Pub Date : 2021-11-19DOI: 10.1109/DISCOVER52564.2021.9663628
S. Anusha, Bommidi Shivanath Nikhil, K. Manoj, Kirti S. Pande
The semiconductor industry is expanding swiftly and the demand for memory and faster access of memory is increasing. The data stability and energy usage are the basic requirements of cache memory in embedded processors that uses SRAM. The SRAM cell parameters that require scrutiny at lower supply voltages are data stability, leakage current and delay. In order to ameliorate the stability further and lower the substrate (junction) leakage current in comparison to the existing SRAM cells, the MTCMOS ST SRAM cell is introduced in this paper. The proposed MTCMOS ST SRAM cell uses HVT and LVT MOSFETs that helps in reduction of the average power consumption by subsiding the leakage current. The proposed MTCMOS ST SRAM cell is implemented, analysed, verified and compared to the existing SRAM cells using Cadence Virtuoso with a channel length of 45 nm at a power supply of 500 mV. In proposed MTCMOS ST SRAM cell, i) read stability RSVNM is increased by 5.89%, 5.72% and 4.74% ii) write stability WTV is increased by 4.16%, 4.16% and 5.05% iii) hold stability HSNM is increased by 0.24% iv) power consumption is decreased by 58.87%, 3.164% and 66.49% in comparison to conventional 6T, existing 8T and existing 9T SRAM cell respectively v) overall read path leakage current is reduced by 94.83% and 87.420%, when compared with existing 8T and existing 9T SRAM cell respectively.
半导体产业正在迅速发展，对存储器和更快存取存储器的需求也在增加。数据稳定性和能耗是采用SRAM的嵌入式处理器对高速缓存的基本要求。在较低的电源电压下，需要仔细检查的SRAM单元参数是数据稳定性、泄漏电流和延迟。为了进一步提高SRAM电池的稳定性，降低衬底(结)漏电流，本文介绍了MTCMOS ST SRAM电池。所提出的MTCMOS ST SRAM单元使用HVT和LVT mosfet，通过降低泄漏电流有助于降低平均功耗。使用Cadence Virtuoso实现、分析、验证了所提出的MTCMOS ST SRAM单元，并将其与现有的SRAM单元进行了比较，该SRAM单元的通道长度为45 nm，电源为500 mV。在提出MTCMOS圣SRAM单元,我)读稳定RSVNM是增长了5.89%,5.72%和4.74%(二)写稳定WTV是增长了4.16%,4.16%和5.05% iii)持有稳定HSNM增加了0.24%(四)能耗下降了58.87%,3.164%和66.49%相比传统6 t,现有8 t和现有9 SRAM t细胞分别v)整体阅读路径漏电流降低94.83%和87.420%,相比与现有8 t和现有9分别SRAM t细胞。
{"title":"MTCMOS 8T SRAM Cell with Improved Stability and Reduced Power Consumption","authors":"S. Anusha, Bommidi Shivanath Nikhil, K. Manoj, Kirti S. Pande","doi":"10.1109/DISCOVER52564.2021.9663628","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663628","url":null,"abstract":"The semiconductor industry is expanding swiftly and the demand for memory and faster access of memory is increasing. The data stability and energy usage are the basic requirements of cache memory in embedded processors that uses SRAM. The SRAM cell parameters that require scrutiny at lower supply voltages are data stability, leakage current and delay. In order to ameliorate the stability further and lower the substrate (junction) leakage current in comparison to the existing SRAM cells, the MTCMOS ST SRAM cell is introduced in this paper. The proposed MTCMOS ST SRAM cell uses HVT and LVT MOSFETs that helps in reduction of the average power consumption by subsiding the leakage current. The proposed MTCMOS ST SRAM cell is implemented, analysed, verified and compared to the existing SRAM cells using Cadence Virtuoso with a channel length of 45 nm at a power supply of 500 mV. In proposed MTCMOS ST SRAM cell, i) read stability RSVNM is increased by 5.89%, 5.72% and 4.74% ii) write stability WTV is increased by 4.16%, 4.16% and 5.05% iii) hold stability HSNM is increased by 0.24% iv) power consumption is decreased by 58.87%, 3.164% and 66.49% in comparison to conventional 6T, existing 8T and existing 9T SRAM cell respectively v) overall read path leakage current is reduced by 94.83% and 87.420%, when compared with existing 8T and existing 9T SRAM cell respectively.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126107978","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 : 2021-11-19DOI: 10.1109/DISCOVER52564.2021.9663430
A. Ananth, Sachin S. Bhat, P. S. Venugopala
Kannada is one of the most spoken languages in India. Despite the large usage base, like other major Indian languages, there exist minimal linguistic resources for computing and processing. Rich morphology and agglutinative nature of this language pose a great challenge to even the most basic of natural language processing applications like lemmantization, parts of speech tagging, summarization etc. In this paper, we have discussed a deep learning based perspective} for the grammatical tagging by utilizing hybrid models of bidirectional long short term memory(BDLSTM) and linear chain conditional random fields(CCRF). A database of Kannada documents with 15500 manually tagged words is used for this task. Proposed hybrid model shows a promising result of 81.02%.
