Smoothing Filter Research Articles

Deep Learning Ensemble for Flood Probability Analysis

Predicting flood events is complex due to uncertainties from limited gauge data, high data and computational demands of traditional physical models, and challenges in spatial and temporal scaling. This research innovatively uses only three remotely sensed and computed factors: rainfall, runoff and temperature. We also employ three deep learning models—Feedforward Neural Network (FNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)—along with a deep neural network ensemble (DNNE) using synthetic data to predict future flood probabilities, utilizing the Savitzky–Golay filter for smoothing. Using a hydrometeorological dataset from 1993–2022 for the Nile River basin, six flood predictors were derived. The FNN and LSTM models exhibited high accuracy and stable loss, indicating minimal overfitting, while the CNN showed slight overfitting. Performance metrics revealed that FNN achieved 99.63% accuracy and 0.999886 ROC AUC, CNN had 95.42% accuracy and 0.893218 ROC AUC, and LSTM excelled with 99.82% accuracy and 0.999967 ROC AUC. The DNNE outperformed individual models in reliability and consistency. Runoff and rainfall were the most influential predictors, while temperature had minimal impact.

Detecting and sizing the Earth with PLATO: A feasibility study based on solar data

The PLAnetary Transits and Oscillations of stars (PLATO) mission will observe the same area of the sky continuously for at least two years in an effort to detect transit signals of an Earth-like planet orbiting a solar-like star. We aim to study how short-term solar-like variability caused by oscillations and granulation would affect PLATO's ability to detect and size Earth if PLATO were to observe the Solar System itself. We also compare different approaches to mitigate noise caused by short-term solar-like variability and perform realistic transit fitting of transit signals in PLATO-like light curves. We injected Earth-like transit signals onto real solar data taken by the Helioseismic and Magnetic Imager (HMI) instrument on board the Solar Dynamics Observatory (SDO). We isolated short-term stellar variability in the HMI observations by removing any variability with characteristic timescales longer than five hours using a smooth Savitzky-Golay filter. We then added a noise model for a variety of different stellar magnitudes computed by assuming an observation by all 24 normal cameras. We first compared four different commonly used treatments of correlated noise in the time domain by employing them in a transit fitting scheme. We then tried to recover pairs of transit signals using an algorithm similar to the transit least squares algorithm. Finally, we performed transit fits using realistic priors on planetary and stellar parameters and assessed how accurately the pair of two injected transits was recovered. We find that short-term solar-like variability affects the correct retrieval of Earth-like transit signals in PLATO data. Variability models accounting for variations with typical timescales at the order of one hour are sufficient to mitigate these effects. We find that when the limb-darkening coefficients of the host star are properly constrained, the impact parameter does not negatively affect the detectability of a transit signal or the retrieved transit parameters, except for high values ($b > 0.8$). For bright targets (8.5 - 10.5 mag), the transit signal of an Earth analogue can reliably be detected in PLATO data. For faint targets a detection is still likely, though the results of transit search algorithms have to be verified by transit-fitting algorithms to avoid false positive detections being flagged. For bright targets (V-mag leq 9.5), the radius of an Earth-like planet orbiting a solar-like star can be correctly determined at a precision of 3<!PCT!> or less, assuming that at least two transit events are observed and the characteristics of the host star are well understood.

Synthesis of High-Selectivity Two-Dimensional Filter Banks Using Sigmoidal Function

This paper explores an application of the sigmoidal function—the complementary integral Gaussian error function (erfc(.))—in two-dimensional (2D) uniform and nonuniform filter bank synthesis. The complementary integral Gaussian error function graph represents a smooth low-pass filter magnitude response. A parameter changes the function slope and increases the magnitude response selectivity. The theory is applied to 2D band-pass filter banks. Exact expressions for the magnitude response parameters are determined. As a result, 2D uniform and nonuniform filter banks with very high selectivity and exact shapes are obtained. Three synthesis examples of 2D filter banks with circular and fan-shaped magnitude responses are provided. The theoretical exposition is supplemented with two examples of image analysis using 2D uniform and nonuniform filter banks. A procedure to reduce the computations in image analysis is proposed. A comparison of filter synthesis between Parks–McLellan’s 2D filters and the erfc(.) demonstrates the significantly shorter calculation time of the proposed method.

