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The present study is focused on investigating the mechanical properties of hybrid polymer composites. The reinforcement materials are animal bone (ox) particulate and E-glass fiber. The matrix material is epoxy resin. The following combinations are considered for investigation: (a) bone particulate weight percent (20%, 30%, and 40%), (b) E-glass fiber weight percent (20%, 30%, and 40%), and (c) bone particulate (10%, 20%, and 30%) and E-glass fiber (30%, 20%, and 10%) with epoxy resin 60% by weight percent. The test specimens are prepared as per the required ASTM standard for tensile, compressive, and flexural tests. The test results show that maximum tensile and compressive strength observed in 40% of E-glass fiber with 60% of epoxy matrix, correspondingly, is 254.964 MPa and 37.52 MPa. The maximum flexural strength observed in E-glass fiber reinforced composites is 250.43 MPa.

In this paper, we propose to use DWT coefficients as features for emotion recognition from EEG signals. Previous feature extraction methods used power spectra density values dervied from Fourier Transform or sub-band energy and entropy derived from Wavelet Transform. These feature extracion methods eliminate temporal information which are essential for analyzing EEG signals. The DWT coefficients represent the degree of correlation between the analyzed signal and the wavelet function at different instances of time; therefore, DWT coefficients contain temporal information of the analyzed signal. The proposed feature extraction method fully utilizes the simultaneous time-frequency analysis of DWT by preserving the temporal information in the DWT coefficients. In this paper, we also study the effects of using different wavelet functions (Coiflets, Daubechies and Symlets) on the performance of the emotion recognition system. The input EEG signals were obtained from two electrodes according to 10-20 system: F(p1) and F(p2). Visual stimuli from International Affective Picture System (IAPS) were used to induce two emotions: happy and sad. Two classifiers were used: Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Experimental results confirmed that the proposed DWT coefficients method showed improvement of performance compared to previous methods.