Modern Spectral Analysis Techniques Research Articles

CFNet: Cross-modal data augmentation empowered fuzzy neural network for spectral fluctuation

Modern spectral analysis techniques are rapidly advancing, with Laser-induced breakdown spectroscopy (LIBS) gaining attention for its revolutionary potential in analytical chemistry. However, poor repeatability due to spectral fluctuation remains a common challenge. Improving LIBS repeatability involves improving instrument performance, standardizing sample handling, and refining data processing. While instrument performance and sample handling can be standardized, optimizing data processing is crucial for improving spectral reproducibility. This research addresses this issue through a 7-day experiment by proposing a cross-modal data augmentation empowered fuzzy neural network (CFNet). We first introduce a cross-modal data augmentation method that considers the spatial distribution of LIBS elemental lines. This method expands from a single spectrum modality to an image-spectrum dual modality, enhancing the ability to capture spectral fluctuation and thereby improving LIBS repeatability. We then introduce a cross-modal data augmentation empowered fuzzy neural network, which allows each spectrum to belong to multiple categories simultaneously, increasing adaptability to spectral fluctuation. Results show that both Accuracy and MacF exceed 91% across three tests, demonstrating the CFNet’s effectiveness in managing data fluctuation and serving as a reference for other spectral technologies. Integrating fuzzy logic into spectroscopy not only expands its applications but also improves the repeatability of spectral data. The cross-modal augmented data is available at https://github.com/aoao0206/CFNet.

Silver Carp (Hypophthalmichthys molitrix) Scales Collagen Peptides (SCPs): Preparation, Whitening Activity Screening and Characterization

This study involves the preparation of scale collagen peptides (SCPs) with whitening activity from silver carp (Hypophthalmichthys molitrix) and their characterization and peptide sequence identification. In this article, scanning electron microscopy (SEM) was used to observe structure changes of sliver carp scales; enzymatic hydrolysis was optimized through protease screening and response surface optimization. The ultrafiltration was used to separate SCPs and the whitening activity was comprehensively evaluated using radical scavenging rate and tyrosinase-inhibiting activity, among others. An optimal component was characterized and identified using various modern spectral analysis techniques. The results showed that the surface of silver carp scales after decalcification was smooth and clear. The pepsin had the highest peptide yield and tyrosinase-inhibiting activity (90.01% and 82.25%, respectively). The optimal enzymatic hydrolysis conditions were an enzyme dosage of 16.1%, a solid-liquid ratio of 1:15.6 and a time of 4.9 h. The proportions of hydrophobic and basic amino acids in the peptide composition were 32.15% and 13.12%, respectively. Compared with SCPs2, SCPs1 (6096.68-9513.70 Da) showed better ·OH scavenging ability, tyrosinase-inhibiting activity and moisture absorption. SCPs1 was a macromolecular fragment of type I collagen with a triple helix structure, containing three peptide sequences with the potential for tyrosinase activity inhibition (AGPPGADGQTGQRGE, SGPAGIAGPAGPRGPAGPNGPPGKD and KRGSTGEQGSTGPLGMRGPRGAA). These results show that SCPs1 is a collagen peptide product with whitening potential.

Polymerisation of an industrial resin monitored by infrared fiber evanescent wave spectroscopy

In order to monitor the polymerisation process of an industrial resin, a study by infrared fiber evanescent wave spectroscopy has been carried out. To design the optical fiber, a special glass composition has been optimized in the Se–Sb–Ge–Ga vitreous quaternary system. This glass exhibits a large transparency in the mid-infrared range, and shows a temperature–viscosity behaviour that enables the preparation of optical fibers. Most important is the glass transition temperature, which is high enough to allow fiber measurement at about 200 °C. Infrared spectra have been recorded every 5 min during the curing process of the resin. A direct measurement of the integrated intensities of the relevant absorption band enabled to determine the times when the curing process started and when it finished. This direct analysis has been confirmed by principle component analysis, which is an unsupervised and very efficient method for applications at the industrial scale. Moreover, it has been shown that an autocatalytic kinetic model should be used to give a good account for the absorbency evolution versus time. From a practical point of view, the optical fiber can be integrated directly into the mould and be considered as a consumable. The use of chalcogenide glass fiber to record remotely infrared signals, coupled with modern spectral analysis techniques constitute a very powerful route for monitoring the curing of thermo-hardening resins.

