Large Window Size Research Articles

Longitudinal synchroextracting transform: A useful tool for characterizing signals with strong frequency modulation and application to machine fault diagnosis

In this paper, we propose a new time–frequency analysis (TFA) method termed longitudinal synchrosqueezing transform (LSST). As a post-processing time–frequency analysis method, the theory of this method is based on the Reassignment method (RM) and Fourier-based synchrosqueezing transform (FSST). LSST combines the advantages of RM and FSST in signal processing and avoids their drawbacks. Compared with RM and FSST, this method can achieve compact time–frequency representation (TFR) while retaining the ability of modes extraction and reconstruction. However, when addressing strong frequency modulation (FM) signals, there will still be energy smear in the TFR generated by LSST. Therefore, longitudinal synchroextracting transform (LSET) is further proposed to cope with signals with strong FM components and generate TFR with high energy concentration. On the basis of LSST, we only keep the energy most relevant to the instantaneous frequency (IF) of the signal through the Dirichlet function to obtain LSET. LSET estimates the signal's instantaneous frequency using a second-order approximation, which enables an accurate characterization of fast time-varying features of the signal at large window size range and it is more robust against noise. In addition, the proposed method is applied to the early fault diagnosis of rotor rub-impact and the fault feature extraction of rolling bearings under variable speed. Compared with other advanced TFA technologies, the simulated and measured signals can verify the effectiveness and competitiveness of the proposed method.

Information Aggregation for Constrained Online Control

This paper considers an online control problem involving two controllers. A central controller chooses an action from a feasible set that is determined by time-varying and coupling constraints, which depend on all past actions and states. The central controller's goal is to minimize the cumulative cost; however, the controller has access to neither the feasible set nor the dynamics directly, which are determined by a remote local controller. Instead, the central controller receives only an aggregate summary of the feasibility information from the local controller, which does not know the system costs. We show that it is possible for an online algorithm using feasibility information to nearly match the dynamic regret of an online algorithm using perfect information whenever the feasible sets satisfy a causal invariance criterion and there is a sufficiently large prediction window size. To do so, we use a form of feasibility aggregation based on entropic maximization in combination with a novel online algorithm, named Penalized Predictive Control (PPC) and demonstrate that aggregated information can be efficiently learned using reinforcement learning algorithms. The effectiveness of our approach for closed-loop coordination between central and local controllers is validated via an electric vehicle charging application in power systems.

Genome-wide discovery of G-quadruplexes in barley

G-quadruplexes (G4s) are four-stranded nucleic acid structures with closely spaced guanine bases forming square planar G-quartets. Aberrant formation of G4 structures has been associated with genomic instability. However, most plant species are lacking comprehensive studies of G4 motifs. In this study, genome-wide identification of G4 motifs in barley was performed, followed by a comparison of genomic distribution and molecular functions to other monocot species, such as wheat, maize, and rice. Similar to the reports on human and some plants like wheat, G4 motifs peaked around the 5′ untranslated region (5′ UTR), the first coding domain sequence, and the first intron start sites on antisense strands. Our comparative analyses in human, Arabidopsis, maize, rice, and sorghum demonstrated that the peak points could be erroneously merged into a single peak when large window sizes are used. We also showed that the G4 distributions around genic regions are relatively similar in the species studied, except in the case of Arabidopsis. G4 containing genes in monocots showed conserved molecular functions for transcription initiation and hydrolase activity. Additionally, we provided examples of imperfect G4 motifs.

Application of Fast Non-Local Means Algorithm for Noise Reduction Using Separable Color Channels in Light Microscopy Images.

