By Stavros Alifragkis, George Papakonstantinou (auth.), Constantinos T. Angelis, Dimitrios Fotiadis, Alexandros T. Tzallas (eds.)

This booklet constitutes the completely refereed post-conference court cases of the 3rd overseas ICST convention on Ambient Media and structures, AMBI-SYS 2013, held in Athens, Greece, in March 2013. The 12 revised complete papers provided have been conscientiously reviewed and chosen from quite a few submissions. The papers concentrate on rising applied sciences, providers and strategies for brand new, human-centric clever ambient environments.

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Extra resources for Ambient Media and Systems: Third International ICST Conference, AMBI-SYS 2013, Athens, Greece, March 15, 2013, Revised Selected Papers

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Wavelet transformation is a linear operation that decomposes the signal into a number of scales corresponding to frequency components and evaluates every scale with a certain resolution [15, 16]. The implementation of the WT results to a serial list of coefficients named wavelet coefficients, which represent the evolution of the correlation between the signal and the mother function at different levels of analysis (or different ranges of frequencies) all along the HRV series [17]. 2 Continuous Wavelet Transform Wavelet transforms categorized in essentially two distinct classes: the continuous wavelet transform CWT and the discrete wavelet transform DWT.

Although the concept of the wavelets presented earlier, the first algorithm was developed in 1988 and since then many modification of wavelets has been published [14]. A wavelet is a “small wave” of small duration having an average value that is zero. Unlike fourier transform, where fourier sine and cosine functions are smooth, predictable and extend from minus to plus infinity, wavelets could be chosen from an unlimited tank of basis functions, they are usually non-symmetrical, with small duration and a finite period.

Eventually, if the underlying sampling period of the signal is , we also accept that the scale is expressed as frequency from the equation 2. (2) The frequency is inversely proportional to scale . Large scale corresponds to a low frequency and small scales correspond to high frequencies providing details about the HRV signal. Table 1. 90 Frequency decomposition and related scale range are listed in Table 1. After several trials we decided that using a 124 linear scales decomposition of CWT provides high resolution.

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