2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)最新文献_第8页

An improved speech enhancement approach based on combination of compressed sensing and Kalman filter 一种基于压缩感知和卡尔曼滤波相结合的改进语音增强方法

2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) Pub Date : 2015-12-01 DOI: 10.1109/ICCIC.2015.7435699

Kalpana Naruka, Dr.O.P. Sahu

引用次数: 1

Learning mechanism for RT task scheduling RT任务调度的学习机制

2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) Pub Date : 2015-12-01 DOI: 10.1109/ICCIC.2015.7435795

A. Rao, Swathi Agarwal, K. Srinivas, B. Rani

引用次数: 1

Automated signature generation for polymorphic worms using substrings extraction and principal component analysis 基于子串提取和主成分分析的多态蠕虫自动签名生成

2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) Pub Date : 2015-12-01 DOI: 10.1109/ICCIC.2015.7435724

Avijit Mondal, Subrata Paul, A. Mitra, Biswajit Gope

引用次数: 4

Evaluation of PSE, STFT and probability coefficients for classifying two directions from EEG using radial basis function 利用径向基函数对EEG进行两个方向分类的PSE、STFT和概率系数的评价

2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) Pub Date : 2015-12-01 DOI: 10.1109/ICCIC.2015.7435664

Vivek P. Patkar, Lekha Das, Prakruti J. Joshi

{"title":"Evaluation of PSE, STFT and probability coefficients for classifying two directions from EEG using radial basis function","authors":"Vivek P. Patkar, Lekha Das, Prakruti J. Joshi","doi":"10.1109/ICCIC.2015.7435664","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435664","url":null,"abstract":"EEG (Electroencephalography) is a recording of electrical activities of brain measured from scalp. Brain is a control center for almost all functions of body. As EEG originates from brain, it contains various components related to cognitive activities of brain. Hence, it also contains information regarding the motor functions associated with movement of the body. EEG is commonly recorded for purposes of diagnosis and research associated with diseases like epilepsy, seizures, sleep disorders etc. But apart from these applications it can also be used to map various motor movements being thought of. This may lead to development of landmark devices in the field of rehabilitation of physically challenged individuals. Here we intend to extract the features and classify the directions using EEG. At initial stage it is desired to classify two movements i.e. left and right, but the method can be extended for the classification of other directions as well. In present scenario the most suitable methods for classification problems can be developed using machine learning algorithms. In this work the features like probability co efficient, PSE (power spectral entropy) and STFT (Short Time Fourier Transform) are extracted and evaluated for their efficiency in classification. Radial Basis Function is used for classifying these features. The study shows probability co efficient and STFT have yielded about 60% accuracy in classifying raw EEG signals proving them advantageous over power spectral entropy.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114386284","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

Minimization of fuel cost in solving the power economic dispatch problem including transmission losses by using modified Particle Swarm Optimization 利用改进粒子群算法求解包含输电损耗的电力经济调度问题中的燃料成本最小化

2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) Pub Date : 2015-12-01 DOI: 10.1109/ICCIC.2015.7435718

J. Rizwana, R. Jeevitha, R. Venkatesh, K. Parthiban

引用次数: 6

Automated verification of spacecraft auxiliary data 航天器辅助数据的自动验证

2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) Pub Date : 2015-12-01 DOI: 10.1109/ICCIC.2015.7435722

A. Savitha, Rajiv R. Chetwani, Y. R. Bhanumathy, M. Ravindra

{"title":"Automated verification of spacecraft auxiliary data","authors":"A. Savitha, Rajiv R. Chetwani, Y. R. Bhanumathy, M. Ravindra","doi":"10.1109/ICCIC.2015.7435722","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435722","url":null,"abstract":"ISRO programmes are realizing 8 to 10 spacecrafts a year. The complexity of the spacecraft is increasing in each of geostationary, IRS and interplanetary missions, leading to increase in computational load of the onboard computer. Hence there is a need for quick testing and analysis of various spacecrafts. The attitude and orbit control electronics system receives data from various sensors and does the processing and maintains the spacecraft in station by generating control signals for actuators. It has various subsystems which interact with it. In addition to this package there is data handling remote terminal package which is implemented to provide other interfaces like star sensor, satellite positioning system, solid state recorder, baseband data handling and many more. These systems communicate with attitude and orbit control electronics through MIL-STD-1553 Bus. This paper explains the methodology developed for the analysis of this spacecraft baseband data handling auxiliary data. This analysis helps for both internal logic verification and external component interface. The actual results are recorded for further analysis and reports are generated.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122175714","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 noise removal techniques in MRI brain images 脑MRI图像去噪技术的对比分析

