Publications by authors named "Swapna Banerjee"

The use of near infra red (NIR) photoacoustic spectroscopy (PAS) for continuous non-invasive glucose measurement is outlined in the paper. A photoacoustic (PA) measurement apparatus was constructed and PA measurements were made on glucose solutions at multiple NIR excitation wavelengths. A variety of time and frequency domain features, including amplitude and area based features, were extracted from the PA measurements.

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This paper examines the use of photoacoustic spectroscopy (PAS) at an excitation wavelength of 905 nm for making continuous non-invasive blood glucose measurements. The theoretical background of the measurement technique is verified through simulation. An apparatus is fabricated for performing photoacoustic measurements in vitro on glucose solutions and in vivo on human subjects.

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This paper examines the history of child-care by non-kin, “non-family” members and their representations in colonial India. It focuses primarily on Bengal and relies on several genres of literary documents. Bengal harboured the seat of the British imperial capital in the city of Calcutta until 1911 and its culture was shaped in unique ways compared with other Indian cities.

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Oral submucous fibrosis (OSF) is a precancerous condition of the oral cavity and oropharynx and a significant number of such cases transform into oral squamous cell carcinoma (OSCC). Presently, diagnosis of OSF is done mainly through qualitative histopathological techniques and in the level of diagnostic molecular biology no specific genetic marker is evident. Keeping these facts in mind this study evaluates histopathological changes in the epithelium and subepithelial connective tissue of OSF through quantitative digital image analysis in respect to specific candidate features and analyses null mutations in the GSTM1 and GSTT1 by PCR amplification.

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This paper presents an automatic method for classification of progressive stages of oral precancerous conditions like oral submucous fibrosis (OSF). The classifier used is a three-layered feed-forward neural network and the feature vector, is formed by calculating the wavelet coefficients. Four wavelet decomposition functions, namely GABOR, HAAR, DB2 and DB4 have been used to extract the feature vector set and their performance has been compared.

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