The integration of artificial intelligence (AI) in healthcare has led to the development intelligent auxiliary diagnosis systems, enhancing diagnostic capabilities across various medical domains. These AI-assisted systems leverage deep learning algorithms aid professionals disease screening, localization focal areas, and treatment plan selection. With policies emphasizing innovation AI technology, particularly China, have emerged as valuable tools improving accuracy efficiency. categorized...

Introduction Active rehabilitation requires active neurological participation when users use equipment. A brain-computer interface (BCI) is a direct communication channel for detecting changes in the nervous system. Individuals with dyskinesia have unclear intentions to initiate movement due physical or psychological factors, which not conducive detection. Virtual reality (VR) technology can be potential tool enhance intention from pre-movement neural signals clinical exercise therapy....

This study discusses the application of deep learning technology in network intrusion detection systems (IDS) and focuses on a new model named CNN-Focal. First, through review traditional IDS technology, it analyzes its limitations dealing with complex traffic. Then, design principle CNN-Focal is described detail, which uses threshold convolution SoftMax multi-class classification to effectively improve abnormal traffic detection's accuracy efficiency. The experimental results show that...

The surface electromyography (sEMG) signal has been used for volitional control of robotic assistive devices. There are still challenges in improving system performance accuracy and processing to remove systematic noise. A sEMG-controlled ambulation exoskeleton was developed this study, aiming achieve harmonic interactions between a user the system. gait cycle duration (GCD) extracted from sEMG signals using autocorrelation algorithm Bayesian redundant fusion algorithm. GCDs various walking...

ABSTRACT How does the politician's reputation concern affect information provision when is endogenously provided by a biased lobbyist? I develop model to study this problem and show that answer depends on transparency design. When lobbyist's preference publicly known, induces lobbyist provide more information. unknown, may induce less One implication of result given transparent preferences, decision consequences can impede moderating reputational incentive.

This study discusses the application of deep learning technology in network intrusion detection systems (IDS) and focuses on a new model named CNN-Focal. First, reviewing traditional IDS technology, it analyzes its limitations dealing with complex traffic. Then, design principle CNN-Focal is described detail, which uses threshold convolution SoftMax multi-class classification to improve abnormal traffic detections accuracy efficiency effectively. The experimental results show that performs...

In critical clinical medical image analysis applications, such as surgical navigation and tumor monitoring, registration is crucial. Recognizing the potential for enhanced accuracy in existing unsupervised techniques single-modal imagery, this research introduces an innovative deep learning-based algorithm. Its novelty resides integrating short long connections to create a densely connected structure, markedly refining feature map interconnectivity within U-Net architecture. This advancement...

High-Fat-Diet (HFD)-induced obesity is a major contributor to heart and mobility premature aging mortality in both

Orchard target-oriented variable rate spraying is an effective method to reduce pesticide drift and excessive residues. To accomplish this task, the orchard targets' characteristic information needed control liquid flow airflow rate. One of most important characteristics canopy density. In order establish density model for a planar target which indispensable calculation, detection testing system was developed based on ultrasonic sensor. A time-domain energy analysis employed analyze signal....

One of the most exciting areas rehabilitation research is brain-controlled prostheses, which translate electroencephalography (EEG) signals into control commands that operate prostheses. However, existing brain-control methods have an obstacle between selection brain computer interface (BCI) and its performance. In this paper, a novel BCI system based on facial expression paradigm proposed to prostheses uses characteristics theta alpha rhythms prefrontal motor cortices. A portable prosthesis...

In the field of lower limb exoskeletons, besides its electromechanical system design and control, attention has been paid to realizing linkage exoskeleton robots humans via electroencephalography (EEG) electromyography (EMG). However, even state art performance voluntary movement intention decoding still faces many obstacles. following work, focusing on perspective inner mechanism, a homology characteristic EEG EMG for was conducted. A mathematical model built based which consists neural...

