2016 IEEE Annual India Conference (INDICON)最新文献

Comparison of Adaboost.M2 and perspective based model ensemble in multispectral image classification Adaboost的比较。M2与基于透视的模型集成在多光谱图像分类中的应用

2016 IEEE Annual India Conference (INDICON) Pub Date : 2017-07-01 DOI: 10.1109/IGARSS.2017.8127798

L. Eeti, K. Buddhiraju

引用次数: 3

SEU mitigation of rad-tolerant Xilinx FPGA using external scrubbing for geostationary mission 用于地球静止任务的Xilinx FPGA耐辐射SEU的外部擦洗

2016 IEEE Annual India Conference (INDICON) Pub Date : 2017-02-01 DOI: 10.1109/SPIN.2017.8049985

M. Kumar, Durga Digdarsini, NEERAJ KUMAR MISRA, T. Ram

引用次数: 0

Photovoltaic H6-type transformerless inverter topology 光伏h6型无变压器逆变器拓扑

2016 IEEE Annual India Conference (INDICON) Pub Date : 2017-02-01 DOI: 10.1109/ICECCT.2017.8118031

Aparna Das, G. Sheeja

引用次数: 1

Design and implementation of image processing algorithms for cardiac blockage detection on FPGA 基于FPGA的心脏阻塞检测图像处理算法的设计与实现

2016 IEEE Annual India Conference (INDICON) Pub Date : 2016-12-01 DOI: 10.1109/INDICON.2016.7839159

Shrinivas B Mudigoudar, A. Rasheed

引用次数: 10

PAPR analysis of superimposed training based MIMO-OFDM systems using an orthogonal affine precoder 基于正交仿射预编码器的MIMO-OFDM系统的PAPR分析

2016 IEEE Annual India Conference (INDICON) Pub Date : 2016-12-01 DOI: 10.1109/INDICON.2016.7839003

E. Sharma, H. B. Mishra, K. Vasudevan

引用次数: 0

Characterisation of adaptive filters for air traffic control 空中交通管制自适应滤波器的特性

2016 IEEE Annual India Conference (INDICON) Pub Date : 2016-12-01 DOI: 10.1109/INDICON.2016.7839041

Aiman Javed, Nilanjan Patra

引用次数: 1

Classifying musical instruments using speech signal processing methods 用语音信号处理方法对乐器进行分类

2016 IEEE Annual India Conference (INDICON) Pub Date : 2016-12-01 DOI: 10.1109/INDICON.2016.7839034

Seema Ghisingh, V. K. Mittal

{"title":"Classifying musical instruments using speech signal processing methods","authors":"Seema Ghisingh, V. K. Mittal","doi":"10.1109/INDICON.2016.7839034","DOIUrl":"https://doi.org/10.1109/INDICON.2016.7839034","url":null,"abstract":"Identification of musical instruments from the acoustic signal using speech signal processing methods is a challenging problem. Further, whether this identification can be carried out by a single musical note, like humans are able to do, is an interesting research issue that has several potential applications in the music industry. Attempts have been made earlier using the spectral and temporal features of the music acoustic signals. The process of identifying the musical instrument from monophonic audio recording basically involves three steps — pre-processing of music signal, extracting features from it and then classifying those. In this paper, we present an experiment-based comparative study of different features for classifying few musical instruments. The acoustic features, namely, the Mel-Frequency Cepstral Coefficients (MFCCs), Spectral Centroids (SC), Zero-Crossing Rate (ZCR) and signal energy are derived from the music acoustic signal using different speech signal processing methods. A Support Vector Machine (SVM) classifier is used with each feature for the relative comparisons. The classification results using different combinations of training by features from different music instrument and testing with another/same type of music instruments are compared. Our results indicate that the most significant feature for classifying Guitar, Violin and Drum is MFCC as it gives the better accurate results. Also, the feature which gives better accuracy results for the drum instrument is ZCR. Among the features used, after MFCC, ZCR proved to be the optimal feature for the classification of drum instrument.","PeriodicalId":283953,"journal":{"name":"2016 IEEE Annual India Conference (INDICON)","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125938970","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: 11

Design and analysis of symmetric and asymmetric multilevel inverter for renewable energy applications 用于可再生能源的对称和非对称多电平逆变器的设计与分析

2016 IEEE Annual India Conference (INDICON) Pub Date : 2016-12-01 DOI: 10.1109/INDICON.2016.7839087

P. N. V. S. Ayyappa, S. Andrade, Y. Manjunatha

{"title":"Design and analysis of symmetric and asymmetric multilevel inverter for renewable energy applications","authors":"P. N. V. S. Ayyappa, S. Andrade, Y. Manjunatha","doi":"10.1109/INDICON.2016.7839087","DOIUrl":"https://doi.org/10.1109/INDICON.2016.7839087","url":null,"abstract":"Multilevel converters offer high power capacity, connected with high quality output and lower compensation misfortunes. Utilization of multilevel inverters (MLI) for higher voltage objectives in renewable vitality has turned out to be more prevalent. In this paper, at first another topology for sub-multilevel inverter is proposed and afterward arrangement association of the sub-multilevel inverters is proposed as a summed up multilevel inverter. The proposed multilevel inverter utilizes lessened number of exchanging gadgets. Uncommon consideration has been paid to acquire ideal structures seeing diverse criteria, for example, number of switches, standing voltage on the switches, number of dc voltage sources, and so forth. The proposed multilevel inverter has been dissected in both 9-level symmetric and 31-level asymmetric configurations. For exchanging measure up to equal step technique and fundamental frequency switching strategies are utilized and near investigation is done between them. Verification of the expository results is performed utilizing MATLAB/SIMULINK programming and Laboratory proto type was implemented for 31-level asymmetric configuration with fundamental frequency switching technique.","PeriodicalId":283953,"journal":{"name":"2016 IEEE Annual India Conference (INDICON)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126130079","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

High performance and adaptive lab report generation in hospital management information systems 医院管理信息系统中高性能和自适应的实验室报告生成

2016 IEEE Annual India Conference (INDICON) Pub Date : 2016-12-01 DOI: 10.1109/INDICON.2016.7839138

S. Srivastava, P. Khurana, A. Rai, A. Cheema, P. K. Srivastava

引用次数: 5

Cognitive agent based stable routing protocol for vehicle-to-vehicle communication 基于认知代理的车对车通信稳定路由协议

2016 IEEE Annual India Conference (INDICON) Pub Date : 2016-12-01 DOI: 10.1109/INDICON.2016.7839102

Hridya C. Premkumar, V. Budyal, M. Kakkasageri

引用次数: 8