The purpose of this study is to evaluate the various control parameters of a modeled fast non-local means (FNLM) noise reduction algorithm which can separate color channels in light microscopy (LM) images. To achieve this objective, the tendency of image characteristics with changes in parameters, such as smoothing factors and kernel and search window sizes for the FNLM algorithm, was analyzed. To quantitatively assess image characteristics, the coefficient of variation (COV), blind/referenceless image spatial quality evaluator (BRISQUE), and natural image quality evaluator (NIQE) were employed. When high smoothing factors and large search window sizes were applied, excellent COV and unsatisfactory BRISQUE and NIQE results were obtained. In addition, all three evaluation parameters improved as the kernel size increased. However, the kernel and search window sizes of the FNLM algorithm were shown to be dependent on the image processing time (time resolution). In conclusion, this work has demonstrated that the FNLM algorithm can effectively reduce noise in LM images, and parameter optimization is important to achieve the algorithm’s appropriate application.

Two Zn(II)-organic frameworks: catalytic Knoevenagel condensation and treatment activity on spine surgery incision infection via inhibiting Staphylococcus aureus biofilms formation

By utilizing the mixed-ligand method, two novel metal-organic frameworks (MOFs) based on Zn(II) ions as nodes with the chemical formulae of {[Zn2.5(abta)(trz)2(H2O)]·3H2O}n (1, Htrz = 1H-1,2,4-triazole) and [Zn3(abta)2(bibb)2]n (2, bibb = 1,4-bis(benzimidazol-1-yl)-2-butene) were produced via Zn(NO3)2·6H2O reacting with the 1-aminobenzene-3,4,5-tricarboxylic acid (H3abta) in the existence of distinct nitrogen-donor co-ligands. The different N-donor ligands result in their distinct framework structures, and the compound 1 with higher solvent accessible void and large window size shows highly heterogeneous catalytic activities for Knoevenagel condensation. The treatment of Staphylococcus aureus biofilms formation during spine surgery incision infection and the related mechanism was explored at the same time. First of all, the S. aureus bacterial numbers in the infectious site was measured under compound 1 or 2 treatment. In addition to this, the relative expression levels of the genes related with S. aureus biofilms was determined with real time RT-PCR.

A Parallel Retrodiction Algorithm for Large-Scale Multitarget Tracking

Kalman filter-based retrodiction plays an indispensable role in modern multitarget tracking and retrodiction (MTTR) algorithms. To this end, the Rauch-Tung-Striebel (RTS) smoother is a widely used Kalman filter-based target state smoother. With a large number of targets, MTTR algorithms, particularly with large window sizes, become very computationally intensive. If not addressed, these algorithms will not meet the requirements for tracking a large number of targets in real time. A natural approach is to parallelize these algorithms to render them useful, particularly in the context of emerging multicore platforms. However, this is nontrivial, as the governing mathematical framework of the RTS smoother, namely the dependencies between complex computations, prevents any form of parallelization. Although the MTTR component can naively be parallelized ignoring the smoothing component, the overall benefit, as we demonstrate in this article, is a fraction of the best possible benefits. In this article, by carefully reformulating the underlying mathematical framework that is necessary for retrodiction, we propose a novel, easily parallelizable RTS smoother. The proposed parallelized RTS smoother we outline in this article has best data reuse and enables the overall MTTR problem to be parallelized more efficiently. We demonstrate this on a state-of-the-art multicore processor platform using the shared-memory parallelism. Our results show that the parallel MTTR solution, which includes gating, assignment, tracking, and retrodiction, can offer nearly 150 times speed up against a fully sequential version. With excellent computational performance, our proposed RTS smoother enables very large window sizes with little or no impact on the overall performance.

Regional Mapping of Vineyards Using Machine Learning and LiDAR Data

This study evaluates the use of LiDAR data and machine learning algorithms for mapping vineyards. Vineyards are planted in rows spaced at various distances, which can cause spectral mixing within individual pixels and complicate image classification. Four resolution where used for generating normalized digital surface model and intensity derivatives from the LiDAR data. In addition, texture measures with window sizes of 3x3 and 5x5 were generated from the LiDAR derivatives. The different combinations of the resolutions and window sizes resulted in eight data sets that were used as input to 11 machine learning algorithms. A larger window size was found to improve the overall accuracy for all the classifier–resolution combinations. The results showed that random forest with texture measures generated at a 5x5 window size outperformed the other experiments, regardless of the resolution used. The authors conclude that the random forest algorithm used on LiDAR derivatives with a resolution of 1.5m and a window size of 5x5 is the recommend configuration for vineyard mapping using LiDAR data.

