CHRONIC WASTING DISEASE (CWD) PREDICTION, MODELING AND MAPPING
Project utilizing Python tools to process and inspect images provided by volunteer land owners and hunters. Compare images of confirmed CDW positive deer harvested and inspected at Fish and Game inspections stations, when leveraged with all other available data to help predict where CWD will either appear or increase to allow Fish and Game and how to better manage and reduce the impact of the disease. (Sustainable Agriculture, Big Data, Machine Learning, NoSQL)
DATA ACQUISITION UTILIZING DRONE TECHNOLOGY WITH SWARM METHODOLOGIES
Creating autonomous inspection and data collection tools for remote data gathering. Several applications under development, including crop and health and vigor data. The use of swarms of commodity priced drones improves data collection and efficiency while keeping costs low. This project involves Big Data and NoSQL solutions to store and process the large amounts of images captured.
DIRECTED BIOFEEDBACK RELAXATION MOBILE APP
This project involves the development of a mobile Android app (Java) which implements cutting edge research in directed visualization and biofeedback to assist in relaxation to assist subjects to cope with anxiety disorders and process high anxiety situations. The app is intended to utilize bidirectional communications to monitor the effects of the treatment to customize and improve the algorithm utilizing detailed datalogging and Machine Learning.
HUMAN INTERFACE DATA COLLECTION AND CONTROL THROUGH NEUROFEEDBACK
Project Management effort, with some device driver and embedded systems software development in (C/C++) effort to develop proprietary technologies which was intended to assist clients in overcoming performance and anxiety issues in their own homes. The concept was centered a station which monitored and directed the treatments of a proprietary neurofeedback system which gathered data from the client, relied it to a central processing station. The central processing station coordinates series of algorithms to determine what actions should be taken by the client and would then direct the remote unit to administer the corrective actions as needed. The remote stations (Java), the central processor algorithms, corrective actions, and results as well as the data stream were constantly monitored and processed by a Deep Learning system designed to improve all levels of the system as well as assist in improvement and help find insights which might lead to new discoveries. (Deep Learning, Big Data, C/C++, Java)