Spectral Distance Research Articles

An intelligent spectral identification approach for the simultaneous detection of endocrine-disrupting chemicals in aquatic environments.

With the rapid progression of industrialization, the application and release of endocrine disruptors (EDCs), including bisphenol A (BPA), octylphenol and nonylphenol have significantly increased, presenting substantial health hazards. Conventional analytical techniques, such as high-performance liquid chromatography and gas chromatography-mass spectrometry, are highly sophisticated but suffer from complex procedures and high costs. To overcome these limitations, this study introduces an innovative spectral methodology for the simultaneous detection of multiple aquatic multicomponent EDCs. By leveraging chemical machine vision, specifically with convolutional neural network (CNN) models, we employed a long-path holographic spectrometer for rapid, cost-effective identification of BPA, 4-tert-octylphenol, and 4-nonylphenol in aqueous samples. The CNN, refined with the ResNet-50 architecture, demonstrated superior predictive performance, achieving detection limits as low as 3.34, 3.71 and 4.36 μg/L, respectively. The sensitivity and quantification capability of our approach were confirmed through the analysis of spectral image Euclidean distances, while its universality and resistance properties were validated by assessments of environmental samples. This technology offers significantly advantages over conventional techniques in terms of efficiency and cost, offering a novel solution for EDC monitoring in aquatic environments. The implications of this research extend beyond improved detection speed and cost reduction, presenting new methodologies for analyzing complex chemical systems and contributing to environmental protection and public health.

A Variable-Iterative Fully Convolutional Neural Network for Sparse Unmixing

Neural networks have greatly promoted the development of hyperspectral unmixing (HU). Most data-driven deep networks extract features of hyperspectral images (HSIs) by stacking convolutional layers to achieve endmember extraction and abundance estimation. Some model-driven networks have strong interpretability but fail to mine the deep feature. We propose a variable-iterative fully convolutional neural network (VIFCNN) for sparse unmixing, combining the characteristics of these two networks. Under the model-driven iterative framework guided by sparse unmixing by variable splitting and augmented lagrangian (SUnSAL), a data-driven spatial-spectral feature learning module and a spatial information updating module are introduced to enhance the learning of data information. Experimental results on synthetic and real datasets show that VIFCNN significantly outperforms several traditional unmixing methods and two deep learning???based methods. On real datasets, our method improves signal-to-reconstruction error by 17.38%, reduces abundance root-mean-square error by 25.24%, and reduces abundance spectral angle distance by 31.40% compared with U-ADMM-ßUNet.

Spectrally narrowband simultaneous dual-wavelength emission from Y-branch DBR diode lasers at 785 nm

Y-branch distributed Bragg reflector (DBR) diode lasers with a stable narrowband emission in simultaneous dual-wavelength operation with spectral distances below 3.2 nm are presented. The Y-branch laser consists of two laser branches with different DBR gratings serving as wavelength-selective rear-side mirrors. Therefore, two emission wavelengths with a spectral distance defined by the DBR grating periods can be generated simultaneously. A Y-coupler combines the two ridge waveguide (RW) branches into a single straight output RW. Devices with a spectral distance of 0.6 nm and 2.0 nm emitting around 785 nm are manufactured. Selecting the operation parameters carefully, stable narrowband emission for both wavelengths is obtained. Resistors serving as heaters implemented next to the DBR gratings allow for wavelength adjustment and a tuning of the spectral distance. At an optical output power of 100 mW, the spectral distance can be shifted from 0 to 1.55 nm (0–0.76 THz) for the former device or from 1.00 to 3.15 nm (0.49–1.54 THz) for the latter device, respectively. This makes the Y-branch DBR diode laser particularly interesting for the generation of THz beat-note signals, needed to generate THz radiation via photo-mixing.

