Thien Minh Nguyen

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This work reviews Adaline-based techniques for estimating Fourier series. The Adaline, with its linear structure and learning, fits a Fourier series by expressing any periodic signal as a sum of harmonic terms. The learning with elementary harmonic inputs enforces the weights to converge to the amplitudes. The Adaline therefore individually identifies the(More)
A linear Multi Layer Perceptron (MLP) is proposed as a new approach to identify the harmonic content of biomedical signals and to characterize them. This layered neural network uses only linear neurons. Some synthetic sinusoidal terms are used as inputs and represent a priori knowledge. A measured signal serves as a reference, then a supervised learning(More)
We establish several new findings on the relation between open interest in commodity markets and asset returns. High commodity market activity, as measured by high open-interest growth, predicts high commodity returns and low bond returns. Open-interest growth is a more powerful and robust predictor of commodity returns than other known predictors such as(More)
Investigations of the neuro-physiological correlates of mental loads, or states, have attracted significant attention recently, as it is particularly important to evaluate mental fatigue in drivers operating a motor vehicle. In this research, we collected multimodal EEG/ECG/EOG and fNIRS data simultaneously to develop algorithms to explore(More)
—A new approach based on a linear Multi Layer Perceptron (MLP) is introduced for harmonics identification. This neural approach uses linear neurons and inputs composed of synthetic harmonic terms in order to fit Fourier series of periodic signals. The amplitudes of the fundamental and high-order harmonics are deduced from a combination of the weights. The(More)
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