Influence of mesoscale sea‐surface temperature structures on the Mediterranean cyclone Ianos in convection‐permitting simulations: Contributions of surface warming and cold wakes

AbstractThis study examined the influence of mesoscale sea‐surface temperature (SST) structures and warming on the tropical‐like cyclone Ianos. The controlling mechanisms were investigated using a set of sensitivity experiments with the non‐hydrostatic weather research and forecasting model. A simulation forced by a high‐resolution SST field from the mesoscale eddy‐resolving HYCOM reanalysis (CTRLH) performed better than a simulation forced by a coarse‐resolution SST field from Optimum Interpolation Sea Surface Temperature (OISST) in terms of precipitation, wind intensity, and landfall location. The suppression of mesoscale SST variability affected the cyclone's intensity, its path, and associated processes. This effect depends on the nature of the anomalies (cold/warm). The suppression of mesoscale SST forcing intensified wind and precipitation in the presence of cold anomalies, leading to a northwestward shift of the storm's path during its mature phase. The magnitude of these changes varied with the scale of smoothing (mesoscale SST). Compared to the CTRLH simulation, a simulation in which a 60‐km smoothing filter was applied to the SST field resulted in a cyclone centered slightly to the west (or delayed by 3–6 hr), which was further shifted to the west (or delayed) when the smoothing filter was increased to 150 km. The intensification of wind and precipitation may be associated with the reduction in the storm's cold wake, which would lead to enhanced turbulent fluxes and increased atmospheric water vapor. These results highlight the role of small‐scale air–sea interaction processes that could have a strong impact on Mediterranean cyclones' path and intensity.

Sound Sensing: Generative and Discriminant Model-Based Approaches to Bolt Loosening Detection.

The detection of bolt looseness is crucial to ensure the integrity and safety of bolted connection structures. Percussion-based bolt looseness detection provides a simple and cost-effective approach. However, this method has some inherent shortcomings that limit its application. For example, it highly depends on the inspector's hearing and experience and is more easily affected by ambient noise. In this article, a whole set of signal processing procedures are proposed and a new kind of damage index vector is constructed to strengthen the reliability and robustness of this method. Firstly, a series of audio signal preprocessing algorithms including denoising, segmenting, and smooth filtering are performed in the raw audio signal. Then, the cumulative energy entropy (CEE) and mel frequency cepstrum coefficients (MFCCs) are utilized to extract damage index vectors, which are used as input vectors for generative and discriminative classifier models (Gaussian discriminant analysis and support vector machine), respectively. Finally, multiple repeated experiments are conducted to verify the effectiveness of the proposed method and its ability to detect the bolt looseness in terms of audio signal. The testing accuracy of the trained model approaches 90% and 96.7% under different combinations of torque levels, respectively.

Mode-switching cooperative defense strategy for the Orbit Pursuit-Evasion-Defense Game

This paper presents a mode-switching collaborative defense strategy for spacecraft pursuit-evasion-defense scenarios. In these scenarios, the pursuer tries to avoid the defender while capturing the evader, while the evader and defender form an alliance to prevent the pursuer from achieving its goal. First, the behavioral modes of the pursuer, including attack and avoidance modes, were established using differential game theory. These modes are then recognized by an interactive multiple model-matching algorithm (IMM), that uses several smooth variable structure filters to match the modes of the pursuer and update their probabilities in real time. Based on the linear-quadratic optimization theory, combined with the results of strategy identification, a two-way cooperative optimal strategy for the defender and evader is proposed, where the evader aids the defender to intercept the pursuer by performing luring maneuvers. Simulation results show that the interactive multi-model algorithm based on several smooth variable structure filters perform well in the strategy identification of the pursuer, and the cooperative defense strategy based on strategy identification has good interception performance when facing pursuers, who are able to flexibly adjust their game objectives.

