Input Spectrum Research Articles

Computer Modeling of Human Voice System Diagnostic Algorithms

Every year, people are faced with an increasing number of diseases that require timely detection and diagnosis without causing discomfort to the patient. Thus arose the systems of objective and subjective assessment of the quality of hearing, as well as the first systems of analysis of the state of the vocal tract. The problem of voice pathology is inherent in a fairly large risk group, which includes teachers, artists, call center operators. The specifics of the development of such systems is that the hardware and software complex developed by the engineer should be convenient for use by a doctor and relevant in terms of existing solutions to identify certain acoustic parameters of the voice. The study of the voice spectrum can be the main diagnostic test in the diagnosis of persistent voice disorders and, along with the study of the voice field and vibrometry, allows you to determine the form of voice disorders in professionals. Acoustic tests for the presence of high and low singing forms in the singing voice can be highly important in determining the singer's performance and professional prognosis, can serve as a criterion in the diagnosis of persistent voice disorders, and their use in early stages of occupational laryngeal diseases will help prevent preventive measures. MATLAB software comes with a large number of tools, which facilitates the implementation of many engineering, mathematical, computational issues of development and research of various processes related to any field of research. Based on these tools, there are a large number of basic functions of digital signal analysis, including audio signals. FFT - fast Fourier transform algorithms are chosen as a basis, which with a certain modification are divided into parametric and nonparametric. In this case, the nonparametric Welch method and the parametric Berg method are chosen. The user is given access to choose between the parameters required for their operation. For the first it is the dimension of the weight function, for the second it is the order of the autoregressive model. All this gives opportunity to analyze the spectrum of vowel phonemes. The AppDesigner package provides great opportunities for creating interfaces in software development. By manipulating the functions of Callback, you can bring the program to the finest settings, which at first may seem invisible, but generally create comfort when working. It is important to build a certain algorithm of action of each component. It often happens that it is necessary to take into account such details for which a certain component in itself is not responsible. For example, this could be changing the signatures of other components. To simplify code writing, it is important to create m-functions. However, this must be taken into account when editing them after entering them in the main script. Using the above toolbox, a software interface was developed, which is divided into two working areas: time and spectral parts. In addition, the interface is filled with controls for input data and spectrum analyzer parameters, as well as spectrum analysis tools.

Bayesian deep learning-based 1 H-MRS of the brain: Metabolite quantification with uncertainty estimation using Monte Carlo dropout.

To develop a Bayesian convolutional neural network (BCNN) with Monte Carlo dropout sampling for metabolite quantification with simultaneous uncertainty estimation in deep learning-based proton MRS of the brain. Human brain spectra were simulated using basis spectra for 17 metabolites and macromolecules (N=100 000) at 3.0 Tesla. In addition, actual in vivo spectra (N=5) were modified by adjusting SNR and linewidth with increasing severity of spectral degradation (N=50). A BCNN was trained on the simulated spectra to generate a noise-free, line-narrowed, macromolecule signal-removed, metabolite-only spectrum from a typical human brain spectrum. At inference, each input spectrum was Monte Carlo dropout sampled (50 times), and the resulting mean spectrum and variance spectrum were used for metabolite quantification and uncertainty estimation, respectively. Using the simulated spectra, the mean absolute percent errors of the BCNN-predicted metabolite content were < 10% for Cr, Glu, Gln, mI, NAA, and Tau (< 5% for Glu, NAA, and mI). For all metabolites, the correlations (r's) between the ground-truth error and BCNN-predicted uncertainty ranged 0.72-0.94 (0.83 ± 0.06; p < 0.001). Using the modified in vivo spectra, the extent of variation in the estimated metabolite content against the increasing severity of spectral degradation tended to be smaller with BCNN than with linear combination of model spectra (LCModel). Overall, the variation in metabolite content tended to be more highly correlated with the uncertainty from BCNN than with the Cramér-Rao lower-bounds from LCModel (0.938 ± 0.019 vs. 0.881 ± 0.057 [p=0.115]). The BCNN with Monte Carlo dropout sampling may be used in deep learning-based MRS for the estimation of uncertainty in the machine-predicted metabolite content, which is important in the clinical application of deep learning-based MRS.

