Accurate analysis of spectral regrowth of nonlinear power amplifier driven by cyclostationary modulated signals

In this paper we present an exact analytical expression to calculate the spectral regrowth at the output of a nonlinear power amplifier (PA) using the higher order cumulants and Poisson summation formula. This PA is driven by the filtered digitally modulated signals. To improve the accuracy of the calculations, the cyclosationarity of the input signal is considered. Moreover, closed-form expressions for the 1-dB compression and saturation points are extracted as a function of the PA model parameters, higher order statistics of the input signal, and the transfer function of the pulse shaping filter. In addition, an analytical expression for the adjacent channel power (ACP) and a closed-form expression of the ACP ratio are derived. This is followed by investigation of the effect of the PA nonlinearity on the performance of receiver. Simulation studies are carried out to verify the accuracy of the derived expressions. Excellent agreement between the analytical and simulation results is achieved.     

Effect of silver dissolution on electrical properties of As2S3

The paper reports the effect of dissolution of silver into the chalcogenide As₂S₃. The conductivity of As₂S₃ decreases with doping at lower temperatures. The a.c. conductivity is proportional to ^s for undoped as well as doped samples. The value of the frequency exponent s increases with doping. On the other hand, the dielectric constant of As₂S₃ also increases with doping and the effect is quite prominent at higher temperatures. A new structural model together with the correlated barrier hopping model has been used to explain these results. © 2001 Kluwer Academic Publishers     

Fast-forward solver for inhomogeneous media using machine learning methods: artificial neural network,support vector machine and fuzzy logic

Encountering with a nonlinear second-order differential equation including ϵ _r and μ _r spatial distributions, while computing the fields inside inhomogeneous media, persuaded us to find their known distributions that give exact solutions. Similarities between random distributions of electric properties and known functions lead us to estimate them using three mathematical tools of artificial neural networks (ANNs), support vector machines (SVMs) and Fuzzy Logic (FL). Assigning known functions after fitting with minimum error to arbitrary inputs using results of machine learning networks leads to achieve an approximate solution for the field inside materials considering boundary conditions. A comparative study between the methods according to the complexity of the structures as well as the accuracy and the calculation time for testing of unforeseen inputs, including classification, prediction and regression is presented. We examined the extracted pairs of ϵ _r and μ _r with ANN, SVM networks and FL and got satisfactory outputs with detailed results. The application of the presented method in zero reflection subjects is exemplified.

     

PCR amplification of 18S rRNA,single cell protein production and fatty acid evaluation of some naturally isolated microalgae

Microalgae were isolated during a screening program from soil samples collected from paddy-fields of Fars province, south of Iran. The protein content was assayed by the Kochert method. Total genomic DNA were isolated and used for PCR amplification of the 18S rRNA gene. The sequences were determined for 12 species of microalgae. Some bioinformatic tools were used for more investigation on these biologic data. Total lipids from five microalgal species were extracted and used for determination of different types of fatty acids by gas chromatography–mass spectrometry method. In our experiments the green algae yielded a maximum protein of about 42% ± 1.64. The DNA sequences were published in the NCBI under specific accession numbers. The composition of fatty acids was mainly, myristic acid, palmitic acid, oleic acid, α-linolenic acid, and γ-linolenic acid.     

Experimental and Numerical Investigation of Inlet Temperature Effect on Convective Heat Transfer of γ-Al2O3/Water Nanofluid Flows in Microtubes

ABSTRACT

Nanofluids are the combination of a base fluid with nanoparticles with sizes of 1–100 nm. In order to increase the heat transfer performance, nanoparticles with higher thermal conductivity compared to that of base fluid are introduced into the base fluid. Main parameters affecting single-phase and two-phase heat transfer of nanofluids are shape, material type and average diameter of nanoparticles, mass fraction and stability of nanoparticles, surface roughness, and fluid inlet temperature. In this study, the effect of inlet temperature of deionized water/alumina (Al₂O₃) nanoparticle nanofluids was both experimentally and numerically investigated. Nanofluids with a mass fraction of 0.1% were tested inside a microtube having inner and outer diameters of 889 and 1,067 µm, respectively, for hydrodynamically developed and thermally developing laminar flows at Reynolds numbers of 650, 1,000, and 1,300. According to the obtained numerical and experimental results, the inlet temperature effect was more pronounced for the thermally developing region. The performance enhancement with nanoparticles was obtained at rather higher Reynolds numbers and near the inlet of the microtube. There was a good agreement between the experimental and numerical results so that the numerical approach could be further implemented in future studies on nanofluid flows.