Pub Date : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208349
Dr. S. Karthikeyan
Wireless sensor network (WSN) consists of many sensors to monitor physical or environmental conditions, such as health condition monitoring, military applications temperature, sound, vibration, pressure, motion or pollutants and to cooperatively pass the data through network to a main location. The main characteristics of nodes in Wireless Sensor Network are low power and minimum processing. So it is essential to optimize the consumption of energy in WSN application. In this paper we introduce a new algorithm to increase life time of the sensor nodes in the network. Only few sensors are in active state in the covered regions and the remaining are in ideal. All the nodes change their status from active to ideal and ideal to active state periodically. Meantime the nodes which are in ideal state enable for a short period to check whether the active nodes are still active or not. If there is any failure nodes in the region ideal sensor get active and sense the data. As all the nodes changes their status periodically, few nodes only in active state and start to sense the data using its energy. So the energy of ideal nodes is saved and it will be used only when it gets active. The proposed algorithm provides close to optimal enhancement in the network life time and the output performs six times better than existing algorithm.
{"title":"A new approach to increase the life time and efficiency of wireless sensor network","authors":"Dr. S. Karthikeyan","doi":"10.1109/ICPRIME.2012.6208349","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208349","url":null,"abstract":"Wireless sensor network (WSN) consists of many sensors to monitor physical or environmental conditions, such as health condition monitoring, military applications temperature, sound, vibration, pressure, motion or pollutants and to cooperatively pass the data through network to a main location. The main characteristics of nodes in Wireless Sensor Network are low power and minimum processing. So it is essential to optimize the consumption of energy in WSN application. In this paper we introduce a new algorithm to increase life time of the sensor nodes in the network. Only few sensors are in active state in the covered regions and the remaining are in ideal. All the nodes change their status from active to ideal and ideal to active state periodically. Meantime the nodes which are in ideal state enable for a short period to check whether the active nodes are still active or not. If there is any failure nodes in the region ideal sensor get active and sense the data. As all the nodes changes their status periodically, few nodes only in active state and start to sense the data using its energy. So the energy of ideal nodes is saved and it will be used only when it gets active. The proposed algorithm provides close to optimal enhancement in the network life time and the output performs six times better than existing algorithm.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116439397","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208369
P. R. Kumar, S. Palani
With growing of applications of the embedded system technology to mobile systems, energy efficiency is becoming an important issue for designing real time embedded systems. One of the possible techniques to reduce the energy consumption is the Dynamic Voltage Scaling (DVS). DVS utilizes the slack time and adjusts the supply voltage so as to reduce the energy expense. However, how to optimally adjust the supply voltage is a NP hard problem. This paper focuses the combinational optimization problem, namely, the problem of minimizing schedule length with energy consumption constraint and the problem of minimizing energy consumption with schedule length constraint. These problems emphasize the tradeoff between power and performance and are defined such that the power-performance product is optimized by fixing one factor and minimizing the other. We propose the analytical result which gives the variation factor of each power supply which depends on the workload and provides the same power supply while meeting the constraints. We address to the use of genetic algorithm to schedule the tasks and then find the optimal power supplies and determine the schedule length on the multiprocessor system.
{"title":"A dynamic voltage scaling with single power supply and varying speed factor for multiprocessor system using genetic algorithm","authors":"P. R. Kumar, S. Palani","doi":"10.1109/ICPRIME.2012.6208369","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208369","url":null,"abstract":"With growing of applications of the embedded system technology to mobile systems, energy efficiency is becoming an important issue for designing real time embedded systems. One of the possible techniques to reduce the energy consumption is the Dynamic Voltage Scaling (DVS). DVS utilizes the slack time and adjusts the supply voltage so as to reduce the energy expense. However, how to optimally adjust the supply voltage is a NP hard problem. This paper focuses the combinational optimization problem, namely, the problem of minimizing schedule length with energy consumption constraint and the problem of minimizing energy consumption with schedule length constraint. These problems emphasize the tradeoff between power and performance and are defined such that the power-performance product is optimized by fixing one factor and minimizing the other. We propose the analytical result which gives the variation factor of each power supply which depends on the workload and provides the same power supply while meeting the constraints. We address to the use of genetic algorithm to schedule the tasks and then find the optimal power supplies and determine the schedule length on the multiprocessor system.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127615506","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208304
A. Anbarasan, K. Shankar
Fast Fourier transform (FFT) processing is one of the key procedure in popular orthogonal frequency division multiplexing (OFDM) communication systems. Structured pipeline architectures, low power consumption, high speed and reduced chip area are the main concerns in this VLSI implementation. In this paper, the efficient implementation of FFT/IFFT processor for OFDM applications is presented. The processor can be used in various OFDM-based communication systems, such as Worldwide Interoperability for Microwave access (Wi-Max), digital audio broadcasting (DAB), digital video broadcasting-terrestrial (DVB-T). We adopt single-path delay feedback architecture. To eliminate the read only memories (ROM's) used to store the twiddle factors, this proposed architecture applies a reconfigurable complex multiplier to achieve a ROM-less FFT/IFFT processor and to reduce the truncation error we adopt the fixed width modified booth multiplier. The three processing elements (PE's), delay-line (DL) buffers are used for computing IFFT. Thus we consume the low power, lower hardware cost, high efficiency and reduced chip size.
