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1
Nonstationary waveform detection in power quality signals using multisynchrosqueezing transform
Dergi Makalesi

Dergi Makalesi
Kaynak :
Wiley Online Library
Dergi:
International Transactions on Electrical Energy Systems
Yazar(lar) :
Gupteswar Sahu , Sonali Dash
Yıl :
2021
Sonali Dash
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
Time‐frequency (TF) analysis (TFA) is widely used as a reliable signal‐processing tool for the detection and identification of time‐varying waveforms in power quality (PQ) signals. However, the effectiveness of a TFA method is limited by Heisenberg uncertainty principle. The presence of cross terms is also a significant issue in TF analysis of real‐time signals. In PQ signal analysis, it is highly necessary to apply a suitable TFA technique with higher clarity and fewer cross‐term problems. This paper introduces a newly developed TFA method known as multisynchrosqueezing transform (MSST) to detect the presence of nonstationary waveforms in PQ disturbance signals. The MSST is a high‐resolution TFA method, which is similar to synchrosqueezing transform (SST) but has an iterative reassignment process to enhance the energy concentration of the TF representation. We apply the MSST to various types of single and combined PQ disturbance signals and show its potential application to PQ disturbance signal analysis. Results show that MSST provides higher TF resolution compared to other contemporary methods.
DOI : 10.1002/2050-7038.13013
ISSN: 2050-7038
Sayı: 9
Cilt: 31
2
Texture classification using Steerable Pyramid based Laws’ Masks
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
Directory of Open Access Journals (DOAJ)
Dergi:
Journal of Electrical Systems and Information Technology
Yazar(lar) :
Sonali Dash , Uma Ranjan Jena
Yıl :
2017
Sonali Dash
Yayıncı Web Sitesi
 Piri Tam Metin Bulucu
Kaydet Alıntıla
This paper progress towards a new feature extraction technique by combining the two existing methods named as Laws’ mask and steerable pyramid for texture classification. Texture parameters are derived and classified for accepted Laws’ mask method. In this paper texture features are extracted and classified using new approaches, which are carried out by integrating both steerable pyramid and Laws’ mask (SPLM) methods. The comparison of the methods yields that the Steerable Pyramid based Laws’ Mask (SPLM) texture feature extraction technique using fifth level of image decomposition level resulted in the best classification accuracy. We use simple k-NN classifier for classification purpose. Our proposed approaches are tested on Brodatz database. Experimental results on fused features established the combination of two feature sets always outperform the conventional Laws’ mask method.
DOI : 10.1016/j.jesit.2016.10.001
Anahtar Kelimeler : Steerable pyramid, Lawsu2019 mask, Feature extraction, Texture classification
ISSN: 2314-7172
Cilt: 4
Sayı: 1
Sayfa: 185 - 197
Sınıflandırma :
3
Noise robust Laws’ filters based on fuzzy filters for texture classification
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
Directory of Open Access Journals (DOAJ)
Dergi:
Egyptian Informatics Journal
Yazar(lar) :
Sonali Dash , Manas Ranjan Senapati
Yıl :
2020
Sonali Dash
Yayıncı Web Sitesi
Kaydet Alıntıla
Laws’ mask method has achieved wide acceptance in texture analysis, however it is not robust to noise. Fuzzy filters are well known for denoising applications. This work proposes a noise-robust Laws’ mask descriptor by integrating the exiting fuzzy filters with the traditional Laws’ mask for the improvement of texture classification of noisy texture images. Images are corrupted by adding Gaussian noise of different values. These noisy images are transformed into fuzzy images through fuzzy filters of different windows. Then the texture features are extracted using Laws’ mask descriptor. To investigate the proposed techniques two texture databases i.e. Brodatz and STex are used. The proposals are assessed by comparing the performance of the traditional Laws’ mask descriptor alone and after combined with the fuzzy filters on noisy images. The k-Nearest Neighbor (k-NN) classifier is utilized in the classification task. Results indicate that the proposed approach delivers higher classification accuracy than the traditional Laws’ mask method. Hence, validate that the suggested methods significantly improve the noised texture classification. Keywords: Fuzzy filter, Laws’ mask, Texture classification, Texture features
ISSN: 1110-8665
Cilt: 21
Sayı: 1
Sayfa: 37 - 49
Sınıflandırma :
4
Enhancing detection of retinal blood vessels by combined approach of DWT, Tyler Coye and Gamma correction
Dergi Makalesi

