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Cancer Colony Identification VIA Digital Image Processing, Barry Dunn, Gavin Mantica, Jessica Jensen, Joshua Kruzan, Juliette Caffrey

 Title: Cancer Colony Identification VIA Digital Image Processing


Barry Dunn, Gavin Mantica, Jessica Jensen, Joshua Kruzan, Juliette Caffrey

University affiliation: Roger Williams University
Department: School of Engineering, Computing and Construction Management


In an effort to provide a standardized method of quantification for researchers to develop treatments for lung cancer, which is the most lethal form of cancer, a numerical analysis method was created for preclinical use, in which it will accurately and affordably quantify the presence of cancer colonies on mouse lungs in an automated fashion. Using digital image processing, two different algorithms were developed to examine whole lungs and their cross sections. Once samples of the cancerous samples are obtained and photographed; the algorithm would be utilized. It starts by pixelating the photos and converting them to grayscale, before applying filters and other threshold driven image processing techniques, in order to present quantitative analysis on the presence of cancer colonies. The principal result of this project is to have created a method that has less error and can be completed faster than those that currently exist.

Submitted by: Barry Dunn
Major: Engineering
Degree being pursued: BS
Type of student: Senior

Jin Z, Shao Y, So M: A MultiSensor Data Fusion Approach For Simultaneous Localization And Mapping

Title: A MultiSensor Data Fusion Approach For Simultaneous Localization And Mapping

Authors: Jin Z, Shao Y, So M

University affiliation: The Cooper Union for the Advancement of Science and Art
Department: Electrical Engineering

Abstract: Simultaneous localization and mapping (SLAM) has been an emerging research topic in the fields of robotics, autonomous driving, and unmanned aerial vehicles over the last 30 years. Unfortunately, SLAM research is generally inaccessible for student researchers due to expensive hardware and painful software setup. By introducing a loosely coupled, modular multi-sensor data fusion architecture, we present an autonomous driving research platform that can be adapted to computing platforms with various computational constraints and serve multiple applications and educational purposes. Our goal is to create an easily accessible SLAM module with cost-friendly hardware dependencies and minimal software setup for SLAM researchers, teachers, and learners.

Submitted by: Zhekai Jin
Major: Electrical Engineering
Degree being pursued: BS
Type of student: Senior

IEEE Region 1 Student Conference 2013

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Registrants: 205

23 Schools participating are New Jersey Institute of Technology (NJIT), New York Institute of Technology (NYIT), Stevens Institute of Technology, Cooper Union, Massachusetts Institute of Technology (MIT), University of Buffalo, Rutgers University, University of Maine, Stony Brook University, Monmouth University, WPI,  NYU Poly, Columbia University, The College of New Jersey,  Syracuse University, Cornell University, SUNY New Paltz, Boston University, Wentworth Institute of Technology, Curry College, Clarkson University, UMass Amherst, Suffolk University