IDEEA: information diffusion model for integrating gene expression and EEG data in identifying Alzheimer’s disease markers

Abstract Understanding the genetic components of Alzheimer’s disease (AD) via transcriptome analysis often necessitates the use of invasive methods. This work focuses on overcoming the difficulties associated with the invasive process of collecting brain tissue samples in order to measure and investigate the transcriptome behavior of AD. Our approach called IDEEA (Information Diffusion model for integrating gene Expression and EEG data in identifying Alzheimer’s disease markers) involves systematically linking two different but complementary modalities: transcriptomics and EEG data. We preprocess these two data types by calculating the spectral and transcriptional sample distances, over 11 brain regions encompassing 6 distinct frequency bands. Subsequently, we employ a genetic algorithm approach to integrate the distinct features of the preprocessed data. Our experimental results show that
IDEEA converges rapidly to local optima gene subsets, in fewer than 250 iterations. Our algorithm identifies novel genes along with genes that have previously been linked to AD. It is also capable of detecting genes with transcription patterns specific to individual EEG bands as well as those with common patterns among bands. In particular, the alpha2 (10-13 Hz) frequency band yielded 8 AD associated genes out of the top 100 most frequently selected genes by our algorithm, with a p-value of
0.05. Our method not only identifies AD-related genes but also genes that interact with AD genes in terms of transcription regulation. We evaluated various aspects of our approach, including the genetic algorithm performance, band-pair association and gene interaction topology. Our approach reveals AD-relevant genes with transcription patterns inferred from EEG alone, across various frequency bands, avoiding the risky brain tissue collection process. This is a significant advancement toward the early identification of AD using non-invasive EEG recordings.

Abnormal Stokes V profiles observed by Hinode in a sunspot. Detection of hidden photospheric fine-structures

Hidden magnetic components in a sunspot are present as small-scale structures that are absent in low-resolution observations. We search for traces of the hidden magnetic components in spectro-polarimetric observations of a mature sunspot close to the disk center recorded by Hinode. To find extra humps in the far blue and red lobes of Stokes V, we examined the sign reversal in the second derivative of the profile in the umbra and penumbra. We also looked for the hump signature in the Stokes I and the linear polarization profiles. The amplitudes of the humps are small compared to the main component. More than half of the profiles show one extra hump, while 21<!PCT!> show an extra hump on both the blue and the red lobe of the 630.15 nm line with the same magnetic polarity as the sunspot. The location of the pixels where the extra hump is seen on both lobes has a pseudo-grainy structure in the single wavelength Stokes V magnetograms. This type of profile is better detected in darker parts of the penumbra, as well as in the umbra-penumbra border toward the umbra. The spectral distance between the two humps averaged over elliptical rings levels off in the umbra, decreases toward the penumbra, and levels off again there. We find no correlations between the wavelength positions of the two humps. We discuss two scenarios that could potentially produce the simultaneously observed blue and red humps: one in which a single hidden magnetic component is responsible for the two humps, and another in which the two humps emanate from two hidden magnetic components.

Discovery of 118 New Ultracool Dwarf Candidates Using Machine-learning Techniques

We present the discovery of 118 new ultracool dwarf candidates, discovered using a new machine-learning tool, named SMDET, applied to time-series images from the Wide-field Infrared Survey Explorer. We gathered photometric and astrometric data to estimate each candidate’s spectral type, distance, and tangential velocity. This sample has a photometrically estimated spectral class distribution of 28 M dwarfs, 64 L dwarfs, and 18 T dwarfs. We also identify a T-subdwarf candidate, two extreme T-subdwarf candidates, and two candidate young ultracool dwarfs. Five objects did not have enough photometric data for any estimations to be made. To validate our estimated spectral types, spectra were collected for two objects, yielding confirmed spectral types of T5 (estimated T5) and T3 (estimated T4). Demonstrating the effectiveness of machine-learning tools as a new large-scale discovery technique.