Dergi Makalesi
Kaynak :
Science Direct
Dergi:
Biomedical Signal Processing and Control
Yayıncı:
Elsevier
Yazar(lar) :
Sonali Dash , Manas Ranjan Senapati
Yıl :
2020
Sonali Dash
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
Retinal imaging plays the most significant roles among all the medical imaging technology, because of its ability to derive various features that is related to various eye diseases. Thus, precise extraction is highly required for the blood vessels that lead to accurate identification of the diseases. Because of some instabilities in the retinal images such as noise, non-uniform illumination etc. it may motivate towards a false detection. This paper suggests an automated method to extract the blood vessels from fundus by integrating discrete wavelet transform (DWT) with Tyler Coye algorithm. Furthermore, in the retinal blood vessel segmentation methods the most vital step is to enhance the contrast. The authenticity of the segmentation is purely subjected to the uniformity of the contrast over the image. Thus, an illumination normalization technique known as Gamma correction is also utilized for the contrast enhancement. Three different types of experiments are carried out: original Tyler Coye algorithm, DWT with different wavelet filters integrated with Tyler Coye, and Gamma correction normalization applied to the integrated model. The approaches are tested with DRIVE dataset. The performance of the normalization-integrated model is found superior to the other two approaches.
DOI : 10.1016/j.bspc.2019.101740
Anahtar Kelimeler : DWT, Tyler Coye algorithm, Illumination normalization, Gamma correction
ISSN: 1746-8094
Cilt: 57
Sayfa: 101740
Sınıflandırma :
5
Gray level run length matrix based on various illumination normalization techniques for texture classification
Dergi Makalesi

Dergi Makalesi
Kaynak :
Springer Link
Dergi:
Evolutionary Intelligence
Yazar(lar) :
Sonali Dash , Manas Ranjan Senapati
Konu :
Artificial Intelligence, Cognitive Neuroscience, Computer Vision and Pattern Recognition, Mathematics (miscellaneous)
Yıl :
2018
Sonali Dash
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
Texture classification under varying illumination conditions is one of the most important challenges. This paper presents a new texture classification approach by taking the combinations of robust illumination normalization techniques applied on gray level run length matrix (GLRLM) for texture features extraction. The purpose of selecting the GRLM, as texture descriptor is that, it extracts information of an image from its gray level runs. A set of consecutive, collinear picture points having the same gray level values is considered as a gray level run. The textured materials usually go through a deep change in their images with variations in illumination and camera pose. For instance, keeping all the parameters fixed but just changing the scale and rotation can result in a completely new texture. Hence, change in gray level values also occurred. Dealing with these variations successfully by utilizing GRLM descriptor for texture classification is the main purpose of this paper. In the suggested approach, 2D wavelet, Tan and Triggs (TT) normalization methods are employed to compensate illumination variations. Experimental results on the Brodatz, VisTex, STex and ALOT databases show that the suggested approach improves the performance significantly as compared to the classical GLRLM descriptor.
DOI : 10.1007/s12065-018-0164-2
Anahtar Kelimeler : Gray level run length matrix, 2D wavelet, Tan and Triggs, Feature extraction, Texture classification
ISSN: 1864-5909 1864-5917
Sayı: 2
Cilt: 14
Sayfa: 217-226
6
Retinal Blood Vessel Segmentation by Employing Various Upgraded Median Filters
Konferans Bildirileri

Konferans Bildirileri
Kaynak :
IEEE Xplore
Yazar(lar) :
Sonali Dash , Gupteswar Sahu
Konu :
Aerospace, Communication, Networking and Broadcast Technologies, Components, Circuits, Devices and Systems, Computing and Processing, Engineering Profession, Fields, Waves and Electromagnetics, General Topics for Engineers, Power, Energy and Industry Applications, Robotics and Control Systems, Signal Processing and Analysis
Yıl :
2019
Sonali Dash
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
Retinal image analysis plays a vital part in diagnosis of Diabetic Retinopathy. It is a progressive disease and leads to blindness which can be prevented if detected at the earlier stage. In this paper, a robust method of blood tree segmentation is proposed by using some advanced median filters such as improved median, hybrid median and weighted median filters. The algorithm for vessel extraction is carried out in three different stages: preprocessing, extraction of blood vessel, and post processing. The results are compared with the median filter segmentation approach. The approach is validated on DRIVE database. The suggested method delivers more accurate outputs than the median filter in terms of accuracy, sensitivity, and specificity.
DOI : 10.1109/icisgt44072.2019.00023
Anahtar Kelimeler : Standards, Image segmentation, Biomedical imaging, Blood vessels, Retinal vessels, Filtering, biomedical optical imaging, blood vessels, diseases, eye, image filtering, image segmentation, median filters, medical image processing, retinal blood vessel segmentation, retinal image analysis, diabetic retinopathy, blood tree segmentation, advanced median filters, weighted median filters, vessel extraction, median filter segmentation approach, DRIVE database, Retinal Image, blood vessel, vessel extraction, advanced median filter
7
Enhanced antibacterial potential of berberine via synergism with chitosan nanoparticles
Dergi Makalesi