Alterations in surface EMG during gait in children with Fragile X Syndrome

This study aimed to characterize the biomechanical stresses in the neck and shoulder, self-reported discomfort, and usability by different target distance or size during augmented reality (AR) interactions. In a repeated-measures laboratory-based study, 20 participants (10 males) performed three standardized AR tasks (3-dimensional (3-D) cube, omni-directional pointing, and web-browsing tasks) with three target distances (0.3, 0.6, and 0.9 m from each participant denoted by near, middle, far targets) for the 3-D cube and omni-directional pointing tasks or three target sizes: small (30% smaller than default), medium (default: 1.0 × 1.1 m), and large (30% larger than default) for the web-browsing task. Joint angle, joint moment, muscle activity, self-reported discomfort and comfort in the neck and shoulders; and subjective usability ratings were measured. The results showed that shoulder angle (flexion and abduction), shoulder moment (flexion), middle deltoid muscle activity significantly increased as the target distance increased during the 3-D cube task (p's < 0.001). Self-reported neck and shoulder discomfort significantly increased after completing each task (p's < 0.001). The participants preferred the near to middle distance (0.3–0.6 m) or the medium to large window size due to task easiness (p's < 0.005). The highest task performance (speed) was occurred at the near distance or the large window size during the 3-D cube and web-browsing tasks (p's < 0.001). The results indicate that AR interactions with the far target distance (close to maximum reach envelop) may increase the risk for musculoskeletal discomfort in the shoulder regions. Given the increased usability and task performance, the near to middle distance (less than 0.6 m) or the medium to large window size (greater than 1.0 × 1.1 m) would be recommended for AR interactions.

Mining frequent itemsets from streaming transaction data using genetic algorithms

This paper presents a study of mining frequent itemsets from streaming data in the presence of concept drift. Streaming data, being volatile in nature, is particularly challenging to mine. An approach using genetic algorithms is presented, and various relationships between concept drift, sliding window size, and genetic algorithm constraints are explored. Concept drift is identified by changes in frequent itemsets. The novelty of this work lies in determining concept drift using frequent itemsets for mining streaming data, using the genetic algorithm framework. Formulas have been presented for calculating minimum support counts in streaming data using sliding windows. Testing highlighted that the ratio of the window size to transactions per drift was a key to good performance. Getting good results when the sliding window size was too small was a challenge since normal fluctuations in the data could appear to be a concept drift. Window size must be managed in conjunction with support and confidence values in order to achieve reasonable results. This method of detecting concept drift performed well when larger window sizes were used.

Evaluation of the biomechanical stress in the neck and shoulders during augmented reality interactions

This study aimed to characterize the biomechanical stresses in the neck and shoulder, self-reported discomfort, and usability by different target distance or size during augmented reality (AR) interactions. In a repeated-measures laboratory-based study, 20 participants (10 males) performed three standardized AR tasks (3-dimensional (3-D) cube, omni-directional pointing, and web-browsing tasks) with three target distances (0.3, 0.6, and 0.9 m from each participant denoted by near, middle, far targets) for the 3-D cube and omni-directional pointing tasks or three target sizes: small (30% smaller than default), medium (default: 1.0 × 1.1 m), and large (30% larger than default) for the web-browsing task. Joint angle, joint moment, muscle activity, self-reported discomfort and comfort in the neck and shoulders; and subjective usability ratings were measured. The results showed that shoulder angle (flexion and abduction), shoulder moment (flexion), middle deltoid muscle activity significantly increased as the target distance increased during the 3-D cube task (p's < 0.001). Self-reported neck and shoulder discomfort significantly increased after completing each task (p's < 0.001). The participants preferred the near to middle distance (0.3–0.6 m) or the medium to large window size due to task easiness (p's < 0.005). The highest task performance (speed) was occurred at the near distance or the large window size during the 3-D cube and web-browsing tasks (p's < 0.001). The results indicate that AR interactions with the far target distance (close to maximum reach envelop) may increase the risk for musculoskeletal discomfort in the shoulder regions. Given the increased usability and task performance, the near to middle distance (less than 0.6 m) or the medium to large window size (greater than 1.0 × 1.1 m) would be recommended for AR interactions.

