Processing Of Spectral Information Research Articles

Research on the Processing of Image and Spectral Information in an Infrared Polarization Snapshot Spectral Imaging System

In order to solve the problems of a low target recognition rate and poor real-time performance brought about by conventional infrared imaging spectral detection technology under complex background conditions or in the detection of targets of weak radiation or long distance, a kind of infrared polarization snapshot spectral imaging system (PSIFTIS) and a spectrum information processing method based on micro-optical devices are proposed in this paper, where the synchronous acquisition of polarization spectrum information is realized through the spatial modulation of phase with a rooftop-shaped multi-stage micro-mirror and the modulation of the polarization state of light with a micro-nanowire array. For the polarization interference image information obtained, the infrared polarization spectrum decoupling is realized by image segmentation, optical path difference matching, and image registration methods, the infrared polarization spectrum reconstruction is realized by Fourier transform spectral demodulation, and the infrared polarization image fusion is realized by decomposing and reconstructing the high- and low-frequency components of the polarization image based on the Haar wavelet transform. The maximum spectral peak wavenumber error of the four polarization channels of the polarization spectrum reconstruction is less than 2 cm−1, and the polarization angle error is within 1°. Ultimately, compared with the unprocessed polarization image unit, the peak signal-to-noise ratio is improved by 45.67%, the average gradient is improved by 8.03%, and the information entropy is improved by 56.98%.

FGRC-Net: A high-information interactive convolutional neural network for identifying ink spectral information

Modifying some keywords or numbers on documents to change the original intention is illegal. In some litigation cases, especially economic cases, there is often a need to examine the type of ink on documents. This paper proposes a nondestructive and accurate detection method for ink inspection. In this work, a high-information interactive, full group residual convolution network (FGRC-Net) is proposed and combined with spectral information to classify the inks of six different brands of pens. First, FGRC-Net realizes the information interaction among channels to effectively improve the feature extraction ability through multigroup convolution and multipath cascade. Meanwhile, to avoid feature degradation, the residual connection is introduced. Second, the ink spectral information of six pens based on the hyperspectral system is obtained. Finally, in comparing FGRC-Net and other deep learning methods, FGRC-Net shows the best classification performance with 98.38% accuracy, 98.53% precision, and 98.40% recall. The results show that combining FGRC-Net with a hyperspectral system is an effective detection method for handwritten ink classification. It also provides an effective detection method for handwritten document ink inspection. At the same time, the universality and validity of FGRC-Net in processing spectral information are verified by the comparison of multiple datasets.

Compact multi-foci metalens spectrometer

A lightweight and portable spectrometer is desirable for miniaturization and integration. The unprecedented capability of optical metasurfaces has shown much promise to perform such a task. We propose and experimentally demonstrate a compact high-resolution spectrometer with a multi-foci metalens. The novel metalens is designed based on wavelength and phase multiplexing, which can accurately map the wavelength information into its focal points located on the same plane. The measured wavelengths in the light spectra agree with simulation results upon the illumination of various incident light spectra. The uniqueness of this technique lies in the novel metalens that can simultaneously realize wavelength splitting and light focusing. The compactness and ultrathin nature of the metalens spectrometer render this technology have potential applications in on-chip integrated photonics where spectral analysis and information processing can be performed in a compact platform.

Dual Optical Frequency Comb Neuron: Co‐Developing Hardware and Algorithm

Previous studies on photonic neural network have demonstrated that algorithm can inspire hardware design. This study seeks to demonstrate that hardware can also inspire algorithm design. To further exploit the advantages of photonic analog computing, the authors develop hardware and algorithm simultaneously for photonic convolutional neural networks. Specifically, this work developed an architecture called dual optical frequency comb neuron (DOFCN) enabled by an integrated microcomb to perform cosinusoidal nonlinear activation and vector convolution without temporal or spatial dispersion and large‐scale modulator arrays. Furthermore, DOFCN‐based composite vector convolutional neural networks (CVCNNs), an optical‐electric hybrid model, are proposed to perform classification and regression tests in signal modulation format identification and optical structure inverse design tasks, respectively. The ablation experiments show that under 4‐bit precision limit, the element‐wise activation CVCNN has 14% higher classification accuracy, 76% lower regression residuals, and 100% higher training efficiency than that of the 32‐bit standard convolutional neural network (CNN). DOFCN exhibits impressive spectral information processing ability to facilitate signal‐processing tasks related to optics and electromagnetics.