{"title":"Grammatical Tagging for the Kannada Text Documents using Hybrid Bidirectional Long-Short Term Memory Model","authors":"A. Ananth, Sachin S. Bhat, P. S. Venugopala","doi":"10.1109/DISCOVER52564.2021.9663430","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663430","url":null,"abstract":"Kannada is one of the most spoken languages in India. Despite the large usage base, like other major Indian languages, there exist minimal linguistic resources for computing and processing. Rich morphology and agglutinative nature of this language pose a great challenge to even the most basic of natural language processing applications like lemmantization, parts of speech tagging, summarization etc. In this paper, we have discussed a deep learning based perspective} for the grammatical tagging by utilizing hybrid models of bidirectional long short term memory(BDLSTM) and linear chain conditional random fields(CCRF). A database of Kannada documents with 15500 manually tagged words is used for this task. Proposed hybrid model shows a promising result of 81.02%.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121471639","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 : 2021-11-19DOI: 10.1109/DISCOVER52564.2021.9663670
D. Pavithra, R. Vanithamani
Chronic Kidney Disease (CKD) is a concerning health issue worldwide as it affects a huge population with a high mortality rate. CKD patients are at increased risk of developing adverse effects such as anemia, bone diseases, cardiac disorders and hormonal problems. Since the loss of renal function occurs gradually and its symptoms are devoid, advanced technologies are needed to find the patterns and relationships in medical data for early diagnosis. This work aims to focus on detecting CKD from clinical data using Convolutional Neural Network (CNN) and comparing their findings with various machine learning algorithms. As the data available has some missing values, numerical data are imputed using k-nearest neighbor and categorical data are imputed with the most frequently occurring category. Hence, this article exposes the best method to automatically diagnose CKD from clinical data. The empirical results indicated that CNN outperforms other classifiers, with a promising accuracy of 99.12%.
{"title":"Chronic Kidney Disease Detection from Clinical Data using CNN","authors":"D. Pavithra, R. Vanithamani","doi":"10.1109/DISCOVER52564.2021.9663670","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663670","url":null,"abstract":"Chronic Kidney Disease (CKD) is a concerning health issue worldwide as it affects a huge population with a high mortality rate. CKD patients are at increased risk of developing adverse effects such as anemia, bone diseases, cardiac disorders and hormonal problems. Since the loss of renal function occurs gradually and its symptoms are devoid, advanced technologies are needed to find the patterns and relationships in medical data for early diagnosis. This work aims to focus on detecting CKD from clinical data using Convolutional Neural Network (CNN) and comparing their findings with various machine learning algorithms. As the data available has some missing values, numerical data are imputed using k-nearest neighbor and categorical data are imputed with the most frequently occurring category. Hence, this article exposes the best method to automatically diagnose CKD from clinical data. The empirical results indicated that CNN outperforms other classifiers, with a promising accuracy of 99.12%.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115546742","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 : 2021-11-19DOI: 10.1109/DISCOVER52564.2021.9663681
S. Ranjana, Ramakrishna Hegde, C. Divya
IOT devices are employed in a variety of industries to make people’s lives easier. Many smart sensors are used to measure and identify various health indicators, however having many health monitoring equipment becomes expensive and time-consuming. The most common and fundamental health detectors required for each patient in every health care institution are temperature, humidity and heart rate. As a result, we attempted to coordinate all of these health indicators into a single health monitoring station that connects with a chosen device through WiFi. This paper proposes a method that may be used by patients friends and relatives, as well as doctors, to keep track of their health. The proposed system takes live data of patients and their environment from 5 sensors: pulse sensor, body temperature sensors, MQ-2 sensors, MQ-135 sensor and room temperature sensor. All these data are processed in ESP32 processor and the output is displayed on Blynk android application. The developed system shows how efficient the system is and it is best suited in the current pandemic situation.