An efficient non-binary QC-LDPC coded modulation scheme for long-haul optical systems

Abstract Coded modulation (CM), which combines channel codes with high-dimensional modulation schemes, is an attractive technique to improve the spectral efficiency in high-speed optical transmission systems. Non-binary low-density parity check (NB LDPC) codes can provide larger coding gains with smaller codeword lengths compared to their binary counterpart, in addition to reducing the latency of the system. The majority of works based on NB LDPC CM focus on bit-interleaved CM (BICM) and the widely used additive white Gaussian noise (AWGN) channel model. However, the Gaussian channel model is not a realistic model for long-haul intensity-modulated direct-detection (IM/DD) optical systems using erbium-doped fiber amplifiers. In this paper, it is mathematically proved that noise in the received signals after photodetection can be more accurately represented using chi-squared distribution. The efficiency of CM depends on the mapping between coded symbols and the constellation signal points of the modulation scheme. Hence, a symbol interleaved CM with subcarrier modulation for NB LDPC codes based on the proposed channel model is presented in this paper to improve the bit-error rate (BER) performance. The simulation results show that the proposed CM for NB LDPC codes with M-ary quadrature amplitude modulation schemes provides better performance than BICM. The analysis of the error performance of the proposed system gives a coding gain of 1 dB at a BER of 10−7 compared to that of the AWGN channel model. The performance improvement of the system is also evaluated in terms of spectral efficiency and link distance.

Effect of Modulation Domain Coupled KalmanSpectral Filter on Speech Enhancement over Wireless Voiced Communication System

Noise suppression in speech signal has received great attention in signal processing research community. However, existing methods of noise suppression such as the time-domain Kalman filter suffers from the psychoacoustic and physiological characteristic feature while the spectral subtraction technique performed in the modulation domain is associated with remnant noise in the enhanced speech signal. Therefore, in this paper, a Modulation-Domain coupled Kalman-Spectral Filtering (MD-KSF) in a single channel system is proposed to address the aforementioned shortcomings in the two existing techniques. The simulation of the proposed coupled MD-KSF was performed in MATLAB software environment. The evaluation of the proposed coupled MD-KSF technique in terms of Spectral waveform, Mean Square Error (MSE) and Log Spectral Distance (LSD) was performed. Furthermore, Perceptual Evaluation of Speech Quality (PESQ) and Short-Time Objective Intelligibility (STOI) tests were employed to validate the quality and intelligibility of the proposed coupled MD- KSF using the NOIZEUS corpus data set. The proposed technique shows significant noise suppression over the existing techniques.

Hyperspectral endmember extraction using convexity based purity index

The endmember extraction is a challenging problem in spectral unmixing (SU) of a mixed pixel in hyperspectral imagery. There are plenty of attempts to solve the endmember extraction problem. Still, the pure pixel assumption-based algorithms have probably been used most in solving the endmember extraction of SU due to the light computational burden. These pure pixel assumption-based algorithms usually follow one of the criteria: (1) Maximum simplex volume or (2) Extreme projection on a subspace. We propose a novel integrated framework that uses both the criteria mentioned above and the proposed one is referred to as the Convexity-based Pure Index (CPI) algorithm. The CPI generates a fixed number of convex sets based on the number of available bands in the hyperspectral image. The algorithm defines the purity score based on the availability of pixels in the convex sets for the two-band data. The CPI has been compared with contemporary algorithms such as Automatic Target Generation Process (ATGP), Vertex Component Analysis (VCA), Pixel Purity Index (PPI), Successive Volume MAXimization (SVMAX), Alternating Volume MAXimization (AVMAX), TRIple-P: P-norm based Pure Pixel identification (TRIP), Successive Decoupled Volume Max–Min (SDVMM), Negative ABundance-Oriented (NABO), and Entropy-based Convex Set Optimization (ECSO). The metrics, Spectral Angle Distance (SAD) and Spectral Information Divergence (SID) used in the comparison were improved up to 5.9% and 9%, respectively. The CPI outperforms prevailing algorithms on real benchmark data and new AVIRIS-NG data. The robustness of the CPI is also tested for various noisy synthetic data. The efficacy of the proposed algorithm is also tested by using qualitative analysis by visualizing the spectra comparison, and abundance maps for all real data.

