Exploring Motor Imagery EEG: Enhanced EEG Microstate Analysis with GMD-Driven Density Canopy Method

The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity.Traditional methods such as Atomic Agglomerative Hierarchical Clustering(AAHC),K-means clustering,Principal Component Analysis(PCA),and Independent Component Analysis(ICA)are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction.Tackling these limitations,this study introduces a Global Map Dissimilarity(GMD)-driven density canopy K-means clustering algorithm.This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for dynamic modeling of EEG data.Utilizing this advanced algorithm,the study analyzes the Motor Imagery(MI)dataset from the GigaScience database,GigaDB.The findings reveal six distinct microstates during actual right-hand movement and five microstates across other task conditions,with microstate C showing superior performance in all task states.During imagined movement,microstate A was significantly enhanced.Comparison with existing algorithms indicates a significant improvement in clustering performance by the refined method,with an average Calinski-Harabasz Index(CHI)of 35517.29 and a Davis-Bouldin Index(DBI)average of 2.57.Furthermore,an information-theoretical analysis of the microstate sequences suggests that imagined movement exhibits higher complexity and disorder than actual movement.By utilizing the extracted microstate sequence parameters as features,the improved algorithm achieved a classification accuracy of 98.41%in EEG signal categorization for motor imagery.A performance of 78.183%accuracy was achieved in a four-class motor imagery task on the BCI-IV-2a dataset.These results demonstrate the potential of the advanced algorithm in microstate analysis,offering a more effective tool for a deeper understanding of the spatiotemporal features of EEG signals.

ConvNeXt-UperNet-Based Deep Learning Model for Road Extraction from High-Resolution Remote Sensing Images

When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in incomplete road extraction and low accuracy.We propose the introduction of spatial and channel attention modules to the convolutional neural network ConvNeXt.Then,ConvNeXt is used as the backbone network,which cooperates with the perceptual analysis network UPerNet,retains the detection head of the semantic segmentation,and builds a new model ConvNeXt-UPerNet to suppress noise interference.Training on the open-source DeepGlobe and CHN6-CUG datasets and introducing the DiceLoss on the basis of CrossEntropyLoss solves the problem of positive and negative sample imbalance.Experimental results show that the new network model can achieve the following performance on the DeepGlobe dataset:79.40%for precision(Pre),97.93% for accuracy(Acc),69.28% for intersection over union(IoU),and 83.56% for mean intersection over union(MIoU).On the CHN6-CUG dataset,the model achieves the respective values of 78.17%for Pre,97.63%for Acc,65.4% for IoU,and 81.46% for MIoU.Compared with other network models,the fused ConvNeXt-UPerNet model can extract road information better when faced with the influence of noise contained in high-resolution remote sensing images.It also achieves multiscale image feature information with unified perception,ultimately improving the generalization ability of deep learning technology in extracting complex roads from high-resolution remote sensing images.

Methods for ice velocity mapping in West Antarctica using historical optical satellite images

The Antarctic Ice Sheet harbors more than 90%of the Earth ice mass,with significant losses experienced through dynamic thinning,particularly in West Antarctica.The crucial aspect of investigating ice mass balance in historical periods preceding 1990 hinges on the utilization of ice velocities derived from optical satellite images.We employed declassified satellite images and Landsat images with normalized cross correlation based image matching,adopting an adaptive combination of skills and methods to overcome challenges encountered during the mapping of historical ice velocity in West Antarctica.A basin-wide synthesis velocity map encompassing the coastal regions of most large-scale glaciers and ice shelves in West Antarctica has already been successfully generated.Our results for historical ice velocities cover over 70%of the grounding line in most of the West Antarctic basins.Through adjustments,we uncovered overestimations in ice velocity measurements over an extended period,transforming our ice velocity map into a spatially deterministic,temporally average version.Among all velocity measurements,Thwaites Glacier exhibited a notable spatial variation in the fastest ice flowline and velocity distribution.Overestimation distributions on Thwaites Glacier displayed a clear consistency with the positions of subsequent front calving events,offering insights into the instabilities of ice shelves.

