Learning is a tiring job. We should adapt to local conditions and strive for excellence\u5b66\u4e60\u662f\u4e00\u95e8\u6280\u672f\u6d3b\uff0c\u56e0\u5730\u5236\u5b9c\uff0c\u7cbe\u76ca\u6c42\u7cbe<\/p>\n\n\n\n
Total Scholarships Earned<\/strong> $45,037.674 <\/pre>\n<\/div><\/div>\n\n\n\n\n<\/figure>\n\n\n\n$10,000 Cornell Scholarship<\/figcaption><\/figure>\n\n\n\n$10,000 Annual Dean Scholarship<\/figcaption><\/figure>\n\n\n\nAlibaba Cloud Certification<\/strong><\/figcaption><\/figure>\n\n\n\nDean’s Superior Scholarship<\/strong>Fall2024<\/strong><\/figcaption><\/figure>\n\n\n\nDean’s Superior Scholarship<\/strong>Fall2024<\/strong><\/figcaption><\/figure>\n\n\n\nDean’s Superior Scholarship <\/strong>Fall2023<\/strong><\/figcaption><\/figure>\n\n\n\nDean’s Superior Scholarship<\/strong>Spring2023<\/strong><\/figcaption><\/figure>\n\n\n\nDean’s Superior Scholarship <\/strong>Fall2022<\/strong><\/figcaption><\/figure>\n\n\n\nGZHU University<\/strong>Second Class Scholarship<\/strong><\/figcaption><\/figure>\n\n\n\n16th Programming Competition Excellent Award 2020Fall<\/strong><\/figcaption><\/figure>\n\n\n\nEnglish Test for International Communication<\/strong><\/figcaption><\/figure>\n\n\n\nCIIA National College Student Career Development Competition<\/strong><\/figcaption><\/figure>\n<\/figure>\n\n\n\n<\/div>\n\n\n\nProjects and Designs<\/h2>\n\n\n\n<\/div>\n\n\n\nGesture Recognition, (Python \/ HMM)<\/strong><\/p>\n\n\n\n<\/div>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>OHXTO\/Gesture-Recognition<\/span><\/a><\/div><\/div><\/div><\/i>\u00a0<\/span><\/span><\/i>\u00a0<\/span><\/span><\/div><\/div>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n<\/div>\n\n\n<\/div>\n\n\n\nThis project implements a full end-to-end gesture recognition pipeline:<\/p>\n\n\n\n\nVisualize raw IMU data example<\/li>\n\n\n\nDiscretize continuous sensor readings using K-means clustering<\/li>\n\n\n\nTrain one Hidden Markov Model (HMM) for each gesture using Baum-Welch (EM)<\/li>\n\n\n\nClassify unknown gestures using log-likelihood comparison<\/li>\n<\/ol>\n\n\n\nSix gestures: Wave, Circle, Eight, Infinity, Beat3, Beat4<\/p>\n\n\n\n<\/div>\n\n\n\nCone Detection & Distance Estimation (HSV + GMM)<\/strong><\/p>\n\n\n\n<\/div>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>OHXTO\/cone-color-segmentation<\/span><\/a><\/div><\/div><\/div><\/i>\u00a0<\/span><\/span><\/i>\u00a0<\/span><\/span><\/div><\/div>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n<\/div>\n\n\n<\/div>\n\n\n\nThis project detects orange traffic cones in images using pixel-wise color segmentation with a Gaussian Mixture Model (GMM) trained via EM (implemented from scratch), then estimates cone distance from bounding-box pixel height.<\/p>\n\n\n\n<\/div>\n\n\n\nNLP with Disaster Tweets (Python \/ Logistic Regression) Kaggle<\/strong><\/p>\n\n\n\n<\/div>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>OHXTO\/Natural-Language-Processing-with-Disaster-Tweets<\/span><\/a><\/div><\/div><\/div><\/i>\u00a0<\/span><\/span><\/i>\u00a0<\/span><\/span><\/div><\/div>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n<\/div>\n\n\n<\/div>\n\n\n\nA lightweight NLP baseline for the Kaggle competition \u201cNatural Language Processing with Disaster Tweets\u201d: predict whether a tweet is about a real disaster (target=1) or not (target=0).