Abstract: In wireless sensor network, sensor node transmits the
sensed data to the sink node in multi-hop communication
periodically. This high traffic induces congestion at the node which is
present one-hop distance to the sink node. The packet transmission
and reception rate of these nodes should be very high, when
compared to other sensor nodes in the network. Therefore, the energy
consumption of that node is very high and this effect is known as the
“funneling effect”. The tree based-data aggregation technique
(TBDA) is used to reduce the energy consumption of the node. The
throughput of the overall performance shows a considerable decrease
in the number of packet transmissions to the sink node. The proposed
scheme, TBDA, avoids the funneling effect and extends the lifetime
of the wireless sensor network. The average case time complexity for
inserting the node in the tree is O(n log n) and for the worst case time
complexity is O(n2).
Abstract: In rotating machinery one of the critical components
that is prone to premature failure is the rolling bearing.
Consequently, early warning of an imminent bearing failure is much
critical to the safety and reliability of any high speed rotating
machines. This study is concerned with the application of Recurrence
Quantification Analysis (RQA) in fault detection of rolling element
bearings in rotating machinery. Based on the results from this study it
is reported that the RQA variable, percent determinism, is sensitive
to the type of fault investigated and therefore can provide useful
information on bearing damage in rolling element bearings.