Online health monitoring of rotating machine elements using statistical spectral distances

Online health monitoring of rotating machine elements using statistical spectral distances

Assessment of the spaceborne EnMAP hyperspectral data for alteration mineral mapping: A case study of the Reko Diq porphyry Cu[sbnd]Au deposit, Pakistan

For over four decades, spaceborne multispectral data have played a crucial role in supporting mineral exploration and geologic mapping. The spaceborne multispectral datasets, however, have a restricted number of bands with coarse spectral resolution and, thus are very limited in mineral mapping. The advent of high-quality spaceborne imaging spectroscopic data like the Environmental Mapping and Analysis Program (EnMAP), has bridged this gap initiating a new era in global hyperspectral mineral mapping. The EnMAP satellite, operational since November 2022, covers the spectral range of 420 and 2450 nm in 224 bands, offering a spatial resolution of 30 m and a mean spectral sampling distance of 8.1 and 12.5 nm in the visible-near infrared and shortwave infrared regions, respectively. In this paper, we demonstrate the enhanced mapping capabilities of EnMAP using datasets acquired over the Reko Diq mining district, a cluster of Miocene porphyries located in Pakistan's Chagai Belt hosting an undeveloped world-class porphyry Cu-Au ± Mo deposit. The EnMAP's Level 2A data product was processed using the polynomial fitting technique to characterize the diagnostic absorption features of the alteration minerals in the Reko Diq porphyry system. This involved retrieving the minimum wavelength, depth, width, and asymmetry parameters for key absorption features and employing them interactively for mineral characterization. A diverse array of minerals were successfully mapped over the study area and validated by ground spectroscopy. This includes abundance/composition maps for white micas, chlorite, epidote, calcite, kaolinite, gypsum, jarosite, and ferric and ferrous iron minerals. The minimum wavelength of white mica was found to vary between 2195 and 2210 nm, with the shorter wavelengths (Al-rich) white mica occurring proximal to the known mineralized zones. The potassic alteration cores and the outer propylitic zones were identified by the ferrous iron and chlorite-epidote-calcite mineral maps, respectively. This study demonstrated the superiority of EnMAP hyperspectral data in delineating the alteration mineralogy and zonation pattern of porphyry copper systems. This capability can potentially contribute to the exploration of new deposits in exposed terrains worldwide.

Diagnostics on Power Electronics Converters by Means of Autoregressive Modelling

Power conversion systems for wireless power transfer (WPT) applications have demanding requirements for continuity of service, besides being operated with stressing environmental conditions. Diagnostic and prognostic programs are thus quite useful and this work shows a novel approach based on the analysis of spectra of an autoregressive (AR) model to recognize a wide range of faulty devices, including incipient faults, when deviations from nominal parameters begin to manifest. AR modeling provides cleaner and easier to interpret spectra, where only the salient features remain, and they are more sensitive to variations in the corresponding time domain waveforms. A log spectral distance is calculated that successfully separates healthy and faulty states of the feeding single-phase inverter, even in challenging scenarios of poor signal-to-noise ratio.

Overcoming laser phase noise for low-cost coherent optical communication

Artificial-intelligence-generated content has driven explosive data traffic growth in data-center interconnects. Traditional direct detection solutions struggle with limited spectral efficiency and distance, prompting the shift to coherent optics for cost-sensitive short-reach links. One specific challenge is integrating low-cost lasers while overcoming severe phase noise on high-order modulation formats. Here, we propose a residual carrier modulation scheme for precise and efficient carrier frequency and phase recovery. The residual optical carrier can continuously track phase fluctuations without redundancy compared with discrete time-domain pilots, and address the digital-to-analog convertor resolution reduction issue of frequency-domain digital pilots. In proof-of-concept experiments, we transmit a net 1-Tb/s probabilistic-shaped 256-ary quadrature amplitude modulated (PS-256-QAM) signal using a 3 MHz distributed feedback (DFB) laser. Our scheme improves bitrate by 41% compared to conventional time-domain pilots, achieving a record laser linewidth sum and symbol duration product of 6.89 × 10−5. This approach supports MHz linewidth DFB lasers in low-cost coherent optical communications.

A Multidimensional Health Indicator Based on Autoregressive Power Spectral Density for Machine Condition Monitoring.

