Artificial Neural Networks

Course Nos. ECE.09.560 (Grad), ECE.09.454 (Senior Elective)

Fall 2010
Instructor &
Office Hours

Course Schedule,
Notes and Assignments

Textbook &
Reference Texts

Links and Demos

Grading &
Classroom Policies




Classroom Policies:

  • As per the Rowan College of Engineering policy, no eating or drinking in the classrooms and laboratories.
  • Lab projects must be worked in teams of not more than 2students. Each individual is expected  submit a separate Lab Report.  For report format, click here.

Final Project:


As part of your final exam, you are required to design and develop an artificial neural network application of your choice; simulate the system, obtain results and write a paper suitable for IEEE Conference Proceedings . Your network can perform typical functions such as signal/image classification and/or characterization (interpolation); although you are not limited to these. The development of new network architectures, measures of network performance and prediction confidence, etc. are also good projects. Discussion and implementation of current ANN literature (IEEE Transactions on Neural Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, etc.) are also welcome.


An e-mail (, Fax (856-256-5241) or a written/printed document presenting your project proposal must be sent to the instructor not later than Monday, Oct 25, 2010. This proposal must not exceed one page.
Project deliverables include a  paper suitable for IEEE conference proceedings  (format follows) and a comprehensive presentation (~45 min + Questions) describing your work. The paper is due by the end of the Fall semester Final’s week (Dec 21, 2010).

Final Paper

The paper should not exceed 8 pages (excluding appendix) in the format provided at IEEE Manuscript Templates for Conference Proceedings, . The paper should address the following topics:
  1. Introduction – general description of the project topic/research paper.
  2. The problem statement – why is an artificial neural network required? What are the issues?
  3. Objective – what are you proposing to do/show?
  4. Approach – describe the method you have adopted.
  5. Implementation results – include your graphs, tables, etc. here.
  6. Conclusions – what have you learned? How can the algorithm be improved?
  7. Appendix – listing of the source code (exclude these pages from the total page count).