BCI+VR Rehabilitation Design of Closed-Loop Motor Imagery Based on the Degree of Drug Addiction

After abusing drugs for long,drug users will experience deteriorated self-control cognitive ability,and poor emotional regulation.This paper designs a closed-loop virtual-reality(VR),motorimagery(MI)rehabilitation training system based on brain-computer interface(BCI)(MI-BCI+VR),aiming to enhance the self-control,cognition,and emotional regulation of drug addicts via personalized rehabilitation schemes.This paper is composed of two parts.In the first part,data of 45 drug addicts(mild:15;moderate:15;and severe:15)is tested with electroencephalogram(EEG)and near-infrared spectroscopy(NIRS)equipment(EEG-NIRS)under the dual-mode,synchronous signal collection paradigm.Using these data sets,a dual-modal signal convolutional neural network(CNN)algorithm is then designed based on decision fusion to detect and classify the addiction degree.In the second part,the MIBCI+VR rehabilitation system is designed,optimizing the Filter Bank Common Spatial Pattern(FBCSP)algorithm used in MI,and realizing MI-EEG intention recognition.Eight VR rehabilitation scenes are devised,achieving the communication between MI-BCI and VR scene models.Ten subjects are selected to test the rehabilitation system offline and online,and the test accuracy verifies the feasibility of the system.In future,it is suggested to develop personalized rehabilitation programs and treatment cycles based on the addiction degree.

New Progress in the Application of Satellite Thermal Infrared Imagery to Earthquake Prediction

The concept of stochastic resonance (SR) has been introduced into the analysis of satellite thermal infrared images. Six kinds of anomalous phenomena related to crustal movement were recognized in satellite thermal infrared images. Six diagnostic indicators for the prediction of global earthquakes with magnitude ≥6.0 and their quantitative evaluation standards have been established. The microscopic behavior of global crustal movement is successfully controlled by using satellite thermal infrared imagery, and the occurrence time and magnitude of over 80% of global strong earthquakes occurred since the foundation of the observation station have been successfully predicted. It is believed that the combination of satellite thermal infrared information with macroscopic anomalous phenomena will play an important role in earthquake hazard reduction.

Transformer-Based Cloud Detection Method for High-Resolution Remote Sensing Imagery

Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose an innovative transformer-based method.This method leverages transformers,which are adept at processing data sequences,to enhance cloud detection accuracy.Additionally,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud detection.Our extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud segmentation.By integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation accuracy.Ultimately,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.

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.

Pseudo channel:time embedding for motor imagery decoding

Motor imagery(MI)based electroencephalogram(EEG)represents a frontier in enabling direct neural control of external devices and advancing neural rehabilitation.This study introduces a novel time embedding technique,termed traveling-wave based time embedding,utilized as a pseudo channel to enhance the decoding accuracy of MI-EEG signals across various neural network architectures.Unlike traditional neural network methods that fail to account for the temporal dynamics in MI-EEG in individual difference,our approach captures time-related changes for different participants based on a priori knowledge.Through extensive experimentation with multiple participants,we demonstrate that this method not only improves classification accuracy but also exhibits greater adaptability to individual differences compared to position encoding used in Transformer architecture.Significantly,our results reveal that traveling-wave based time embedding crucially enhances decoding accuracy,particularly for participants typically considered“EEG-illiteracy”.As a novel direction in EEG research,the traveling-wave based time embedding not only offers fresh insights for neural network decoding strategies but also expands new avenues for research into attention mechanisms in neuroscience and a deeper understanding of EEG signals.

The Analysis of Wolf Imagery in The Company of Wolves

Imagery analysis is a commonly used analytical method in literary analysis.In Angela Carter’s work,the image of wolves is particularly prominent.Her“Werewolf Tetralogy”rewrites traditional culture and subverts traditional consciousness,and is the research object of many scholars.Starting from the analysis of the wolf image in The Company of Wolves,this paper uses Deleuze’s Becoming-Animal Theory to explore the construction of harmony between nature,humans and gender relations in The Company of Wolves.

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.

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.

DICTION文本分析工具及其在情感分析中的应用

计算机辅助文本分析软件——DICTION应用广泛,在会计与金融研究、政治学、语言学、传播学等不同领域发挥了重要作用。此外,将该软件应用于817篇中美50强企业年报的话语情感对比研究,笔者发现,中美企业年报话语情感存在显著差异。该工具使自动量化分析文本内容成为可能,无须格式转换和人工标注,具有较高的信度和速度,大大促进了内容分析这一研究方法的应用和发展。

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.

Enhanced hyperspectral imagery representation via diffusion geometric coordinates

The concise and informative representation of hyperspectral imagery is achieved via the introduced diffusion geometric coordinates derived from nonlinear dimension reduction maps - diffusion maps. The huge-volume high- dimensional spectral measurements are organized by the affinity graph where each node in this graph only connects to its local neighbors and each edge in this graph represents local similarity information. By normalizing the affinity graph appropriately, the diffusion operator of the underlying hyperspectral imagery is well-defined, which means that the Markov random walk can be simulated on the hyperspectral imagery. Therefore, the diffusion geometric coordinates, derived from the eigenfunctions and the associated eigenvalues of the diffusion operator, can capture the intrinsic geometric information of the hyperspectral imagery well, which gives more enhanced representation results than traditional linear methods, such as principal component analysis based methods. For large-scale full scene hyperspectral imagery, by exploiting the backbone approach, the computation complexity and the memory requirements are acceptable. Experiments also show that selecting suitable symmetrization normalization techniques while forming the diffusion operator is important to hyperspectral imagery representation.

