Pub Date : 2011-11-03DOI: 10.1109/RAICS.2011.6069329
N. Rao, V. Dinesh Kumar
In this paper, a novel design of an electromagnetic band-gap (EBG) structure using finite difference time-domain (FDTD) solver has been proposed for application in microstrip antennas. This EBG structure when incorporated with microstrip patch antenna is found to increase its gain remarkably. The designed EBG structures suppress propagation of surface waves at a particular band-gap frequency and have been found to decrease reflection loss significantly. The EBG structure has been designed for a band-gap 6.5 to 8.5 GHz. This structure has been applied on a rectangular patch substrate with dielectric constant 6.6. The gain using the designed structure has been found to be 6.306 dB at 7.5 GHz.
{"title":"Investigation of a microstrip patch antenna with EBG structures using FDTD method","authors":"N. Rao, V. Dinesh Kumar","doi":"10.1109/RAICS.2011.6069329","DOIUrl":"https://doi.org/10.1109/RAICS.2011.6069329","url":null,"abstract":"In this paper, a novel design of an electromagnetic band-gap (EBG) structure using finite difference time-domain (FDTD) solver has been proposed for application in microstrip antennas. This EBG structure when incorporated with microstrip patch antenna is found to increase its gain remarkably. The designed EBG structures suppress propagation of surface waves at a particular band-gap frequency and have been found to decrease reflection loss significantly. The EBG structure has been designed for a band-gap 6.5 to 8.5 GHz. This structure has been applied on a rectangular patch substrate with dielectric constant 6.6. The gain using the designed structure has been found to be 6.306 dB at 7.5 GHz.","PeriodicalId":394515,"journal":{"name":"2011 IEEE Recent Advances in Intelligent Computational Systems","volume":"45 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124432024","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 : 2011-11-03DOI: 10.1109/RAICS.2011.6069359
V. Raj, T. Venkateswarlu
In Medical diagnosis operations such as feature extraction and object recognition will play the key role. These tasks will become difficult if the images are corrupted with noises. So the development of effective algorithms for noise removal became an important research area in present days. Developing Image denoising algorithms is a difficult task since fine details in a medical image embedding diagnostic information should not be destroyed during noise removal. Many of the wavelet based denoising algorithms use DWT (Discrete Wavelet Transform) in the decomposition stage which is suffering from shift variance. To overcome this in this paper we are proposing the denoising method which uses Undecimated Wavelet Transform to decompose the image and we performed the shrinkage operation to eliminate the noise from the noisy image. In the shrinkage step we used semi-soft and stein thresholding operators along with traditional hard and soft thresholding operators and verified the suitability of different wavelet families for the denoising of medical images. The results proved that the denoised image using UDWT (Undecimated Discrete Wavelet Transform) have a better balance between smoothness and accuracy than the DWT. We used the SSIM (Structural similarity index measure) along with PSNR to assess the quality of denoised images.
{"title":"Denoising of medical images using undecimated wavelet transform","authors":"V. Raj, T. Venkateswarlu","doi":"10.1109/RAICS.2011.6069359","DOIUrl":"https://doi.org/10.1109/RAICS.2011.6069359","url":null,"abstract":"In Medical diagnosis operations such as feature extraction and object recognition will play the key role. These tasks will become difficult if the images are corrupted with noises. So the development of effective algorithms for noise removal became an important research area in present days. Developing Image denoising algorithms is a difficult task since fine details in a medical image embedding diagnostic information should not be destroyed during noise removal. Many of the wavelet based denoising algorithms use DWT (Discrete Wavelet Transform) in the decomposition stage which is suffering from shift variance. To overcome this in this paper we are proposing the denoising method which uses Undecimated Wavelet Transform to decompose the image and we performed the shrinkage operation to eliminate the noise from the noisy image. In the shrinkage step we used semi-soft and stein thresholding operators along with traditional hard and soft thresholding operators and verified the suitability of different wavelet families for the denoising of medical images. The results proved that the denoised image using UDWT (Undecimated Discrete Wavelet Transform) have a better balance between smoothness and accuracy than the DWT. We used the SSIM (Structural similarity index measure) along with PSNR to assess the quality of denoised images.","PeriodicalId":394515,"journal":{"name":"2011 IEEE Recent Advances in Intelligent Computational Systems","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123473542","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 : 2011-11-03DOI: 10.1109/RAICS.2011.6069325
N. Yadaiah, R. Bapi, Lakshman Singh, B. Deekshatulu
In this paper decoupled extended kalman filter (DEKF) based Recurrent Neural Network (RNN) has been proposed for state estimation of nonlinear dynamical systems. The proposed state estimator uses cascading of recurrent neural network structures to learn the internal behavior of the dynamical system along with the measuring relations of the system from the input-output data through prediction error minimization. A dynamic learning algorithm for the recurrent neural network has been developed using DEKF. The performance of the proposed method is illustrated for an induction motor which is a typical nonlinear dynamical system and has been compared with that of the conventional state estimation method such as EKF.
