Power components, power factors, and pollution factor are defined according to the IEEE standard 1459-2000 based on the fast Fourier transform (FFT). However, the FFT in the presence of nonstationary power quality (PQ) disturbances results in inaccurate values due to its sensitivity to the spectral leakage problem. In this paper, a new perspective for the IEEE standard 1459-2000 definitions is introduced using the stationary wavelet transform (SWT). As a time-frequency transform, the SWT can provide variable frequency resolution while preserving time information without spectral leakage as the FFT. Moreover, unlike other time-frequency transforms, such as discrete wavelet transform (DWT), SWT possesses the time-invariance property that keeps the time and frequency characteristics throughout all of the decomposition levels. Results of different case studies including stationary, nonstationary of synthetic, and real PQ disturbances proves the effectiveness of applying the SWT over FFT or DWT.

A New Perspective for the IEEE Standard 1459-2000 Via Stationary Wavelet Transform in the Presence of Nonstationary Power Quality Disturbance

Handwritten Marathi character recognition using R -HOG Feature

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. Cancer has generally an abnormal cell that grows and invade healthy cells in the body. Breast cancer occurs when malignant tumors develop in the breast. Early detection of the breast cancer can effective in increasing the rate of survival. There are several Medical image techniques which has been used for detecting breast cancer. The survey explored the needs for the thermography over other screening tools. Thermography de-tects and records temperature changes on the surface of the skin which is known as Digital Infrared Thermal Imaging (DITI). Thermography is known for its features like non-invasive, painless, cost effective and high chances of recovery, which has got ability to detect the breast cancer at near begin-ning. There is no such tool which provides accurate predictability unless biopsy. But the combination of tools with thermography may boost the sensitivity and specificity when compared with other mechanisms.

 




	
		
			How to Cite this Article
		
		
			Pubmed Style
			
			Pavithra P, Ravichandran K, Sekar K, Manikandan R. The Effect of Thermography on Breast Cancer Detection. SRP. 2018; 9(1): 10-16. doi:10.5530/srp.2018.1.3
			
			Web Style
			
			Pavithra P, Ravichandran K, Sekar K, Manikandan R. The Effect of Thermography on Breast Cancer Detection. http://www.sysrevpharm.org/?mno=302644541 [Access: March 28, 2021]. doi:10.5530/srp.2018.1.3

			
			AMA (American Medical Association) Style

			Pavithra P, Ravichandran K, Sekar K, Manikandan R. The Effect of Thermography on Breast Cancer Detection. SRP. 2018; 9(1): 10-16. doi:10.5530/srp.2018.1.3

			
			Vancouver/ICMJE Style

			Pavithra P, Ravichandran K, Sekar K, Manikandan R. The Effect of Thermography on Breast Cancer Detection. SRP. (2018), [cited March 28, 2021]; 9(1): 10-16. doi:10.5530/srp.2018.1.3

			
			Harvard Style

			Pavithra, P., Ravichandran, . K., Sekar, . K. & Manikandan, . R. (2018) The Effect of Thermography on Breast Cancer Detection. SRP, 9 (1), 10-16. doi:10.5530/srp.2018.1.3

			
			Turabian Style

			Pavithra, PR, KS Ravichandran, KR Sekar, and R Manikandan. 2018. The Effect of Thermography on Breast Cancer Detection. Systematic Reviews in Pharmacy, 9 (1), 10-16. doi:10.5530/srp.2018.1.3

			
			Chicago Style

			Pavithra, PR, KS Ravichandran, KR Sekar, and R Manikandan. "The Effect of Thermography on Breast Cancer Detection." Systematic Reviews in Pharmacy 9 (2018), 10-16. doi:10.5530/srp.2018.1.3

			
			MLA (The Modern Language Association) Style

			Pavithra, PR, KS Ravichandran, KR Sekar, and R Manikandan. "The Effect of Thermography on Breast Cancer Detection." Systematic Reviews in Pharmacy 9.1 (2018), 10-16. Print. doi:10.5530/srp.2018.1.3

			
			APA (American Psychological Association) Style

			Pavithra, P., Ravichandran, . K., Sekar, . K. & Manikandan, . R. (2018) The Effect of Thermography on Breast Cancer Detection. Systematic Reviews in Pharmacy, 9 (1), 10-16. doi:10.5530/srp.2018.1.3

/pdf/the-effect-of-thermography-on-breast-cancer-detection-a-1d7e6u9725.pdf

The Effect of Thermography on Breast Cancer Detection-A Survey

The offline character recognition is very useful software in the field of research. Authoritative field of research has made by character recognition due to its need in various fields of research as in banks, post offices to fulfill all recognition requirements. This paper is an exploration on the different scripts including Mathematical digits, Hindi consonants and vowels, Gurumukhi characters as well as numerals. Different handwritten samples of all these language scripts are taken from different persons in a good environment. First of all preprocessing including different operations for the reduction of noise is operated. The pre-processed documents are then segmented for the feature extraction. In this technique, different features like lines, corners etc. are to be extracted. Number of hidden layers taken are 15. Then neural network is trained for the testing of all these handwritten samples. Out of 100% samples, 90% are used for training, 5% are for testing and the remaining of the samples are used for validation. Comparison of recognition rates achieved on different scripts is accessible. Out of all samples taken, highest accuracy is achieved on the Gurumukhi script which is 99.9%.

