Spectral Envelope Research Articles

How Nature Makes O2: an Electronic Level Mechanism for Water Oxidation in Photosynthesis.

In this paper, we combine broken symmetry density functional calculations and electron paramagnetic resonance analysis to obtain the electronic structure of the penultimate S3 state of nature’s water-oxidizing complex and determine the electronic pathway of O–O bond formation. Analysis of the electronic structure changes along the reaction path shows that two spin crossovers, facilitated by the geometry and magnetism of the water-oxidizing complex, are used to provide a unique low-energy pathway. The pathway is facilitated via the formation and stabilization of the [O2]3– ion. This ion is formed between ligated deprotonated substrate waters, O5 and O6, and is stabilized by antiferromagnetic interaction with the Mn ions of the complex. Combining the computational, crystallographic, and spectroscopic data, we show that an equilibrium exists between the O5 oxo and O6 hydroxo forms with an S = 3 spin state and a deprotonated O6 form containing a two-center one-electron bond in [O5O6]3– which we identify as the form detected using crystallography. This form corresponds to an S = 6 spin state which we demonstrate gives rise to a low-intensity EPR spectrum compared with the accompanying S = 3 state, making its detection via EPR difficult and overshadowed by the S = 3 form. Simulations using 70% of the S = 6 component give rise to a superior fit to the experimental W-band EPR spectral envelope compared with an S = 3 only form. Analyses of the most recent X-ray emission spectroscopy first moment changes for solution and time-resolved crystal data are also shown to support the model. The computational, crystallographic, and spectroscopic data are shown to coalesce to the same picture of a predominant S = 6 species containing the first one-electron oxidation product of two water molecules, that is, [O5O6]3–. Progression of this form to the two-electron-oxidized peroxo and three-electron-oxidized superoxo forms, leading eventually to the evolution of triplet O2, is proposed to be the pathway nature adopts to oxidize water. The study reveals the key electronic, magnetic, and structural design features of nature’s catalyst which facilitates water oxidation to O2 under ambient conditions.

Efficient time-dependent orbital-free density functional theory: Semilocal adiabatic response

Orbital-free density functional theory and its time-dependent extension are efficient ab initio methods for calculating the electronic structure and dynamics of large systems. Through the calculation of the optical spectra of selected clusters, we reach three important conclusions: (1) The quality of the spectra is strongly affected by the quality of the corresponding ground-state electron density; (2) the adiabatic part of the electronic response to external perturbations can be safely evaluated at the semilocal level; and (3) the nonadiabatic, current-dependent part of the time-dependent Pauli potential is key to recover correct spectral envelopes.

The effects of aging and musicianship on the use of auditory streaming cues.

Auditory stream segregation, or separating sounds into their respective sources and tracking them over time, is a fundamental auditory ability. Previous research has separately explored the impacts of aging and musicianship on the ability to separate and follow auditory streams. The current study evaluated the simultaneous effects of age and musicianship on auditory streaming induced by three physical features: intensity, spectral envelope and temporal envelope. In the first study, older and younger musicians and non-musicians with normal hearing identified deviants in a four-note melody interleaved with distractors that were more or less similar to the melody in terms of intensity, spectral envelope and temporal envelope. In the second study, older and younger musicians and non-musicians participated in a dissimilarity rating paradigm with pairs of melodies that differed along the same three features. Results suggested that auditory streaming skills are maintained in older adults but that older adults rely on intensity more than younger adults while musicianship is associated with increased sensitivity to spectral and temporal envelope, acoustic features that are typically less effective for stream segregation, particularly in older adults.

Theoretical analysis of beam quality degradation induced by laser spectral characteristics in spectral beam combining system

We establish a model based on the dispersion of the diffractive grating to investigate the influence of the spectral envelope and wavelength stability on the combined beam quality M 2 of the spectral beam combining (SBC) system. We obtain the analytic expressions of combined beam quality corresponding to the second-moment-spectral width of the beamlets, which can be used to forecast the combined beam quality for any integrable spectral envelope shape of beamlets in an SBC system. Additionally, we find that the center wavelength shift of beamlets has a significant impact on the combined beam quality. The second-moment-based width of the center wavelength shift distribution among all beamlets should be less than 0.5 times the spectral width. Here, the spectral width is the weighted root mean square of all beamlets’ spectral width, where the weight is the power of each single channel.