Marine oil slicks detection using spaceborne and airborne SAR data

Naval transportations have grown exponentially in the past few years’ and the quantity of illegitimate oil discharges in the oceans has developed with the volume of traffic imposing a severe threat to marine lives and ecosystem. Oil spills dampen the capillary wave’s effect hence create a dark signature on satellite image which is often called a dark object/matter that can be easily detected by SAR sensors. This study reviews the potential of quad-pol and hybrid-pol datasets in detecting oil spills. The data used for this study is the UAVSAR quad-pol and RISAT-1 hybrid/compact-pol dataset. These datasets were acquired during an oil spill experiment named the Norwegian Radar oil Spill Experiment (NORSE2015) which was carried out in the North Sea in June 2015. Basic pre-processing and decomposition modeling techniques namely Freeman and Durden, Van-Zyl, Yamaguchi, and Multiple-Component Scattering Model (MCSM) were applied on UAVSAR dataset, while Raney and Compact-Pol decompositions were applied to RISAT-1 dataset to know different surface backscattering behavior shown by the sea and oil patches. It was observed that Van-Zyl decomposition for UAVSAR and Compact-Pol decomposition for the RISAT-1 dataset gave the most suitable outcome based on the separability analysis to distinguish between oil and water. Two classification techniques Wishart Supervised Classifier (WSC) and Support Vector Machine (SVM) classifications were implemented on decomposed scattering elements of Van-Zyl and Compact-Pol. Radial Basis Function (RBF) kernel parameter values in SVM were used to precisely classify the oil patches from seawater. All the applied decomposition experiments provide good separability between water and oil. Later on, WSC and SVM had been used to finally classify both the dataset outputs. WSC had given better results with an accuracy of 83% for UAVSAR and 86% for RISAT-1, with a larger window size by minimizing the misclassification of sea class into oil spill class. Later the paper concludes the scope of further research possibilities for making a robust system for detecting the oil spills, both precisely and effectually.

Weighted average of shared trajectory: A new estimator for dynamic functional connectivity efficiently estimates both rapid and slow changes over time

BackgroundDynamic functional network connectivity (dFNC) of the brain has attracted considerable attention recently. Many approaches have been suggested to study dFNC with sliding window Pearson correlation (SWPC) being the most well-known. SWPC needs a relatively large sample size to reach a robust estimation but using large window sizes prevents us to detect rapid changes in dFNC. New methodHere we first calculate the gradients of each time series pair and use the magnitude of these gradients to calculate weighted average of shared trajectory (WAST) as a new estimator for dFNC. ResultsUsing WAST to compare healthy control and schizophrenia patients using a large dataset, we show disconnectivity between different regions associated with schizophrenia. In addition, WAST results reveals patients with schizophrenia stay longer in a connectivity state with negative connectivity between motor and sensory regions than do healthy controls. Comparison with existing methodsWe compare WAST with SWPC and multiplication of temporal derivatives (MTD) using different simulation scenarios. We show that WAST enables us to detect very rapid changes in dFNC (undetected by SWPC) while MTD performance is generally lower. ConclusionsAs large window sizes are unable to detect short states, using shorter window size is desirable if the estimator is robust enough. We provide evidence that WAST requires fewer samples (compared to SWPC) to reach a robust estimation. As a result, we were able to identify rapidly varying dFNC patterns undetected by SWPC while still being able to robustly estimate slower dFNC patterns.

