I attended the first day of the NNBA Conference on Saturday, October 3rd at the Westgate Resort & Casino in Las Vegas, NV.
To answer my final question thoroughly after attending the 2015 NNBA Conference, "Is this worth my time?" I can attest to the extreme value of each item on the agenda.
Consequently, this NNBA model has nxm input layer nodes and the same number of first hidden layer neurons.
According to this NNBA model, firstly sensor nodes collect the acoustic intensity measurements from the target and preprocess them according to the neuron function of the first hidden layer.
Neurons in NNBA algorithm need some operation parameters (such as weight, thresholds, etc.) at runtime; as a result, the neuron network model parameters should be trained after the clustering and CH election.
The proposed CSIFP scheme consumes the least energy, because the CH selection method makes advantage of CSIP2, and the NNBA model is utilized to reduce the time redundancy as well.
It is because that CSIFP reduces time redundancy by NNBA model.
This gain benefits from not only the CH selection method but also the fusion algorithm based on NNBA. From the above results, a good tradeoff between tracking accuracy and energy cost is achieved.
Two exploratory scales were developed from a list of 69 items pretested on a sample population, which was a subset of the NNBA membership randomly selected from that universe.
Although demographic information on the NNBA membership was not available to help directly assess the potential for bias in the respondent group, statistics provided by the National League of Nurses was helpful in this regard.