Improving PAPR performance of filtered OFDM for 5G communications using PTS

The filtered orthogonal frequency division multiplexing (F-OFDM) system has been recommended as a waveform candidate for fifth-generation (5G) communications. The suppression of out-of-band emission (OOBE) and asynchronous transmission are the distinctive features of the filtering-based waveform frameworks. Meanwhile, the high peak-to-average power ratio (PAPR) is still a challenge for the new waveform candidates. Partial transmit sequence (PTS) is an effective technique for mitigating the trend of high PAPR in multicarrier systems. In this study, the PTS technique is employed to reduce the high PAPR value of an F-OFDM system. Then, this system is compared with the OFDM system. In addition, the other related parameters such as frequency localization, bit error rate (BER), and computational complexity are evaluated and analyzed for both systems with and without PTS. The simulation results indicate that the F-OFDM based on PTS achieves higher levels of PAPR, BER, and OOBE performances compared with OFDM. Moreover, the BER performance of F-OFDM is uninfluenced by the use of the PTS technique.     

Multiple responses analysis and modeling of Fenton process for treatment of high strength landfill leachate

Landfill leachate is one of the most recalcitrant wastes for biotreatment and can be considered a potential source of contamination to surface and groundwater ecosystems. In the present study, Fenton oxidation was employed for degradation of stabilized landfill leachate. Response surface methodology was applied to analyze, model and optimize the process parameters, i.e. pH and reaction time as well as the initial concentrations of hydrogen peroxide and ferrous ion. Analysis of variance showed that good coefficients of determination were obtained (R2 > 0.99), thus ensuring satisfactory agreement of the second-order regression model with the experimental data. The results indicated that, pH and its quadratic effects were the main factors influencing Fenton oxidation. Furthermore, antagonistic effects between pH and other variables were observed. The optimum H2O2 concentration, Fe(II) concentration, pH and reaction time were 0.033 mol/L, 0.011 mol/L, 3 and 145 min, respectively, with 58.3% COD, 79.0% color and 82.1% iron removals.     

Reservoir Inflow Prediction by Ensembling Wavelet and Bootstrap Techniques to Multiple Linear Regression Model

In this study, a new hybrid model, bootstrap multiple linear regression (BMLR) is suggested to investigate the potential of bootstrap resampling technique for daily reservoir inflow prediction. The proposed model compares with three other models: Multiple linear regression (MLR), wavelet multiple linear regression (WMLR) and wavelet bootstrap multiple linear regression (WBMLR). River stage data of monsoon season (1st July 2010 to 30 September 2010) from three gauging stations of Chenab river basin are used. In wavelet transformation, input vectors are decomposed using discrete wavelet transformation (DWT) into discrete wavelet components (DWCs). Then suitable DWCs are used to provide input to MLR model to develop WMLR model. Bootstrap technique coupled with MLR model to build up BMLR model. While WBMLR model is the conjunction of suitable DWCs and bootstrap technique to MLR model. Performance indices namely root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe coefficient of efficiency (NSC), and persistence index (CP) are used in study to evaluate the performance of model. Results showed that hybrid model BMLR produce significantly better results on performance indices than other models MLR, WMLR and WBMLR.

     

Software Assisted Digital RF Processor (DRP™) for Single-Chip GSM Radio in 90 nm CMOS

This paper proposes and describes a new software and application programming interface view of an RF transceiver. It demonstrates benefits of using highly programmable digital control logic in an RF wireless system realized in a digital nanoscale CMOS process technology. It also describes a microprocessor architecture design in Digital RF Processor (DRP™) and how it controls calibration and compensation for process, temperature and voltage variations of the analog and RF circuits to meet the required RF performance. A few calibration examples to reduce a DCO bias current and improve device reliability, as well as to optimize transmit modulation and receive performance, are given. The presented circuits and techniques have enabled successful implementation of a commercial single-chip GSM radio in 90 nm CMOS.      

