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Wireless indoor localization is a key technology for the future Internet of things (IoT) paradigm. In this paper, we perform an experimental comparative study of machine learning-based localization schemes, such as k-nearest neighbor (k-NN) and variants of support vector machine (SVM), based on the received signal strength (RSS) measurements of the ambient frequency modulation (FM) and digital video...
In this paper, we propose a real-time architecture of multiple features extraction for vehicle verification. First, we set a range of YCbCr values to extract the pixels belonging to vehicle back lights. The density of light is computed by the number of the extracted pixels, and considered as the first feature. The second feature is the location of license plate. It is determined by Searching Area...
With the wide application of sensitive power electronic devices in industry, the power quality (PQ) disturbance problems become more concerned. The S-transform is a time-frequency localization technique that bridges the gap between the short-time Fourier transform and wavelet transform. A new PQ disturbances identification method based on S-transform time-frequency analysis and fuzzy expert system...
This paper proposed a power quality disturbances classification system based on wavelet transforms and novel probabilistic neural network (PNN). Wavelet transform is utilized to extract feature vectors for various power quality disturbances based on multi-resolution analysis. The decomposition signal is divided into 5 equal length bins in each level. Root mean square (RMS) value of the wavelet coefficients...
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