Details on DARPA Robotics Challenge Trial Events

source article : Details on DARPA Robotics Challenge Trial Events

This may be too ambitious? To accomplish 8 tasks in hazard/emergency situations! Check it out ūüôā

Task 1: Drive Utility Vehicle

Task 2: Travel Dismounted

Task 3: Remove Debris Blocking Entry

Task 4: Open Door, Enter Building

Task 5: Climb Industrial Ladder

Task 6: Break Through Wall

Task 7: Locate and Close Valves

Task 8: Carry, Unspool, and Connect Fire Hose

Data Visualization Gallery from USA Census Bureau

DataViz Gallery,  USA Census Bureau websites provides a number of interactive data access/visualization tool that shows census data in many categories.

They also provide Census Data APIs for developers to access data, send/recv query requests/responses, and JSON scripts as well.


Their web pages are well established and data sets are comparatively well presented on the public domain. For instance, the captured image above is from Coastline County Population, which enables end-users to control the slider at the bottom to show the changes in time (years) and the accumulated population in coastline counties in USA.

Most of the visualization here are, however, just 2D plain graphs that are just for 1 or 2D data arrays. It would be much better and effective if there are some fast & light-weighted 3D interactive visualization which provides more interactive controls for the end-users for in-depth data analysis and exploration. But still, data sets here are very well presented in my opinion, as a Data Visualization Expert, Senior Software Engineer, and an Assistant Professor in Computer Science.

Single Variable View tool, svVu for data analysis

svVu - Single Variable View, data analysis tool for N-dimensional data sets
svVu – Single Variable View, data analysis tool for N-dimensional data sets

This small tool, Single Variable View svVu was developed for in-depth data analysis on single feature (attribute) variable as a part of research collaboration work with research team in School of Computer Science, Edith Cowan University, Western Australia.

Functionalities of this tool;

1. Loading N-dimensional data files,

2. Selecting one of the N-dimension features,

3. Selecting one of the data distribution function types (Gaussian Normal Distribution by default),

4. (‘View’ button) Plotting distribution functions for each selected feature w.r.t. all the output decision classes,

5. Showing the “membership values (closeness / belonging degrees)” of a new unknown input value within [min max] range (‘Analyze’ button),

6. Displaying statistics of the selected feature variable,

7. Calculating overlap degrees of distribution of the selected feature variable w.r.t. its output classes,

8. Calculating SNR (Signal-to-Noise Ratio) for the selected feature to show how much its output groups are separated from each other,

9. Calculating membership degrees for the unknown ‘test’ N-dimensional data points and Generating the list of rank on those unknown test data set.

Even though this is a small tool for data analysis, it has achieved a significant part of the research for in-depth data analysis.

Microsoft Research Project: “Fetal Heart Rate Monitoring using Smart Phones”

May.2008 ~ Aug.2009


The goal of this Microsoft Research Project was to develop a fetal heart rate monitoring system on Windows mobile smart phones. Dr. Chang Su Lee worked on this project as a Post Doctoral Research Fellow in School of Computer Science at Edith Cowan University, Mt. Lawley, Western Australia.

Over the last decade, the rate of premature births and fetal deaths among the Indigenous population of rural and remote Australia was more than twice that of the non-Indigenous population. Because of the distance that women in these isolated communities often must travel to reach health centers and hospitals, many do not get the proper medical care to prevent or address prenatal issues. The tyranny of distance and its associated costs remain a barrier for delivering reasonable quality [prenatal] care to remote and rural populations.
For example in reality, one of the phone calls we had was from a woman who lost the twins she was carrying because she lived too far from a medical facility to get regular checkups. By the time doctors discovered a problem with her pregnancy, it was too late to save the babies.

Our team (Dr. A.Tan, Dr. M.Masek, Dr. C.Lee, Dr. C.P.Lam, Angela Fyneman) developed this system to improve the quality of prenatal care in rural and remote communities by providing expectant mothers with an inexpensive, portable, Doppler-based ultrasound device connected to a smartphone running Microsoft¬ģ Windows Mobile¬ģ.
The first prototype has been produced in mid 2009 resulted in a stand-alone Mobile application on Microsoft¬ģ Windows smartphone for calcuating fetal heart rates on front-end mobile devices and sending calculated heart rates to a back-end web server for reporting to the medical team in our partner, Mercy Hospital Mt. Lawely. Since then the system has been in trials with the help from volunteers to evaluate and enhance the accuracy and performance of our system.

This project is listed in Microsoft Research web pages;
* Microsoft Research webpages: mHealth Summit 2009, research abstract, socially relevant computing brochure:p#15, MSResearch_smartphone_based_fetal_monitor

Published technical papers are listed below;
*¬†“Towards Higher Accuracy and Better Noise-Tolerance for Fetal Heart Rate Monitoring using Doppler Ultrasound” – IEEE TENCON 2009
*¬†“Advances in Fetal Heart Rate Monitoring using Smart Phones” – IEEE ISCIT 2009
*¬†“Remote Home-Based Ante and Post Natal Care” – IEEE HealthCom 2009

Since then, it became a student project in School of Computer Security and Science, Eidth Cowan University, Western Australia. In November 2011, our team won a prize AUD $15,000 as a top 5 finalist for the ‚ÄėMobile Fetal and Maternal Health mobile application‚Äô in Univation WApp Competition in Perth, Western Australia.¬†Link – ECU

Interactive Data Visualizer

Interactive Data Visualizer v.0.15

This is an on-going project which I have started and supervised for one of the Visualization projects at the School of Computer Science, Edith Cowan University, Western Australia.

The ultimate main purpose for data visualization is to provide the followings to data analysts, scientists, statisticians, engineers and business stakeholders;

1. Intuitive visual displays for data points, clusters, distribution, and structures,

2. In-depth data analysis at a glance,

3. Trends of data in the past and current, also for prediction on possible future data patterns,

4. Specific data examination / investigation in a particular domain.

The interactive data visualizer v.0.15 shown above is the first interactive data visualization tool (java applet) I have developed with two students in Java for its first prototype. It loads data files Рthere are 10 example data files retrieved from UCI Machine Learning Repository and shows data points, data distribution in Gaussian-basis distribution functions, and statistics using box plots for each attribute/feature.

I also have been working on other data visualization applications for the last two years and at the moment. Once ready, I will put them on here to show the prototypes and the details.


HeatMap Generator – Another data visualization tool

HeatMap - Correlation Matrix on Features vs Features of Alzheimer Disease data set

HeatMap – Correlation Matrix on Features vs Features of Alzheimer Disease data set

HeatMap - Correlation Matrix on Features vs Features of Alzheimer Disease data set

HeatMap – Correlation Matrix on Features vs Features of Alzheimer Disease data set

HeatMap - Correlation Matrix on Features vs Features of Alzheimer Disease data set

HeatMap – Correlation Matrix on Features vs Features of Alzheimer Disease data set

HeatMap is another data visualization tool which has been widely used for examining correlation between data rows or columns.

All three pictures above show correlations between features (column data values) in regards to ‘how much they are co-related to each other’. For instance, if their correlation is very high (i.e., very similar) then the color of the each small square dot will be 100% black in first BLACK-WHITE map, and 100% red in second RED_GREEN and third RED_BLUE maps. The data set used here is one of the Alzheimer Disease data sets that I utilized for collaboration work between CSJL solutions and my research team in Edith Cowan University.

This is one way of showing or revealing relationships of data points on the given data set. Obviously, it is much easier to pick up some facts by looking at these pictures, rather than staring at a bunch of high precision numbers.