 7 times.Conclusion. Application of the discrete Fourier transform and Fourier spectrogram provides for fine resolution in the frequency domain. However, separation of signal components in the time-frequency domain is impeded. The continuous-time wavelet transform ensures sufficient resolution in the low-frequency domain for component localization and visualization of irregularities.

Exploring Be phenomena in OBA stars: A mid-infrared search

As early-type stars with a rotation speed close to their critical velocity, Be stars experience an event called the Be phenomenon. The material in their equator is ejected into outside space during the Be phenomenon and forms a circumstellar disk. The mechanism triggering these events remains poorly understood, and observations of these events are limited because the duration of these events ranges from months to years. Long-term epoch photometry in the infrared bands is expected to be ideal for detecting Be phenomena because the brightness variation is larger than that in the optical, and the effect of interstellar extinction is weaker as well. We conducted a systematic search for Be phenomena among Milky Way OBA stars in the mid-infrared. We examined the brightness and colour variations of known classical Be stars using the WISE W1 and W2 photometry bands to quantify their characteristics. Subsequently, we established a set of criteria to identify similar photometric variations in a large sample of OBA stars. We found 916 OBA stars that show Be phenomena in the past 13 yr, 736 of which are newly discovered. The peak-to-peak variations in magnitude and colour were found to be correlated, indicating that a decretion disk is common. The increase in colour was observed to be strongly correlated with the emission of the Hα line, providing further evidence of the association with circumstellar disks. The brightness variation of a star with Be phenomena can be up to 1.5 mag, and the colour variations can be up to 0.4 mag. The median durations for the disk build-up and decay phases are 474 and 524 days, respectively (durations shorter than 180 days are not sampled). The search for Be phenomena in the WISE bands greatly enlarges the number of stars showing disk variation, and it enables multi-band photometry analysis of these events with the help of current and future optical photometry surveys.

Cardiovascular Diseases Diagnosis Using an ECG Multi-Band Non-Linear Machine Learning Framework Analysis.

cardiovascular diseases (CVDs), which encompass heart and blood vessel issues, stand as the leading cause of global mortality for many people. the present study intends to perform discrimination between seven well-known CVDs (bundle branch block, cardiomyopathy, myocarditis, myocardial hypertrophy, myocardial infarction, valvular heart disease, and dysrhythmia) and one healthy control group, respectively, by feeding a set of machine learning (ML) models with 10 non-linear features extracted every 1 s from electrocardiography (ECG) lead signals of a well-known ECG database (PTB diagnostic ECG database) using multi-band analysis performed by discrete wavelet transform (DWT). The ML models were trained and tested using a leave-one-out cross-validation approach, assessing the individual and combined capabilities of features, per each lead or combined, to distinguish between pairs of study groups and for conducting a comprehensive all vs. all analysis. the Accuracy discrimination results ranged between 73% and 100%, the Recall between 68% and 100%, and the AUC between 0.42 and 1. the results suggest that our method is a good tool for distinguishing CVDs, offering significant advantages over other studies that used the same dataset, including a multi-class comparison group (all vs. all), a wider range of binary comparisons, and the use of classical non-linear analysis under ECG multi-band analysis performed by DWT.

Research on high-precision gas concentration inversion based on adaptive infrared multi-band joint spectral analysis

Research on high-precision gas concentration inversion based on adaptive infrared multi-band joint spectral analysis

A Multi-Band Circularly Polarized-Shared Aperture Antenna for Space Applications at S and X Bands

In this article, a compact multiband antenna design and analysis is presented with a view of ensuring efficient uplink/downlink communications at the same time from a single antenna for CubeSat applications. This design shares the aperture of an S-band slot antenna to accommodate a square patch antenna operating in the X-band. Shared aperture antennas, along with an air gap and dielectric loading, provided good gain in both frequency bands. The S-band patch had an S11 = −10 dB bandwidth of 30 MHz (2013–2043 MHz, 1.5%), and the X-band antenna demonstrated a bandwidth of 210 MHz (8320–8530 MHz, 2.5%). The Axial Ratio (&lt;3 dB) bandwidth of the slot antenna in the S-band is 7 MHz (2013–2020 MHz, 0.35%), and it is 67 MHz (8433–8500 MHz, 0.8%) in the case of patch antenna in the X-band. While the maximum gain in the S-band reached 7.7 dBic, in the X-band, the peak gain was 12.8 dBic. This performance comparison study shows that the antenna is advantageous in terms of high gain, maintains circular polarization over a wideband, and can replace two antennas needed in CubeSats for uplink/downlink, which essentially saves space.