Mapping Air Quality Using Remote Sensing Technology: A Case Study of Nairobi County

Nairobi County experiences rapid industrialization and urbanization that contributes to the deteriorating state of air quality, posing a potential health risk to its growing population. Currently, in Nairobi County, most air quality monitoring stations use low-cost, inaccurate monitors prone to defects. The study’s objective was to map Nairobi County’s air quality using freely available remotely sensed imagery. The Air Pollution Index (API) formula was used to characterize the air quality from cloud-free Landsat satellite images i.e., Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI from Google Earth Engine. The API values were computed based on vegetation indices namely NDVI, TVI, DVI, and the SWIR1 and NIR bands on the QGIS platform. Qualitative accuracy assessment was done using sample points drawn from residential, industrial, green spaces, and traffic hotspot categories, based on a passive-random sampling technique. In this study, Landsat 5 API imagery for 2010 provided a reliable representation of local conditions but indicated significant pollution in green spaces, with recorded values ranging from -143 to 334. The study found that Landsat 7 API imagery in 2002 showed expected results with the range of values being -55 to 287, while Landsat 8 indicated high pollution levels in Nairobi. The results emphasized the importance of air quality factors in API calibration and the unmatched spatial coverage of satellite observations over ground-based monitoring techniques. The study recommends the recalibration of the API formula for characteristic regions, exploring newer satellite sensors like those onboard Landsat 9 and Sentinel 2, and involving key stakeholders in a discourse to develop a suitable Kenyan air quality index.

A Study on the Translation of Plant Images in Qu Yuan’s Li Sao

The renowned masterpiece“Li Sao”by Qu Yuan contains numerous plant images for“expressing emotions and aspirations.”Exploring methods of translating plant imagery has greatly assisted in disseminating Chinese classical culture and facilitating cross-cultural communication.This study conducts a comparative analysis of three translations of“Li Sao”by Xu Yuanchong,Yang Xianyi,and Hawkes,aiming to understand the different approaches to translating plant imagery and explore variations in translation effectiveness.Through data collection,comparative analysis,and case studies,this research reveals that Xu Yuanchong emphasizes free translation,Yang Xianyi tends towards literal translation,and Hawkes adopts a combination of literal,free,and phonetic translation methods.

Migration and Spatiotemporal Land Cover Change: A Case of Bosomtwe Lake Basin, Ghana

Internal migration is highly valued due to its increasingly acknowledged potential for social and economic development. However, despite its significant contribution to the development of towns and cities, it has led to the deterioration of many ecosystems globally. Lake Bosomtwe, a natural Lake in Ghana and one of the six major meteoritic lakes in the world is affected by land cover changes caused by the rising effects of migration, population expansion, and urbanization, owing to the development of tourist facilities on the lakeshore. This study investigated land cover change trajectories using a post-classification comparison approach and identified the factors influencing alteration in the Lake Bosomtwe Basin. Using Landsat imagery, an integrated approach of remote sensing, geographical information systems (GIS), and statistical analysis was successfully employed to analyze the land cover change of the basin. The findings show that over the 17 years, the basin’s forest cover decreased significantly by 16.02%, indicating that population expansion significantly affects changes in land cover. Ultimately, this study will raise the awareness of stakeholders, decision-makers, policy-makers, government, and non-governmental agencies to evaluate land use development patterns, optimize land use structures, and provide a reference for the formulation of sustainable development policies to promote the sustainable development of the ecological environment.

Color Expression and Mood Creation in Imagery Oil Painting

Oil painting is a traditional Western painting form.With the introduction of China and the influence of China’s traditional painting and aesthetics,the painting style became more distinctive,expanding a new development direction of oil painting,and thus imagery oil painting came into being.Color,as the most important element in imagery oil painting,mainly plays the role of mood creation and emotional expression.Many creators are good at injecting their thoughts and emotions into the paintings through color matching,so as to enhance the artistic expression of the paintings.This paper analyzes the color expression characteristics of imagery oil painting and explores the color expression techniques in imagery oil painting and mood creation of imagery oil painting from several aspects.