{"title":"DEKF based Recurrent Neural Network for state estimation of nonlinear dynamical systems","authors":"N. Yadaiah, R. Bapi, Lakshman Singh, B. Deekshatulu","doi":"10.1109/RAICS.2011.6069325","DOIUrl":"https://doi.org/10.1109/RAICS.2011.6069325","url":null,"abstract":"In this paper decoupled extended kalman filter (DEKF) based Recurrent Neural Network (RNN) has been proposed for state estimation of nonlinear dynamical systems. The proposed state estimator uses cascading of recurrent neural network structures to learn the internal behavior of the dynamical system along with the measuring relations of the system from the input-output data through prediction error minimization. A dynamic learning algorithm for the recurrent neural network has been developed using DEKF. The performance of the proposed method is illustrated for an induction motor which is a typical nonlinear dynamical system and has been compared with that of the conventional state estimation method such as EKF.","PeriodicalId":394515,"journal":{"name":"2011 IEEE Recent Advances in Intelligent Computational Systems","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122629496","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 : 2011-11-03DOI: 10.1109/RAICS.2011.6069446
Praveen S. Babu, S. Ushakumari
Direct Torque Control (DTC) is one of the most excellent strategies of torque control of an induction machine. They aim to provide a decoupled control of torque and flux. The main drawback associated with the conventional DTC was the high torque and flux ripples and also variable switching frequency of the devices. This drawback was rectified with the usage of space vector modulation(SVM) technique. Here, it is intended to make an analysis study of two such direct torque control schemes for induction motor drive which incorporates the space vector modulation technique a) Modified Direct Torque Control scheme-1 (MDTC-1) comprising of the PI controllers and the SVM technique and b) Modified DTC scheme-2 (MDTC-2) comprising of comprising of the sliding mode controllers and the SVM technique. Finally the effectiveness and validity of MDTC -1 and MDTC-2 schemes for the induction motor drives has been analysed, studied and confirmed by simulation using MATLAB®/SIMULINK®.