A Priori Test of Scale-Similarity Parameterizations for Subgrid-Scale Fluxes in Convective Storms

Abstract This study evaluates a parameterization scheme for subgrid-scale (SGS) fluxes based on the scale-similarity assumption and employing a large-eddy simulation of an idealized back-building convective system. In this parameterization, the SGS fluxes are decomposed into the “Leonard term,” which depends only on the resolved scale components, the “Reynolds term,” which depends only on the SGS components, and the “cross term,” which corresponds to the interaction between the resolved scale and SGS components. Assuming a linear relationship between the Leonard term and the Reynolds and cross terms, SGS fluxes are expressed as the product of an empirical coefficient and the Leonard term. The Leonard term reasonably represents the SGS flux derived by a smooth filter operation, including the countergradient vertical SGS transport of potential temperature, which cannot be represented by a traditional eddy-diffusivity model. The dependence of the empirical coefficient on filter width is also evaluated. This dependence is related mainly to the Reynolds term, the magnitude of which varies widely with the filter width. The estimation based on the spectral decomposition of the Reynolds term explains the obtained dependence of the empirical coefficient for the vertical flux on filter width. In contrast, the variation of the empirical coefficient with filter width is not required to obtain the horizontal flux. For the parameterization of SGS fluxes in kilometer-scale models that use finite difference or volume methods, the Leonard term is expressed by the horizontal gradient of variables on a discrete grid. The Leonard term on a discrete grid also accurately represents the amplitude and spatial pattern of the SGS flux. Significance Statement Kilometer-scale numerical weather prediction models can reproduce deep convection explicitly. However, they cannot reproduce smaller inner structures within this deep convection. Therefore, a parameterization scheme that can express the nature of the transport associated with such structures is required. Using a large-eddy simulation with a horizontal resolution sufficient to express such inner structures, we evaluated a parameterization based on the scale similarity assumption, including an empirical coefficient. Based on spectral decomposition, we quantitatively estimated the relationship between the empirical coefficient and grid spacing. Our results provide guidance in selecting the value of the empirical coefficient in this parameterization.

A Novel Method for Online Diagnostic Analysis of Partial Discharge in Instrument Transformers and Surge Arresters from the Correlation of HFCT and IEC Methods

In this work, a new methodology is proposed for the online and non-invasive extraction of partial discharge (PD) pulses from raw measurement data obtained using a simplified setup. This method enables the creation of sub-windows with optimized size, each containing a single candidate PD pulse. The proposed approach integrates mathematical morphological filtering (MMF) with kurtosis, a first-order Savitzky-Golay smoothing filter, the Otsu method for thresholding, and a specific technique to associate each sub-window with the phase angle of the applied voltage waveform, enabling the construction of phase-resolved PD (PRPD) patterns. The methodology was validated against a commercial PD detection device adhering to the IEC (International Electrotechnical Commission) standard. Experimental results demonstrated that the proposed method, utilizing an off-the-shelf 8-bit resolution data acquisition system and a low-cost high-frequency current transformer (HFCT) sensor, effectively diagnoses and characterizes PD activity in high-voltage equipment, such as surge arresters and instrument transformers, even in noisy environments. It was able to characterize PD activity using only a few cycles of the applied voltage waveform and identify low amplitude PD pulses with low signal-to-noise ratio signals. Other contribution of this work is the diagnosis and fault signature obtained from a real surge arrester (SA) with a nominal voltage of 192 kV, corroborated by destructive disassembly and internal inspection of the tested equipment. This work provides a cost-effective and accurate tool for real-time PD monitoring, which can be embedded in hardware for continuous evaluation of electrical equipment integrity.

Microscale insights into deep bed membrane filtration: Influence of internal surface roughness

Deep bed filtration is widely applied in bioprocessing, virus filtration, and water purification to remove small impurities, such as particles or virus fragments. However, more needs to be understood about the parameters that influence particle capture and deposition. Detailed simulations and microfluidic filtration experiments with straight pores have been widely investigated, yet realistic membrane porosity features such as pore gradients and roughness have not been addressed in detail. The internal membrane morphology is assumed to have a strong effect on filtration performance. Therefore, this study introduces a new microfluidic membrane mimicking device (MMD) and methodology that analyzes spatio-temporal particle collections in a deep bed filtration MMD with gradually decreasing pore size toward the retentate side. The design allows us to systematically tailor an internal filter surface roughness to investigate its influence on differently sized soft particles and the consequences for filtration performance, in particular, particle capture. Optical investigation and quantitative evaluation show enhanced particle–pillar interactions for increased roughness. This results in a reduced penetration depth and reduced particle breakthrough. The modes of capture or filtration phenomena, such as pore blocking, bridging, or dendrite formation, differ significantly between smooth and rough membrane filter structures. In the case of rough inner filter surfaces, those phenomena lead to a less distinct decrease in volumetric flow, allowing a longer filtration process. The microfluidic study offers a detailed investigation of how microscale phenomena influence the filtration process. The results provide a fundamental understanding of microscale filtration effects based on roughness and, hence, motivate a tailoring of the internal membrane filter surface according to the filtration problem at hand.

