Process Improvement of Finishing Machine by Real Time Monitoring using IoT
2022 IEEE Delhi Section Conference (DELCON)
At present many small-scale fabric industries finishing machines are not automated, Human interve... more At present many small-scale fabric industries finishing machines are not automated, Human intervention is required for analysis and highlighting the technical parameters deviations. In fabric industry parameter analysis like temperature, water level, humidity and pressure has to be done and usually it's done manually. This led to high workload on engineers and operators for parameters logging and communication to engineering department and also its time consuming for maintenance team members to get different types of analysis, e.g., down time %, Repeated breakdown % etc, which may lead to poor analysis process for defectives in fabric. So, to overcome this problem in fabric industry, process improvement can be done by real-time monitoring of finishing machines using IOT. This paper presents the technique to make the finishing process of fabric industry automated i.e. to do real time monitoring of finishing process, using IoT technology. By automation of the finishing process there will be good analysis of poor fabric, and this will also prevent fabric wastage. This process saves human power, as the output is directly received through WhatsApp or Email in form of graphs which prevent manual analysis of parameter. Since the finishing process is automated the cost is controlled by reduction in rejection of fabric and it also increases productivity of machines and human resource. This leads to smart working practices by eliminating paper works and time-consuming methods.
Extraction of discriminate features is very important task in classification algorithms. This pap... more Extraction of discriminate features is very important task in classification algorithms. This paper presents technique for extraction cosine modulated feature for classification of the T2-weighted MRI images of human brain. Better discrimination and low design implementation complexity of the cosine-modulated wavelets has been effectively utilized to give better features and more accurate classification results. The proposed technique consists of two stages, namely, feature extraction, and classification. In the first stage, the energy features from MRI images are obtained from sub-band images obtained after decomposition using cosine modulated wavelet transform. In the classification stage, Mahalanobis distance metric is used to classify the image as normal or abnormal. Average Classification accuracy with a success rate of 100% has been obtained.
Segmentation of Left Ventricle of 2D Echocardiographic Image Using Active Contouring
2011 International Conference on Computational Intelligence and Communication Networks, 2011
... Shweta Deopujari Electronics engineering department Yeshwantrao Chavan College of Engineering... more ... Shweta Deopujari Electronics engineering department Yeshwantrao Chavan College of Engineering Nagpur, India [email protected] ... Although, at first, the curve has to be initialized the active-contours try to find object boundaries by heading for pixels where abrupt ...
Left Ventricular Segmentation of 2-D Echocardiographic Image: A Survey
2011 International Conference on Communication Systems and Network Technologies, 2011
Abstract Cardiovascular diseases are a major health concern worldwide. The left ventricle and in ... more Abstract Cardiovascular diseases are a major health concern worldwide. The left ventricle and in particular the endocardium is a structure of particular interest since it performs the task of pumping oxygenated blood to the entire body. Therefore, segmentation of the left ...
Wavelet-based technique for texture classification
SPIE Proceedings, 2010
ABSTRACT This paper presents a technique for texture feature extraction and classification using ... more ABSTRACT This paper presents a technique for texture feature extraction and classification using wavelet transform. A image is decomposed into no. of sub-bands after applying Wavelet transform to it. A three level decomposition is carried out. A number of sub-bands are generated after wavelet decomposition. An energy signature is computed for each sub-band of these wavelet coefficients. A k-nearest neighbor's classifier is then employed to classify texture patterns. To test and evaluate the method, several sets of textures along with different wavelet bases are employed. Experimental results show superiority of the proposed method. Bibtex entry for this abstract Preferred format for this abstract (see Preferences) Find Similar Abstracts: Use: Authors Title Abstract Text Return: Query Results Return items starting with number Query Form Database: Astronomy Physics arXiv e-prints
Signal & Image Processing : An International Journal, 2012
Extraction of discriminate features is very important task in classification algorithms. This pap... more Extraction of discriminate features is very important task in classification algorithms. This paper presents technique for extraction cosine modulated feature for classification of the T2-weighted MRI images of human brain. Better discrimination and low design implementation complexity of the cosine-modulated wavelets has been effectively utilized to give better features and more accurate classification results. The proposed technique consists of two stages, namely, feature extraction, and classification. In the first stage, the energy features from MRI images are obtained from sub-band images obtained after decomposition using cosine modulated wavelet transform. In the classification stage, Mahalanobis distance metric is used to classify the image as normal or abnormal. Average Classification accuracy with a success rate of 100% has been obtained.