{"title":"Modified Direct Torque Control of induction motor drives","authors":"Praveen S. Babu, S. Ushakumari","doi":"10.1109/RAICS.2011.6069446","DOIUrl":"https://doi.org/10.1109/RAICS.2011.6069446","url":null,"abstract":"Direct Torque Control (DTC) is one of the most excellent strategies of torque control of an induction machine. They aim to provide a decoupled control of torque and flux. The main drawback associated with the conventional DTC was the high torque and flux ripples and also variable switching frequency of the devices. This drawback was rectified with the usage of space vector modulation(SVM) technique. Here, it is intended to make an analysis study of two such direct torque control schemes for induction motor drive which incorporates the space vector modulation technique a) Modified Direct Torque Control scheme-1 (MDTC-1) comprising of the PI controllers and the SVM technique and b) Modified DTC scheme-2 (MDTC-2) comprising of comprising of the sliding mode controllers and the SVM technique. Finally the effectiveness and validity of MDTC -1 and MDTC-2 schemes for the induction motor drives has been analysed, studied and confirmed by simulation using MATLAB®/SIMULINK®.","PeriodicalId":394515,"journal":{"name":"2011 IEEE Recent Advances in Intelligent Computational Systems","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123803741","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 : 2011-11-03DOI: 10.1109/RAICS.2011.6069435
Z. Ansari, A. V. Babuy, W. Ahmed, Mohammad Fazle Azeemz
Due to the continuous increase in growth and complexity of WWW, web site publishers are facing increasing difficulty in attracting and retaining users. In order to design attractive web sites, designers must understand their users' needs. Therefore analysing navigational behaviour of users is an important part of web page design. Web Usage Mining (WUM) is the application of data mining techniques to web usage data in order to discover the patterns that can be used to analyse the user's navigational behaviour. Preprocessing, knowledge extraction and results analysis are the three main steps of WUM. Due to large amount of irrelevant information present in the web logs, the original log file can not be directly used in the WUM process. During the preprocessing stage of WUM raw web log data is to transformed into a set of user profiles. Each user profile captures a set of URLs representing a user session. This sessionized data can be used as the input for a variety of data mining tasks such as clustering, association rule mining, sequence mining etc. If the data mining task at hand is clustering, the session files are filtered to remove very small sessions in order to eliminate the noise from the data. But direct removal of these small sized sessions may result in loss of a significant amount of information specially when the number of small sessions is large. We propose a “Fuzzy Set Theoretic” approach to deal with this problem. Instead of directly removing all the small sessions below a specified threshold, we assign weights to all the sessions using a “Fuzzy Membership Function” based on the number of URLs accessed by the sessions. After assigning the weights we apply a “Fuzzy c-Mean Clustering” algorithm to discover the clusters of user profiles. In this paper, we provide a detailed review of various techniques to preprocess the web log data including data fusion, data cleaning, user identification and session identification. We also describe our methodology to perform feature selection (or dimensionality reduction) and session weight assignment tasks. Finally we compare our soft computing based approach of session weight assignment with the traditional hard computing based approach of small session elimination.
{"title":"A Fuzzy Set Theoretic approach to discover user sessions from web navigational data","authors":"Z. Ansari, A. V. Babuy, W. Ahmed, Mohammad Fazle Azeemz","doi":"10.1109/RAICS.2011.6069435","DOIUrl":"https://doi.org/10.1109/RAICS.2011.6069435","url":null,"abstract":"Due to the continuous increase in growth and complexity of WWW, web site publishers are facing increasing difficulty in attracting and retaining users. In order to design attractive web sites, designers must understand their users' needs. Therefore analysing navigational behaviour of users is an important part of web page design. Web Usage Mining (WUM) is the application of data mining techniques to web usage data in order to discover the patterns that can be used to analyse the user's navigational behaviour. Preprocessing, knowledge extraction and results analysis are the three main steps of WUM. Due to large amount of irrelevant information present in the web logs, the original log file can not be directly used in the WUM process. During the preprocessing stage of WUM raw web log data is to transformed into a set of user profiles. Each user profile captures a set of URLs representing a user session. This sessionized data can be used as the input for a variety of data mining tasks such as clustering, association rule mining, sequence mining etc. If the data mining task at hand is clustering, the session files are filtered to remove very small sessions in order to eliminate the noise from the data. But direct removal of these small sized sessions may result in loss of a significant amount of information specially when the number of small sessions is large. We propose a “Fuzzy Set Theoretic” approach to deal with this problem. Instead of directly removing all the small sessions below a specified threshold, we assign weights to all the sessions using a “Fuzzy Membership Function” based on the number of URLs accessed by the sessions. After assigning the weights we apply a “Fuzzy c-Mean Clustering” algorithm to discover the clusters of user profiles. In this paper, we provide a detailed review of various techniques to preprocess the web log data including data fusion, data cleaning, user identification and session identification. We also describe our methodology to perform feature selection (or dimensionality reduction) and session weight assignment tasks. Finally we compare our soft computing based approach of session weight assignment with the traditional hard computing based approach of small session elimination.","PeriodicalId":394515,"journal":{"name":"2011 IEEE Recent Advances in Intelligent Computational Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133778346","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 : 2011-11-03DOI: 10.1109/RAICS.2011.6069340
R. Bhakthavatchalu, G. Deepthy, S. Sreenivasa Mallia, R. Harikrishnan, Arun Krishnan, B. Sruthi
The BIST technique for logic circuits improves access to internal signals from primary input/outputs. This paper presents programmable logic BIST architecture for testing ASIC chips. The scheme is based on STUMPS [6] (Self Test Using MISR [4, 6] and Parallel Shift register) architecture which uses an on-chip circuitry to generate the test patterns and analyze the responses with no or little help from an ATE. External operations are required only to initialize the Built-in tests and to check the test results. The system is synthesized in Xilinx ISE 10.1 to get the frequency of operation and in Design Compiler for timing Analysis. Multi Voltage design for power reduction is successfully implemented.