<\/p>\n\n\n\nCompetition: https:\/\/www.kaggle.com\/competitions\/nlp-getting-startedKaggle score (public): 0.79619<\/p>\n\n\n\n<\/div>\n\n\n\nTurtleBot4 Interactive Quiz Game (ROS2 + OAK-D \/ Python)<\/strong><\/p>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>OHXTO\/PvZ-quiz-robotic-game-HRI-Cornell-Tech<\/span><\/a><\/div><\/div><\/div><\/i>\u00a0<\/span><\/span><\/i>\u00a0<\/span><\/span><\/div><\/div>\n\n\n\n<\/div>\n\n\n\n<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\n<\/figure>\n<\/figure>\n\n\n\n<\/video><\/figure>\n<\/div>\n\n\n\n<\/div>\n\n\n\nAn educational Human\u2013Robot Interaction (HRI) quiz game using ROS2, TurtleBot4, and OAK-D RGB color detection. Inspired by Plants vs. Zombies, the robot behaves like a \u201czombie\u201d moving toward a target brain. Students defend by answering True\/False questions with \ud83d\udfe9green-True and \ud83d\udfe5red-False cards.<\/p>\n\n\n\nCorrect answers slow the robot. Wrong answers speed it up. Students win if they reach the required number of correct answers before the robot reaches the brain.<\/p>\n\n\n\n<\/div>\n\n\n\nAutonomous Plant-Watering Robot (Teensy 4.0 \/ Arduino C)<\/strong>(Motion & Watering)<\/p>\n\n\n\n<\/div>\n\n\n\nA household plant-watering robot; I owned low-level motion control and the watering mechanism on Teensy 4.0.<\/p>\n\n\n\n<\/div>\n\n\n\nTech:<\/strong> Teensy 4.0 (Arduino\/C), wheel encoders, 4\u00d7 Mecanum, per-wheel PID, PWM motor driver (12 V, TB4223), stepper lift (A4988), simple digital on\/off line for pump<\/p>\n\n\n\n<\/div>\n\n\n\nMy scope (essentials)<\/strong><\/p>\n\n\n\n\nImplemented per-wheel PID velocity control<\/strong> with encoder feedback; PWM into a 12 V motor driver.<\/li>\n\n\n\nPractical, observation-based tuning<\/strong> (team effort) to get smooth, fast target-speed tracking on carpet vs. smooth floors<\/strong>.<\/li>\n\n\n\nBuilt the A4988 stepper lift<\/strong> from Teensy STEP\/DIR for adjustable sprayer height.<\/li>\n\n\n\nProvided a simple on\/off control line<\/strong> for the pump; external driver \/ Raspberry Pi wiring were teammate-owned.<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\nWiring & bring-up<\/strong><\/p>\n\n\n\n\nSeparated 12 V \/ 5 V \/ GND rails; color-coded leads bundled in tidy runs with right-angle turns to each breadboard pin; verified connections with DMM continuity before power-on.<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\nMaterials<\/strong><\/strong><\/p>\n\n\n\n\nDemo video, Poster, Wiring photo, Notes, Code repo: Repo (coming soon)<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\n\n\n\n<\/figure>\n\n\n\n<\/div>\n<\/div><\/div>\n\n\n\n<\/figure>\n<\/div>\n\n\n\n\n<\/video><\/figure>\n<\/div>\n\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n<\/div>\n<\/div>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\nSelf-Balancing Car<\/strong><\/strong><\/strong> (ItsyBitsy M4 \/ Arduino C)<\/strong><\/p>\n\n\n\n(Balance Control) <\/p>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>
Gesture Recognition, (Python \/ HMM)<\/strong><\/p>\n\n\n\n<\/div>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>OHXTO\/Gesture-Recognition<\/span><\/a><\/div><\/div><\/div><\/i>\u00a0<\/span><\/span><\/i>\u00a0<\/span><\/span><\/div><\/div>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n<\/div>\n\n\n<\/div>\n\n\n\nThis project implements a full end-to-end gesture recognition pipeline:<\/p>\n\n\n\n\nVisualize raw IMU data example<\/li>\n\n\n\nDiscretize continuous sensor readings using K-means clustering<\/li>\n\n\n\nTrain one Hidden Markov Model (HMM) for each gesture using Baum-Welch (EM)<\/li>\n\n\n\nClassify unknown gestures using log-likelihood comparison<\/li>\n<\/ol>\n\n\n\nSix gestures: Wave, Circle, Eight, Infinity, Beat3, Beat4<\/p>\n\n\n\n<\/div>\n\n\n\nCone Detection & Distance Estimation (HSV + GMM)<\/strong><\/p>\n\n\n\n<\/div>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>OHXTO\/cone-color-segmentation<\/span><\/a><\/div><\/div><\/div><\/i>\u00a0<\/span><\/span><\/i>\u00a0<\/span><\/span><\/div><\/div>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n<\/div>\n\n\n<\/div>\n\n\n\nThis project detects orange traffic cones in images using pixel-wise color segmentation with a Gaussian Mixture Model (GMM) trained via EM (implemented from scratch), then estimates cone distance from bounding-box pixel height.