An Adaptive Mapping Method Using Spectral Envelope Approach for DNA Spectral Analysis.

The digital signal processing approaches were investigated as a preliminary indicator for discriminating between the protein coding and non-coding regions of DNA. This is because a three-base periodicity (TBP) has already been proven to exist in protein-coding regions arising from the length of codons (three nucleic acids). This demonstrates that there is a prominent peak in the energy spectrum of a DNA coding sequence at frequency rad/sample. However, because DNA sequences are symbolic sequences, these should be mapped into one or more signals such that the hidden information is highlighted. We propose, therefore, two new algorithms for computing adaptive mappings and, by using them, finding periodicities. Both such algorithms are based on the spectral envelope approach. This adaptive approach is essentially important since a single mapping for any DNA sequence may ignore its intrinsic properties. Finally, the improved performance of the new methods is verified by using them with synthetic and real DNA sequences as compared to the classical methods, especially the minimum entropy mapping (MEM) spectrum, which is also an adaptive method. We demonstrated that our method is both more accurate and more responsive than all its counterparts. This is especially important in this application since it reduces the risks of a coding sequence being missed.

Dynamic and static feature fusion for increased accuracy in signature verification

The success rate in offline signature verification studies has reached high and limiting levels recently. However, any increase in this performance is and will be highly valuable in terms of fraud detection. This study assesses the impact of the sound arising from the friction of pen and paper on handwritten signature verification. A dataset was built containing static data from the signature image and dynamic data from the signature sound by taking samples from 75 participants according to different combinations of pen, paper types, and mobile phone models for recording the sounds of the signatures with their internal microphones. It was aimed to increase verification success by fusing dynamic and static features. From the static data, the features are extracted by the LBP and SIFT algorithms. For dynamic data, spectral flux onset envelopes and spectral centroids of audio signals are plotted and converted to image files. Thus, the dynamic data of the signature sound signal became static data and as in the static image of the signature, feature extraction was performed with the LBP and SIFT algorithms. Classification is performed with the OC-SVM algorithm. Moreover, instead of LBP and SIFT features, another verification method with the deep features obtained with a CNN-based model was also proposed and comparatively analyzed. Test results indicate that the aforementioned fusion of these two traits leads to increased signature verification success rates (statistical significance test results are provided), without incurring large costs, considering the sensor availability and acquisition times.

Far- and mid-infrared examination of nontronite-1 clay mineral – Redox and cation saturation effects

Infrared Spectroscopy is a very useful tool studying clay minerals in a structural level as well as interactions on their surfaces. In this study, middle- and far- infrared spectroscopy was utilised to better understand how iron-bearing smectites and more specifically the nontronite-1 (NAu1) alters upon iron reduction. Changes in the middle-infrared spectral range have been studied extensively, such as the 3570 cm−1, 1020 cm−1 and 823 cm−1, while far-infrared spectral range changes have not drawn much attention. In this study, NAu1 spectra of oxidised, partially, and fully reduced state are presented noting the differences that occur on different vibration bands at this low frequency infrared range. Key changes took place at the region of 430–494 cm−1, as well as the 145 cm−1 band. From the 175 cm−1 to the 380 cm−1 region, vibrating bands disappeared or convoluted in a wider spectral envelope, upon full iron reduction. At the same time, NAu1 is saturated with different cations, such as Na+, Ca2+, K+, isolating effects on vibrating bands, depending on these cations, across the oxidation states and infrared range examined. These effects are more prominent at the region of 1620–1650 cm−1, where adsorbed H2O bending vibration takes place, for the middle-infrared range. Far-infrared range changes, due to the cation present, are less prominent, but recorded and discussed in detail. Eventually, the redox state of iron-bearing clay minerals controls the structural configuration of the lattice and consequently the interaction strength of clay mineral/saturating cations. The increasing interaction strength observed was Ca2+ < Na+ < K+, which will govern clay mineral hydration and cation exchange processes taking place on the clay mineral surface, as demonstrated, and discussed in this study. These outcomes can be useful in agriculture, waste disposal, and the oil & gas industry.