Application of a hospital–community–family trinity rehabilitation nursing model combined with motor imagery therapy in patients with cerebral infarction

BACKGROUND Rehabilitation nursing is considered an indispensable part of the cerebral infarction treatment system.The hospital–community–family trinity rehabilitation nursing model can provide continuous nursing services across hospitals,communities,and families for patients.AIM To explore the application of a hospital–community–family rehabilitation nursing model combined with motor imagery therapy in patients with cerebral infarction.METHODS From January 2021 to December 2021,88 patients with cerebral infarction were divided into a study(n=44)and a control(n=44)group using a simple random number table.The control group received routine nursing and motor imagery therapy.The study group was given hospital–community–family trinity rehabilitation nursing based on the control group.Motor function(FMA),balance ability(BBS),activities of daily living(BI),quality of life(SS-QOL),activation status of the contralateral primary sensorimotor cortical area to the affected side,and nursing satisfaction were evaluated before and after intervention in both groups.RESULTS Before intervention,FMA and BBS were similar(P>0.05).After 6 months’intervention,FMA and BBS were significantly higher in the study than in the control group(both P<0.05).Before intervention,BI and SS-QOL scores were not different between the study and control group(P>0.05).However,after 6months’intervention,BI and SS-QOL were higher in the study than in the control group(P<0.05).Before intervention,activation frequency and volume were similar between the study and the control group(P>0.05).After 6 months’intervention,the activation frequency and volume were higher in the study than in the control group(P<0.05).The reliability,empathy,reactivity,assurance,and tangibles scores for quality of nursing service were higher in the study than in the control group(P<0.05).CONCLUSION Combining a hospital–community–family trinity rehabilitation nursing model and motor imagery therapy enhances the motor function and balance ability of patients with cerebral infarction,improving their quality of life.

YOLOv5-Based Seabed Sediment Recognition Method for Side-Scan Sonar Imagery

Seabed sediment recognition is vital for the exploitation of marine resources.Side-scan sonar(SSS)is an excellent tool for acquiring the imagery of seafloor topography.Combined with ocean surface sampling,it provides detailed and accurate images of marine substrate features.Most of the processing of SSS imagery works around limited sampling stations and requires manual interpretation to complete the classification of seabed sediment imagery.In complex sea areas,with manual interpretation,small targets are often lost due to a large amount of information.To date,studies related to the automatic recognition of seabed sediments are still few.This paper proposes a seabed sediment recognition method based on You Only Look Once version 5 and SSS imagery to perform real-time sedi-ment classification and localization for accuracy,particularly on small targets and faster speeds.We used methods such as changing the dataset size,epoch,and optimizer and adding multiscale training to overcome the challenges of having a small sample and a low accuracy.With these methods,we improved the results on mean average precision by 8.98%and F1 score by 11.12%compared with the original method.In addition,the detection speed was approximately 100 frames per second,which is faster than that of previous methods.This speed enabled us to achieve real-time seabed sediment recognition from SSS imagery.

A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery

The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field sampling data for leaf area index(LAI),canopy photosynthetic pigments(CPP;including chlorophyll a,chlorophyll b and carotenoids)and leaf nitrogen concentration(LNC)can be time-consuming and costly.Here we evaluated the use of high-precision unmanned aerial vehicle(UAV)multispectral imagery for estimating the LAI,CPP and CNC of winter wheat over the whole growth period.A total of 23 spectral features(SFs;five original spectrum bands,17 vegetation indices and the gray scale of the RGB image)and eight texture features(TFs;contrast,entropy,variance,mean,homogeneity,dissimilarity,second moment,and correlation)were selected as inputs for the models.Six machine learning methods,i.e.,multiple stepwise regression(MSR),support vector regression(SVR),gradient boosting decision tree(GBDT),Gaussian process regression(GPR),back propagation neural network(BPNN)and radial basis function neural network(RBFNN),were compared for the retrieval of winter wheat LAI,CPP and CNC values,and a double-layer model was proposed for estimating CNC based on LAI and CPP.The results showed that the inversion of winter wheat LAI,CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs.The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI,CPP and CNC.The proposed double-layer models(R^(2)=0.67-0.89,RMSE=13.63-23.71 mg g^(-1),MAE=10.75-17.59 mg g^(-1))performed better than the direct inversion models(R^(2)=0.61-0.80,RMSE=18.01-25.12 mg g^(-1),MAE=12.96-18.88 mg g^(-1))in estimating winter wheat CNC.The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs(R^(2)=0.89,RMSE=13.63 mg g^(-1),MAE=10.75 mg g^(-1)).The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.