The frequency-following response to assess the neural representation of spectral speech cues in older adults

Older adults often present difficulties understanding speech that cannot be explained by age-related changes in sound audibility. Psychoacoustic and electrophysiologic studies have linked these suprathreshold difficulties to age-related deficits in the auditory processing of temporal and spectral sound information. These studies suggest the existence of an age-related temporal processing deficit in the central auditory system, but the existence of such deficit in the spectral domain remains understudied. The FFR is an electrophysiological evoked response that assesses the ability of the neural auditory system to reproduce the spectral and temporal patterns of a sound. The main goal of this short review is to investigate if the FFR can identify and measure spectral processing deficits in the elderly compared to younger adults (for both, without hearing loss or competing noise). Furthermore, we want to determine what stimuli and analyses have been used in the literature to assess the neural encoding of spectral cues in older adults. Almost all reviewed articles showed an age-related decline in the auditory processing of spectral acoustic information. Even when using different speech and non-speech stimuli, studies reported an age-related decline at the fundamental frequency, at the first formant, and at other harmonic components using different metrics, such as the response's amplitude, inter-trial phase coherence, signal-to-response correlation, and signal-to-noise ratio. These results suggest that older adults may present age-related spectral processing difficulties, but further FFR studies are needed to clarify the effect of advancing age on the neural encoding of spectral speech cues. Spectral processing research on aging would benefit from using a broader variety of stimuli and from rigorously controlling for hearing thresholds even in the absence of disabling hearing loss. Advances in the understanding of the effect of age on FFR measures of spectral encoding could lead to the development of new clinical tools, with possible applications in the field of hearing aid fitting.

Assessing temporal responsiveness of primary stimulated neurons in auditory brainstem and cochlear implant users

The reasons why clinical outcomes with auditory brainstem implants (ABIs) are generally poorer than with cochlear implants (CIs) are still somewhat elusive. Prior work has focused on differences in processing of spectral information due to possibly poorer tonotopic representation and higher channel interaction with ABIs than with CIs. In contrast, this study examines the hypothesis that a potential contributing reason for poor speech perception in ABI users may be the relative lack of temporal responsiveness of the primary neurons that are stimulated by the ABI. The cochlear nucleus, the site of ABI stimulation, consists of different neuron types, most of which have much more complex responses than the auditory nerve neurons stimulated by a CI. Temporal responsiveness of primary stimulated neurons was assessed in a group of ABI and CI users by measuring recovery of electrically evoked compound action potentials (ECAPs) from single-pulse forward masking. Slower ECAP recovery tended to be associated with poorer hearing outcomes in both groups. ABI subjects with the longest recovery time had no speech understanding or even no hearing sensation with their ABI device; speech perception for the one CI outlier with long ECAP recovery time was well below average. To the extent that ECAP recovery measures reveal temporal properties of the primary neurons that receive direct stimulation form neural prosthesis devices, they may provide a physiological underpinning for clinical outcomes of auditory implants. ECAP recovery measures may be used to determine which portions of the cochlear nucleus to stimulate, and possibly allow us to enhance the stimulation paradigms.

SRNSSI: A Deep Light-Weight Network for Single Image Super Resolution Using Spatial and Spectral Information

Design of a residual block that provides a rich set of features while requiring only small numbers of parameters and operations is crucial for the task of single image super resolution. This is especially important in applications with limited power and storage capacity. In this paper, a new multi-domain residual block is proposed in order to generate richer set of features for the task of image super resolution. The proposed residual block consists of two feature generation modules. The first one is a spatial information processing module and the second one is a spectral information processing module. The feature maps obtained by these two feature generation modules are concatenatively fused to obtain block's output. The new residual block is used to build light-weight super resolution networks. Extensive experiments are performed using several benchmark datasets in order to evaluate the performance of the networks using the new multi-domain residual block. It is shown that the use of both the spatial and spectral features enhances the performance of the light-weight super resolution networks.

Colored visual stimuli evoke spectrally tuned neuronal responses across the central nervous system of zebrafish larvae