Improving TVB-N prediction in pork using portable spectroscopy with just-in-time learning model updating method

Near infrared spectroscopy (NIR) technology is an effective method for nondestructive prediction of total volatile basic nitrogen (TVB-N) in pork. However, the NIR models lack robustness and often fail when used on a new batch. To handle the problem and obtain better prediction performance, a model updating method based on just-in-time learning (JITL) was proposed in this study. A comprehensive similarity criterion considering both input (spectra) and output (TVB-N content) information was designed. Combining a defined similarity factor, the most relevant samples to new batch samples were selected and a local least square support vector machine model was established in real time based on the selected samples. The results showed that the models updated with JITL approach kept a high predictive performance on new independent batch with prediction error decreasing from 2.95 to 1.60 mg/100 g. The robust models made on selected similar samples combined with JITL model updating strategy can support to make NIR spectroscopy a preferred choice for non-destructive assessment of quality features in pork meat.

Closed-loop identification for aircraft flutter model parameters

PurposeThe purpose of this paper is to extend the authors’ previous contributions on aircraft flutter model parameters identification. Because closed-loop condition is more widely used in today’s practice, a closed-loop stochastic model of the aircraft flutter test is constructed to model the aircraft flutter process, whose input–output signals are all corrupted by the observed noises. Through using a rational transfer function, the equivalent property between the aircraft flutter model parameters and polynomial coefficients is established, and then the problem of aircraft flutter model parameters identification is turned to one closed-loop identification problem. An iterative identification algorithm is proposed to identify the unknown polynomial coefficients, being benefit for the latter flutter model parameter identification. Furthermore, as the closed-loop output corresponds to the flutter amplitude, so from the point of the minimization with respect to the variance of the closed-loop output, the optimal input signal and optimal feedback controller are all derived to achieve the zero flutter, respectively, for example, the optimal input spectrum and the detailed form for optimal feedback controller.Design/methodology/approachFirst, model parameter identification for aircraft flutter is reviewed as one problem of parameter identification and this aircraft flutter model corresponds to one closed-loop stochastic model, whose input signal and output are corrupted by external noises. Second, for aircraft flutter closed-loop statistical model with statistical noise, an iterative identification algorithm is proposed to identify the unknown model parameters. Third, from the point of minimizing with respect to the variance of the closed-loop output, the optimal input signal and optimal feedback controller are all derived to achieve the zero flutter, respectively, for example, the optimal input spectrum and the detailed form for optimal feedback controller.FindingsThis aircraft flutter model corresponds to one closed-loop stochastic model, whose input signal and output are corrupted by external noises. Then, identification algorithm and optimal input signal design are studied for aircraft flutter model parameter identification with statistical noise, respectively. It means the optimal input signal and optimal feedback controller are useful for the aircraft flutter model parameter identification within the constructed new closed-loop stochastic model.Originality/valueTo the best of the authors’ knowledge, this problem of the model parameter identification for aircraft flutter is proposed by their previous work, and they proposed many identification strategies to identify these model parameters. This paper proposes a new closed-loop stochastic model to construct the aircraft flutter test, and some related topics are considered about this closed-loop identification for aircraft flutter model parameter identification in the framework of closed-loop condition.

A Broadband Spectrum Channelizer With PWM-LO-Based Sub-Band Gain Control

A broadband spectrum channelizer employing pulsewidth-modulated local-oscillator (PWM-LO)-based sub-band amplitude control is demonstrated. The channelizer has an analog bandwidth of 800 MHz and partitions the input spectrum into eight contiguous sub-bands. The design employs an approach based on frequency folding from harmonics of multi-phase periodic clocks and allows for scaling the relative gain of the sub-bands over a 20-dB range. This relaxes the compression performance of the channelizer baseband and sub-analog-to-digital converter (ADC) dynamic range in the presence of large sub-bands. Gain control on individual sub-bands is performed by employing customized PWM-LO waveforms, where the PWM-LO pulses are generated using delay-locked loops (DLLs). Off-chip learning employing a neural network is used to estimate the PWM symbol pulsewidths required for setting the desired LO harmonic levels. A 1.6-GS/s spectrum channelizer integrated circuit (IC) is implemented in a 65-nm CMOS process to verify the architecture. The measured channelizer gain is 51.6–56.5 dB without gain scaling and provides a range of 37–59 dB with PWM-LO gain control. Gain scaling at a specific harmonic improves blocker compression in an unattenuated sub-band from −34 to −16 dBm. The in-band gain compression with gain scaling also increases from −32 to −17 dBm.

