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The unprotected wireless network utilizes the current MAC layer (802.11-Current-MAC layer).
The network utilizes a linear function in its output layer to transfer the data to the output.
The second neural network utilizes the natural residual function with the cone projection function to achieve low computation complexity.
The network utilizes a passive architecture in which any mobile mote receives the Beacon signals to localize itself.
Thus, conversely to all previous works, in [29], the authors considered a scenario in which the secondary cooperative network utilizes a FD relay subject to self-interference.
This network utilizes 4 input data in the input layer with 7 and 13 neurons in its first and second hidden layers to predict the output.
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Therefore, training a neural network utilizing evolutionary algorithms has been an important field.
Figure 1 A cognitive relay network utilizing distributed beamforming for interference cancellation.
GORMANN is a self-organizing neural network utilizing Newton's law of universal gravitation.
Having primary network of 25 PUs using 15 frequencies, CR network utilizing D-CSUM algorithm can operate 23 SUs with an average interference 0.1, CR network utilizing DminInt algorithm can handle 40 SUs, and the CR network utilizing CminSumInt algorithm can operate 47 SUs.
The retrieval performance of Siamese network, utilizing end-to-end learning, is lower, compared to 109 and 4144-dimensional representations.
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