<\/p>\n\n\n\n<\/div>\n\n\n\nNLP with Disaster Tweets (Python \/ Logistic Regression) Kaggle<\/strong><\/p>\n\n\n\n<\/div>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>OHXTO\/Natural-Language-Processing-with-Disaster-Tweets<\/span><\/a><\/div><\/div><\/div><\/i>\u00a0<\/span><\/span><\/i>\u00a0<\/span><\/span><\/div><\/div>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n<\/div>\n\n\n<\/div>\n\n\n\nA lightweight NLP baseline for the Kaggle competition \u201cNatural Language Processing with Disaster Tweets\u201d: predict whether a tweet is about a real disaster (target=1) or not (target=0).<\/p>\n\n\n\nCompetition: https:\/\/www.kaggle.com\/competitions\/nlp-getting-startedKaggle score (public): 0.79619<\/p>\n\n\n\n<\/div>\n\n\n\nTurtleBot4 Interactive Quiz Game (ROS2 + OAK-D \/ Python)<\/strong><\/p>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>OHXTO\/PvZ-quiz-robotic-game-HRI-Cornell-Tech<\/span><\/a><\/div><\/div><\/div><\/i>\u00a0<\/span><\/span><\/i>\u00a0<\/span><\/span><\/div><\/div>\n\n\n\n<\/div>\n\n\n\n<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\n<\/figure>\n<\/figure>\n\n\n\n<\/video><\/figure>\n<\/div>\n\n\n\n<\/div>\n\n\n\nAn educational Human\u2013Robot Interaction (HRI) quiz game using ROS2, TurtleBot4, and OAK-D RGB color detection. Inspired by Plants vs. Zombies, the robot behaves like a \u201czombie\u201d moving toward a target brain. Students defend by answering True\/False questions with \ud83d\udfe9green-True and \ud83d\udfe5red-False cards.<\/p>\n\n\n\nCorrect answers slow the robot. Wrong answers speed it up. Students win if they reach the required number of correct answers before the robot reaches the brain.<\/p>\n\n\n\n<\/div>\n\n\n\nAutonomous Plant-Watering Robot (Teensy 4.0 \/ Arduino C)<\/strong>(Motion & Watering)<\/p>\n\n\n\n<\/div>\n\n\n\nA household plant-watering robot; I owned low-level motion control and the watering mechanism on Teensy 4.0.<\/p>\n\n\n\n<\/div>\n\n\n\nTech:<\/strong> Teensy 4.0 (Arduino\/C), wheel encoders, 4\u00d7 Mecanum, per-wheel PID, PWM motor driver (12 V, TB4223), stepper lift (A4988), simple digital on\/off line for pump<\/p>\n\n\n\n<\/div>\n\n\n\nMy scope (essentials)<\/strong><\/p>\n\n\n\n\nImplemented per-wheel PID velocity control<\/strong> with encoder feedback; PWM into a 12 V motor driver.<\/li>\n\n\n\nPractical, observation-based tuning<\/strong> (team effort) to get smooth, fast target-speed tracking on carpet vs. smooth floors<\/strong>.<\/li>\n\n\n\nBuilt the A4988 stepper lift<\/strong> from Teensy STEP\/DIR for adjustable sprayer height.<\/li>\n\n\n\nProvided a simple on\/off control line<\/strong> for the pump; external driver \/ Raspberry Pi wiring were teammate-owned.<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\nWiring & bring-up<\/strong><\/p>\n\n\n\n\nSeparated 12 V \/ 5 V \/ GND rails; color-coded leads bundled in tidy runs with right-angle turns to each breadboard pin; verified connections with DMM continuity before power-on.<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\nMaterials<\/strong><\/strong><\/p>\n\n\n\n\nDemo video, Poster, Wiring photo, Notes, Code repo: Repo (coming soon)<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\n\n\n\n<\/figure>\n\n\n\n<\/div>\n<\/div><\/div>\n\n\n\n<\/figure>\n<\/div>\n\n\n\n\n<\/video><\/figure>\n<\/div>\n\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n<\/div>\n<\/div>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\nSelf-Balancing Car<\/strong><\/strong><\/strong> (ItsyBitsy M4 \/ Arduino C)<\/strong><\/p>\n\n\n\n(Balance Control) <\/p>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>
