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Simulations results in Table 10 show a high modulation recognition percentage over fading channels.
In particular, we provide in Table 9 the total recognition percentage using several wavelet filters in the case of full-class recognition for SNR = 1 dB.
In [33], those authors obtained preliminary results using MFCC representation and spiking neurons network that recognizes short, complex temporal patterns with a recognition percentage of 67.92%.
Simulations on the same modulation pool examined in [13] (16-QAM, 4-PSK, and 4-FSK) show that our recognition percentage is higher than 99% when SNR is not lower than dB.
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Recognition percentages are shown in Table 1.
Word Error Rate (WER) % Adult voices Older voices -value Overall 28.7 38.6.001 Male 28.7 37.9.001 Female 28.2 41.3.001 Table 11 Likelihood scores (Negative log likelihood per frame) and Correct recognition percentages of all the phonemes (see Figures 6 and 7).
These predictions are very near the arithmetic mean recognition percentages that are plotted in Fig. 5 for contiguous and random subsets at each of the T3 intervals.
Two-dimensional projection is the most desirable, allowing examination of the classification results in terms of recognition ability (percentage of members of the training set correctly classified) and prediction ability (percentage of members of the test set correctly classified using the rules developed during the training).
Horizontal axis: input instances, vertical axis: average recognition accuracy (percentage) Fig. 6 Total number of instances in the ATs during runtime learning.
AFM: Atomic force microscopy; P: Poplar; SG: Switchgrass; CS: Corn stover; CBM: Carbohydrate-binding module; DAP: Dilute acid pretreatment; RAP: Recognition area percentage.
The surface structural changes were studied at single molecule level based on the recognition area percentage (RAP) of exposed crystalline cellulose over the imaged cell wall surface.
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