Hollow-core fiber delivery of broadband mid-infrared light for remote spectroscopy.

High-resolution multi-species spectroscopy is achieved by delivering broadband3-4-μmmid-infrared light through a 4.5-meter-long silica-based hollow-core optical fiber. Absorptions from H37Cl, H35Cl, H2O and CH4 present in the gas within the fiber core are observed, and the corresponding gas concentrations are obtained to 5-ppb precision using a high-resolution Fourier-transform spectrometer and a full-spectrum multi-species fitting algorithm. We show that by fully fitting the narrow absorption features of these light molecules their contributions can be nulled, enabling further spectroscopy of C3H6O and C3H8O contained in a Herriott cell after the fiber. As a demonstration of the potential to extend fiber-delivered broadband mid-infrared spectroscopy to significant distances, we present a high-resolution characterization of the transmission of a 63-meter length of hollow-core fiber, fully fitting the input and output spectra to obtain the intra-fiber gas concentrations. We show that, despite the fiber not having been purged, useful spectroscopic windows are still preserved which have the potential to enable hydrocarbon spectroscopy at the distal end of fibers with lengths of tens or even hundreds of meters.

Deep regression with ensembles enables fast, first-order shimming in low-field NMR

Shimming in the context of nuclear magnetic resonance aims to achieve a uniform magnetic field distribution, as perfect as possible, and is crucial for useful spectroscopy and imaging. Currently, shimming precedes most acquisition procedures in the laboratory, and this mostly semi-automatic procedure often needs to be repeated, which can be cumbersome and time-consuming. The paper investigates the feasibility of completely automating and accelerating the shimming procedure by applying deep learning (DL). We show that DL can relate measured spectral shape to shim current specifications and thus rapidly predict three shim currents simultaneously, given only four input spectra. Due to the lack of accessible data for developing shimming algorithms, we also introduce a database that served as our DL training set, and allows inference of changes to 1H NMR signals depending on shim offsets. In situ experiments of deep regression with ensembles demonstrate a high success rate in spectral quality improvement for random shim distortions over different neural architectures and chemical substances. This paper presents a proof-of-concept that machine learning can simplify and accelerate the shimming problem, either as a stand-alone method, or in combination with traditional shimming methods. Our database and code are publicly available.

Proteoform identification based on top-down tandem mass spectra with peak error corrections.

In this paper, we study the problem for finding complex proteoforms from protein databases based on top-down tandem mass spectrum data. The main difficulty to solve the problem is to handle the combinatorial explosion of various alterations on a protein. To overcome the combinatorial explosion of various alterations on a protein, the problem has been formulated as the alignment problem of a proteoform mass graph (PMG) and a spectrum mass graph (SMG). The other important issue is to handle mass errors of peaks in the input spectrum. In previous methods, an error tolerance value is used to handle the mass differences between the matched consecutive nodes/peaks in PMG and SMG. However, such a way to handle mass error can not guarantee that the mass difference between any pairs of nodes in the alignment is approximately the same for both PMG and SMG. It may lead to large error accumulation if positive (or negative) errors occur consecutively for a large number of consecutive matched node pairs. The problem is severe so that some existing software packages include a step to further refine the alignments. In this paper, we propose a new model to handle the mass errors of peaks based on the formulation of the PMG and SMG. Note that the masses of sub-paths on the PMG are theoretical and suppose to be accurate. Our method allows each peak in the input spectrum to have a predefined error range. In the alignment of PMG and SMG, we need to give a correction of the mass for each matched peak within the predefined error range. After the correction, we impose that the mass between any two (not necessarily consecutive) matched nodes in the PMG is identical to that of the corresponding two matched peaks in the SMG. Intuitively, this kind of alignment is more accurate. We design an algorithm to find a maximum number of matched node and peak pairs in the two (PMG and SMG) mass graphs under the new constraint. The obtained alignment can show matched node and peak pairs as well as the corrected positions of peaks. The algorithm works well for moderate size input instances and takes very long time as well as huge size memory for large input size instances. Therefore, we propose an algorithm to do diagonal alignment. The diagonal alignment algorithm can solve large input size instances in reasonable time. Experiments show that our new algorithms can report alignments with much larger number of matched node pairs. The software package and test data sets are available at https://github.com/Zeirdo/TopMGRefine.