BackgroundVisually guided behaviors such as optomotor and optokinetic responses, phototaxis, and prey capture are crucial for survival in zebrafish and become apparent after just a few days of development. Color vision, which in zebrafish is based on a spatially anisotropic tetrachromatic retina, provides an additional important component of world representation driving fundamental larval behaviors. However, little is known about the central nervous system (CNS) circuitry underlying color vision processing downstream of the retina, and its activity correlates with behavior. Here, we used the transparent larva of zebrafish to image CNS neurons and their activity in response to colored visual stimuli.ResultsTo investigate the processing of chromatic information in the zebrafish larva brain, we mapped with cellular resolution, spectrally responsive neurons in the larva encephalon and spinal cord. We employed the genetically encoded calcium indicator GCaMP6s and two-photon microscopy to image the neuronal activity while performing visual stimulation with spectrally distinct stimuli at wavelengths matching the absorption peaks of the four zebrafish cone types. We observed the presence of a high number of wavelength-selective neurons not only in the optic tectum, but also in all other regions of the CNS, demonstrating that the circuitry involved in processing spectral information and producing color-selective responses extends to the whole CNS.ConclusionsOur measurements provide a map of neurons involved in color-driven responses, revealing that spectral information spreads in all regions of the CNS. This suggests the underlying complexity of the circuits involved and opens the way to their detailed future investigation.

A Pearl Spectrometer.

Information recovery from incomplete measurements, typically performed by a numerical means, is beneficial in a variety of classical and quantum signal processing. Random and sparse sampling with nanophotonic and light scattering approaches has received attention to overcome the hardware limitations of conventional spectrometers and hyperspectral imagers but requires high-precision nanofabrications and bulky media. We report a simple spectral information processing scheme in which light transport through an Anderson-localized medium serves as an entropy source for compressive sampling directly in the frequency domain. As implied by the "lustrous" reflection originating from the exquisite multilayered nanostructures, a pearl (or mother-of-pearl) allows us to exploit the spatial and spectral intensity fluctuations originating from strong light localization for extracting salient spectral information with a compact and thin form factor. Pearl-inspired light localization in low-dimensional structures can offer an alternative of spectral information processing by hybridizing digital and physical properties at a material level.

Remote Compact Seismic Sensor for the Moving Person Detection

The results of the development of a remote compact seismic sensor for the detection of a moving person, as well as sensor testing, are presented in this letter. A choice of a seismic transducer and a seismic signal processing unit is justified. Characteristics of seismic signals from a moving person are analyzed, and an algorithm for the person detection is proposed. As tests have shown, the developed seismic sensor has high-level parameters, which allows it to be successfully used both for reconnaissance purposes and for a various objects guard. It is shown that such parameters can be achieved in a relatively simple device that uses temporal parameters of seismic signals without the use of complex and energy expenditure spectral, time-frequency, statistical, learning, or self-learning information processing methods.

Spectrotemporal Modulation Sensitivity in Cochlear-Implant and Normal-Hearing Listeners: Is the Performance Driven by Temporal or Spectral Modulation Sensitivity?

This study examined the contribution of temporal and spectral modulation sensitivity to discrimination of stimuli modulated in both the time and frequency domains. The spectrotemporally modulated stimuli contained spectral ripples that shifted systematically across frequency over time at a repetition rate of 5 Hz. As the ripple density increased in the stimulus, modulation depth of the 5 Hz amplitude modulation (AM) reduced. Spectrotemporal modulation discrimination was compared with subjects’ ability to discriminate static spectral ripples and the ability to detect slow AM. The general pattern from both the cochlear implant (CI) and normal hearing groups showed that spectrotemporal modulation thresholds were correlated more strongly with AM detection than with static ripple discrimination. CI subjects’ spectrotemporal modulation thresholds were also highly correlated with speech recognition in noise, when partialing out static ripple discrimination, but the correlation was not significant when partialing out AM detection. The results indicated that temporal information was more heavily weighted in spectrotemporal modulation discrimination, and for CI subjects, it was AM sensitivity that drove the correlation between spectrotemporal modulation thresholds and speech recognition. The results suggest that for the rates tested here, temporal information processing may limit performance more than spectral information processing in both CI users and normal hearing listeners.

Spectral–Spatial Hyperspectral Image Classification Using Cascaded Markov Random Fields

Joint spectral and spatial information processing is an effective means to improve the classification accuracy of hyperspectral remote sensing images. The Markov random field (MRF) is a powerful tool for integrating spectral and contextual information into the classification framework. However, the shallow structure of the MRF cannot fully exploit the information of hyperspectral imagery. In this article, a cascaded MRF model is proposed to combine the benefit of the MRF and cascaded model. The model consists mainly of two phases. In the first phase, the predicted probability vector generated by the support vector machine classifier and the MRF model is combined. Then, the combined feature vector is concatenated with the original spectral feature to generate an enhanced feature vector with more discriminating power. In the subsequent stage, the enhanced feature vector is used as the input of the next level of the cascaded MRF model. Experiments based on three widely used hyperspectral data show that the proposed method has state-of-the-art performance.

