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)
Three adaptive line enhancer (ALE) algorithms and architectures-namely, conventional ALE, ALE with double filtering, and ALE with coherent accumulation-axe investigated for fast carrier acquisition in the time domain. The advantages of these algorithms axe their simplicity, flexibility, robustness, and applicability to general situations including 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)
—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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