Pub Date : 2016-12-01DOI: 10.1109/ICAECCT.2016.7942547
U. Ghanekar, R. Pandey
The choice of appropriate value of threshold in median-based impulse detection method becomes difficult due to its dependence on the noise density and image characteristics. In the case of random valued noise(RVIN), if a fixed value of threshold is used then it will result into large percentage of missed and false detection. Therefore, a variable threshold governed by the local image characteristics, is required for detection of RVIN. Here, we present an impulse detector in which the value of threshold depends on window under observation. The extensive simulations exhibit the efficacy of the method in respect of both random valued as well as salt and pepper noise.
{"title":"Adaptive threshold based impulse detection for restoration of digital images","authors":"U. Ghanekar, R. Pandey","doi":"10.1109/ICAECCT.2016.7942547","DOIUrl":"https://doi.org/10.1109/ICAECCT.2016.7942547","url":null,"abstract":"The choice of appropriate value of threshold in median-based impulse detection method becomes difficult due to its dependence on the noise density and image characteristics. In the case of random valued noise(RVIN), if a fixed value of threshold is used then it will result into large percentage of missed and false detection. Therefore, a variable threshold governed by the local image characteristics, is required for detection of RVIN. Here, we present an impulse detector in which the value of threshold depends on window under observation. The extensive simulations exhibit the efficacy of the method in respect of both random valued as well as salt and pepper noise.","PeriodicalId":6629,"journal":{"name":"2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT)","volume":"2 1","pages":"12-16"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75990994","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}
Pub Date : 2016-12-01DOI: 10.1109/ICAECCT.2016.7942556
A. Shinde, Sharad S. Jagtap, V. Puranik
This paper based on the classification of the voltage signal on basis of quality. It can be achieved by various techniques according to applications and required accuracy. Feeder points at various locations of electrical substation play important role in reduction of noise from the supply voltage. However this is of less amount and considerable for general applications. In some industrial applications, this may cause a large loss due to the presence of noise. So for controlling the accuracy one can design a system which overcomes the problems arising due to noise. Using MATLAB software it is implemented for detection and identification. It has various algorithms like KNN, SVM and RBF. SVM is the powerful tool in MATLAB for identification and Classification of voltage signals, images as well as music signals. For this detection of signals, a database is applied for any type of transform. It is better to use wavelet transform for feature extraction purpose. This paper gives solution for identification and sorting of different noises in voltage signals using the pair of wavelet transform and SVM.
{"title":"Identification and sorting of power quality disturbances using signal processing with GUI","authors":"A. Shinde, Sharad S. Jagtap, V. Puranik","doi":"10.1109/ICAECCT.2016.7942556","DOIUrl":"https://doi.org/10.1109/ICAECCT.2016.7942556","url":null,"abstract":"This paper based on the classification of the voltage signal on basis of quality. It can be achieved by various techniques according to applications and required accuracy. Feeder points at various locations of electrical substation play important role in reduction of noise from the supply voltage. However this is of less amount and considerable for general applications. In some industrial applications, this may cause a large loss due to the presence of noise. So for controlling the accuracy one can design a system which overcomes the problems arising due to noise. Using MATLAB software it is implemented for detection and identification. It has various algorithms like KNN, SVM and RBF. SVM is the powerful tool in MATLAB for identification and Classification of voltage signals, images as well as music signals. For this detection of signals, a database is applied for any type of transform. It is better to use wavelet transform for feature extraction purpose. This paper gives solution for identification and sorting of different noises in voltage signals using the pair of wavelet transform and SVM.","PeriodicalId":6629,"journal":{"name":"2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT)","volume":"17 1","pages":"60-63"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78669839","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}
Pub Date : 2016-12-01DOI: 10.1109/ICAECCT.2016.7942588
Deepa N. Reddy, Y. Ravinder
The most important components in cognitive radio (CR) system is spectrum sensing. In this paper we propose a novel method to determine the optimum number of Secondary users (SUs) necessary in the cooperative spectrum sensing environment for perfect reporting channel. At first the threshold selection is carried out considering present conditions of noise levels. The noise variance is estimated using Maximum likelihood estimator. Secondly the optimum number of SUs required in cooperative sensing are determined using the proposed scheme of threshold selection. The results show that the proposed method provides detection probability very close to the clairvoyant detector with known parameters. The optimum number of SUs and the error rates achieved in Cooperative sensing using the proposed method are close to that obtained by the classical detection.