Peak-aware guided filtering for spectrum signal denoising

In the analysis of spectrum signals, it is especially important to identify the peaks of a spectrum signal. Due to various noise, spectrum signals must be smoothed in advance, while preserving peaks information. In this paper, we propose a peak-aware guided filtering (PAGF) for peak-preserving smoothing of a noisy spectrum signal. The content of a guidance signal is taken into account in the filtering process, which can provide smooth structure information and peak position of the input signal. The guidance signal is generated from the input signal by a nonlinear diffusion filtering (NDF) since the output of NDF provides sufficiently smooth structure and accurate peak position for the input signal. The proposed PAGF is validated by experiments on commonly tested signals, in comparison to previous peak-preserving smoothing methods. The results show that the PAGF can better preserve the peak height, width and position of the input spectrum signal while removing the noise.

Introduction

Inclusivity challenges that are prevailing in African cities and beyond, require concerted multi-thronged efforts from all stakeholders to address them. This is so because inclusivity issues cannot just be viewed from a narrow periscope but a comprehensive spectrum of inputs and actions. On the strength of the aforesaid, it became expedient for the SARChI-Chair for Inclusive Cities to organise a critical symposium that interrogated the concept of inclusivity from a multi-disciplinary dimension. Such an enquiry into the nature and extent of urban inclusivity in an African city context, provides an appropriate platform to understand factors underpinning the need to establish desired inclusivity (Medayese et al., 2016).

Probabilistic Energy-to-Amplitude Mapping in a Tapered Superconducting Nanowire Single-Photon Detector.

A spectrum-resolved photon detector is crucial for cutting-edge quantum optics, astronomical observation, and spectroscopic sensing. However, such an ability is rarely obtained because a direct linear conversion from weak single-photon energy to a readable electrical signal above the noise level without causing an avalanche is challenging. Here, we overcame these difficulties by building a probabilistic energy-to-amplitude mapping in a tapered superconducting nanowire single-photon detector and combining a computational reconstruction to obtain equivalent spectral resolving capacity. Distinguished dependence of pulse amplitude distributions on varied input spectra has been observed experimentally. As the energy-to-amplitude mapping is probabilistic, statistical measurements are required. By collecting around a few hundred photons, we have demonstrated wavelength perception over a wide spectral range from 600 to 1700 nm with a resolution of 100 nm. These findings represent a new approach to designing spectrum-sensitive SNSPDs for low-light spectroscopic applications.

Neural network aided diffractive metagratings for efficient beam splitting at terahertz frequencies

The merging of neural networks with metasurfaces is a rising subject in photonics design, which offers an abstract bridge between the geometry of the subwavelength element and the optical response. The commonly involved optical response is the transmission or reflection spectrum, while here we focus on metasurfaces with superwavelength elements and predict multiple diffraction spectra in all the possible orders and orthogonal polarization modes given the geometry. This is achieved by parallel arrangement of several fully connected neural networks with shared input and diverse output diffraction spectra. As an application example, the model is used to find a metagrating as a 1:1 beam splitter in TE mode and 1:1:1 beam splitter in TM mode. The design is taken into fabrication and experimentally tested at 0.14 THz with results that are highly consistent with the prediction.

