2021 IEEE 18th India Council International Conference (INDICON)最新文献_第6页

Label-Free Biosensing using Dielectric Modulated GaAs1-x Sbx FinFET under Dry/Wet Environment 干/湿环境下介质调制GaAs1-x Sbx FinFET的无标签生物传感

2021 IEEE 18th India Council International Conference (INDICON) Pub Date : 2021-12-19 DOI: 10.1109/INDICON52576.2021.9691731

Ankit Dixit, D. P. Samajdar, N. Bagga

引用次数: 0

Modelling and Control of a nonlinear distillation column: A, using fractional-order controllers 非线性精馏塔的建模和控制:a，使用分数阶控制器

2021 IEEE 18th India Council International Conference (INDICON) Pub Date : 2021-12-19 DOI: 10.1109/INDICON52576.2021.9691748

Omar Hanif, Shipra Tiwari, Vivek Kumar

{"title":"Modelling and Control of a nonlinear distillation column: A, using fractional-order controllers","authors":"Omar Hanif, Shipra Tiwari, Vivek Kumar","doi":"10.1109/INDICON52576.2021.9691748","DOIUrl":"https://doi.org/10.1109/INDICON52576.2021.9691748","url":null,"abstract":"This paper takes up the challenge of modelling a complex nonlinear distillation column type-A and designs four optimal controllers for the same. The nonlinear plant is first linearized into a linear higher-order model. Thereafter, it is reduced into a lower-order model. Subsequently, the controllers are designed for the lower-order linear model. The controllers are designed by reducing the error cost functions, namely Integral Square Error (ISE), Integral Absolute Error (IAE) and Integral Time Absolute Error (ITAE). The process of optimization is done heuristically using the Genetic Algorithm method of tuning. The four controllers designed are Proportional Integral Derivative, Fractional-order Proportional Integral Derivative, Tilt Integral Derivative and Fractional-order Internal Model Control. A thorough comparison is made between the three designed controllers, first on the Single Input Single Output (SISO), then to the reduced-order linear model and finally to the main plant. The FO-IMC controller is shown as an exhibition controller designed using GA to demonstrate the novelty of its kind. It has also been compared, but the results show poor performances as it has not been tuned with the same parameters as the rest.","PeriodicalId":106004,"journal":{"name":"2021 IEEE 18th India Council International Conference (INDICON)","volume":"42 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132360840","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

A Robust Zero-attracting Proportionate Logarithmic Hyperbolic Cosine Adaptive Filter against Impulsive Noise for Sparse Systems 一种抗脉冲噪声的鲁棒零吸引比例对数双曲余弦自适应滤波器

2021 IEEE 18th India Council International Conference (INDICON) Pub Date : 2021-12-19 DOI: 10.1109/INDICON52576.2021.9691600

Joel Fernandez, R. Das

引用次数: 0

A Novel Triple Pulse Test Circuit for Complete Characterization of SiC MOSFET 一种全新的三脉冲测试电路，可全面表征SiC MOSFET

2021 IEEE 18th India Council International Conference (INDICON) Pub Date : 2021-12-19 DOI: 10.1109/INDICON52576.2021.9691636

S. Acharya, Nitesh Agrawal, S. Samanta

引用次数: 0

VLSI Implementation of A Robust Threshold-Based Earthquake Early Warning System 基于鲁棒阈值的地震预警系统的VLSI实现

2021 IEEE 18th India Council International Conference (INDICON) Pub Date : 2021-12-19 DOI: 10.1109/INDICON52576.2021.9691540

Sudipta Bose, Arijit De, Indrajit Chakrabarti

{"title":"VLSI Implementation of A Robust Threshold-Based Earthquake Early Warning System","authors":"Sudipta Bose, Arijit De, Indrajit Chakrabarti","doi":"10.1109/INDICON52576.2021.9691540","DOIUrl":"https://doi.org/10.1109/INDICON52576.2021.9691540","url":null,"abstract":"The earthquake-early-warning-system (EEWS) is one of the effective tools to mitigate seismic hazards in real-time. In previous studies, the product of two parameters viz. ground motion period (τc) and high-pass filtered vertical displacement peak amplitude (Pd) has been extensively used to establish threshold-based EEWS. However, the threshold-based approach by employing parameter τc × Pd suffers from error and generates false alarms for low-magnitude events data corrupted with high background noise. We have proposed a robust threshold-based methodology where noise-removal by employing adaptive filter is performed to increase the SNR of events data. Moreover, owing to the fact that τc exhibits uncertainty in predicting low-magnitude events though reliably predicting high-magnitude ones, whereas Pd can reliably predict low-magnitude events, we have used a two-step threshold-based approach, where in step I, if the estimated magnitude based on parameter Pd does not cross the predetermined threshold value, then low-magnitude event is predicted; next in step II, if the estimated magnitude based on parameter τc overshoots the threshold value, then high-magnitude event is predicted and warning is generated. In this paper, a novel VLSI architecture of the proposed two-step τc and Pd estimation unit has been presented and realized in both FPGA and ASIC platforms.","PeriodicalId":106004,"journal":{"name":"2021 IEEE 18th India Council International Conference (INDICON)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127778062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Multimodal Fusion for Segment Classification in Folk Music 多模态融合在民乐音段分类中的应用

