Pub Date : 2016-12-01DOI: 10.1109/ICSPCOM.2016.7980627
A. Chattopadhyay, M. H. Khondekar, A. Bhattacharjee
In this article, an effort has been made to investigate the nonlinear and chaotic nature of daily CME linear speed time series data collected from the Solar and Heliospheric Observatory for solar cycle 23 over the period of February 1999 to December 2007. To explore the nonlinear characteristic of the CME linear speed signal delay vector variance algorithm is used whereas 0–1 test, information entropy and also correlation dimension methods are performed to investigate the chaotic behaviour of the signal. The result of these analyses suggests that the CME linear speed time series signal generated source is definitely nonlinear and deterministic with chaotic behaviour which validates that the possibilities of forecasting for long duration is nearly impossible but forecasting for short span can be achieved on condition that the underlying dynamics of the process must be known.
{"title":"Complexity of CME linear speed time series","authors":"A. Chattopadhyay, M. H. Khondekar, A. Bhattacharjee","doi":"10.1109/ICSPCOM.2016.7980627","DOIUrl":"https://doi.org/10.1109/ICSPCOM.2016.7980627","url":null,"abstract":"In this article, an effort has been made to investigate the nonlinear and chaotic nature of daily CME linear speed time series data collected from the Solar and Heliospheric Observatory for solar cycle 23 over the period of February 1999 to December 2007. To explore the nonlinear characteristic of the CME linear speed signal delay vector variance algorithm is used whereas 0–1 test, information entropy and also correlation dimension methods are performed to investigate the chaotic behaviour of the signal. The result of these analyses suggests that the CME linear speed time series signal generated source is definitely nonlinear and deterministic with chaotic behaviour which validates that the possibilities of forecasting for long duration is nearly impossible but forecasting for short span can be achieved on condition that the underlying dynamics of the process must be known.","PeriodicalId":213713,"journal":{"name":"2016 International Conference on Signal Processing and Communication (ICSC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121094832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-01DOI: 10.1109/ICSPCOM.2016.7980567
Veervrat Singh Chandrawanshi, R. Tripathi, N. U. Khan
The k-means initialization technique for a wireless sensor network is a newly emerging area for researchers. There are many constraints in designing the wireless sensor network. The primary constraint is energy consumption. Clustering is used for improving the lifetime of the system by reducing the power consumption. The most popular clustering technique is k-means algorithm but it exhibits local minima problem due to initial center selection. This paper provides the comprehensive survey of different initialization techniques such as Uniform Sampling, Random Sampling, k-means++ and Density based initialization. The above comparison has been made by taking the account of energy consumption and the lifetime of the wireless sensor network.
{"title":"A comprehensive study on k-means algorithms initialization techniques for wireless sensor network","authors":"Veervrat Singh Chandrawanshi, R. Tripathi, N. U. Khan","doi":"10.1109/ICSPCOM.2016.7980567","DOIUrl":"https://doi.org/10.1109/ICSPCOM.2016.7980567","url":null,"abstract":"The k-means initialization technique for a wireless sensor network is a newly emerging area for researchers. There are many constraints in designing the wireless sensor network. The primary constraint is energy consumption. Clustering is used for improving the lifetime of the system by reducing the power consumption. The most popular clustering technique is k-means algorithm but it exhibits local minima problem due to initial center selection. This paper provides the comprehensive survey of different initialization techniques such as Uniform Sampling, Random Sampling, k-means++ and Density based initialization. The above comparison has been made by taking the account of energy consumption and the lifetime of the wireless sensor network.","PeriodicalId":213713,"journal":{"name":"2016 International Conference on Signal Processing and Communication (ICSC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121647034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-01DOI: 10.1109/ICSPCOM.2016.7980571
W. Akaram, D. Rano, A. Q. Ansari
This paper present different design methods to improve the performance of MPA. A comparative analysis of EBG and DGS structures in terms of performance has been given. The proposed DGS structure improves gain, efficiency and the return loss of the antenna. The antenna w/o EBG and DGS resonates at 2.4 GHz offering bandwidth of 67.8 MHz. Three different types of DGS structure have been designed and their effects on the antenna performance have been tabulated. The DGS structure provides good matching with reduction in size more than 4%.