This project implements a full end-to-end gesture recognition pipeline:<\/p>\n\n\n\n
Six gestures: Wave, Circle, Eight, Infinity, Beat3, Beat4<\/p>\n\n\n\n
Cone Detection & Distance Estimation (HSV + GMM)<\/strong><\/p>\n\n\n\n<\/div>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>OHXTO\/cone-color-segmentation<\/span><\/a><\/div><\/div><\/div><\/i>\u00a0<\/span><\/span><\/i>\u00a0<\/span><\/span><\/div><\/div>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n<\/div>\n\n\n<\/div>\n\n\n\nThis project detects orange traffic cones in images using pixel-wise color segmentation with a Gaussian Mixture Model (GMM) trained via EM (implemented from scratch), then estimates cone distance from bounding-box pixel height.<\/p>\n\n\n\n<\/div>\n\n\n\nNLP with Disaster Tweets (Python \/ Logistic Regression) Kaggle<\/strong><\/p>\n\n\n\n<\/div>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>OHXTO\/Natural-Language-Processing-with-Disaster-Tweets<\/span><\/a><\/div><\/div><\/div><\/i>\u00a0<\/span><\/span><\/i>\u00a0<\/span><\/span><\/div><\/div>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n<\/div>\n\n\n<\/div>\n\n\n\nA lightweight NLP baseline for the Kaggle competition \u201cNatural Language Processing with Disaster Tweets\u201d: predict whether a tweet is about a real disaster (target=1) or not (target=0).<\/p>\n\n\n\nCompetition: https:\/\/www.kaggle.com\/competitions\/nlp-getting-startedKaggle score (public): 0.79619<\/p>\n\n\n\n<\/div>\n\n\n\nTurtleBot4 Interactive Quiz Game (ROS2 + OAK-D \/ Python)<\/strong><\/p>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>OHXTO\/PvZ-quiz-robotic-game-HRI-Cornell-Tech<\/span><\/a><\/div><\/div><\/div><\/i>\u00a0<\/span><\/span><\/i>\u00a0<\/span><\/span><\/div><\/div>\n\n\n\n<\/div>\n\n\n\n<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\n<\/figure>\n<\/figure>\n\n\n\n<\/video><\/figure>\n<\/div>\n\n\n\n<\/div>\n\n\n\nAn educational Human\u2013Robot Interaction (HRI) quiz game using ROS2, TurtleBot4, and OAK-D RGB color detection. Inspired by Plants vs. Zombies, the robot behaves like a \u201czombie\u201d moving toward a target brain. Students defend by answering True\/False questions with \ud83d\udfe9green-True and \ud83d\udfe5red-False cards.<\/p>\n\n\n\nCorrect answers slow the robot. Wrong answers speed it up. Students win if they reach the required number of correct answers before the robot reaches the brain.<\/p>\n\n\n\n<\/div>\n\n\n\nAutonomous Plant-Watering Robot (Teensy 4.0 \/ Arduino C)<\/strong>(Motion & Watering)<\/p>\n\n\n\n<\/div>\n\n\n\nA household plant-watering robot; I owned low-level motion control and the watering mechanism on Teensy 4.0.<\/p>\n\n\n\n<\/div>\n\n\n\nTech:<\/strong> Teensy 4.0 (Arduino\/C), wheel encoders, 4\u00d7 Mecanum, per-wheel PID, PWM motor driver (12 V, TB4223), stepper lift (A4988), simple digital on\/off line for pump<\/p>\n\n\n\n<\/div>\n\n\n\nMy scope (essentials)<\/strong><\/p>\n\n\n\n\nImplemented per-wheel PID velocity control<\/strong> with encoder feedback; PWM into a 12 V motor driver.<\/li>\n\n\n\nPractical, observation-based tuning<\/strong> (team effort) to get smooth, fast target-speed tracking on carpet vs. smooth floors<\/strong>.<\/li>\n\n\n\nBuilt the A4988 stepper lift<\/strong> from Teensy STEP\/DIR for adjustable sprayer height.<\/li>\n\n\n\nProvided a simple on\/off control line<\/strong> for the pump; external driver \/ Raspberry Pi wiring were teammate-owned.<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\nWiring & bring-up<\/strong><\/p>\n\n\n\n\nSeparated 12 V \/ 5 V \/ GND rails; color-coded leads bundled in tidy runs with right-angle turns to each breadboard pin; verified connections with DMM continuity before power-on.