{"title":"Design and implementation of low power FFT/IFFT processor for wireless communication","authors":"A. Anbarasan, K. Shankar","doi":"10.1109/ICPRIME.2012.6208304","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208304","url":null,"abstract":"Fast Fourier transform (FFT) processing is one of the key procedure in popular orthogonal frequency division multiplexing (OFDM) communication systems. Structured pipeline architectures, low power consumption, high speed and reduced chip area are the main concerns in this VLSI implementation. In this paper, the efficient implementation of FFT/IFFT processor for OFDM applications is presented. The processor can be used in various OFDM-based communication systems, such as Worldwide Interoperability for Microwave access (Wi-Max), digital audio broadcasting (DAB), digital video broadcasting-terrestrial (DVB-T). We adopt single-path delay feedback architecture. To eliminate the read only memories (ROM's) used to store the twiddle factors, this proposed architecture applies a reconfigurable complex multiplier to achieve a ROM-less FFT/IFFT processor and to reduce the truncation error we adopt the fixed width modified booth multiplier. The three processing elements (PE's), delay-line (DL) buffers are used for computing IFFT. Thus we consume the low power, lower hardware cost, high efficiency and reduced chip size.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121436334","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208343
M. Karthika, C. Rajasekaran
Scratchpad Memories are widely employed in embedded systems as an alternative to caches because they achieve comparable performance with higher power efficiency. Here, Optimal SPM Mapping and Memory Power-Down techniques are used for minimize the total energy of the SPM. SPM mapping simply targets the minimum number of accesses to the main memory, i.e., active power. A global optimization should explicitly take into account memory access energy, leakage energy, and power-down/up energy penalty, to define the Optimal SPM mapping and Optimal memory power-down scheduling for minimizing the total energy in the memory sub-system. Synthesis results based on 1.32V CMOS standard-cell library shows that the proposed SPM reduces the power consumption by 25-30%.
{"title":"Scratchpad memory-global power optimization","authors":"M. Karthika, C. Rajasekaran","doi":"10.1109/ICPRIME.2012.6208343","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208343","url":null,"abstract":"Scratchpad Memories are widely employed in embedded systems as an alternative to caches because they achieve comparable performance with higher power efficiency. Here, Optimal SPM Mapping and Memory Power-Down techniques are used for minimize the total energy of the SPM. SPM mapping simply targets the minimum number of accesses to the main memory, i.e., active power. A global optimization should explicitly take into account memory access energy, leakage energy, and power-down/up energy penalty, to define the Optimal SPM mapping and Optimal memory power-down scheduling for minimizing the total energy in the memory sub-system. Synthesis results based on 1.32V CMOS standard-cell library shows that the proposed SPM reduces the power consumption by 25-30%.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134187577","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208281
S. Balaji, S. Sarumathi
Web Mining Systems make use of the redundancy of data published on the Web to automatically extract formation from existing web documents. The crawler is an important module of a web search engine. The quality of a crawler directly affects the searching quality of such web search engines. Such a web crawler may interact with millions of hosts over a period of weeks or months, and thus issues of robustness, flexibility, and manageability are of major importance. Given some URLs, the crawler should retrieve the web pages of those URLs, parse the HTML files, add new URLs into its queue and go back to the first phase of this cycle. The crawler also can retrieve some other information from the HTML files as it is parsing them to get the new URLs. This paper proposes a framework and algorithm, TOPCRAWL for mining. The proposed TOPCRAWL algorithm is a new crawling method which emphasis on topic relevancy and outperforms state-of-the-art approaches with respect to recall values achievable within a given period of time. This method also tries to offer the result in community format and it makes use of a new combination of ideas and techniques used to identify and exploit navigational structures of websites, such as hierarchies, lists or maps. This algorithm is simulated with web mining tool Deixto and the basic idea has been implemented using the JAVA and Results are given. Comparisons with existing focused crawling techniques reveal that the new crawling method leads to a significant increase in recall whilst maintaining precision.