{"title":"Real Time Patient Monitoring System Using BLYNK","authors":"S. Ranjana, Ramakrishna Hegde, C. Divya","doi":"10.1109/DISCOVER52564.2021.9663681","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663681","url":null,"abstract":"IOT devices are employed in a variety of industries to make people’s lives easier. Many smart sensors are used to measure and identify various health indicators, however having many health monitoring equipment becomes expensive and time-consuming. The most common and fundamental health detectors required for each patient in every health care institution are temperature, humidity and heart rate. As a result, we attempted to coordinate all of these health indicators into a single health monitoring station that connects with a chosen device through WiFi. This paper proposes a method that may be used by patients friends and relatives, as well as doctors, to keep track of their health. The proposed system takes live data of patients and their environment from 5 sensors: pulse sensor, body temperature sensors, MQ-2 sensors, MQ-135 sensor and room temperature sensor. All these data are processed in ESP32 processor and the output is displayed on Blynk android application. The developed system shows how efficient the system is and it is best suited in the current pandemic situation.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116189356","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 : 2021-11-19DOI: 10.1109/DISCOVER52564.2021.9663615
Akhila Jagarlapudi, Amey Patil, D. Rathod
As the progressive development unfolds the utilization of applications using Internet of Things, this is a great opportunity to explore newer capabilities and understand the difficulties in healthcare. Because of these advancements, we can accelerate the transition from neuroscience and clinical research to real-life and hands-on modules to detect and experience migraine. This paper will review and extrapolate the importance of applications such as Virtual Reality Headsets, portable EEG sensors and novel applications available to detect migraine using smartphones. Post this groundwork, the approach we propose comprises a perfect blend of these three ideas that would lead to the most robust healthcare solutions. The proposed model helps identify the key migraine triggers and suggest quick remedies to deal with the situation at hand at the earliest. Not only this, the model will work on a predictive basis to foresee any migraine attacks possible.
{"title":"A Proposed Model on Merging IoT Applications and Portable EEGs for Migraine Detection and Prevention","authors":"Akhila Jagarlapudi, Amey Patil, D. Rathod","doi":"10.1109/DISCOVER52564.2021.9663615","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663615","url":null,"abstract":"As the progressive development unfolds the utilization of applications using Internet of Things, this is a great opportunity to explore newer capabilities and understand the difficulties in healthcare. Because of these advancements, we can accelerate the transition from neuroscience and clinical research to real-life and hands-on modules to detect and experience migraine. This paper will review and extrapolate the importance of applications such as Virtual Reality Headsets, portable EEG sensors and novel applications available to detect migraine using smartphones. Post this groundwork, the approach we propose comprises a perfect blend of these three ideas that would lead to the most robust healthcare solutions. The proposed model helps identify the key migraine triggers and suggest quick remedies to deal with the situation at hand at the earliest. Not only this, the model will work on a predictive basis to foresee any migraine attacks possible.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122013211","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 : 2021-11-19DOI: 10.1109/DISCOVER52564.2021.9663618
Roshan Fernandes, K. Madhu Rai, Anisha P. Rodrigues, B. A. Mohan, N. Sreenivasa, N. Megha
Nowadays video cameras have become gradually deployed, hence the hassle of video enhancement has also been increased. Video enhancement is a process of illuminating the occurrence using gentle techniques to maintain the integrity of pixel quality. The standard of the original video recording gives the success for the enhancement. The purpose of video enhancement is to refine the visual look of the video or to give an extra changed illustration for future video processing which consists of analysis, detection, segmentation, recognition, and used for surveillance and the criminal justice system. In the proposed work vehicle number plate is enhanced and recognition of a number plate is performed using Convolutional Neural Network and Support Vector Machine. There are a lot of challenges in recognizing the number plate due to the presence of blur, low-intensity, snow, rain, hit and run cases. In such a case, recognizing the vehicle number plate is challenging. So to overcome all these problems video enhancement has to be performed. The proposed work involves converting the video into image frames, pre-processing the frames and then performing enhancement, and finally recognizing the vehicle number plate using CNN and Support Vector Machine. The result analysis proves that CNN gives better classification accuracy over the Support Vector Machine model.