Improved Faraday Rotation Estimation in Spaceborne PolSAR Data Using Total Variation Denoising

Faraday rotation (FR) is a serious problem for spaceborne polarization SAR (PolSAR) systems at L and P bands. One way to solve the problem is to estimate the FR from PolSAR data for further compensation. Therefore, precise estimation of FR from PolSAR data not only determines the compensation effect of polarimetric systems but also benefits the ionospheric sounding with high spatial resolution. Among the factors that affect the FR estimation, system noise is a non-neglectable factor. Although average filtering (AF) has been widely used in previous works for noise removing it depends on large window size, and therefore reduces the spatial resolution of FR estimation. In order to realize optimal noise suppression with minimized resolution loss, the total variation (TV) denoising method is applied in this paper. By testing the Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) full-pol datasets, TV and AF are compared and validated. Results using synthetic and real data show that, after TV denoising, the FR can be recovered with high spatial resolution and the noise level in estimated FR is reduced more effectively than that after AF.

Thermal comfort analysis of a high-speed train cabin considering the solar radiation effects

Due to the fast development of high-speed rail (HSR) around the world, high-speed trains (HSTs) are becoming a strong competitor against airliners in terms of long-distance travel. Compared with airliner cabins, HST cabins have much larger window sizes. When the big windows provide better lighting and view of the scenery, they also have significant effects on the thermal conditions in the cabins due to the solar radiation through them. This study presents a numerical study on the solar radiation on the thermal comfort in a typical HST cabin. The effect of solar radiation was discussed in terms of airflow pattern, temperature distribution and thermal comfort indices. Parametric studies with seven different daytime hours were carried out. The effect of using the roller curtain was also studied. The overall cabin air temperature, especially near passengers, was found to have significantly increased by solar radiation. Passengers sitting next to windows were recorded to have an obvious thermal comfort variation at different hours of the day. To improve the passengers’ comfort and reduce energy consumption during hot weather, the use of a curtain could effectively reduce the solar radiation effect in the cabin environment.

Optical and Radar Data Analysis for Land Use Land Cover Mapping in Peru

This analysis determined the best individual band and combinations of various numbers of bands for land use land cover mapping for three sites in Peru. The data included Landsat Thematic Mapper (TM) optical data, PALSAR L-band dual-polarized radar, and derived radar texture images. Spectral signatures were first obtained for each site class and separability between classes determined using divergence measures. Results show that the best single band for analysis was a TM band, which was different for each site. For two of the three sites, the second best band was a radar texture image from a large window size. For all sites the best three bands included two TM bands and a radar texture image. The original PALSAR bands were of limited value. Finally upon further analysis it was determined that no more than six bands were needed for viable classification at each study site.

Constraining the geotherm beneath the British Isles from Bayesian inversion of Curie depth: integrated modelling of magnetic, geothermal, and seismic data

Abstract. Curie depth offers a valuable constraint on the thermal structure of the lithosphere, based on its interpretation as the depth to 580 ∘C, but current methods underestimate the range of uncertainty. We formulate the estimation of Curie depth within a Bayesian framework to quantify its uncertainty across the British Isles. Uncertainty increases exponentially with Curie depth but this can be moderated by increasing the size of the spatial window taken from the magnetic anomaly. The choice of window size needed to resolve the magnetic thickness is often ambiguous but, based on our chosen spectral method, we determine that significant gains in precision can be obtained with window sizes 15–30 times larger than the deepest magnetic source. Our Curie depth map of the British Isles includes a combination of window sizes: smaller windows are used where the magnetic base is shallow to resolve small-scale features, and larger window sizes are used where the magnetic base is deep in order to improve precision. On average, the Curie depth increases from Laurentian crust (22.2±5.3 km) to Avalonian crust (31.2±9.2 km). The temperature distribution in the crust, and associated uncertainty, was simulated from the ensemble of Curie depth realizations assigned to a lower thermal boundary condition of a crustal model (sedimentary thickness, Moho depth, heat production, thermal conductivity), constructed from various geophysical and geochemical datasets. The uncertainty in the simulated heat flow field substantially increases from ±10 mW m−2 for shallow Curie depths at ∼15 km to ±80 mW m−2 for Curie depths &gt;40 km. Surface heat flow observations are concordant with the simulated heat flow field except in regions that contain igneous bodies. Heat flow data within large batholiths in the British Isles exceed the simulated heat flow by ∼25 mW m−2 as a result of their high rates of heat production (4–6 µW m−3). Conversely, heat refraction around thermally resistive mafic volcanics and thick sedimentary layers induce a negative heat flow misfit of a similar magnitude. A northward thinning of the lithosphere is supported by shallower Curie depths on the northern side of the Iapetus Suture, which separates Laurentian and Avalonian terranes. Cenozoic volcanism in Northern Britain and Ireland has previously been attributed to a lateral branch of the proto-Icelandic mantle plume. Our results show that high surface heat flow (&gt;90 mW m−2) and shallow Curie depth (∼15 km) occur within the same region, which supports the hypothesis that lithospheric thinning occurred due to the influence of a mantle plume. The fact that the uncertainty is only ±3–8 km in this region demonstrates that Curie depths are more reliable in hotter regions of the crust where the magnetic base is shallow.