{"title":"Comparative analysis of performance improvement of MPA by using EBG and DGS structures","authors":"W. Akaram, D. Rano, A. Q. Ansari","doi":"10.1109/ICSPCOM.2016.7980571","DOIUrl":"https://doi.org/10.1109/ICSPCOM.2016.7980571","url":null,"abstract":"This paper present different design methods to improve the performance of MPA. A comparative analysis of EBG and DGS structures in terms of performance has been given. The proposed DGS structure improves gain, efficiency and the return loss of the antenna. The antenna w/o EBG and DGS resonates at 2.4 GHz offering bandwidth of 67.8 MHz. Three different types of DGS structure have been designed and their effects on the antenna performance have been tabulated. The DGS structure provides good matching with reduction in size more than 4%.","PeriodicalId":213713,"journal":{"name":"2016 International Conference on Signal Processing and Communication (ICSC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115544743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-01DOI: 10.1109/ICSPCOM.2016.7980550
G. Jandieri
Statistical characteristics of radio waves scattered in the turbulent ionospheric plasma are investigated analytically by modify perturbation method taking into account both polarization coefficients and diffraction effects of both ordinary and extraordinary waves. Numerical investigations are carried out for anisotropic Gaussian correlation function containing anisotropy coefficient and the angle of inclination of elongated plasma irregularities with respect to the geomagnetic lines of forces. Scintillation level is analyzed for different parameters characterizing anisotropic irregularities using experimental data.
{"title":"Scintillation spectra of multiple scattered radio waves in the ionospheric plasma","authors":"G. Jandieri","doi":"10.1109/ICSPCOM.2016.7980550","DOIUrl":"https://doi.org/10.1109/ICSPCOM.2016.7980550","url":null,"abstract":"Statistical characteristics of radio waves scattered in the turbulent ionospheric plasma are investigated analytically by modify perturbation method taking into account both polarization coefficients and diffraction effects of both ordinary and extraordinary waves. Numerical investigations are carried out for anisotropic Gaussian correlation function containing anisotropy coefficient and the angle of inclination of elongated plasma irregularities with respect to the geomagnetic lines of forces. Scintillation level is analyzed for different parameters characterizing anisotropic irregularities using experimental data.","PeriodicalId":213713,"journal":{"name":"2016 International Conference on Signal Processing and Communication (ICSC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130838202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-01DOI: 10.1109/ICSPCOM.2016.7980597
Nitin Jonathan Myers, R. Yasarla
This paper proves the optimality of circular 8QAM in modulating 3 bits/symbol over an AWGN channel, using a parametrised constellation. Incorporating power amplifier's limitations, we move to a differential scheme that modulates 2 bits/symbol and analytically prove that maximum energy efficiency for this scheme can be achieved when 8PSK is the underlying constellation, and not circular 8QAM. We also discuss about non data aided carrier frequency offset estimation when circular 8QAM is used and compare the performance of modified Mth power algorithm with the generalised QPSK partitioning algorithm.
{"title":"Analysis of 8 point constellations - optimality and recovery","authors":"Nitin Jonathan Myers, R. Yasarla","doi":"10.1109/ICSPCOM.2016.7980597","DOIUrl":"https://doi.org/10.1109/ICSPCOM.2016.7980597","url":null,"abstract":"This paper proves the optimality of circular 8QAM in modulating 3 bits/symbol over an AWGN channel, using a parametrised constellation. Incorporating power amplifier's limitations, we move to a differential scheme that modulates 2 bits/symbol and analytically prove that maximum energy efficiency for this scheme can be achieved when 8PSK is the underlying constellation, and not circular 8QAM. We also discuss about non data aided carrier frequency offset estimation when circular 8QAM is used and compare the performance of modified Mth power algorithm with the generalised QPSK partitioning algorithm.","PeriodicalId":213713,"journal":{"name":"2016 International Conference on Signal Processing and Communication (ICSC)","volume":"296 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128536496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-01DOI: 10.1109/ICSPCOM.2016.7980599
Pushpendra Singh, Amit Singhal
In this paper, we present a simple and intuitive approach to estimate the frequency of a single-tone signal in the presence of noise. We obtain three optimum discrete-time Fourier transform (DTFT) points for application of non-polynomial parabola interpolation to determine the frequency corresponding to its maxima. The results indicate that high performance, in terms of root-mean-square error (RMSE) values comparable to Cramer-Rao lower bound (CRLB), can be achieved over a large range of signal to noise ratio (SNR).
