Pub Date : 2020-12-11DOI: 10.1109/MPCIT51588.2020.9350469
Santosh Kumar Bairappaka, Anumoy Ghosh
In this paper, a coplanar waveguide (CPW) fed dual band circular polarized (CP) slot antenna is proposed. A modified asymmetric stub loaded feedline is used along with a square shaped slot in the ground plane to achieve two broad impedance bandwidths (|S11|< −10 dB). The top right corner of the slot is truncated with suitable dimensions to obtain two CP bands within the dual resonances. The simulated results show that a band is resonated with center frequency of 1.6 GHz with 41.8% impedance bandwidth and another band is resonated at 3.3 GHz with 30.7% impedance bandwidth (IBW). Axial Ratio bandwidth (ARBW; Axial Ratio ≤ 3 dB) of 260MHz (13.2%) and 110 MHz (2.9 %) are obtained within the lower and upper resonances respectively. The antenna is packed with a layout area of 0.33λ × 0.33λ, where λ being the wavelength for the lower resonant frequency in free space medium. The antenna simulation is done by assuming FR4 substrate with 1.6mm thickness and tan δ= 0.02. The CP radiation patterns are investigated and found to be stable with satisfactory with dominant left hand circular polarization. The antenna has satisfactory gain suitable for GPS and WiMAX applications.
{"title":"Co-Planar Waveguide Fed Dual Band Circular Polarized Slot Antenna","authors":"Santosh Kumar Bairappaka, Anumoy Ghosh","doi":"10.1109/MPCIT51588.2020.9350469","DOIUrl":"https://doi.org/10.1109/MPCIT51588.2020.9350469","url":null,"abstract":"In this paper, a coplanar waveguide (CPW) fed dual band circular polarized (CP) slot antenna is proposed. A modified asymmetric stub loaded feedline is used along with a square shaped slot in the ground plane to achieve two broad impedance bandwidths (|S11|< −10 dB). The top right corner of the slot is truncated with suitable dimensions to obtain two CP bands within the dual resonances. The simulated results show that a band is resonated with center frequency of 1.6 GHz with 41.8% impedance bandwidth and another band is resonated at 3.3 GHz with 30.7% impedance bandwidth (IBW). Axial Ratio bandwidth (ARBW; Axial Ratio ≤ 3 dB) of 260MHz (13.2%) and 110 MHz (2.9 %) are obtained within the lower and upper resonances respectively. The antenna is packed with a layout area of 0.33λ × 0.33λ, where λ being the wavelength for the lower resonant frequency in free space medium. The antenna simulation is done by assuming FR4 substrate with 1.6mm thickness and tan δ= 0.02. The CP radiation patterns are investigated and found to be stable with satisfactory with dominant left hand circular polarization. The antenna has satisfactory gain suitable for GPS and WiMAX applications.","PeriodicalId":136514,"journal":{"name":"2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133156082","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 : 2020-12-11DOI: 10.1109/MPCIT51588.2020.9350325
K. Bhargavi, Praveena K S, S. Tejaswini, M. Sahana, H. S. Bhanu
Face Recognition of Identical Twin is a challenging task due to the presence of a high degree of correlation in the overall appearance of the face. Few monozygotic twins help with business tricks such as fake insurance compensation. Most importantly, if one of the indistinguishable twins commits a serious crime, their unclear personalities cause confusion and uncertainty in court trials. The proposed method can be employed for these applications to overcome such harms. In this paper, The AdaBoost Technique is employed for the face detection using Haar features. This algorithm identifies the face region of the input image. The Pseudo Zernike Moment (PZM) and Difference of Gaussian (DoG) methods are utilized to extract the features from the face region detected by AdaBoost algorithm and stored in the databases in both training and testing phase. The Support Vector Machine (SVM) classifier distinguishes the twin’s features by comparing both trained and tested features and identifies the culprit who is required as a result. The experimental results demonstrated the ability of the proposed method to recognize a pair of Identical twins.
