Distributed Neural Networks for Signal Change Detection: On the Way to Cognition in Sensor Networks

Abstract

Cognition is a fundamental feature of natural intelligence, which a modern technology has not yet been able to reproduce in full capacity. Sensor networks provide a new technological support for a substantial increase in an amount and quality of information that might be collected and communicated in complex adaptive systems. Their application may significantly raise the degree of intelligence in system design and implementation into the levels where effects of cognition will start kicking in. The paper describes the results of an empirical study aiming to demonstrate that a cognition ability may be treated as a generic sensor network feature. The new architecture with neural networks distributed over the sensor network platforms was developed for sensor network engineering applications. The detection system learns to detect the change of not only the signal levels but also sensor signal shapes and parameters that represent a more complicated task. The architecture allows for a significant reduction in resource consumption without compromising the change detection performance. Implemented as an agent controlling the sensor network self-adjustment to the objects under measurement in the sensor network composed from typical sensor motes, the novel neural network structures may achieve a significant saving in power consumption and an increase in a possible network deployment time from a few days to a few years. The experiments prove that a neural-network-based change detection system is feasible for sensor networks application designs and could be successfully implemented on the technological platforms currently available on the market.Index Terms-Artificial neural networks (ANNs), distributed artificial intelligence, sensor networks, signal change detection.

The problem of developing the architecture of modern cognitive radar system

“…Information in the radar system is perceived by «smart» sensors, i.e. sensors with primary processing and control of the measurement process [7], as well as through network sensors that demonstrate «distrib-uted intelligence» with self-monitoring capabilities, automatic solution of changes in their environment [8] , [9]. Cognitive radar has the ability to adapt to transmission in the probing process, imitating human perception as an interactive process where the cognitive entity responds to or changes its behavior as a result of external stimuli.…”

Development of a spherical motor with a 3-DOF sensing system

“…Unlike the forward modeling-to solve MFD with given orientation, which can be solved under superposition principle once MFD of one or one pair of PMs is computed by analysis or FEM, the inverse modeling-to obtain three Euler angles from measured MFD is much more challenging, and it is very difficult to acquire a closed-loop equation unless high-order terms of the magnetic field are ignored. Neglecting high-order terms results in low accuracy, so neural networks[105][106][107][108][109][110][111][112] which can fit any practical function have been adopted here to approximate the relationship between inputs-measured MFD and outputsorientation angles.3-axis Hall Effect sensors as presented inFigure 4.1 are applied in the sensing system.A 3-axis Hall Effect sensor can output voltages in response to the magnetic field in 3 mutually perpendicular directions. The output voltages are proportional to magnetic flux density components in each direction over the working range of the sensors.…”