Iris pattern recognition using biorthogonal Wavelet Packet Analysis

Hariprasath, S.; Venkatasubramaniam, S.

doi:10.1109/icccct.2010.5670727

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…Morlet wavelet is obtained by localizing a complex sine wave with a Gaussian envelope. This wavelet has both complex and real parts and, therefore, enables the identification and fine tuning of significant frequencies [ Lau and Weng , ; Hariprasath and Mohan , ].…”

Section: Methodsmentioning

confidence: 99%

Temporal dynamics of biogeochemical processes at the Norman Landfill site

et al. 2013

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[1] The temporal variability observed in redox sensitive species in groundwater can be attributed to coupled hydrological, geochemical, and microbial processes. These controlling processes are typically nonstationary, and distributed across various time scales. Therefore, the purpose of this study is to investigate biogeochemical data sets from a municipal landfill site to identify the dominant modes of variation and determine the physical controls that become significant at different time scales. Data on hydraulic head, specific conductance, 2 H, chloride, sulfate, nitrate, and nonvolatile dissolved organic carbon were collected between 1998 and 2000 at three wells at the Norman Landfill site in Norman, OK. Wavelet analysis on this geochemical data set indicates that variations in concentrations of reactive and conservative solutes are strongly coupled to hydrologic variability (water table elevation and precipitation) at 8 month scales, and to individual eco-hydrogeologic framework (such as seasonality of vegetation, surface-groundwater dynamics) at 16 month scales. Apart from hydrologic variations, temporal variability in sulfate concentrations can be associated with different sources (FeS cycling, recharge events) and sinks (uptake by vegetation) depending on the well location and proximity to the leachate plume. Results suggest that nitrate concentrations show multiscale behavior across temporal scales for different well locations, and dominant variability in dissolved organic carbon for a closed municipal landfill can be larger than 2 years due to its decomposition and changing content. A conceptual framework that explains the variability in chemical concentrations at different time scales as a function of hydrologic processes, site-specific interactions, and/or coupled biogeochemical effects is also presented.

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Section: Methodsmentioning

confidence: 99%

Temporal dynamics of biogeochemical processes at the Norman Landfill site

et al. 2013

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“…One of the objectives of this work is focused on the performance of features extracted from biorthogonal wavelet decomposition by applying the most suitable sub-band of either approximation coefficient or detail coefficient for extracting features for classification. Majority of the prior work using biorthogonal wavelet [ 31 , 32 , 33 , 34 , 35 , 36 , 37 ] used either approximation coefficient or detail coefficient mostly from signals extracted using an intrusive method. The use of smartwatch reduces the intrusiveness in the extraction of the signal; therefore, a comparison analysis is to determine the most efficient biorthogonal wavelet family for extracting features.…”

Section: State Of the Artmentioning

confidence: 99%

Continuous m-Health Data Authentication Using Wavelet Decomposition for Feature Extraction

Enamamu

Otebolaku

Marchang

et al. 2020

Sensors

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The World Health Organization (WHO) in 2016 considered m-health as: “the use of mobile wireless technologies including smart devices such as smartphones and smartwatches for public health”. WHO emphasizes the potential of this technology to increase its use in accessing health information and services as well as promoting positive changes in health behaviours and overall management of diseases. In this regard, the capability of smartphones and smartwatches for m-health monitoring through the collection of patient data remotely, has become an important component in m-health system. It is important that the integrity of the data collected is verified continuously through data authentication before storage. In this research work, we extracted heart rate variability (HRV) and decomposed the signals into sub-bands of detail and approximation coefficients. A comparison analysis is done after the classification of the extracted features to select the best sub-bands. An architectural framework and a used case for m-health data authentication is carried out using two sub-bands with the best performance from the HRV decomposition using 30 subjects’ data. The best sub-band achieved an equal error rate (EER) of 12.42%.

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“…Blood vessel segmentation involves image compliment, Median filtering, contrast enhancement and Wavelet Transformation through symmlet filters [13]. Optic Disc extraction involves border suppression, optic disc segmentation through K-Means Clustering [14][15], Optic Disc Segmentation through Haar Wavelet Transformation [16] and Optic Disc Segmentation through Histogram analysis [17] followed by maximum voting to delineate final Optic disc boundary.…”

Section: Image Processing Phasementioning

confidence: 99%

Automatic Detection of Glaucoma in Retinal Fundus Images through Image Processing and Data Mining Techniques

Ramani¹,

Sugirtharani²,

Lakshmi³

2017

IJCA

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Computational techniques are highly used in medical image analysis to aid the medical professionals. Glaucoma is a sight threatening retinal disease that needs attention at its early stages, though it does not reveal any symptoms. Glaucoma is identified usually through cup to disc ratio and ISNT rule. This work involves segmentation of blood vessels, segmentation of optic disc through proposed maximum voting of three segmentation algorithms (K-Means, Wavelet and Histogram based), segmentation of optic cup through intensity thresholding, feature extraction from these segmented structures, feature selection to identify siginificant features, hybrid model involving Naive Bayes to remove noise in data followed by ensemble classification of Reduced Error Pruning Tree. Optic disc segmentation methodology attains an average accuracy of 99.33%. Glaucoma detection accuracy reaches a maximum of 96.42%.

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Iris pattern recognition using biorthogonal Wavelet Packet Analysis

Cited by 3 publications

References 8 publications

Temporal dynamics of biogeochemical processes at the Norman Landfill site

Temporal dynamics of biogeochemical processes at the Norman Landfill site

Continuous m-Health Data Authentication Using Wavelet Decomposition for Feature Extraction

Automatic Detection of Glaucoma in Retinal Fundus Images through Image Processing and Data Mining Techniques

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