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Reconfigurable Array Control Via Convolutional Neural Networks, Garrett A. Harris
Reconfigurable Array Control Via Convolutional Neural Networks, Garrett A. Harris
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A method for the beam forming control of an array of reconfigurable antennas is presented. The method consists of using two parallel convolutional neural networks (CNNs) to analyze a desired radiation pattern image, or mask, and provide a suggestion for the reconfigurable element state, array shape, and steering weights necessary to obtain the radiation pattern. This research compares beam forming systems designed for three distinct element types: a patch antenna, a reconfigurable square spiral antenna restricted to a single reconfigurable state, and the fully reconfigurable square spiral. The parametric sweeps for the design of the CNNs are presented along with …
Few-Shot Malware Detection Using A Novel Adversarial Reprogramming Model, Ekula Praveen Kumar
Few-Shot Malware Detection Using A Novel Adversarial Reprogramming Model, Ekula Praveen Kumar
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The increasing sophistication of malware has made detecting and defending against new strains a major challenge for cybersecurity. One promising approach to this problem is using machine learning techniques that extract representative features and train classification models to detect malware in an early stage. However, training such machine learning-based malware detection models represents a significant challenge that requires a large number of high-quality labeled data samples while it is very costly to obtain them in real-world scenarios. In other words, training machine learning models for malware detection requires the capability to learn from only a few labeled examples. To address …