<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\nMaterials<\/strong><\/strong><\/p>\n\n\n\n\nDemo video, Poster, Wiring photo, Notes, Code repo: Repo (coming soon)<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\n\n\n\n<\/figure>\n\n\n\n<\/div>\n<\/div><\/div>\n\n\n\n<\/figure>\n<\/div>\n\n\n\n\n<\/video><\/figure>\n<\/div>\n\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n<\/div>\n<\/div>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\nSelf-Balancing Car<\/strong><\/strong><\/strong> (ItsyBitsy M4 \/ Arduino C)<\/strong><\/p>\n\n\n\n(Balance Control) <\/p>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>
This project detects orange traffic cones in images using pixel-wise color segmentation with a Gaussian Mixture Model (GMM) trained via EM (implemented from scratch), then estimates cone distance from bounding-box pixel height.<\/p>\n\n\n\n
NLP with Disaster Tweets (Python \/ Logistic Regression) Kaggle<\/strong><\/p>\n\n\n\n<\/div>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>OHXTO\/Natural-Language-Processing-with-Disaster-Tweets<\/span><\/a><\/div><\/div><\/div><\/i>\u00a0<\/span><\/span><\/i>\u00a0<\/span><\/span><\/div><\/div>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n<\/div>\n\n\n<\/div>\n\n\n\nA lightweight NLP baseline for the Kaggle competition \u201cNatural Language Processing with Disaster Tweets\u201d: predict whether a tweet is about a real disaster (target=1) or not (target=0).<\/p>\n\n\n\nCompetition: https:\/\/www.kaggle.com\/competitions\/nlp-getting-startedKaggle score (public): 0.79619<\/p>\n\n\n\n<\/div>\n\n\n\nTurtleBot4 Interactive Quiz Game (ROS2 + OAK-D \/ Python)<\/strong><\/p>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>OHXTO\/PvZ-quiz-robotic-game-HRI-Cornell-Tech<\/span><\/a><\/div><\/div><\/div><\/i>\u00a0<\/span><\/span><\/i>\u00a0<\/span><\/span><\/div><\/div>\n\n\n\n<\/div>\n\n\n\n<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\n<\/figure>\n<\/figure>\n\n\n\n<\/video><\/figure>\n<\/div>\n\n\n\n<\/div>\n\n\n\nAn educational Human\u2013Robot Interaction (HRI) quiz game using ROS2, TurtleBot4, and OAK-D RGB color detection. Inspired by Plants vs. Zombies, the robot behaves like a \u201czombie\u201d moving toward a target brain. Students defend by answering True\/False questions with \ud83d\udfe9green-True and \ud83d\udfe5red-False cards.<\/p>\n\n\n\nCorrect answers slow the robot. Wrong answers speed it up. Students win if they reach the required number of correct answers before the robot reaches the brain.<\/p>\n\n\n\n<\/div>\n\n\n\nAutonomous Plant-Watering Robot (Teensy 4.0 \/ Arduino C)<\/strong>(Motion & Watering)<\/p>\n\n\n\n<\/div>\n\n\n\nA household plant-watering robot; I owned low-level motion control and the watering mechanism on Teensy 4.0.<\/p>\n\n\n\n<\/div>\n\n\n\nTech:<\/strong> Teensy 4.0 (Arduino\/C), wheel encoders, 4\u00d7 Mecanum, per-wheel PID, PWM motor driver (12 V, TB4223), stepper lift (A4988), simple digital on\/off line for pump<\/p>\n\n\n\n<\/div>\n\n\n\nMy scope (essentials)<\/strong><\/p>\n\n\n\n\nImplemented per-wheel PID velocity control<\/strong> with encoder feedback; PWM into a 12 V motor driver.<\/li>\n\n\n\nPractical, observation-based tuning<\/strong> (team effort) to get smooth, fast target-speed tracking on carpet vs. smooth floors<\/strong>.<\/li>\n\n\n\nBuilt the A4988 stepper lift<\/strong> from Teensy STEP\/DIR for adjustable sprayer height.<\/li>\n\n\n\nProvided a simple on\/off control line<\/strong> for the pump; external driver \/ Raspberry Pi wiring were teammate-owned.<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\nWiring & bring-up<\/strong><\/p>\n\n\n\n\nSeparated 12 V \/ 5 V \/ GND rails; color-coded leads bundled in tidy runs with right-angle turns to each breadboard pin; verified connections with DMM continuity before power-on.