同卵双胞胎的面部识别是一项具有挑战性的任务，因为在面部的整体外观存在高度的相关性。很少有同卵双胞胎能帮上忙，比如伪造保险赔偿。最重要的是，如果这对难以区分的双胞胎中的一个犯了重罪，他们不清楚的性格会在法庭审判中造成混乱和不确定性。所提出的方法可以用于这些应用，以克服这些危害。本文采用AdaBoost技术对Haar特征进行人脸检测。该算法对输入图像的人脸区域进行识别。利用伪泽尼克矩(Pseudo Zernike Moment, PZM)和高斯差分(Difference of Gaussian, DoG)方法从AdaBoost算法检测到的人脸区域中提取特征，并在训练和测试阶段分别存储在数据库中。支持向量机(SVM)分类器通过比较训练和测试的特征来区分双胞胎的特征，并识别出结果需要的罪魁祸首。实验结果表明，该方法能够识别出一对同卵双胞胎。
{"title":"PZM and DoG based Feature Extraction Technique for Facial Recognition among Monozygotic Twins","authors":"K. Bhargavi, Praveena K S, S. Tejaswini, M. Sahana, H. S. Bhanu","doi":"10.1109/MPCIT51588.2020.9350325","DOIUrl":"https://doi.org/10.1109/MPCIT51588.2020.9350325","url":null,"abstract":"Face Recognition of Identical Twin is a challenging task due to the presence of a high degree of correlation in the overall appearance of the face. Few monozygotic twins help with business tricks such as fake insurance compensation. Most importantly, if one of the indistinguishable twins commits a serious crime, their unclear personalities cause confusion and uncertainty in court trials. The proposed method can be employed for these applications to overcome such harms. In this paper, The AdaBoost Technique is employed for the face detection using Haar features. This algorithm identifies the face region of the input image. The Pseudo Zernike Moment (PZM) and Difference of Gaussian (DoG) methods are utilized to extract the features from the face region detected by AdaBoost algorithm and stored in the databases in both training and testing phase. The Support Vector Machine (SVM) classifier distinguishes the twin’s features by comparing both trained and tested features and identifies the culprit who is required as a result. The experimental results demonstrated the ability of the proposed method to recognize a pair of Identical twins.","PeriodicalId":136514,"journal":{"name":"2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114825576","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 : 2020-12-11DOI: 10.1109/MPCIT51588.2020.9350423
Akanksha Soni, Avinash Rai
The role of digital image processing in medical science is very advantageous. Colon malignancy is one of the perilous infections which are very hazardous for human health. It starts on the large intestine and later infects other nearest organs of the body, which is lethal if left untreated. Colorectal diagnosis is very expensive if it is not treated timely, so the early phase identification of malignancy is necessary for better health. To diminishing this problem we develop an automated system for recognizing colorectal malignancy in an initial stage. The prime aspire of this framework is to inspect the colorectal CT image to identify whether the colon has malignancy or not. Usually, most of the existing techniques may distort the actual detail that creates false prediction and may reduce accuracy and precision which is very dangerous for patients but a proposed novel approach is capable of accurately detect colorectal cancer at very less processing instant. It consists of different phases namely Pre-processing, Thresholding, Sobel filter, and morphological dilation operation. Sobel algorithm executes a 2-D spatial gradient measurement on the picture and emphasizes the vicinity of high spatial frequency that corresponds to edges. It is easy to apply and gives more accurate edges information about the scene. After that, we apply a morphological operation for extracting picture elements and also advantageous for telling about object shape. The system obtained 98.48% accuracy by testing 198 colon CT samples.