Dergi Makalesi
Kaynak :
Science Direct
Dergi:
Materials Today: Proceedings
Yayıncı:
Elsevier
Yazar(lar) :
Sonali Dash , Manish Kumar , Nidhi Pareek
Yıl :
2020
Sonali Dash
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
From the last three decades nanoparticles have captivated tremendous attention in the field of biomedical research owing to their diverse pharmaceutical and therapeutic applications. Chitosan has gained significant attention in pharmaceutics and food industries due to its remarkable antimicrobial activities and biocompatibility. Furthermore, chitosan nanoparticles have been explored widely for increased antimicrobial activities against broad range of multi drug resistant bacterial strains. Antimicrobials derived from natural herbs have also been explored. Berberine, a plant alkaloid has also proven efficacy against a broad spectrum of microorganisms. Combinatorial employment of both berberine and chitosan nanoparticles could be employed to enhance their antibacterial action. Chitosan nanoparticles were developed by ionic gelation methods and physicochemically characterized. Chitosan nanoparticles and berberine alone have showed notable antibacterial activity against both Bacillus subtilis and Staphylococcus aureus. However, synergy has been observed to improve their inhibitory action. The time kill kinetics revealed that the synergy has augmented the antibacterial potential of chitosan nanoparticles and berberine. The synergy between berberine and chitosan nanoparticles demonstrates combinatorial application of compounds as an efficient, novel therapeutic agents against antibiotic resistant bacterial infections.
DOI : 10.1016/j.matpr.2020.05.506
Anahtar Kelimeler : Chitosan, Berberine, Nanoparticles, Synergy, Antibacterial
ISSN: 2214-7853

Sınıflandırma :
Biyotıp %100
8
Time‐frequency analysis of power quality disturbances using synchroextracting transform
Dergi Makalesi

Dergi Makalesi
Kaynak :
Wiley Online Library
Dergi:
International Transactions on Electrical Energy Systems
Yazar(lar) :
Gupteswar Sahu , Sonali Dash , Birendra Biswal
Yıl :
2020
Sonali Dash
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
The joint time‐frequency representation (TFR) provides the useful hidden information of a nonstationary signal consisting of several components. The time‐frequency analysis methods with high resolution are of utmost importance in visual localization and recognition of nonstationary waveform in power quality (PQ) disturbance signals. This paper introduces a newly developed TFR technique known as synchroextracting transform (SET) for the detection and identification of various kinds of single and combined nonstationary PQ disturbances. The SET is a high‐resolution TFR technique that belongs to a post‐processing procedure of short‐time Fourier transform (STFT) incorporating components of mode decomposition and signal reconstruction. The SET aims to retain the TF information related to time‐varying features of the signal and removes most smeared TF energy. In the case studies considered here, several PQ disturbance signals are thoroughly analyzed by using SET.
DOI : 10.1002/2050-7038.12278
ISSN: 2050-7038
Sayı: 4
Cilt: 30

