Day 2 :
GunderFish LLC, USA
Time : 10:00-10:40
James P Gunderson has completed his PhD from the University of Virginia in 2003. His research has focused on Artificial Intelligence and the effective application of the same in automated systems and robotics. He has focused on Artificial Intelligence applications for over 30 years, and been part of two successful robotics/automation start-ups. He is the Growth Director for GunderFish LLC., which focuses on integrating cutting edge AI and predictive analytics with existing business flow. He has published more than 25 papers in journals and conferences.
The next wave of automation and robotics is benefiting from adding more \'intelligence\' to the systems deployed. The continuing decrease in cost and increase in processing power and sensor capability is leading a drive to smarter and smarter automated systems. However, there is a balance point between not smart enough and too smart.rnrnThis presentation will focus on three key elements:rn• What are the new capabilities and applications for the new smarter industrial and service robots?rn• What are the costs and potential problems introduced by these new artificial intelligence applications?rn• How does a systems engineer or designer determine how smart is smart enough?rnrnLike any other engineering decision, there are trade offs that lead toward an optimal design choice. Adding intelligence to an automated system is no different. However for many designers and engineers with the responsibility to make these critical decisions, there are no good \'best practices\' that can be applied. This often leads to decisions being made based on the input from the vendors alone, and this input is targeted towards the upper end of the decision space. I will present solid \'hands on\' tools for narrowing down the decision space to what is appropriate for the given problem. These tools will enable the responsible engineer to make solid \'best practices\' choices when looking at “How smart is smart enough?”
National University of Computer & Emerging Sciences, Pakistan
Time : 11:00-11:40
Asim ur Rehman Khan received BSc (EE) from UET, Lahore, Pakistan in 1981. He received MS (EE) from South Dakota State University, Brookings, South Dakota in 1987. He received PhD degree from Polytechnic University, now New York University, NY, USA in 1993. He worked in the Space Agency of Pakistan, SUPARCO, where he worked on the design & development of a small satellite. He has taught courses at SSUET, Karachi University, and NED University in Karachi, Pakistan. He worked in a software house for 5 years, where he was involved in the automation of MCI, USA fiber optic nation wide link. Since 2002, he is teaching at NU-FAST. His principle interests are image processing, nework protocols, and network security. He is a senior member of IEEE, and Pakistan Engineering Council (PEC).
The detection of important features of a moving object is a challenging task especially when these images are corrupted with heavy noise. This research proposes two statistical base techniques. The first technique performs three-way nested design using the analysis of variance (ANOVA). The three-way nested design corresponds to three-layers. The top layer is based on the temporal analysis where the model compares two consecutive image frames and identifies regions having sufficient temporal interframe changes. The next two layers perform statistical approach to see if there are sufficient intraframe variations. A large amount of intraframe variations are accounted for important features that may have edges to track across multiple image frames. In case of affirmative results in all the three layers, a second method based on the contrast function (CF) is used to identify edges in four possible directions. These four directions are horizontal, vertical, and two diagonal directions. The presence or absence of an effect is confirmed by testing a hypothesis. The test uses F-test, and Tukey’s T-test. The results are quite good for image frames that are previously corrupted with heavy Gaussain noise.