{"title":"TOPCRAWL: Community mining in web search engines with emphasize on topical crawling","authors":"S. Balaji, S. Sarumathi","doi":"10.1109/ICPRIME.2012.6208281","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208281","url":null,"abstract":"Web Mining Systems make use of the redundancy of data published on the Web to automatically extract formation from existing web documents. The crawler is an important module of a web search engine. The quality of a crawler directly affects the searching quality of such web search engines. Such a web crawler may interact with millions of hosts over a period of weeks or months, and thus issues of robustness, flexibility, and manageability are of major importance. Given some URLs, the crawler should retrieve the web pages of those URLs, parse the HTML files, add new URLs into its queue and go back to the first phase of this cycle. The crawler also can retrieve some other information from the HTML files as it is parsing them to get the new URLs. This paper proposes a framework and algorithm, TOPCRAWL for mining. The proposed TOPCRAWL algorithm is a new crawling method which emphasis on topic relevancy and outperforms state-of-the-art approaches with respect to recall values achievable within a given period of time. This method also tries to offer the result in community format and it makes use of a new combination of ideas and techniques used to identify and exploit navigational structures of websites, such as hierarchies, lists or maps. This algorithm is simulated with web mining tool Deixto and the basic idea has been implemented using the JAVA and Results are given. Comparisons with existing focused crawling techniques reveal that the new crawling method leads to a significant increase in recall whilst maintaining precision.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131168655","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208278
R. Muralidharan, C. Chandrasekar
In this paper, a framework for recognizing an object from the given image based on the local and global feature is discussed. The proposed method is based on the combination of the two methods in the literature, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). For feature vector formation, Hu's Moment Invariant is computed to represent the image, which is invariant to translation, rotation and scaling as a global feature and Hessian-Laplace detector and PCA-SIFT descriptor as local feature. In this framework, first the KNN is applied to find the closest neighbors to a query image and then the local SVM is applied to find the object that belongs to the object set. The proposed method is implemented as two stage process. In the first stage, KNN is utilized to compute distances of the query to all training and pick the nearest K neighbors. During the second stage SVM is applied to recognize the object. The proposed method is experimented in MATLAB and tested with the COIL-100 database and the results are shown. To prove the efficiency of the proposed method, Neural Network model (BPN) is performed and the comparative results are given.
{"title":"Combining local and global feature for object recognition using SVM-KNN","authors":"R. Muralidharan, C. Chandrasekar","doi":"10.1109/ICPRIME.2012.6208278","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208278","url":null,"abstract":"In this paper, a framework for recognizing an object from the given image based on the local and global feature is discussed. The proposed method is based on the combination of the two methods in the literature, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). For feature vector formation, Hu's Moment Invariant is computed to represent the image, which is invariant to translation, rotation and scaling as a global feature and Hessian-Laplace detector and PCA-SIFT descriptor as local feature. In this framework, first the KNN is applied to find the closest neighbors to a query image and then the local SVM is applied to find the object that belongs to the object set. The proposed method is implemented as two stage process. In the first stage, KNN is utilized to compute distances of the query to all training and pick the nearest K neighbors. During the second stage SVM is applied to recognize the object. The proposed method is experimented in MATLAB and tested with the COIL-100 database and the results are shown. To prove the efficiency of the proposed method, Neural Network model (BPN) is performed and the comparative results are given.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130970241","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208379
R. Singh
Herbicides, pesticides, and other chemicals are employed in crop lands to increase the agricultural food productivity. These chemicals increase the concentration of non point pollutant in river systems. Non point pollution affects the health of human and aquatic environment. The transport mechanism of chemical pollutants into river or streams is not straight forward but complex function of applied chemicals and land use patterns in a given river or stream basin which are difficult to quantify accurately. Development of models based on temporal observations may improve understanding the underlying the hydrological processes in such complex transports. Present work utilized temporal patterns extracted from temporal observations using wavelet theory at single as well as multi resolution levels. These patterns are then utilized by an artificial neural network (ANN) based on feed forward backpropogation algorithm. The integrated model, Wavelet-ANN conjunction model, is then utilized to predict the monthly concentration of non point pollution in a river system. The application of the proposed methodology is illustrated with real data to estimate the diffuse pollution concentration in a river system due to application of a typical herbicide, atrazine, in corn fields. The limited performance evaluation of the methodology was found to work better than simple time series.
