The Technology Collaborative
Round 16 Awards
1. "Application Acceleration: Extraction of Parallelism from Sequential Software"
Concurrent EDA
Concurrent EDA seeks to prove that the arduous task of manually rewriting sequential software applications is not necessary to realize the full benefits of parallel processing hardware. Concurrent EDA will develop a software product that automatically extracts parallelism from sequential software and outputs optimized parallel hardware. By exploiting the power of parallel execution, compute intensive applications can be accelerated 10 to 100 times faster than their sequential form. Acceleration of this caliber is analogous to the leap in speed from a world-class sprinter to an F-16 fighter jet. This level of high-performance computing is desirable anywhere time is a critical factor. Example applications include: Fingerprint Identification (Biometrics), Protein Sequencing (Computational Biology), Derivative Analysis (Financial Services) and 3-Dimensional MRI Scans (Medical Imaging).
2. "Hierarchical Mission Planning System for Autonomous Vehicles"
National Robotics Engineering Center, CMU
Today’s farmer needs to be ever vigilant in task management and component scheduling in order to maintain profit margins. The National Robotics Engineering Center, partnering with John Deere & Company, is proposing a predictive and dynamic planner for farming tasks that involve calculating capacity constraints and machine allocation – especially during times when the same vehicles are being used for multiple tasks. Both low and high level missions will be planned using combined software to calculate the most efficient plan of operations. There are many different implications for this type of technology including more productive crop layout design and ideal placement of fueling stations.
3. "Applications of Event Detection Technology"
Robotics Institute, Carnegie Mellon University
Carnegie Mellon University teams up with Seagate to demonstrate innovative video indexing technology and produce commercial applications for this tool. The starting point for this project is the development of an approach to event detection in videos. The basis of the TTC funded project is to develop algorithms that come closer to real time operation, improving accuracy of the technology, testing the technology using large data sets from real world applications, and molding the technology in a way which non-expert users will be able to easily use.
4. "3D Imaging with Binary-Coded Laser Projection"
Tomo Technology
Tomo Technology manufactures standard and custom 3D imagers used for object modeling, analysis, and reverse engineering. Their current product offering utilizes a laser to generate a high accuracy scan an object. The accepted proposal will develop a hybrid 3D scanner that will replace a sweeping plane of laser light with a binary-coded structured laser light pattern. This new system will scan an object 50 times faster, yet retain the high accuracy characteristics of their current system.
5. "Reconfigurable Development Platform for Next Generation Networks"
Valley Technologies, Inc.
Valley Technologies Inc. (VTI) plans a reconfigurable development platform for optical next generation networks (NGNs). Networks are increasingly requiring larger bandwidth and augmented security due to new multimedia services. This proposal seeks to minimize the cost and deployment time associated with transitioning to optical NGNs while improving future functionality by incorporating remotely reconfigurable options that will improve optical network efficiency. VTI is currently integrating advanced boarder security assets through NGNs for government prime contractors.
6. "Sensor Networks for Disease Management in Specialty Crops"
ZedX, Inc.
ZedX is involved in developing technologies that will increase knowledge of how to better sustain the viability of our food and energy systems. Specialty crops are susceptible to pestilence, much like most produce. This proposal aims to use wireless sensor networks (WSNs) to monitor crops and pests, according to previously developed disease models. Cost efficient WSNs will be installed, subfield, to transmit information into a web-based data management system which with allow the grower (user) to easily interpret and manage the field data. This will allow the grower to make timely decisions, and respond quicker, regarding the treatment of crops. ZedX’s project is unique because it uses the CMU sensor network, which allows for integration with other sensors, and because it proposes an end-to-end system.