{"title":"Automatic Colon Malignancy Recognition using Sobel & Morphological Dilation","authors":"Akanksha Soni, Avinash Rai","doi":"10.1109/MPCIT51588.2020.9350423","DOIUrl":"https://doi.org/10.1109/MPCIT51588.2020.9350423","url":null,"abstract":"The role of digital image processing in medical science is very advantageous. Colon malignancy is one of the perilous infections which are very hazardous for human health. It starts on the large intestine and later infects other nearest organs of the body, which is lethal if left untreated. Colorectal diagnosis is very expensive if it is not treated timely, so the early phase identification of malignancy is necessary for better health. To diminishing this problem we develop an automated system for recognizing colorectal malignancy in an initial stage. The prime aspire of this framework is to inspect the colorectal CT image to identify whether the colon has malignancy or not. Usually, most of the existing techniques may distort the actual detail that creates false prediction and may reduce accuracy and precision which is very dangerous for patients but a proposed novel approach is capable of accurately detect colorectal cancer at very less processing instant. It consists of different phases namely Pre-processing, Thresholding, Sobel filter, and morphological dilation operation. Sobel algorithm executes a 2-D spatial gradient measurement on the picture and emphasizes the vicinity of high spatial frequency that corresponds to edges. It is easy to apply and gives more accurate edges information about the scene. After that, we apply a morphological operation for extracting picture elements and also advantageous for telling about object shape. The system obtained 98.48% accuracy by testing 198 colon CT samples.","PeriodicalId":136514,"journal":{"name":"2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116097114","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 : 2020-12-11DOI: 10.1109/MPCIT51588.2020.9350388
Akanksha Soni, Avinash Rai
The kidneys are a pair of fist-structured organs placed beneath the rib cage. Kidneys function is indispensable to having a healthful body. Kidney disorder happens when it cannot execute its role and can lead to other health predicaments, including puny bones, nerve damage, and malnutrition. If the disease gets worse then kidneys may stop functioning totally and it may cause lethal if left untreated. Kidney disorder may also occur because of stone formation, malignancy, congenital anomalies, blockage of the urinary system, etc. The existence of stone in the kidney called Nephrolithiasis and it is a tremendously painful disorder. For surgical operations, it is incredibly essential to foresee the exact place of tumors in the kidney. The CT scan pictures have poor contrast and also contain noise; this creates complications for recognizing kidney abnormalities manually. So, there is a must wanted an accurate and intelligent system to foresee the stone automatically; it will be really advantageous for necessary treatment. The prime intention of this effort is to develop an automatic stone detection system from the CT picture. A learning model-Support Vector Machine is a proficient algorithm for classifying stone. It classifies the vector space of stone affected & normal kidneys into two separate districts. Before classifying the stone, the image may refer to some kind of improvements such as histogram equalization and Emboss that directionally calculates the differences in colors. Generally, existing approaches may deform the genuine information that degrades the accurateness of the system. The System obtained 98.71% accuracy by testing 156 CT samples that have a stone or tumor as well as a healthful kidney.
{"title":"Kidney Stone Recognition and Extraction using Directional Emboss & SVM from Computed Tomography Images","authors":"Akanksha Soni, Avinash Rai","doi":"10.1109/MPCIT51588.2020.9350388","DOIUrl":"https://doi.org/10.1109/MPCIT51588.2020.9350388","url":null,"abstract":"The kidneys are a pair of fist-structured organs placed beneath the rib cage. Kidneys function is indispensable to having a healthful body. Kidney disorder happens when it cannot execute its role and can lead to other health predicaments, including puny bones, nerve damage, and malnutrition. If the disease gets worse then kidneys may stop functioning totally and it may cause lethal if left untreated. Kidney disorder may also occur because of stone formation, malignancy, congenital anomalies, blockage of the urinary system, etc. The existence of stone in the kidney called Nephrolithiasis and it is a tremendously painful disorder. For surgical operations, it is incredibly essential to foresee the exact place of tumors in the kidney. The CT scan pictures have poor contrast and also contain noise; this creates complications for recognizing kidney abnormalities manually. So, there is a must wanted an accurate and intelligent system to foresee the stone automatically; it will be really advantageous for necessary treatment. The prime intention of this effort is to develop an automatic stone detection system from the CT picture. A learning model-Support Vector Machine is a proficient algorithm for classifying stone. It classifies the vector space of stone affected & normal kidneys into two separate districts. Before classifying the stone, the image may refer to some kind of improvements such as histogram equalization and Emboss that directionally calculates the differences in colors. Generally, existing approaches may deform the genuine information that degrades the accurateness of the system. The System obtained 98.71% accuracy by testing 156 CT samples that have a stone or tumor as well as a healthful kidney.","PeriodicalId":136514,"journal":{"name":"2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121919118","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 : 2020-12-11DOI: 10.1109/MPCIT51588.2020.9350502
C. Maheshan, H. Kumar
This paper proposes an innovative method in the investigation and analysis of real time transformer oil images at different temperatures along with different ages using haralick image texture features. Haralick texture feature method based on Gray-Level Co-occurrence Matrix (GLCM) used in this paper to enumerate the spatial relation between the neighborhood pixels in an image. A theoretical examination performed on oil test images to characterize its textural properties. The statistical features extracted for original as well as filtered transformer oil image at different temperatures, and features of one year to twenty five year aged oils determined. The results through this analysis indicate the identification of significant textures of the test images. The experimental results demonstrated that texture feature extraction derived from the haralick features realize a new technique in the analysis of transformer oil images under different ages as well as operating conditions.