9
Gaussian pyramid based laws' mask descriptor for texture classification
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Konferans Bildirileri
Kaynak :
IEEE Xplore
Yazar(lar) :
Sonali Dash , Uma Ranjan Jena
Konu :
Communication, Networking and Broadcast Technologies, Fields, Waves and Electromagnetics, Signal Processing and Analysis, Transportation
Yıl :
2017
Sonali Dash
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
An image pyramid is a special type of subband transformation. It is constructed with a recursive method that represents multi-resolution and multi-scale format. Pyramid analysis is useful for texture mapping at different resolutions. Laws has developed to detect various types of textures by using local masks named as Laws' mask descriptor. Laws' mask descriptor does not consider about the multiple scales of an image while extracting the texture features. In this paper, we have suggested a novel method of integrating Gaussian pyramid with Laws' mask descriptor for feature extraction. Experimental results show that the proposed method of feature extraction technique provides better classification accuracy the traditional Laws' mask descriptor.
DOI : 10.1109/wispnet.2017.8299841
Anahtar Kelimeler : Feature extraction, Energy measurement, Image resolution, Databases, Kernel, Image edge detection, Standards, feature extraction, Gaussian processes, image classification, image representation, image resolution, image texture, texture classification, image pyramid, pyramid analysis, texture mapping, texture features, integrating Gaussian pyramid, Laws mask descriptor, feature extraction, multiresolution format, multiscale format, Texture feature, Gaussian pyramid, Laws' mask, Classification
10
K-NN based automated reasoning using bilateral filter based texture descriptor for computing texture classification
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
Directory of Open Access Journals (DOAJ)
Dergi:
Egyptian Informatics Journal
Yazar(lar) :
Sonali Dash , Manas Ranjan Senapati , Uma Ranjan Jena
Yıl :
2018
Sonali Dash
Yayıncı Web Sitesi
Kaydet Alıntıla
Regions in the visual field can be characterized by differences in texture, brightness, colour, or other attributes. Bilateral filter is an efficient way to smooth any digital image while preserving the fine information. In bilateral filter, it has been observed that by selecting carefully, the bilateral filter range parameter and bilateral filter domain parameter the ability to smooth any arbitrary digital image while preserving the edges can be improved. This trait of bilateral filter helps to adapt it to application specific requirements. In this study, a new feature extraction method is recommended by integrating the conventional Laws’ mask method with bilateral filter, which results in the improvement of classification accuracy. The texture features are extracted by using different values of range parameter and domain parameter and are fed as input to k-Nearest Neighbor (k-NN) classifier for classification. The new fusion model is tested with Brodatz, VisTex, STex and ALOT databases. The results of the proposed method are also compared with the conventional Laws’ mask descriptor for all the aforementioned four datasets. The experimental results show that bilateral filter based Laws’ mask feature extraction technique provides better classification accuracy for all the four databases for various combinations of bilateral filter range and domain parameters. Keywords: Bilateral filter, Laws mask descriptor, Feature extraction, Texture classification
ISSN: 1110-8665
Cilt: 19
Sayı: 2
Sayfa: 133 - 144
Sınıflandırma :
11
Complex wavelet co-occurrence based on thresholding for texture classification
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Konferans Bildirileri
Kaynak :
IEEE Xplore
Yazar(lar) :
Sonali Dash , Uma Ranjan Jena
Konu :
Communication, Networking and Broadcast Technologies, Fields, Waves and Electromagnetics, Signal Processing and Analysis, Transportation
Yıl :
2017
Sonali Dash
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
The complex wavelet transform is superior to discrete wavelet transform in two aspects. Those are its shift invariant property and good directional selectivity. In this paper, we propose a new approach of combining dual-tree complex wavelet transform (DT-CWT) with traditional co-occurrence method for texture classification. Thresholding is a method to preserve the significant data of the image while discarding the insignificant part. On this basis for further enhancement of classification, we have acquired the texture images by applying thresholding technique to the entire texture database. These thresholded images are then applied to the fusion model of DTCWT with co-occurrence method. Results show that our proposed methods provide better classification accuracy than classical co-occurrence method.
DOI : 10.1109/wispnet.2017.8299811
Anahtar Kelimeler : Feature extraction, Databases, Discrete wavelet transforms, Conferences, Standards, discrete wavelet transforms, image classification, image fusion, image segmentation, image texture, trees (mathematics), DT-CWT, texture classification, texture images, thresholding technique, thresholded images, classification accuracy, classical co-occurrence method, shift invariant property, directional selectivity, dual-tree complex wavelet transform, texture database, fusion model, discrete wavelet transform, Texture feature, GLCM, Dual-tree complex wavelet, Classification
12
Improvement on Deep Features through Various Enhancement Techniques for Vehicles Classification
Dergi Makalesi