Machine-Learning-Based Diagnosis of an Inverter-Fed Induction Motor

The principal objective of this paper is to detect and automatically monitor switch open-circuit faults in a two-level three-phase voltage source inverter fed induction motor from the processing of its current signals. The proposed diagnostic method uses both signal processing techniques and machine learning techniques in order to detect and localize the switch under an open-circuit fault. First, the Hilbert-Huang transform using the empirical ensemble mode decomposition is employed for each phase current signal, which leads to extracting the intrinsic mode functions. In order to optimally choose the function indicating the open-circuit fault harmonic, two factors, namely, the root mean square and the correlation coefficient are calculated out for each function. In this regard, two criteria are proposed that lead to choose the optimal function giving better information about the defected phase. The spectral envelope of the optimal function permits extra0cting the fault harmonic of the switch. Second, different machine learning techniques are applied to locate and classify the switch open-circuit faults with the hyper-parameters optimization for a better design of the different models. Finally, a comparative study of the different machine learning techniques is carried out for determining the best classifier for the open-circuit faults. The experimental results effectively demonstrate a very high classification rate of 98.98%.

A novel spectral coherence-based envelope spectrum for railway axle-box bearing damage identification

Damage identification of axle-box bearings is essential to ensure the safe operation of railway trains. The envelope spectrums generated by spectral coherence are effective bearing damage identification tools, but the traditional spectral coherence-based envelope spectrums cannot effectively reveal the bearing damage features under strong interference noise or cannot fully extract the damage information distributed in multiple spectral frequency bands. To solve these problems, a weighted combined envelope spectrum (WCES) based on spectral coherence is proposed as an enhanced bearing damage detector in this paper. First, the frequency domain signal-to-noise ratio (FDSNR) is devised to measure the damage information in each spectral frequency component of spectral coherence. Then, an information threshold is introduced into the estimated FDSNR to construct a weighting function to enhance the informative spectral frequency components and eliminate the interference components. Eventually, the spectral coherence normalized by the weighting function is integrated to generate WCES for bearing damage identification. The simulation and experimental results indicate that the proposed method can effectively excavate the fault-related information and detect railway axle-box bearing damages, and the comparisons with the state-of-the-art methods demonstrate the superiority of the proposed method.

Rhythm May Be Key to Linking Language and Cognition in Young Infants: Evidence From Machine Learning.

Rhythm is key to language acquisition. Across languages, rhythmic features highlight fundamental linguistic elements of the sound stream and structural relations among them. A sensitivity to rhythmic features, which begins in utero, is evident at birth. What is less clear is whether rhythm supports infants' earliest links between language and cognition. Prior evidence has documented that for infants as young as 3 and 4 months, listening to their native language (English) supports the core cognitive capacity of object categorization. This precocious link is initially part of a broader template: listening to a non-native language from the same rhythmic class as (e.g., German, but not Cantonese) and to vocalizations of non-human primates (e.g., lemur, Eulemur macaco flavifrons, but not birds e.g., zebra-finches, Taeniopygia guttata) provide English-acquiring infants the same cognitive advantage as does listening to their native language. Here, we implement a machine-learning (ML) approach to ask whether there are acoustic properties, available on the surface of these vocalizations, that permit infants' to identify which vocalizations are candidate links to cognition. We provided the model with a robust sample of vocalizations that, from the vantage point of English-acquiring 4-month-olds, either support object categorization (English, German, lemur vocalizations) or fail to do so (Cantonese, zebra-finch vocalizations). We assess (a) whether supervised ML classification models can distinguish those vocalizations that support cognition from those that do not, and (b) which class(es) of acoustic features (including rhythmic, spectral envelope, and pitch features) best support that classification. Our analysis reveals that principal components derived from rhythm-relevant acoustic features were among the most robust in supporting the classification. Classifications performed using temporal envelope components were also robust. These new findings provide in principle evidence that infants' earliest links between vocalizations and cognition may be subserved by their perceptual sensitivity to rhythmic and spectral elements available on the surface of these vocalizations, and that these may guide infants' identification of candidate links to cognition.

