Published Article / In this article we will describe the use of 3D medical image information of individual patients as well as selected patient populations, combined with CAE tools and processes, in the rapid product development of custom and standard implantable devices. The combination of medical image information with CAE methods such as CAD, RP, FEA and CFD, allows the engineer to develop implantable devices faster and better, with optimized designs tailored to the anthropometry of the targeted patient (population), using virtual instead of mechanical prototype testing. Case studies will be demonstrated for a variety of surgical fields such as orthopaedic, cranio-maxillofacial and cardiovascular surgery.

The evolution of the Graphics Processing Units (GPUs) over the last decade, has reinforced general purpose computing while sustaining a steady performance growth in graphics intensive applications. However, the immense performance improvement is generally associated with a steep rise in GPU power consumption. Consequently, GPUs are already close to the abominable power wall. With a massive popularity of the mobile devices running general-purpose GPU (GPGPU) applications, it is of utmost importance to ensure a high energy efficiency, while meeting the strict performance requirements. In this work, we demonstrate that, customizing a Streaming Multiprocessor (SM) of a GPU, at a lower frequency, is significantly more energy efficient, compared to employing Dynamic Voltage and Frequency Scaling (DVFS) on an SM, designed for a high frequency operation. Using a system level Computer Aided Design (CAD) technique, we propose SSAGA - Streaming Multiprocessors Sculpted for Asymmetric GPGPU Applications, an energy efficient GPU design paradigm. SSAGA creates architecturally identical SM cores, customized for different voltage-frequency domains.

On-road vehicles are a major source of transportation carbon dioxide (CO2) greenhouse gas emissions in all the developed countries, and in many of the developing countries in the world. Similarly, several criteria air pollutants are associated with transportation, e.g., carbon monoxide (CO), nitrogen oxides (NOx), and particulate matter (PM). The need to accurately quantify transportation-related emissions from vehicles is essential. Transportation agencies and researchers in the past have estimated emissions using one average speed and volume on a long stretch of roadway. With MOVES, there is an opportunity for higher precision and accuracy. Integrating a microscopic traffic simulation model (such as VISSIM) with MOVES allows one to obtain precise and accurate emissions estimates. The new United States Environmental Protection Agency (USEPA) mobile source emissions model, MOVES2010a (MOVES) can estimate vehicle emissions on a second-by-second basis creating the opportunity to develop new software ―VIMIS 1.0‖ (VISSIM/MOVES Integration Software) to facilitate the integration process. This research presents a microscopic examination of five key transportation parameters (traffic volume, speed, truck percentage, road grade and temperature) on a 10-mile stretch of Interstate 4 (I- 4) test bed prototype; an urban limited access highway corridor in Orlando, Florida. iv The analysis was conducted utilizing VIMIS 1.0 and using an advanced custom design technique; D-Optimality and I-Optimality criteria, to identify active factors and to ensure precision in estimating the regression coefficients as well as the response variable. The analysis of the experiment identified the optimal settings of the key factors and resulted in the development of Micro-TEM (Microscopic Transportation Emissions MetaModel). The main purpose of Micro-TEM is to serve as a substitute model for predicting transportation emissions on limited access highways in lieu of running simulations using a traffic model and integrating the results in an emissions model to an acceptable degree of accuracy. Furthermore, significant emission rate reductions were observed from the experiment on the modeled corridor especially for speeds between 55 and 60 mph while maintaining up to 80% and 90% of the freeway‘s capacity. However, vehicle activity characterization in terms of speed was shown to have a significant impact on the emission estimation approach. Four different approaches were further examined to capture the environmental impacts of vehicular operations on the modeled test bed prototype. First, (at the most basic level), emissions were estimated for the entire 10-mile section ―by hand‖ using one average traffic volume and average speed. Then, three advanced levels of detail were studied using VISSIM/MOVES to analyze smaller links: average speeds and volumes (AVG), second-bysecond link driving schedules (LDS), and second-by-second operating mode distributions (OPMODE). This research analyzed how the various approaches affect predicted emissions of CO, NOx, PM and CO2. v The results demonstrated that obtaining accurate and comprehensive operating mode distributions on a second-by-second basis improves emission estimates. Specifically, emission rates were found to be highly sensitive to stop-and-go traffic and the associated driving cycles of acceleration, deceleration, frequent braking/coasting and idling. Using the AVG or LDS approach may overestimate or underestimate emissions, respectively, compared to an operating mode distribution approach. Additionally, model applications and mitigation scenarios were examined on the modeled corridor to evaluate the environmental impacts in terms of vehicular emissions and at the same time validate the developed model ―Micro-TEM‖. Mitigation scenarios included the future implementation of managed lanes (ML) along with the general use lanes (GUL) on the I-4 corridor, the currently implemented variable speed limits (VSL) scenario as well as a hypothetical restricted truck lane (RTL) scenario. Results of the mitigation scenarios showed an overall speed improvement on the corridor which resulted in overall reduction in emissions and emission rates when compared to the existing condition (EX) scenario and specifically on link by link basis for the RTL scenario. The proposed emission rate estimation process also can be extended to gridded emissions for ozone modeling, or to localized air quality dispersion modeling, where temporal and spatial resolution of emissions is essential to predict the concentration of pollutants near roadways