C. Sowmya, C. D. Naidu, Rajendra Prasad Somineni, D. R. Reddy
Water is a precious source vital for healthy living. Most of the infectious diseases are due to contaminated water which leads to millions of deaths every year. There is a need to establish Water quality monitoring system to verify whether the determined water quality is suitable for intended use. This paper presents the application of Wireless Sensor Network (WSN) technology for real time online Water quality monitoring. In this paper, the details of system design and implementation of WSN are presented. Wireless Sensor Network (WSN) for a water quality monitoring is composed of number of sensor nodes with networking capability which are deployed at different overhead tanks and water bodies in an area. Each sensor node consists of an Arduino microcontroller, Xbee module and water quality sensors, the sensor probes shall continuously measure the different water quality parameters like pH, Temperature, Conductivity. The parameters are measured in real time by the sensors and send the data to the data center. Solar panel is used to power the system for each node. Data collected from remote nodes are displayed in the user PC. This developed system will demonstrate online sensor data analysis and has the advantages of power optimization, portability and easy installation.
{"title":"Implementation of Wireless Sensor Network for Real Time Overhead Tank Water Quality Monitoring","authors":"C. Sowmya, C. D. Naidu, Rajendra Prasad Somineni, D. R. Reddy","doi":"10.1109/IACC.2017.0118","DOIUrl":"https://doi.org/10.1109/IACC.2017.0118","url":null,"abstract":"Water is a precious source vital for healthy living. Most of the infectious diseases are due to contaminated water which leads to millions of deaths every year. There is a need to establish Water quality monitoring system to verify whether the determined water quality is suitable for intended use. This paper presents the application of Wireless Sensor Network (WSN) technology for real time online Water quality monitoring. In this paper, the details of system design and implementation of WSN are presented. Wireless Sensor Network (WSN) for a water quality monitoring is composed of number of sensor nodes with networking capability which are deployed at different overhead tanks and water bodies in an area. Each sensor node consists of an Arduino microcontroller, Xbee module and water quality sensors, the sensor probes shall continuously measure the different water quality parameters like pH, Temperature, Conductivity. The parameters are measured in real time by the sensors and send the data to the data center. Solar panel is used to power the system for each node. Data collected from remote nodes are displayed in the user PC. This developed system will demonstrate online sensor data analysis and has the advantages of power optimization, portability and easy installation.","PeriodicalId":248433,"journal":{"name":"2017 IEEE 7th International Advance Computing Conference (IACC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117111504","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}
Multiplication is an essential vital role in arithmetic operations. In fact, multiplication is allotted on operations like Multiply and Accumulate (MAC). The exaggerate form of Braun multiplier is the Baugh-Wooley multiplier. This work proposes the design of Low-power Baugh-Wooley Multiplier with CMOS full adder with different topologies like 10T, 14T, 17T as well as with CNTFET full adders using different topologies like 10T, 14T, 17T. All circuits are designed and simulated using HSPICE Tool.
{"title":"Design of Low Power Multiplier using CNTFET","authors":"Rajendra Prasad Somineni, S. Jaweed","doi":"10.1109/IACC.2017.0120","DOIUrl":"https://doi.org/10.1109/IACC.2017.0120","url":null,"abstract":"Multiplication is an essential vital role in arithmetic operations. In fact, multiplication is allotted on operations like Multiply and Accumulate (MAC). The exaggerate form of Braun multiplier is the Baugh-Wooley multiplier. This work proposes the design of Low-power Baugh-Wooley Multiplier with CMOS full adder with different topologies like 10T, 14T, 17T as well as with CNTFET full adders using different topologies like 10T, 14T, 17T. All circuits are designed and simulated using HSPICE Tool.","PeriodicalId":248433,"journal":{"name":"2017 IEEE 7th International Advance Computing Conference (IACC)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121804383","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}
Given an application of a spatial data set, we discover a set of co-location patterns using a GUI (Graphical User Interface) model in a less amount of time, as this application is implemented using a parallel approach-A Map-Reduce framework. This framework uses a grid based approach to find the neighboring paths using a Euclidean distance. The framework also uses a dynamic algorithm in finding the spatial objects and discovers co-location rules from them. Once co-location rules are identified, we give the input as a threshold value which is used to form clusters of similar behavior. If the threshold value is too low more clusters are formed, if it is too high less clusters are formed. The comparison of the results shows that the proposed system is computationally good and gives the co-location patterns in a less amount of time.