Hybridized synergy effect among TiO2, Pt and graphite silica on photocatalytic hydrogen production from water–methanol solution

The photocatalytic production of hydrogen gas from aqueous methanol solution was performed using powdered mixtures of graphite silica (GS) and platinized TiO₂ (Pt/TiO₂) or those of GS, Pt and TiO₂. The addition of GS to Pt/TiO₂ resulted in the decrease of the amount of H₂ gas, whereas the addition of GS to mixtures of TiO₂ and Pt led to the incremental production of H₂ gas. This effect is attributed to the aggregation of GS, TiO₂ and Pt in suspension. The new additional electron transfer process of TiO₂ → Pt → GS caused the increment of the amount of hydrogen gas.     

Influence of Ultrasonic Waves on the Processing of Cotton with Cationic Softener

In recent years, the textile industry has been forced to develop new technologies to reduce energy and water consumption. The use of ultrasound in textile wet processing is one solution to this problem. The aim of this work was to investigate the effects of ultrasonic energy on the processing of cotton with a cationic softener. For this purpose, cotton fabric was treated with a fatty acid amide derivative cationic softener in water using ultrasonic energy during treatment. The physical properties of the fabrics treated under different conditions are discussed. The results show that the treatment of fabrics with softeners in an ultrasound bath is more effective compared to conventional methods and that it enhances the physical properties of the cotton.     

Senti‐CS: Building a lexical resource for sentiment analysis using subjective feature selection and normalized Chi‐Square‐based feature weight generation

Sentiment analysis involves the detection of sentiment content of text using natural language processing. Natural language processing is a very challenging task due to syntactic ambiguities, named entity recognition, use of slangs, jargons, sarcasm, abbreviations and contextual sensitivity. Sentiment analysis can be performed using supervised as well as unsupervised approaches. As the amount of data grows, unsupervised approaches become vital as they cut down on the learning time and the requirements for availability of a labelled dataset. Sentiment lexicons provide an easy application of unsupervised algorithms for text classification. SentiWordNet is a lexical resource widely employed by many researchers for sentiment analysis and polarity classification. However, the reported performance levels need improvement. The proposed research is focused on raising the performance of SentiWordNet3.0 by using it as a labelled corpus to build another sentiment lexicon, named Senti‐CS. The part of speech information, usage based ranks and sentiment scores are used to calculate Chi‐Square‐based feature weight for each unique subjective term/part‐of‐speech pair extracted from SentiWordNet3.0. This weight is then normalized in a range of ?1 to +1 using min–max normalization. Senti‐CS based sentiment analysis framework is presented and applied on a large dataset of 50000 movie reviews. These results are then compared with baseline SentiWordNet, Mutual Information and Information Gain techniques. State of the art comparison is performed for the Cornell movie review dataset. The analyses of results indicate that the proposed approach outperforms state‐of‐the‐art classifiers.     

Polyacrylonitrile–poly(1-vinyl pyrrolidone-co-vinyl acetate) blend based gel polymer electrolytes incorporated with sodium iodide salt for dye-sensitized solar cell applications

New gel polymer electrolytes (GPEs) were prepared via the blending of a polyacrylonitrile polymer and a poly(1-vinyl pyrrolidone-co-vinyl acetate) copolymer. The sodium iodide (NaI) salt concentration was varied for each GPE sample. From ionic conductivity (σ) studies, we observed that the sample with a 40 wt % NaI salt content (N40) showed the highest σ of 3.54 × 10⁻³ ± 0.05 S/cm at room temperature, and all of the GPE samples obeyed Arrhenius behavior. The dielectric properties of the GPE samples were also analyzed to study the electrical polarization of the materials. The developed GPE samples were also characterized with X-ray diffraction and Fourier transform infrared spectroscopy. We also then used the developed GPE samples for the fabrication of dye-sensitized solar cells by sandwiching them between a photoanode and Pt counter electrode for photovoltaic studies. The highest photovoltaic performance was achieved by N40, with an efficiency of 3.04%. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47810.     

Zinc oxide-decorated multiwalled carbon nanotubes: a selective electrochemical sensor for the detection of Pb(II) ion in aqueous media

Journal of Materials Science: Materials in Electronics - Multiwalled carbon nanotubes, due to high conductivity, stability, and large specific surface area, have a potential ability to promote...