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Full-Text Articles in Engineering
Towards A Mobile Ad-Hoc Mesh Network Establishing Emergency Drone System, Ryan Integlia
Towards A Mobile Ad-Hoc Mesh Network Establishing Emergency Drone System, Ryan Integlia
36th Florida Conference on Recent Advances in Robotics
A drone based mobile ad-hoc mesh network for emergency communications is discussed. The in-progress project seeks to improve emergency and disaster area communication systems by creating a mobile, ad-hoc wireless network with an array of microcomputers, a GPS receiver, IMU, network adapter and drone. The Linux based platform includes network management, data collection, and integration with visualization. The expected outcome of this project is the establishment of a wireless mesh network capable of self-healing to support emergency response.
Data Preprocessing For Machine Learning Modules, Rawan El Moghrabi
Data Preprocessing For Machine Learning Modules, Rawan El Moghrabi
Undergraduate Student Research Internships Conference
Data preprocessing is an essential step when building machine learning solutions. It significantly impacts the success of machine learning modules and the output of these algorithms. Typically, data preprocessing is made-up of data sanitization, feature engineering, normalization, and transformation. This paper outlines the data preprocessing methodology implemented for a data-driven predictive maintenance solution. The above-mentioned project entails acquiring historical electrical data from industrial assets and creating a health index indicating each asset's remaining useful life. This solution is built using machine learning algorithms and requires several data processing steps to increase the solution's accuracy and efficiency. In this project, the …
Deep Neural Network Architectures For Modulation Classification Using Principal Component Analysis, Sharan Ramjee, Shengtai Ju, Diyu Yang, Aly El Gamal
Deep Neural Network Architectures For Modulation Classification Using Principal Component Analysis, Sharan Ramjee, Shengtai Ju, Diyu Yang, Aly El Gamal
The Summer Undergraduate Research Fellowship (SURF) Symposium
In this work, we investigate the application of Principal Component Analysis to the task of wireless signal modulation recognition using deep neural network architectures. Sampling signals at the Nyquist rate, which is often very high, requires a large amount of energy and space to collect and store the samples. Moreover, the time taken to train neural networks for the task of modulation classification is large due to the large number of samples. These problems can be drastically reduced using Principal Component Analysis, which is a technique that allows us to reduce the dimensionality or number of features of the samples …