This paper outlines the design and testing of a digital imaging system that utilizes an artificial neural network with unsupervised and supervised learning to convert streaming input (real time) image space into parameter space. The primary objective of this work is to investigate the effectiveness of using a neural network to significantly reduce the information density of streaming images so that objects can be readily identified by a limited set of primary parameters and act as an enhanced human machine interface (HMI). Many applications are envisioned including use in biomedical imaging, anomaly detection and as an assistive device for the visually impaired. A digital circuit was designed and tested using a Field Programmable Gate Array (FPGA) and an off the shelf digital camera. Our results indicate that the networks can be readily trained when subject to limited sets of objects such as the alphabet. We can also separate limited object sets with rotational and positional invariance. The results also show that limited visual fields form with only local connectivity.
The Photonics Technology program at Niagara College was first launched in 2001. Since that time, in an attempt to meet the joint needs of industry and students, Niagara has developed the technology program into a cluster of four programs related to photonic technology. Niagara is also building relationships with universities to deliver photonic course material to physics undergrad students using Niagara College Photonics facilities and faculty to create an undergraduate specialization in lasers.
This paper will review the development of the photonics cluster at Niagara College and present the current state of its evolution.
Launched in 2001, the Ontario Photonics Education and Training project (PET) has established an completely new Photonics Engineering Technician (2 years) and Photonics Engineering Technologist (3 years) programs at Niagara and Algonquin Colleges. The programs have now completed a full academic cycle at both colleges. This paper will review the history of the program, its collaborators, and industry climate changes. This paper will present recruitment statistics, which will include percentage uptake, student retention, and profiles of the student group. The first year’s intake was characterized by high achieving 'early adopters', including those with non-technical backgrounds and University converts. Lessons learned from recruitment and high school outreach activities will be discussed. We observe that 'photonics' is not a term recognized by the populace at large. An improved public understanding of the pervasive nature of electro-optic technologies in everyday life is desired. Curriculum highlights, recommendations; and the evolution of our facilities will be discussed. We will review employment and destination statistics of our graduates. Challenges for the future will be addressed, including the need for greater program visibility amongst regional photonics employers. In summary, the PET program has created an optics specialist with a practical skill-set that will fill the expertise gap that exists in traditional and non-traditional consumers of optical technologies.