/pdf/offline-handwritten-character-recognition-using-neural-2tt1uc2hp1.pdf

Offline Handwritten Character Recognition using Neural Network

Fungal keratitis is an inflammation of the cornea that results from infection by fungal organisms. It has a high rate of blindness, which makes the accurate diagnosis of fungal keratitis important. Confocal microscopy is an optical imaging technique that assists doctors in diagnosing fungal keratitis, and cornea images obtained by confocal microscopy can be used to detect hyphae. The current challenges are how to classify normal cornea images with nerves and abnormal cornea images with hyphae and how to detect the hyphae in a complicated background. To address this problem, this paper proposes a novel automatic hyphae detection method that assists doctors in making diagnoses. It includes two primary steps: texture classification of images and hyphae detection. In texture classification step, first, after image enhancement using a subregional contrast stretching algorithm, an adaptive robust binary pattern (ARBP), which is an improvement on the adaptive median binary pattern (AMBP), is proposed and adopted to extract texture features; and a support vector machine model is used to classify the normal and abnormal images. In the hyphae detection step, binarization and a connected domain process are used to further enhance the targets, and a line segment detector algorithm is adopted to detect the hyphae; then, the hyphal density is defined to quantitatively evaluate the infection severity. The contributions of this study include the improvement of the AMBP and the design of a novel framework. ARBP can extract effective texture features of images with relatively bright and small targets. The experimental results demonstrate the effectiveness of the proposed framework.

Hyphae Detection in Fungal Keratitis Images With Adaptive Robust Binary Pattern

Compound character recognition of Devanagari
script is one of the challenging tasks since the characters are complex
in structure and can be modified by writing combination of
two or more characters. These compound characters occurs 12 to
15% in the Devanagari Script. The moment based techniques are
being successfully applied to several image processing problems
and represents a fundamental tool to generate feature descriptors
where the Zernike moment technique has a rotation invariance
property which found to be desirable for handwritten character
recognition. This paper discusses extraction of features from
handwritten compound characters using Zernike moment feature
descriptor and proposes SVM and k-NN based classification system.
The proposed classification system preprocess and normalize
the 27000 handwritten character images into 30x30 pixels images
and divides them into zones. The pre-classification produces three
classes depending on presence or absence of vertical bar. Further
Zernike moment feature extraction is performed on each zone.
The overall recognition rate of proposed system using SVM and
k-NN classifier is upto 98.37%, and 95.82% respectively.

/pdf/zernike-moment-feature-extraction-for-handwritten-devanagari-4mvlaxq1bg.pdf

Zernike Moment Feature Extraction for Handwritten Devanagari (Marathi) Compound Character Recognition

Compound character recognition of Devanagari script is one of the challenging tasks since the characters are complex in structure and can be modified by writing combination of two or more characters. These compound characters occurs 12 to 15% in the Devanagari Script. The moment based techniques are being successfully applied to several image processing problems and represents a fundamental tool to generate feature descriptors where the Zernike moment technique has a rotation invariance property which found to be desirable for handwritten character recognition. This paper discusses extraction of features from handwritten compound characters using Zernike moment feature descriptor and proposes SVM and k-NN based classification system. The proposed classification system preprocess and normalize the 27000 handwritten character images into 30×30 pixels images and divides them into zones. The pre-classification produces three classes depending on presence or absence of vertical bar. Further Zernike moment feature extraction is performed on each zone. The overall recognition rate of proposed system using SVM and k-NN classifier is upto 98.37%, and 95.82% respectively.

Zernike moment feature extraction for handwritten Devanagari compound character recognition

A huge increase in medical image database volume has set new challenges to clinical routine for patients record about diagnosis, treatment & follow -up, with help of data & image mining it is possible to assist or automate the radiologist for diagnosis. Detection of breast cancer is done with mammogram, which are low dose x-ray images. Mammogram images play a very significant role in early detection of breast cancer. Usually in image mining texture analysis is used for clustering and classification based on content of image. Up till now in breast cancer detection only first and second order GLCM features were mostly used, to the best of our knowledge there is no evidence of use of third order features. In this research paper we retrieved a novel third order features and observed the results by clustering the effects of higher order features in recognition of malignancy in a novel breast mammogram's regional database.

Clustering of breast cancer tumor using third order GLCM feature

In machine translation system, the text is translated from one language known as source language into another language known as target language. The development of a machine translation system needs to identify the patterns of divergence between two languages. A detail study of divergence issues in machine translation is required for their proper classification and detection. The primary objective of this paper is to understand the types of divergence problems that operate behind English to Marathi translation. In this paper, the various divergence patterns between English-Marathi language pair are considered. This will enable us to come up with strategies to handle these situations and coming up with correct translation. General Terms Natural Language Processing, Machine Translation, Computational Linguistics

/pdf/linguistic-divergence-patterns-in-english-to-marathi-148p50vxzn.pdf

Linguistic Divergence Patterns in English to Marathi Translation

The translation divergence is a challenging problem in the area of machine translation. Language divergence arises when lexically and syntactically similar sentences of the source language do not translate into sentences that are similar in lexical and syntactic structure in the target language. A detailed study of divergence issues in machine translation is required for their proper classification and detection. Though a number of Machine Translation developers have given attention to this problem, it is difficult to derive general strategies which can be used for any language pair. In this paper, we discuss the translation pattern between English to Marathi and Marathi to English of various constructions to identify the structural and syntactic divergences in English-Marathi language pair.

http://ieeexplore.ieee.org/iel7/6657191/6724301/06724351.pdf

Prapti Deshmukh

Papers

Zernike Moment Feature Extraction for Handwritten Devanagari (Marathi) Compound Character Recognition

Zernike moment feature extraction for handwritten Devanagari compound character recognition

Clustering of breast cancer tumor using third order GLCM feature

Linguistic Divergence Patterns in English to Marathi Translation

Syntactic and Structural Divergence in English-to-Marathi Machine Translation