Overcast sky condition prevails on and influences the biometeorology of the tropical Andean Páramos

Clouds play a major role in modulating the biometeorological processes. We studied the influence of cloudiness on four biometeorological variables:daily air temperature(Tair), relative humidity(RH),reference evapotranspiration(ETr),and photosynthetic active radiation(PAR), recorded at four sites of Andean Páramos in southern Ecuador during 2.5 to 5.5 years. First, we quantified both the cloud cover percentage(Cloud%) creating cloud masks over the visible bands of Landsat 7 images and the sky condition(K_(T)) using the records of solar and extraterrestrial radiation. Second, we estimated KTfrom Cloud%. Finally, we quantified T_(air), RH, ET_(r), and PAR under clear, cloudy, and overcast K_(T) and their dependence on KT. The average Cloud% ranged between 65%–76%, and KTcorroborated the prevailing overcast sky(between 55% and 72.5% of the days) over the páramos. The proposed model performed well in the sites of calibration(R^(2)= 0.80;MBE = 0.00;RMSE = 0.05) and validation(R^(2)= 0.74;MBE =-0.07;RMSE = 0.11). The overcast sky diminished T_(air)(≤ 10℃), ET_(r)(≤ 1.6 mm day-1), and PAR(4 MJ m^(-2)day^(-1)) and increased RH(≥ 88%),while the variables showed the opposite behavior during the uncommon clear sky(≤ 5.5% of the days).Thus, mostly the dynamic of RH(R^(2)≥ 0.62), ETr(R^(2)≥ 0.85), and PAR(R2≥ 0.77) depended on K_(T). Hence,the prevailing overcast sky influenced the biometeorology of the páramos.

Assessing the severity of cotton Verticillium wilt disease from in situ canopy images and spectra using convolutional neural networks

Verticillium wilt(VW)is a common soilborne disease of cotton.It occurs mainly in the seedling and bollopening stages and severely impairs the yield and quality of the fiber.Rapid and accurate identification and evaluation of VW severity(VWS)forms the basis of field cotton VW control,which has great significance to cotton production.Cotton VWS values are conventionally measured using in-field observations and laboratory test diagnoses,which require abundant time and professional expertise.Remote and proximal sensing using imagery and spectrometry have great potential for this purpose.In this study,we performed in situ investigations at three experimental sites in 2019 and 2021 and collected VWS values,in situ images,and spectra of 361 cotton canopies.To estimate cotton VWS values at the canopy scale,we developed two deep learning approaches that use in situ images and spectra,respectively.For the imagery-based method,given the high complexity of the in situ environment,we first transformed the task of healthy and diseased leaf recognition to the task of cotton field scene classification and then built a cotton field scenes(CFS)dataset with over 1000 images for each scene-unit type.We performed pretrained convolutional neural networks(CNNs)training and validation using the CFS dataset and then used the networks after training to classify scene units for each canopy.The results showed that the Dark Net-19 model achieved satisfactory performance in CFS classification and VWS values estimation(R^(2)=0.91,root-mean-square error(RMSE)=6.35%).For the spectroscopy-based method,we first designed a one-dimensional regression network(1D CNN)with four convolutional layers.After dimensionality reduction by sensitive-band selection and principal component analysis,we fitted the 1D CNN with varying numbers of principal components(PCs).The 1D CNN model with the top 20 PCs performed best(R^(2)=0.93,RMSE=5.77%).These deep learning-driven approaches offer the potential of assessing crop disease severity from spatial and spectral perspectives.

