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Drone Detection by Event Cameras Using DNN
CS-B-09
Danielle Elnekave; chailelnekave@gmail.com Yakir Zindani; hehr345@gmail.com Yoav Kivity; yoavkivity@gmail.com
Advisor: Prof. Shlomo Greenberg
SCE - Shamoon College of Engineering, Be’er-Sheva
In recent years, there has been a growing use of unique-event cameras, which have the advantages of ultra-low power consumption and an inherent ability to compress information. However, most classical image-processing algorithms assume input in the form of RGB images. This project developed algorithms for detecting drones and UAVs based on images captured by these cameras. The approach leveraged deep neural networks (DNNs) to process the generated data, thereby adapting existing detection methods to the distinct data format provided by event-based imaging technology.
Keywords: DNN, drone detection, event camera
Finding the Security Level and Max-Min in a Stock Market Game
CS-B-10
Tom Ben Baruch; tombenbaruch@icloud.com Tal Yosef; yoseftal95@gmail.com Daniel Shkoratov; danielsk128@gmail.com
Advisor: Dr. Jeremy Miller
SCE - Shamoon College of Engineering, Be’er-Sheva
The goal of this project was to build an algorithm to calculate the moving maximum-minimum value in a game of n trading players using the live stock market. The max-min value in game theory is a quantity that can be calculated in a simple case of two players. We generalized the value to include more than two players. We also investigated whether there is a link between price movements and the moving max-min indicator. This is a new tool that can potentially be used as a signal for entering long or short positions, analogous to moving averages.
Keywords: financial modeling, game theory, mixed strategies, Nash equilibrium, stock data