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Principle component analysis (PCA) is a fundamental tool for dimension reduction. It’s operations is quite simple: center the data, calculate the covariance matrix, perform eigenvalue decomposition and these eigenvector are “principle components”, which is also called eigenface if the data is face images.
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Short description of portfolio item number 1
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Short description of portfolio item number 2
Published in , 2019
Pursuit-evasion is a classic research topic in mobile robotics and gaming theory. Deep reinforcement learning advances rapidly in recent years, and solves many decision-making problem like go, atari and robots control. However, learning an end-to-end strategy in a complex task can be prohibitive. We propose a hierachical framework which composes the neural network and classical tracking algorithms and outperforms either single method.
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Published in , 2019
Indoor localization is a key technique to provide services about interaction with the indoor environment such as Virtual Reality and airport navigation. However, GPS can hardly be used in the indoor space, due to the obstruction from buildings itself. WiFi, which are widely used, are rich and accurate signal sources for localization. Much like using lidar or camera for mapping and localzation, we first build the WiFi signal strength map, then localize the users’ smartphone according to their received signal strength.
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Published in , 2020
This projects contains my robotics knowledge and common robotics algorithm demo.
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.