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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...
As a machine learning method under sparse Bayesian framework, classical Relevance Vector Machine (RVM) applies kernel methods to construct Radial Basis Function(RBF) networks using a least number of relevant basis functions. Compared to the well-known Support Vector Machine (SVM), the RVM provides a better sparsity, and an automatic estimation of hyperparameters. However, the performance of the original...
The power quality (PQ) signals are traditionally analyzed in the time-domain by skilled engineers. However, PQ disturbances may not always be obvious in the original time-domain signal. Fourier analysis transforms signals into frequency domain, but has the disadvantage that time characteristics will become unobvious. Wavelet analysis, which provides both time and frequency information, can overcome...
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