Smoking status classification by optical spectroscopy and partial least square regression

Smoking status of individuals is often revealed through self-reported data and interviews. The incidence of false reports severely impairs the proper assessment of the individuals’ health conditions and their risk to tobacco associated diseases, delays clinical intervention and treatment services. This paper presents the use of optical technique combined with partial least square (PLS) regression model in the classification of smoking status. The focus of this work is on light absorbance signals (by skin tissue) in the wavelength range of 520 – 600 nm; based on these data dendogram and PLS techniques are used to estimate the number of likely clusters within the considered dataset and to predict smoking status of individuals, respectively. The results from the processing of spectral information of smoking and nonsmoking populations revealed a high misclassification rate of 26.67 % using dendrogram method, but a considerably high accuracy of 90 % evaluated via leave one out cross validation was obtained using PLS component number 4. This study concluded that the spectral oscillation patterns and descending rates corresponded to nonsmoking and smoking individuals could be differentiated and specified using PLS technique in the determination of smoking status.

Application of Multivariate Analysis of Broadband Transmission Spectra for Calibration of Physico-Chemical Parameters of Wines

The use of multivariate processing of spectral information has recently been favored due to the express nature of this method, the ease of use of mathematical packages, and the lack of the need to add chemical reagents. The aim of the work is using the methods of multivariate analysis of broadband transmission spectra to calibrate the physicochemical parameters of wines and to improve the accuracy of this calibration by selecting spectral variables.Using the interval projection to latent structures of the transmission spectra in the range of 220– 2500 nm, the physicochemical characteristics of the varietal unblended Moldovan wine are calibrated. Interval methods of multivariate data analysis allow signifi reducing the root mean square calibration error in comparison with the broadband multivariate methods. Residual predictive deviations exceed the threshold value of 2.5 for K, Ca, Mg, oxalic, malic and succinic acids, 2,3-butylene glycol, ash and phenolic compounds for red wines and Mg, tartaric, citric and lactic acids, 2,3-butylene glycol, ash, phenolic compounds and soluble salts for white wines. These values demonstrate good calibration quality.The application of the proposed method for calibrating the physicochemical parameters of wines makes it possible to replace traditional methods with spectral measurements, which are available not only in laboratory but also in the fi and characterized by small values of the root mean square error of calibration.

Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles.

Deep learning technologies are permeating every field from image and speech recognition to computational and systems biology. However, the application of convolutional neural networks (CCNs) to "omics" data poses some difficulties, such as the processing of complex networks structures as well as its integration with transcriptome data. Here, we propose a CNN approach that combines spectral clustering information processing to classify lung cancer. The developed spectral-convolutional neural network based method achieves success in integrating protein interaction network data and gene expression profiles to classify lung cancer. The performed computational experiments suggest that in terms of accuracy the predictive performance of our proposed method was better than those of other machine learning methods such as SVM or Random Forest. Moreover, the computational results also indicate that the underlying protein network structure assists to enhance the predictions. Data and CNN code can be downloaded from the link: https://sites.google.com/site/nacherlab/analysis.

Automatic Building Footprint Extraction from Multi-Resolution Remote Sensing Images Using a Hybrid FCN

Recent technical developments made it possible to supply large-scale satellite image coverage. This poses the challenge of efficient discovery of imagery. One very important task in applications like urban planning and reconstruction is to automatically extract building footprints. The integration of different information, which is presently achievable due to the availability of high-resolution remote sensing data sources, makes it possible to improve the quality of the extracted building outlines. Recently, deep neural networks were extended from image-level to pixel-level labelling, allowing to densely predict semantic labels. Based on these advances, we propose an end-to-end U-shaped neural network, which efficiently merges depth and spectral information within two parallel networks combined at the late stage for binary building mask generation. Moreover, as satellites usually provide high-resolution panchromatic images, but only low-resolution multi-spectral images, we tackle this issue by using a residual neural network block. It fuses those images with different spatial resolution at the early stage, before passing the fused information to the Unet stream, responsible for processing spectral information. In a parallel stream, a stereo digital surface model (DSM) is also processed by the Unet. Additionally, we demonstrate that our method generalizes for use in cities which are not included in the training data.

