harshal patil | Sheffield Hallam University (original) (raw)
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Institute of Archaeology and Ethnography of National Academy of Sciences, Armenia
University of the Basque Country, Euskal Herriko Unibertsitatea
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Papers by harshal patil
JOURNAL OF CLINICAL AND DIAGNOSTIC RESEARCH, 2015
International Journal of Computer Applications, 2013
Pattern is an arrangement of features which are defined by various characteristics of image such ... more Pattern is an arrangement of features which are defined by various characteristics of image such as shape, color and texture. Texture is an important characteristic for image analysis. The major trend of the research today in terms of feature extraction for classification is accuracy oriented, however usually the newer algorithms that promises better accuracy is much more complicated in its calculations and often sacrifices the speed of the algorithm. This paper contains study and review of various techniques used for feature extraction and texture classification. The objective of study is to find technique or combination of techniques to reduce complexity, speed while increasing the accuracy at the same time. Here we are studying and reviewing the three feature extraction methods: Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter method. Also two classification methods KNN and SVM are used on the texture datasets Brodatz, CUReT, VisTex and OuTex for the experimental purpose.
Applied Mathematical Modelling, 2011
In this paper, an indirect identification scheme is proposed for identifying the parameters of th... more In this paper, an indirect identification scheme is proposed for identifying the parameters of the continuous-time first-order plus time delay (FOPTD) model and the second-order plus time delay (SOPTD) model from step responses. Unlike the existing direct identification scheme, which identifies the parameters of the continuous-time FOPTD and SOPTD models directly from the continuous-time step response data, the proposed indirect scheme is to pre-identify discrete-time FOPTD and SOPTD models from the discretized continuous-time step response input-output data, then convert the obtained discrete-time models to the desirable continuous-time models. The proposed method is then extended to identify the afore-mentioned models from the step responses of the systems contaminated with input noise and constant output disturbance. The proposed simple alternative method exhibits good estimation performances in both the time domain and the frequency domain. Illustrative examples are presented to demonstrate the effectiveness of the proposed scheme.
JOURNAL OF CLINICAL AND DIAGNOSTIC RESEARCH, 2015
International Journal of Computer Applications, 2013
Pattern is an arrangement of features which are defined by various characteristics of image such ... more Pattern is an arrangement of features which are defined by various characteristics of image such as shape, color and texture. Texture is an important characteristic for image analysis. The major trend of the research today in terms of feature extraction for classification is accuracy oriented, however usually the newer algorithms that promises better accuracy is much more complicated in its calculations and often sacrifices the speed of the algorithm. This paper contains study and review of various techniques used for feature extraction and texture classification. The objective of study is to find technique or combination of techniques to reduce complexity, speed while increasing the accuracy at the same time. Here we are studying and reviewing the three feature extraction methods: Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter method. Also two classification methods KNN and SVM are used on the texture datasets Brodatz, CUReT, VisTex and OuTex for the experimental purpose.
Applied Mathematical Modelling, 2011
In this paper, an indirect identification scheme is proposed for identifying the parameters of th... more In this paper, an indirect identification scheme is proposed for identifying the parameters of the continuous-time first-order plus time delay (FOPTD) model and the second-order plus time delay (SOPTD) model from step responses. Unlike the existing direct identification scheme, which identifies the parameters of the continuous-time FOPTD and SOPTD models directly from the continuous-time step response data, the proposed indirect scheme is to pre-identify discrete-time FOPTD and SOPTD models from the discretized continuous-time step response input-output data, then convert the obtained discrete-time models to the desirable continuous-time models. The proposed method is then extended to identify the afore-mentioned models from the step responses of the systems contaminated with input noise and constant output disturbance. The proposed simple alternative method exhibits good estimation performances in both the time domain and the frequency domain. Illustrative examples are presented to demonstrate the effectiveness of the proposed scheme.