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Home / programming language / Online Matlab Projects / Detection and Classification of OFDM Waveforms Using Cepstral Analysis
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Detection and Classification of OFDM Waveforms Using Cepstral Analysis

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SKU: PROJ5197 Categories: 2016 Projects, 2015 Projects, Final Year Projects, Online Matlab Projects, Wireless Sensor Networking Tags: academic projects, Android project 2013-2014, Android project 2015-2016, Android project Abstract, Android project list, btech projects, dotnet project 2013-2014, dotnet project 2015-2016, dotnet project Abstract, dotnet project list, elysium technologies abstract, elysium technologies chennai, elysium technologies coimbatore, elysium technologies company, elysium technologies courses, elysium technologies erode, elysium technologies inpant traning, elysium technologies internship, elysium technologies jobs, elysium technologies madurai, elysium technologies mou, elysium technologies pondychery, elysium technologies projectlist, elysium technologies projects, elysium technologies ramnad, elysium technologies salem, elysium technologies software, elysium technologies tirunelveli, elysium technologies trichy, Final Year Projects, java projects 2013-2014, java projects 2015-2016, java projects Abstract, madurai software company, matlab project 2013-2014, matlab project 2015-2016, matlab project Abstract, matlab project list, mtech projects, phd research work, Php project 2013-2014, Php project 2015-2016, Php project Abstract, Php project list, Power Electronic project 2013-2014, Power Electronic project 2015-2016, Power Electronic project Abstract, Power Electronic project list, project center Bangalore, project center chennai, project center coimbatore, project center Erode, project center Hyderabad, project center Kollam, project center madurai, project center Pandicherry, project center ramnad, project center Salem, project center Tiruneveli, project center trichy, research center, Students Projects, Vlsi project 2013-2014, Vlsi project 2015-2016, Vlsi project Abstract, Vlsi project list
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Detection and Classification of OFDM Waveforms Using Cepstral Analysis

Abstract—Detection and Classification of OFDM Waveforms Using Cepstral Analysis .Also presence of cyclic prefix in the orthogonal frequency division multiplexing induces periodicities. Thus the distributions of coefficients are derived for two schemes. As of all, significantly different from the additive white Gaussian noise (AWGN) and can be used to detect waveforms. Therefore OFDM is rich in features and to estimate the number of subcarriers and length of CP in a symbol. Secondly these parameters are used to automatically identify or classify different waveforms, which are important for the cognitive radios, coexistence of heterogeneous networks and signal intelligence. Since then it has been in various applications such as radar, sonar, marine and earth seismology, speech processing, image processing and deconvolution of the probability density functions. Probably two methods are suggested based on the primary user (PU). While the test information under two theory is created. Consequently Neyman–Pearson technique is used. Almost the algorithms for calculating the number of subcarriers and the length of the CP are introduced. Seems like their performances are studied through simulations. Later the schemes are extended to cooperative sensing events with the multiple secondary users < Final Year Projects 2016 >. As a result, it is shown that the combination between them importantly, improve the detection and analysis. After then it has been used in various applications such as radar, sonar, marine and also especially the earth seismology, image processing and of the probability density functions. May be the ability of the cepstrum to find the periodicities in the signal and to separate a glottal action , a vocal tract transfer is proven. Which resulted in its general use in the processing such as the voice pitch, vocoders.Furthermore it is mostly used in a very high communication applications. As a result the response of a selective channel is judged.

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