Dergi Makalesi
Kaynak :
Springer Link
Dergi:
Sensing and Imaging
Yazar(lar) :
V. Keerthi Kiran , Sonali Dash , Priyadarsan Parida
Konu :
Electrical and Electronic Engineering, Instrumentation
Yıl :
2021
Sonali Dash
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
In the smart transport network, the classification of vehicles plays a significant role. However, the traditional classification systems of vehicles can not satisfy the specifications of real-time applications because of disparities such as luminescence, weather, noise, and many more factors. Convolutional neural network (CNN) has gained more attraction since it boosts the recognition performance considerably. CNN comprises various types of pre-trained networks through which features can be extracted. The focus of the paper is to enhance the features of the existing pre-trained networks by integrating with most advanced enhancement techniques for increasing the vehicle recognition rate. This work utilizes different enhancement techniques to achieve improved features. Different CNN networks chosen in this work are Residual networks (ResNet-18, 50, 101), AlexNet, GoogLeNet, DenseNet-201, VGG-19. The enhancement techniques like Discrete Wavelet Transform (DWT), Histogram Equalization (HE), Adaptive gamma correction with a weighting distribution function (AGCWD), Homomorphic Filter (HF), and Joint Histogram Equalization (JHE) are chosen for the suggested method. SoftMax layer of CNN and Support Vector Machine (SVM) are used for the classification task. Extensive experiments are conducted using 1510 images with 10 different classes of Comprehensive Cars-Surveillance view (CompCars) dataset. The results show that the suggested approaches deliver higher recognition rate than the different traditional CNN networks. Among all the suggested integrated models, the best classification result is achieved mostly for the AGCWD integrated with different networks. The suggested approaches outperform many existing methods.
DOI : 10.1007/s11220-021-00363-1
Anahtar Kelimeler : Convolutional neural networks, Discrete wavelet transform, Histogram equalization, Homomorphic filter, Joint histogram equalization, Support vector machine
ISSN: 1557-2064 1557-2072
Sayı: 1
Cilt: 22
13
Homomorphic Normalization-Based Descriptors for Texture Classification
Dergi Makalesi

Dergi Makalesi
Kaynak :
Springer Link
Dergi:
Arabian Journal for Science and Engineering
Yazar(lar) :
Sonali Dash , Uma Ranjan Jena , Manas Ranjan Senapati
Konu :
Multidisciplinary
Yıl :
2017
Sonali Dash
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
Illumination variation is an essential trait in texture analysis, since the same texture can be surrounded with different illuminations, which can greatly affect the classification rate. This paper introduces a new approach to extract the texture features applying illumination normalization-based texture descriptors. The prime objective is to enhance the classification accuracy by normalizing the illumination of colour textures. Normalization of illumination is achieved by applying homomorphic filter. For feature extraction, two relevant approaches grey-level co-occurrence matrix (GLCM) and Laws’ mask are utilized. Experiments are conducted for normalized co-occurrence and Laws’ filter for colour images. Classification rates of the traditional GLCM and Laws’ mask descriptors are included for baseline comparison. The effectiveness of the introduced techniques is assessed on three benchmark texture datasets, i.e. STex, VisTex, and ALOT. A k-nearest neighbour (k-NN) classifier is utilized to perform texture classification. Results show that the proposed approach has achieved higher classification rates and outperformed existing methods.
DOI : 10.1007/s13369-017-2961-9
Anahtar Kelimeler : Texture, Colour, Lawsu2019 mask, GLCM, Homomorphic filter, Classification
ISSN: 2193-567X 2191-4281
Sayı: 8
Cilt: 43
Sayfa: 4303-4313
14
Comparative study of image texture classification techniques
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Konferans Bildirileri
Kaynak :
IEEE Xplore
Yazar(lar) :
Sonali Dash , K. Chiranjeevi , U.R. Jena , Akula Trinadh
Konu :
Communication, Networking and Broadcast Technologies, Engineering Profession, Power, Energy and Industry Applications, Signal Processing and Analysis
Yıl :
2015
Sonali Dash
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
Removed.
DOI : 10.1109/eesco.2015.7253732
15
Illumination normalized based technique for retinal blood vessel segmentation
Dergi Makalesi

Dergi Makalesi
Kaynak :
Wiley Online Library
Dergi:
International Journal of Imaging Systems and Technology
Yazar(lar) :
Sonali Dash , Manas Ranjan Senapati , Pradip Kumar Sahu , P. S. R. Chowdary
Konu :
Electrical and Electronic Engineering, Software, Electronic, Optical and Magnetic Materials, Computer Vision and Pattern Recognition
Yıl :
2020
Sonali Dash
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
The Retinal image carries important information about the health of the sensory part of the visual system. In this paper, a new approach is suggested by utilizing the homomorphic filter integrated with Contrast limited adaptive histogram equalization (CLAHE) method for the illumination normalization and contrast enhancement of the retinal images. Then segmentation is done through several steps by using the existing methods such as morphological filtering, a second derivative operator that is followed by a final morphological filtering stage and hysteresis thresholding. The suggested method is verified on DRIVE and CHASE‐DB1 databases and has average accuracy of 72.03% and 64.54%, accordingly. The obtained results demonstrate that the proposed approaches achieve higher accuracy than the traditional method. The suggested approach not only contributes to the successful result, but also minimizes the computing time.
DOI : 10.1002/ima.22461
ISSN: 0899-9457

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