Adapted from Mike Bostock's Zoomable Sunburst to include arc labels.
Click on any arc to zoom in. Click on the center circle to zoom out. Click on canvas background to reset zoom.
Adapted from Mike Bostock's Zoomable Sunburst to include arc labels.
Click on any arc to zoom in. Click on the center circle to zoom out. Click on canvas background to reset zoom.
Treemaps for visualizing hierarchical data. Click to zoom to the next level. Click on the top orange band to zoom out. Based on Mike Bostock's Zoomable Treemaps
This template follows pigshell's convention for "gist templates":
license: mit |
<!doctype html> | |
<html class="no-js" lang=""> | |
<head> | |
<meta charset="utf-8"> | |
<title>Canvas City</title> | |
<meta name="description" content=""> | |
<meta name="viewport" content="width=device-width, initial-scale=1"> | |
</head> | |
<body> | |
<canvas></canvas> |
license: gpl-3.0 | |
height: 302 | |
license: gpl-3.0 |
Sensor networks allow for regions to be scanned for the presence of a particular object. Many small devices, each capable of making some measurements, can be distributed over the desired region. The types of measurements a sensor might make include taking temperature readings, detecting electromagnetic frequencies, and recording sound levels. A more complex sensor could process video footage and find valuable information automatically.
A critical consideration for sensor networks is the “blanket” of coverage that is produced. Wherever holes exist in this coverage, it is possible to miss critical information in the region we would like to scan.
If we have sophisticated sensors capable of measuring their exact position, then it is a fairly easy task to compute the coverage of the network. However, if we have simple sensors which can only record local information, then we are
license: gpl-3.0 | |
height: 600 |
license: gpl-3.0 |