Modern Digital-Analog Jamming Transmitter

Abstract- Principle analysis results provide construction new digital-analog jammers from the point of view minimizing out-of-band undesirable electromagnetic radiation. Reviewed methods of compensating for nonlinear distortions, which are suitable for use in power amplifiers of jammers, based on linearization methods of the characteristics of powerful amplifying devices. The substantiation of a new compensation method for eliminating undesirable combination components in the spectrum of jamming transmitters with quadrature modulation (the method of depletion of the input signal spectrum).

Validation of the HERA Phase I Epoch of Reionization 21 cm Power Spectrum Software Pipeline

Abstract We describe the validation of the HERA Phase I software pipeline by a series of modular tests, building up to an end-to-end simulation. The philosophy of this approach is to validate the software and algorithms used in the Phase I upper-limit analysis on wholly synthetic data satisfying the assumptions of that analysis, not addressing whether the actual data meet these assumptions. We discuss the organization of this validation approach, the specific modular tests performed, and the construction of the end-to-end simulations. We explicitly discuss the limitations in scope of the current simulation effort. With mock visibility data generated from a known analytic power spectrum and a wide range of realistic instrumental effects and foregrounds, we demonstrate that the current pipeline produces power spectrum estimates that are consistent with known analytic inputs to within thermal noise levels (at the 2σ level) for k &gt; 0.2h Mpc−1 for both bands and fields considered. Our input spectrum is intentionally amplified to enable a strong “detection” at k ∼ 0.2 h Mpc−1—at the level of ∼25σ—with foregrounds dominating on larger scales and thermal noise dominating at smaller scales. Our pipeline is able to detect this amplified input signal after suppressing foregrounds with a dynamic range (foreground to noise ratio) of ≳107. Our validation test suite uncovered several sources of scale-independent signal loss throughout the pipeline, whose amplitude is well-characterized and accounted for in the final estimates. We conclude with a discussion of the steps required for the next round of data analysis.

A Probability Metric-Based Autoencoder for Hyperspectral Unmixing

In recent years, hyperspectral unmixing has been a crucial preprocess in hyperspectral imagery analysis. Current learning-based methods of hyperspectral unmixing are built on the autoencoder framework. Most of them hold a point-wise loss function only comparing the individual difference without the overall difference. As a remedy, we propose a probability metric based autoencoder (PMAE) for hyperspectral unmixing. Specifically, we combine a distribution distance called maximum mean discrepancy (MMD) with underlying loss spectral angle distance (SAD) as the final loss function for training the unmixing autoencoder. The MMD can be seen as a regularization that is designed to match all orders of statistics between the input spectrums and the reconstructed spectrums. The SAD is to compare the individual difference while the MMD compares the overall difference. Combing these two points, we can obtain more prior information to guide the autoencoder training towards boosting the unmixing performance. In order to validate this viewpoint, we conduct model analysis and a series of ablation studies showing that adding the MMD into the autoencoder unmixing model indeed boosts the unmixing performance a lot compared to the single SAD objective. Experimental results on five real hyperspectral scenes demonstrate the effectiveness and competitiveness of the proposed PMAE compared with several classical and learning-based approaches.

Deep Learning-Aided Signal Enumeration for Lens Antenna Array

This work investigates a data-driven approach to detect the number of incoming signals for a lens antenna array (LAA). First, the energy-focusing property of an electromagnetic (EM) lens is utilized to generate an input spectrum, which can be used to enumerate both the multipath and independent signals. Next, we present the deep learning (DL)-assisted sharp peak recognition method referred to as the power spectrum-based convolutional neural network (PSCNet). Unlike classical techniques, such as constant false alarm rate (CFAR) detection, this data-driven detector can count received signals adaptively based on the LAA power spectrum without requiring any initial configurations. In addition, the PSCNet outperforms other state-of-the-art subspace-based detectors, even under challenging conditions, such as a low signal-to-noise ratio (SNR), a small observation size, and angular ambiguity. For the training phase, we propose a pretrained-model reusing strategy and an input pre-processing approach referred to as the power spectrum shortening (PSS) to alleviate the training burden and achieve lower complexity compared to fully retraining all isolated networks. The simulation results demonstrate that our proposed sharp peak-recognition algorithm not only accomplishes the improved signal enumeration performance but also requires lower computational resources than other subspace-based approaches.