Condition monitoring (CM) is the basis of prognostics and health management (PHM), which is gaining more and more importance in the industrial world. CM, which refers to the tracking of industrial equipment's state of health during operations, plays, in fact, a significant role in the reliability, safety, and efficiency of industrial operations. This paper proposes a data-driven CM approach based on the autoregressive (AR) modeling of the acquired sensor data and their analysis within frequency subbands. The number and size of the bands are determined with negligible human intervention, analyzing only the time-frequency representation of the signal of interest under normal system operating conditions. In particular, the approach exploits the synchrosqueezing transform to improve the signal energy distribution in the time-frequency plane, defining a multidimensional health indicator built on the basis of the AR power spectral density and the symmetric Itakura-Saito spectral distance. The described health indicator proved capable of detecting changes in the signal spectrum due to the occurrence of faults. After the initial definition of the bands and the calculation of the characteristics of the nominal AR spectrum, the procedure requires no further intervention and can be used for online condition monitoring and fault diagnosis. Since it is based on the comparison of spectra under different operating conditions, its applicability depends neither on the nature of the acquired signal nor on a specific system to be monitored. As an example, the effectiveness of the proposed method was favorably tested using real data available in the Case Western Reserve University (CWRU) Bearing Data Center, a widely known and used benchmark.

Land cover fraction mapping across global biomes with Landsat data, spatially generalized regression models and spectral-temporal metrics

Mapping land cover in highly heterogeneous landscapes is challenging, and classifications have inherent limitations where the spatial resolution of remotely sensed data exceeds the size of small objects. For example, classifications based on 30-m Landsat data do not capture urban or other heterogeneous environments well. This limitation may be overcome by quantifying the subpixel fractions of different land cover types. However, the selection process and transferability of models designed for subpixel land cover mapping across biomes is yet challenging. We asked to what extent (a) locally trained models can be used for sub-pixel land cover fraction estimates in other biomes, and (b) training data from different regions can be combined into spatially generalized models to quantify fractions across global biomes. We applied machine learning regression-based fraction mapping to quantify land cover fractions of 18 regions in five biomes using Landsat data from 2022. We used spectral-temporal metrics to incorporate intra-annual temporal information and compared the performance of local, spatially transferred, and spatially generalized models. Local models performed best when applied to their respective sites (average mean absolute error, MAE, 9–18%), and also well when transferred to other sites within the same biome, but not consistently so for out-of-biome sites. However, spatially generalized models that combined input data from many sites worked very well when analyzing sites in many different biomes, and their MAE values were only slightly higher than those of the respective local models. A weighted training data selection approach, preferring training data with a lower spectral distance to the image data to be predicted, further enhanced the performance of generalized models. Our results suggest that spatially generalized regression-based fraction models can support multi-class sub-pixel fraction estimates based on medium-resolution satellite images globally. Such products would have great value for environmental monitoring in heterogeneous environments and where land cover varies along spatial or temporal gradients.

Modulation format conversion between BPSK and 8QAM signals using coherent interference and four-wave mixing

Optical fiber networks need to transmit various types of client data with different requirements of bandwidth, reach, latency, and capacity by interconnecting backbone, metro, and local access networks. Since the optimal modulation format usually differs, it should be efficiently converted at the intermediate node connecting different networks. Among the multilevel modulation format, 8-ary quadrature amplitude modulation (8QAM) is a candidate for terrestrial networks thanks to the good balance between spectral efficiency and transmission distance. In this paper, we propose modulation format conversion systems from binary phase shift keying (BPSK) to 8QAM using coherent interference in delay interferometer and its reverse conversion system using four-wave mixing in a single highly nonlinear fiber. Conversion performances are numerically evaluated based on bit error rate, constellation diagrams, 8QAM and BPSK propagation lengths, and parameters in electrical signal regeneration. As a result, error-free format conversion is achieved for both conversion systems.

The Effects of Interactive Context on Acoustic Characteristics of Speech in People With Dysarthria: A Preliminary Study.

