Signal Correction Research Articles

Unlocking Osseointegration: Surface Engineering Strategies for Enhanced Dental Implant Integration.

Tooth loss is a prevalent problem faced by individuals of all ages across the globe. Various biomaterials, such as metals, bioceramics, polymers, composites of ceramics and polymers, etc., have been used for the manufacturing of dental implants. The success of a dental implant primarily depends on its osseointegration rate. The current surface modification techniques fail to imbibe the basics of tooth development, which can impart better mineralization and osseointegration. This can be improved by developing an understanding of the developmental pathways of dental tissue. Stimulating the correct signaling pathways through inductive material systems can bring about a paradigm shift in dental implant materials. The current review focuses on the developmental pathway and mineralization process that happen during tooth formation and how surface modifications can help in biomimetic mineralization, thereby enhancing osseointegration. We further describe the effect of dental implant surface modifications on mineralization, osteoinduction, and osseointegration; both in vitro and in vivo. The review will help us to understand the natural process of teeth development and mineralization and how the surface properties of dental implants can be further improved to mimic teeth development, in turn increasing osseointegration.

Rapid Correction of Turbidity Interference on Chemical Oxygen Demand Measurements by Using Ultraviolet-Visible Spectrometry

We developed a simple, rapid, and high-precision method to compensate for the turbidity interference in the measurement of water parameters using ultraviolet-visible spectrometry. By combining direct orthogonal signal correction (DOSC) with partial least squares (PLS), we corrected the full spectra (220 nm to 600 nm), significantly enhancing the accuracy of the water parameter calculations. First, DOSC was applied to filter out turbidity-related components, retaining only the spectral elements most closely associated with the target substance, without requiring a standard baseline for the turbidity effect. Then, 13 wavelengths were selected from the corrected full spectra to construct the discrete absorption spectra. Further, a PLS regression model was established based on the corrected discrete absorption spectra and their corresponding concentrations. In our experiment, this method effectively eliminated the blue shift and peak height reduction caused by turbidity, especially in shorter wavelengths, which are more sensitive to interference. Moreover, when applied to new samples, the correlation coefficients (R2) between the predicted and actual values improved from 0.5455 to 0.9997, and the root mean square error (RMSE) decreased from 12.3604 to 0.2295 after correction. Overall, the DOSC-PLS method, together with ultraviolet-visible spectrometry, posed a great potential for the precise monitoring of target water parameters in field studies.

Evaluation of an integrated variable flip angle protocol to estimate coil B1 for hyperpolarized MRI

AbstractPurposeThe purpose of this work is to validate a simple and versatile integrated variable flip angle (VFA) method for mapping B1 in hyperpolarized MRI, which can be used to correct signal variations due to coil inhomogeneity.Theory and MethodsSimulations were run to assess performance of the VFA B1 mapping method compared to the currently used constant flip angle (CFA) approach. Simulation results were used to inform the design of VFA sequences, validated in four volunteers for hyperpolarized xenon‐129 imaging of the lungs and another four volunteers for hyperpolarized carbon‐13 imaging of the human brain. B1 maps obtained were used to correct transmit and receive inhomogeneity in the images.ResultsSimulations showed improved performance of the VFA approach over the CFA approach with reduced sensitivity to T1. For xenon‐129, the B1 maps accurately reflected the variation of signal depolarization, but in some cases could not be used to correct for coil receive inhomogeneity due to a lack of transmit‐receive reciprocity resulting from suboptimal coil positioning. For carbon‐13, the B1 maps showed good agreement with a separately acquired B1 map of a phantom and were effectively used to correct coil‐induced signal inhomogeneity.ConclusionA simple, versatile, and effective VFA B1 mapping method was implemented and evaluated. Inclusion of the B1 mapping method in hyperpolarized imaging studies can enable more robust signal quantification.