{"title":"Evaluation of cooperative sensing for perfect reporting channels using dynamic detection threshold","authors":"Deepa N. Reddy, Y. Ravinder","doi":"10.1109/ICAECCT.2016.7942588","DOIUrl":"https://doi.org/10.1109/ICAECCT.2016.7942588","url":null,"abstract":"The most important components in cognitive radio (CR) system is spectrum sensing. In this paper we propose a novel method to determine the optimum number of Secondary users (SUs) necessary in the cooperative spectrum sensing environment for perfect reporting channel. At first the threshold selection is carried out considering present conditions of noise levels. The noise variance is estimated using Maximum likelihood estimator. Secondly the optimum number of SUs required in cooperative sensing are determined using the proposed scheme of threshold selection. The results show that the proposed method provides detection probability very close to the clairvoyant detector with known parameters. The optimum number of SUs and the error rates achieved in Cooperative sensing using the proposed method are close to that obtained by the classical detection.","PeriodicalId":6629,"journal":{"name":"2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT)","volume":"343 1","pages":"231-235"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79748560","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}
Pub Date : 2016-12-01DOI: 10.1109/ICAECCT.2016.7942591
S. Vernekar, Ingrid Nazareth, J. Parab, G. Naik
With the growing concern for environmental pollution and shrinking land resources available for agriculture, the need for sustainable agriculture is increasing. Soil sensing plays an important role in sustainable agriculture as it provides an insight into the various soil properties thus enabling the farmer to adjust the inputs accordingly. The aim of the study is to design a soil sensor and analyze the errors in the prediction of a soil nutrient. The manuscript describes a new method for soil nutrient sensing using RF spectroscopy. The technique can predict soil urea content and is based on multivariate analysis using the PLSR (Partial Least Square Regression) mathematical tool. Eight different combinations of five important soil nutrients (Urea, Potash, Phosphate, Salt, and Lime) at varying concentration were used to develop multivariate block. The Urea prediction algorithm takes into account the effect of various other soil nutrients present in the sample. The results obtained show that the percentage error in prediction of urea is within the tolerable limits of +/−5% of the actual value, when other soil nutrient concentrations are varied below and above their normal values. The method can be extended for sensing multiple nutrients simultaneously by modifying the algorithm.
{"title":"Error analysis in soil urea prediction based on RF spectroscopy","authors":"S. Vernekar, Ingrid Nazareth, J. Parab, G. Naik","doi":"10.1109/ICAECCT.2016.7942591","DOIUrl":"https://doi.org/10.1109/ICAECCT.2016.7942591","url":null,"abstract":"With the growing concern for environmental pollution and shrinking land resources available for agriculture, the need for sustainable agriculture is increasing. Soil sensing plays an important role in sustainable agriculture as it provides an insight into the various soil properties thus enabling the farmer to adjust the inputs accordingly. The aim of the study is to design a soil sensor and analyze the errors in the prediction of a soil nutrient. The manuscript describes a new method for soil nutrient sensing using RF spectroscopy. The technique can predict soil urea content and is based on multivariate analysis using the PLSR (Partial Least Square Regression) mathematical tool. Eight different combinations of five important soil nutrients (Urea, Potash, Phosphate, Salt, and Lime) at varying concentration were used to develop multivariate block. The Urea prediction algorithm takes into account the effect of various other soil nutrients present in the sample. The results obtained show that the percentage error in prediction of urea is within the tolerable limits of +/−5% of the actual value, when other soil nutrient concentrations are varied below and above their normal values. The method can be extended for sensing multiple nutrients simultaneously by modifying the algorithm.","PeriodicalId":6629,"journal":{"name":"2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT)","volume":"71 1","pages":"244-246"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77397354","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}
Pub Date : 2016-12-01DOI: 10.1109/ICAECCT.2016.7942555
Vaishali Shirsath, M. Dongare
Two gait phase estimation method to control the above knee prosthesis is discussed in this paper. A rule base quantization and an ANN based system is preferred for controlling various parameters such as motion, torque required in walking with the help of prosthetic leg. Microcontroller based semi-active knee prosthesis in order to respond patients demands and adapt environmental conditions such as whether are considered. A design is suggested to measure experimental environment in which gait data is collected for both inertial as well as image based measurement systems. The inertial measurement system consist of MEM accelerometers as well as gyroscopes to identify direct motion measurement of controlling parameter using microcontroller. The image based measurement system is used to verify the above measured data from the prosthetic leg. Various advantages of proposed system is discussed in this paper.