2021 IEEE 18th India Council International Conference (INDICON) Pub Date : 2021-12-19 DOI: 10.1109/INDICON52576.2021.9691751

Aravind Krishnan, Amal Vincent, Geevar Jos, R. Rajan

{"title":"Multimodal Fusion for Segment Classification in Folk Music","authors":"Aravind Krishnan, Amal Vincent, Geevar Jos, R. Rajan","doi":"10.1109/INDICON52576.2021.9691751","DOIUrl":"https://doi.org/10.1109/INDICON52576.2021.9691751","url":null,"abstract":"A folk music segment classification system that uses a multimodal fusion of acoustic features, textual information and duration based feature on Thiruvathirakali music corpus is proposed. Acoustic features are learned from musical texture features (MTF) using a long short term memory (LSTM) model. A term frequency-inverse document frequency (TF-IDF) model is employed to derive text-based features from transcription data. For multimodal fusion, early integration of the LSTM derived features, TF-IDF features and duration feature is employed. An attempt to optimise the LSTM model is carried out through frame fusion in the temporal domain. Frame fusion is seen to increase classification efficiency by 13 percent and reduce computational expense by tenfold. The system reports an overall precision, recall and F1 measure of 0.53, 0.52 and 0.51 respectively for an LSTM model with frame fusion, with better performance over a baseline SVM classifier. The classification efficiency is seen to improve by 15 percent (absolutely) with the addition of each multimodal component. For complete multimodal fusion, the metrics improve to 0.83, 0.78 and 0.80 respectively.","PeriodicalId":106004,"journal":{"name":"2021 IEEE 18th India Council International Conference (INDICON)","volume":"800 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133550166","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

Synchronization of Nonlinear Systems in Chain Network Configuration with Parametric Uncertainty 具有参数不确定性的链式网络结构非线性系统的同步

2021 IEEE 18th India Council International Conference (INDICON) Pub Date : 2021-12-19 DOI: 10.1109/INDICON52576.2021.9691586

Yashasvi Chauhan, B. Sharma, R. K. Ranjan

引用次数: 3

Ultra-Wideband Microstrip Filter Using Interdigital Coupled Line Ring Resonator 采用数字间耦合线环谐振器的超宽带微带滤波器

2021 IEEE 18th India Council International Conference (INDICON) Pub Date : 2021-12-19 DOI: 10.1109/INDICON52576.2021.9691671

Deepraj Chowdhury, Vasu Nand, Maifuz Ali, L. Murmu, K. Mondal, T. Mandal

引用次数: 1

Deep Learning Oriented Satellite Remote Sensing for Drought and Prediction in Agriculture 面向深度学习的卫星遥感农业干旱与预测

2021 IEEE 18th India Council International Conference (INDICON) Pub Date : 2021-12-19 DOI: 10.1109/INDICON52576.2021.9691608

Yogesh Dhyani, R. Pandya

{"title":"Deep Learning Oriented Satellite Remote Sensing for Drought and Prediction in Agriculture","authors":"Yogesh Dhyani, R. Pandya","doi":"10.1109/INDICON52576.2021.9691608","DOIUrl":"https://doi.org/10.1109/INDICON52576.2021.9691608","url":null,"abstract":"Drought is a challenging problem in agriculture due to its random and nonlinear nature. Moreover, in bad weather situations, satellites do not capture the precise data of the earth, which ultimately affects the training of machine learning models. This project proposes an approach for drought prediction and anti-drought strategies. The task is divided into two parts- (1) Predicting the short-term severity of drought, (2) Crop selection. Based on time duration, agricultural drought occurs after meteorological drought. Meteorological drought leads to the deficiency of precipitation, increased evaporation, and transpiration. Therefore, we employed the Standard Precipitation and Evapotranspiration Index (SPEI) to predict the first-stage by passing it to Long-Short Term Memory (LSTM) oriented neural network demonstrated an accuracy of 74%. Subsequently, for agricultural drought, which leads to reduced biomass yield and soil water deficiency, indices known as Normalized Difference Vegetation Index (NDVI) and Soil Moisture Index (SMI) are formulated. Stage 2 prediction is made using these indices as input to time distributed Convolutional Neural Network (CNN) network, which demonstrated an accuracy of 96%. Furthermore, based on the proposed methodology, anti-drought strategies are suggested to improve the agriculture outcome.","PeriodicalId":106004,"journal":{"name":"2021 IEEE 18th India Council International Conference (INDICON)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115808817","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: 7

Tree-Based Group Diffie-Hellman for subgroup communication in M2M networks M2M网络中基于树的分组Diffie-Hellman通信

2021 IEEE 18th India Council International Conference (INDICON) Pub Date : 2021-12-19 DOI: 10.1109/INDICON52576.2021.9691617

Kousik Rajesh, Manoj Das, Sukumar Nandi

引用次数: 1