A Quantification Method for Supraharmonic Emissions Based on Outlier Detection Algorithms

Based on outlier detection algorithms, a feasible quantification method for supraharmonic emission signals is presented. It is designed to tackle the requirements of high-resolution and low data volume simultaneously in the frequency domain. The proposed method was developed from the skewed distribution data model and the self-tuning parameters of density-based spatial clustering of applications with noise (DBSCAN) algorithm. Specifically, the data distribution of the supraharmonic band was analyzed first by the Jarque–Bera test. The threshold was determined based on the distribution model to filter out noise. Subsequently, the DBSCAN clustering algorithm parameters were adjusted automatically, according to the k-dist curve slope variation and the dichotomy parameter seeking algorithm, followed by the clustering. The supraharmonic emission points were analyzed as outliers. Finally, simulated and experimental data were applied to verify the effectiveness of the proposed method. On the basis of the detection results, a spectrum with the same resolution as the original spectrum was obtained. The amount of data declined by more than three orders of magnitude compared to the original spectrum. The presented method will benefit the analysis of quantification for the amplitude and frequency of supraharmonic emissions.

Spectral denoising in hyperspectral imaging using the discrete wavelet transform

The use of hyperspectral sensors has gained relevance in agriculture due to its potential in the phytosanitary management of crops. However, these sensors are sensitive to spectral noise, which makes their real application difficult. Therefore, this work focused on the analysis of the spectral noise present in a bank of 180 hyperspectral images of mango leaves acquired in the laboratory, and the implementation of a denoising technique based on the discrete wavelet transform. The noise analysis consisted in the identification of the highest noisy bands, while the performance of the technique was based on the PSNR and SNR metrics. As a result, it was determined that the spectral noise was present at the ends of the spectrum (417-421nm and 969-994nm) and that the Neigh-Shrink method achieved a SNR of the order of 1011 with respect to the order of 102 of the original spectrum.

Circular dichroism for secondary structure determination of proteins with unfolded domains using a self-organising map algorithm SOMSpec

Circular dichroism secondary structure fitting by analysing derandomized spectra using the SOMSpec approach then regenerating data for the original spectrum.

Fourier Domain Anomaly Detection and Spectral Fusion for Stripe Noise Removal of TIR Imagery

Stripe noise is a common and unwelcome noise pattern in various thermal infrared (TIR) image data including conventional TIR images and remote sensing TIR spectral images. Most existing stripe noise removal (destriping) methods are often difficult to keep a good and robust efficacy in dealing with the real-life complex noise cases. In this paper, based on the intrinsic spectral properties of TIR images and stripe noise, we propose a novel two-stage transform domain destriping method called Fourier domain anomaly detection and spectral fusion (ADSF). Considering the principal frequencies polluted by stripe noise as outliers in the statistical spectrum of TIR images, our naive idea is first to detect the potential anomalies and then correct them effectively in the Fourier domain to reconstruct a desired destriping result. More specifically, anomaly detection for stripe frequencies is achieved through a regional comparison between the original spectrum and the expected spectrum that statistically follows a generalized Laplacian regression model, and then an anomaly weight map is generated accordingly. In the correction stage, we propose a guidance-image-based spectrum fusion strategy, which integrates the original spectrum and the spectrum of a guidance image via the anomaly weight map. The final reconstruction result not only has no stripe noise but also maintains image structures and details well. Extensive real experiments are performed on conventional TIR images and remote sensing spectral images, respectively. The qualitative and quantitative assessment results demonstrate the superior effectiveness and strong robustness of the proposed method.

Investigation on Texture Changes and Classification between Cold-Fresh and Freeze-Thawed Tan Mutton

To study the texture, microstructural changes, and classification of cold-fresh (C-F), freeze-thawed once (F-T0), and freeze-thawed twice Tan mutton (F-Tt), the aforementioned three types of Tan mutton were subjected to near-infrared hyperspectrum scanning, scanning electron microscopy, and TPA testing. The original spectrum of Tan mutton was obtained at a wavelength range of 900∼1,700 nm after hyperspectrum scanning; a spectrum fragment ranging from 918 nm to 1,008 nm was intercepted, and the remaining original spectrum was used as a studied spectrum (“full spectrum” hereafter). The full spectrum was pretreated by SNV (standard normal variate), MSC (multiple scattering correction), and SNV + MSC and then extracted feature wavelengths by SPA (successive projections algorithm) and CARS (competitive adaptive reweighted sampling) algorithm, and 25 feature wavelengths were obtained. By combining these feature wavelengths with classified variables, the SNV + MSC−CARS−PLS-DA (partial least squares-discriminate analysis, PLS-DA) and SNV + MSC−SPA−PLS-DA models for classification of C-F and F-T Tan mutton were established. In contrast, SNV + MSC−CARS−PLS-DA yielded the highest classification rate of 98% and 100% for calibration set and validation set, respectively. The results indicated that the texture and surface microstructure of F-T Tan mutton deteriorated, and more worsely with F-T time. SNV+MSC-CARS-PLS-DA could be well used to classify C-F, F-T0, and F-Tt Tan mutton.