Super-resolution of polarimetric SAR images of ship targets

This paper deals with the application of modern spectral analysis techniques to polarimetric Synthetic Aperture Radar (SAR) data. The purpose (Section 1) is to use all the information provided by images of the same SAR scene at the different polarimetric channels (HH, VV, HV and VH) in order to extract all the information on the scene back-scattering properties with better geometrical resolution than the resolution corresponding to the compressed coded waveform and the synthetic aperture. Super-resolved images are obtained by replacing the classical spectral estimator (i.e. the Fast Fourier Transform, FFT) with parametric spectral estimators such as those built around an autoregressive model of the dechirped signal. The proposed processing schemes—based on two-dimensional covariance method—are presented in Section 2 for both single channel data and polarimetric data. The joint use of different polarimetric images requires the choice of a suitable fusion technique to obtain a single super-resolved SAR image: to this aim both a decentralised fusion strategy (fusion of separately super-resolved images) and a centralised fusion strategy (super-resolution of the fused image) are proposed. The application of the techniques to polarimetric SIR-C SAR images of a ship is presented in Section 3 where the results obtained by using single channel images and multipolarisation images are discussed and compared. A perspective for future research and applications (Section 4) closes the paper.

Two-dimensional super-resolution spectral analysis applied to SAR images

The authors describe the application of modern spectral analysis techniques to synthetic aperture radar data. The purpose is to improve the geometrical resolution of the image with respect to the numerical values related to the compressed coded waveform and the synthetic aperture, so that subsequent classification procedures will have improved performance as well. The classical spectral estimator, i.e. the FFT, produces an image with resolution in azimuth and range bounded by the Rayleigh limits. Super-resolved images are obtained by replacing the FFT with parametric spectral estimators such as those built around an autoregressive model of the dechirped signal. The proposed processing scheme is based on a two-dimensional covariance method. The expected improvement in resolution is discussed together with the results of a simulation analysis. The application of the technique to images captured by an airborne SAR resulted in a resolution gain factor of about two. The paper concludes with a perspective on future research and applications.

Quantification of the difference between detection and resolution thresholds for multiple closely spaced emitters

The problem of estimating the parameters of multiple signals closely spaced in either space or frequency has been much researched because it is a difficult case for modern spectral analysis techniques. Based on empirical evidence, it has been noted by others that signal-to-noise ratio (SNR) detection thresholds typically are lower than resolution thresholds. That is, one can detect the occurrence of n closely spaced signals at SNRs lower than those at which one can resolve the signals and reliably estimate their parameters. The difference between detection and resolution thresholds is quantified for the direction-finding problem in which source locations are specified by a single angular coordinate (e.g., azimuth).< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Modern spectral analysis techniques for blood flow velocity and spectral measurements with pulsed Doppler ultrasound

Four spectral analysis techniques were applied to pulsed Doppler ultrasonic quadrature signals to compare the relative merits of each technique for estimation of flow velocity and Doppler spectra. The four techniques were 1) the fast Fourier transform method, 2) the maximum likelihood method, 3) the Burg autoregressive algorithm, and 4) the modified covariance approach to autoregressive modeling. Both simulated signals and signals obtained from an in vitro flow system were studied. Optimal parameter values (e.g., model orders) were determined for each method, and the effects of signal-to-noise ratio and signal bandwidth were investigated. The modern spectral analysis techniques were shown to be superior to Fourier techniques in most circumstances, provided the model order was chosen appropriately. Robustness considerations tended to recommend the maximum likelihood method for both velocity and spectral estimation. Despite the restrictions of steady laminar flow, the results provide important basic information concerning the applicability of modern spectral analysis techniques to Doppler ultrasonic evaluation of arterial disease.

Noise measurements on machinery near a reflecting plane

Noise test specifications for large equipment often requires that the measurements be made with the equipment out‐of‐doors resting on the ground (usually a smooth, paved surface), and with the sound level meter microphone positioned between 1 and 2 m above the ground. Constructive and destructive interference between the sound radiated directly from the equipment and the sound reflected from the ground produces a sound field in which the variation of the A‐weighted sound level with distance from the unit does not exactly follow the “inverse square law.” The directional characteristics of the equipment under test further complicate the interpretation of the noise measurements, so that using sound levels at one distance, together with the inverse square law to predict the sound levels at other distances, will not be very precise. This paper discusses the problem of machinery noise measurement made above a reflecting plane and how modern spectral analysis techniques can be used to extract information from the noise data.

An autoregressive approach to the identification of multipath ray parameters from field measurements

This study is concerned with using modern spectral analysis techniques to derive estimates of multipath ray parameters (delays and amplitudes) from measurements of selective fading over a wide band at microwave frequencies. The applicability of the general category of autoregressive methods is addressed, and the specific method adopted, the Prony algorithm, is described. An illustrative example in which this is applied to a typical field measurement is followed by an investigation into the capabilities and limitations of the algorithm. A statistical analysis of the method has been carried out using a database of laboratory measurements. The Prony algorithm's ability to resolve delays to better than half the Fourier limit is shown, and its tendency to yield biased estimates is studied.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>