Fading channel modeling is generally defined as the variation of the attenuation of a signal with various variables. Time, geographical position, and radio frequency which is included. Fading is often modeled as a random process. Thus, a fading channel is a communication channel that experiences fading. In this paper, the proposed system presents a new design and simulate a wireless channel using Rayleigh channels. Rayleigh channels using two approaches (flat and frequency-selective fading channels) in order to calculate some path space loss efforts and analysis the performance of different wireless fading channel modeling. The results show that the bite error rate (BER) performance is dramatically improved in the value of signal to noise ratio (SNR) is equal to 45dB. Finally, the experimental results show that the proposed method enhances the performance of fading channel modeling by reducing the error of BER when the SNR is reduced also. Moreover, the more accurate model is Rayleigh model which can be considered for developing fading channel model.
The current work studies the effect of adding chopped carbon fiber (CCF) on gypsum plaster properties (precisely the compressive strength and the modulus of rupture). The research plan consists of using six mixes of gypsum plaster; these mixes are divided into two groups according to the (Water/Gypsum) ratios (0.5 & 0.6). Each group was divided into three subgroups according to CCF volume fraction (Vf): 0.0%, 0.2% and 0.4%. Three cubic (50×50×50) mm and three prismatic (40×40×160) mm samples were performed for each mix. It was found that, the addition of CCF to the gypsum plaster mixes increases both the compressive strength and the modulus of rupture for both (W/G) ratios, an
Today with increase using social media, a lot of researchers have interested in topic extraction from Twitter. Twitter is an unstructured short text and messy that it is critical to find topics from tweets. While topic modeling algorithms such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) are originally designed to derive topics from large documents such as articles, and books. They are often less efficient when applied to short text content like Twitter. Luckily, Twitter has many features that represent the interaction between users. Tweets have rich user-generated hashtags as keywords. In this paper, we exploit the hashtags feature to improve topics learned
This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback
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