Simultaneous Voltammetric Determination of Kanamycin and Ampicillin Using an Ultrasensitive Electrochemical Nanosensor with Partial Least Squares (PLS) and Orthogonal Signal Correction (OSC-PLS)

A sensitive electrochemical nanosensor based on a glassy carbon electrode modified by carbon nanofibers (CNFs) was developed to simultaneously determine kanamycin and ampicillin. Under optimized conditions, the nanosensor showed a wide dynamic linear range (1.0 × 10−11-1.0 × 10−6 M and 1.0 × 10−10-1.0 × 10−6 M) and a low detection limit (6.12 × 10−12 M and 1.47 × 10−11 M) for kanamycin and ampicillin, respectively. The multivariable calibration of OSC-PLS was adopted to separate the obtained voltammograms. The root mean square error of prediction (RMSEPs) of kanamycin and ampicillin using the OSC-PLS model were 0.05 and 0.08, respectively. The method was used to simultaneously determine kanamycin and ampicillin in tap water.

Exploration of grade distribution in iron mines based on rough set extreme learning machine and multispectral

Ore grade is an important indicator for evaluating the mining value of a mine. Reasonable mining based on ore grade distribution can aid in sustainable development and grade control within mining areas. Therefore, this paper aims to utilize Sentinel-2 multispectral data and an extreme learning machine (ELM) to rapidly explore the grade distribution in iron ore mines. First, direct orthogonal signal correction and mathematical transforms are used to correct and enhance multispectral information. Then, we propose a rough set ELM (RS-ELM) to learn multispectral data for grade distribution exploration. Specifically, we first discretize the hidden layer matrix (H) of the ELM and calculate the dependency degrees of the nodes on each other according to the rough set method. Then, the nodes with high dependency degree are deleted to ensure the lightness of the network structure and reduce the complexity of the ELM. Furthermore, a whitening transformation is performed on H to improve numerical stability. Comparison experiments prove the effectiveness of the RS-ELM. Finally, the mine grade distribution exploration model is established by using Sentinel-2 multispectral data and RS-ELM, and the grade distribution map of the mine is drawn. The distribution map clearly shows the trend of grade distribution in the mine. Moreover, based on the cut-off and industrial grades, we categorize the mine area into three zones: non-ore, secondary ore, and main ore areas. This categorization provides a foundation for developing the mine’s mining plan and grade control.

Ultratrace CO/SF6 Detection System Based on Differential Fourier Transform Infrared Spectroscopy Combined with the Weighted Sine Spectral Reconstruction Convolutional Neural Network (WSSR-CNN) Model.

Carbon monoxide (CO), as an indicator gas for fault diagnosis of gas-insulated switchgear, has the strongest absorption coefficient in the mid-infrared region, making Fourier transform infrared (FTIR) spectroscopy an ideal method for its concentration detection. However, there is extremely abundant absorption of SF6 in this region, resulting in a great challenge for CO/SF6 detection. To address this challenge, we report a high sensitivity online detection system for ultratrace levels of CO, which combines differential Fourier transform infrared (DFTIR) spectroscopy with a weighted sine spectral reconstruction convolutional neural network (WSSR-CNN). The differential technique is first introduced into FTIR for baseline correction of CO absorption signals. The novel WSSR method achieves feature extraction and denoising of weak spectral signals in strong interference by discretizing interfering signals and enhancing target signals. On this basis, the detection of CO concentration is realized using the CNN model. Finally, WSSR-CNN is compared with four other methods. Results showed that WSSR-CNN outperforms all four models with evaluation index R2 reaching 0.99982 and 0.99999 in the range of low concentration (0.096-0.986 ppm) and high concentration (1.984-52.193 ppm) and mean absolute percentage error of 0.97% and 0.22%, respectively. The lowest detection limit of the system at 1σ is 13 ppb, which is the best result reported so far for detecting CO/SF6 concentration based on FTIR.

Raman spectroscopy coupled with the PLSR model: A rapid method for analyzing gamma-oryzanol content in rice bran oil

Rice bran oil (RBO) is widely used in food, nutraceutical, and cosmetic industries, due to its γ-oryzanol content, a key quality indicator. This study developed a rapid, non-destructive method for quantifying γ-oryzanol in RBO using Raman spectroscopy combined with partial least squares regression (PLSR). The optimal PLSR model, based on orthogonal signal correction (OSC)-pretreated data of Raman spectra from 800 to 1800 cm−1, demonstrated high accuracy with a strong R2-Pearson correlation coefficient of 0.9827 and low root mean square error of prediction (RMSEP) of 0.5314. Principal component analysis (PCA) of OSC-pretreated data showed improved sample grouping by concentration of γ-oryzanol compared to untreated data. Additionally, Bland-Altman plots comparing results from Raman and HPLC methods showed random scatter within ±2 SD of the mean difference, confirming the method's reliability. This study indicates that Raman spectroscopy can serve as a reliable method for determining γ-oryzanol content in RBO products within the related industries.