Shallow water bathymetry based on a back propagation neural network and ensemble learning using multispectral satellite imagery

The back propagation(BP)neural network method is widely used in bathymetry based on multispectral satellite imagery.However,the classical BP neural network method faces a potential problem because it easily falls into a local minimum,leading to model training failure.This study confirmed that the local minimum problem of the BP neural network method exists in the bathymetry field and cannot be ignored.Furthermore,to solve the local minimum problem of the BP neural network method,a bathymetry method based on a BP neural network and ensemble learning(BPEL)is proposed.First,the remote sensing imagery and training sample were used as input datasets,and the BP method was used as the base learner to produce multiple water depth inversion results.Then,a new ensemble strategy,namely the minimum outlying degree method,was proposed and used to integrate the water depth inversion results.Finally,an ensemble bathymetric map was acquired.Anda Reef,northeastern Jiuzhang Atoll,and Pingtan coastal zone were selected as test cases to validate the proposed method.Compared with the BP neural network method,the root-mean-square error and the average relative error of the BPEL method can reduce by 0.65–2.84 m and 16%–46%in the three test cases at most.The results showed that the proposed BPEL method could solve the local minimum problem of the BP neural network method and obtain highly robust and accurate bathymetric maps.

Deep Neural Network Based Detection and Segmentation of Ships for Maritime Surveillance

In recent years,computer visionfinds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture.Auto-matic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies.Waterways being an important medium of transport require continuous monitoring for protection of national security.The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea.This paper proposes a deep learning based model capable enough to classify between ships and no-ships as well as to localize ships in the original images using bounding box tech-nique.Furthermore,classified ships are again segmented with deep learning based auto-encoder model.The proposed model,in terms of classification,provides suc-cessful results generating 99.5%and 99.2%validation and training accuracy respectively.The auto-encoder model also produces 85.1%and 84.2%validation and training accuracies.Moreover the IoU metric of the segmented images is found to be of 0.77 value.The experimental results reveal that the model is accu-rate and can be implemented for automatic ship detection in water bodies consid-ering remote sensing satellite images as input to the computer vision system.

Earthquake-triggered landslide interpretation model of high resolution remote sensing imageries based on bag of visual word

Traditional visual interpretation is often inefficient due to its excessively workload professional knowledge and strong subjectivity.Therefore,building an automatic interpretation model on high spatial resolution remote sensing images is the key to the quick and efficient interpretation of earthquake-triggered landslides.Aiming at addressing this problem,a landslide interpretation model of high-resolution images based on bag of visual word(BoVW)feature was proposed.The high-resolution images were pre-processed,and then BoVW feature and support vector machine(SVM)was adopted to establish an automatic landslide interpretation model.This model was further compared with the currently widely used Histogram of Oriented Gradient(HoG)feature extraction model.In order to test the effectiveness of the method,typical landslide images were selected to construct a landslide sample library,which was subsequently utilized as the foundation for conducting an experimental study.The results show that the accuracy of landslide extraction using this method reaches as high as 89%,indicating that the method can be used for the automatic interpretation of landslides in disaster-prone areas,and has high practical value for regional disaster prevention and damage reduction.

Monitoring Wildfires in Thailand: A Case Study of the ECSTAR-TeroSpace’s Earth Observation Project

The primary objective of this paper is to present a comprehensive case study on monitoring wildfires in Nakhon Nayok, Thailand, utilizing Earth observation platforms. This initiative project has been undertaken by the Excellence Center of Space Technology and Research (ECSTAR), in partnership with its spin-off startup, TeroSpace. The study aims to provide an in-depth analysis of the wildfire incidents in the region, utilizing advanced technologies such as satellite imagery and data analytics, and to identify ways to improve future wildfire management. In particular, the paper focuses on the wildfires including thermal area comparison that ravaged the land in Nakhon Nayok Province in central Thailand from March to April 18th, 2023. To conduct this study, the ECSTAR-TeroSpace analytic team utilized satellite images from Earth observation platforms: MODIS and Sentinel-2A. By presenting this case study, this paper contributes to the broader understanding of how to monitor and manage wildfires in a changing climate. The findings of this study underscore the importance of proactive and collaborative efforts in mitigating the negative impacts of wildfires in Nakhon Nayok and other regions in Thailand.