{"title":"Frequency estimation of a sinusoidal signal","authors":"Pushpendra Singh, Amit Singhal","doi":"10.1109/ICSPCOM.2016.7980599","DOIUrl":"https://doi.org/10.1109/ICSPCOM.2016.7980599","url":null,"abstract":"In this paper, we present a simple and intuitive approach to estimate the frequency of a single-tone signal in the presence of noise. We obtain three optimum discrete-time Fourier transform (DTFT) points for application of non-polynomial parabola interpolation to determine the frequency corresponding to its maxima. The results indicate that high performance, in terms of root-mean-square error (RMSE) values comparable to Cramer-Rao lower bound (CRLB), can be achieved over a large range of signal to noise ratio (SNR).","PeriodicalId":213713,"journal":{"name":"2016 International Conference on Signal Processing and Communication (ICSC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126770135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-01DOI: 10.1109/ICSPCOM.2016.7980635
V. Saxena
In this paper an experiment has been done on proposed algorithm for calculation of wall parameters. 2D-beam forming image has been developed by using calculated wall parameters, but calculated wall parameters are not so exact so auto focusing techniques based on higher order statistics has been presented that correct wall ambiguities. All data has been taken in real time environments by interfacing the VNA to the laptop.
{"title":"Characterization of wall parameters and autofocusing techniques for through - the-wall-imaging system","authors":"V. Saxena","doi":"10.1109/ICSPCOM.2016.7980635","DOIUrl":"https://doi.org/10.1109/ICSPCOM.2016.7980635","url":null,"abstract":"In this paper an experiment has been done on proposed algorithm for calculation of wall parameters. 2D-beam forming image has been developed by using calculated wall parameters, but calculated wall parameters are not so exact so auto focusing techniques based on higher order statistics has been presented that correct wall ambiguities. All data has been taken in real time environments by interfacing the VNA to the laptop.","PeriodicalId":213713,"journal":{"name":"2016 International Conference on Signal Processing and Communication (ICSC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126614891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-01DOI: 10.1109/ICSPCOM.2016.7980619
Shourya Gupta, K. Gupta, N. Pandey
In this paper, different dual-port SRAM cell structures have been analysed in deep submicron regions. Dual port cells have different ports for read and write operations. Two 9 transistor and two 10 transistor SRAM cells have been evaluated using the N-Curve Method. This method provides better analysis than the traditionally used Butterfly Curve method in submicron regions. The performance evaluation in this paper also includes Leakage Current, Cell Standby Current, Read Current and Data Retention Voltage (DRV). The SRAM cell simulations are performed on 22 nm, 32 nm and 45 nm CMOS technology nodes. All SRAM cells showed moderately desirable parameters, with each cell displaying a performance edge in a certain niche. However, the 9T SRAM cell with supply feedback provided considerably good performance parameters across all technology nodes, exhibiting the highest noise margins, lowest leakage currents, lowest data retention voltage and the lowest read currents.
{"title":"Stability analysis of different dual-port SRAM cells in deep submicron region using N-Curve Method","authors":"Shourya Gupta, K. Gupta, N. Pandey","doi":"10.1109/ICSPCOM.2016.7980619","DOIUrl":"https://doi.org/10.1109/ICSPCOM.2016.7980619","url":null,"abstract":"In this paper, different dual-port SRAM cell structures have been analysed in deep submicron regions. Dual port cells have different ports for read and write operations. Two 9 transistor and two 10 transistor SRAM cells have been evaluated using the N-Curve Method. This method provides better analysis than the traditionally used Butterfly Curve method in submicron regions. The performance evaluation in this paper also includes Leakage Current, Cell Standby Current, Read Current and Data Retention Voltage (DRV). The SRAM cell simulations are performed on 22 nm, 32 nm and 45 nm CMOS technology nodes. All SRAM cells showed moderately desirable parameters, with each cell displaying a performance edge in a certain niche. However, the 9T SRAM cell with supply feedback provided considerably good performance parameters across all technology nodes, exhibiting the highest noise margins, lowest leakage currents, lowest data retention voltage and the lowest read currents.","PeriodicalId":213713,"journal":{"name":"2016 International Conference on Signal Processing and Communication (ICSC)","volume":"56 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120811701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-01DOI: 10.1109/ICSPCOM.2016.7980624
Abhijit Mohanta, V. K. Mittal
In the field of Human Computer Interaction (HCI), human emotion recognition from speech signal is evolving as a recent research area. Speech is the most common way for communication among human beings. Speech consists of sentences, which can be further segregated into words. Words consist of phonemes which are considered to be the primary voice construction elements. This paper presents a classification of four basic emotional states, namely anger, happy, sad, and neutral by extracting acoustic features from the speech signal. Production features mainly F0, i.e., pitch and formants F1, F2, and F3 are derived from the speech signal using only the vowel parts of English language i.e., /a/, /e/, /i/, /o/, and /u/, without requiring to process the speech signal of entire utterances or sentences. Using the pitch and formants feature vectors, the emotion classification has been carried out using a Support Vector Machine (SVM) classifier. In this preliminary investigation, the vowel regions have been separated manually, so as to assess their efficacy in classifying the emotions. The approach has been validated using an emotional speech dataset in English language, collected especially for this study. The performance evaluation results obtained are encouraging. This approach can be further refined for wider applications.