{"title":"Investigation And Analysis of Real Time Transformer oil Images Using Haralick Texture Features","authors":"C. Maheshan, H. Kumar","doi":"10.1109/MPCIT51588.2020.9350502","DOIUrl":"https://doi.org/10.1109/MPCIT51588.2020.9350502","url":null,"abstract":"This paper proposes an innovative method in the investigation and analysis of real time transformer oil images at different temperatures along with different ages using haralick image texture features. Haralick texture feature method based on Gray-Level Co-occurrence Matrix (GLCM) used in this paper to enumerate the spatial relation between the neighborhood pixels in an image. A theoretical examination performed on oil test images to characterize its textural properties. The statistical features extracted for original as well as filtered transformer oil image at different temperatures, and features of one year to twenty five year aged oils determined. The results through this analysis indicate the identification of significant textures of the test images. The experimental results demonstrated that texture feature extraction derived from the haralick features realize a new technique in the analysis of transformer oil images under different ages as well as operating conditions.","PeriodicalId":136514,"journal":{"name":"2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125821488","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 : 2020-12-11DOI: 10.1109/MPCIT51588.2020.9350390
Padidela Swarochish Rao, S. Rao, R. Ranjan
India has witnessed an unprecedented increase in garbage levels in the past 20 years. Massive quantities of waste, particularly solid waste, are generated daily and seldom picked up. Consequently, garbage is being dumped in landfills and water bodies, hence not managed effectively. This mismanagement has detrimental consequences on our environment. Thus, there is a need to develop an efficient system to manage waste. In this paper, an IoT-based, automated smart bin monitoring system is proposed. Moreover, a deep learning model was used to forecast future garbage levels from the data collected. The proposed neural network model was able to predict garbage levels with an accuracy of 80.33%. Results verify the accurate prognosis of garbage levels. Additionally, data were analysed using bar charts. The amalgamation of IoT and Deep learning can bring a revolutionary change in technology and be applied to waste management. Consequently, prediction and examination of garbage levels may help municipal authorities incorporate an efficient garbage management system and reduce the overflow of garbagebins.
{"title":"Deep Learning Based Smart Garbage Monitoring System","authors":"Padidela Swarochish Rao, S. Rao, R. Ranjan","doi":"10.1109/MPCIT51588.2020.9350390","DOIUrl":"https://doi.org/10.1109/MPCIT51588.2020.9350390","url":null,"abstract":"India has witnessed an unprecedented increase in garbage levels in the past 20 years. Massive quantities of waste, particularly solid waste, are generated daily and seldom picked up. Consequently, garbage is being dumped in landfills and water bodies, hence not managed effectively. This mismanagement has detrimental consequences on our environment. Thus, there is a need to develop an efficient system to manage waste. In this paper, an IoT-based, automated smart bin monitoring system is proposed. Moreover, a deep learning model was used to forecast future garbage levels from the data collected. The proposed neural network model was able to predict garbage levels with an accuracy of 80.33%. Results verify the accurate prognosis of garbage levels. Additionally, data were analysed using bar charts. The amalgamation of IoT and Deep learning can bring a revolutionary change in technology and be applied to waste management. Consequently, prediction and examination of garbage levels may help municipal authorities incorporate an efficient garbage management system and reduce the overflow of garbagebins.","PeriodicalId":136514,"journal":{"name":"2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115910007","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 : 2020-12-11DOI: 10.1109/MPCIT51588.2020.9350457
Suman De, Dhriti Agarwal
Unorganized data is a massive source of cluttered information available over the web. It possesses a major problem when this data originates from unauthenticated sources creating confusion among the general public. The amount of fake news regarding the current COVID-19 scenario and political movements have had an adverse effect on the world. It is necessary to devise models and a step by step algorithm to tackle this challenge. This paper talks about a model that identifies data available over the web and performs crawling to get information about the data sources and maps the information with regards to the authenticity of the source. We look at possible web perspectives of data sources, official social media handles, reviewed agency lists, sentiment analysis, and calculate a value for a piece of particular news. The observed critical value looks for identifying the authenticity of the news and forms the basis of this idea. This paper also looks at a model that uses supervised learning to classify various news items depending on the defined criteria.
