The document provides details about the research background and interests of researcher Fang Can. It outlines his educational background in electrical engineering and computer science. It also describes his technical skills in mathematics, optimization, algorithm development, and optics. The document discusses Fang's PhD research projects which took graph-theoretic and geometric approaches to problems in communication networks and wireless sensor networks. It provides examples of his current work developing multi-spectral optical probes and a spectrum-scanning microscope to analyze tissue and cell samples.

1. Research Statement
By: Fang, Can 方灿
mrfunder@hotmail.com,
fangc2@upmc.edu

2. Academic Background
 B.S of Electrical Engineering (Xi’An
JiaoTong University, China)
◦ Dissertation: “DS1302 Chip Coding and Encrypting
Study in Contact-less IC Card System”
 PhD of Computer Science (Nanyang
Technological University, Singapore)
◦ Dissertation: “Graph-Theoretic and Geometric
Optimizations in Communication Networks”

3. Technical Skill Highlights
 Computer Science
◦ Mathematical modeling, optimization and simulations
◦ Algorithm prototyping, code developing
 Optics
◦ Device design, setup and alignment
◦ Monte Carlo simulation
 Good knowledge in broad range of general
science and technology

4. PhD Researches
 My PhD research work is composed of four
projects:
a. Optimal Wavelength Converter with Guaranteed Wavelength Usage
b. Load-Balanced Clustering in Wireless Sensor Networks (WSNs)
c. Worst-case and Best-case k-coverage Problem in WSNs
d. Generalized Study on Movement-assisted Sensor deployment problem
 These four projects can be classified by
approaches used:
◦ Graph-theoretic approach
◦ Geometric approach

5. Graph-Theoretic VS Geometric
Optimizations
Graph-theoretic approach Geometric approach
Formulate the original Study the measurable properties
problem into “graphs” (tree, of figures (size, shape, angle,
path, star, ring, etc) position, etc)
A graph is composed of edges Deals with problems of
and vertices (nodes) computing geometric objects

6. Graph-Theoretic VS Geometric
Optimizations (cont’)
Project A: Project C:
·I
P
F
·
Project B:
Project D:

7. Current Work
As team player:
As lead player:
Spectrum-scanning
microscope
Tissue light
Multi-spectral optical probe scattering
fingerprinting
system

8. Light Scattering Fingerprinting
System

9. Data Acquired
 Raw data:
◦ CCD arm: two dimensional scattering
intensity distribution;
◦ Spectrometer arm: scattering angle/
wavelength distribution
 Parameters (markers) after processed:
◦ LEBS peak height, width, correlation, spectral
slope, etc

10. Data Example
processed LEBS peak
scattering angle
wavelength
scattering angle
wavelength

11. Spectrum-Scanning Microscope
objective
splitter
Xe interchangeable
splitter
telescopic
objective
commercial
microscope
slit
scanning stage
and CCD

12. Data Acquired
 Raw data:
◦ Three dimensional data cube, reflected
intensity R(x, y, λ), where λ is the spectrum
from 400nm to 700nm.
 After processed:
◦ Refractive index distribution within the cell

13. 1
Example
Cervix cells(cancer)
2
3
1 2
3

14. Multi-Spectral Optical Probe
spectrometer
fiber switch
Xe fiber switch
405nm laser probe head
coupling optics

15. Data Acquired
 Raw data:
◦ Depth-selective spectrum of back scattered
light
 Proceesed:
◦ Scattering feature: slope
◦ Absorption feature: concentration of
absorbers (Hb, HbO2, Beta-carotene, etc)

16. Data example
Hemoglobin phantom measurement
(Hb increased from 1g/L to 15g/L)