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Abstract An intelligent sensing framework using Machine Learning (ML) and Deep Learning (DL) architectures to precisely quantify dielectrophoretic force invoked on microparticles in a textile electrode-based DEP sensing device is reported. The prediction accuracy and generalization ability of the framework was validated using experimental results. Images of pearl chain alignment at varying input voltages were used to build deep regression models using modified ML and CNN architectures that can correlate pearl chain alignment patterns of Saccharomyces cerevisiae(yeast) cells and polystyrene microbeads to DEP force. Various ML models such as K-Nearest Neighbor, Support Vector Machine, Random Forest, Neural Networks, and Linear Regression along with DL models such as Convolutional Neural Network (CNN) architectures of AlexNet, ResNet-50, MobileNetV2, and GoogLeNet have been analyzed in order to build an effective regression framework to estimate the force induced on yeast cells and microbeads. The efficiencies of the models were evaluated using Mean Absolute Error, Mean Absolute Relative, Mean Squared Error, R-squared, and Root Mean Square Error (RMSE) as evaluation metrics. ResNet-50 with RMSPROP gave the best performance, with a validation RMSE of 0.0918 on yeast cells while AlexNet with ADAM optimizer gave the best performance, with a validation RMSE of 0.1745 on microbeads. This provides a baseline for further studies in the application of deep learning in DEP aided Lab-on-Chip devices.
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A First Look at the Security of EEG-based Systems and Intelligent Algorithms under Physical Signal Injections
Electroencephalography (EEG) based systems utilize machine learning (ML) and deep learning (DL) models in various applications such as seizure detection, emotion recognition, cognitive workload estimation, and brain-computer interface (BCI). However, the security and robustness of such intelligent systems under analog-domain threats have received limited attention. This paper presents the first demonstration of physical signal injection attacks on ML and DL models utilizing EEG data. We investigate how an adversary can degrade the performance of different models by non-invasively injecting signals into EEG recordings. We show that the attacks can mislead or manipulate the models and diminish the reliability of EEG-based systems. Overall, this research sheds light on the need for more trustworthy physiological-signal-based intelligent systems in the healthcare field and opens up avenues for future work.
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- PAR ID:
- 10422597
- Date Published:
- Journal Name:
- Proceedings of the 2023 Secure and Trustworthy Deep Learning Systems Workshop (SecTL ’23)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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