We propose a method for identifying image spam by training an artificial neural network. A detailed process for preprocessing spam image files is given, followed by a description on how to train an artificial neural network to distinguish between ham and spam. Finally, we exercise the trained network by testing it against unknown images.
We propose mote technology as a potential source for wireless communication of medical signals in body area networks. In order to determine if the proposed system is useful, a robustness metric is developed specifically for a biosensor shirt application. The objective is to provide a tool to evaluate whether the mote hardware will deliver acceptable… (More)
We propose a web-based architecture using standard protocols to provide a user-friendly interface and remote access to satellite emulation test-bed facilities on a parallel computing cluster. Researchers can remotely control an emulation to evaluate performance of proposed space-based Internet architectures, backbone networks, formation clusters and… (More)