{"title":"Recognition of Moving Vehicle Number Plates using Convolutional Neural Network and Support Vector Machine Techniques","authors":"Roshan Fernandes, K. Madhu Rai, Anisha P. Rodrigues, B. A. Mohan, N. Sreenivasa, N. Megha","doi":"10.1109/DISCOVER52564.2021.9663618","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663618","url":null,"abstract":"Nowadays video cameras have become gradually deployed, hence the hassle of video enhancement has also been increased. Video enhancement is a process of illuminating the occurrence using gentle techniques to maintain the integrity of pixel quality. The standard of the original video recording gives the success for the enhancement. The purpose of video enhancement is to refine the visual look of the video or to give an extra changed illustration for future video processing which consists of analysis, detection, segmentation, recognition, and used for surveillance and the criminal justice system. In the proposed work vehicle number plate is enhanced and recognition of a number plate is performed using Convolutional Neural Network and Support Vector Machine. There are a lot of challenges in recognizing the number plate due to the presence of blur, low-intensity, snow, rain, hit and run cases. In such a case, recognizing the vehicle number plate is challenging. So to overcome all these problems video enhancement has to be performed. The proposed work involves converting the video into image frames, pre-processing the frames and then performing enhancement, and finally recognizing the vehicle number plate using CNN and Support Vector Machine. The result analysis proves that CNN gives better classification accuracy over the Support Vector Machine model.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121731834","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 : 2021-11-19DOI: 10.1109/DISCOVER52564.2021.9663601
K. Kailash Chandra Shenoy, Sukesh. Rao
Bioelectric potential is generated in a plant by its physiological activities. Therefore, evaluation and monitoring of the plant activities can be done by measuring the changes in its bioelectric potential. The objective of this project was to develop a suitable instrumentation system that magnifies the plant bioelectric signals which are read at the surface of a leaf using a copper needle electrode. Further, it will also help in understanding the physiological behavior of plants which in turn may enable farmers to cultivate the plant crops in a more effective way. Copper needle electrodes were used as they are less susceptible to movement and also have less impendences compared to surface electrodes. Bioelectric potentials of the Bryophyllum plant were measured using copper needle electrodes. Bioelectric signal was amplified by an instrumentation amplifier (AD 620) and then converted to digital signal using a Data Acquisition device (DAQ) and monitored on a Cathode Ray Oscilloscope (CRO).
{"title":"Design and Development of an Instrumentation System to Detect the Bioelectric Signals of Plants","authors":"K. Kailash Chandra Shenoy, Sukesh. Rao","doi":"10.1109/DISCOVER52564.2021.9663601","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663601","url":null,"abstract":"Bioelectric potential is generated in a plant by its physiological activities. Therefore, evaluation and monitoring of the plant activities can be done by measuring the changes in its bioelectric potential. The objective of this project was to develop a suitable instrumentation system that magnifies the plant bioelectric signals which are read at the surface of a leaf using a copper needle electrode. Further, it will also help in understanding the physiological behavior of plants which in turn may enable farmers to cultivate the plant crops in a more effective way. Copper needle electrodes were used as they are less susceptible to movement and also have less impendences compared to surface electrodes. Bioelectric potentials of the Bryophyllum plant were measured using copper needle electrodes. Bioelectric signal was amplified by an instrumentation amplifier (AD 620) and then converted to digital signal using a Data Acquisition device (DAQ) and monitored on a Cathode Ray Oscilloscope (CRO).","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126422228","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 : 2021-11-19DOI: 10.1109/DISCOVER52564.2021.9663690
Sofia D'souza, K. Prema, S. Balaji
Predicting the binding affinity of compounds is an essential task in drug discovery. In silico QSAR regression and classification models to predict drug-target interaction can help speed up identifying the most potent compounds. Machine learning-based QSAR models were developed to predict the binding affinity of compounds against different targets using the experimental values or labels. In this work, we modeled the binding affinity prediction of SARS-3CL protease inhibitors using hierarchical modeling. We developed the Base classification and regression models using KNN, SVM, RF, and XGBoost techniques. Further, the predictions of the base models were concatenated and provided as inputs for the stacked models. The results indicate that stacking of models hierarchically leads to improved performances on both classification and regression endpoints.