Assessment of Texture Features for Bermudagrass (Cynodon dactylon) Detection in Sugarcane Plantations

Sugarcane products contribute significantly to the Brazilian economy, generating U.S. $12.2 billion in revenue in 2018. Identifying and monitoring factors that induce yield reduction, such as weed occurrence, is thus imperative. The detection of Bermudagrass in sugarcane crops using remote sensing data, however, is a challenge considering their spectral similarity. To overcome this limitation, this paper aims to explore the potential of texture features derived from images acquired by an optical sensor onboard anunmanned aerial vehicle (UAV) to detect Bermudagrass in sugarcane. Aerial images with a spatial resolution of 2 cm were acquired from a sugarcane field in Brazil. The Green-Red Vegetation Index and several texture metrics derived from the gray-level co-occurrence matrix were calculated to perform an automatic classification using arandom forest algorithm. Adding texture metrics to the classification process improved the overall accuracy from 83.00% to 92.54%, and this improvement was greater considering larger window sizes, since they representeda texture transition between two targets. Production losses induced by Bermudagrass presence reached 12.1 tons × ha−1 in the study site. This study not only demonstrated the capacity of UAV images to overcome the well-known limitation of detecting Bermudagrass in sugarcane crops, but also highlighted the importance of texture for high-accuracy quantification of weed invasion in sugarcane crops.

Comparative Analysis of the Digital Terrain Models Extracted from Airborne LiDAR Point Clouds Using Different Filtering Approaches in Residential Landscapes

Light Detection And Ranging (LiDAR) is a well-established active remote sensing technology that can provide accurate digital elevation measurements for the terrain and non-ground objects such as vegetations and buildings, etc. Non-ground objects need to be removed for creation of a Digital Terrain Model (DTM) which is a continuous surface representing only ground surface points. This study aimed at comparative analysis of three main filtering approaches for stripping off non-ground objects namely; Gaussian low pass filter, focal analysis mean filter and DTM slope-based filter of varying window sizes in creation of a reliable DTM from airborne LiDAR point clouds. A sample of LiDAR data provided by the ISPRS WG III/4 captured at Vaihingen in Germany over a pure residential area has been used in the analysis. Visual analysis has indicated that Gaussian low pass filter has given blurred DTMs of attenuated high-frequency objects and emphasized low-frequency objects while it has achieved improved removal of non-ground object at larger window sizes. Focal analysis mean filter has shown better removal of nonground objects compared to Gaussian low pass filter especially at large window sizes where details of non-ground objects almost have diminished in the DTMs from window sizes of 25 × 25 and greater. DTM slope-based filter has created bare earth models that have been full of gabs at the positions of the non-ground objects where the sizes and numbers of that gabs have increased with increasing the window sizes of filter. Those gaps have been closed through exploitation of the spline interpolation method in order to get continuous surface representing bare earth landscape. Comparative analysis has shown that the minimum elevations of the DTMs increase with increasing the filter widow sizes till 21 × 21 and 31 × 31 for the Gaussian low pass filter and the focal analysis mean filter respectively. On the other hand, the DTM slope-based filter has kept the minimum elevation of the original data, that could be due to noise in the LiDAR data unchanged. Alternatively, the three approaches have produced DTMs of decreasing maximum elevation values and consequently decreasing ranges of elevations due to increases in the filter window sizes. Moreover, the standard deviations of the created DTMs from the three filters have decreased with increasing the filter window sizes however, the decreases have been continuous and steady in the cases of the Gaussian low pass filter and the focal analysis mean filters while in the case of the DTM slope-based filter the standard deviations of the created DTMs have decreased with high rates till window size of 31 × 31 then they have kept unchanged due to more increases in the filter window sizes.

