YoonLee-lab
#Ph.D. M.S. B.S. #Electrical and Computer Engineering #Love to troubleshoot.
Atlanta, GA / Madison, WI / Columbus, OH
Pinned Repositories
.CodeBits
:books: List of resources for Algorithms and Data Structures in Python & other CS topics @2017
ad5940-examples
AD594x related application examples and block level examples.
ad5940lib
Firmware library for AD594x and ADuCM355
Advanced-Lane-Detection
Detecting Lane Boundaries in Images and Videos using Advanced Computer Vision Techniques (Python/OpenCV).
awesome-opensource-security
A list of interesting open-source tools
euler
C++ solutions for more than 300 Project Euler problems
stat479-machine-learning-fs19
Course material for STAT 479: Machine Learning (FS 2019) taught by Sebastian Raschka at University Wisconsin-Madison
YoonLee-lab's Repositories
YoonLee-lab/Karabiner-Elements
Karabiner-Elements is a powerful utility for keyboard customization on macOS Sierra (10.12) or later.
YoonLee-lab/middleman
Hand-crafted frontend development
YoonLee-lab/quadtarium-python-simulator
Robotarium quadcopter simulator in python.
YoonLee-lab/MachineLearningStocks
Using python and scikit-learn to make stock predictions
YoonLee-lab/kw_condition
키움 증권 조건 검색식 사용 Console App
YoonLee-lab/protobuf
Protocol Buffers - Google's data interchange format
YoonLee-lab/CS-7641-Machine-Learning-Notes
In this repository, I will publish my notes for GaTech's Machine Learning course CS7641.
YoonLee-lab/openhardwaremonitor
Open Hardware Monitor
YoonLee-lab/cs-video-courses
List of Computer Science courses with video lectures.
YoonLee-lab/Deep-Reinforcement-Learning-Hands-On
Hands-on Deep Reinforcement Learning, published by Packt
YoonLee-lab/Interactive-Semi-Automatic-Image-2D-Bounding-Box-Annotation-Tool-using-Multi-Template_Matching
Interactive Semi Automatic Image 2D Bounding Box Annotation and Labelling Tool using Multi Template Matching An Interactive Semi Automatic Image 2D Bounding Box Annotation/Labelling Tool to aid the Annotater/User to rapidly create 2D Bounding Box Single Object Detection masks for large number of training images in a semi automatic manner in order to train an object detection deep neural network such as Mask R-CNN or U-Net. As the Annotater/User starts annotating/labelling by drawing a bounding box for a few number of images in the selected folder then the algorithm suggests bounding box predictions for the rest of the yet to be annotated/labelled images in the folder. If the predictions are right then the user/annotater can simply press the keyboard key 'y' which indicates that the detected bounding box is correct. If the prediction is wrong then the user/annotater can manually draw a rectangular 2D bounding box over the correct ROI (Region of interest) in the image and then press the key 'y' to proceed further to the rest of the images in the folder. If the user/annotater made a mistake while drawing the 2D bounding box, then he/she can press the key 'n' in order to remove the incorrectly marked 2D bounding box and he/she can repeat the process for the same image until he/she draws the correct 2D bounding box and then after drawing the correct 2D bounding box, the user/annotater may press the key 'y' to continue to the rest of the images. The 2D bounding box prediction over the whole image data set improves as the user/annotater annotates/labels more number of images by drawing 2D bounding boxes. This tool allows the user/annotater to not only interactively and rapidly annotate large number of images but also to validate the predictions at the same time interactively. This tool helps the user/annotater to save a lot of time when annotating/labelling and validating the predictions for a large number of training images in a folder. Instructions to use:- 1. If the training images are in JPEG or any other format, then convert them to PNG format using some other tool or program before using these images for annotation. 2. All the training images must contain the object of interest which is to be annotated. 3. Currently the application only supports 2D bounding box annotation for single object detection per image, but in the future semantic segmentation based annotation features will be added which will allow precise boundary segmentation masks of an object in an image. 4. If some or all of the training images have varying dimensions(shapes/resolutions), then resize them to the same dimensions using this tool by providing the height and width to which all the training images need to be resized to. The height and width are inputed separately in two different dialog boxes which pop up once the program is executed. If the training images need not be resized then press the cancel button in the dialog boxes requesting the height and width. 5. Select the folder containing the training images by navigating to the folder containing the training images through a dialog box which pops up after the program is executed. If the images need to be resized then two dialog boxes pop up. The first dialog box is to navigate to the destination folder containing the unresized raw training images and after resizing another dialog box pops up to navigate to the folder containing the saved resized training images named as "resized_data". If the images need not be resized then only one dialog box pops up so that the user can navigate to the raw training images folder directly. 6. The images in the folder pop up one by one. After drawing the correct 2D bounding box over the ROI (region of Interest), press the 'y' key. Except the first image, the rest of the images will have a 2D bounding box drawn over them. If the predicted box is accurate, then continue by pressing the 'y' key. If the prediction is incorrect, then draw the accurate bounding box and press the 'y' key. If any mistake occured while drawing the 2D box, then reset the image by removing the incorrect drawing by pressing the 'n' key and then draw the correct box and press the 'y' key. 7. The output images are stored in four different folders in the same directory containing the training images folder. among the four folders, one contains the cropped templates of the bounding boxes, black and white mask images, training images and the images with 2D box detection markings.
YoonLee-lab/teensy4
Teensy 4 sketches and such
YoonLee-lab/cse6040fa20
In-class demo notebooks for the on-campus MSA version of CSE 6040, Fall 2020
YoonLee-lab/MUX74HC4067
Arduino library for interfacing with the 74HC4067 mux / demux
YoonLee-lab/trading-eye
Developing trading tools to help investors/traders to track and analyze their trading history and hopefully improve trading performance
YoonLee-lab/espNowFloodingMeshLibrary2
ESP8266/ESP32 ESPNOW/Broadcast Arduino Flooding mesh library
YoonLee-lab/esp_mesh_pir_sensor
EspNow Flooding mesh example PIR sensor node. Part of the ESP32, ESP8266 flooding mesh project.
YoonLee-lab/textiles_display
Large-area display textiles integrated with functional systems
YoonLee-lab/Cell-Nuclei-Detection-and-Segmentation
Detect location and draw boundary of nuclei from microscopic images
YoonLee-lab/Fair-Multiple-Decision-Making
YoonLee-lab/PyTorch
Deep Learning Zero to All - Pytorch
YoonLee-lab/CS6476_Computer_Vision
Georgia Tech CS6476 Computer Vision Fall 2020
YoonLee-lab/EPSI
Here, you can find the custom Matlab codes we have used for analysis of 2D EPSI data from our hyperpolarized 13C-MRSI studies in preclinical brain tumor models. The data were acquired on a Bruker horizontal MR system.
YoonLee-lab/ECE6607_Project
Final Project for ECE 6607
YoonLee-lab/PythonAlgorithms
List of Leetcode algorithms Python
YoonLee-lab/ProtoCentral_fdc1004_breakout
A breakout board for the Texas Instruments FDC1004 capacitance to digital converter
YoonLee-lab/FAANG-Coding-Interview-Questions
A curated List of Coding Questions Asked in FAANG Interviews
YoonLee-lab/Azure-Kinect-Samples
Samples for Azure Kinect
YoonLee-lab/cs6290
Root repository for CS 6290: High Performance Computer Architecture
YoonLee-lab/algorithm-visualizer
:fireworks:Interactive Online Platform that Visualizes Algorithms from Code