2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) Pub Date : 2015-12-01 DOI: 10.1109/ICCIC.2015.7435737

B. Deepa, M. Sumithra

{"title":"Comparative analysis of noise removal techniques in MRI brain images","authors":"B. Deepa, M. Sumithra","doi":"10.1109/ICCIC.2015.7435737","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435737","url":null,"abstract":"Noise removal techniques have become an essential exercise in medical imaging applications, for the study of anatomical structures. To address this issue many denoising algorithm has been proposed both in spatial and frequency domain. Among them, few techniques in spatial domain are hybrid median filter, Weiner filter, bilateral filter, histogram equalization and in frequency domain are wavelet transform, independent component analysis were successfully used in medical imaging. The most commonly affected noises in medical image are salt and pepper, Gaussian, Speckle and Brownian noise. In this paper, the medical images taken for comparison include MRI brain images, in gray scale and RGB. The performances of these algorithms are analyzed for various noise types at different noise levels ranging from 0 dB to 30 dB. The evaluation of these algorithms is done by measures like peak signal to noise ratio (PSNR), root mean square error value (RMSE), universal quality index (UQI) and picture quality scale(PQS). Experimental results suggest that, independent component analysis performs better for removing salt and pepper noise in RGB and gray scale and Gaussian noise for images in RGB. Wavelet transform gives superior performance for removing speckle and Brownian noise for images in RGB and grayscale, irrespective of the noise level considered. Whereas histogram equalization gives better quality results while removing Gaussian noise at all noise levels for the images in gray scale only. On the other hand all spatial filtering techniques give comparative results at all dB levels in gray scale, which is inferior to frequency domain techniques.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117012776","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: 24

Identification of selfish attack in cognitive radio ad-hoc networks 认知无线电自组织网络中自私攻击的识别

2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) Pub Date : 2015-12-01 DOI: 10.1109/ICCIC.2015.7435805

Sharad Wagh, Avinash More, Aditya Khavnekar

引用次数: 2

Palm fruit harvester algorithm for elaeis guineensis oil palm fruit grading using UML 棕榈果实收割机算法中基于UML的油棕果实分级

2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) Pub Date : 2015-12-01 DOI: 10.1109/ICCIC.2015.7435700

G. Patkar, G. Anjaneyulu, P. Mouli

引用次数: 3

Seeded region growing segmentation on ultrasound image using particle swarm optimization 基于粒子群算法的超声图像种子区域生长分割

2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) Pub Date : 2015-12-01 DOI: 10.1109/ICCIC.2015.7435715

Parineeta Suman, D. Parasar, V. Rathod

{"title":"Seeded region growing segmentation on ultrasound image using particle swarm optimization","authors":"Parineeta Suman, D. Parasar, V. Rathod","doi":"10.1109/ICCIC.2015.7435715","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435715","url":null,"abstract":"Ultrasound imaging is one of the most popular and cheapest noninvasive medical scans. At the time of image acquisition, there may be degradation in the quality of image in the form of speckle noise. In recent times, many researches have made various experiments to enhance the quality of medical imaging. However, there is scope to further enhance it. In the proposed method, finding out the seed pixel randomly is the basic problem, which is treated as an optimization problem. This problem can be solved by Particle Swarm Optimization. Using Particle Swarm Optimization algorithm, the fitness function can give us the appropriate seed pixel for the desired ultrasound imaging. In this paper, a novel method is proposed, wherein segmentation will be applied on a fuzzy filtered image. The fuzzy filter applies fuzzy rules to detect regions in the image viz. edge region, homogeneous region, and noisy region by using different gradients, and then filters the noisy region using fuzzy membership rules. The proposed method has been tested on different ultrasound images, and the experimental results demonstrate its effectiveness.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129609629","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: 5