Accurate classification and counting of blood components is crucial in detection of illnesses of an individual. The widely used methods to count blood components are manual counting and hematology analyzer. With advancement in the field of image processing and machine learning, new and better methods are available for counting and classifying blood components. Deep leaning is training the computer with labelled data for classification tasks. Such techniques have shown high performance and accuracy. Most Deep learning models uses neural network architecture. One of the most popular type of deep learning model is Convolutional Neural Network. CNN convolves learned features with input data, and uses 2D convolutional layers, making this architecture well suited to processing 2D data, such as images. CNN's extract the features from the image automatically using numerous hidden layers. Most Deep learning models use transfer learning that is fine-tuning a pre-trained model. RCNN stands for Region based CNN. Unlike CNN which is used for image classification, RCNN is used for object detection. Thus in this paper, we have proposed a method to classify various components of blood : RBCs, WBCs (Monocyte, Lymphocytes, Eosinophils, Neutrophils and Basophils) and find their count from a microscopic blood image using Faster R-CNN model. Thus generating a CBC (Complete Blood Count) report which can be used by medical professionals to diagnose, suggest tests and treatments to their patients.
{"title":"Recognizing Presence of Hematological Disease using Deep Learning","authors":"Bhagyeshri Darane, Prathamesh Rajput, Yogesh Sondagar, Reeta Koshy","doi":"10.1109/I-SMAC47947.2019.9032639","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032639","url":null,"abstract":"Accurate classification and counting of blood components is crucial in detection of illnesses of an individual. The widely used methods to count blood components are manual counting and hematology analyzer. With advancement in the field of image processing and machine learning, new and better methods are available for counting and classifying blood components. Deep leaning is training the computer with labelled data for classification tasks. Such techniques have shown high performance and accuracy. Most Deep learning models uses neural network architecture. One of the most popular type of deep learning model is Convolutional Neural Network. CNN convolves learned features with input data, and uses 2D convolutional layers, making this architecture well suited to processing 2D data, such as images. CNN's extract the features from the image automatically using numerous hidden layers. Most Deep learning models use transfer learning that is fine-tuning a pre-trained model. RCNN stands for Region based CNN. Unlike CNN which is used for image classification, RCNN is used for object detection. Thus in this paper, we have proposed a method to classify various components of blood : RBCs, WBCs (Monocyte, Lymphocytes, Eosinophils, Neutrophils and Basophils) and find their count from a microscopic blood image using Faster R-CNN model. Thus generating a CBC (Complete Blood Count) report which can be used by medical professionals to diagnose, suggest tests and treatments to their patients.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131614795","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 : 2019-12-01DOI: 10.1109/I-SMAC47947.2019.9032435
S. Thangavelu, A. S, K C Naetra, Krishna Sathya A C, V. Lasya
Microarray databases are the most frequently used datasets for cancer analytics. Microarray databases are characterized by the presence of a very large number of genes, which exceeds the very little number of samples. So, the feature set accumulates the curse of dimensionality. Therefore, selecting a small subset of genes among thousands of genes in microarray data can potentially increase the accuracy for the classification of cancer. Many approaches, from the field of classical machine learning and soft computing, have been used to address the issue of feature selection and feature extraction for better classifications and clustering accuracy. The research outlined in this paper strives to look at a two-stage approach using minimum Redundancy Maximum Relevancy (mRMR), a feature ranking framework as the first stage followed by a hybrid genetic algorithm in the second stage that works on the features ranked by the mRMR. The proposed method is aimed to select the optimal feature subsets for better classification results in binary and multi class datasets to compensate for the curse of dimensionality in microarray datasets. The classifiers used to test the two-stage proposition are SVM, Naive-Bayes, Linear Discriminant Analysis, decision trees and random forest classifiers. The experimental results show that the gene subset selected by the mRMR-GA pipeline gives good results.
{"title":"Feature Selection in Cancer Genetics using Hybrid Soft Computing","authors":"S. Thangavelu, A. S, K C Naetra, Krishna Sathya A C, V. Lasya","doi":"10.1109/I-SMAC47947.2019.9032435","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032435","url":null,"abstract":"Microarray databases are the most frequently used datasets for cancer analytics. Microarray databases are characterized by the presence of a very large number of genes, which exceeds the very little number of samples. So, the feature set accumulates the curse of dimensionality. Therefore, selecting a small subset of genes among thousands of genes in microarray data can potentially increase the accuracy for the classification of cancer. Many approaches, from the field of classical machine learning and soft computing, have been used to address the issue of feature selection and feature extraction for better classifications and clustering accuracy. The research outlined in this paper strives to look at a two-stage approach using minimum Redundancy Maximum Relevancy (mRMR), a feature ranking framework as the first stage followed by a hybrid genetic algorithm in the second stage that works on the features ranked by the mRMR. The proposed method is aimed to select the optimal feature subsets for better classification results in binary and multi class datasets to compensate for the curse of dimensionality in microarray datasets. The classifiers used to test the two-stage proposition are SVM, Naive-Bayes, Linear Discriminant Analysis, decision trees and random forest classifiers. The experimental results show that the gene subset selected by the mRMR-GA pipeline gives good results.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116957671","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 : 2019-12-01DOI: 10.1109/I-SMAC47947.2019.9032429
Thulasi Bikku, V. Narayana, A. Gopi, Sk. Reshmi Khadherbhi
Nowadays the number of vehicles on the road has been expanded exponentially, but the limitations of roads and transportation frameworks have not created in a comparable method to effectively adapt with the number of vehicles going on them. Because of this, road congestion has expanded around the world. Sensor systems have increased by expanding consideration in rush hour traffic identification and maintaining a strategic distance from heavy traffic. WSNs are extremely smart because of their quicker exchange of data, simple establishment and for being more affordable contrasted with other systems. Remote sensor systems are an innovation which has assumed an enormous job empowering smarter city urban communities is utilizing this innovation to accumulate information identified with movement. The goal is to have an entire framework that empowers the observing of activity practices so choices on city advancement can be made smarter. This paper provides a survey on road traffic congestion control with the help of sensors which communicate with other vehicles nearby for avoiding traffic as well as road accidents. This paper performs a survey on various techniques on road traffic reduction methods of road accidents using sensors.
{"title":"Sensors Systems for Traffic Congestion Reduction Methodologies","authors":"Thulasi Bikku, V. Narayana, A. Gopi, Sk. Reshmi Khadherbhi","doi":"10.1109/I-SMAC47947.2019.9032429","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032429","url":null,"abstract":"Nowadays the number of vehicles on the road has been expanded exponentially, but the limitations of roads and transportation frameworks have not created in a comparable method to effectively adapt with the number of vehicles going on them. Because of this, road congestion has expanded around the world. Sensor systems have increased by expanding consideration in rush hour traffic identification and maintaining a strategic distance from heavy traffic. WSNs are extremely smart because of their quicker exchange of data, simple establishment and for being more affordable contrasted with other systems. Remote sensor systems are an innovation which has assumed an enormous job empowering smarter city urban communities is utilizing this innovation to accumulate information identified with movement. The goal is to have an entire framework that empowers the observing of activity practices so choices on city advancement can be made smarter. This paper provides a survey on road traffic congestion control with the help of sensors which communicate with other vehicles nearby for avoiding traffic as well as road accidents. This paper performs a survey on various techniques on road traffic reduction methods of road accidents using sensors.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116543811","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 : 2019-12-01DOI: 10.1109/I-SMAC47947.2019.9032701
B. C. Naik, B. Anuradha
Recently, the remote sensing data is widely used for the extraction of water body from the satellite images. The accuracy assessment of the extracted water features from the satellite images is highly correlated with the real time data. Spatiotemporal changes in nagarjunasagar reservoir, located in India in a period of 2014 to 2019 time series and analysis using multi temporal Landsat-8 (OLI) images. Unsupervised classification (Isodata) and spectral water indexing methods, including NDVI, NDWI, MNDWI and AWEI were evaluated for surface water body extraction and change detection. The overall accuracy and kappa coefficients were evaluated for water indexing methods. The statistical parameters of the accuracy results show that AWEI achieved 96.26% overall accuracy, 0.94 kappa coefficient and MNDWI achieved 96.94% overall accuracy, 0.95 kappa coefficient. The AWEI and MNDWI water indexes performed better results as compared to other water indexing methods.
{"title":"Time Series Analysis of Water Feature Extraction using Water Index Techniques from Landsat Remote Sensing Images","authors":"B. C. Naik, B. Anuradha","doi":"10.1109/I-SMAC47947.2019.9032701","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032701","url":null,"abstract":"Recently, the remote sensing data is widely used for the extraction of water body from the satellite images. The accuracy assessment of the extracted water features from the satellite images is highly correlated with the real time data. Spatiotemporal changes in nagarjunasagar reservoir, located in India in a period of 2014 to 2019 time series and analysis using multi temporal Landsat-8 (OLI) images. Unsupervised classification (Isodata) and spectral water indexing methods, including NDVI, NDWI, MNDWI and AWEI were evaluated for surface water body extraction and change detection. The overall accuracy and kappa coefficients were evaluated for water indexing methods. The statistical parameters of the accuracy results show that AWEI achieved 96.26% overall accuracy, 0.94 kappa coefficient and MNDWI achieved 96.94% overall accuracy, 0.95 kappa coefficient. The AWEI and MNDWI water indexes performed better results as compared to other water indexing methods.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116253832","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 : 2019-12-01DOI: 10.1109/I-SMAC47947.2019.9032627
Sunil Annareddy, Srikanth Tammina
Since the last decade, internet plays an imperative and vital role in the creation and retrieval of colossal amounts of information. With ever-increasing advancements in technological field and creation of data at an exponential rate, impertinent or irrelevant data is proliferating at a vast scale in commensuration with relevant data. Moreover, the usage of mobile phones has increased drastically, and phones are becoming an evident part of everyone's lives. With this, there is a notable increase in the number of spam messages from spammers. According to recent statistics, 96% of Indians receive unsolicited text messages every day. SMS spam is any unwanted or unsolicited text note in the form of weblink, promotional message or irrelevant text sent uncritically and non-selectively to your mobile phone, regularly for advertising purposes. The surge in unsolicited information across all platforms including mobile text messages and emails has created an expedited need for the advancement and refinement of more reliable filters to counteract the spam in these messages. Traditionally, rule-based approach is employed to counteract spam messages. According to this approach, a set of rules are employed on the messages by some authority manually. By this method, no favorable or assuring results will be shown because the rules need to regularly be restructured based on the source of spam messages, which is an arduous process. Instead, we use deep learning methods that are efficient and does not require any rules. Deep learning models require a set of training dataset samples to learn the rules from these SMS messages and build a text classifier that efficiently classifies spam from these messages. This paper presents a systematic review of employing deep learning methods namely, convolutional neural network and recurrent neural network on huge corpus of SMS texts to build a spam classifier that classifies messages as ham or spam.
{"title":"A Comparative Study of Deep Learning Methods for Spam Detection","authors":"Sunil Annareddy, Srikanth Tammina","doi":"10.1109/I-SMAC47947.2019.9032627","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032627","url":null,"abstract":"Since the last decade, internet plays an imperative and vital role in the creation and retrieval of colossal amounts of information. With ever-increasing advancements in technological field and creation of data at an exponential rate, impertinent or irrelevant data is proliferating at a vast scale in commensuration with relevant data. Moreover, the usage of mobile phones has increased drastically, and phones are becoming an evident part of everyone's lives. With this, there is a notable increase in the number of spam messages from spammers. According to recent statistics, 96% of Indians receive unsolicited text messages every day. SMS spam is any unwanted or unsolicited text note in the form of weblink, promotional message or irrelevant text sent uncritically and non-selectively to your mobile phone, regularly for advertising purposes. The surge in unsolicited information across all platforms including mobile text messages and emails has created an expedited need for the advancement and refinement of more reliable filters to counteract the spam in these messages. Traditionally, rule-based approach is employed to counteract spam messages. According to this approach, a set of rules are employed on the messages by some authority manually. By this method, no favorable or assuring results will be shown because the rules need to regularly be restructured based on the source of spam messages, which is an arduous process. Instead, we use deep learning methods that are efficient and does not require any rules. Deep learning models require a set of training dataset samples to learn the rules from these SMS messages and build a text classifier that efficiently classifies spam from these messages. This paper presents a systematic review of employing deep learning methods namely, convolutional neural network and recurrent neural network on huge corpus of SMS texts to build a spam classifier that classifies messages as ham or spam.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123974624","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 : 2019-12-01DOI: 10.1109/I-SMAC47947.2019.9032542
Timmana Hari Krishna, C. Rajabhushnam
In recently observed that breast related diseases affects women present all over the globe, where it emerges as the second most common disease in the world. In 2012, 12 % cancer patients were present and from these patients 25 % are breast cancer patients. In the traditional method to cure the breast cancer is malignant tumor. Most of the doctors manually presumed the bosom malignant growth region. Various examinations have referred that this manual presumed requires more time and it relies upon the operation and machine. Therefore, it is necessary to design a perfect algorithm for the identification of bosom diseases. In this report, we have developed an algorithm to identify the breast cancer patient automatically. This algorithm can automatically detect the tumor of breast cancer by observing the biopsy pictures. Also, the calculation must be very precise, as the lives of individuals are at risk. All the performance operations are done on the microscopy pictures and the data set for this microscopy pictures is designed for the clustering analysis of a picture. The experimental results of the proposed scheme show accuracy 98.3 %, precision 0.65, Recall 0.95, F1 score 0.77 and ROC - AUC 0.692.
{"title":"Bosom Malignant Diseases (Cancer) Identification by using Deep Learning Technique","authors":"Timmana Hari Krishna, C. Rajabhushnam","doi":"10.1109/I-SMAC47947.2019.9032542","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032542","url":null,"abstract":"In recently observed that breast related diseases affects women present all over the globe, where it emerges as the second most common disease in the world. In 2012, 12 % cancer patients were present and from these patients 25 % are breast cancer patients. In the traditional method to cure the breast cancer is malignant tumor. Most of the doctors manually presumed the bosom malignant growth region. Various examinations have referred that this manual presumed requires more time and it relies upon the operation and machine. Therefore, it is necessary to design a perfect algorithm for the identification of bosom diseases. In this report, we have developed an algorithm to identify the breast cancer patient automatically. This algorithm can automatically detect the tumor of breast cancer by observing the biopsy pictures. Also, the calculation must be very precise, as the lives of individuals are at risk. All the performance operations are done on the microscopy pictures and the data set for this microscopy pictures is designed for the clustering analysis of a picture. The experimental results of the proposed scheme show accuracy 98.3 %, precision 0.65, Recall 0.95, F1 score 0.77 and ROC - AUC 0.692.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123413856","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 : 2019-12-01DOI: 10.1109/I-SMAC47947.2019.9032678
Veeramalai Sankaradass, P. Karthikeyan, T. Ravishankar, J. Murugan
Content Based Image Retrieval (CBIR) aims the system to compel the recovery of pictures from a very large store of collected pictures. The recovered picture approaches shading, color, texture and size. In this paper, a privacy saving substance based on picture recovery computes by utilizing Earth Moveable Distance (EMD) which is proposed because of the administrations of information proprietor to reappropriate picture from the database that is powerfully accessible in the cloud without extracting the entire substance from the database that should be given to the client's precise query. The proposed scheme supports the neighborhood highlight based CBIR with EMD as closeness metric. The EMD matches perceptual similarity for substance based picture recovery. It is additionally dependent on transportation issue from straight improvement, for which proficient calculations are accessible and to get comparability metric effectively. The sensitive (LSH) Local Sensitive Hash is improved for search efficiency. We look at the recovery execution of EMD and examine the protection and security of pictures dependent on client query.
基于内容的图像检索(CBIR)的目的是迫使系统从非常大的收集图像存储中恢复图像。恢复的图像接近阴影、颜色、纹理和大小。本文提出了一种基于图像恢复的隐私保存物质，利用地球可移动距离(Earth mobile Distance, EMD)进行计算，因为信息所有者的管理需要从云中可强大访问的数据库中重新获取图像，而无需从数据库中提取应提供给客户端精确查询的整个物质。该方案支持基于邻域突出的CBIR，并以EMD作为接近度度量。EMD匹配基于物质的图像恢复的感知相似性。此外，它还依赖于直接改进的运输问题，对此可以进行熟练的计算，并有效地获得可比性度量。提高了敏感(LSH)局部敏感散列的搜索效率。我们将查看EMD的恢复执行，并检查依赖于客户机查询的图片的保护和安全性。
{"title":"An Enhanced Content Based Image Retrieval in Cloud Computing with Privacy Towards EMD","authors":"Veeramalai Sankaradass, P. Karthikeyan, T. Ravishankar, J. Murugan","doi":"10.1109/I-SMAC47947.2019.9032678","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032678","url":null,"abstract":"Content Based Image Retrieval (CBIR) aims the system to compel the recovery of pictures from a very large store of collected pictures. The recovered picture approaches shading, color, texture and size. In this paper, a privacy saving substance based on picture recovery computes by utilizing Earth Moveable Distance (EMD) which is proposed because of the administrations of information proprietor to reappropriate picture from the database that is powerfully accessible in the cloud without extracting the entire substance from the database that should be given to the client's precise query. The proposed scheme supports the neighborhood highlight based CBIR with EMD as closeness metric. The EMD matches perceptual similarity for substance based picture recovery. It is additionally dependent on transportation issue from straight improvement, for which proficient calculations are accessible and to get comparability metric effectively. The sensitive (LSH) Local Sensitive Hash is improved for search efficiency. We look at the recovery execution of EMD and examine the protection and security of pictures dependent on client query.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125101736","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 : 2019-12-01DOI: 10.1109/I-SMAC47947.2019.9032537
M. Selvaperumal, D. Kirubakaran
Three-phase asymmetric 9 level inverter is presented with another configuration proposed uneven staggered inverter has topsy-turvy voltage source 1:2:4. To expand the come to of level by the advance get nearer to of intensity electronic parts it is recommended to use by including the number of switches. The planned circuit exchanging gadget is reduced, three-stage inverter circuit control technique and exchanging design is Mat lab extremely hard for this reason switches are supplanted by a diode. The stockpile recurrence adjustment procedure is anything but difficult to control the yield capability of an inverter. The recurrence regulation strategy is anything but difficult to produce the reasonable exchanging gate signal additionally Configuration can be made as got by the equipment and recreation results guarantees the similarity of this recurrence balance technique.
{"title":"Novel Harmonic Diminution of 3phase Asymmetric Cascaded Multilevel Inverter","authors":"M. Selvaperumal, D. Kirubakaran","doi":"10.1109/I-SMAC47947.2019.9032537","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032537","url":null,"abstract":"Three-phase asymmetric 9 level inverter is presented with another configuration proposed uneven staggered inverter has topsy-turvy voltage source 1:2:4. To expand the come to of level by the advance get nearer to of intensity electronic parts it is recommended to use by including the number of switches. The planned circuit exchanging gadget is reduced, three-stage inverter circuit control technique and exchanging design is Mat lab extremely hard for this reason switches are supplanted by a diode. The stockpile recurrence adjustment procedure is anything but difficult to control the yield capability of an inverter. The recurrence regulation strategy is anything but difficult to produce the reasonable exchanging gate signal additionally Configuration can be made as got by the equipment and recreation results guarantees the similarity of this recurrence balance technique.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124064232","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 : 2019-12-01DOI: 10.1109/I-SMAC47947.2019.9032445
V. R. Machavaram, B. Nistala
A compact very low loss onchip bandpass filter which suits the 5G radio frequency front end (RFFE) filtering requirements, is reported here. The proposed filter is modeled using $0.18 mumathrm{m}$ CMOS Silicon substrate IPD technology. A series LC resonant onchip BPF structure is designed and simulated by combining a passive multilayer (ML) spiral inductor and a planar spiral capacitor in High Frequency Structural Simulator (HFSS) at component level. The filter showed a quality factor (Q) value of 7.3125 and a fractional bandwidth of 13% (< 20%). It had exhibited very good insertion loss of −0.415 dB and also excellent return loss of −42.9 dB, at a self-resonant (SRF) frequency of 3.5 GHz. The physical dimensions of the Inductor, Capacitor and bandpass filter are $340times 240 mumathrm{m}^{2},quad 280times 240 mumathrm{m}^{2}$ and $480times 240 mumathrm{m}^{2}$ respectively. It had demonstrated with an excellent loss along with a narrow passband characteristics, still occupying very small onchip area. Hence, this compact resonator filter definitely suits the 5G front end filter applications. We simulated this filter by focusing around 3.5 GHz, as this spectral band is used in 4G and also being actively considered for several 5G trials and installations across several countries.
本文报道了一种紧凑型极低损耗片上带通滤波器，适合5G射频前端(RFFE)滤波要求。该滤波器采用$0.18 mu mathm {m}$ CMOS硅衬底IPD技术建模。采用无源多层螺旋电感与平面螺旋电容相结合的方法，在高频结构模拟器(HFSS)中设计并仿真了串联LC谐振片上BPF结构。该滤波器的质量因子(Q)值为7.3125，分数带宽为13%(< 20%)。在自谐振(SRF)频率为3.5 GHz时，其插入损耗为- 0.415 dB，回波损耗为- 42.9 dB。电感器、电容和带通滤波器的物理尺寸分别为$340乘以240 mu mathm {m}^{2}， $ quad 280乘以240 mu mathm {m}^{2}$和$480乘以240 mu mathm {m}^{2}$。实验证明，该芯片具有良好的损耗和窄通带特性，且占用的片上面积很小。因此，这款紧凑型谐振器滤波器绝对适合5G前端滤波器应用。我们通过聚焦3.5 GHz左右来模拟该滤波器，因为该频段用于4G，并且正在积极考虑在几个国家进行几次5G试验和安装。
{"title":"A Compact Low Loss Onchip Bandpass Filter For 5G Radio Front End Using Integrated Passive Device Technology","authors":"V. R. Machavaram, B. Nistala","doi":"10.1109/I-SMAC47947.2019.9032445","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032445","url":null,"abstract":"A compact very low loss onchip bandpass filter which suits the 5G radio frequency front end (RFFE) filtering requirements, is reported here. The proposed filter is modeled using $0.18 mumathrm{m}$ CMOS Silicon substrate IPD technology. A series LC resonant onchip BPF structure is designed and simulated by combining a passive multilayer (ML) spiral inductor and a planar spiral capacitor in High Frequency Structural Simulator (HFSS) at component level. The filter showed a quality factor (Q) value of 7.3125 and a fractional bandwidth of 13% (< 20%). It had exhibited very good insertion loss of −0.415 dB and also excellent return loss of −42.9 dB, at a self-resonant (SRF) frequency of 3.5 GHz. The physical dimensions of the Inductor, Capacitor and bandpass filter are $340times 240 mumathrm{m}^{2},quad 280times 240 mumathrm{m}^{2}$ and $480times 240 mumathrm{m}^{2}$ respectively. It had demonstrated with an excellent loss along with a narrow passband characteristics, still occupying very small onchip area. Hence, this compact resonator filter definitely suits the 5G front end filter applications. We simulated this filter by focusing around 3.5 GHz, as this spectral band is used in 4G and also being actively considered for several 5G trials and installations across several countries.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128994307","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 : 2019-12-01DOI: 10.1109/I-SMAC47947.2019.9032472
G. Shobana, M. Suguna
Blockchain is decentralized architecture, where data are stored in the form of blocks for processing. The data has to be transferred from one person to another with safety and security and updated with smart contract in the blockchain. But there are some challenges such as data spoofing, integrity, authentication of the data. In the health sector the privacy of the patients' data has to be maintained. The proposed system, “Insurance Management in Healthcare Sector” uses blockchain combined with identity management to access the identity of a person when authorized by the person. After verifying the details, the insured amount will be transferred to the policy holder or the hospital with the help of matching smart contracts in the blockchain of the Ethereum platform. As a result, the insurance claim can reach the policy holder who has initiated the claim process with proof of work. The other use cases such as health care industries, social media networks are also discussed and the analysis of how the blockchain can be used in various fields.
{"title":"Block Chain Technology towards Identity Management in Health Care Application","authors":"G. Shobana, M. Suguna","doi":"10.1109/I-SMAC47947.2019.9032472","DOIUrl":"https://doi.org/10.1109/I-SMAC47947.2019.9032472","url":null,"abstract":"Blockchain is decentralized architecture, where data are stored in the form of blocks for processing. The data has to be transferred from one person to another with safety and security and updated with smart contract in the blockchain. But there are some challenges such as data spoofing, integrity, authentication of the data. In the health sector the privacy of the patients' data has to be maintained. The proposed system, “Insurance Management in Healthcare Sector” uses blockchain combined with identity management to access the identity of a person when authorized by the person. After verifying the details, the insured amount will be transferred to the policy holder or the hospital with the help of matching smart contracts in the blockchain of the Ethereum platform. As a result, the insurance claim can reach the policy holder who has initiated the claim process with proof of work. The other use cases such as health care industries, social media networks are also discussed and the analysis of how the blockchain can be used in various fields.","PeriodicalId":275791,"journal":{"name":"2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131009656","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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