Multiscale intuitionistic fuzzy roughness measure for image segmentation
2014 International Conference on Communication and Signal Processing, 2014
In this paper, a method for image segmentation using multiscale intuitionistic fuzzy roughness me... more In this paper, a method for image segmentation using multiscale intuitionistic fuzzy roughness measure is proposed. The traditional roughness measure tends to over-focus on the little important homogeneous regions but is not accurate enough to measure the homogeneity in an image. By applying the theories of scale-space and using intuitionistic fuzzy representation for images, roughness is measured under multiple scales. Multiscale representation can tolerate the disturbance of trivial regions, and intuitionistic fuzzy representation deals with hesitancy in image boundary, therefore produces precise segmentation results.
International Journal of Engineering and Advanced Technology, 2019
Non-contact-based baby monitoring system using image processing is proposed in this paper which i... more Non-contact-based baby monitoring system using image processing is proposed in this paper which is used for proper safety and monitoring the activity of baby by their busy parents. The system detects the motion, crying and present position of the baby. If any abnormal action is detected, then the system sends a message in the form of text and images of baby to the particular user through email. Raspberry Pi B+ module is used to process the videos taken by pi camera, MIC is used for crying detection and image processing is used for detection of real-time motion of babies and boundary condition of the bed. The system required to first install OS Raspbian, and all the other packages like OpenCV, Numpy and Virtual environment. Face detection algorithm is trained using Haar classifier for positive face images and negative nonface images. This system will help in decreasing the chances of the baby’s falling from the bed. Also, this system can be used in hospitals while baby is sleeping wh...
This paper proposes segmentation and detection of tumor of MRI brain images using a novel method ... more This paper proposes segmentation and detection of tumor of MRI brain images using a novel method provided by Attanassov intuitionistic fuzzy set theory. Segmentation of such type can be important in detecting different type of tumor, stroke, paralysis etc which are developed inside brain. This type of segmentation is very important in detecting. Segmentation becomes very difficult in medical images which are not properly illuminated. A image segmentation approach intuitionistic fuzzy set theory and a new membership function called restricted equivalence function from automorphisms, for finding the membership values of the pixels of the image is proposed here. An intuitionistic fuzzy image is constructed using Sugeno type intuitionistic fuzzy generator. A new distance measure Intuitionistic Fuzzy Divergence is used. From this Intuitionistic Fuzzy Divergence edge detection is carried out. The results showed a much better performance on poor illuminated medical images, where the brain ...
Stabilization of Flight Controller for Unmanned Aerial Vehicle
2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), 2021
This paper presents the technique to implement stability and control of multirotor unmanned aeria... more This paper presents the technique to implement stability and control of multirotor unmanned aerial vehicle (UAV). The output of the inertial sensors like accelerometer and gyroscope is noisy. These sensors are used to get the orientation and attitude of the UAV. Most of the flight controllers available these days are built to fly in a very specific environment. One cannot fly with these flight controllers in heavy wind or where precise control is required. As a result, the UAV’s that use these flight controllers feel unresponsive and sloppy. The goal here is to get excellent flight dynamics and robust flight control which can fly in any conditions. The UAV’s should be able to do aggressive maneuvering, fault tolerance etc. In the proposed design, Kalman Filter algorithm is implemented to get precise values from Inertial Measurement Unit. Extended Kalman Filter (EKF) is used for sensor fusion and to get precise values from Inertial Measurement Unit PID algorithms are used to stabiliz...
Brain Tumor Type Detection Using Texture Features in MR Images
2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), 2018
In this paper, the algorithms for the detection of brain tumor and then classifcation of the tumo... more In this paper, the algorithms for the detection of brain tumor and then classifcation of the tumor into meningioma and glioma are proposed. Firstly, automated method is proposed for skull stripping using mathematical morphology and thresholding. Stationary wavelet transform features, Self-organizing map (SOM) and watershed algorithm are used for the segmentation of brain tumor. Gray level co- occurrence matrix (GLCM) features are extracted from tumor and feed forward neural network is used for classification. Proposed algorithm reported classification accuracy of 95% with the available dataset of real brain images from the hospital.
Object Shadow Detection From Remote Sensing Images
In accordance with the characteristics of urban high-resolution color remote sensing images, we p... more In accordance with the characteristics of urban high-resolution color remote sensing images, we put forward an object shadow detection method. In this method, during image segmentation, shadow features are taken into consideration and after that using statistical feature of the images, suspected shadows are extracted. According to object properties and spatial relationship between objects, some dark objects could be ruled out. In our method, first color image is transform to gray image, after that global thresholding process is performed to detect the shadow region. Next to that morphology erosion process is performed. Then convolution filtering to construct coarse-shadow map, to classify the input color image into the candidate shadow pixels and the nonshadow pixels. Experiments show that the new method can accurately detect shadows from urban remote sensing images.
