Day 3 :
- Track 9: Automation systemsTrack 10: Automation solutions
Cranfield University, UK
Asim ur Rehman Khan
National University of Computer & Emerging Sciences, Pakistan
University of Southern California, USA
Time : 10:00-10:30
Mary M Eshaghian-Wilner is an interdisciplinary Scientist and Patent Attorney. She is currently a Professor of Engineering Practice at the Electrical Engineering Department of USC. She is best known for her work in the areas of Optical Computing, Heterogeneous Computing, and Nanocomputing. Her current research involves the applications and implications of these and other emerging technologies in medicine and law. She has founded and/or chaired numerous IEEE conferences and organizations, and serves on the editorial board of several journals. She is the recipient of several prestigious awards, and has authored and/or edited hundreds of publications, including three books.
Modern CMOS encounter issues altering NMOS and PMOS threshold voltages. Negative Bias Temperature Instability (NBTI) and Positive Bias Temperature Instability (PBTI) decrease drain-to-source current and increase propagation delay, due to operating temperature and stress time. NBTI also affects the timing of the circuit by varying the different propagation delays, thus vastly degrading overall performance. We therefore propose a generation-evaluation algorithm to minimize NBTI/PBTI by reducing transistor stress time through signal probability-based relative transistor repositioning. The algorithm takes a stack configuration as input and outputs the optimal configuration. For equiprobable signals, PMOS transistors connected to power supplies experience more Vth degradation than transistors indirectly connected. Therefore, the number of transistors connected to VDD in stack must be manipulated to reduce NBTI. However, assuming equal signal arrival probability is impractical. For non-equiprobable signals, the probability that a transistor is under stress is a signal probabilities function. Input degradation probability is obtained by multiplying input signal probability with the worst-case signal probability of the transistor stacked above/below depending on PMOS/NMOS, respectively. The total switching activity/equivalent stress time is the sum of each input’s degradation probability. Given a structure, the algorithm calculates the probabilities of all possible stack configurations. The structure with smallest switching activity will undergo minimum degradation. Two configurations of an AOI logic structure under equal and unequal signal probability cases were analyzed. The preferred optimal choice is the configuration with minimal probability of being under stress.
Festo Didactic Solution Center, USA
Time : 10:30-11:00
Ted Rozier is the Engineering Development Manager for Festo Didactic Solution Center head quartered in Eatontown NJ. Before joining Festo Didactic, he has 18 years of experience in leading the Automation Engineering Department for Doosan Infracore Machine Tool Corporation. He specialized in the design and development of Robotics and Machine tool turnkey systems for the Automotive, Aerospace and Pharmaceutical industry. He has managed and developed software that is the foundation for Automated Robotic Manufacturing systems on a global scale and has been acknowledged in several Manufacturing Engineering magazines for his innovative user friendly software development. As Engineering Development Manager, he is passionately looking to advance Festo Didactic as a global leader in designing and implementing learning factories and training programs with the view to systematically prepare individuals to excel working in dynamic and complex industrial automation environments. He is also a member of the AMT Global Service and Technical committee as well as a member of the Technical Work Group formed to support the NIMS standard.
It has been acknowledged that tomorrow’s automation manufacturing process includes Smart Factories, Smart Machines, Smart Materials and smart Products that have the ability to communicate with each other, alternately driving production, being interconnected and traceable at all times within an “Internet of Things”. We prepare to develop the talent needed to support the demand for bringing manufacturing back to the US. It is essential to interrogate and define what type of skills will be needed to support the game changing technology of tomorrow. During this presentation, I will dive deep into the classroom of a few Universities and Community colleges to discuss educational strategies as well as case studies and best practices that have been put in place to shape a strong Advanced Automation Manufacturing and Mechatronics program that will breed the best talent for the future.
Petroleum Training Institute Nigeria
Time : 11:20-12:00
Stanley Okiy is a Training Officer in the Department of Welding Engineering and Offshore Technology, Petroleum Training Institute, Effurun, Delta State, Nigeria. He obtained a BTech in Marine Engineering, from Rivers State University of Science and Technology, Port Harcourt, Nigeria, an MEng in Industrial Engineering from University of Benin, Benin City, Nigeria and also, an MPA in Public Administration from University of Benin, Nigeria. He is involved in active academic research and engineering consultancy with multinational oil companies in Nigeria. He has 13 years in teaching/training experience and has about 6 papers in local and international conference proceedings with 2 local journals.
Electricity provides power that drives automation and industrialization. Most developing countries are experiencing acute power shortages and Nigeria is not an exception. The power situation in Nigeria is so bad that a lot of local industries have been forced to close down. In this work, the state of power generation in Nigeria is surveyed and the current level compared to the level needed to successfully power and industrialize the economy. The results show that for the industrial development of Nigeria, it needs to generate 160 MW daily. With sufficient power supply, investment in automation technologies and robotics would boost the productivity and output of local industries thereby creating a buoyant economy.
Maven Machines, USA
Time : 12:00-12:40
Avishai Geller is the CEO and Founder of Maven Machines, a leader in Mobile Technology and Driver Safety for the Trucking Industry. He has a BS in Electrical Engineering and Computer Science from MIT and an MBA from Kellogg. Maven Machines has developed the first smart headset for drivers that is capable of detecting driver fatigue and distraction in real time.
Safety in the trucking industry is a major problem affecting public safety on the nation’s highways and is a substantial economic burden. There are over 400,000 trucking accidents each year resulting in over 100,000 injuries and 4,000 fatalities. Current safety technology largely focuses on placing sensors on trucks but does not monitor or aid the most important factor in safe driving – the driver. Fatigue and distraction are a leading cause of trucking accidents. By providing drivers with a wearable device that constantly monitors them for the onset of fatigue and attention grabbing distractions, we can alert drivers before a situation becomes dangerous. In this presentation, we will discuss how a “hearable” can serve truck drivers to communicate effectively and safely while driving and reduce the occurence of accidents on the roads. The federal guideline is for truck drivers to check their mirrors every 5 to 8 seconds. Wearable technology can act as a co-pilot to drivers, constantly monitoring their behavior and their environment to maintain the highest level of performance and avoid high risk situations.