<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\nMaterials<\/strong><\/strong><\/p>\n\n\n\n\nDemo video, Poster, Wiring photo, Notes, Code repo: Repo (coming soon)<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\n\n\n\n<\/figure>\n\n\n\n<\/div>\n<\/div><\/div>\n\n\n\n<\/figure>\n<\/div>\n\n\n\n\n<\/video><\/figure>\n<\/div>\n\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n<\/div>\n<\/div>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\nSelf-Balancing Car<\/strong><\/strong><\/strong> (ItsyBitsy M4 \/ Arduino C)<\/strong><\/p>\n\n\n\n(Balance Control) <\/p>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>
A lightweight NLP baseline for the Kaggle competition \u201cNatural Language Processing with Disaster Tweets\u201d: predict whether a tweet is about a real disaster (target=1) or not (target=0).<\/p>\n\n\n\n
Competition: https:\/\/www.kaggle.com\/competitions\/nlp-getting-startedKaggle score (public): 0.79619<\/p>\n\n\n\n
TurtleBot4 Interactive Quiz Game (ROS2 + OAK-D \/ Python)<\/strong><\/p>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>OHXTO\/PvZ-quiz-robotic-game-HRI-Cornell-Tech<\/span><\/a><\/div><\/div><\/div><\/i>\u00a0<\/span><\/span><\/i>\u00a0<\/span><\/span><\/div><\/div>\n\n\n\n<\/div>\n\n\n\n<\/div>\n\n\n\n\n\n<\/figure>\n\n\n\n<\/figure>\n<\/figure>\n\n\n\n<\/video><\/figure>\n<\/div>\n\n\n\n<\/div>\n\n\n\nAn educational Human\u2013Robot Interaction (HRI) quiz game using ROS2, TurtleBot4, and OAK-D RGB color detection. Inspired by Plants vs. Zombies, the robot behaves like a \u201czombie\u201d moving toward a target brain. Students defend by answering True\/False questions with \ud83d\udfe9green-True and \ud83d\udfe5red-False cards.<\/p>\n\n\n\nCorrect answers slow the robot. Wrong answers speed it up. Students win if they reach the required number of correct answers before the robot reaches the brain.<\/p>\n\n\n\n<\/div>\n\n\n\nAutonomous Plant-Watering Robot (Teensy 4.0 \/ Arduino C)<\/strong>(Motion & Watering)<\/p>\n\n\n\n<\/div>\n\n\n\nA household plant-watering robot; I owned low-level motion control and the watering mechanism on Teensy 4.0.<\/p>\n\n\n\n<\/div>\n\n\n\nTech:<\/strong> Teensy 4.0 (Arduino\/C), wheel encoders, 4\u00d7 Mecanum, per-wheel PID, PWM motor driver (12 V, TB4223), stepper lift (A4988), simple digital on\/off line for pump<\/p>\n\n\n\n<\/div>\n\n\n\nMy scope (essentials)<\/strong><\/p>\n\n\n\n\nImplemented per-wheel PID velocity control<\/strong> with encoder feedback; PWM into a 12 V motor driver.<\/li>\n\n\n\nPractical, observation-based tuning<\/strong> (team effort) to get smooth, fast target-speed tracking on carpet vs. smooth floors<\/strong>.<\/li>\n\n\n\nBuilt the A4988 stepper lift<\/strong> from Teensy STEP\/DIR for adjustable sprayer height.<\/li>\n\n\n\nProvided a simple on\/off control line<\/strong> for the pump; external driver \/ Raspberry Pi wiring were teammate-owned.<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\nWiring & bring-up<\/strong><\/p>\n\n\n\n\nSeparated 12 V \/ 5 V \/ GND rails; color-coded leads bundled in tidy runs with right-angle turns to each breadboard pin; verified connections with DMM continuity before power-on.<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\nMaterials<\/strong><\/strong><\/p>\n\n\n\n\nDemo video, Poster, Wiring photo, Notes, Code repo: Repo (coming soon)<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\n\n\n\n<\/figure>\n\n\n\n<\/div>\n<\/div><\/div>\n\n\n\n<\/figure>\n<\/div>\n\n\n\n\n<\/video><\/figure>\n<\/div>\n\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n<\/div>\n<\/div>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\nSelf-Balancing Car<\/strong><\/strong><\/strong> (ItsyBitsy M4 \/ Arduino C)<\/strong><\/p>\n\n\n\n(Balance Control) <\/p>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>