Ultrasound image smoothing based on adaptive and non-adaptive filters.

To model adaptive and non-adaptive filters to ensure smooth ultrasound images. The comparative study was conducted at Al-Yarmouk Teaching Hospital, Al Mustansiriyah University, Baghdad, Iraq, in 2019, and comprised ultrasound images of kidney (303x208 pixel) and foetus (111x109 pixel). These images were smoothed based on 8 filters; 1 non-adaptive (median), and 7 adaptive enhanced filters (Gamma, Wiener, Lee, Frost, Kuan, Adaptive Lee and Adaptive Frost). They were applied to the images by windows measuring 3x3, 5x5, 7x7. The additive noise and the multiplicative noise factor were calculated using histogram to determine the noise type for each image. Statistical criteria included mean square error, normalised absolute error and signalto- noise ratio. The relationship between noise ratio and filter type showed that Wiener was the best filter and the best sliding window was 3x3. The worst filters were Gamma, EFrost and Kuan. The relationship between sliding window size and noise ratio for all the smoothing filters clearly identified the best filter for the type of noise.

Towards Optimising the Derivation of Phenological Phases of Different Crop Types over Germany Using Satellite Image Time Series

Operational crop monitoring applications, including crop type mapping, condition monitoring, and yield estimation, would benefit from the ability to robustly detect and map crop phenology measures related to the crop calendar and management activities like emergence, stem elongation, and harvest timing. However, this has proven to be challenging due to two main issues: first, the lack of optimised approaches for accurate crop phenology retrievals, and second, the cloud cover during the crop growth period, which hampers the use of optical data. Hence, in the current study, we outline a novel calibration procedure that optimises the settings to produce high-quality NDVI time series as well as the thresholds for retrieving the start of the season (SOS) and end of the season (EOS) of different crops, making them more comparable and related to ground crop phenological measures. As a first step, we introduce a new method, termed UE-WS, to reconstruct high-quality NDVI time series data by integrating a robust upper envelope detection technique with the Whittaker smoothing filter. The experimental results demonstrate that the new method can achieve satisfactory performance in reducing noise in the original NDVI time series and producing high-quality NDVI profiles. As a second step, a threshold optimisation approach was carried out for each phenophase of three crops (winter wheat, corn, and sugarbeet) using an optimisation framework, primarily leveraging the state-of-the-art hyperparameter optimization method (Optuna) by first narrowing down the search space for the threshold parameter and then applying a grid search to pinpoint the optimal value within this refined range. This process focused on minimising the error between the satellite-derived and observed days of the year (DOY) based on data from the German Meteorological Service (DWD) covering two years (2019–2020) and three federal states in Germany. The results of the calculation of the median of the temporal difference between the DOY observations of DWD phenology held out from a separate year (2021) and those derived from satellite data reveal that it typically ranged within ±10 days for almost all phenological phases. The validation results of the detection of dates of phenological phases against separate field-based phenological observations resulted in an RMSE of less than 10 days and an R-squared value of approximately 0.9 or greater. The findings demonstrate how optimising the thresholds required for deriving crop-specific phenophases using high-quality NDVI time series data could produce timely and spatially explicit phenological information at the field and crop levels.