Soliton linear-wave scattering in a Kerr microresonator

The nonlinear scattering of a linear optical wave from a conservative soliton has been widely studied in optical fibers as a mechanism for nonlinear frequency conversion. Here we extend this analysis to consider the scattering of an externally injected probe wave from a dissipative cavity soliton circulating in a Kerr microresonator. We demonstrate, both theoretically and experimentally, that this nonlinear interaction can be harnessed for useful expansion of the soliton frequency comb via the formation of a secondary idler comb. We explore the physics of the process, showing that the phase detuning of the injected probe from a cavity resonance plays a key role in setting the central frequency of the idler comb, thus providing a convenient parameter through which to control the spectral envelope of that comb. Our results elucidate the dynamics that govern the interactions between dissipative Kerr cavity solitons and externally injected probe waves, and could prove useful in the design of future Kerr frequency comb systems by enabling the possibility to provide high-power comb lines in a specified spectral region simply through the injection of a suitably chosen probe.

Spectral variational mode extraction and its application in fault detection of rolling bearing

The core of fault diagnosis of rolling bearing is to extract the narrowband sub-components containing fault feature information from the bearing fault signal. Variational mode extraction (VME), a novel single sub-component separation algorithm originated from variational mode decomposition (VMD), provides a promising solution to bearing fault detection. However, its performance is closely related to the hyperparameter selection, including the center frequency ω d and the penalty factor α. This paper proposes a non-recursive and adaptive signal decomposition algorithm termed spectral variational mode extraction (SVME). SVME can be seen as a spectral decomposition technique whose framework is composed of the adaptive spectral boundary division and boundary constrained VME. In the adaptive spectral boundary division, an adaptive iterative spectral envelope method referring to the continuous envelope correlation (CCE) index is developed to integrate with the parameterless scale-space division to adaptively locate the frequency band boundary. The presented adaptive spectral boundary division approach can effectively inhibit the spectral boundary over-division. In the boundary constrained VME, the dominant frequency of each frequency band determined by the optimal spectral division is distinguished as the preset center frequency. Meanwhile, the optimal penalty factor is determined based on the envelope spectral kurtosis (ESK) index and the boundary-constraint principle. The SVME method is utilized in the simulation and experimental case studies to investigate its capability. Furthermore, its superiority is highlighted through the comparison with the variational mode decomposition (VMD) and Autogram methods.

Tone colour in music and bifurcation control

An intrinsic characteristic of musical sounds is the qualitative evolution of their timbre (tone colour). In this paper, we interpret each qualitative change by a bifurcation. We employ spectral and temporal envelopes as two of the most salient features of tone colour. Fourier analysis of a note generates its n-leading harmonic partials and an amplitude spectral n-vectorA for n≥2. We introduce harmonic manifolds associated with amplitude spectral vectors. This is to accommodate the n-leading harmonic partials of the note. Then, harmonic partials are modelled by solutions of a radial coupling of n-tuple parametric Hopf oscillators. We use one bifurcation parameter and interpolate the qualitative evolution of temporal envelope over a set of consecutive time-intervals via bifurcation analysis and control. Bifurcation scenarios involve harmonic asymptotic sets as time-asymptotic limits of harmonic partials. Each harmonic asymptotic set is homeomorphic to a k-tori, where k≤n is the number of contributing harmonic partials. Bifurcation classification of tone colour is then drawn up by encoding the spectral geometry of acoustic signals into harmonic manifolds and harmonic asymptotic sets. For instance, we treat audio C♯4 files obtained from piano and violin. Different bifurcation scenarios distinguish temporal amplitude bifurcations of these sounds. A complete hysteresis cycle is observed within the temporal amplitude bifurcations of C♯4 note played by violin.

In vivo assessment of pulmonary edema and fibrosis in rats using separation of single and multiple scattering contributions and quantitative ultrasound

The application of spectral quantitative ultrasound (QUS) and envelope statistical methods to lung ultrasound is complicated due to the presence of multiple scattering (MS). In this study, the relationship between QUS assessment of lung disease and scattering regime was explored by applying QUS to lung ultrasound data, where the single scattering (SS) and MS components were separated. Data were acquired by a Verasonics Vantage ultrasound scanner from 15 healthy rats, 19 edematous rats, and 20 fibrotic rats. SS and MS components of the data were separated by a random matrix theory approach. Spectral QUS and envelope statistical parameters were then estimated from the SS and MS data separately. Initial results were obtained using the healthy and pulmonary edema data, where the extracted QUS parameters were correlated to the wet-to-dry (W/D) weight ratio, a gold standard measurement of edema. Several QUS parameters were significantly correlated to W/D ratio for both the SS and MS data, and the highest correlation coefficient was 0.53 using the SS data and 0.48 using the MS data. These results demonstrate the utility of QUS for assessing lung disease and provide further insight on the effects of SS and MS on spectral QUS and envelope statistical parameters.