«Atom» software for atomic spectral analysis

«Atom» software is used in integrated spectral devices for atomic emission and atomic absorption analysis produced by «VMK-Optoélektronika». Development of the new spectral instruments and upgrading of existing devices, birth of new mathematical methods and algorithms for processing spectral information, as well as user stories motivate and promote improvement of the software. This paper presents the main developments and most noticeable changes in «Atom «software for the period 2017 – 2018. The developed 64-bit version of «Atom» software removed the limitation on the amount of recorded sequences of spectra and significantly speeded up their processing by parallelizing the calculations on the processor cores (rather important step, for example, in scintillation atomic emission spectrometry). To implement continuum source electrothermal atomic absorption spectrometry for simultaneous multi-element analysis, we have developed the «Automatic Dispenser» and «Absorption Spectrometer» tools and have implemented the method proposed by D.A. Katskov to linearize calibration dependences in a wide range of concentrations. The possibility of spectrophotometric determination of substances in the solutions of their mixtures has been also implemented. The «Barcode Scanner» and the «Automatic Start of Measurement» tools have been developed to speed up and automate the analysis procedure. The software is supplemented with a set of useful functions designed for further development of analytical methods and programs of analysis.

Determination of trace elements in plants using the direct current arc atomic-emission spectrometry

Plants connect together the atmosphere, the biosphere and the hydrosphere, participate in the migration of chemical elements and indicate an anthropogenic environmental pollution. For this reason, it is important to determine the content of a wide range of essential and toxic elements in plants. Atomic emission spectrometry with different sources of excitation of atoms is one of the most frequently used instrumental methods for determining the elemental composition of plants. Express determination of 23 essential and toxic elements in plant powders (without ashing and acid decomposition) using the atomic emission spectrometry with arc discharge (DC-arc AES) was developed. The special ways of obtaining spectra and of spectral information processing provided an increased accuracy of the analytical results in the concentration ranges from n ∙(10 -6 -10 -4 ) to 10 wt. %. The correctness of the results of the developed technique was verified by the means of the encrypted certified reference materials of the plants and matching them with the results obtained by the certified techniques of atomic emission and mass spectrometry with inductively coupled plasma. The application of the DC-arc AES technique to determine trace elements in plants from the ecologically clean and polluted areas with different landscapes, provided new analytical data for the interdisciplinary research. These data are traceable to the well-known international certified reference materials of plants. Keywords : plants, trace elements, direct current arc atomic emission spectrometry, traceability. DOI: http://dx.doi.org/10.15826/analitika.2019.23.3.011 (Russian) I.E. Vasil'eva and E.V. Shabanova Vinogradov Institute of Geochemistry, Siberian Branch of the Russian Academy of Sciences, Russian Federation, 664033, Irkutsk, Favorsky Str., 1A

Age-related differences in Voice-Onset-Time in Polish language users: An ERP study

Using the Mismatch Negativity (MMN) paradigm we investigated for the first time cortical responses to consonant - vowel (CV) syllables differing in Voice-Onset-Time (VOT) for Polish, a member of the Slavic group of languages. The study aimed at testing age-related effects on different ERP responses in young (20–30 years of age) and elderly (60–68 years) native Polish speakers. Participants were presented with a sequence of voiced and voiceless stop CV syllables /to/ and /do/ with different VOT values (−100 ms, −70 ms, −30 ms, −20 ms, +20 ms, +50 ms). We analyzed MMN and P1, N1, N1′, P2, N2 components. Our results showed an age-related decline in voicing perception in all tested ERP components. This decline could be explained in relation to a general slowing in neural processing with advancing age and may be associated with difficulties in temporal- and spectral-information processing in elderly people. Our findings revealed also that specific features of Slavic languages influence ERP morphology in a different way than reported in the literature for aspirating languages.

Syntactic processing in music and language: Effects of interrupting auditory streams with alternating timbres

Music and language both rely on the processing of spectral (pitch, timbre) and temporal (rhythm) information to create structure and meaning from incoming auditory streams. Behavioral results have shown that interrupting a melodic stream with unexpected changes in timbre leads to reduced syntactic processing. Such findings suggest that syntactic processing is conditional on successful streaming of incoming sequential information. The current study used event-related potentials (ERPs) to investigate whether (1) the effect of alternating timbres on syntactic processing is reflected in a reduced brain response to syntactic violations, and (2) the phenomenon is similar for music and language. Participants listened to melodies and sentences with either one timbre (piano or one voice) or three timbres (piano, guitar, and vibraphone, or three different voices). Half the stimuli contained syntactic violations: an out-of-key note in the melodies, and a phrase-structure violation in the sentences. We found smaller ERPs to syntactic violations in music in the three-timbre compared to the one-timbre condition, reflected in a reduced early right anterior negativity (ERAN). A similar but non-significant pattern was observed for language stimuli in both the early left anterior negativity (ELAN) and the left anterior negativity (LAN) ERPs. The results suggest that disruptions to auditory streaming may interfere with syntactic processing, especially for melodic sequences.