Dust Enrichment and Grain Growth in a Smooth Disk around the DG Tau Protostar Revealed by ALMA Triple Bands Frequency Observations

Characterizing the physical properties of dust grains in a protoplanetary disk is critical to comprehending the planet formation process. Our study presents Atacama Large Millimeter/submillimeter Array (ALMA) high-resolution observations of the young protoplanetary disk around DG Tau at a 1.3 mm dust continuum. The observations, with a spatial resolution of ≈0.″04, or ≈5 au, revealed a geometrically thin and smooth disk without substantial substructures, suggesting that the disk retains the initial conditions of the planet formation. To further analyze the distributions of dust surface density, temperature, and grain size, we conducted a multiband analysis with several dust models, incorporating ALMA archival data of the 0.87 and 3.1 mm dust polarization. The results showed that the Toomre Q parameter is ≲2 at a 20 au radius, assuming a dust-to-gas mass ratio of 0.01. This implies that a higher dust-to-gas mass ratio is necessary to stabilize the disk. The grain sizes depend on the dust models, and for the DSHARP compact dust, they were found to be smaller than ∼400 μm in the inner region (r ≲ 20 au) while exceeding larger than 3 mm in the outer part. Radiative transfer calculations show that the dust scale height is lower than at least one-third of the gas scale height. These distributions of dust enrichment, grain sizes, and weak turbulence strength may have significant implications for the formation of planetesimals through mechanisms such as streaming instability. We also discuss the CO snowline effect and collisional fragmentation in dust coagulation for the origin of the dust size distribution.

A Unique Low-mass-ratio Contact Eclipsing Binary System under the Period Cutoff

We present a multiband photometric analysis of CRTS J163819.6+03485, the first low-mass-ratio contact binary system with a period of 0.2053321 day under the contact binary period limit. The unprecedented combination of mass ratio and period makes this system unique for eclipsing binary (EB) research. Using new multiband photometric observations, we explored the parameter space of this unique total EB system through a detailed scan in the mass ratio–inclination plane and using the pikaia genetic algorithm optimizer. The best set of relative physical parameters and corresponding uncertainties was adopted through Markov Chain Monte Carlo sampling of the parameter space. The resulting mass ratio of the system is q = 0.16 ± 0.01. The absolute parameters were derived by adopting an empirical mass–luminosity relation. Period changes are also investigated by using new observations and archival photometric light curves from massive astronomical surveys, which revealed in a preliminary solution the presence of a possible low-mass tertiary companion. The origin and evolutionary status of the system are investigated through the detached binary formation scenario.

Electroencephalography-Based Depression Detection Using Multiple Machine Learning Techniques.

The growth of biomedical engineering has made depression diagnosis via electroencephalography (EEG) a trendy issue. The two significant challenges to this application are EEG signals' complexity and non-stationarity. Additionally, the effects caused by individual variances may hamper the generalization of detection systems. Given the association between EEG signals and particular demographics, such as gender and age, and the influences of these demographic characteristics on the incidence of depression, it would be preferable to include demographic factors during EEG modeling and depression detection. The main objective of this work is to develop an algorithm that can recognize depression patterns by studying EEG data. Following a multiband analysis of such signals, machine learning and deep learning techniques were used to detect depression patients automatically. EEG signal data are collected from the multi-modal open dataset MODMA and employed in studying mental diseases. The EEG dataset contains information from a traditional 128-electrode elastic cap and a cutting-edge wearable 3-electrode EEG collector for widespread applications. In this project, resting EEG readings of 128 channels are considered. According to CNN, training with 25 epoch iterations had a 97% accuracy rate. The patient's status has to be divided into two basic categories: major depressive disorder (MDD) and healthy control. Additional MDD include the following six classes: obsessive-compulsive disorders, addiction disorders, conditions brought on by trauma and stress, mood disorders, schizophrenia, and the anxiety disorders discussed in this paper are a few examples of mental illnesses. According to the study, a natural combination of EEG signals and demographic data is promising for the diagnosis of depression.