{"title":"Non point pollution predictions in river system using time series patterns in multi level wavelet-ANN model","authors":"R. Singh","doi":"10.1109/ICPRIME.2012.6208379","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208379","url":null,"abstract":"Herbicides, pesticides, and other chemicals are employed in crop lands to increase the agricultural food productivity. These chemicals increase the concentration of non point pollutant in river systems. Non point pollution affects the health of human and aquatic environment. The transport mechanism of chemical pollutants into river or streams is not straight forward but complex function of applied chemicals and land use patterns in a given river or stream basin which are difficult to quantify accurately. Development of models based on temporal observations may improve understanding the underlying the hydrological processes in such complex transports. Present work utilized temporal patterns extracted from temporal observations using wavelet theory at single as well as multi resolution levels. These patterns are then utilized by an artificial neural network (ANN) based on feed forward backpropogation algorithm. The integrated model, Wavelet-ANN conjunction model, is then utilized to predict the monthly concentration of non point pollution in a river system. The application of the proposed methodology is illustrated with real data to estimate the diffuse pollution concentration in a river system due to application of a typical herbicide, atrazine, in corn fields. The limited performance evaluation of the methodology was found to work better than simple time series.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131110570","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208376
S. Jayanthi, S. Sasikala
Internet today is huge, dynamic, self-organized, and strongly interlinked. Web spam can significantly worsen the quality of search engine results. The motivation of the paper is based on the logical perspective of approaching the web spam problem as cancer caused to the internet, and the solution could be derived by formulating the algorithms based on genetic algorithm (GA) based on content and link attributes. Web mining tools GATree [15] and PermutMatrix [14] has been used to simulate the experiments. JAVA is used to develop program that analyze and report the spamdexing instance. This paper proposes an algorithm WESPACT, to detect the web spam. This algorithm performs well as shown through experiments.
{"title":"WESPACT: — Detection of web spamdexing with decision trees in GA perspective","authors":"S. Jayanthi, S. Sasikala","doi":"10.1109/ICPRIME.2012.6208376","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208376","url":null,"abstract":"Internet today is huge, dynamic, self-organized, and strongly interlinked. Web spam can significantly worsen the quality of search engine results. The motivation of the paper is based on the logical perspective of approaching the web spam problem as cancer caused to the internet, and the solution could be derived by formulating the algorithms based on genetic algorithm (GA) based on content and link attributes. Web mining tools GATree [15] and PermutMatrix [14] has been used to simulate the experiments. JAVA is used to develop program that analyze and report the spamdexing instance. This paper proposes an algorithm WESPACT, to detect the web spam. This algorithm performs well as shown through experiments.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132156154","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208298
D. Parthiban, A. Philomina, N. R. Raajan, B. Monisha, M. Priya, S. Suganya
Wavelet theory has emerged as a new mathematical tool that can be applied in many fields such as image processing, biomedical engineering, radar, physics, control systems and communication systems. The important area of application of wavelets in communication: multiple accesses. Among the multiple access applications one of the most notable work is wavelet packet-based multiple access communication. The two new multiple access systems are Scale-Time-Code Division Multiple Access (STCDMA) and Scale-Code Division Multiple Access (SCDMA). In a STCDMA system, Direct-Sequence (DS) Code-Division Multiple Access (CDMA) is used in each time slot to identify multiple users. If time division multiplexing is excluded in each scale, SCDMA, which is a multimedia system, is obtained. These systems are analyzed over a synchronous Additive White Gaussian Noise (AWGN) by using a conventional detector and a multiuser detector based on decorrelating detector for real and complex-valued PN sequences. These systems have better performance for complex-valued sequences compared to real-valued sequences. SCDMA can also be analyzed over an asynchronous AWGN by using a conventional detector for real-valued sequences. SCDMA is attractive compared to DS-CDMA, because it is capable of transmitting different rates of information messages. To be more specific, STCDMA is user-advantageous and SCDMA is information-advantageous. In STCDMA and SCDMA good PN sequences such as Kasami sequences are required because of the reuse capability while DS-CDMA has only limited number of them. Kasami sequences are optimal since the maximum cross correlation value achieves the Welch Lower Bound. The main purpose of using Kasami sequences is that, it decreases the multiple access interference. These PN sequences are very useful for multipath, jamming environments and synchronization purposes.