Authentication of the shelf-life of NPH injectable insulins samples using NIR and MS spectral fingerprints

Insulin is essential for regulating glucose levels, and its hormonal deficiency leads to diabetes. Treatment involves the use of different types of insulin tailored to individual needs. Properly stored and transported Neutral Protamine Hagedorn (NPH) insulin remains effective and stable, highlighting the importance of quality control to ensure its efficacy and patient safety. This study aims to develop an analytical method to evaluate the shelf-life of NPH injectable insulin samples while preserving packaging integrity. The approach consisted of analyzing intact insulin samples, with and without expired shelf-life dates, using near-infrared spectroscopy (NIR) obtained from two instruments: a benchtop and a portable device. These measurements were coupled with chemometric methods, specifically single-class classification algorithms. The classifiers considered were Data Driven-Soft Independent Modeling of Class Analogy (DD-SIMCA) and One-Class Partial Least Squares (OC-PLS). For benchtop NIR, the DD-SIMCA method, with Savitzky-Golay smoothing preprocessing (41-point window) and baseline offset (BO), achieved excellent results, reaching 100% sensitivity, specificity, and efficiency in both training and test sets. As for portable NIR, the most effective methods were DD-SIMCA and OC-PLS, using datasets preprocessed with Savitzky-Golay smoothing with first derivative, second-order polynomial (SGD-1st-2nd), and 9-point windows combined with Standard Normal Variate (SNV), and Savitzky-Golay smoothing filters (SGS) using second-order polynomial and 7-point windows combined with baseline offset (BO) and Multiplicative Scattering Correction (MSC). The results were corroborated by mass spectrometry, correlating the structural changes of degraded insulin with NIR data. The proposed methodology is a promising tool for non-destructive, rapid, and cost-effective authentication of the preservation state of NPH insulin, aligning with green chemistry principles and allowing for in-situ evaluation, thereby preserving medication integrity.

Precision fMRI and cluster-failure in the individual brain.

Advances in neuroimaging acquisition protocols and denoising techniques, along with increasing magnetic field strengths, have dramatically improved the temporal signal-to-noise ratio (tSNR) in functional magnetic resonance imaging (fMRI). This permits spatial resolution with submillimeter voxel sizes and ultrahigh temporal resolution and opens a route toward performing precision fMRI in the brains of individuals. Yet ultrahigh spatial and temporal resolution comes at a cost: it reduces tSNR and, therefore, the sensitivity to the blood oxygen level-dependent (BOLD) effect and other functional contrasts across the brain. Here we investigate the potential of various smoothing filters to improve BOLD sensitivity while preserving the spatial accuracy of activated clusters in single-subject analysis. We introduce adaptive-weight smoothing with optimized metrics (AWSOM), which addresses this challenge extremely well. AWSOM employs a local inference approach that is as sensitive as cluster-corrected inference of data smoothed with large Gaussian kernels, but it preserves spatial details across multiple tSNR levels. This is essential for examining whole-brain fMRI data because tSNR varies across the entire brain, depending on the distance of a brain region from the receiver coil, the type of setup, acquisition protocol, preprocessing, and resolution. We found that cluster correction in single subjects results in inflated family-wise error and false positive rates. AWSOM effectively suppresses false positives while remaining sensitive even to small clusters of activated voxels. Furthermore, it preserves signal integrity, that is, the relative activation strength of significant voxels, making it a valuable asset for a wide range of fMRI applications. Here we demonstrate these features and make AWSOM freely available to the research community for download.

Quantum Molecular Docking with a Quantum-Inspired Algorithm.

Molecular docking (MD) is a crucial task in drug design, which predicts the position, orientation, and conformation of the ligand when it is bound to a target protein. It can be interpreted as a combinatorial optimization problem, where quantum annealing (QA) has shown a promising advantage for solving combinatorial optimization. In this work, we propose a novel quantum molecular docking (QMD) approach based on a QA-inspired algorithm. We construct two binary encoding methods to efficiently discretize the degrees of freedom with an exponentially reduced number of bits and propose a smoothing filter to rescale the rugged objective function. We propose a new quantum-inspired algorithm, hopscotch simulated bifurcation (hSB), showing great advantages in optimizing over extremely rugged energy landscapes. This hSB can be applied to any formulation of an objective function under binary variables. An adaptive local continuous search is also introduced for further optimization of the discretized solution from hSB. Concerning the stability of docking, we propose a perturbation detection method to help rank the candidate poses. We demonstrate our approach on a typical data set. QMD has shown advantages over the search-based Autodock Vina and the deep-learning DIFFDOCK in both redocking and self-docking scenarios. These results indicate that quantum-inspired algorithms can be applied to solve practical problems in drug discovery even before quantum hardware become mature.