{"title":"A Map-Reduce Framework for Finding Clusters of Colocation Patterns - A Summary of Results","authors":"M. Sheshikala, D. Rao, R. Prakash","doi":"10.1109/IACC.2017.0039","DOIUrl":"https://doi.org/10.1109/IACC.2017.0039","url":null,"abstract":"Given an application of a spatial data set, we discover a set of co-location patterns using a GUI (Graphical User Interface) model in a less amount of time, as this application is implemented using a parallel approach-A Map-Reduce framework. This framework uses a grid based approach to find the neighboring paths using a Euclidean distance. The framework also uses a dynamic algorithm in finding the spatial objects and discovers co-location rules from them. Once co-location rules are identified, we give the input as a threshold value which is used to form clusters of similar behavior. If the threshold value is too low more clusters are formed, if it is too high less clusters are formed. The comparison of the results shows that the proposed system is computationally good and gives the co-location patterns in a less amount of time.","PeriodicalId":248433,"journal":{"name":"2017 IEEE 7th International Advance Computing Conference (IACC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124196232","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}
Satellite Data concisely convey information about positions, sizes and interrelationships between objects. The satellite image losses information due to lack of Acquisition capability of sensor and atmosphere's effect. It is very difficult to extract useful information at intensity level with low SNR, non wavelet segmented schemes losing high frequency contact with results texture is blurred several preprocesses are applied to make textual image clear and segmentation. Unsatisfied results due with lack of directionality with DWT, Here we can implement advance image processing technique for improving texture based features to multispectral satellite image, find discrepancy distribution of observed and normal region using Higher order statistical methods(HOS) like skewness, Kurtosis. The shape of the distribution of intensity levels are examined by HOG. For improving the visualization quality we examine features based on edges, lines and their gradients using Curvelet and Histogram of oriented Gradient (HOG), intensity distribution using Higher order Statistics (HOS).
{"title":"Higher Order Statistics for Multispectral Satellite Data","authors":"T. V. Krishnamoorthy, G. Reddy","doi":"10.1109/IACC.2017.0056","DOIUrl":"https://doi.org/10.1109/IACC.2017.0056","url":null,"abstract":"Satellite Data concisely convey information about positions, sizes and interrelationships between objects. The satellite image losses information due to lack of Acquisition capability of sensor and atmosphere's effect. It is very difficult to extract useful information at intensity level with low SNR, non wavelet segmented schemes losing high frequency contact with results texture is blurred several preprocesses are applied to make textual image clear and segmentation. Unsatisfied results due with lack of directionality with DWT, Here we can implement advance image processing technique for improving texture based features to multispectral satellite image, find discrepancy distribution of observed and normal region using Higher order statistical methods(HOS) like skewness, Kurtosis. The shape of the distribution of intensity levels are examined by HOG. For improving the visualization quality we examine features based on edges, lines and their gradients using Curvelet and Histogram of oriented Gradient (HOG), intensity distribution using Higher order Statistics (HOS).","PeriodicalId":248433,"journal":{"name":"2017 IEEE 7th International Advance Computing Conference (IACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125866715","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}
Ravichand Kancharla, Shanthi Karpurapu, Vadeghar Ramesh Kumar, G. P. Reddy
Fourier transformation infrared spectroscopy has been used widely for understanding the structure and organization of membranes. The data obtained from FTIR spectroscopy will be huge and preprocessing of the data is an essential step. For accomplishing this task, we need to depend on various software which are not open source. Also the data processing will be time consuming and rigorous. This paper discusses a novel approach for analysis of multiple FTIR spectra. This paper discusses our tool developed Using MATLAB Image processing tool box functions for analyzing the multiple FTIR spectroscopy's.
{"title":"A Novel Approach for Analysis of FTIR Membrane Spectroscopy","authors":"Ravichand Kancharla, Shanthi Karpurapu, Vadeghar Ramesh Kumar, G. P. Reddy","doi":"10.1109/IACC.2017.0151","DOIUrl":"https://doi.org/10.1109/IACC.2017.0151","url":null,"abstract":"Fourier transformation infrared spectroscopy has been used widely for understanding the structure and organization of membranes. The data obtained from FTIR spectroscopy will be huge and preprocessing of the data is an essential step. For accomplishing this task, we need to depend on various software which are not open source. Also the data processing will be time consuming and rigorous. This paper discusses a novel approach for analysis of multiple FTIR spectra. This paper discusses our tool developed Using MATLAB Image processing tool box functions for analyzing the multiple FTIR spectroscopy's.","PeriodicalId":248433,"journal":{"name":"2017 IEEE 7th International Advance Computing Conference (IACC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130644071","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}
J. Panda, Akshay Uppal, Akhil S. Nair, Bhavesh Agrawal
This paper presents a new optimized DWT-SVD based watermarking technique using Genetic Algorithm. The singular value component of the original Image is modified by adding the singular component of the watermark image along with a suitable scaling factor. This scaling factor is optimized by GA using the PSNR values as the fitness criteria in order to achieve high values or robustness without compromising the transparency of the watermark. Further application based analysis is done by using the Noise Correlation as a fitness function to test for better results in robustness.
