Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and ...
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Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.
We present NewsBench, a novel evaluation framework to systematically assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism. Our constructed benchmark dataset is focus...
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Speech Emotion Recognition (SER) is a challenging task due to the complexity and variability of human emotions. In this paper, we propose an innovative approach to improve SER performance on the EMODB dataset. Our app...
Speech Emotion Recognition (SER) is a challenging task due to the complexity and variability of human emotions. In this paper, we propose an innovative approach to improve SER performance on the EMODB dataset. Our approach employs data augmentation techniques, such as noise addition and spectrogram shift, as well as balancing techniques, including random oversampling. We also extract five different features from the dataset samples: MFCC, Chroma, Mel Spectrogram, ZCR, and RMS. We compare the performance of four different classifiers - MLP, SVM, KNN, and CNN - with and without the use of our proposed approach. Our results demonstrate that the proposed approach significantly enhances the accuracy of speech emotion recognition compared to the approach without data augmentation and balancing techniques. Our experiments reveal that the proposed approach achieves higher accuracy and F1-score compared to other approaches, with MLP and CNN models achieving 100% accuracy. These findings highlight the effectiveness of data augmentation and balancing techniques in improving the performance of speech emotion recognition. Moreover, our approach holds great potential for application in various real-life scenarios, including mental health monitoring, human-robot interaction, and speech-based virtual assistants.
This paper proposes an energy-efficient federated learning method and its application in human activity monitoring and recognition. In the proposed approach, the device that needs a model for an application requests i...
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Variational mode decomposition (VMD) is extensively utilized in the field of industrial signal processing due to its superior anti-mixing and denoising capabilities. However, in practice, the performance of VMD is sig...
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ISBN:
(数字)9798350368604
ISBN:
(纸本)9798350368611
Variational mode decomposition (VMD) is extensively utilized in the field of industrial signal processing due to its superior anti-mixing and denoising capabilities. However, in practice, the performance of VMD is significantly influenced by the initial center frequency $\omega^{1}$ . An improper setting of $\omega^{1}$ can significantly reduce the stability of decomposition, resulting in the loss or masking of feature information by noise. To address the above issue, an improved VMD algorithm is proposed in this work. Our algorithm redefines $\omega^{1}$ based on the narrowband characteristics of intrinsic mode function (IMF). Under the new definition, $\omega^{1}$ can be adaptively adjusted based on the signal spectrum processed by Wiener filtering. This adjustment significantly enhances the flexibility and anti-mixing performance of the original VMD. To validate our proposed method, comparative experiments were conducted using two sets of fault data from Case Western Reserve University (CWRU). The results demonstrate that our method is more effective and superior in extracting fault features.
Ultrasound imaging (US) is one of the most commonly used techniques for detecting breast lesions. However, due to the inherent properties of low contrast, speckle noise, and blurred boundaries in B-mode ultrasound ima...
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Computation offloading at lower time and lower energy consumption is crucial for resource limited mobile devices. This paper proposes an offloading decision-making model using federated learning. Based on the task typ...
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Federated learning (FL) has emerged as a prominent machine learning paradigm in edge computing environments, enabling edge devices to collaboratively optimize a global model without sharing their private data. However...
With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an...
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