This research studies the development and synthesis of blended nanocomposites filled with Titanium dioxide (TiO2). Blended nanocomposites based on unsaturated polyester resin (UPR) and epoxy resins were synthesized by reactive blending. The optimum quantity from nano partical of titanium dioxide was selected and different weight proportions 1%, 3%, 5%, and 7% ratios of new epoxy are blended with UPR resin. The dielectric breakdown strength and thermal conductivity properties of the blended nanocomposites were compared with those of the basis material (UPR and 3% TiO2).The results show good compatibility epoxy resins with the UPR resin on blending, dielectric breakdown strength values are higher while thermal conductivity values of blends nanocomposites are significantly lower compared to that of the(UPR and 3% TiO2), semi-interpenetrating UPR/Epoxy blends (semi-IPNs) for one type of new epoxy [P2]was prepared and noticed the blend nanocomposites show higher dielectric breakdown strength than the semi- IPNs (UPR/Epoxy) at low loading of new epoxies but the thermal conductivity is a higher than the semi- IPNs UPR/Epoxy at all loading. Thermogravimetric analysis (TGA) was employed to study the thermal properties of the blended nanocomposites.
Using the Neural network as a type of associative memory will be introduced in this paper through the problem of mobile position estimation where mobile estimate its location depending on the signal strength reach to it from several around base stations where the neural network can be implemented inside the mobile. Traditional methods of time of arrival (TOA) and received signal strength (RSS) are used and compared with two analytical methods, optimal positioning method and average positioning method. The data that are used for training are ideal since they can be obtained based on geometry of CDMA cell topology. The test of the two methods TOA and RSS take many cases through a nonlinear path that MS can move through tha
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