{"title":"Neural network based gait phases of above knee prosthesis","authors":"Vaishali Shirsath, M. Dongare","doi":"10.1109/ICAECCT.2016.7942555","DOIUrl":"https://doi.org/10.1109/ICAECCT.2016.7942555","url":null,"abstract":"Two gait phase estimation method to control the above knee prosthesis is discussed in this paper. A rule base quantization and an ANN based system is preferred for controlling various parameters such as motion, torque required in walking with the help of prosthetic leg. Microcontroller based semi-active knee prosthesis in order to respond patients demands and adapt environmental conditions such as whether are considered. A design is suggested to measure experimental environment in which gait data is collected for both inertial as well as image based measurement systems. The inertial measurement system consist of MEM accelerometers as well as gyroscopes to identify direct motion measurement of controlling parameter using microcontroller. The image based measurement system is used to verify the above measured data from the prosthetic leg. Various advantages of proposed system is discussed in this paper.","PeriodicalId":6629,"journal":{"name":"2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT)","volume":"40 1","pages":"55-59"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82643133","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}
Pub Date : 2016-12-01DOI: 10.1109/ICAECCT.2016.7942587
A. Deshmukh, Mohil Gala, S. Agrawal
Various configurations of slotted and shorted 60° Sector microstrip antennas for wider bandwidth are discussed. Slot tunes the spacing between shorted patch TM1/4,1 and TM1/4,0 resonant modes, that gives bandwidth of more than 800 MHz (>60%). The surface current distributions at modified shorted resonant modes were studied. Based on current variations against slot length, formulation in resonant length for shorted modes is proposed. Using proposed formulations frequencies were calculated. They show close agreement with simulated frequencies. The proposed formulations were further used to design slot cut gap-coupled shorted variations at different frequency on thicker air substrate. In all the configurations design procedure achieves wide band response.
{"title":"Design of gap-coupled variations of slotted and shorted 60° Sector microstrip antennas","authors":"A. Deshmukh, Mohil Gala, S. Agrawal","doi":"10.1109/ICAECCT.2016.7942587","DOIUrl":"https://doi.org/10.1109/ICAECCT.2016.7942587","url":null,"abstract":"Various configurations of slotted and shorted 60° Sector microstrip antennas for wider bandwidth are discussed. Slot tunes the spacing between shorted patch TM1/4,1 and TM1/4,0 resonant modes, that gives bandwidth of more than 800 MHz (>60%). The surface current distributions at modified shorted resonant modes were studied. Based on current variations against slot length, formulation in resonant length for shorted modes is proposed. Using proposed formulations frequencies were calculated. They show close agreement with simulated frequencies. The proposed formulations were further used to design slot cut gap-coupled shorted variations at different frequency on thicker air substrate. In all the configurations design procedure achieves wide band response.","PeriodicalId":6629,"journal":{"name":"2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT)","volume":"50 1","pages":"226-230"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86716858","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}
Pub Date : 2016-12-01DOI: 10.1109/ICAECCT.2016.7942626
S. Bhisikar, S. Kale
Arthritis is an inflammatory disease which causes erosion in bones or narrowing of joint space in various joints of the body. First symptom of this disease is seen in joints of hand finger and wrist joints thus making hand radiograph analysis extremely important. Lately Reading hand X-ray radiographic image to measure joint space width is very tedious and time consuming task for the radiologist since there are 14 joints in hand and also the structure of hand is complicated to carry out joint space width measurement and analysis. It has certain disadvantages like inaccuracy because of visual measurement and also variation from one reader to another, which can be overcome by automatic technique that can serve as a powerful aid for peoples suffering from disability due to pain, stiffness in joints. In this paper, Image processing based algorithm is developed to yield solution to two major problems joint detection and JSW measurement. The algorithm is divided into following steps, First image preprocessing is carried out using Gaussian filter. Second hand mask is extracted by separating foreground and background by using Otsu's binarization method. Third morphological thinning is applied to get thinned skeleton of binarized image. Fourth To detect joint location in original X-ray image Gabor filter is used. Fifth edge Finally of minimal joint space width is extracted and analyzed automatically. We have experimented 10 digital hand X-ray radiograph of resolution 2000pixels×2000pixels and calculated 120 readings of JSW of finger joints successfully.
