Modified time-based multilayer perceptron for sensor networks and image processing applications

The paper introduces a modified time-based multilayer perceptron (MTBMLP), which is a complex structure composed by a few time-based multilayer perceptrons. This modification reduces connections, isolates information for each function and produces knowledge about the system of functions as a whole. This neural network is applied for novelty and change detection in signals delivered by sensor networks and for edge detection in image processing. In both applications a MTBMLP is utilized for function predictions and, after a further structure development is implemented, for an error prediction also. In sensor network applications, a number of experiments with Crossbow sensor kits and the MTBMLP acting as a function predictor have been conducted and analyzed for detecting a significant change in signals of various shapes and nature. A series of experiments with Lena image have been conducted for edge detection applications. The results demonstrate that MTBMLP is more efficient and reliable than other methodologies in sensor network change detection and that its application in change detection is more effective than in edge detection.