Intelligent computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of physicians and reduce the time required for accurate diagnosis. CAD systems could provide physicians with a suggestion about the diagnostic of heart diseases. The objective of this paper is to review the recent published preprocessing, feature extraction and classification techniques and their state of the art of phonocardiogram (PCG) signal analysis. Published literature reviewed in this paper shows the potential of machine learning techniques as a design tool in PCG CAD systems and reveals that the CAD systems for PCG signal analysis are still an open problem. Related studies are compared to their datasets, feature extraction techniques and the classifiers they used. Current achievements and limitations in developing CAD systems for PCG signal analysis using machine learning techniques are presented and discussed. In the light of this review, a number of future research directions for PCG signal analysis are provided.

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In this Letter, funnel-shaped silicon nanowires (SiNWs) are newly introduced for highly efficient light trapping. The proposed designs of nanowires are inspired by the funnel shape, which enhances the light trapping with reduced reflections in the wavelength range from 300 to 1100 nm. Composed of both cylindrical and conical units, the funnel nanowires increase the number of leaky mode resonances, yielding an absorption enhancement relative to a uniform nanowire array. The optical properties of the suggested nanowires have been numerically investigated using the 3D finite difference time domain (FDTD) method and compared to cylindrical and conical counterparts. The structural geometrical parameters are studied to maximize the ultimate efficiency and hence the short-circuit current. Carefully engineered structure geometry is shown to yield improved light absorption useful for solar cell applications. The proposed funnel-shaped SiNWs offer an ultimate efficiency of 41.8%, with an enhancement of 54.8% relative to conventional cylindrical SiNWs. Additionally, short-circuit current of 34.2  mA/cm2 is achieved using the suggested design.

This paper presents an intelligent algorithm for heart diseases diagnosis using phonocardiogram (PCG). The proposed technique consists of four stages: Data acquisition, pre-processing, feature extraction and classification. PASCAL heart sound database is used in this research. The second stage concerns with removing noise and artifacts from the PCG signals. Feature extraction stage is carried out using discrete wavelet transform (DWT). Finally, artificial neural network (ANN) has been used for classification stage with an overall accuracy 97%.