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2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)最新文献

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Determination of coordinate transformations in UAVS 无人机坐标变换的确定
2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802861
Sandhya Rani Chapala, Gangadhara Sai Pirati, U. R. Nelakuditi
{"title":"Determination of coordinate transformations in UAVS","authors":"Sandhya Rani Chapala, Gangadhara Sai Pirati, U. R. Nelakuditi","doi":"10.1109/CCIP.2016.7802861","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802861","url":null,"abstract":"Estimation of vehicle attitude during autonomous flying of Unmanned Aerial Vehicles (UAVs) can be performed with Global Positioning System (GPS). But when vehicle is flying this method results in error, hence another approach i.e., Direction Cosine Matrix (DCM) is used. But while extracting attitude (Euler Angles) of UAVs with DCM creates a drawback known as Gimbal lock which is also known as singularity problem in mathematics. This problem is due to deviation in one of the angles of attitude and leads to loss of degree of freedom. To avoid this problem the proposed technique is quaternion which represents orientation of a body. Quaternion, which is produced from rotation sequence of Euler angles and it implements Euler angles with any one of rotation sequences. This paper deals with the mathematical modelling and implementation of the quaternion in Matlab. Finally results are represented for each orientation in terms of Euler angles due to quaternion. This method is more accurate even body is under motion.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132354547","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 picture fuzzy clustering approach for brain tumor segmentation 一种图像模糊聚类方法用于脑肿瘤分割
2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802852
S. A. Aruna Kumar, B. Harish, V. N. Manjunath Aradhya
{"title":"A picture fuzzy clustering approach for brain tumor segmentation","authors":"S. A. Aruna Kumar, B. Harish, V. N. Manjunath Aradhya","doi":"10.1109/CCIP.2016.7802852","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802852","url":null,"abstract":"This paper presents a Picture Fuzzy Clustering (PFC) method for MRI brain image segmentation. The PFC is based on the Picture fuzzy set, which is the generalization of the traditional fuzzy set and intuitionistic fuzzy set. In traditional fuzzy set, the problem of uncertainty arises in defining the membership function. Intuitionistic fuzzy set handles this uncertainty by considering hesitation degree. However, intuitionistic fuzzy set fails to solve real time problems which require answers like yes, abstain, no and refusal. The picture fuzzy set solves these problems by considering refusal degree along with membership, neutral and nonmembership degree. Thus, the cluster centers in the PFC may converge to a desirable location than the cluster centers obtained using traditional Fuzzy C-Means (FCM) and Intuitionistic Fuzzy Clustering (IFC). Experimentation is carried out on the standard MRI brain image dataset. To assess the performance, the proposed method is compared with the existing FCM and IFC methods. Results show that the proposed method gives the better result.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"23 20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130633803","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: 23
Automation of device validation using digital power technology and PMBus communication 利用数字电源技术和PMBus通信实现设备验证自动化
2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802855
M. Shivakumar, B. S. Premananda, Anil Keste
{"title":"Automation of device validation using digital power technology and PMBus communication","authors":"M. Shivakumar, B. S. Premananda, Anil Keste","doi":"10.1109/CCIP.2016.7802855","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802855","url":null,"abstract":"Power subsystems today are no longer stand-alone and are being integrated into total systems. Digital power technology helps in reducing power consumption and managing the power complexity of the recent electronic systems. For the success of digital power management, it is very much necessary to have a standardized way of communication with the power subsystem. One of the first standards for such communications is the Intel's System Management Bus (SMBus) based on I2C bus from Philips. PMBus specification is an improved version of SMBus to allow digital control of a power supply over a specified physical bus. Power related testing of devices/products including power consumption, margining, sequencing in the traditional system requires manually changing the passive components and additional setup for each test case which is cumbersome. Digital power technology provides a good cost-effective approach to automate this process. The paper provides the automation of device validation using the concept of digital power and the Texas Instrument's UCD9224 Digital PWM controller.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132405218","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
Analysis of associativity among mirror neurons for financial profiling 镜像神经元在财务分析中的关联性分析
2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802869
Tarun Dash, Vinayak Jaiswal, Anoosha Sagar, Gaurav Vazirani, N. Giri
{"title":"Analysis of associativity among mirror neurons for financial profiling","authors":"Tarun Dash, Vinayak Jaiswal, Anoosha Sagar, Gaurav Vazirani, N. Giri","doi":"10.1109/CCIP.2016.7802869","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802869","url":null,"abstract":"Mirror neurons, observed first in macaque monkeys, are neurons which fire not only on the performance of an action but also during the perception of the same action by some being. This paper presents the application of the concept of mirror neurons in financial profiling. In addition, this concept has been extended to establish associativity among the mirror neurons. This financial application makes use of stock market data from the official Bombay Stock Exchange (BSE) Web site and uses the concepts of artificial neural networks, hierarchical agglomerative clustering, and dimensionality reduction for implementation. The performance of this system has been established using the concept of root mean square error.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125726913","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
Association rule analysis in cardiovascular disease 心血管疾病的关联规则分析
2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802881
S. Khare, Deepa Gupta
{"title":"Association rule analysis in cardiovascular disease","authors":"S. Khare, Deepa Gupta","doi":"10.1109/CCIP.2016.7802881","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802881","url":null,"abstract":"Data mining in healthcare is a rising field due to the vast amount of patient specific data which is freely available for analysis. While the majority of this data has been analyzed using various data mining techniques like classification, but association rule mining in this field is still largely unexplored. Association Rule Mining is a simple yet powerful tool that brings to light hidden relationships among data attributes in addition to statistically validating those which are already known. These relationships can help in understanding diseases and their causes in a better way, which in turn will help to prevent them. This report presents exploration of this field and the conclusions drawn from analyzing heart disease dataset from UCI repository. In this paper association rule mining is applied to cardiovascular disease. Cardiovascular diseases are diseases related to heart and circulatory system. Heart disease is explored in this paper.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129845357","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: 27