Optimal Input Spectrum Design for Closed-Loop System Identification

Optimal Input Spectrum Design for Closed-Loop System Identification

Simultaneous label-free autofluorescence-multiharmonic microscopy driven by femtosecond sources based on self-phase modulation enabled spectral selection

Nonlinear optical microscopy technique has unique advantages in tissue imaging, such as enhanced contrast, high resolution, and label-free deep optical sectioning capabilities. Nonlinear optical microscopy also has multiple imaging modalities, corresponding to various components in biological tissues. Unfortunately, its wide applications are hindered due to the lack of broadly tunable femtosecond sources designed for driving multimodalities simultaneously. To solve this challenge, we propose a new wavelength conversion approach—self-phase modulation (SPM) enabled spectral selection, dubbed as SESS. The SESS employs SPM to broaden the input spectrum in a short fiber, and the broadened spectrum features well-isolated spectral lobes. Using the suitable optical filters to select the outermost spectral lobes produces nearly transform-limited femtosecond pulses. In this work, we demonstrate a fiber-optic SESS source for multimodal nonlinear optical microscopy. Based on a 43-MHz Yb-fiber laser, this SESS source can emit 990-nm, 84-fs pulses with &gt;5-nJ energy and ~84-fs pulse duration; it can also produce 1110-nm, 48-fs pulses with 15-nJ energy. The 990-nm pulses are used to drive two-photon excitation fluorescence of many important fluorophores and second-harmonic generation microscopy, which, combined with image splicing technology, enables us to obtain a large field of view image of the gastric tissue. We also employ the 1110-nm pulses to drive simultaneous label-free autofluorescence-multiharmonic microscopy for multimodal imaging of gastric tissue. Two-photon excitation fluorescence, three-photon excitation fluorescence, second-harmonic generation and third-harmonic generation signals of gastric tissue are simultaneously excited efficiently. Such a multimodal nonlinear optical microscopy driven by SESS sources becomes a powerful tool in biomedical imaging.

Deep Learning on Synthesized Sensor Characteristics and Transmission Spectra Enabling MEMS-Based Spectroscopic Gas Analysis beyond the Fourier Transform Limit

Miniaturized Fourier transform infrared spectrometers serve emerging market needs in many applications such as gas analysis. The miniaturization comes at the cost of lower performance than bench-top instrumentation, especially for the spectral resolution. However, higher spectral resolution is needed for better identification of the composition of materials. This article presents a convolutional neural network (CNN) for 3X resolution enhancement of the measured infrared gas spectra using a Fourier transform infrared (FTIR) spectrometer beyond the transform limit. The proposed network extracts a set of high-dimensional features from the input spectra and constructs high-resolution outputs by nonlinear mapping. The network is trained using synthetic transmission spectra of complex gas mixtures and simulated sensor non-idealities such as baseline drifts and non-uniform signal-to-noise ratio. Ten gases that are relevant to the natural and bio gas industry are considered whose mixtures suffer from overlapped features in the mid-infrared spectral range of 2000–4000 cm−1. The network results are presented for both synthetic and experimentally measured spectra using both bench-top and miniaturized MEMS spectrometers, improving the resolution from 60 cm−1 to 20 cm−1 with a mean square error down to 2.4×10−3 in the transmission spectra. The technique supports selective spectral analysis based on miniaturized MEMS spectrometers.

Transformation of Water Wave Spectra into Time Series of Surface Elevation

Spectral wave modelling is widely used to simulate large-scale wind–wave processes due to its low computation cost and relatively simpler formulation, in comparison to phase-resolving or hydrodynamic models. However, some applications require a time-domain representation of sea waves. This article proposes a methodology to transform the wave spectrum into a time series of water surface elevation for applications that require a time-domain representation of ocean waves. The proposed method uses a generated phase spectrum and the inverse Fourier transform to turn the wave spectrum into a time series of water surface elevation. The consistency of the methodology is then verified. The results show that it is capable of correctly transforming the wave spectrum, and the significant wave height of the resulting time series is within 5% of that of the input spectrum.