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Once the kernel computation has been optimized, the biggest processing part becomes the features computation.
The first one is the features computation cpp: it is lower for SoA.
Our future work will focus on (1) the optimization of the features computation and (2) the multi-threading of the tracking in order to perform multi-target tracking with load balancing on the available core.
Furthermore, after ψ 2,0 and ψ 5,1 features computation, intra-pulse modulation signals are described by matrices, which are used for recognition by using KNN classifier [22].
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Note that as the data rate decreases, the number of symbols involved in features' computations also decreases for fixed values of Ns and fs.
The methods are comparable in terms of computational cost as it is the image processing, feature computation and, particularly, the k-means clustering that has the highest cost (the clustering is done over objects and even this evaluation set of ∼12 K images resulted in ∼750 K objects).
Three parts are included in this research: ST-RFT spectrogram computation, moments feature computation, and recognition.
A 21-channel filterbank is used for 13-dimensional MFCC ( to ) feature computation.
After ψ 3,3 feature computation, intra-pulse modulation signals are described by vectors.
First-stage feature computation and data rate reduction play a crucial role in an efficient visual information processing system.
Filter design and feature computation from samples are discussed and recognition performance with different choices is presented.
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