A Hybrid Path Planning Technique for the Time-Efficient Navigation of Unmanned Vehicles in an Unconstrained Environment

Optimal path planning refers to choosing a collision-free, shortest, and smooth path, which is achieved by proposing a new technique, Optimized A* (OA*). The proposed OA* is the hybrid version of three techniques that are ABA* (Adaptive Bidirectional A*), VTM (Vector triangulation method), and PSF (Path smoothing filter which is the modification of Bezier curve technique). The conventional A* only deal with the heuristic values of concerned nodes, but the proposed ABA* algorithm also considers the occupied cells/ obstacles present near the starting and goal nodes in the grid cells to select the shortest path from start to destination. Due to the involvement of the occupied cells, the execution time, path curves, and the number of operations is primarily reduced because occupied nodes are considered as closed nodes, and its heuristic calculation is not considered in the conventional method. The trajectory generated by the proposed ABA* technique is also further made smoother by implementing a path smoothing filter that is the combination of VTM and modified Bezier curve technique based on selecting optimal control point for the collision free trajectory. While implementing the proposed OA* makes the path trajectory become smoother by reducing the number of sharp turns by 85.36% w.r.t traditional ACO, 73.91% w.r.t improved ACO, and 64.70% w.r.t hybrid solution of ACO+ A*. Due to the reduction in the number of sharp turns in the path trajectory, the acceleration of the mobile robot is increased by 52.96% w.r.t traditional ACO, 28.63% w.r.t improved ACO, and 19.96% w.r.t hybrid solution of ACO+ A*.

A new dynamic state estimation method for distribution networks based on modified SVSF considering photovoltaic power prediction

The fluctuations brought by the renewable energy access to the distribution network make it difficult to accurately describe the state space model of the distribution network’s dynamic process, which is the basis of the existing dynamic state estimation methods such as the Kalman filter. The inaccurate state space model directly causes an error of dynamic state estimation results. This paper proposed a new dynamic state estimation method which can mitigates the impact of renewable energy fluctuation by considering PV power prediction in establishing distribution network state space model. Firstly, the proposed method mitigates the impact of renewable energy fluctuation by considering PV power prediction in establishing distribution network state space model. Secondly, SVSF filter is introduced to achieve more accurate estimation under noise. The case study and evaluations are carried out based on MATLAB simulation. The results prove that the smooth variable structure filter with photovoltaic power prediction has a better dynamic state estimation effect under the fluctuation of the distribution network compared with the existing Kalman filter.

Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images.

Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and obstacles characterizes computer-assisted diagnosis, which relies on the precise and effective analysis of pathology images. In recent years, pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection have witnessed the considerable potential of artificial intelligence, especially deep learning techniques. In this context, an artificial intelligence (AI)-based methodology for lung cancer diagnosis is proposed in this research work. As a first processing step, filtering using the Butterworth smooth filter algorithm was applied to the input images from the LUNA 16 lung cancer dataset to remove noise without significantly degrading the image quality. Next, we performed the bi-level feature selection step using the Chaotic Crow Search Algorithm and Random Forest (CCSA-RF) approach to select features such as diameter, margin, spiculation, lobulation, subtlety, and malignancy. Next, the Feature Extraction step was performed using the Multi-space Image Reconstruction (MIR) method with Grey Level Co-occurrence Matrix (GLCM). Next, the Lung Tumor Severity Classification (LTSC) was implemented by using the Sparse Convolutional Neural Network (SCNN) approach with a Probabilistic Neural Network (PNN). The developed method can detect benign, normal, and malignant lung cancer images using the PNN algorithm, which reduces complexity and efficiently provides classification results. Performance parameters, namely accuracy, precision, F-score, sensitivity, and specificity, were determined to evaluate the effectiveness of the implemented hybrid method and compare it with other solutions already present in the literature.