用于逻辑电路的BIST技术改进了对初级输入/输出的内部信号的访问。本文提出了用于测试ASIC芯片的可编程逻辑BIST体系结构。该方案基于STUMPS[6](使用MISR[4,6]和并行移位寄存器的自测)架构，该架构使用片上电路生成测试模式并分析响应，而无需或很少需要ATE的帮助。只有在初始化内置测试和检查测试结果时才需要外部操作。该系统在Xilinx ISE 10.1中合成以获得运行频率，并在Design Compiler中进行时序分析。成功实现了多电压降耗设计。
{"title":"32-bit reconfigurable logic-BIST design using Verilog for ASIC chips","authors":"R. Bhakthavatchalu, G. Deepthy, S. Sreenivasa Mallia, R. Harikrishnan, Arun Krishnan, B. Sruthi","doi":"10.1109/RAICS.2011.6069340","DOIUrl":"https://doi.org/10.1109/RAICS.2011.6069340","url":null,"abstract":"The BIST technique for logic circuits improves access to internal signals from primary input/outputs. This paper presents programmable logic BIST architecture for testing ASIC chips. The scheme is based on STUMPS [6] (Self Test Using MISR [4, 6] and Parallel Shift register) architecture which uses an on-chip circuitry to generate the test patterns and analyze the responses with no or little help from an ATE. External operations are required only to initialize the Built-in tests and to check the test results. The system is synthesized in Xilinx ISE 10.1 to get the frequency of operation and in Design Compiler for timing Analysis. Multi Voltage design for power reduction is successfully implemented.","PeriodicalId":394515,"journal":{"name":"2011 IEEE Recent Advances in Intelligent Computational Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128391149","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 : 2011-11-03DOI: 10.1109/RAICS.2011.6069401
S. Meher, Punyaban Patel
The present article proposes an efficient restoration model for images corrupted with impulse noise of varying values that follow a random distribution over some dynamic range. The model extracts a set of informative features, uses a fuzzy detector based on product aggregation reasoning rule for noisy pixels detection and noise removal operator for filtration. The fuzzy set-based detector provides a better learning and generalization capability for improved detection. The model thus explores mutually the advantages of both fuzzy detector and noise removal operator. Superiority of the proposed model to other similar methods is established both visually and quantitatively in removing impulse noise from highly corrupted images. With experimental results, it is found that the proposed model performs better and at the same time takes less computational time than others.
