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📁Other: Controls and Dashboards
👤Luis Fernando Aljure Munoz (MSWinTools)
📅Apr 26, 2019
This is a project development based on 'Inverse Thermodynamics Technology' taking advantage of Hilsch Tube efficiency to design a levitation vehicle control. According to this technology, a few small pressure vessels (air tanks) are enough to produce high rotating kinethic energy that makes the vehicle levitate and move forward.

The use of a high speed FPGA is imperative to keep track of all physical varibles involved in the process to synchronize and control the mechanism of the vehicle. A dashboard display is implemented to gather all sensor variables and control information.
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📁Machine Learning
👤Euclides Chuma (Unicamp)
📅Mar 20, 2019
The project consists of developing a platform to identify chemical materials using microwave sensors. This project will use deep learning resources to recognize patterns of dielectric properties of the materials under test to identify which are chemical compounds of these materials.
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📁Machine Learning
👤Hong Lin (University of Houston-Downtown)
📅Mar 20, 2019
Existing physiological reading systems, e.g., those used in patient monitoring, are ineffective for daily practices. This project aims to simulate an environment for daily physiological tracking using a FPGA DE10 board with physiological sensors. On a single board computer, we will an affordable reusable, expandable, and wireless, machine that can monitor a user’s temperature, ECG heart activity and EEG brain waves. With the integrated sensors and coding, the device should be capable of live streaming and exporting collected data on a local web server for rendering.
For the collection of EEG brain waves signals, a hand-made headset will be connected to the FPGA board via a Bluetooth module. Data will be sent and collected from the EEG headset using a Python Library.
The hosting microcomputer will be made capable of configuring and programming the required html, PHP, and python files. Overall, the data rendering software simulates a professional medical interface and is available to both the mobile devices and internet browser. The user should be able to connect the device to a remote server via the internet wirelessly, attach the reusable sensors to his/her body, and download the information gathered.
This project aims to challenge the affordability and accessibility of existing healthcare oriented monitoring equipment. From this point on, a system with the ability to collect data, and perform machine learning tasks based on the collected data is desirable. Ideally, with the computational power provided by the remote server, such a system will be able to diagnose the user’s mental states based on the knowledge gathered with the machine learning power and the organized data collection and processing.
A virtual reality system on mobile phone will be connected to the DE10 board to render mind intervention activities based on the diagnosis of the user’s mental state. Continual diagnosis and intervention will be studied to find the best routine for certain type of mental health problems.
The FPGA DE10 board will exert its power in this project, especially in the stages of machine learning for brain state recognition and rendering of virtual reality scenes. Those machine learning and virtual reality tasks will be handled using packages that run on full-fledged operating systems supported by FPGA DE10.
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📁Internet of Things
👤Mohamed El-Hadedy (California Polytechnic State University)
📅Mar 22, 2019
We teamed up with partners from NASA to use the dynamic re-configuration for implementing the open-source flight control software Fprime. So far the software was implemented by NASA/JPL on raspberry Pi and we are looking for exploring different way on using FPGA to make the software available on a wide range of the reconfigurable computing platforms.
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📁Internet of Things
👤Mohamed El-Hadedy (California State Polytechnic University Pomona)
📅Mar 22, 2019
NIST has just launched a new Lightweight cryptographic standardization competition for the small devices. The team will be implementing one of the NIST competition candidate algorithms (gage and engage) on FPGA targeting fewer resources on the chip with acceptable performance aligned with the requirements of the small device. Both OpenCL and Custom System-Verilog tools will be involved,
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