Iterative robust peak-aware guided filter for signal smoothing

Due to the imperfection of experimental measurements, the measured spectrum signals are inevitably corrupted by random error (noise). It is very useful in applications to remove the noise in the measured data while preserving some significant features (such as peaks) by a smoothing filtering process. This paper presents a peak-preserving smoothing technique in the framework of guided filtering for spectrum signals, called robust peak-aware guided filter (RPAGF). Firstly, the robust peak-aware weight (RPAW) is introduced by means of hyperbolic tangent with threshold parameter, where the parameter is estimated by the median of absolute deviations from the median of local variances of the signal under consideration. Then the RPAGF is presented by incorporating the RPAW of a guidance signal into the cost function of guided filter as the regularization coefficient. The RPAGF uses the Gaussian-weighted averaging to deal with the problem of overlapping windows involved in computing filtering output, while the guidance signal is generated by a time fractional diffusion filtering from the input signal. Finally, the self-iteration scheme of RPAGF called iterative RPAGF (IRPAGF) is designed to provide a more faithful guidance signal for RPAGF at each iteration. The IRPAGF is evaluated by experiments on simulated and real spectrum signals, in comparison to the relevant state-of-the-art methods. Results show that the IRPAGF achieves higher performance in terms of peak-preserving smoothing of spectrum signals.

Hierarchical three-layered fibers for bioaerosol and CO2 capture, and antimicrobial performance

As the duration of indoor activities has increased owing to environmental issues and the prevalence of respiratory diseases, it has become important to reduce indoor air pollutants. In this study, air filters with structurally modified surfaces are fabricated to effectively capture and remove indoor air pollutants. High-temperature alkali treatment is applied to a smooth fiber filter to convert it into porous calcium silicate hydrate and create a hierarchical three-layered (H3L) fibrous structure. The outer, middle, and inner layers of the H3L fiber are designed for bioaerosol capture and disinfection, CO2 capture, and maintenance of mechanical strength, respectively. An antimicrobial agent ((3-(trimethoxysilyl)propyldimethyloctadecyl ammonium chloride, TPMMC) is covalently grafted onto the surface of H3L fibers to impart antimicrobial performance. The H3L-TPMMC filter outperforms the bare filter in capturing E. coli and S. aureus aerosols, displaying improved quality factors of 31.9 % and 63.9 %, respectively. It achieves a 99.999 % reduction and 99.790 % for the collected E. coli and S. aureus aerosols, respectively. The H3L-TPMMC filter demonstrates selective CO2 capture and maintains consistent performance and antimicrobial efficacy even in environments with bioaerosols and CO2. This hierarchical filter membrane provides an efficient approach for reducing various indoor air pollutants using a single filter system.

Non-destructive authentication of melon seed (Cucumeropsis mannii) powder using a pocket-sized near-infrared (NIR) spectrophotometer with multiple spectral preprocessing

Increasing demand for plant-based proteins has elicited suspicions of fraudulent practices among some producers of melon seed powder. The powder is suspected to be adulterated with carbohydrate-rich foods including maize, dried-milled cassava (gari) and soy flour to boost its quantity for increased profit. This study aimed to develop robust models using near-infrared spectroscopy (NIRS) for rapid authentication of melon seed powder. Melon seed powder samples containing 0–50 %w/w maize, gari and soy were prepared in triplicates and scanned using a handheld NIRS device. Proximate composition and color were significantly (p<0.05) affected by adulterant presence in melon seed powder. Classification models using linear discriminant analysis yielded accuracies of adulterant classification that were further confirmed by the high sensitivity, specificity and precision of the models (higher than 80 % in all cases). Low errors (RMSEC 3.28, RMSECV 3.92, RMSEP 6.87) % w/w, limits of detection and quantification and high coefficients (R2 0.96, R2CV 0.94, R2PRED 0.81) % w/w, indicative of reliable partial least squares models were obtained for the prediction of adulterants, protein, carbohydrate, total colour change, fat and lightness. Overall, Savitisky-Golay smoothing filter yield the best results in all LDA analysis. NIRS is a viable option for rapid non-invasive authentication of melon seed powder. The handheld device presents an added advantage of remote analysis. Instruments with other wavelength ranges can be explored in a similar approach study but with a higher sample size for more rigorous applications.