{"title":"Fuzzy impulse noise detector for efficient image restoration","authors":"S. Meher, Punyaban Patel","doi":"10.1109/RAICS.2011.6069401","DOIUrl":"https://doi.org/10.1109/RAICS.2011.6069401","url":null,"abstract":"The present article proposes an efficient restoration model for images corrupted with impulse noise of varying values that follow a random distribution over some dynamic range. The model extracts a set of informative features, uses a fuzzy detector based on product aggregation reasoning rule for noisy pixels detection and noise removal operator for filtration. The fuzzy set-based detector provides a better learning and generalization capability for improved detection. The model thus explores mutually the advantages of both fuzzy detector and noise removal operator. Superiority of the proposed model to other similar methods is established both visually and quantitatively in removing impulse noise from highly corrupted images. With experimental results, it is found that the proposed model performs better and at the same time takes less computational time than others.","PeriodicalId":394515,"journal":{"name":"2011 IEEE Recent Advances in Intelligent Computational Systems","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131136911","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 : 2011-11-03DOI: 10.1109/RAICS.2011.6069274
B. Jaganathan, S. Venkatesh, Yougank Bhardwaj, V. Sridhar
Brushless DC motors are the widely used motors for they possess many advantages when compared with induction motors such as higher efficiencies, High torque to inertia ratios, Greater speed capabilities, Lower audible noise, Better thermal efficiencies, Lower EMI characteristics, electronically commutated etc., In the design of such advantageous motors it becomes necessary for the estimation of the performance characteristics parameters such as back EMF, stator current, rotor speed, Torque etc., Many ideas have been proposed for the estimation of these characteristic parameters. This paper proposes an unsupervised learning method i.e., Kohonen's Self Organizing Feature Map method of estimation of BLDCM drive parameters. Since the method makes use of ‘winner takes it all’ of neurons, the values obtained by this, will be the optimal values. Simulation of the drive is first performed under ideal conditions and the values of the above mentioned parameters are obtained. Matlab coding is then written for KSOFM which is run and various maps of KSOFM are obtained. The values obtained using these two methods are compared and is found to match with each other. Because of the idea of “Winner takes it all” and the comparison with the ideal simulation, it can be concluded that the values obtained are optimal. As mentioned Matlab/Simulink is used for the simulation and the results obtained are shown with the inferences.
{"title":"Optimal parameters estimation of a BLDC motor by Kohonen's Self Organizing Map Method","authors":"B. Jaganathan, S. Venkatesh, Yougank Bhardwaj, V. Sridhar","doi":"10.1109/RAICS.2011.6069274","DOIUrl":"https://doi.org/10.1109/RAICS.2011.6069274","url":null,"abstract":"Brushless DC motors are the widely used motors for they possess many advantages when compared with induction motors such as higher efficiencies, High torque to inertia ratios, Greater speed capabilities, Lower audible noise, Better thermal efficiencies, Lower EMI characteristics, electronically commutated etc., In the design of such advantageous motors it becomes necessary for the estimation of the performance characteristics parameters such as back EMF, stator current, rotor speed, Torque etc., Many ideas have been proposed for the estimation of these characteristic parameters. This paper proposes an unsupervised learning method i.e., Kohonen's Self Organizing Feature Map method of estimation of BLDCM drive parameters. Since the method makes use of ‘winner takes it all’ of neurons, the values obtained by this, will be the optimal values. Simulation of the drive is first performed under ideal conditions and the values of the above mentioned parameters are obtained. Matlab coding is then written for KSOFM which is run and various maps of KSOFM are obtained. The values obtained using these two methods are compared and is found to match with each other. Because of the idea of “Winner takes it all” and the comparison with the ideal simulation, it can be concluded that the values obtained are optimal. As mentioned Matlab/Simulink is used for the simulation and the results obtained are shown with the inferences.","PeriodicalId":394515,"journal":{"name":"2011 IEEE Recent Advances in Intelligent Computational Systems","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134486356","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 : 2011-11-03DOI: 10.1109/RAICS.2011.6069361
Prabhjot Kaur, I. M. S. Lamba, A. Gosain
The toughest challenges in medical diagnosis are uncertainty handling and noise. This paper presents a novel kernelized type-2 fuzzy c-means algorithm that is a generalization of conventional type-2 fuzzy c-means (T2FCM). Although T2FCM has proven effective for spherical data, it fails when the data structure of input patterns is non-spherical and complex. In this paper, we present a novel kernelized type-2 fuzzy c-means (KT2FCM) where type-2 fuzzy c-means is extended by adopting a kernel induced metric in the data space to replace the original Euclidean norm metric. Use of kernel function makes it possible to cluster data that is linearly non-separable in the original space into homogeneous groups in the transformed high dimensional space. From our experiments, we found that different kernel with different kernel widths lead to different clustering results. Thus a key point is to choose an appropriate value for the kernel width. Experimental are done using synthetic and real medical images (CT Scan and MR images) to show the effectiveness of method.
