The high level of English is a standard for a top student.
1. “There is not much to choose between men. They are all a hotchpotch of greatness and littleness, of virtue and vice, of nobility and baseness. “
2. What would you do in the morning?
Oh, well, if you're asking me about my morning routines, I usually get up at 7. I cannot get up earlier than that because I would be physically awake, but mentally asleep. So basically at 7, and then I would go through my morning routines. That usually takes an hour. It involves a lot of things. I would go brush my teeth, wash my face, and then put on cosmetics and makeup. And then I would have some breakfast. I usually boil myself a bowl of wantons, a dozen wantons. I would usually keep the soup heavy and thick. That's how I like and it is very delicious. And that's about it.
3. Life is too short to wake up with regrets. So love the people who treat you right. Forget about the one’s who don’t.
4. Find a right person, is to give oneself after one of the best gift in my life.
5. Everyone has darkness in their lives, but without the shadows we wouldn’t know we were standing in the light.
6. Love, there is no reason for love and without precondition tolerance.
7. If people treat you like you're not worth it, show them that they're the ones not good enough for you.
8. If you stop learning, you will forget what you already know.
9. There is a time in life that is full of uneasiness.We have no other choice but to face it.
10. There can be too much truth in any relationships
Paper
1. A Navigational Cognitive Assistant for the Blind (http://www.cs.cmu.edu/~kkitani/pdf/AGKITA-MHCI16.pdf)
ABSTRACT Turn-by-turn navigation is a useful paradigm for assisting people with visual impairments during mobility as it reduces the cognitive load of having to simultaneously sense, localize and plan. To realize such a system, it is necessary to be able to automatically localize the user with sufficient accuracy, provide timely and efficient instructions and have the ability to easily deploy the system to new spaces. We propose a smartphone-based system that provides turn-byturn navigation assistance based on accurate real-time localization over large spaces. In addition to basic navigation capabilities, our system also informs the user about nearby points-of-interest (POI) and accessibility issues (e.g., stairs ahead). After deploying the system on a university campus across several indoor and outdoor areas, we evaluated it with six blind subjects and showed that our system is capable of guiding visually impaired users in complex and unfamiliar environments.
2. A Complete Navigation System for Goal Acquisition in Unknown Environments
(http://www.frc.ri.cmu.edu/~axs/doc/autron95.pdf)
Abstract Most autonomous outdoor navigation systems tested on actual robots have centered on local navigation tasks such as avoiding obstacles or following roads. Global navigation has been limited to simple wandering, path tracking, straight-line goal seeking behaviors, or executing a sequence of scripted local behaviors. These capabilities are insufficient for unstructured and unknown environments, where replanning may be needed to account for new information discovered in every sensor image. To address these problems, we have developed a complete system that integrates local and global navigation. The local system uses a scanning laser rangefinder to detect obstacles and recommend steering commands to ensure robot safety. These obstacles are passed to the global system which stores them in a map of the environment. With each addition to the map, the global system uses an incremental path planning algorithm to optimally replan the global path and recommend steering commands to reach the goal. An arbiter combines the steering recommendations to achieve the proper balance between safety and goal acquisition. This system was tested on a real robot and successfully drove it 1.4 kilometers to find a goal given no a priori map of the environment.
3. Mapping Orchards for Autonomous Navigation
(https://www.ri.cmu.edu/pub_files/2014/7/ASABE_2014.pdf)
Abstract. Autonomous orchard vehicles have been shown to increase worker efficiency in tasks including pruning, thinning, tree maintenance, and pheromone placing. To cover entire blocks, they must be able to repeatedly exit an orchard row, turn, and enter the next. The authors’ experience deploying autonomous vehicles for five years in commercial and research orchards shows that, when the map of the block is available, navigation errors decrease and the vehicle’s probability of finding the next row increases. From a cost perspective, it is important that the map be built without expensive surveying equipment, and preferably using the same sensors used for vehicle guidance. Here, we present a landmark-based method that creates a local map of the block in which the vehicle operates using the laser scanner already present on the vehicle. We describe the mapping procedure and present results obtained in commercial orchards.
Keywords. Agricultural Robotics; Autonomous Orchard Vehicle; Autonomous Navigation; Mapping; Lidar.
4. 16-385 Computer Vision, Spring 2018 (http://www.cs.cmu.edu/~16385/)