The electroencephalogram (EEG) and surface electromyogram (sEMG) fusion has been widely used in the detection of human movement intention for human–robot interaction, but internal relationship EEG sEMG signals is not clear, so their still some shortcomings. A precise method using CNN-LSTM model was investigated to detect lower limb voluntary this study. At first, signal processing each stage analyzed that response time difference between can be estimated movement, it calculated by symbolic...

Most control methods deployed in lower extremity rehabilitation robots cannot automatically adjust to different gait cycle stages and training modes for impairment subjects. This article presents a continuous seamless assist-as-needed method based on sliding mode adaptive control. A forgetting factor is introduced, small trajectory deviation from reference normal used learn the level of human subject real time. The assistance torque needed complete learned through radial basis function...

Abstract Individuals with severe tetraplegia frequently require to control their complex assistive devices using body movement the remaining activity above neck. Electromyography (EMG) signals from contractions of facial muscles enable people produce multiple command by conveying information about attempted movements. In this study, a novel EMG-controlled system based on actions was developed. The mechanism different processed an EMG model. Four asymmetric and symmetry were defined...

Focusing on the specific requirements for prosthesis operation, a real-time Facial-Expression-BCI method with 5 commands was proposed in this paper. 4 active facial expressions were adopted to control directional movement, and 1 Normal expression used send hold command. The functional connectivity analyzed verify among related brain regions under expression. signal processing `one vs one' CSP quadratic SVM detail dealing 100ms-length EEG. offline training accuracy of Facial-Expression-BC!...

There are huge and complex power grids in China, but the high-voltage transmission lines inspection relies mainly on manual inspection. The non-manual line methods include flight robot online However, these two kinds of have some limitations shortcomings leading that they can't be widely used harsh environments, such as wind cold Xin Jiang province. This paper presents a new method combines advantages inspection, aiming at making functions flight, glide crawl to can carry out feasibility...

Electroencephalogram (EEG) modeling in brain-computer interface (BCI) provides a theoretical foundation for its development. However, limited by the lack of guidelines model parameter selection and inability to obtain personal tissue information practice, EEG BCI is mainly focused on qualitative level which shows gap between theory application. Based such problems, this work combined surface simulation with converter based generative adversarial network (GAN), establish connection from...

The estimation of joint angle based on sEMG is one the key technologies in bioelectric processing, which could be used for medical rehabilitation robots, intelligent prosthetics, industrial mechanical arms and so on. However, continuous wrist rare. This paper implements sEMG. motion data were acquired firstly. Then six kinds features extracted actual was calculated. Thirdly, BP neural network established to analysis relationship between joint. Finally, verification experiment performed,...

Real-time application of exoskeleton remains a challenge due to the limited stability electromyogram (EMG) collected on lower limb. To enhance generalization EMG based pattern recognition (PR), this work proposed novel fault-tolerant algorithm conventional liner discriminant analysis (LDA). Based two patterns collection (static data and dynamic data), most accurate feature set was first selected in static guarantee basic performance LDA. Detections LDA formed decision stream, provided later...

Considering the limitations of stimulator redundancy and user-unfriendliness in mainstream brain control paradigm, a facial expression assisted method had been proposed by our research group for realtime precise needs peripheral control. Focused on further research, this paper an updated version with semi-asynchronous strategy up to eight instruction sets, which EEGs 100 ms window length were selected realizing real-time decoding. An algorithm based `one vs one' CSP combined SVM was applied...

Background and Objective Exoskeleton robot control should ideally be based on human voluntary movement intention. The readiness potential (RP) component of the motion-related cortical is observed before in electroencephalogram can used for intention prediction. However, its single-trial features are weak highly variable, existing methods cannot achieve high cross-temporal cross-subject accuracies practical online applications. Therefore, this work aimed to combine a deep convolutional neural...

Aiming at the problems of assistance efficiency evaluate technology upper limb assist exoskeleton, a continuous torque estimation method based on surface electromyography signal (sEMG) and convolutional long short-term memory (CNN-LSTM) was proposed in this paper. Firstly, dynamic analysis human is carried out, then sEMG signals directly related to lifting process are collected, from which features muscle extracted. On basis, CNN-LSTM network used construct model mapping multi joint limb,...