Abstract-Presently considerable amount of work has been done in tele-monitoring which involves the transmission of bio-signals and medical images in the wireless media.

Simultaneous occurrence of saturable absorption nonlinearity and two-photon absorption nonlinearity in the same medium is well sought for the devices like optical limiter and laser mode-locker. Pristine graphene sheet consisting entirely of sp2-hybridized carbon atoms has already been identified having large optical nonlinearity. However, graphene oxide (GO), a precursor of graphene having both sp2 and sp3-hybridized carbon atom, is increasingly attracting cross-discipline researchers for its controllable properties by reduction of oxygen containing groups. In this work, GO has been prepared by modified Hummers method, and it has been further reduced by infrared (IR) radiation. Characterization of reduced graphene oxide (RGO) by means of Raman spectroscopy, X-ray photoelectron spectroscopy, and UV-Visible absorption measurements confirms an efficient reduction with infrared radiation. Here, we report precise control of non-linear optical properties of RGO in femtosecond regime with increased degrees of IR reduction measured by open aperture z-scan technique. Depending on the intensity, both saturable absorption and two-photon absorption effects are found to contribute to the non-linearity of all the samples. Saturation dominates at low intensity (∼127 GW/cm2) while two-photon absorption becomes prominent at higher intensities (from 217 GW/cm2 to 302 GW/cm2). The values of two-photon absorption co-efficient (∼0.0022–0.0037 cm/GW for GO, and ∼0.0128–0.0143 cm/GW for RGO) and the saturation intensity (∼57 GW/cm2 for GO, and ∼194 GW/cm2 for RGO) increase with increasing reduction, indicating GO and RGO as novel tunable photonic devices. We have also explained the reason of tunable nonlinear optical properties by using amorphous carbon model.

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The present work proposes a computer-aided normal and abnormal heart sound identification based on Discrete Wavelet Transform (DWT), it being useful for tele-diagnosis of heart diseases. Due to the presence of Cumulative Frequency components in the spectrogram, DWT is applied on the spectrogram up to n level to extract the features from the individual approximation components. One dimensional feature vector is obtained by evaluating the Row Mean of the approximation components of these spectrograms. For this present approach, the set of spectrograms has been considered as the database, rather than raw sound samples. Minimum Euclidean distance is computed between feature vector of the test sample and the feature vectors of the stored samples to identify the heart sound. By applying this algorithm, almost 82% of accuracy was achieved.

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