{"title":"Automatic joint detection and measurement of joint space width in arthritis","authors":"S. Bhisikar, S. Kale","doi":"10.1109/ICAECCT.2016.7942626","DOIUrl":"https://doi.org/10.1109/ICAECCT.2016.7942626","url":null,"abstract":"Arthritis is an inflammatory disease which causes erosion in bones or narrowing of joint space in various joints of the body. First symptom of this disease is seen in joints of hand finger and wrist joints thus making hand radiograph analysis extremely important. Lately Reading hand X-ray radiographic image to measure joint space width is very tedious and time consuming task for the radiologist since there are 14 joints in hand and also the structure of hand is complicated to carry out joint space width measurement and analysis. It has certain disadvantages like inaccuracy because of visual measurement and also variation from one reader to another, which can be overcome by automatic technique that can serve as a powerful aid for peoples suffering from disability due to pain, stiffness in joints. In this paper, Image processing based algorithm is developed to yield solution to two major problems joint detection and JSW measurement. The algorithm is divided into following steps, First image preprocessing is carried out using Gaussian filter. Second hand mask is extracted by separating foreground and background by using Otsu's binarization method. Third morphological thinning is applied to get thinned skeleton of binarized image. Fourth To detect joint location in original X-ray image Gabor filter is used. Fifth edge Finally of minimal joint space width is extracted and analyzed automatically. We have experimented 10 digital hand X-ray radiograph of resolution 2000pixels×2000pixels and calculated 120 readings of JSW of finger joints successfully.","PeriodicalId":6629,"journal":{"name":"2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT)","volume":"6 1","pages":"429-432"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74392947","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}
Pub Date : 2016-12-01DOI: 10.1109/ICAECCT.2016.7942614
D. R. Sonawane, S. Apte
Now days to prevent malicious use of original companies logos or identity, the automated image processing based frameworks are presented. The process of logo detection and recognition hence becoming the vital task for various applications. In this project we are presenting automated framework for logo detection using the real world logos images and its test image. Basically the working is that input query image is taken and big database of logos with goal of recognizing the logo in query image if any. Previously efficient method presented which outperform the existing method in terms of FRR and FPR. During this paper we are contributing by using RANSAC in which Fast Retina Keypoint (FREAK) descriptor is extracted for further matching and recognition process rather than using existing SIFT technique. The recent method for logo recognition and detection process is based on methodology of CDS (Context Dependent Similarity) which directly local features spatial context. Basically this CDS method using the SIFT method for initial keypoints extraction and then further matching process along with detection is done. The goal of our proposed CDS with RANSAC is to improve the recognition accuracy and to minimize the error rate performance. The RANSAC method is using FREAK technique for keypoints extraction which is superior as compared to SIFT.
