2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC)最新文献_第8页

Determination of α, β and γ-components of a switching state without Clarke transformation 无Clarke转变的开关态α、β和γ组分的测定

2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC) Pub Date : 1900-01-01 DOI: 10.1109/CIEC.2016.7513764

A. Chakraborty, Bhaskar Bhattachaya

引用次数: 3

Optimal state feedback controller and observer design for twin rotor MIMO system 双转子MIMO系统的最优状态反馈控制器和观测器设计

2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC) Pub Date : 1900-01-01 DOI: 10.1109/CIEC.2016.7513774

R. Maiti, Kaushik Das Sharma, G. Sarkar

引用次数: 4

OPF governed determination of location and size of distribution generators using gravitational search algorithm OPF利用引力搜索算法确定配电发电机的位置和大小

2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC) Pub Date : 1900-01-01 DOI: 10.1109/CIEC.2016.7513766

B. K. Sarkar, A. Chakrabarti

引用次数: 2

Discrimination of black tea grades by means of cyclic voltammetry using polyacrylamide/exfoliated graphite composite electrode 聚丙烯酰胺/脱落石墨复合电极循环伏安法鉴别红茶品级

2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC) Pub Date : 1900-01-01 DOI: 10.1109/CIEC.2016.7513799

T. Chatterjee, R. B. Roy, B. Tudu, S. Biswas, R. Bandyopadhyay, P. Pramanik, N. Bhattacharyya

引用次数: 1

Frequency and voltage droop control of parallel inverters in microgrid 微电网并联逆变器的频率和电压下垂控制

2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC) Pub Date : 1900-01-01 DOI: 10.1109/CIEC.2016.7513771

D. Raj, D. N. Gaonkar

引用次数: 29

Characterizing cardiac arrhythmia by optimized window length based PRSA technique 基于优化窗长PRSA技术表征心律失常

2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC) Pub Date : 1900-01-01 DOI: 10.1109/CIEC.2016.7513743

U. Maji, Saswati Mondal, A. Biswas, Ivy Barman, S. Pal

{"title":"Characterizing cardiac arrhythmia by optimized window length based PRSA technique","authors":"U. Maji, Saswati Mondal, A. Biswas, Ivy Barman, S. Pal","doi":"10.1109/CIEC.2016.7513743","DOIUrl":"https://doi.org/10.1109/CIEC.2016.7513743","url":null,"abstract":"Phase Rectified Signal Averaging (PRSA) technique is a promising method for analysis of quasi-periodic signals which helps to identify characteristic frequencies contained in the data by disregarding the artifacts and noises. But the performance of the PRSA technique largely depends on the choice of window length (WL). It is required to optimize WL for better detection of precise but important periods present in signal. In this paper a method to optimize the window length is proposed based on the spectral analysis of original and PRSA signal. The proposed method is applied on ECG signal to characterize and classify the atrial fibrillation (AF), atrial flatter (AFL) and ventricular flutter (VFL) rhythms with statistical features. Classification of the fibrillatory episode is done by K-nearest-neighbor (KNN) and support vector machine (SVM) clustering method with derived features. Extracted features are clustered with a new approach of Root Mean Square (RMS) Technique. This algorithm is applied on to the MIT-BIH arrhythmia database and checks the performance. Both quantitative and qualitative analysis is made and sensitivity and specificity 98.24% and 96.08% respectively is achieved.","PeriodicalId":443343,"journal":{"name":"2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124940096","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

An efficient hardware accelerated design for image denoising using Extended Trilateral filter 基于扩展三边滤波器的图像去噪硬件加速设计

2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC) Pub Date : 1900-01-01 DOI: 10.1109/CIEC.2016.7513830

M. Dey, Chandrajit Pal, A. Chakrabarti, R. Ghosh

{"title":"An efficient hardware accelerated design for image denoising using Extended Trilateral filter","authors":"M. Dey, Chandrajit Pal, A. Chakrabarti, R. Ghosh","doi":"10.1109/CIEC.2016.7513830","DOIUrl":"https://doi.org/10.1109/CIEC.2016.7513830","url":null,"abstract":"Trilateral filtering presents an edge preserving smoothing filter. The predecessor of Trilateral filtering, the bilateral filter is a non-linear filtering technique that can reduce noise from an image while preserving the strong and sharp edges, but it cannot provide desired result when the edges have valley or ridge like features. The Trilateral filter is extended to be a gradient-preserving filter, including the local image gradient (signal plane) into the filtering process. This filter has the added benefit that it requires only one user-set parameter (neighborhood size used for bilateral gradient smoothing), and the rest are self-tuning to the image. In this paper, we introduce an extended version of Trilateral filtering where domain and range filtering are applied on the image separately followed by applying the original Trilateral filter on the image that is produced by combination of domain and range filtered outputs. This extended approach gives better noise reduction than the original Trilateral approach and improves the image quality. We also provide an efficient Field Programmable Gate Array (FPGA) based implementation of the proposed Extended Trilateral filter(ETF) on a hardware software co-simulation environment to validate the design.","PeriodicalId":443343,"journal":{"name":"2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121214790","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: 3

A Naïve Bayesian approach to lower limb classification from EEG signals 一种Naïve基于脑电信号的下肢分类贝叶斯方法

2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC) Pub Date : 1900-01-01 DOI: 10.1109/CIEC.2016.7513812

Arnab Rakshit, A. Khasnobish, D. Tibarewala

{"title":"A Naïve Bayesian approach to lower limb classification from EEG signals","authors":"Arnab Rakshit, A. Khasnobish, D. Tibarewala","doi":"10.1109/CIEC.2016.7513812","DOIUrl":"https://doi.org/10.1109/CIEC.2016.7513812","url":null,"abstract":"Lower limb movement decoding is the primary objective for designing Brain-computer interface (BCI) controlled leg prosthesis. In this paper the main objective is to classify left-right leg movement directly from electroencephalography (EEG) signals by probabilistic Naive Bayesian approach. Wavelet based decomposition has been taken as feature extractor to take care of non-stationary nature of brain waves, that have been recorded from 12 healthy subjects. The proposed method achieved average accuracy of 78.33% with 11ms of execution time. Specificity, sensitivity, type 1 and type 2 error rate have also been determined for each case. Results of the classifier is compared with other standard classifiers and statistically validated by Friedman Test. Novelty of the paper lies in the fact that it considers the both frequency and spatial domain (location in time) features of EEG signal without sacrificing accuracy and very low execution time makes it feasible for real time application. It also shows the Naïve Bayes classifier with uniform prior probability is better classifier than standard Naïve Bayes classifier in recognizing left-right lower limb movement.","PeriodicalId":443343,"journal":{"name":"2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128892211","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

A particle swarm optimization based gene identification technique for classification of cancer subgroups 基于粒子群优化的癌症亚群基因鉴定技术

2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC) Pub Date : 1900-01-01 DOI: 10.1109/CIEC.2016.7513800

Subhajit Kar, Kaushik Das Sharma, M. Maitra

引用次数: 4

Bias field estimation and segmentation of MRI images using a Spatial Fuzzy C-means algorithm 基于空间模糊c均值算法的MRI图像偏置场估计与分割

2016 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC) Pub Date : 1900-01-01 DOI: 10.1109/CIEC.2016.7513733

S. Adhikari, J. Sing, D. K. Basu

引用次数: 1