lexmao,Stone Li,github

ai_artist

AI Artist for Machine Learning for Hackers #5

ECE 155 | Engineering Design with Embedded Systems | Winter 2015 All labs are Android applications and were tested on Nexus 5 and Nexus 7 devices. Lab 1 - Sensor Reader Displays informaton from an android phone sensors. Sensors include Light sensor: Displays light intensity Accelerometer: Displays X,Y,Z acceleration and displays it on a graph with respect to time Magnetic Field Sensor: Displays X,Y,Z field values Rotation Sensor: Displays X,Y,Z rotation values Lab 2 - Step Counter Detects and counts user steps (device held in both hands with screen facing upwards) Uses accelerometer data to detect a step in the user's motion. Step counter is then incremented and displayed on screen. Step counter can be reset by pressing the reset button.> Lab 3 - Displacement Tracker Measures user displacement in NS/EW coordinates Uses existing code from Lab 2 and adds the user's direction to track their displacement. North has to be calibrated by facing North and hitting the calibrate button. Lab 4 - Map Guide Gives directions to the user to move from one point of a room to another. Uses existing code from Lab 3 to track the user's displacement. A map in an SVG format must be added to the app's files directory. The user can set their initial location and direction by pressing and holding on the map.

anna-molly

Anomaly Detection Framework

anomalous-driving

A Unity demo of anomaly detection using NuPIC

anomalydetection

Anomaly Detection with R

arbitrary_style_transfer

Fast Neural Style Transfer with Arbitrary Style using AdaIN Layer - Based on Huang et al. "Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization"