Over last few decades, wireless communication system has sought more attention and plays predominant role in different areas for human personal and commercial applications. Day by day, with advancements in technology, wireless gadgets got more compact due to microelectronics fabrication and integration techniques. So, such applications put great demand over new design specifications on antenna structures used in transmitter and receiver for radio wave communication. In wireless applications depending upon, frequency bands and bandwidth requirements numerous compact antenna structures are used with improved efficiency. Microstrip patch antennas are highly regarded due to its compact size, easy integration with microwave circuits. In study of patch antenna, Defected Ground structures gain popularity these days due to its various benefits to enhance antenna performance. This research article, provides extensive literature survey over use of Defected Ground Structures (DGS) in microstrip patch antenna with its design consequences. This article also explores the enhancement in antenna parameters with implementation of DGS’s. DGS concept is used in microstrip patch antenna and microwave engineering for performance improvement of these devices. DGS can be merged with other techniques to enhance antenna operational parameters like gain, bandwidth, VSWR and spurious radiations.
{"title":"Microstrip patch antenna performance analysis with Defected Ground structures: A review","authors":"Amandeep Kaur","doi":"10.51735/IJICCN/001/03","DOIUrl":"https://doi.org/10.51735/IJICCN/001/03","url":null,"abstract":"Over last few decades, wireless communication system has sought more attention and plays predominant role in different areas for human personal and commercial applications. Day by day, with advancements in technology, wireless gadgets got more compact due to microelectronics fabrication and integration techniques. So, such applications put great demand over new design specifications on antenna structures used in transmitter and receiver for radio wave communication. In wireless applications depending upon, frequency bands and bandwidth requirements numerous compact antenna structures are used with improved efficiency. Microstrip patch antennas are highly regarded due to its compact size, easy integration with microwave circuits. In study of patch antenna, Defected Ground structures gain popularity these days due to its various benefits to enhance antenna performance. This research article, provides extensive literature survey over use of Defected Ground Structures (DGS) in microstrip patch antenna with its design consequences. This article also explores the enhancement in antenna parameters with implementation of DGS’s. DGS concept is used in microstrip patch antenna and microwave engineering for performance improvement of these devices. DGS can be merged with other techniques to enhance antenna operational parameters like gain, bandwidth, VSWR and spurious radiations.","PeriodicalId":266028,"journal":{"name":"International Journal of Intelligent Communication, Computing and Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117227191","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}
Massive MIMO (mMIMO) systems become a primary advantage to overcome the problem of bandwidth restrictions. It improves the channel capacity of remote systems.The paper reviews about mMIMO systems. mMIMO consists of several number of antennas at base station (BS) which improves spectrum efficacy. The extra benefit of the mMIMO system is that the components cost is low because of utilization of less power components. The paper also discusses about the channel estimation at the BS and generally time division mode (TDD) is assumed for mMIMO systems. The paper also discusses system model, benefits for 5G wireless communication and its challenges.
{"title":"A Review of Massive Multiple Input Multiple Output for 5G Communication: Benefits and Challenges","authors":"S. Nilofer","doi":"10.51735/IJICCN/001/04","DOIUrl":"https://doi.org/10.51735/IJICCN/001/04","url":null,"abstract":"Massive MIMO (mMIMO) systems become a primary advantage to overcome the problem of bandwidth restrictions. It improves the channel capacity of remote systems.The paper reviews about mMIMO systems. mMIMO consists of several number of antennas at base station (BS) which improves spectrum efficacy. The extra benefit of the mMIMO system is that the components cost is low because of utilization of less power components. The paper also discusses about the channel estimation at the BS and generally time division mode (TDD) is assumed for mMIMO systems. The paper also discusses system model, benefits for 5G wireless communication and its challenges.","PeriodicalId":266028,"journal":{"name":"International Journal of Intelligent Communication, Computing and Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121990430","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 : 2018-11-02DOI: 10.20944/preprints201810.0762.v1
Rajesh Singh, A. Gehlot, P. Malik, M. Kaushik
The accumulation of waste in one place for much time makes it rotten and which leads to the attraction of flies and rodents, which can harbor many of health hazardous conditions? Likewise, some have the cure as burning waste, and this is worst planned for getting rid of this problem of waste as this further it pollutes air so as living being to as we inhale it. To avoid such disastrous situations, we are going to make these smart bins named as "IoT enabled Devices for Waste Management'. These dustbins are interfaced with IOT system with MSP-EXP430G2 Launchpad using Wi-Fi and GPRS modem. Hence, the state will be updated on to the HTML page along with it we have an App it will be making this system to be user handy for everyone. The heart of this system is a Wi-Fi module and GPRS modem; essential for its real-time data updates for picking up the team to function fluently. The main aim of this project is to protect the environment, peoples as well as the proper management of resource by adequate waste management devices.
{"title":"Dynamic Modeling and Decision System for Smart Bin in Cities for Waste Management","authors":"Rajesh Singh, A. Gehlot, P. Malik, M. Kaushik","doi":"10.20944/preprints201810.0762.v1","DOIUrl":"https://doi.org/10.20944/preprints201810.0762.v1","url":null,"abstract":"The accumulation of waste in one place for much time makes it rotten and which leads to the attraction of flies and rodents, which can harbor many of health hazardous conditions? Likewise, some have the cure as burning waste, and this is worst planned for getting rid of this problem of waste as this further it pollutes air so as living being to as we inhale it. To avoid such disastrous situations, we are going to make these smart bins named as \"IoT enabled Devices for Waste Management'. These dustbins are interfaced with IOT system with MSP-EXP430G2 Launchpad using Wi-Fi and GPRS modem. Hence, the state will be updated on to the HTML page along with it we have an App it will be making this system to be user handy for everyone. The heart of this system is a Wi-Fi module and GPRS modem; essential for its real-time data updates for picking up the team to function fluently. The main aim of this project is to protect the environment, peoples as well as the proper management of resource by adequate waste management devices.","PeriodicalId":266028,"journal":{"name":"International Journal of Intelligent Communication, Computing and Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132296242","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}
{"title":"Performance Analysis and Optimization of Customer E-Commerce using Data Segmentation Algorithm","authors":"","doi":"10.51735/ijiccn/001/20","DOIUrl":"https://doi.org/10.51735/ijiccn/001/20","url":null,"abstract":"","PeriodicalId":266028,"journal":{"name":"International Journal of Intelligent Communication, Computing and Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115658068","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}
{"title":"Implementation of Ethereum Blockchain in Healthcare Using IPFS","authors":"","doi":"10.51735/ijiccn/001/17","DOIUrl":"https://doi.org/10.51735/ijiccn/001/17","url":null,"abstract":"","PeriodicalId":266028,"journal":{"name":"International Journal of Intelligent Communication, Computing and Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114731656","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}
{"title":"Design and Analysis of Grid Tied Single Stage Three Phase P-V System","authors":"","doi":"10.51735/ijiccn/001/18","DOIUrl":"https://doi.org/10.51735/ijiccn/001/18","url":null,"abstract":"","PeriodicalId":266028,"journal":{"name":"International Journal of Intelligent Communication, Computing and Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134279293","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}
: Today, for Internet users, micro-blogging has become a popular networking forum. Millions of people exchange views on different aspects of their lives. Thus, microblogging websites are a rich source of opinion mining data or Sentiment Analysis (SA) information. Because of the recent advent of microblogging, there are a few research papers dedicated to this subject. In our paper, we concentrate on Reddit.com, one of the leading microblogging sites, to explore the opinion of the public. We will demonstrate how to collect real-time Reddit data and use algorithms such as Support Vector Machine, KNN, and Multinomial Naive Bayes (MNB) for sentiment analysis or opinion mining purposes. We are able to assess positive and negative feelings using the algorithms selected above for the real-time Reddit depression info. The following experimental evaluations show that the algorithms used are accurate and can be used as an application for diagnosing the depression of individuals. After deployment User can write the content as input and after submitting the input text model API will be called from where result will come as User is going through depression or suicide. We worked with English in this post, but it can be used with any other language. English in this document, but it can be used for any other language this will be in future scope.
{"title":"Prediction of depression using Machine Learning and NLP approach","authors":"Amrat Mali, R. Sedamkar","doi":"10.51735/ijiccn/001/16","DOIUrl":"https://doi.org/10.51735/ijiccn/001/16","url":null,"abstract":": Today, for Internet users, micro-blogging has become a popular networking forum. Millions of people exchange views on different aspects of their lives. Thus, microblogging websites are a rich source of opinion mining data or Sentiment Analysis (SA) information. Because of the recent advent of microblogging, there are a few research papers dedicated to this subject. In our paper, we concentrate on Reddit.com, one of the leading microblogging sites, to explore the opinion of the public. We will demonstrate how to collect real-time Reddit data and use algorithms such as Support Vector Machine, KNN, and Multinomial Naive Bayes (MNB) for sentiment analysis or opinion mining purposes. We are able to assess positive and negative feelings using the algorithms selected above for the real-time Reddit depression info. The following experimental evaluations show that the algorithms used are accurate and can be used as an application for diagnosing the depression of individuals. After deployment User can write the content as input and after submitting the input text model API will be called from where result will come as User is going through depression or suicide. We worked with English in this post, but it can be used with any other language. English in this document, but it can be used for any other language this will be in future scope.","PeriodicalId":266028,"journal":{"name":"International Journal of Intelligent Communication, Computing and Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131835606","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}
: Plant diseases will wreak havoc on agricultural products' quality and quantity. It is important to recognize plant pathogens early on for the sake of global health and well-being. Deep learning's popularity in machine vision has recently inspired many researchers to improve the performance of plant disease detection systems. Unfortunately, most of these studies relied on AlexNet, GoogleNet, and other similar structural design rather than more recent deep designs. Furthermore, the research did not employ deep learning visualization techniques, which classify deep classifiers as "black boxes" due to their opacity. We used these three learning techniques to assess various state-of-the-art Convolutional Neural Network (CNN), AlexNet, and VGG16 architectures on a public dataset for plant disease classification in this article. In comparison to other designs, the VGG16 outperforms state-of-the-art findings in plant disease classification, with an accuracy of 97 percent. In addition, we have suggested the use of saliency maps as a means of visualizing and interpreting the CNN classification mechanism. This method of visualization improves the clarity of deep learning models and provides further insight into plant disease symptoms. This paper compares the disease classification of CNN, AlexNet, and VGG16 designs on mangoes, grapes, potatoes, rice, and corn leaves. In comparison to CNN and AlexNet designs, the VGG16 architecture has high accuracy and recall. Precision separates predictive positive from actual positive, while recall separates actual positive from predictive, positive, and high precision and recall mean that the classifier is generating accurate results. The next project for the research team will be to develop a smartphone app that will diagnose the disease and be useful to farmers. Farmers will photograph diseased leaves, and the mobile device will identify the issue and include instructions about how to address it. It would be very good for farmers with vast fields because it will be more efficient and less time intensive. DL designs have also been discovered to be capable of identifying essential and irrelevant features from a series of images through this research.
{"title":"Detection of Plant Diseases Using Convolutional Neural Network Architectures","authors":"Shraddha Mahale, Kamal Shah","doi":"10.51735/ijiccn/001/19","DOIUrl":"https://doi.org/10.51735/ijiccn/001/19","url":null,"abstract":": Plant diseases will wreak havoc on agricultural products' quality and quantity. It is important to recognize plant pathogens early on for the sake of global health and well-being. Deep learning's popularity in machine vision has recently inspired many researchers to improve the performance of plant disease detection systems. Unfortunately, most of these studies relied on AlexNet, GoogleNet, and other similar structural design rather than more recent deep designs. Furthermore, the research did not employ deep learning visualization techniques, which classify deep classifiers as \"black boxes\" due to their opacity. We used these three learning techniques to assess various state-of-the-art Convolutional Neural Network (CNN), AlexNet, and VGG16 architectures on a public dataset for plant disease classification in this article. In comparison to other designs, the VGG16 outperforms state-of-the-art findings in plant disease classification, with an accuracy of 97 percent. In addition, we have suggested the use of saliency maps as a means of visualizing and interpreting the CNN classification mechanism. This method of visualization improves the clarity of deep learning models and provides further insight into plant disease symptoms. This paper compares the disease classification of CNN, AlexNet, and VGG16 designs on mangoes, grapes, potatoes, rice, and corn leaves. In comparison to CNN and AlexNet designs, the VGG16 architecture has high accuracy and recall. Precision separates predictive positive from actual positive, while recall separates actual positive from predictive, positive, and high precision and recall mean that the classifier is generating accurate results. The next project for the research team will be to develop a smartphone app that will diagnose the disease and be useful to farmers. Farmers will photograph diseased leaves, and the mobile device will identify the issue and include instructions about how to address it. It would be very good for farmers with vast fields because it will be more efficient and less time intensive. DL designs have also been discovered to be capable of identifying essential and irrelevant features from a series of images through this research.","PeriodicalId":266028,"journal":{"name":"International Journal of Intelligent Communication, Computing and Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128861875","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}
{"title":"Design and Implementation of Routing Algorithm to Enhance Network Lifetime in Wireless Body Area Network for Health Monitoring","authors":"","doi":"10.51735/ijiccn/001/25","DOIUrl":"https://doi.org/10.51735/ijiccn/001/25","url":null,"abstract":"","PeriodicalId":266028,"journal":{"name":"International Journal of Intelligent Communication, Computing and Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125110171","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}
{"title":"Comparison between Algorithms of Deep Learning to Detect Brain Tumor","authors":"","doi":"10.51735/ijiccn/001/23","DOIUrl":"https://doi.org/10.51735/ijiccn/001/23","url":null,"abstract":"","PeriodicalId":266028,"journal":{"name":"International Journal of Intelligent Communication, Computing and Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129607940","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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