{"title":"Hierarchical Modeling of Binding Affinity Prediction Using Machine LearningTechniques","authors":"Sofia D'souza, K. Prema, S. Balaji","doi":"10.1109/DISCOVER52564.2021.9663690","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663690","url":null,"abstract":"Predicting the binding affinity of compounds is an essential task in drug discovery. In silico QSAR regression and classification models to predict drug-target interaction can help speed up identifying the most potent compounds. Machine learning-based QSAR models were developed to predict the binding affinity of compounds against different targets using the experimental values or labels. In this work, we modeled the binding affinity prediction of SARS-3CL protease inhibitors using hierarchical modeling. We developed the Base classification and regression models using KNN, SVM, RF, and XGBoost techniques. Further, the predictions of the base models were concatenated and provided as inputs for the stacked models. The results indicate that stacking of models hierarchically leads to improved performances on both classification and regression endpoints.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130345153","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 : 2021-11-19DOI: 10.1109/DISCOVER52564.2021.9663620
P. Abhishek, Akash Chobari, D. Narayan
The increased usage of cloud computing technology and its industry-wide adoption has led to almost all modern consumer services being heavily dependent on cloud computing platforms. The user and the Cloud Service Provider (CSP) must agree on a Service Level Agreement (SLA) to keep performance above a given threshold and maintain a certain Quality of Service (QoS) (quality of service). This SLA is enforced by the CSP, who monitors the performance of the computers and compensates the user using the logs created by the underlying machinery. Similarly, a cautious customer can keep a monitoring system in place to examine the operation of the virtualized resources assigned to them regularly. This leads to distrust between the CSP and the customer, as neither party believes the other’s monitoring system is reliable. In this work, we propose a blockchain-based which guarantees the integrity of the client’s logs and verifies the SLA violations creating a trustworthy ecosystem. Furthermore, we carry out the scalability and performance analysis of proposed system using Hyperledger Fabric blockchain platform.
{"title":"SLA Violation Detection in Multi-Cloud Environment using Hyperledger Fabric Blockchain","authors":"P. Abhishek, Akash Chobari, D. Narayan","doi":"10.1109/DISCOVER52564.2021.9663620","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663620","url":null,"abstract":"The increased usage of cloud computing technology and its industry-wide adoption has led to almost all modern consumer services being heavily dependent on cloud computing platforms. The user and the Cloud Service Provider (CSP) must agree on a Service Level Agreement (SLA) to keep performance above a given threshold and maintain a certain Quality of Service (QoS) (quality of service). This SLA is enforced by the CSP, who monitors the performance of the computers and compensates the user using the logs created by the underlying machinery. Similarly, a cautious customer can keep a monitoring system in place to examine the operation of the virtualized resources assigned to them regularly. This leads to distrust between the CSP and the customer, as neither party believes the other’s monitoring system is reliable. In this work, we propose a blockchain-based which guarantees the integrity of the client’s logs and verifies the SLA violations creating a trustworthy ecosystem. Furthermore, we carry out the scalability and performance analysis of proposed system using Hyperledger Fabric blockchain platform.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132931447","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 : 2021-11-19DOI: 10.1109/DISCOVER52564.2021.9663688
F. V. Jayasudha, I. Sheeba, I. S. Sanju, K. Srilatha
A compact Microstrip antenna with and without slot was initiated for communication systems. Circular patch antenna focusing on size reduction, gain, directivity and bandwidth. Based on the performance of the antenna characteristics and optimization technique is introduced. Multiband is achieved which can be applied for multiband operations and applications. The characteristics such as narrow band and low gain in microstrip patch antenna is overcome by introducing metamaterials, The use of electromagnetic metamaterial gives a new solution in the antenna size reduction and improves the gain and multiband operations. Simulation analysis done by HFSS, the antenna is fabricated and is measured using Network analyzer the simulated and measured results are analyzed and compared for multiband operations.
{"title":"A Microstrip Antenna Using Metamaterials For Satellite Communication","authors":"F. V. Jayasudha, I. Sheeba, I. S. Sanju, K. Srilatha","doi":"10.1109/DISCOVER52564.2021.9663688","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663688","url":null,"abstract":"A compact Microstrip antenna with and without slot was initiated for communication systems. Circular patch antenna focusing on size reduction, gain, directivity and bandwidth. Based on the performance of the antenna characteristics and optimization technique is introduced. Multiband is achieved which can be applied for multiband operations and applications. The characteristics such as narrow band and low gain in microstrip patch antenna is overcome by introducing metamaterials, The use of electromagnetic metamaterial gives a new solution in the antenna size reduction and improves the gain and multiband operations. Simulation analysis done by HFSS, the antenna is fabricated and is measured using Network analyzer the simulated and measured results are analyzed and compared for multiband operations.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129875019","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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