The Physics of the Accelerating Universe Survey: narrow-band image photometry

ABSTRACT PAUCam is an innovative optical narrow-band imager mounted at the William Herschel Telescope built for the Physics of the Accelerating Universe Survey (PAUS). Its set of 40 filters results in images that are complex to calibrate, with specific instrumental signatures that cannot be processed with traditional data reduction techniques. In this paper, we present two pipelines developed by the PAUS data management team with the objective of producing science-ready catalogues from the uncalibrated raw images. The Nightly pipeline takes care of entire image processing, with bespoke algorithms for photometric calibration and scatter-light correction. The Multi-Epoch and Multi-Band Analysis pipeline performs forced photometry over a reference catalogue to optimize the photometric redshift (photo-z) performance. We verify against spectroscopic observations that the current approach delivers an inter-band photometric calibration of 0.8 per cent across the 40 narrow-band set. The large volume of data produced every night and the rapid survey strategy feedback constraints require operating both pipelines in the Port d’Informació Cientifica data centre with intense parallelization. While alternative algorithms for further improvements in photo-z performance are under investigation, the image calibration and photometry presented in this work already enable state-of-the-art photo-z down to iAB = 23.0.

Multi-band analysis on physical properties of superconducting FeSe films

The origins of superconductivity and pairing symmetry of order parameters are still controversial problems for FeSe thin films up to date. Under the Neumann boundary conditions, the electromagnetic properties of this system are investigated using the two-band Ginzburg–Landau theory. We calculate the temperature dependence of upper critical field in arbitrary direction and critical supercurrent density through the FeSe film. It is revealed that the normalized upper critical field is independent of the film thickness and all of our theoretical results are in accordance with the experimental data. These thus strongly indicate the existence of two-gap s-wave superconductivity in this material.

Frequency-specific brain network architecture in resting-state fMRI

The analysis of brain function in resting-state network (RSN) models, ascertained through the functional connectivity pattern of resting-state functional magnetic resonance imaging (rs-fMRI), is sufficiently powerful for studying large-scale functional integration of the brain. However, in RSN-based research, the network architecture has been regarded as the same through different frequency bands. Thus, here, we aimed to examined whether the network architecture changes with frequency. The blood oxygen level-dependent (BOLD) signal was decomposed into four frequency bands—ranging from 0.007 to 0.438 Hz—and the clustering algorithm was applied to each of them. The best clustering number was selected for each frequency band based on the overlap ratio with task activation maps. The results demonstrated that resting-state BOLD signals exhibited frequency-specific network architecture; that is, the networks finely subdivided in the lower frequency bands were integrated into fewer networks in higher frequency bands rather than reconfigured, and the default mode network and networks related to perception had sufficiently strong architecture to survive in an environment with a lower signal-to-noise ratio. These findings provide a novel framework to enable improved understanding of brain function through the multiband frequency analysis of ultra-slow rs-fMRI data.

Constraints on the Physics of the Prompt Emission from Distant and Energetic Gamma-Ray Burst GRB 220101A

The emission region of γ-ray bursts (GRBs) is poorly constrained. The uncertainty on the size of the dissipation site spans over 4 orders of magnitude (1012–1017 cm) depending on the unknown energy composition of the GRB jets. The joint multiband analysis from soft X-rays to high energies (up to ∼1 GeV) of one of the most energetic and distant GRBs, GRB 220101A (z = 4.618), allows us to make an accurate distinction between prompt and early afterglow emissions. The enormous amount of energy released by GRB 220101A (E iso ≈ 3 × 1054 erg) and the spectral cutoff at MeV observed in the prompt emission spectrum constrain the parameter space of the GRB dissipation site. We put stringent constraints on the prompt emission site, requiring 700 < Γ0 < 1160 and R γ ∼ 4.5 × 1013 cm. Our findings further highlight the difficulty of finding a simple self-consistent picture in the electron–synchrotron scenario, favoring instead a proton–synchrotron model, which is also consistent with the observed spectral shape. Deeper measurements of the time variability of GRBs, together with accurate high-energy observations (MeV–GeV), would unveil the nature of the prompt emission.