Secondary halo bias through cosmic time II. Reconstructing halo properties using clustering information

When constructing mock galaxy catalogs based on suites of dark matter halo catalogs generated with approximated, calibrated, or machine-learning approaches, assigning intrinsic properties for these tracers is a step of paramount importance, given that they can shape the abundance and spatial distribution of mock galaxies and galaxy clusters. We explore the possibility of assigning properties of dark matter halos within the context of calibrated or learning approaches, explicitly using clustering information. The goal is to retrieve the correct signal of primary and secondary large-scale effective bias as a function of properties reconstructed solely based on phase-space properties of the halo distribution and dark matter density field. The algorithm reconstructs a set of halo properties (such as virial mass, maximum circular velocity, concentration, and spin) constrained to reproduce both primary and secondary (or assembly) bias. The key ingredients of the algorithm are the implementation of individually-assigned large-scale effective bias, a multi-scale approach to account for halo exclusion, and a hierarchical assignment of halo properties. The method facilitates the assignment of halo properties, aiming to replicate the large-scale effective bias, both primary and secondary. This constitutes an improvement over previous methods in the literature, especially for the high-mass end population. We have designed a strategy for reconstructing the main properties of dark matter halos obtained using calibrated or learning algorithms, such that the one- and two-point statistics (on large scales) replicate the signal from detailed $N$-body simulations. We encourage the application of this strategy (or the implementation of our algorithm) for the generation of mock catalogs of dark matter halos based on approximated methods.

Rapid determination of the geographical origin of kimchi by Fourier transform near-infrared spectroscopy coupled with chemometric techniques.

Determining the geographical origin of kimchi holds significance because of the considerable variation in quality and price among kimchi products from different regions. This study explored the feasibility of employing Fourier transform near-infrared spectroscopy in conjunction with supervised chemometric techniques to differentiate domestic and imported kimchi products. A total of 30 domestic and 30 imported kimchi products were used to build datasets. Three categories of preprocessing methods such as scattering correction (multiplicative signal correction and standard normal variate), spectral derivatives (the first derivative and the second derivative), and data smoothing (Savitzky-Golay filtering and Norris derivative filtering) were used. K-nearest neighbors, support vector machine, random forest, and partial least squares-discriminant analysis were employed. By appropriately preprocessing spectral data, these four methods successfully distinguished between the two sample groups based on their origin. Notably, the k-nearest neighbors method exhibited exceptional performance, accurately classifying the sample groups irrespective of the preprocessing method employed and swiftly achieving this classification. In comparison, classification and regression tree as well as naïve Bayes methods were outperformed by the aforementioned four classification techniques. Particularly, the efficiency and accuracy of the k-nearest neighbors method make it the most recommended chemometric tool for determining the geographical origins of kimchi.

Modulation Classification of Underwater Communication Signals Based on Channel Estimation

Classifying modulated signals for non-cooperative underwater acoustic communication is challenging due to signal distortion caused by fading and multipath effects in the underwater acoustic channel. Our proposed method utilizes channel estimation parameters to measure and correct signal distortion, thereby enhancing the recognition performance of the received signal. Modulation classification experiments were conducted on a public dataset with various modulation schemes, as well as on the same dataset with simulated underwater acoustic channels. The results indicate that our method effectively mitigates the impact of the underwater acoustic channel on modulation signal classification, improves recognition accuracy, and is broadly applicable to a wide range of machine learning classifiers. Finally, we validated these findings using real underwater communication data.

Ensemble percepts of colored targets among distractors are influenced by hue similarity, not categorical identity.