Novel frequency-based approach for detection of steady-state visual evoked potentials for realization of practical brain computer interfaces

ABSTRACT Various algorithms for recognizing Steady-State Visual Evoked Potentials have been proposed over the last decade for realizing Brain-Computer Interfaces. However, frequency-domain techniques aside from classical FFT have been generally neglected. While close to perfect accuracies have been reported in SSVEP-based BCI studies, achieving high accuracy in a realistic scenario is still challenging. Here several frequency-domain algorithms were evaluated for SSVEP detection for the first time, and a new algorithm based on spectral averaging on resampled signal (SAoRS) was proposed, when a single EEG channel and high-frequency flickers were considered to improve user experience. Spectral Envelope (SE) and Maximum Entropy (ME) methods outperformed Burg, MUSIC, and Welch for processing window lengths of 0.5–2 s. The newly developed SAoRS algorithm significantly outperformed SE and the benchmark CCA algorithms for 0.5 s processing window. The results suggest that Spectral Envelop and SAoRS algorithms can provide high accuracies in SSVEP BCI systems.

Relevance of the Spectral Analysis Method of Tilted Fiber Bragg Grating-Based Biosensors: A Case-Study for Heart Failure Monitoring.

Optical fiber technology has rapidly progressed over the years, providing valuable benefits for biosensing purposes such as sensor miniaturization and the possibility for remote and real-time monitoring. In particular, tilted fiber Bragg gratings (TFBGs) are extremely sensitive to refractive index variations taking place on their surface. The present work comprises a case-study on the impact of different methods of analysis applied to decode spectral variations of bare and plasmonic TFBGs during the detection of N-terminal B-type natriuretic peptide (NT-proBNP), a heart failure biomarker, namely by following the most sensitive mode, peaks of the spectral envelopes, and the envelopes’ crossing point and area. Tracking the lower envelope resulted in the lowest limits of detection (LOD) for bare and plasmonic TFBGs, namely, 0.75 ng/mL and 0.19 ng/mL, respectively. This work demonstrates the importance of the analysis method on the outcome results, which is crucial to attain the most reliable and sensitive method with lower LOD sensors. Furthermore, it makes the scientific community aware to take careful attention when comparing the performance of different biosensors in which different analysis methods were used.

Combined-Vernier effect based on hybrid fiber interferometers for ultrasensitive temperature and refractive index sensing.

A kind of hybrid fiber interferometer consisting of a fiber Sagnac interferometer (FSI), a closed-cavity Fabry-Perot interferometer (FPI), and an open-cavity FPI is proposed for generating combined-Vernier-effect. Through adjusting the polarization-maintaining fiber (PMF) length of the FSI, the free spectral range (FSR) is tailored to be similar to that of the parallel-connected reference FPI for producing the first Vernier effect, of which the spectrum is used to match the sensing FPI spectrum for obtaining the second Vernier effect. Noticeable lower and upper spectral envelopes are achieved in the first and second Vernier effects, respectively, so called the combined-Vernier spectrum. Accessibly, the upper envelope is only sensitive to the refractive index (RI) owing to the characteristics of the open-cavity FPI, while the lower one is immune to the RI and employed to detect the temperature by taking advantage of the FSI. Most importantly, the sensitivities of RI and temperature can be significantly improved simultaneously without crosstalk. The experimental results show that the RI sensitivity is -19844.67 nm/RIU and the temperature sensitivity is -46.14 nm/°C, which can be used for high-precision temperature and RI simultaneous measurement.