{"title":"Genetic Algorithm Based Optimized Color Image Watermarking Technique Using SVD and DWT","authors":"J. Panda, Akshay Uppal, Akhil S. Nair, Bhavesh Agrawal","doi":"10.1109/IACC.2017.0124","DOIUrl":"https://doi.org/10.1109/IACC.2017.0124","url":null,"abstract":"This paper presents a new optimized DWT-SVD based watermarking technique using Genetic Algorithm. The singular value component of the original Image is modified by adding the singular component of the watermark image along with a suitable scaling factor. This scaling factor is optimized by GA using the PSNR values as the fitness criteria in order to achieve high values or robustness without compromising the transparency of the watermark. Further application based analysis is done by using the Noise Correlation as a fitness function to test for better results in robustness.","PeriodicalId":248433,"journal":{"name":"2017 IEEE 7th International Advance Computing Conference (IACC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127294910","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}
As the users on the cloud network increase, the consumption of the Compute, Network and Storage resources also increases. This leads to increase in the cost of deployment, configuration and maintenance. Hence, the Capital Expenditure (CAPEX) of the organization providing the cloud network increases. Network Function Virtualization (NFV) is a technology which virtualizes network functionalities. This paper studies the influence of NFV on CAPEX of cloud based networks and compares it with traditional implementation (without NFV) of such networks. A prototype cloud network based on NFV implementation is developed and implemented. Based on the test cases developed on the prototype, CAPEX of the resources used for both NFV based and traditional implementations are studied and analyzed. RESTful web services are created for the users of the cloud network to orchestrate and manage the network on the cloud. The results accomplished show that NFV based implementation reduces the CAPEX, when compared with the traditional implementation. It is also observed that orchestration mechanism reduces complexity of management of cloud network. A use case with simple web server is developed to compare the performance of a system on Cloud with that of a physical system.
{"title":"Cost and Performance Analysis of Network Function Virtualization Based Cloud Systems","authors":"M. Ananth, Rinki Sharma","doi":"10.1109/IACC.2017.0029","DOIUrl":"https://doi.org/10.1109/IACC.2017.0029","url":null,"abstract":"As the users on the cloud network increase, the consumption of the Compute, Network and Storage resources also increases. This leads to increase in the cost of deployment, configuration and maintenance. Hence, the Capital Expenditure (CAPEX) of the organization providing the cloud network increases. Network Function Virtualization (NFV) is a technology which virtualizes network functionalities. This paper studies the influence of NFV on CAPEX of cloud based networks and compares it with traditional implementation (without NFV) of such networks. A prototype cloud network based on NFV implementation is developed and implemented. Based on the test cases developed on the prototype, CAPEX of the resources used for both NFV based and traditional implementations are studied and analyzed. RESTful web services are created for the users of the cloud network to orchestrate and manage the network on the cloud. The results accomplished show that NFV based implementation reduces the CAPEX, when compared with the traditional implementation. It is also observed that orchestration mechanism reduces complexity of management of cloud network. A use case with simple web server is developed to compare the performance of a system on Cloud with that of a physical system.","PeriodicalId":248433,"journal":{"name":"2017 IEEE 7th International Advance Computing Conference (IACC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121038310","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}
Fractal Dimension was firstly introduced by Mandelbort [1]. Fractal Dimension describe about the shape and appearance of object, which have the property of self similarity. Fractal Dimension of several objects are calculated by using the concept of self similarity. Because Fractal objects are self similar to the original object and dimensions are little varies as per scale length. Our main purpose is to find smoothness and roughness of images and image analysis. Various methods were proposed to estimate the fractal dimension of Grey scale images. Some existing methods were described using Fractal Dimension methodology for finding the roughness and smoothness of images. So many experiments has been done by using existing methods of fractal dimension and found various results. In this report we have described some proposed approach for finding the fractal dimension of color images. We found out fractal dimension of both gray scale and color image using Differential Box Count (DBC) method, cell counting method and our proposed approach.