Creation and Analysis of Earth’s Surface Roughness Maps from Airborne LiDAR Measurements in Downtown Urban Landscape

The Earth’s surface roughness constitutes an important parameter in terrain analysis for studying different environmental and engineering problems. Authors gave different definitions and measures for the earth’s surface roughness that usually depend on exploitation of digital elevation data for its reliable determination. This research aimed at exploring the different approaches for defining and extraction of the Earth’s surface roughness from Airborne LiDAR Measurements. It also aimed at evaluating the effects of the window size of the standard deviation filter on the created roughness maps in downtown landscapes using three known approaches namely; standard deviation filtering of the Digital Elevation Model (DEM), standard deviation filtering of the slope gradient model and standard deviation filtering of the profile curvature model. In this context, different roughness maps have been created from Airborne LiDAR measurements of the City of Toronto, Canada using the three filtering approaches with varying window sizes. Visual analysis has shown color tones of small roughness values with smooth textures dominate the roughness maps from small window sizes of the standard deviation filter, however, increasing the window sizes has produced wider variations of the color tones and rougher texture roughness maps. The standard deviations and ranges of the roughness maps from LiDAR DEM have increased due to increasing the filter window size while the skewness and kurtosis have decreased due to increasing the window size, indicating that the roughness maps from larger window sizes are statistically more symmetrical and more consistent. Thus, kurtosis has decreased by 53% and 82% due to increasing the window size to 7 × 7 and 15 × 15 respectively. The standard deviations of the roughness maps from the slope gradient model have increased due to increasing the window size till 15 × 15 while they have decreased with more increases. However, skewness has decreased due to increasing the window size till 15 × 15 and the kurtosis has decreased with higher rate till window size of 11 × 11. In the roughness maps from the profile curvature model, the ranges and skewness have decreased by 93.6% and 82.6% respectively due to increasing the window size to 15 × 15 while, kurtosis has decreased by 58.6%, 76.3% and 93.76% due to increases in the filter window size to 5 × 5, 7 × 7 and 15 × 15 respectively.

Pore Engineering of Mesoporous Tungsten Oxides for Ultrasensitive Gas Sensing

AbstractThe mesoporous tungsten oxides have shown great potential in various fields, including energy storage and conversion, catalysis, and gas sensor, because they have ordered porous architectures and unique semiconducting property for host–guest interaction. Most of the reported mesoporous tungsten oxides have monomodal mesopores, which are not favorable for the mass diffusion and host–guest interactions. To date, it still remains a great challenge to synthesize ordered mesoporous WO3 with bimodal or hierarchical pores and crystalline frameworks. Herein, a pore engineering strategy is demonstrated for the synthesis of ordered mesoporous WO3 with well‐connected bimodal pores, and crystalline pore walls by using hydrophilic resols as the sacrificial carbon source which can interact preferentially with poly(ethylene oxide) (PEO) domains and serves a glue to bridge the tungsten species and poly(ethylene oxide)‐block‐polystyrene block copolymers. The obtained ordered mesoporous tungsten oxide materials possess dual mesopore size (5.8 and 15.8 nm), high surface area (128 m2 g−1), large window size (7.7 nm), and highly crystalline mesostructure. The dual mesoporous WO3‐based gas sensor exhibits significantly excellent gas sensing performance toward H2S with a rapid response (3 s) and recovery (14 s) even at low concentration (0.2 ppm), and high selectivity, which is much better than previously reported WO3‐based sensors.