Spectral envelope-based adaptive empirical Fourier decomposition method and its application to rolling bearing fault diagnosis

Adaptive empirical Fourier decomposition (AEFD) is a recently developed approach of nonstationary signal mode separation. However, it requires to set the spectrum segmentation boundary relying on the users’ professional experience ahead of time. In this paper, a novel spectral envelope-based adaptive empirical Fourier decomposition (SEAEFD) method is proposed to improve the performance of AEFD for rolling bearing vibration signal analysis. In the proposed SEAEFD approach, fast Fourier transform (FFT) of the raw signal is calculated to obtain the frequency spectrum at first. Then, the spectral envelope processing is implemented on the spectrum signal obtained by FFT to achieve an adaptive segmentation. In the traditional segmentation method, generally, the minima and midpoints between adjacent extreme points are taken as the spectrum segmentation boundary, in which the obtained frequency band contains more interference components. To achieve the effect of denoising and restrain the noise that existed in the collected vibration signal, SEAEFD is proposed to optimize the spectrum segmentation boundary so that the obtained frequency band contains the least noise components. Lastly, the inverse FFT is used to reconstruct the component signal within each frequency band and the gained signals are termed as Fourier intrinsic mode functions (FIMFs). Therefore, SEAEFD enables a nonstationary signal to be decomposed into several single-component signals with instantaneous frequencies of physical significance. The proposed SEAEFD method is compared with recently developed methods, including EAEFD, AEFD, EWT, VMD and EMD methods, by analyzing the simulation signals and the measured data of rolling bearing. The results indicate that SEAEFD is valid in diagnosing rolling bearing faults and gets a better diagnosis performance than the compared methods.

Microwave Photonic Interrogation of a High-Speed and High-Resolution Multipoint Refractive Index Sensor

Microwave photonic interrogation of a high-speed and high-resolution multipoint refractive index (RI) sensor based on an Au-coated tilted fiber grating (Au-TFBG) array is proposed and experimentally demonstrated. Due to the surface plasmon resonance (SPR), the optical spectrum of an Au-TFBG has a dip in the spectral envelope. When an Au-TFBG is immersed in a solution and the RI of the solution changes, the location of the dip will shift, reflecting the change of the RI. For multipoint sensing, an Au-TFBG array with the array elements located at different locations is employed. However, due to the overlap of the spectra of the Au-TFBGs in the array, the dips cannot be precisely located by optical spectrum measurement. A solution is to convert the spectra to the time domain based on spectral shaping and wavelength-to-time (SS-WTT) mapping with the mapped temporal waveforms separated by different time delays. By using a digital signal processor (DSP), the sensing information can be extracted at a high speed and high resolution. The proposed approach is evaluated experimentally. Experimental results show that the sensor has high RI accuracies of 3.1&#x00D7;10^&#x2212;5, 2.5&#x00D7;10^&#x2212;5 and 2.6&#x00D7;10^&#x2212;5 refractive index unit (RIU) and a fast sensing speed of 11.75 kHz.

Disruption of large-scale electrophysiological networks in stroke patients with visuospatial neglect.

Stroke frequently produces attentional dysfunctions including symptoms of hemispatial neglect, which is characterized by a breakdown of awareness for the contralesional hemispace. Recent studies with functional MRI (fMRI) suggest that hemineglect patients display abnormal intra- and interhemispheric functional connectivity. However, since stroke is a vascular disorder and fMRI signals remain sensitive to nonneuronal (i.e., vascular) coupling, more direct demonstrations of neural network dysfunction in hemispatial neglect are warranted. Here, we utilize electroencephalogram (EEG) source imaging to uncover differences in resting-state network organization between patients with right hemispheric stroke (N = 15) and age-matched, healthy controls (N = 27), and determine the relationship between hemineglect symptoms and brain network organization. We estimated intra- and interregional differences in cortical communication by calculating the spectral power and amplitude envelope correlations of narrow-band EEG oscillations. We first observed focal frequency-slowing within the right posterior cortical regions, reflected in relative delta/theta power increases and alpha/beta/gamma decreases. Secondly, nodes within the right temporal and parietal cortex consistently displayed anomalous intra- and interhemispheric coupling, stronger in delta and gamma bands, and weaker in theta, alpha, and beta bands. Finally, a significant association was observed between the severity of left-hemispace search deficits (e.g., cancellation test omissions) and reduced functional connectivity within the alpha and beta bands. In sum, our novel results validate the hypothesis of large-scale cortical network disruption following stroke and reinforce the proposal that abnormal brain oscillations may be intimately involved in the pathophysiology of visuospatial neglect.