The development of practical Brain-computer Interface (BCI) systems has been hindered by significant issues related to data, specifically the lack of sufficient data needed for training. To address this challenge, gen...
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ISBN:
(纸本)9798350312249
The development of practical Brain-computer Interface (BCI) systems has been hindered by significant issues related to data, specifically the lack of sufficient data needed for training. To address this challenge, generating synthetic data that mimics real recorded data has been proposed to augment the real data. One promising technique for data augmentation is through the use of Generative Adversarial networks (GANs), which have been successfully applied in many other fields. This paper proposes a novel GAN-based approach for generating synthetic spectrum images of Motor Imagery (MI) Electroencephalogram (EEG). The proposed GAN is examined with two Convolutional Neural Network (CNN) architectures in the context of MI classification. Using the public dataset BCI competition IV, our findings reveal that the generated EEG spectrum images using GANs exhibit temporal, spectral, and spatial characteristics similar to the real ones. The average classification accuracy of right-hand versus left-hand MI using the proposed GAN/CNN models has improved to 76.71% with an enhancement of 2.5% in comparison to using the CNN applied to the real data only. These results suggest that using GANs could improve MI BCI systems with limited data.
Edge computing is crucial for IoT applications, especially those needing quick, private data handling. However, these applications are resource-intensive, and edge computing resources are limited compared to cloud cap...
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In India, farmers often face significant challenges in selecting the appropriate crop variety for specific soil types, leading to substantial productivity losses. This project leverages precision agriculture by utiliz...
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This paper presents a robust adaptive diffusion algorithm based on exponential hyperbolic cosine cost function to enhance the distributed estimation performance. Mathematical analysis shows the convergence criteria of...
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Quantum Computing (QC) works based on the principle of quantum mechanics, which is different from traditional computers. Heart disease remains the leading cause of mortality worldwide and the development of advanced p...
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This paper describes a novel virtual platform for university teaching, which in particular allows the creation and use of complex IT infrastructures even for non-experts. Until now, complex network infrastructures in ...
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This paper introduces a novel approach to enhance privacy-preserving machine learning (PPML) by integrating adversarial techniques with Homomorphic Encryption (HE) and Differential Privacy (DP). Privacy-Preserving mac...
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Current software evaluation work based on complex networks rarely considers the complexity of nodes themselves and the multiple coupling between nodes, making it difficult to accurately identify high complexity and hi...
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Heterogeneous Graph Neural networks (HGNNs) have emerged as powerful tools for handling heterogeneous graphs. However, current HGNNs often rely on meta-paths or intricate aggregation operations. In response, we introd...
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Lip detecion(LD) holds great promise for mobile human-computer interaction(HCI) terminals such as hearing aids, robots, smartphones etc. However they suffer from the massive computation and resource overhead from main...
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ISBN:
(纸本)9798350387186;9798350387179
Lip detecion(LD) holds great promise for mobile human-computer interaction(HCI) terminals such as hearing aids, robots, smartphones etc. However they suffer from the massive computation and resource overhead from mainstream models as Viola-Jones framework, convolutional neural networks(CNN), recurrent neural networks(RNN) and vision transformer(ViT). To solve this problem, we propose a resource-efficient lip detector(RELD) for mobile HCI applications. For lip region of interest(ROI) detection, a hybrid feature criteria is constructed utilizing the hump-like curve formed by row-summation of lip ROI. And a L-order predictive tracking method is proposed to track the lip bounding box in conitnuous image flows with low computation and latency. For behavioural validation, RELD ahieves test accuracy over 95% on a database of 204000 images generated from GRID dataset. To verify its hardware feasibility, an RTL implementation has been accomplished based on 200 x 200 images read from OV7670 image sensor, showing that RELD requires only 352 bytes of SRAM and <= 5000 MAC operations per frame to perform lip detection task.
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