{"title":"Wavelet-based multiple access technique for mobile communications","authors":"D. Parthiban, A. Philomina, N. R. Raajan, B. Monisha, M. Priya, S. Suganya","doi":"10.1109/ICPRIME.2012.6208298","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208298","url":null,"abstract":"Wavelet theory has emerged as a new mathematical tool that can be applied in many fields such as image processing, biomedical engineering, radar, physics, control systems and communication systems. The important area of application of wavelets in communication: multiple accesses. Among the multiple access applications one of the most notable work is wavelet packet-based multiple access communication. The two new multiple access systems are Scale-Time-Code Division Multiple Access (STCDMA) and Scale-Code Division Multiple Access (SCDMA). In a STCDMA system, Direct-Sequence (DS) Code-Division Multiple Access (CDMA) is used in each time slot to identify multiple users. If time division multiplexing is excluded in each scale, SCDMA, which is a multimedia system, is obtained. These systems are analyzed over a synchronous Additive White Gaussian Noise (AWGN) by using a conventional detector and a multiuser detector based on decorrelating detector for real and complex-valued PN sequences. These systems have better performance for complex-valued sequences compared to real-valued sequences. SCDMA can also be analyzed over an asynchronous AWGN by using a conventional detector for real-valued sequences. SCDMA is attractive compared to DS-CDMA, because it is capable of transmitting different rates of information messages. To be more specific, STCDMA is user-advantageous and SCDMA is information-advantageous. In STCDMA and SCDMA good PN sequences such as Kasami sequences are required because of the reuse capability while DS-CDMA has only limited number of them. Kasami sequences are optimal since the maximum cross correlation value achieves the Welch Lower Bound. The main purpose of using Kasami sequences is that, it decreases the multiple access interference. These PN sequences are very useful for multipath, jamming environments and synchronization purposes.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"10 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120834926","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208359
S. Shanthi, V. Bhaskaran
This study presents a computer aided system for automatic detection and classification of breast cancer in mammogram images. First the suspicious region or the Region of Interest is identified and extracted using Intuitionistic Fuzzy C-Means Clustering technique. Next multilevel Discrete Wavelet Transformation is applied to the extracted Region of Interest. After applying Discrete Wavelet Transformation, histogram features, Gray Level Concurrence wavelet features, and wavelet energy features are extracted from each Region of Interest of the image. Before classification, Principal Component Analysis is applied on the extracted features to reduce the feature dimension. Finally, the feature database is submitted to self-adaptive resource allocation network classifier for classification. The proposed system is verified with 295 mammograms in the Mammographic Image Analysis Society Database. The result shows that the proposed algorithm produces better results.
{"title":"Computer aided detection and classification of mammogram using self-adaptive resource allocation network classifier","authors":"S. Shanthi, V. Bhaskaran","doi":"10.1109/ICPRIME.2012.6208359","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208359","url":null,"abstract":"This study presents a computer aided system for automatic detection and classification of breast cancer in mammogram images. First the suspicious region or the Region of Interest is identified and extracted using Intuitionistic Fuzzy C-Means Clustering technique. Next multilevel Discrete Wavelet Transformation is applied to the extracted Region of Interest. After applying Discrete Wavelet Transformation, histogram features, Gray Level Concurrence wavelet features, and wavelet energy features are extracted from each Region of Interest of the image. Before classification, Principal Component Analysis is applied on the extracted features to reduce the feature dimension. Finally, the feature database is submitted to self-adaptive resource allocation network classifier for classification. The proposed system is verified with 295 mammograms in the Mammographic Image Analysis Society Database. The result shows that the proposed algorithm produces better results.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129524602","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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