Comparing Pan-Sharpening Algorithms to Access an Agriculture Area: A Mississippi Case Study

Numerous satellites collect imagery of the Earth’s surface daily, providing information to the public and private sectors. The fusion (pan-sharpening) of high-resolution panchromatic satellite imagery with lower-resolution multispectral satellite imagery has shown promise for monitoring natural resources and farming areas. It results in new imagery with more detail than the original multispectral or panchromatic images. In agricultural areas in Mississippi, landscapes can range from complex mixtures of vegetation and built-up areas to dense vegetative regions. More information is needed on pan-sharpened imagery for assessing landscapes in rural areas of Mississippi. WorldView 3 satellite imagery consisting of landscapes commonly found in rural areas of Mississippi was subjected to 17 pan-sharpening algorithms. The pan-sharpened images were compared qualitatively and quantitatively with three quality indices: 1) Erreur Relative Globale Addimensionelle de Synthese;2) Universal Image Quality Index;3) Bias. à trous wavelet transform with the injection model 3 and hyperspherical color spaced fusion methods were ranked among the best for maintaining image integrity for qualitative and quantitative analyses. The optimized high-pass filter method was often ranked last by the quality indices. The smoothing filter-based intensity modulation algorithm and the gaussian modulation transfer function match filtered with high-pass modulation injection model added artifacts to the images. Pan-sharpened satellite imagery has great potential to enhance the survey of Mississippi’s agricultural areas. The key to success is selecting an image fusion process that increases spatial content while not compromising the image integrity.

Land Use Land Cover Dynamics of Upper Benue River Basin, Nigeria

This study examined land use land cover (LULC) dynamics in Upper Benue River Basin, Nigeria. The study makes use of primary and secondary data. Landsat Imageries for the years 1981, 2001 and 2021 were used in the study. Supervised approach with maximum likelihood classifier was adopted for the classification and generation of LULC maps. Markov Cellular Automata model was used to predict the status of LULC of the catchment for year 2070. The findings of the study reveal remarkable changes in the land use land cover of the Upper Benue River Basin. The land cover has witnessed downward trend in the percentage area covered by vegetation and bare surface resulting in 15.4% and 2.6% losses respectively. The result of the findings reveals that the built-up area and rock outcrop has shown significant gains of 15.2% and 2.9% of the study area respectively. Water body has been stable with 0% change, though, it witnessed a marginal decline in 2001. The land use land cover change observed in the Upper Benue River Basin was as a result of anthropogenic factors characterized by deforestation, expansion of agricultural lands, overgrazing among others. Based on the findings, the study recommended controlled grazing activity, deforestation and indiscriminate fuelwood exploitation and improved agronomic practices in the basin.

3D Fractals, Axiom of Algebra (Δn)n = +1

After having laid down the Axiom of Algebra, bringing the creation of the square root of -1 by Euler to the entire circle and thus authorizing a simple notation of the nth roots of unity, the author uses it to organize homogeneous divisions of the limited development of the exponential function, that is opening the way to the use of a whole bunch of new primary functions in Differential Calculus. He then shows how new supercomplex products in dimension 3 make it possible to calculate fractals whose connexity depends on the product considered. We recall the geometry of convex polygons and regular polygons.

Analysis of the Application of Ink Art in Graphic Design Imagery

The graphic design industry has been developing rapidly in recent years.People have begun to focus on steering the development of graphic design in the direction of localization,integrating more traditional Chinese elements,raising the level of acceptance toward graphic design content,and disseminating traditional culture on this basis.Ink art plays an important role in the historical and cultural development process.It uses simple color contrast to construct different situations and possesses unique artistic charm and cultural heritage.Incorporating ink elements into graphic design may enhance the graphic design style and provide inspiration.This article focuses on the reasons,advantages,and strategies of using ink art in graphic design imagery,hoping to provide references for graphic design activities.