Color can be used to group similar elements, and ensemble percepts of color can be formed for such groups. In real-life settings, however, elements of similar color are often spatially interspersed among other elements and seen against a background. Forming an ensemble percept of these elements would require the segmentation of the correct color signals for integration. Can the human visual system do this? We examined whether observers can extract the ensemble mean hue from a target hue distribution among distractors and whether a color category boundary between target and distractor hues facilitates ensemble hue formation. Observers were able to selectively judge the target ensemble mean hue, but the presence of distractor hues added noise to the ensemble estimates and caused perceptual biases. The more similar the distractor hues were to the target hues, the noisier the estimates became, possibly reflecting incomplete or inaccurate segmentation of the two hue ensembles. Asymmetries between nominally equidistant distractors and substantial individual variability, however, point to additional factors beyond simple mixing of target and distractor distributions. Finally, we found no evidence for categorical facilitation in selective ensemble hue formation.

Predicting water content in engine oil using SPA-ISSA-BP methods  

Contamination of engine oil with water can accelerate oil oxidation and decomposition, resulting in oil degradation. Therefore, the detection and prediction of water content in engine oil is crucial to ensure the long-term safe operation of engines. In this study, near-infrared spectroscopy was used to analyse engine oils with varying water contents. Spectral data were obtained, and various methods, including Savitzky-Golay (SG) smoothing, orthogonal signal correction (OSC), and successive projection algorithm (SPA) were employed to compare the performance differences of principal component regression models. An Improved Sparrow Searches Algorithm (ISSA) was then applied to optimise the Back-Propagation Neural Network (BPNN) for predicting water content in SAE 10W-30 engine oil. The results showed that the performance of the principal component regression model constructed from spectral data after SG smoothing, OSC, and the SPA feature wavelength selection had improved. The BPNN optimised by the improved Sparrow Search Algorithm accelerated the convergence speed of the model and effectively improved the prediction accuracy of the BPNN. The obtained coefficient of determination (R2) was 0.99103, the root mean square error (RMSE) was 2. 2136 10-4, and the mean absolute error (MAE) was 1. 8889×10-4. These results provide an effective method for detecting and predicting the water content in engine oil.

Design of Self–Checking Digital Devices with Boolean Signals Correction Using Weight-Based Bose–Lin Codes

This paper proposes a method for designing self-checking digital devices with Boolean signals correction and weight-based Bose–Lin codes. Unlike previous studies, the method involves Boolean signals correction (BSC) in the concurrent error-detection (CED) circuit for those functions describing the outputs of source devices that participate in the formation of data symbols of weight-based Bose–Lin codes. In such codes, as in the absolute majority of uniform separable codes, several data vectors correspond to the same check vector; therefore, it is possible to choose a method for determining BSC functions. We describe an algorithm for determining their values for each input combination, considering the testability of the checker and transformation elements in the CED circuit. The method involves the so-called “base” structure for monitoring multi-output devices by output groups. With this method, the designer of a self-checking device has high variability in choosing the design method and can regulate important indicators (structure redundancy, controllability, energy consumption, and others). Experiments with combinational benchmarks from MCNC Benchmarks were carried out. According to the experimental data, the method has high efficiency in terms of structure redundancy compared to the duplication method widespread in practice. The method can be effective when designing real devices with fault detection used in all areas of technology, including critical application systems in industry and transport.

Fast and simultaneous prediction of inner quality parameters on intact mangos by near infrared spectroscopy: Impact of spectra pre-processing on prediction accuracy

Near infrared spectroscopy or known as NIRS has been widely employed in many fields including agriculture, especially for sorting and grading of agricultural products. Spectra pre-processing is one of the main factors affecting model accuracy and prediction capabilities of NIRS. The objective of the present study was to study the impact of different spectra corrections namely mean centering (MC), mean normalization (MN), de-trending (DT), multiplicative scatter correction (MSC), standard normal variate (SNV) and orthogonal signal correction (OSC), to the prediction accuracy of quality parameters: titratable acidity (TA) and soluble solids content (SSC) in intact mango. A total of 91 mango samples (cv. Kent) were used as dataset for calibration and external prediction which was separated by means of systematic sampling based on a property (SSBP) approach. Diffuse reflectance spectra (log1/R) were acquired and recorded in wavelength range of 1000 – 2500 nm by Antaris Fourier transform NIR instrument. Judging from calibration and prediction performance, MSC found to be the best spectra pre-processing method prior to prediction model development with R2 prediction are 0.72 for TA and 0.76 for SSC. Although MSC increase the prediction performances based on R2, RMSE, RPD and RER metrics compared to the baseline, the achieved RPD, 1.9 for TA and 1.8 for SSC of this findings are still poor and need improvements to achieve even higher levels of accuracy and reliability necessitates for real-time applications.