Imagery perspective among young athletes: Differentiation between external and internal visual imagery

Purpose： This study aimed to investigate the construct of external visual imagery （EVI） vs. internal visual imagery （IV/） by comparing the athletes＇ imagery ability with their levels of skill and types of sports. Methods： Seventy-two young athletes in open （n = 45） or closed （n = 27） sports and with different skill levels completed 2 custom-designed tasks. The EVI task involved the subject generating and visualizing the rotated images of different body parts, whereas the IVI task involved the subject visualizing himself or herself performing specific movements. Results： The significant Skill-Level x Sport Type interactions for the EVI task revealed that participants who specialized in open sports and had higher skill-levels had a higher accuracy rate as compared to the other subgroups. For the IVI task, the differences between the groups were less clear： those with higher skill-levels or open sports had a higher accuracy rate than those with lower skill-levels or closed sports. Conclusion： EVI involves the visualization of others and the environment, and would be relevant to higher skill-level athletes who engage in open sports. IVI, in contrast, tends to be more self-oriented and would be relevant for utilization by higher skill-level athletes regardless of sport type.

Implicit Love and Passionate Love-Contrast the Diction in "Song To Celia" by Ben Jonson and that in "The Canonization" by John Donne

Love is a permanent theme in the world's history of literature.With different dictions(choice of words),poets can portray diverse images of love,thus leaving completely distinct impression on the readers' minds.By analyzing and contrasting the diction in "Song To Celia" by Ben Jonson and that in "The Canonization" by John Donne,this essay aims to illustrate that different dramatic effects could be achieved with different choices of words in creative writing,and diction could be a representative symbol of a poet's writing style.

Corticospinal excitability during motor imagery is diminished by continuous repetition-induced fatigue

Application of continuous repetition of motor imagery can improve the performance of exercise tasks.However,there is a lack of more detailed neurophysiological evidence to support the formulation of clear standards for interventions using motor imagery.Moreover,identification of motor imagery intervention time is necessary because it exhibits possible central fatigue.Therefore,the purpose of this study was to elucidate the development of fatigue during continuous repetition of motor imagery through objective and subjective evaluation.The study involved two experiments.In experiment 1,14 healthy young volunteers were required to imagine grasping and lifting a 1.5-L plastic bottle using the whole hand.Each participant performed the motor imagery task 100 times under each condition with 48 hours interval between two conditions:500 mL or 1500 mL of water in the bottle during the demonstration phase.Mental fatigue and a decrease in pinch power appeared under the 1500-mL condition.There were changes in concentration ability or corticospinal excitability,as assessed by motor evoked potentials,between each set with continuous repetition of motor imagery also under the 1500-mL condition.Therefore,in experiment 2,12 healthy volunteers were required to perform the motor imagery task 200 times under the 1500-mL condition.Both concentration ability and corticospinal excitability decreased.This is the first study to show that continuous repetition of motor imagery can decrease corticospinal excitability in addition to producing mental fatigue.This study was approved by the Institutional Ethics Committee at the Nagasaki University Graduate School of Biomedical and Health Sciences(approval No.18121302)on January 30,2019.

The computer-aided design method of cabinet based on style imagery

Due to the practical problems of the high costs and the long development cycle of China’s cabinet production,a computer-aided design method of the cabinet based on style imagery is proposed.According to the principle of the conjoint analysis method, the rough set theory and the weight coefficient of different components of the cabinet,a multi-dimensional model of style imagery to evaluate the cabinet is built. Then the related constants of style imagery are calculated and the cabinet components library is also built by the three-dimensional modeling.Finally,with recombinant technology and the mapping model between cabinet style and external characteristics,the prototype system based on Visual Studio is proposed.This system actualizes the bidirectional reasoning between product style imagery and the shape features,which can assist designers to produce more creative designs,greatly improve the efficiency of cabinet development and increase the profits of companies.

A new CFAR ship target detection method in SAR imagery

Many ship target detection methods have been developed since it was verified that ship could be imaged with the space-based SAR systems. Most developed detection methods mostly emphasized ship detection rate but not computation time. By making use of the advantages of the K-distribution CFAR method and two-parameter CFAR method, a new CFAR ship target detection algorithm was proposed. In that new method, we use the K-distribution CFAR method to calculate a global threshold with a certain false-alarm rate. Then the threshold is applied to the whole SAR imagery to determine the possible ship target, pixcls, and a binary image is given as tile preliminary result. Mathematical morphological filter are used to filter the binary image. After that step, we use tile two-parameter CFAR method to detect the ship targets. In the step, the local sliding window only works in the possible ship target pixels of the SAR imagery. That step avoids the statistical calculation of the background pixels, so the method proposed can much improve the processing speed. In order to test the new method, two SAN imagery with different background were used, and the detection result shows that that method can work well in different background circumstances with high detection rate. Moreover, a synchronous ship detection experiment was carried out in Qingdao port in October 28, 2005 to verify the new method and one ENVISAT ASAR imagery was acquired to detect ship targets. It can be concluded from the experiment that the new method not only has high detection rate, but also is time-consuining, and is suitable for the operational ship detection system.

Ultrawide-band radar imagery from multiple incoherent frequency subband measurements

The problem of combined radar imagery from multiple sparse frequency subbands initially incoherent to each other is of practical importance for radar target discrimination.A new coherent processing technique based on probability density analysis of the subband data is proposed,which is applicable for radar imaging from measurements of two or more initially incoherent radar subbands.The coherence parameters for both amplitude and phase are obtained by combining modern spectral analysis with probability density estimation.The major advantage of the proposed technique lies in that it enables much more robust cohering for the sparse subband data of real-world complex targets.