2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)最新文献_第2页

2021 IEEE Discover Reviewers 2021 IEEE发现审稿人

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/discover52564.2021.9663428

引用次数: 0

A Comprehensive Analysis of 17-level Modified H-Bridge Multilevel Inverter 17电平改进型h桥多电平逆变器综合分析

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663573

Harshavardhan Govulakonda, C. Venkatesh

引用次数: 0

Design and Development of E-Care for Covid-19 Covid-19电子医疗服务的设计与开发

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663708

Akshata, B. Deeksha, Mahima Dev, S. B. Rudraswamy, Varshitha L

引用次数: 1

Object Recognition System for Visually Impaired People 视障人士物体识别系统

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663608

C. Sagana, P. Keerthika, R. Manjula Devi, M. Sangeetha, R. Abhilash, M. Dinesh Kumar, M. Hariharasudhan

引用次数: 2

Joint Trajectory Tracking of Two- link Flexible Manipulator in Presence of Matched Uncertainty 存在匹配不确定性的两连杆柔性机械臂关节轨迹跟踪

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663625

S. Thakur, R. K. Barai

引用次数: 0

MTCMOS 8T SRAM Cell with Improved Stability and Reduced Power Consumption 具有提高稳定性和降低功耗的MTCMOS 8T SRAM单元

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663628

S. Anusha, Bommidi Shivanath Nikhil, K. Manoj, Kirti S. Pande

{"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}

引用次数: 2

A Low Power Diffused Bit Generator as a TRNG for Cryptographic Key Generation 一种用于加密密钥生成的低功耗扩散位发生器

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663619

D. Bharadwaj, P. Anirvinnan, B. S. Premanada

{"title":"A Low Power Diffused Bit Generator as a TRNG for Cryptographic Key Generation","authors":"D. Bharadwaj, P. Anirvinnan, B. S. Premanada","doi":"10.1109/DISCOVER52564.2021.9663619","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663619","url":null,"abstract":"Cryptographic key generation is an important part of the secured communication system where the key that is generated has a major role to play in the strength of the security of the data that is transferred. To enhance the necessary strength of the key, the random number generated has to be highly secure. This is enhanced by the use of a True Random Number Generation. Diffused Bit Generator (DBG) is an entropy source which is used to produce a sequence of random bits. It is composed of a Linear Feedback Shift Register (LFSR) and a Cellular Automata in order to increase the randomness emanating from the DBG. The proposed LFSR has been designed using TSPC based D flip-flops and the XOR gates consisting of 6 transistors, which has enabled to fulfil the objective of low power. The circuit implementation has been done in Cadence Virtuoso in the CMOS 180 nm technology and simulated in Cadence Spectre. The supply voltage used was 1.8 V and the circuits were simulated for the frequencies ranging from 100 MHz to 1 GHz and the proposed DBG was found to consume lesser power when compared to the existing architectures.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"100 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":"133789809","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

An Effective Data Clustering System using Weighted K-Means and Firefly Optimization Algorithms 基于加权k均值和萤火虫优化算法的有效数据聚类系统

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663710

Keerthi Shetty, CV Aravinda

{"title":"An Effective Data Clustering System using Weighted K-Means and Firefly Optimization Algorithms","authors":"Keerthi Shetty, CV Aravinda","doi":"10.1109/DISCOVER52564.2021.9663710","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663710","url":null,"abstract":"Clustering of data is a standard way used for analyzing the data in several applications such as, data mining, image analysis, pattern recognition, etc. The weighted K-means clustering is one amongst the various data mining techniques used for clustering of the data. The key advantages of weighted k-means clustering are efficient in managing huge amount of data, easy to implement, scalable, simple and easily modifiable. In contrast, the major disadvantage of weighted K-means clustering is the problem with choosing the initial centroids. This clustering technique chooses the initial centroids randomly that leads to a local optimum solution. To address this concern, an effective naturally-inspired optimization algorithm: fire-fly optimization is combined with weighted k-means clustering for obtaining the global optimum solution. In this research paper, weighted k-means clustering along with fire-fly optimization algorithm was developed for enhancing the performance of information sharing and searching efficiency among the population. Here, the proposed system was experimented on dissimilar medical datasets such as, heart disease (original), heart disease (stat-log), liver disease and Indian liver patients. In the practical study, the proposed method enhances the performance up to 0.02-0.4 (label value) as compared to the existing systems by using the concept of precision, recall, and FB-cubed.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"47 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":"116987322","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: 2

Severity Classification of Mental Health Related Tweets 心理健康相关推文的严重程度分类

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663651

Praatibh Surana, Mirza Yusuf, Sanjay Singh

{"title":"Severity Classification of Mental Health Related Tweets","authors":"Praatibh Surana, Mirza Yusuf, Sanjay Singh","doi":"10.1109/DISCOVER52564.2021.9663651","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663651","url":null,"abstract":"The use of social media has drastically gone up over the last decade. With this comes more opportunity and also more problems. There is a rise in the number of mental health-related issues, and it is to some extent possible to detect such cases via user posts and tweets (in our case). Previous research has focused on classifying mental health diseases such as depression, bipolar disorder, schizophrenia, etc., from already filtered data. However, not much has been done to filter out tweets that might be sarcastic, which are generally misclassified, or tweets that might not be intended in a harmful way and, in general, classify tweets based on their severity with regards to mental health. This paper uses multiple models to classify tweets based on their severity and classify them into three classes that help determine whether they help people with mental conditions or sarcasm. We use famous neural network architectures such as Bidirectional LSTMs, GRUs, and a custom HYBRID model to carry out the classification. The models could detect sarcasm in tweets and identify tweets that were helpful despite having words like “depression” and “anxiety.” We obtained F1 scores of 74% on completely unseen data, which is a good starting point considering the limited available data. This paper should serve as a utility for future research in this area and act as a primary data collection and segregation filter.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"504 1-2 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":"131931777","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

Comparative Analysis of Bank Loan Defaulter Prediction Using Machine Learning Techniques 利用机器学习技术预测银行贷款违约者的比较分析

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663662

B. Spoorthi, Shwetha S. Kumar, Anisha P. Rodrigues, Roshan Fernandes, N. Balaji

{"title":"Comparative Analysis of Bank Loan Defaulter Prediction Using Machine Learning Techniques","authors":"B. Spoorthi, Shwetha S. Kumar, Anisha P. Rodrigues, Roshan Fernandes, N. Balaji","doi":"10.1109/DISCOVER52564.2021.9663662","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663662","url":null,"abstract":"Nowadays, there are numerous risks identified with the banking sector regarding giving loans to the clients and for the individuals who get the loan. The examination of risk in bank credits needs to understand what is the reason for this risk. Likewise, the quantity of exchanges in the financial area is quickly developing and information volumes are accessible which address the client’s conduct, and the risk of giving loans are expanded. The objective of this paper is to discover the nature or details of the clients who are applying for the loan. This paper proposes a comparative study of three machine learning models, namely, Random Forest, Naive Bayes (Gaussian model, Multinomial model, and Bernoulli Model), and Support Vector Machine (Linear kernel, Gaussian RBF kernel, and Polynomial kernel), to predict whether a customer may get a loan or not. In this paper, we analyze the evaluation parameters, namely, classification accuracy, precision, recall, and F1-Score for these machine learning models to foresee which model is best suitable for predicting a loan.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"30 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":"132153325","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: 2