Research and analysis of the wheeled vehicle load spectrum editing method based on short-time Fourier transform

The measured uniaxial-head load spectrum in the road simulation test has a large number of useless small loads. When applying the measured load spectrum directly, it will take a lot of time. This paper designs a comprehensive road spectrum measurement system to collect data and proposes a method for editing the uniaxial-head acceleration load spectrum using short-time Fourier transform to speed up the reliability test process and reduce time costs. In this method, the time domain and frequency domain information of the signal is obtained by short-time Fourier transform. The concept of accumulated power spectral density is proposed to identify the reduced load data, and the relative fatigue damage is used as the pass criterion. The length of the edited spectrum is only 66% of the original spectrum through the above-mentioned editing method and retains the relative damage amount of 91%. Finally, through the analysis of time domain, frequency domain, and fatigue statistical parameters, it demonstrates that the short-time Fourier transform–based acceleration load spectrum edition method could achieve a similar fatigue damage to the original spectrum in a shorter time.

Methods for Simultaneous Improvement of Comb Pass Band and Folding Bands

This decimation introduces the replicas of the main signal spectrum. If the signal is not properly filtered, the overlapping of the repeated replicas of the original spectrum, called aliasing, may occur. The aliasing may destroy the useful information of the decimated signal and must be eliminated by the filter which precedes the decimation, called decimation filter. The most popular decimation filter is a comb filter, usually used in the first stage of decimation. However, its magnitude characteristic is not flat in the pass band of interest and there is not enough attenuation in the folding bands. Different methods are proposed to improve comb magnitude characteristic. This chapter presents an overview of methods for simultaneous improvement of comb magnitude characteristic in both: pass band and folding bands. The methods are divided into three main groups: sharpening-based methods, corrector-based methods, and methods based on the combination of alias rejection and compensator design methods.

Methods for Simultaneous Improvement of Comb Pass Band and Folding Bands

This decimation introduces the replicas of the main signal spectrum. If the signal is not properly filtered, the overlapping of the repeated replicas of the original spectrum, called aliasing, may occur. The aliasing may destroy the useful information of the decimated signal and must be eliminated by the filter which precedes the decimation, called decimation filter. The most popular decimation filter is a comb filter, usually used in the first stage of decimation. However its magnitude characteristic is not flat in the pass band of interest and there is not enough attenuation in the folding bands. Different methods are proposed to improve comb magnitude characteristic. This article presents an overview of methods for simultaneous improvement of comb magnitude characteristic in both: pass band and folding bands. The methods are divided into three main groups: Sharpening-based methods, Corrector–based methods, and Methods based on the combination of alias rejection and compensator design methods.

Comparative Studies of Two Metameric Black Spectral Dimension Reduction Methods Based on Color Difference Optimization

Two new metameric black spectral dimension reduction methods based on color difference optimization are presented, and dimension reduction effects are compared in colorimetric and spectral accuracy. The method one decomposes firstly the original spectrum into the basic spectrum and the metameric black spectrum using R-matrix theory, and then determines respectively the basis vectors which express linearly the basic spectrum and the metameric black spectrum. The method two applies firstly the principal component method to the original spectrum to get the first three eigenvectors as basis vectors of the basic spectrum, and then calculates the fundamental spectrum using tristimulus values and basis vectors of original spectrum. Results of experiment show the low-dimensional linear model built by method two can improve spectral and colorimetric accuracy, and satisfy the requirement of spectral color reproduction.

Identification of Greengrocery Seeds Based on NIR and Different Pretreatment Methods

An identifiable model based on near-infrared spectra (NIR) was proposed to distinguish the classification of greengrocery seeds. The performance of five pretreatment methods: Original, Smoothing, MSC (Multiplication scatter correction), SNV (Standard Normalized Variable) and FD (First Derivative) were utilized to reduce the noise in the original spectrum. The effective wavelengths were selected to remove the redundancy existing in the spectra by simulating stepwise regression. The performances of the model were optimized by the combination of pretreatments and effective wavelengths selection in this paper. Compared with the five pretreatment methods, SNV was superior to other methods with an accuracy of 100%. It is concluded that SNV coupled with simulating stepwise regression could be used to identify greengrocery seeds effectively.