Implementation of brain MR image segmentation algorithm on DSP
This paper proposes a method for hardware implementation of brain Magnetic Resonance (MR) image s... more This paper proposes a method for hardware implementation of brain Magnetic Resonance (MR) image segmentation. An effective algorithm which combines intuitionistic fuzzy set and rough set is developed to deal with uncertainty and vagueness. This algorithm is implemented on TMS320C6748 Digital Signal Processor starter kit using Code Composer Studio software. A multilevel thresholding method is presented, for which the peak and valley points are obtained from intuitionisic fuzzy roughness measure graph. Using the algorithm, brain MR image is segmented into three regions; Cerebro spinal fluid (CSF), gray matter (GM) and white matter (WM). The quantitative evaluation and experimental result analysis gives the superiority of the algorithm.
The data from remote sensing have been used from so many years for image classification and its d... more The data from remote sensing have been used from so many years for image classification and its development algorithm, which can be applied to several different fields like forestry, educational purpose, management etc. In this paper, a classification method of a high resolution satellite image using neural network is proposed. First noisy bands were removed using dimensionality reduction technique. Minimum noise fraction (MNF) reduces the spatial dimension of hyperspectral image (HSI). Then, learning vector quantization (LVQ) based algorithm and some samples from groundtruth map are used to train the network for image classification and finally, accuracy is estimated. The main goal of this paper is to determine the ability of artificial neural network system for classifying satellite image by algorithm based on LVQ. General Terms Classification of hyperspectral image, neural network
During acquisition of brain Magnetic Resonance images (MRI), spatial distortion may occur due to ... more During acquisition of brain Magnetic Resonance images (MRI), spatial distortion may occur due to hardware imperfection of the scanning device, movement of patients, etc. which makes image registration an important area of research in medical imaging. In this paper, one reference image is used to align another scanned MR image accordingly. Here the global affine transformation is used for performing spatial transformations and calculating mutual information as a similarity metric. Mutual information obtained by calculating the joint entropy of the two images using their joint histogram. Images are then iteratively aligned until maximum mutual information achieved. General Terms Registration of images, Spatial distortion, Similarity Metric.
Process Improvement of Finishing Machine by Real Time Monitoring using IoT
2022 IEEE Delhi Section Conference (DELCON)
At present many small-scale fabric industries finishing machines are not automated, Human interve... more At present many small-scale fabric industries finishing machines are not automated, Human intervention is required for analysis and highlighting the technical parameters deviations. In fabric industry parameter analysis like temperature, water level, humidity and pressure has to be done and usually it's done manually. This led to high workload on engineers and operators for parameters logging and communication to engineering department and also its time consuming for maintenance team members to get different types of analysis, e.g., down time %, Repeated breakdown % etc, which may lead to poor analysis process for defectives in fabric. So, to overcome this problem in fabric industry, process improvement can be done by real-time monitoring of finishing machines using IOT. This paper presents the technique to make the finishing process of fabric industry automated i.e. to do real time monitoring of finishing process, using IoT technology. By automation of the finishing process there will be good analysis of poor fabric, and this will also prevent fabric wastage. This process saves human power, as the output is directly received through WhatsApp or Email in form of graphs which prevent manual analysis of parameter. Since the finishing process is automated the cost is controlled by reduction in rejection of fabric and it also increases productivity of machines and human resource. This leads to smart working practices by eliminating paper works and time-consuming methods.
Extraction of discriminate features is very important task in classification algorithms. This pap... more Extraction of discriminate features is very important task in classification algorithms. This paper presents technique for extraction cosine modulated feature for classification of the T2-weighted MRI images of human brain. Better discrimination and low design implementation complexity of the cosine-modulated wavelets has been effectively utilized to give better features and more accurate classification results. The proposed technique consists of two stages, namely, feature extraction, and classification. In the first stage, the energy features from MRI images are obtained from sub-band images obtained after decomposition using cosine modulated wavelet transform. In the classification stage, Mahalanobis distance metric is used to classify the image as normal or abnormal. Average Classification accuracy with a success rate of 100% has been obtained.
Segmentation of Left Ventricle of 2D Echocardiographic Image Using Active Contouring
2011 International Conference on Computational Intelligence and Communication Networks, 2011
... Shweta Deopujari Electronics engineering department Yeshwantrao Chavan College of Engineering... more ... Shweta Deopujari Electronics engineering department Yeshwantrao Chavan College of Engineering Nagpur, India [email protected] ... Although, at first, the curve has to be initialized the active-contours try to find object boundaries by heading for pixels where abrupt ...