An educational Human\u2013Robot Interaction (HRI) quiz game using ROS2, TurtleBot4, and OAK-D RGB color detection. Inspired by Plants vs. Zombies, the robot behaves like a \u201czombie\u201d moving toward a target brain. Students defend by answering True\/False questions with \ud83d\udfe9green-True and \ud83d\udfe5red-False cards.<\/p>\n\n\n\n
Correct answers slow the robot. Wrong answers speed it up. Students win if they reach the required number of correct answers before the robot reaches the brain.<\/p>\n\n\n\n
Autonomous Plant-Watering Robot (Teensy 4.0 \/ Arduino C)<\/strong>(Motion & Watering)<\/p>\n\n\n\n<\/div>\n\n\n\nA household plant-watering robot; I owned low-level motion control and the watering mechanism on Teensy 4.0.<\/p>\n\n\n\n<\/div>\n\n\n\nTech:<\/strong> Teensy 4.0 (Arduino\/C), wheel encoders, 4\u00d7 Mecanum, per-wheel PID, PWM motor driver (12 V, TB4223), stepper lift (A4988), simple digital on\/off line for pump<\/p>\n\n\n\n<\/div>\n\n\n\nMy scope (essentials)<\/strong><\/p>\n\n\n\n\nImplemented per-wheel PID velocity control<\/strong> with encoder feedback; PWM into a 12 V motor driver.<\/li>\n\n\n\nPractical, observation-based tuning<\/strong> (team effort) to get smooth, fast target-speed tracking on carpet vs. smooth floors<\/strong>.<\/li>\n\n\n\nBuilt the A4988 stepper lift<\/strong> from Teensy STEP\/DIR for adjustable sprayer height.<\/li>\n\n\n\nProvided a simple on\/off control line<\/strong> for the pump; external driver \/ Raspberry Pi wiring were teammate-owned.<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\nWiring & bring-up<\/strong><\/p>\n\n\n\n\nSeparated 12 V \/ 5 V \/ GND rails; color-coded leads bundled in tidy runs with right-angle turns to each breadboard pin; verified connections with DMM continuity before power-on.<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\nMaterials<\/strong><\/strong><\/p>\n\n\n\n\nDemo video, Poster, Wiring photo, Notes, Code repo: Repo (coming soon)<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\n\n\n\n<\/figure>\n\n\n\n<\/div>\n<\/div><\/div>\n\n\n\n<\/figure>\n<\/div>\n\n\n\n\n<\/video><\/figure>\n<\/div>\n\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n<\/div>\n<\/div>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\nSelf-Balancing Car<\/strong><\/strong><\/strong> (ItsyBitsy M4 \/ Arduino C)<\/strong><\/p>\n\n\n\n(Balance Control) <\/p>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>
A household plant-watering robot; I owned low-level motion control and the watering mechanism on Teensy 4.0.<\/p>\n\n\n\n
Tech:<\/strong> Teensy 4.0 (Arduino\/C), wheel encoders, 4\u00d7 Mecanum, per-wheel PID, PWM motor driver (12 V, TB4223), stepper lift (A4988), simple digital on\/off line for pump<\/p>\n\n\n\n<\/div>\n\n\n\nMy scope (essentials)<\/strong><\/p>\n\n\n\n\nImplemented per-wheel PID velocity control<\/strong> with encoder feedback; PWM into a 12 V motor driver.<\/li>\n\n\n\nPractical, observation-based tuning<\/strong> (team effort) to get smooth, fast target-speed tracking on carpet vs. smooth floors<\/strong>.<\/li>\n\n\n\nBuilt the A4988 stepper lift<\/strong> from Teensy STEP\/DIR for adjustable sprayer height.<\/li>\n\n\n\nProvided a simple on\/off control line<\/strong> for the pump; external driver \/ Raspberry Pi wiring were teammate-owned.<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\nWiring & bring-up<\/strong><\/p>\n\n\n\n\nSeparated 12 V \/ 5 V \/ GND rails; color-coded leads bundled in tidy runs with right-angle turns to each breadboard pin; verified connections with DMM continuity before power-on.