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Full-Text Articles in Engineering
Machine Learning Methodology Review For Computational Electromagnetics, He Ming Yao, Lijun Jiang, Huan Huan Zhang, Wei E.I. Sha
Machine Learning Methodology Review For Computational Electromagnetics, He Ming Yao, Lijun Jiang, Huan Huan Zhang, Wei E.I. Sha
Electrical and Computer Engineering Faculty Research & Creative Works
While machine learning is revolutionizing every corner of modern technologies, we have been attempting to explore whether machine learning methods could be used in computational electromagnetic (CEM). In this paper, five efforts in line with this direction are reviewed. They include forward methods such as the method of moments (MoM) solved by the artificial neural network training process, FDTD PML (perfectly matched layer) using the hyperbolic tangent basis function (HTBF), etc. There are also inverse problems that use the deep ConvNets for the effective source reconstruction and subwavelength imaging in the far-field. Benchmarks are provided to demonstrate the feasibility of …
Nonlinearity Of Digital I/Os And Its Behaviour Modeling, He Ming Yao, Huan Huan Zhang, Hui Chun Yu, Xing Yun Luo, Bin Li, Hua Sheng Ren, Li (Lijun) Jun Jiang
Nonlinearity Of Digital I/Os And Its Behaviour Modeling, He Ming Yao, Huan Huan Zhang, Hui Chun Yu, Xing Yun Luo, Bin Li, Hua Sheng Ren, Li (Lijun) Jun Jiang
Electrical and Computer Engineering Faculty Research & Creative Works
Due to the rising signal speed in today's integrated circuits (ICs), the digital input/output (I/O) device modeling becomes a very serious challenge. However, its nonlinearity issue was even less addressed. But for accurate EMC and EMI characterizations, the I/O nonlinearity could become a source of unexpected EMC and EMI troubles in the high-speed system. In this paper, we analyze the nonlinearity of high-speed drivers and loads under the influence of various parameters, such as the rising and falling times, data and clock duty cycle distortion (DCD), signal skew, balance of the circuit, etc. Further based on the spectrum property of …
Speaker Identification Using A Combination Of Different Parameters As Feature Inputs To An Artificial Neural Network Classifier, Viresh Moonasar, Ganesh K. Venayagamoorthy
Speaker Identification Using A Combination Of Different Parameters As Feature Inputs To An Artificial Neural Network Classifier, Viresh Moonasar, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a technique using artificial neural networks (ANNs) for speaker identification that results in a better success rate compared to other techniques. The technique used in this paper uses both power spectral densities (PSDs) and linear prediction coefficients (LPCs) as feature inputs to a self organizing feature map to achieve a better identification performance. Results for speaker identification with different methods are presented and compared.