Browsing by Author "Lee, Wen-Ming"

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Optimal Linear Channel Prediction for LTE-A Uplink Under Channel Estimation Errors
(2013-10-01)
Wu, Jwo-Yuh
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Lee, Wen-Ming
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電機資訊學士班
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Undergraduate Honors Program of Electrical Engineering and Computer Science
This paper proposes an optimal design of the linear channel prediction scheme for LTE-A uplink transmission, taking into account the effect of channel estimation errors. The optimization criterion is to minimize mean square prediction errors, averaged over the distributions of true channel coefficients and estimation errors. We derive a closed-form solution of the optimal channel predictor and provide exact analysis for the achievable mean square error (MSE). The performance gain, in terms of the amount of reduction in MSE, as compared with the conventional channel predictor, is also analytically characterized. Simulation results are used to validate our MSE analyses and to confirm the performance advantage of the proposed solution.
Usage of Case-Based Reasoning, Neural Network and Adaptive Neuro-Fuzzy Inference System Classification Techniques in Breast Cancer Dataset Classification Diagnosis
(2012-04-01)
Huang, Mei-Ling
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Hung, Yung-Hsiang
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Lee, Wen-Ming
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Li, R. K.
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Wang, Tzu-Hao
Breast cancer is a common to females world-wide. Today, technological advancements in cancer treatment innovations have increased the survival rates. Many theoretical and experimental studies have shown that a multiple classifier system is an effective technique for reducing prediction errors. This study compared the particle swarm optimizer (PSO) based artificial neural network (ANN), the adaptive neuro-fuzzy inference system (ANFIS), and a case-based reasoning (CBR) classifier with a logistic regression model and decision tree model. It also applied three classification techniques to the Mammographic Mass Data Set, and measured its improvements in accuracy and classification errors. The experimental results showed that, the best CBR-based classification accuracy is 83.60%, and the classification accuracies of the PSO-based ANN classifier and ANFIS are 91.10% and 92.80%, respectively.