<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\nMaterials<\/strong><\/strong><\/p>\n\n\n\n\nDemo video, Poster, Wiring photo, Notes, Code repo: Repo (coming soon)<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\n\n\n\n<\/figure>\n\n\n\n<\/div>\n<\/div><\/div>\n\n\n\n<\/figure>\n<\/div>\n\n\n\n\n<\/video><\/figure>\n<\/div>\n\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n<\/div>\n<\/div>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\nSelf-Balancing Car<\/strong><\/strong><\/strong> (ItsyBitsy M4 \/ Arduino C)<\/strong><\/p>\n\n\n\n(Balance Control) <\/p>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>
My scope (essentials)<\/strong><\/p>\n\n\n\n\nImplemented per-wheel PID velocity control<\/strong> with encoder feedback; PWM into a 12 V motor driver.<\/li>\n\n\n\nPractical, observation-based tuning<\/strong> (team effort) to get smooth, fast target-speed tracking on carpet vs. smooth floors<\/strong>.<\/li>\n\n\n\nBuilt the A4988 stepper lift<\/strong> from Teensy STEP\/DIR for adjustable sprayer height.<\/li>\n\n\n\nProvided a simple on\/off control line<\/strong> for the pump; external driver \/ Raspberry Pi wiring were teammate-owned.<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\nWiring & bring-up<\/strong><\/p>\n\n\n\n\nSeparated 12 V \/ 5 V \/ GND rails; color-coded leads bundled in tidy runs with right-angle turns to each breadboard pin; verified connections with DMM continuity before power-on.<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\nMaterials<\/strong><\/strong><\/p>\n\n\n\n\nDemo video, Poster, Wiring photo, Notes, Code repo: Repo (coming soon)<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\n\n\n\n<\/figure>\n\n\n\n<\/div>\n<\/div><\/div>\n\n\n\n<\/figure>\n<\/div>\n\n\n\n\n<\/video><\/figure>\n<\/div>\n\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n<\/div>\n<\/div>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\nSelf-Balancing Car<\/strong><\/strong><\/strong> (ItsyBitsy M4 \/ Arduino C)<\/strong><\/p>\n\n\n\n(Balance Control) <\/p>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>
Wiring & bring-up<\/strong><\/p>\n\n\n\n\nSeparated 12 V \/ 5 V \/ GND rails; color-coded leads bundled in tidy runs with right-angle turns to each breadboard pin; verified connections with DMM continuity before power-on.<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\nMaterials<\/strong><\/strong><\/p>\n\n\n\n\nDemo video, Poster, Wiring photo, Notes, Code repo: Repo (coming soon)<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\n\n\n\n<\/figure>\n\n\n\n<\/div>\n<\/div><\/div>\n\n\n\n<\/figure>\n<\/div>\n\n\n\n\n<\/video><\/figure>\n<\/div>\n\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n<\/div>\n<\/div>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\nSelf-Balancing Car<\/strong><\/strong><\/strong> (ItsyBitsy M4 \/ Arduino C)<\/strong><\/p>\n\n\n\n(Balance Control) <\/p>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>
Materials<\/strong><\/strong><\/p>\n\n\n\n\nDemo video, Poster, Wiring photo, Notes, Code repo: Repo (coming soon)<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n\n\n\n\n<\/figure>\n\n\n\n<\/div>\n<\/div><\/div>\n\n\n\n<\/figure>\n<\/div>\n\n\n\n\n<\/video><\/figure>\n<\/div>\n\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n<\/div>\n<\/div>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\n<\/figure>\n\n\n\n<\/div>\n\n\n\nSelf-Balancing Car<\/strong><\/strong><\/strong> (ItsyBitsy M4 \/ Arduino C)<\/strong><\/p>\n\n\n\n(Balance Control) <\/p>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>
Self-Balancing Car<\/strong><\/strong><\/strong> (ItsyBitsy M4 \/ Arduino C)<\/strong><\/p>\n\n\n\n(Balance Control) <\/p>\n\n\n\n<\/i>\u00a0GitHub<\/span><\/a><\/div>
(Balance Control) <\/p>\n\n\n\n