2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT)最新文献_第2页

Co-Planar Waveguide Fed Dual Band Circular Polarized Slot Antenna 共面波导馈电双频圆极化槽天线

2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT) Pub Date : 2020-12-11 DOI: 10.1109/MPCIT51588.2020.9350469

Santosh Kumar Bairappaka, Anumoy Ghosh

{"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}

引用次数: 0

PZM and DoG based Feature Extraction Technique for Facial Recognition among Monozygotic Twins 基于PZM和DoG的同卵双胞胎人脸识别特征提取技术

2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT) Pub Date : 2020-12-11 DOI: 10.1109/MPCIT51588.2020.9350325

K. Bhargavi, Praveena K S, S. Tejaswini, M. Sahana, H. S. Bhanu

{"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}

引用次数: 1

Automatic Colon Malignancy Recognition using Sobel & Morphological Dilation 基于Sobel和形态学扩张的结肠恶性肿瘤自动识别

2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT) Pub Date : 2020-12-11 DOI: 10.1109/MPCIT51588.2020.9350423

Akanksha Soni, Avinash Rai

{"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}

引用次数: 0

Kidney Stone Recognition and Extraction using Directional Emboss & SVM from Computed Tomography Images 基于方向浮雕和支持向量机的计算机断层图像肾结石识别与提取

2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT) Pub Date : 2020-12-11 DOI: 10.1109/MPCIT51588.2020.9350388

Akanksha Soni, Avinash Rai

{"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}

引用次数: 5

Investigation And Analysis of Real Time Transformer oil Images Using Haralick Texture Features 基于Haralick纹理特征的实时变压器油图像研究与分析

2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT) Pub Date : 2020-12-11 DOI: 10.1109/MPCIT51588.2020.9350502

C. Maheshan, H. Kumar

引用次数: 1

Deep Learning Based Smart Garbage Monitoring System 基于深度学习的智能垃圾监测系统

2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT) Pub Date : 2020-12-11 DOI: 10.1109/MPCIT51588.2020.9350390

Padidela Swarochish Rao, S. Rao, R. Ranjan

{"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}

引用次数: 5

A Novel Model of Supervised Clustering using Sentiment and Contextual Analysis for Fake News Detection 基于情感和上下文分析的监督聚类假新闻检测新模型

2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT) Pub Date : 2020-12-11 DOI: 10.1109/MPCIT51588.2020.9350457

Suman De, Dhriti Agarwal

{"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}

引用次数: 3

PCB-Fire: Automated Classification and Fault Detection in PCB PCB- fire: PCB中的自动分类和故障检测

2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT) Pub Date : 2020-12-11 DOI: 10.1109/MPCIT51588.2020.9350324

Tejas Khare, Vaibhav Bahel, A. Phadke

引用次数: 3

Design and Implementation of Automated Image Handwriting Sentences Recognition using Hybrid Techniques on FPGA 基于FPGA的图像手写句子自动识别的设计与实现

2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT) Pub Date : 2020-12-11 DOI: 10.1109/MPCIT51588.2020.9350403

R. Premananada, H. J. Jambukesh, H. Shridhar, U. Rajashekar, K. Harisha

{"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}

引用次数: 0

CareBro (Personal Farm Assistant):An IoT based Smart Agriculture with Edge Computing CareBro(个人农场助理):基于物联网的边缘计算智能农业

2020 Third International Conference on Multimedia Processing, Communication & Information Technology (MPCIT) Pub Date : 2020-12-11 DOI: 10.1109/MPCIT51588.2020.9350481

Atharv Tendolkar, S. Ramya

引用次数: 6