توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Structural and mechanical properties of polypropylene \ phosphor strontium aluminate melt spun nanocomposite fibers
نویسنده(ها): ، ،
اطلاعات انتشار: کنفرانس بین المللی علوم مکانیک و صنعت، سال
تعداد صفحات: ۷
Phosphor strontium aluminate (SrAl2O4: Eu2+, Dy3+) nanoparticles were mixed with polypropylene by an internal mixer to prepare a uniform mixture with polymer. Polypropylene \ phosphor strontium aluminate nanocomposite fibers were produced by melt spinning process. Morphological, thermal and structural properties of produced fibers were investigated. Also the mechanical properties of the produced fibers were studied. The SEM results showed that the nanoparticles were uniformly distributed within the polymer matrix and they were shapeless. DSC results indicated that the melting point and the crystallinity increased with increasing concentrations of nanoparticles in the fibers. XRD images showed nanoparticles in the fibers matrix and also proved that crystal size did not change in the fibers. Mechanical properties show that the tenacity, modulus and the shrinkage decreased by increasing the amount of nanoparticles in the fibers but the linear density increased.<\div>

۲A Time–Frequency approach for EEG signal segmentation
نویسنده(ها): ، ، ،
اطلاعات انتشار: Journal of Artificial Intelligence and Data Mining، دوم،شماره۱، ۲۰۱۴، سال
تعداد صفحات: ۹
The record of human brain neural activities, namely electroencephalogram (EEG), is generally known as a non–stationary and nonlinear signal. In many applications, it is useful to divide the EEGs into segments within which the signals can be considered stationary. Combination of empirical mode decomposition (EMD) and Hilbert transform, called Hilbert–Huang transform (HHT), is a new and powerful tool in signal processing. Unlike traditional time–frequency approaches, HHT exploits the nonlinearity of the medium and non–stationarity of the EEG signals. In addition, we use singular spectrum analysis (SSA) in the pre–processing step as an effective noise removal approach. By using synthetic and real EEG signals, the proposed method is compared with wavelet generalized likelihood ratio (WGLR) as a well–known signal segmentation method. The simulation results indicate the performance superiority of the proposed method.
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