{"title":"Classifying emotional states using pitch and formants in vowel regions","authors":"Abhijit Mohanta, V. K. Mittal","doi":"10.1109/ICSPCOM.2016.7980624","DOIUrl":"https://doi.org/10.1109/ICSPCOM.2016.7980624","url":null,"abstract":"In the field of Human Computer Interaction (HCI), human emotion recognition from speech signal is evolving as a recent research area. Speech is the most common way for communication among human beings. Speech consists of sentences, which can be further segregated into words. Words consist of phonemes which are considered to be the primary voice construction elements. This paper presents a classification of four basic emotional states, namely anger, happy, sad, and neutral by extracting acoustic features from the speech signal. Production features mainly F0, i.e., pitch and formants F1, F2, and F3 are derived from the speech signal using only the vowel parts of English language i.e., /a/, /e/, /i/, /o/, and /u/, without requiring to process the speech signal of entire utterances or sentences. Using the pitch and formants feature vectors, the emotion classification has been carried out using a Support Vector Machine (SVM) classifier. In this preliminary investigation, the vowel regions have been separated manually, so as to assess their efficacy in classifying the emotions. The approach has been validated using an emotional speech dataset in English language, collected especially for this study. The performance evaluation results obtained are encouraging. This approach can be further refined for wider applications.","PeriodicalId":213713,"journal":{"name":"2016 International Conference on Signal Processing and Communication (ICSC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115255818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-01DOI: 10.1109/ICSPCOM.2016.7980579
A. Pandey, D. Rajpoot
In software engineering, information retrieval which is also referred as data mining has attracted many researcher's attention. By the virtue of its definition, data mining is responsible for extracting relevant data from large volume of database or dataset. In this context, several techniques have been proposed in literature. Through this paper, an attempt to comparative analysis of various classification algorithms has been made. Such analysis has been done with the help of data mining tool named WEKA on dataset of alcohol consumption by school students. WEKA is a open framework programming tool consisting of various inbuilt classification algorithms like J48, Random Forest, Decision Tree, Random Tree, NaiveBayes, SimpleNaiveBayes, NaiveBayes, DecisionStump, etc. However, the comparison of these algorithms has been made with the help of approaches that include correctly classified, incorrectly classified, Accuracy and many others parameters.
{"title":"A comparative study of classification techniques by utilizing WEKA","authors":"A. Pandey, D. Rajpoot","doi":"10.1109/ICSPCOM.2016.7980579","DOIUrl":"https://doi.org/10.1109/ICSPCOM.2016.7980579","url":null,"abstract":"In software engineering, information retrieval which is also referred as data mining has attracted many researcher's attention. By the virtue of its definition, data mining is responsible for extracting relevant data from large volume of database or dataset. In this context, several techniques have been proposed in literature. Through this paper, an attempt to comparative analysis of various classification algorithms has been made. Such analysis has been done with the help of data mining tool named WEKA on dataset of alcohol consumption by school students. WEKA is a open framework programming tool consisting of various inbuilt classification algorithms like J48, Random Forest, Decision Tree, Random Tree, NaiveBayes, SimpleNaiveBayes, NaiveBayes, DecisionStump, etc. However, the comparison of these algorithms has been made with the help of approaches that include correctly classified, incorrectly classified, Accuracy and many others parameters.","PeriodicalId":213713,"journal":{"name":"2016 International Conference on Signal Processing and Communication (ICSC)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125009604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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