Left Ventricular Segmentation of 2-D Echocardiographic Image: A Survey
2011 International Conference on Communication Systems and Network Technologies, 2011
Abstract Cardiovascular diseases are a major health concern worldwide. The left ventricle and in ... more Abstract Cardiovascular diseases are a major health concern worldwide. The left ventricle and in particular the endocardium is a structure of particular interest since it performs the task of pumping oxygenated blood to the entire body. Therefore, segmentation of the left ...
Wavelet-based technique for texture classification
SPIE Proceedings, 2010
ABSTRACT This paper presents a technique for texture feature extraction and classification using ... more ABSTRACT This paper presents a technique for texture feature extraction and classification using wavelet transform. A image is decomposed into no. of sub-bands after applying Wavelet transform to it. A three level decomposition is carried out. A number of sub-bands are generated after wavelet decomposition. An energy signature is computed for each sub-band of these wavelet coefficients. A k-nearest neighbor's classifier is then employed to classify texture patterns. To test and evaluate the method, several sets of textures along with different wavelet bases are employed. Experimental results show superiority of the proposed method. Bibtex entry for this abstract Preferred format for this abstract (see Preferences) Find Similar Abstracts: Use: Authors Title Abstract Text Return: Query Results Return items starting with number Query Form Database: Astronomy Physics arXiv e-prints
Signal & Image Processing : An International Journal, 2012
Extraction of discriminate features is very important task in classification algorithms. This pap... more Extraction of discriminate features is very important task in classification algorithms. This paper presents technique for extraction cosine modulated feature for classification of the T2-weighted MRI images of human brain. Better discrimination and low design implementation complexity of the cosine-modulated wavelets has been effectively utilized to give better features and more accurate classification results. The proposed technique consists of two stages, namely, feature extraction, and classification. In the first stage, the energy features from MRI images are obtained from sub-band images obtained after decomposition using cosine modulated wavelet transform. In the classification stage, Mahalanobis distance metric is used to classify the image as normal or abnormal. Average Classification accuracy with a success rate of 100% has been obtained.
Multiscale intuitionistic fuzzy roughness measure for image segmentation
2014 International Conference on Communication and Signal Processing, 2014
In this paper, a method for image segmentation using multiscale intuitionistic fuzzy roughness me... more In this paper, a method for image segmentation using multiscale intuitionistic fuzzy roughness measure is proposed. The traditional roughness measure tends to over-focus on the little important homogeneous regions but is not accurate enough to measure the homogeneity in an image. By applying the theories of scale-space and using intuitionistic fuzzy representation for images, roughness is measured under multiple scales. Multiscale representation can tolerate the disturbance of trivial regions, and intuitionistic fuzzy representation deals with hesitancy in image boundary, therefore produces precise segmentation results.
International Journal of Engineering and Advanced Technology, 2019
Non-contact-based baby monitoring system using image processing is proposed in this paper which i... more Non-contact-based baby monitoring system using image processing is proposed in this paper which is used for proper safety and monitoring the activity of baby by their busy parents. The system detects the motion, crying and present position of the baby. If any abnormal action is detected, then the system sends a message in the form of text and images of baby to the particular user through email. Raspberry Pi B+ module is used to process the videos taken by pi camera, MIC is used for crying detection and image processing is used for detection of real-time motion of babies and boundary condition of the bed. The system required to first install OS Raspbian, and all the other packages like OpenCV, Numpy and Virtual environment. Face detection algorithm is trained using Haar classifier for positive face images and negative nonface images. This system will help in decreasing the chances of the baby’s falling from the bed. Also, this system can be used in hospitals while baby is sleeping wh...
This paper proposes segmentation and detection of tumor of MRI brain images using a novel method ... more This paper proposes segmentation and detection of tumor of MRI brain images using a novel method provided by Attanassov intuitionistic fuzzy set theory. Segmentation of such type can be important in detecting different type of tumor, stroke, paralysis etc which are developed inside brain. This type of segmentation is very important in detecting. Segmentation becomes very difficult in medical images which are not properly illuminated. A image segmentation approach intuitionistic fuzzy set theory and a new membership function called restricted equivalence function from automorphisms, for finding the membership values of the pixels of the image is proposed here. An intuitionistic fuzzy image is constructed using Sugeno type intuitionistic fuzzy generator. A new distance measure Intuitionistic Fuzzy Divergence is used. From this Intuitionistic Fuzzy Divergence edge detection is carried out. The results showed a much better performance on poor illuminated medical images, where the brain ...