Qualitative and quantitative detection of camellia oil adulteration using electronic nose based on wavelet decomposition humidity correction

This study addresses the challenge of environmental humidity impacting the accuracy of metal oxide semiconductor (MOS)-based electronic nose systems in detecting adulterated camellia oil. A self-developed electronic nose platform with eight MOS sensors was used to detect adulteration in camellia oil mixed with varying ratios of rapeseed, soybean, and corn oils at different humidity levels (20%, 40%, and 60%). Wavelet decomposition using fourth-order Symlet wavelet was applied to correct response signals, mitigating humidity's effect on detection accuracy. Linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF) were employed to build qualitative identification models using pre- and post-correction datasets, while partial least squares regression (PLSR) and backpropagation neural network (BPNN) were used for quantitative prediction. Results showed that both qualitative and quantitative models significantly improved performance after correcting signal drift with wavelet decomposition. The qualitative model's 10-fold cross-validation prediction accuracy increased to 98.67% from 88.67%, while the quantitative model's average R2 reached 0.99 with RMSE reduced to 3.64 mL/100 mL, compared to pre-correction R2 of 0.86 and RMSE of 9.63 mL/100 mL. Notably, the RF and BPNN models outperformed others in qualitative and quantitative detection, respectively. These findings highlight the potential of electronic nose technology with humidity correction for authenticating camellia oil.

Optimal antioxidant enzyme activity estimation in melon plant leaves based on microhyperspectral imaging technique

BackgroundIn order to select the most suitable type of light for plant growth, we have developed a quantitative model for the detection of antioxidant enzyme activities on melon leaves, which allows us to monitor plant growth in a timely and accurate manner. Leaf of melon ‘M5147’ and ‘M5346’ were used as test material. Six treatments have been established with different illuminance ratios. By the measurement of leaf superoxide dismutase (SOD), peroxidase (POD), catalase (CAT) activities, the varying response of melons leaf to different grades of light have been studied using microscopic hyperspectral imaging at the plant factory. ResultThe original spectrum has been pre-processed and optimized. And then the characteristic wavelengths were extracted by ant colony optimization (ACO), iterative retained information variable (IRIV), un-information variable elimination (UVE) and genetic partial least squares (GAPLS). Partial Least Squares Regression (PLSR), least squares support vector machine (LSSVM) and convolutional neural network (CNN) are built based on the optimal feature wavelengths. T4 treatment (Light ratio: 7R/3B/5W/1UVa, Flux: μmol (m2-s), photoperi 12 h) was found to be optimum. In the pre-processing of the raw spectral data, orthogonal signal correction (OSC) methods were selected for SOD, POD and CAT. The best performance of SOD prediction model was constructed based on GAPLS-CNN (Rc = 0.751, Rp = 0.638); The best performance of POD prediction model was constructed based on UVE-CNN (Rc = 0.753, Rp = 0.530); The best performance of CAT prediction model constructed based on ACO-CNN (Rc = 0.742, Rp = 0.504). SignificanceThe aim of this study was to provide technical support for the dynamic monitoring of antioxidant enzyme activity in the leaves of other plants by establishing a quantitative detection model for antioxidant enzyme activity in melon leaves by combining chemical methods with microscopic hyperspectral imaging technology. This work provides data and a theoretical basis for screening light ratios in other plants and offers technical guidance for dynamically monitoring antioxidant enzyme activities in their leaves.

Innovative strategies for protein content determination in dried laver (Porphyra spp.): Evaluation of preprocessing methods and machine learning algorithms through short-wave infrared imaging

In this study, we explored the application of Short-Wave Infrared (SWIR) hyperspectral imaging combined with Competitive Adaptive Reweighted Sampling (CARS) and advanced regression models for the non-destructive assessment of protein content in dried laver. Utilizing a spectral range of 900–1700 nm, we aimed to refine the quality control process by selecting informative wavelengths through CARS and applying various preprocessing techniques (standard normal variate [SNV], Savitzky-Golay filtering [SG], Orthogonal Signal Correction [OSC], and StandardScaler [SS]) to enhance the model's accuracy. The SNV-OSC-StandardScaler- Support vector regression (SVR) model trained on CARS-selected wavelengths significantly outperformed the other configurations, achieving a prediction determination coefficient (Rp2) of 0.9673, root mean square error of prediction of 0.4043, and residual predictive deviation of 5.533. These results highlight SWIR hyperspectral imaging's potential as a rapid and precise tool for assessing dried laver quality, aiding food industry quality control and dried laver market growth.

