Orthogonal Frequency Division Multiplexing (OFDM) is widely used technique in high data rate wireless communication system. The major drawback of the OFDM system is large Peak to Average Power Ratio (PAPR) which can overweigh all the potential benefit of the OFDM system. Due to high PAPR, the power amplifier lead to saturation and generates inter modulation among the subcarriers. In this paper, we propose iterative clipping and filtering technique along with convolutional code that clipped the amplitude of the OFDM signal, which reduces the PAPR without much degradation in the bit error rate (BER). The reduction in amplitude variation by clipping creates distortion in the OFDM signal. Due to this distortion at the receiver end result higher BER. This BER performance is further improved by using convolutional code. MATLAB coding result shows that sharp drop on Complementary Cumulative Distribution Function (CCDF)Curve and reduced the PAPR.
Sentiment Analysis (SA) is a task of identifying positive and negative opinions, emotion and evaluation in text available over the social networking sites and the world wide web have been gained quite a popularity in the recent years. The analysis serves as an important feedback for further improvement in the offered services and user experiences. Several techniques have been used recently including machine learning approaches and vocabulary orientated semantic algorithms. This report presents a survey of various techniques and tools have been used in the previous research sentiment analysis process. There is a massive increase in number of people who access various social networking and micro-blogging websites that gives new shapes the impression of today’s generation. Many reviews for a specific product, brand, individual, and movies etc. are helpful in directing the perception of people thus the analysts are begun to create algorithms to automate the classification of distinctive reviews on the basis of their polarities in particular : Positive, Negative and Neutral. This machine-driven classification mechanism is referred as Sentiment Analysis. The ultimate aim of this paper is to use support vector machine (SVM) classification technique to classify the feelings of good phone product review that analyses datasets used for classification of sentiments and texts. Also, data sets are used for training as well as testing and implemented through SVM technique for finding the polarity of the ambiguous tweets. The obtain results show to achieve high accuracy as predicted on the basis of reviews of smart phone.
Clustering As a result of the rapid development in cloud computing, it & fundamental to investigate the performance of extraordinary Hadoop MapReduce purposes and to realize the performance bottleneck in a cloud cluster that contributes to higher or diminish performance. It is usually primary to research the underlying hardware in cloud cluster servers to permit the optimization of program and hardware to achieve the highest performance feasible. Hadoop is founded on MapReduce, which is among the most popular programming items for huge knowledge analysis in a parallel computing environment. In this paper, we reward a particular efficiency analysis, characterization, and evaluation of Hadoop MapReduce Word Count utility. The main aim of this paper is to give implements of Hadoop map-reduce programming by giving a hands-on experience in developing Hadoop based Word-Count and Apriori application. Word count problem using Hadoop Map Reduce framework. The Apriori Algorithm has been used for finding frequent item set using Map Reduce framework.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.