Stabilization of Flight Controller for Unmanned Aerial Vehicle
2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), 2021
This paper presents the technique to implement stability and control of multirotor unmanned aeria... more This paper presents the technique to implement stability and control of multirotor unmanned aerial vehicle (UAV). The output of the inertial sensors like accelerometer and gyroscope is noisy. These sensors are used to get the orientation and attitude of the UAV. Most of the flight controllers available these days are built to fly in a very specific environment. One cannot fly with these flight controllers in heavy wind or where precise control is required. As a result, the UAV’s that use these flight controllers feel unresponsive and sloppy. The goal here is to get excellent flight dynamics and robust flight control which can fly in any conditions. The UAV’s should be able to do aggressive maneuvering, fault tolerance etc. In the proposed design, Kalman Filter algorithm is implemented to get precise values from Inertial Measurement Unit. Extended Kalman Filter (EKF) is used for sensor fusion and to get precise values from Inertial Measurement Unit PID algorithms are used to stabiliz...
Brain Tumor Type Detection Using Texture Features in MR Images
2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), 2018
In this paper, the algorithms for the detection of brain tumor and then classifcation of the tumo... more In this paper, the algorithms for the detection of brain tumor and then classifcation of the tumor into meningioma and glioma are proposed. Firstly, automated method is proposed for skull stripping using mathematical morphology and thresholding. Stationary wavelet transform features, Self-organizing map (SOM) and watershed algorithm are used for the segmentation of brain tumor. Gray level co- occurrence matrix (GLCM) features are extracted from tumor and feed forward neural network is used for classification. Proposed algorithm reported classification accuracy of 95% with the available dataset of real brain images from the hospital.
Object Shadow Detection From Remote Sensing Images
In accordance with the characteristics of urban high-resolution color remote sensing images, we p... more In accordance with the characteristics of urban high-resolution color remote sensing images, we put forward an object shadow detection method. In this method, during image segmentation, shadow features are taken into consideration and after that using statistical feature of the images, suspected shadows are extracted. According to object properties and spatial relationship between objects, some dark objects could be ruled out. In our method, first color image is transform to gray image, after that global thresholding process is performed to detect the shadow region. Next to that morphology erosion process is performed. Then convolution filtering to construct coarse-shadow map, to classify the input color image into the candidate shadow pixels and the nonshadow pixels. Experiments show that the new method can accurately detect shadows from urban remote sensing images.
Implementation of brain MR image segmentation algorithm on DSP
This paper proposes a method for hardware implementation of brain Magnetic Resonance (MR) image s... more This paper proposes a method for hardware implementation of brain Magnetic Resonance (MR) image segmentation. An effective algorithm which combines intuitionistic fuzzy set and rough set is developed to deal with uncertainty and vagueness. This algorithm is implemented on TMS320C6748 Digital Signal Processor starter kit using Code Composer Studio software. A multilevel thresholding method is presented, for which the peak and valley points are obtained from intuitionisic fuzzy roughness measure graph. Using the algorithm, brain MR image is segmented into three regions; Cerebro spinal fluid (CSF), gray matter (GM) and white matter (WM). The quantitative evaluation and experimental result analysis gives the superiority of the algorithm.
The data from remote sensing have been used from so many years for image classification and its d... more The data from remote sensing have been used from so many years for image classification and its development algorithm, which can be applied to several different fields like forestry, educational purpose, management etc. In this paper, a classification method of a high resolution satellite image using neural network is proposed. First noisy bands were removed using dimensionality reduction technique. Minimum noise fraction (MNF) reduces the spatial dimension of hyperspectral image (HSI). Then, learning vector quantization (LVQ) based algorithm and some samples from groundtruth map are used to train the network for image classification and finally, accuracy is estimated. The main goal of this paper is to determine the ability of artificial neural network system for classifying satellite image by algorithm based on LVQ. General Terms Classification of hyperspectral image, neural network
During acquisition of brain Magnetic Resonance images (MRI), spatial distortion may occur due to ... more During acquisition of brain Magnetic Resonance images (MRI), spatial distortion may occur due to hardware imperfection of the scanning device, movement of patients, etc. which makes image registration an important area of research in medical imaging. In this paper, one reference image is used to align another scanned MR image accordingly. Here the global affine transformation is used for performing spatial transformations and calculating mutual information as a similarity metric. Mutual information obtained by calculating the joint entropy of the two images using their joint histogram. Images are then iteratively aligned until maximum mutual information achieved. General Terms Registration of images, Spatial distortion, Similarity Metric.
Uploads
Papers by Yogita Dubey