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Chapter and Conference Paper
Hybrid Cross-entropy Algorithm for Mixed Model U-shaped Assembly Line Balancing Problem
Assembly line balancing problem are widespread in manufacturing industries such as electronics or auto parts. As an efficient metaheuristic, cross-entropy (CE) method can be applied to solve mixed model U-shap...
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Chapter and Conference Paper
Basic Level Advantage during Information Retrieval: An ERP Study
Categorization concept could be divided into three levels according to the degree of generalization: superordinate, basic and the subordinate levels. To investigate the neural mechanism of concept information ...
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Chapter and Conference Paper
Channel Estimation for OFDM Systems with Total Least-Squares Solution
Orthogonal frequency-division multiplexing (OFDM) combines the advantages of high performance and relatively low implementation complexity. However, for reliable coherent detection of the input signal, the OFD...
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Chapter and Conference Paper
Object Detection Combining Recognition and Segmentation
We develop an object detection method combining top-down recognition with bottom-up image segmentation. There are two main steps in this method: a hypothesis generation step and a verification step. In the top...
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Chapter and Conference Paper
HMM-Based Emotional Speech Synthesis Using Average Emotion Model
This paper presents a technique for synthesizing emotional speech based on an emotion-independent model which is called “average emotion” model. The average emotion model is trained using a multi-emotion speec...
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Chapter and Conference Paper
Efficient 3D Face Reconstruction from a Single 2D Image by Combining Statistical and Geometrical Information
In this paper, we present an efficient algorithm for reconstructing 3D head model from a single 2D image based on using a 3D eigenhead model. This system is composed of two components, offline training of the ...
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Chapter and Conference Paper
Feature Selection for Fast Image Classification with Support Vector Machines
According to statistical learning theory, we propose a feature selection method using support vector machines (SVMs). By exploiting the power of SVMs, we integrate the two tasks, feature selection and classifi...