{"title":"A Novel Model of Supervised Clustering using Sentiment and Contextual Analysis for Fake News Detection","authors":"Suman De, Dhriti Agarwal","doi":"10.1109/MPCIT51588.2020.9350457","DOIUrl":"https://doi.org/10.1109/MPCIT51588.2020.9350457","url":null,"abstract":"Unorganized data is a massive source of cluttered information available over the web. It possesses a major problem when this data originates from unauthenticated sources creating confusion among the general public. The amount of fake news regarding the current COVID-19 scenario and political movements have had an adverse effect on the world. It is necessary to devise models and a step by step algorithm to tackle this challenge. This paper talks about a model that identifies data available over the web and performs crawling to get information about the data sources and maps the information with regards to the authenticity of the source. We look at possible web perspectives of data sources, official social media handles, reviewed agency lists, sentiment analysis, and calculate a value for a piece of particular news. The observed critical value looks for identifying the authenticity of the news and forms the basis of this idea. This paper also looks at a model that uses supervised learning to classify various news items depending on the defined criteria.","PeriodicalId":136514,"journal":{"name":"2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114849388","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 : 2020-12-11DOI: 10.1109/MPCIT51588.2020.9350324
Tejas Khare, Vaibhav Bahel, A. Phadke
Printed Circuit Boards (“PCB”) are the foundation for the functioning of any electronic device, and therefore are an essential component for various industries such as automobile, communication, computation, etc. However, one of the challenges faced by the PCB manufacturers in the process of manufacturing of the PCBs is the faulty placement of its components including missing components. In the present scenario the infrastructure required to ensure adequate quality of the PCB requires a lot of time and effort. The authors present a novel solution for detecting missing components and classifying them in a resourceful manner. The presented algorithm focuses on pixel theory and object detection, which has been used in combination to optimize the results from the given dataset.
{"title":"PCB-Fire: Automated Classification and Fault Detection in PCB","authors":"Tejas Khare, Vaibhav Bahel, A. Phadke","doi":"10.1109/MPCIT51588.2020.9350324","DOIUrl":"https://doi.org/10.1109/MPCIT51588.2020.9350324","url":null,"abstract":"Printed Circuit Boards (“PCB”) are the foundation for the functioning of any electronic device, and therefore are an essential component for various industries such as automobile, communication, computation, etc. However, one of the challenges faced by the PCB manufacturers in the process of manufacturing of the PCBs is the faulty placement of its components including missing components. In the present scenario the infrastructure required to ensure adequate quality of the PCB requires a lot of time and effort. The authors present a novel solution for detecting missing components and classifying them in a resourceful manner. The presented algorithm focuses on pixel theory and object detection, which has been used in combination to optimize the results from the given dataset.","PeriodicalId":136514,"journal":{"name":"2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123334728","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 : 2020-12-11DOI: 10.1109/MPCIT51588.2020.9350403
R. Premananada, H. J. Jambukesh, H. Shridhar, U. Rajashekar, K. Harisha
The validation of documents such as recognition of optical character, the sign which is written by hand are the main drawbacks involved in the identification of human and their addresses, codes of the post written on the envelops, manuscript evaluation, understanding the transactions of money and documents of the bank that are written in the English language. The conceptual model was written by hand for the real-time application that deals with the handwritten identification enables a comprehensive computerized system to identify the data written by hand which is more efficient and is free from noise. The proposed framework consists of filters based on Probabilistic Patch (PPB), identification, and analysis of the Canny edge. With the application of a Probabilistic Patch-based filter, the recursive speckle noise and additive Gaussian noise are processed. The words in the document are obtained by using the structure of Lifting transformation, the edges of the word are identified with help of Canny edge recognition. At last, the database validates the text as correct or incorrect. With the application of the Embedded Development Kit (EDK) and Software Development Kit (SDK), the entire framework is developed. The hardware used is in this work is Virtex-5 FPGA board which is the integration of SDK and EDK with XC5VLX50T as the part name.