{"title":"New Approach for Estimating Fractal Dimension of Both Gary and Color Images","authors":"Abadhan Ranganath, J. Mishra","doi":"10.1109/IACC.2017.0142","DOIUrl":"https://doi.org/10.1109/IACC.2017.0142","url":null,"abstract":"Fractal Dimension was firstly introduced by Mandelbort [1]. Fractal Dimension describe about the shape and appearance of object, which have the property of self similarity. Fractal Dimension of several objects are calculated by using the concept of self similarity. Because Fractal objects are self similar to the original object and dimensions are little varies as per scale length. Our main purpose is to find smoothness and roughness of images and image analysis. Various methods were proposed to estimate the fractal dimension of Grey scale images. Some existing methods were described using Fractal Dimension methodology for finding the roughness and smoothness of images. So many experiments has been done by using existing methods of fractal dimension and found various results. In this report we have described some proposed approach for finding the fractal dimension of color images. We found out fractal dimension of both gray scale and color image using Differential Box Count (DBC) method, cell counting method and our proposed approach.","PeriodicalId":248433,"journal":{"name":"2017 IEEE 7th International Advance Computing Conference (IACC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128932673","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}
Local Binary Pattern (LBP) is one of the successful texture analysis methods. However, LBP suffers from noise robustness and rotation invariance. This paper proposes a novel noise insensitive texture descriptor, Adjacent Evaluation Local Ternary Count (AELTC) for rotation invariant texture classification. Unlike LBP, AELTC uses an adjacent evaluation window to change the threshold scheme. It is enhanced to Adjacent Evaluation Completed Local Ternary Count (AECLTC) with three operators to improve the performance of texture classification. During the performance evaluation, various experiments are conducted on Outex and CUReT databases using seven existing LBP variants and with proposed AECLTC. The results demonstrated the superiority of AECLTC when compared to other LBP variants.
{"title":"Adjacent Evaluation of Completed Local Ternary Count for Texture Classification","authors":"Ch. Sudha Sree, M.V.P. Chandra Sekhara Rao","doi":"10.1109/IACC.2017.0144","DOIUrl":"https://doi.org/10.1109/IACC.2017.0144","url":null,"abstract":"Local Binary Pattern (LBP) is one of the successful texture analysis methods. However, LBP suffers from noise robustness and rotation invariance. This paper proposes a novel noise insensitive texture descriptor, Adjacent Evaluation Local Ternary Count (AELTC) for rotation invariant texture classification. Unlike LBP, AELTC uses an adjacent evaluation window to change the threshold scheme. It is enhanced to Adjacent Evaluation Completed Local Ternary Count (AECLTC) with three operators to improve the performance of texture classification. During the performance evaluation, various experiments are conducted on Outex and CUReT databases using seven existing LBP variants and with proposed AECLTC. The results demonstrated the superiority of AECLTC when compared to other LBP variants.","PeriodicalId":248433,"journal":{"name":"2017 IEEE 7th International Advance Computing Conference (IACC)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127362215","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}
Rain will be estimated with the formation of cloud consists of water droplets, at higher frequencies such as millimetre band, that experiences a signal degradation and signal reduction due to the cloud consists of water droplets. The atmospheric gases, clouds, rain, snow, fog, cloud droplets, noise, water vapour, hydrometers absorbs electromagnetic energy, which results in the signal degradation. Clouds in generally consists of water droplets of less than 0.10mm in diameter, whereas raindrops in generally consists of range from 0.10mm to 9.5mm in diameter. So all these effects were leads to degradation in the quality of transmissions and in increase in the error rate of digital transmissions. In general, the higher frequency leads to, the more a signal is susceptible to rain in the atmosphere. For the purpose of cloud impact effect evaluation, the cloud cover statistical data, for low level clouds were derived from the earth-satellite link observations. These Extracted statistical data were used to obtain the seasonal drastic fluctuations.
{"title":"Severe Cause of Cloud Attenuation and Rain Attenuation on Space Communication Link at Millimetre Band and Differentiation between Rain Attenuation and Cloud Attenuation","authors":"K. K. Srinivas, T. Ramana","doi":"10.1109/IACC.2017.0063","DOIUrl":"https://doi.org/10.1109/IACC.2017.0063","url":null,"abstract":"Rain will be estimated with the formation of cloud consists of water droplets, at higher frequencies such as millimetre band, that experiences a signal degradation and signal reduction due to the cloud consists of water droplets. The atmospheric gases, clouds, rain, snow, fog, cloud droplets, noise, water vapour, hydrometers absorbs electromagnetic energy, which results in the signal degradation. Clouds in generally consists of water droplets of less than 0.10mm in diameter, whereas raindrops in generally consists of range from 0.10mm to 9.5mm in diameter. So all these effects were leads to degradation in the quality of transmissions and in increase in the error rate of digital transmissions. In general, the higher frequency leads to, the more a signal is susceptible to rain in the atmosphere. For the purpose of cloud impact effect evaluation, the cloud cover statistical data, for low level clouds were derived from the earth-satellite link observations. These Extracted statistical data were used to obtain the seasonal drastic fluctuations.","PeriodicalId":248433,"journal":{"name":"2017 IEEE 7th International Advance Computing Conference (IACC)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116563405","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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