Orthogonal signal correction assisted multivariate regression approach for the estimation of uranium and acidity in PUREX process streams

A direct UV–Visible absorbance spectrophotometric method was developed for the simultaneous determination of uranium and nitric acid concentration in the PUREX process samples. The simulated system consisted of uranium and nitric acid in concentration range corresponding to reprocessing of spent nuclear fuel discharged from nuclear reactor was prepared. The absorbance of these samples was measured in the range of 400–470 nm at a scan speed of 100 nm/s and resultant spectra were recorded. The changes in wavelength maxima of U(VI) absorption spectrum at different nitric acid concentration was utilized to determine the concentration of uranium and nitric acid in the sample by orthogonal signal correction assisted principal component regression. After the principle component regression the RMSEP for test data (Uranium: 3–21 g/L and acidity: 2–12 M) were 0.7 g/L and 0.4 M respectively. This method is superior to conventional method being followed for routine analysis of plant control samples in view of minimizing the generation of radioactive analytical waste consisting other corrosive reagents and reducing radiation exposure to operators during analysis. This method is amenable for online monitoring also.

Exploring the use of extended multiplicative scattering correction for near infrared spectra of wood with fungal decay

Extended Multiplicative Signal Correction (EMSC) is a multivariate linear modelling technique for multi-channel measurements that can identify and correct for different types of systematic variation patterns, known or unknown. It is typically used for pre-processing to separate light absorbance spectra, obtained by diffuse reflectance of intact samples, into three main sources of variation: additive variations due to chemical composition (≈Beer's law), mixed multiplicative and additive variations due to physical light scattering (≈Lambert's law) and more or less random measurement noise. The present work evaluates the use of EMSC to pre-process near infrared spectra obtained by hyperspectral imaging of Scots pine sapwood, inoculated with two different basidiomycete fungi and at various degradation stages. The spectral changes due to fungal decay and resulting mass loss are assessed by interpretation of the EMSC parameters and the partial least squares regression (PLSR) results. Including a cellulose (analyte) or bound water (interferent) spectral profile in the EMSC pre-processing model generally improves the predictive performance of the PLS modelling, but it can also make it worse. The inclusion of the additional polynomial baselines does not necessarily lead to a better separation of the physical and chemical effects present in the spectra. The estimated EMSC parameters provide insight into the differences in decay mechanisms. A detailed analysis of the EMSC results highlights advantages and disadvantages of using a complex pre-processing model.

Estimation of Time-Since-Deposition of bloodstains on different surfaces using ATR-FTIR Spectroscopy and Chemometrics.

Blood is commonly discovered at crime scenes in various forms, including stains, dried residue, pools, and fingerprints on assorted surfaces. Estimating the age of bloodstains is a crucial aspect of reconstructing crime scenes. This research aimed to investigate how the nature of different surfaces affects the estimation of bloodstain age, utilizing a reliable and non-destructive approach. The study employed ATR-FTIR spectroscopy in conjunction with Chemometric techniques such as PCA (Principal Component Analysis) and OPLSR (Orthogonal Signal Correction Partial Least Square Regression Analysis) to analyze spectral data and develop regression models for estimating bloodstain age on cement, metal, and wooden surfaces for up to eleven days. The chemometric models for bloodstains on all three substrates demonstrated strong performance, with predictive Root Mean Square Error (RMSE) values ranging from 1.1 to 1.43 and R2 values from 0.84 to 0.89. Notably, the model developed for metal surfaces was found to be the most accurate with minimal prediction error. The findings of the study showed that the porosity of the substrates upon which bloodstains were found had a discernible influence on the age-related transformations observed in bloodstains; the majority of which occured within the spectral range of 2800cm- 1 to 3500cm- 1.