文件的验证，如识别光学字符，手写的标志是主要的缺点，涉及到识别人类和他们的地址，信封上写的邮政代码，手稿评估，理解用英语写的货币交易和银行文件。该概念模型是针对手写识别的实时应用而建立的，它使综合计算机系统能够更高效、无噪声地识别手写数据。该框架由基于概率补丁(PPB)的滤波器、Canny边缘的识别和分析组成。应用基于概率patch的滤波器，对递归散斑噪声和加性高斯噪声进行了处理。利用lift变换的结构获取文档中的单词，利用Canny边缘识别方法识别单词的边缘。最后，数据库验证文本是否正确。利用嵌入式开发工具包(Embedded Development Kit, EDK)和软件开发工具包(Software Development Kit, SDK)开发了整个框架。本工作使用的硬件是Virtex-5 FPGA板，它是SDK和EDK的集成，部件名称为XC5VLX50T。
{"title":"Design and Implementation of Automated Image Handwriting Sentences Recognition using Hybrid Techniques on FPGA","authors":"R. Premananada, H. J. Jambukesh, H. Shridhar, U. Rajashekar, K. Harisha","doi":"10.1109/MPCIT51588.2020.9350403","DOIUrl":"https://doi.org/10.1109/MPCIT51588.2020.9350403","url":null,"abstract":"The validation of documents such as recognition of optical character, the sign which is written by hand are the main drawbacks involved in the identification of human and their addresses, codes of the post written on the envelops, manuscript evaluation, understanding the transactions of money and documents of the bank that are written in the English language. The conceptual model was written by hand for the real-time application that deals with the handwritten identification enables a comprehensive computerized system to identify the data written by hand which is more efficient and is free from noise. The proposed framework consists of filters based on Probabilistic Patch (PPB), identification, and analysis of the Canny edge. With the application of a Probabilistic Patch-based filter, the recursive speckle noise and additive Gaussian noise are processed. The words in the document are obtained by using the structure of Lifting transformation, the edges of the word are identified with help of Canny edge recognition. At last, the database validates the text as correct or incorrect. With the application of the Embedded Development Kit (EDK) and Software Development Kit (SDK), the entire framework is developed. The hardware used is in this work is Virtex-5 FPGA board which is the integration of SDK and EDK with XC5VLX50T as the part name.","PeriodicalId":136514,"journal":{"name":"2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115639501","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 : 2020-12-11DOI: 10.1109/MPCIT51588.2020.9350481
Atharv Tendolkar, S. Ramya
Post Covid-19 era redefines farming in terms of ensuring the maximum productivity and safety of the produce by leveraging technology. A contactless approach coupled with reliability and safety in the entre supply chain is the need of the hour. The proposed solution “CareBro”, plays a vital part in ensuring that the entire farm is managed autonomously and remotely without physical presence. The onboard edge computing capabilities interact with the smart farm sensorics in an IOT environment. This ensures seamless farming and allows for increased crop yield, ethical pest management and irrigation control. The CareBro is always in touch with the farmer through the cloud, with real time monitoring and decision making. Thereby ensuring the perfect farm management solution in urban, rural, largescale and small scale farmers throughout our country.
{"title":"CareBro (Personal Farm Assistant):An IoT based Smart Agriculture with Edge Computing","authors":"Atharv Tendolkar, S. Ramya","doi":"10.1109/MPCIT51588.2020.9350481","DOIUrl":"https://doi.org/10.1109/MPCIT51588.2020.9350481","url":null,"abstract":"Post Covid-19 era redefines farming in terms of ensuring the maximum productivity and safety of the produce by leveraging technology. A contactless approach coupled with reliability and safety in the entre supply chain is the need of the hour. The proposed solution “CareBro”, plays a vital part in ensuring that the entire farm is managed autonomously and remotely without physical presence. The onboard edge computing capabilities interact with the smart farm sensorics in an IOT environment. This ensures seamless farming and allows for increased crop yield, ethical pest management and irrigation control. The CareBro is always in touch with the farmer through the cloud, with real time monitoring and decision making. Thereby ensuring the perfect farm management solution in urban, rural, largescale and small scale farmers throughout our country.","PeriodicalId":136514,"journal":{"name":"2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120924049","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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