{"title":"Improved Context Dependent logo matching framework using FREAK method","authors":"D. R. Sonawane, S. Apte","doi":"10.1109/ICAECCT.2016.7942614","DOIUrl":"https://doi.org/10.1109/ICAECCT.2016.7942614","url":null,"abstract":"Now days to prevent malicious use of original companies logos or identity, the automated image processing based frameworks are presented. The process of logo detection and recognition hence becoming the vital task for various applications. In this project we are presenting automated framework for logo detection using the real world logos images and its test image. Basically the working is that input query image is taken and big database of logos with goal of recognizing the logo in query image if any. Previously efficient method presented which outperform the existing method in terms of FRR and FPR. During this paper we are contributing by using RANSAC in which Fast Retina Keypoint (FREAK) descriptor is extracted for further matching and recognition process rather than using existing SIFT technique. The recent method for logo recognition and detection process is based on methodology of CDS (Context Dependent Similarity) which directly local features spatial context. Basically this CDS method using the SIFT method for initial keypoints extraction and then further matching process along with detection is done. The goal of our proposed CDS with RANSAC is to improve the recognition accuracy and to minimize the error rate performance. The RANSAC method is using FREAK technique for keypoints extraction which is superior as compared to SIFT.","PeriodicalId":6629,"journal":{"name":"2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT)","volume":"43 1","pages":"362-366"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80861479","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}
Pub Date : 2016-12-01DOI: 10.1109/ICAECCT.2016.7942599
Ms. Nikhila, Sayali Rawat
Salient region detection is essential theme in computer vision. This paper demonstrates the application of salient region detection for object recognition and classification. The different visual cues are used for salient object detection. The local contrast and compactness visual cues are complementally to each other. The salient regions are not correctly suppressed by compactness cues but local contrast can effectively recover. The bottom-up technique is used for recognition of salient object. The saliency map is getting from compactness and local contrast map. After detection of salient region or objects we will extend our approach towards recognition or of some special objects or shapes. For that we will work on some geometrical features.
{"title":"Recognition of salient object","authors":"Ms. Nikhila, Sayali Rawat","doi":"10.1109/ICAECCT.2016.7942599","DOIUrl":"https://doi.org/10.1109/ICAECCT.2016.7942599","url":null,"abstract":"Salient region detection is essential theme in computer vision. This paper demonstrates the application of salient region detection for object recognition and classification. The different visual cues are used for salient object detection. The local contrast and compactness visual cues are complementally to each other. The salient regions are not correctly suppressed by compactness cues but local contrast can effectively recover. The bottom-up technique is used for recognition of salient object. The saliency map is getting from compactness and local contrast map. After detection of salient region or objects we will extend our approach towards recognition or of some special objects or shapes. For that we will work on some geometrical features.","PeriodicalId":6629,"journal":{"name":"2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT)","volume":"101 1","pages":"283-286"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80619900","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}
Pub Date : 2016-12-01DOI: 10.1109/ICAECCT.2016.7942627
S. V. Raut, D. M. Yadav
This paper presents an fMRI signal analysis methodology using Empirical mean curve decomposition (EMCD) and mutual information (MI) based voxel selection framework. Previously, the fMRI signal analysis has been carried out either using empirical mean curve decomposition (EMCD) model or voxel selection on raw fMRI signal. The first methodology does signal decomposition that makes voxel selection process easy while the latter methodology does selection of relevant voxels (or features). Both these advantages are added by our methodology in which the frequency component is considered by decomposing the raw fMRI signal using Empirical mean and the voxels are selected from EMCD signal. The proposed methodologies are adopted for predicting the neural response. Experimentations are carried out in the openly available fMRI data of six subjects and comparisons are made with existing decomposition model and voxel selection framework. The comparative results demonstrate the superiority of the proposed methodology.
{"title":"Voxel selection framework with signal decomposition for fMRI based brain activity classification","authors":"S. V. Raut, D. M. Yadav","doi":"10.1109/ICAECCT.2016.7942627","DOIUrl":"https://doi.org/10.1109/ICAECCT.2016.7942627","url":null,"abstract":"This paper presents an fMRI signal analysis methodology using Empirical mean curve decomposition (EMCD) and mutual information (MI) based voxel selection framework. Previously, the fMRI signal analysis has been carried out either using empirical mean curve decomposition (EMCD) model or voxel selection on raw fMRI signal. The first methodology does signal decomposition that makes voxel selection process easy while the latter methodology does selection of relevant voxels (or features). Both these advantages are added by our methodology in which the frequency component is considered by decomposing the raw fMRI signal using Empirical mean and the voxels are selected from EMCD signal. The proposed methodologies are adopted for predicting the neural response. Experimentations are carried out in the openly available fMRI data of six subjects and comparisons are made with existing decomposition model and voxel selection framework. The comparative results demonstrate the superiority of the proposed methodology.","PeriodicalId":6629,"journal":{"name":"2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT)","volume":"64 1","pages":"433-437"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91192509","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：