Automatic defect identification and grading system for ‘Jonagold’ apples 乔纳金 "苹果缺陷自动识别和分级系统
2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802851
Shyla Raj, Vinod DS
{"title":"Automatic defect identification and grading system for ‘Jonagold’ apples","authors":"Shyla Raj, Vinod DS","doi":"10.1109/CCIP.2016.7802851","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802851","url":null,"abstract":"A method to grade `Jonagold' apples based on features extracted from defects is described. Database consisting of multi-spectral images of Jonagold apples is used for the work. Fuzzy C-Means (FCM) clustering method is used for defect segmentation, features from defect part is extracted using Histogram of Oriented Gradients (HOG) method and Apple classification is performed by using Multi-Class Support Vector Machine (MSVM) with accuracy of 97.5% for two category grading (healthy and defected) and 94.66% for multi-category grading (healthy apples, slightly defected apples and seriously defected apples).","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130278060","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: 4
A Placement Prediction System using k-nearest neighbors classifier 基于k近邻分类器的位置预测系统
2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802883
Animesh Giri, M. V. V. Bhagavath, Bysani Pruthvi, Naini Dubey
{"title":"A Placement Prediction System using k-nearest neighbors classifier","authors":"Animesh Giri, M. V. V. Bhagavath, Bysani Pruthvi, Naini Dubey","doi":"10.1109/CCIP.2016.7802883","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802883","url":null,"abstract":"In this paper, we propose a Placement Prediction System which predicts the probability of a undergrad student getting placed in an IT company by applying the machine learning model of k-nearest neighbors's classification. We also compare the results of the same against the results obtained from other models like Logistic Regression and SVM. To do so we consider the academic history of the student as well as their skill set like, programming skills, communication skills, analytical skills and team work, which are tested by the hiring companies during the recruitment process. The data that is used for this purpose is the Placement Statistics of PES Institute of Technology, Bangalore South Campus for the previous two academic batches.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116633859","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: 29
Hilbert Huang transform based speech recognition 基于Hilbert Huang变换的语音识别
2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802858
Vani H.Y, M. Anusuya
{"title":"Hilbert Huang transform based speech recognition","authors":"Vani H.Y, M. Anusuya","doi":"10.1109/CCIP.2016.7802858","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802858","url":null,"abstract":"In today's world, to make man-machine interaction more effective speech recognition plays an important role in speech processing. This paper presents the application of Hilbert-Huang transform (HHT), a mathematical tool applied for feature extraction phase of the speech signal processing. These features are modeled and evaluated using Vector Quantization(VQ) and Fuzzy C Means(FCM) techniques. The proposed system highlights the importance of Discrete Cosine Transformation(DCT) applied for Hilbert Huang Transform to extract the better speech signal parameters. The features obtained from this process has better recognition accuracies. It also demonstrates the efficiency of DCT with HHT for FCM clustering technique over the VQ technique. The performance of HHT- FCM is discussed with the termination criteria `ε' and fuzzifier `m' parameters of FCM.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114498433","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: 4
Precise current matching charge pump for digital phase locked loop 数字锁相环精密电流匹配电荷泵
2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802854
D. Rajeshwari, P. V. Rao
{"title":"Precise current matching charge pump for digital phase locked loop","authors":"D. Rajeshwari, P. V. Rao","doi":"10.1109/CCIP.2016.7802854","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802854","url":null,"abstract":"In digital phase locked loop cascode structured charge pump is proposed with current mismatch less than 0.01%. Steady state error in digital phase locked loop can be minimized by reducing current mismatch. The rail to rail operational amplifier and cascode current source circuit is employed to reduce the mismatch between charging and discharging current. The operational amplifier has high gain of 90dB. The proposed charge pump is designed, simulated and verified at power supply of 1.8V in TSMC 90nm CMOS technology.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"56 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120815987","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
Meta-cognitive neural network method for classification of diabetic retinal images 糖尿病视网膜图像分类的元认知神经网络方法
2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802860
R. Banu, V. Arun, N. Shankaraiah, V. Shyam
{"title":"Meta-cognitive neural network method for classification of diabetic retinal images","authors":"R. Banu, V. Arun, N. Shankaraiah, V. Shyam","doi":"10.1109/CCIP.2016.7802860","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802860","url":null,"abstract":"An eye disease which is assorted in person with diabetes that is occurred by change in blood vessels of the retina is called Diabetic Retinopathy. Retinopathy can occur with all types of diabetes and can cause vision loss if it's not treated on time. Detection of exudate by ophthalmologist normally takes time and energy. In this paper, classification and detection of exudate in color retinal image using automated technique have been proposed. This method reduces work of ophthalmologist. A series of steps or actions need to be taken for exudate detection. Firstly in pre-processing step, green channel extraction is used and optic disk is eliminated to prevent optic disk from interfacing with exudates detection. Robust Spatial Kernel FCM (RSKFCM) segmentation method is used for optic disk elimination which gives good result compared to other FCM based method. The significant features are extracted from the segmented images and are used for classification purpose. Meta-cognitive neural network method is used as classifier. The experiments were conducted on standard diabetic retinal image dataset. Experimental results shows that the proposed method gives promising results.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"33 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120922738","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: 10
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