The current study compared temporal and spectral acoustic contrast between vowel segments produced by speakers with dysarthria across three speech tasks-interactive, solo habitual, and solo clear. Nine speakers with dysarthria secondary to amyotrophic lateral sclerosis participated in the study. Each speaker was paired with a typical interlocutor over videoconferencing software. The speakers produced the vowels /i, ɪ, ɛ, æ/ in /h/-vowel-/d/ words. For the solo tasks, speakers read the stimuli aloud in both their habitual and clear speaking styles. For the interactive task, speakers produced a target stimulus for their interlocutor to select among the four possibilities. We measured the duration difference between long and short vowels, as well as the F1/F2 Euclidean distance between adjacent vowels, and also determined how well the vowels could be classified based on their acoustic characteristics. Temporal contrast between long and short vowels was higher in the interactive task than in both solo tasks. Spectral distance between adjacent vowel pairs was also higher for some pairs in the interactive task than the habitual speech task. Finally, vowel classification accuracy was highest in the interactive task. Overall, we found evidence that individuals with dysarthria produced vowels with greater acoustic contrast in structured interactions than they did in solo tasks. Furthermore, the speech adjustments they made to the vowel segments differed from those observed in solo speech.

SampleHST-X: A Point and Collective Anomaly-Aware Trace Sampling Pipeline with Approximate Half Space Trees

The storage requirement for distributed tracing can be reduced significantly by sampling only the anomalous or interesting traces that occur rarely at runtime. In this paper, we introduce an unsupervised sampling pipeline for distributed tracing that ensures high sampling accuracy while reducing the storage requirement. The proposed method, SampleHST-X, extends our recent work SampleHST. It operates based on a budget which limits the percentage of traces to be sampled while adjusting the storage quota of normal and anomalous traces depending on the size of this budget. The sampling process relies on accurately defining clusters of normal and anomalous traces by leveraging the distribution of mass scores, which characterize the probability of observing different traces, obtained from a forest of Half Space Trees (HST). In our experiments, using traces from a cloud data center, SampleHST yields 2.3×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} to 9.5×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} better sampling performance. SampleHST-X further extends the SampleHST approach by incorporating a novel class of Half Space Trees, namely Approximate HST, that uses approximate counters to update the mass scores. These counters significantly reduces the space requirement for HST while the sampling performance remains similar. In addition to this extension, SampleHST-X includes a Family of Graph Spectral Distances (FGSD) based trace characterization component, which, in addition to point anomalies, enables it to sample traces with collective anomalies. For such traces, we observe that the SampleHST-X approach can yield 1.2×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} to 19×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} better sampling performance.

Monolithically integrated multimode interference coupler-based master oscillator power amplifier with dual-wavelength emission around 830 nm

A monolithically integrated dual-wavelength multimode interference coupler-based master oscillator power amplifier is presented. It consists of two shallowly etched, laterally separated ridge waveguide laser cavities as master oscillators with individual distributed Bragg reflector gratings as cavity mirrors. A deeply etched coupling section containing S-bend shaped waveguides and a multimode interference coupler is used to couple the laser emission of the master oscillators into a shallowly etched single waveguide serving as power amplifier. Changing the etch depth for the coupling section enables a compact device layout. In addition, increased radiation angles of modes not coupled into the power amplifier help to suppress beam steering, otherwise indicated by laterally separated far-field intensity distributions. The device provides 0.5 W of dual-wavelength emission around 830 nm in individual and common operation. As designed, both emission wavelengths are separated by 0.5 nm with spectral widths below 20 pm, limited by the spectral resolution of the spectrometer. Both peak wavelengths remain within spectral windows of 50 pm within the available power range. This enables full flexibility selecting operating points for applications such as shifted excitation Raman difference spectroscopy and the generation of THz emission by photomixing. The emission wavelengths can additionally be non-continuously tuned by applying a heater current to resistors implemented next to the distributed Bragg reflector gratings. As an example, selected spectral distances of 0.5 nm, 1.0 nm, 1.5 nm, and 2.0 nm are demonstrated. Near field widths of 5 μm and far field angles of 17° result in beam propagation ratios of 1.4 (1/e2) in all operation modes and enable easy beam shaping or optical single-mode fiber coupling.

Anomaly detection in Structural Health Monitoring using spectral distance and t-SNE–GMM framework under ambient excitation

Anomaly detection in Structural Health Monitoring using spectral distance and t-SNE–GMM framework under ambient excitation