{"title":"Kernelized type-2 fuzzy c-means clustering algorithm in segmentation of noisy medical images","authors":"Prabhjot Kaur, I. M. S. Lamba, A. Gosain","doi":"10.1109/RAICS.2011.6069361","DOIUrl":"https://doi.org/10.1109/RAICS.2011.6069361","url":null,"abstract":"The toughest challenges in medical diagnosis are uncertainty handling and noise. This paper presents a novel kernelized type-2 fuzzy c-means algorithm that is a generalization of conventional type-2 fuzzy c-means (T2FCM). Although T2FCM has proven effective for spherical data, it fails when the data structure of input patterns is non-spherical and complex. In this paper, we present a novel kernelized type-2 fuzzy c-means (KT2FCM) where type-2 fuzzy c-means is extended by adopting a kernel induced metric in the data space to replace the original Euclidean norm metric. Use of kernel function makes it possible to cluster data that is linearly non-separable in the original space into homogeneous groups in the transformed high dimensional space. From our experiments, we found that different kernel with different kernel widths lead to different clustering results. Thus a key point is to choose an appropriate value for the kernel width. Experimental are done using synthetic and real medical images (CT Scan and MR images) to show the effectiveness of method.","PeriodicalId":394515,"journal":{"name":"2011 IEEE Recent Advances in Intelligent Computational Systems","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133647465","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 : 2011-11-03DOI: 10.1109/RAICS.2011.6069263
P. Deepalakshmi, S. Radhakrishnan
Recent development has a tremendous growth in the ad-hoc wireless networks. Ad-hoc wireless networks are dynamic topology networks organized by a collection of mobile nodes that utilize multi-hop radio relaying and are capable of operating without the support of any fixed infrastructure. Ad-hoc wireless networks are very useful in emergency operations, collaborative and distributed computing and military applications. Multicasting plays an important role in the ad-hoc wireless networks, where nodes form groups to carry out certain tasks that require point-to-multipoint and multipoint-to-multipoint voice and data communication. The biggest challenge in the ad-hoc wireless networks is to find an optimized path between the two nodes. In this paper, we present an ant colony based multicast routing protocol for ad-hoc wireless networks, in that we analyzed the performance of proposed algorithm next to on-demand multicast routing protocol (ODMRP). This proposed approach maps the solution capability of swarm intelligence to mathematical and engineering problems. The performance of these protocols have been examined and analyzed under realistic scenarios using NS-2.
{"title":"A receiver initiated mesh based multicasting for MANETs using ACO","authors":"P. Deepalakshmi, S. Radhakrishnan","doi":"10.1109/RAICS.2011.6069263","DOIUrl":"https://doi.org/10.1109/RAICS.2011.6069263","url":null,"abstract":"Recent development has a tremendous growth in the ad-hoc wireless networks. Ad-hoc wireless networks are dynamic topology networks organized by a collection of mobile nodes that utilize multi-hop radio relaying and are capable of operating without the support of any fixed infrastructure. Ad-hoc wireless networks are very useful in emergency operations, collaborative and distributed computing and military applications. Multicasting plays an important role in the ad-hoc wireless networks, where nodes form groups to carry out certain tasks that require point-to-multipoint and multipoint-to-multipoint voice and data communication. The biggest challenge in the ad-hoc wireless networks is to find an optimized path between the two nodes. In this paper, we present an ant colony based multicast routing protocol for ad-hoc wireless networks, in that we analyzed the performance of proposed algorithm next to on-demand multicast routing protocol (ODMRP). This proposed approach maps the solution capability of swarm intelligence to mathematical and engineering problems. The performance of these protocols have been examined and analyzed under realistic scenarios using NS-2.","PeriodicalId":394515,"journal":{"name":"2011 IEEE Recent Advances in Intelligent Computational Systems","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133413293","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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