User:Dllu

From Wikipedia, the free encyclopedia
  (Redirected from User:Purpy Pupple)
Jump to: navigation, search
en This user is a native speaker of English.
zh-1 該用戶能以基本中文進行交流。
该用户能以基本中文进行交流。
 
MAT-3 This user is an advanced MATLAB programmer.
C-2 This user is an intermediate C programmer.
C++-3 This user is an advanced C++ programmer.
C#-1 This user is a beginning C# programmer.
Java-0 This user has some undisclosed problems with Java.
js-2 This user is an intermediate JavaScript programmer.
py-2 This user is an intermediate Python programmer.
vhdl-1 This user is a beginning VHDL chip designer.
HTML-2 This user is an intermediate HTML user.
svg-2 This user is an intermediate SVG user.
 
math-2 This user can contribute with intermediate mathematical skills
wiki-3 This user is an advanced Wikipedia editor.
pno-1 This user is a novice pianist.
 
21 This user is 21 years old.
ubx-0 This user does not enjoy userboxes.

This is the userpage of dllu. I enjoy programming, 2D vector graphics, 3D graphics, physics, and photography.

I contribute mostly to articles related to computing, robotics, geometry, and various niche topics. Please feel free to add things to the articles listed below or post any questions to my talk page.

Articles I've contributed significantly to[edit]

Smaller articles I've contributed to[edit]

Featured Pictures[edit]

Organic Molecules Render[edit]

Created with Rhinoceros 3D and VRay.

BML Traffic Model[edit]

Main page: User:dllu/BML
A globally jammed phase observed on a 144x89 rectangular lattice with a traffic density of 60%. (MATLAB)
A free flowing phase observed on a 144x89 rectangular lattice with a traffic density of 28%. (MATLAB)
A periodic intermediate phase observed on a 144x89 rectangular lattice with a traffic density of 38%. (MATLAB)
A disordered intermediate phase observed on a 144x89 rectangular lattice with a traffic density of 39%. (MATLAB)

Maze generation[edit]

Main page: User:dllu/Maze
30x20 "backtracking" maze generation.
30x20 "prim" maze generation.

Arbitrary selection of other diagrams I've made[edit]

Coherent point drift[edit]

These were made with the reference implementation of coherent point drift by Myronenko and Song.

Affine point set registration without noise.
Rigid point set registration with noise.
Non-rigid point set registration with noise.

Orbits of the Swinging Atwood's Machine[edit]

Type A orbits of the Swinging Atwood's machine.
Type B orbits of the Swinging Atwood's machine.

The following family of diagrams are generated by my C++ code, which can be found at [1].

Orbit with mu = 3 starting from rest.
Orbit with mu = 5 starting from rest.
Orbit with mu = 16 starting from rest.
Orbit with mu = 20 starting from rest.

Monte Carlo localization[edit]

I see a door.
I don't see a door.

A robot using Monte Carlo localization to determine its position in a one-dimensional circular corridor containing three doors, using only a sensor that detects whether or not there is a door. The vertical grey bars at the bottom are the locations of the particles which represent the robot's current belief of its position. More particles clustered together means the robot is more likely to be there.

The following family of diagrams are generated by my C++ code, which can be found at [2].

1. The algorithm initializes with a uniform distribution of particles. The robot has no idea where it is.
2. Sensor update the robot detects a door. It assigns a weight to each of the particles. The particles which are likely to give this sensor reading receive a higher weight.
3. Resampling the robot generates a set of new particles, with most of them generated around the previous particles with more weight. The robot now believes it is at one of the three doors.
4. Motion update the robot moves some distance to the right. All particles also move to the right, and some noise is applied to simulate the real world. The robot is now slightly less sure of where it is.
5. Sensor update the robot detects the absence of a door. It assigns a weight to each of the particles. The particles which are likely to give this sensor reading receive a higher weight.
6. Resampling the robot generates a set of new particles, with most of them generated around the previous particles with more weight. The robot now believes it is at one of two doors.
7. Motion update t he robot moves some distance to the left. All particles also move to the left, and some noise is applied to simulate the real world. The robot is now slightly less sure of where it is.
8. Sensor update the robot detects a door. It assigns a weight to each of the particles. The particles which are likely to give this sensor reading receive a higher weight.
9. Resampling the robot generates a set of new particles, with most of them generated around the previous particles with more weight. The robot has successfully localized itself!

Teletherapy Capsule[edit]

Teletherapy capsule.

Photography[edit]

The Tair 11a, a 135mm prime lens that I use.
Photo of the University of British Columbia Student Union Building. At the moment this area is full of construction since the new Student Union Building is being built.
Panoramic view from Eagle Mountain in Abbotsford, BC looking southeast with field of view 146º.
Panoramic view from Abbotsford, BC looking northwest with field of view 90º.
Panoramic view of Port of Seattle from the Space Needle.
Panoramic view of Pittsburgh as seen from Mount Washington at the top of the Monongahela Incline.