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P. Satyanarayana

Abstract

No Abstract.

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P. Satyanarayana ¹, K. Thothathri ¹

Affiliations
1 Botanical Survey of India, Howrah, IN

Abstract

No Abstract.

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P. Satyanarayana ¹, K. Thothathri ¹

Affiliations
1 Botanical Survey of India, Howrah, IN

Abstract

No Abstract.

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P. Satyanarayana ¹, K. Thothathri ¹

Affiliations
1 Botanical Survey of India, Calcutta, IN

Abstract

Notes on a few endemic species of Rhynchosia Lour., and nomenclature of Dunbaria fusca Kurz are discussed.

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P. Satyanarayana ¹

Affiliations
1 Botanical Survey of India, Howrah, IN

Abstract

DUNBARIA Wight & Arn. (Leguminosae) was established in 1834. Since then a number of species have been added to this genus from different regions of the Eastern and South Eastern Asia. This genus has been treated in earlier works for over a century without mention of type species. However, Hutchinson (1964) mentioned Dunbaria heynei Wight & Arn. as its lectotype species. Due to the nomenclatural changes the above species has since been transferred to the nearest related genus CAJANUS DC. Hence, a correct and proper selection of lectotype species has been necessitated for the generic name DUNBARIA Wight & Arn.

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S. Swarnalatha ¹, P. Satyanarayana ², B. Shoban Babu ³

Affiliations
1 Department of E.C.E., Sri Venkateswara University College of Engineering, Tirupati - 517502, Andhra Pradesh, IN
2 Department of E.C.E., Annamacharya Institute of Technology and Sciences, Tirupati - 517520, Andhra Pradesh, IN
3 Department of E.C.E., Sri Venkateswara College of Engineering and Technology, Chittoor - 5170127, Andhra Pradesh, IN

Abstract

Objectives: Image Denoising refers to the recovery of an image that has been corrupted by noise due to poor quality of image acquisition and transmission. Accordingly, there is a need to reduce the noise present in the image as a consequence of the denoised image formed. Methods/Statistical Analysis: This paper presents Image denoising using Wavelet transforms, Contourlet transforms and Block Matching Transforms governed by bivariate shrinkage (Bi-shrink) filter techniques. The Wavelet transform uses up-sampling, down-sampling, low pass filter and high pass filter to perform denoising operation, the Contourlet transform uses up-sampling, down-sampling, low pass filter and high pass filter and directional filter banks to perform denoising operation, the Block Matching Transform uses Haar Transforms, Discrete cosine transforms and Karhunen Loeve transform to perform denoising operation. Findings: The performance of wavelet transforms, Contourlet transforms and Block Matching Transforms are evaluated for Reference images (such as towers, shades and ruler images) corrupted by gaussian noise and salt and pepper noise, by computing two error metrics Peak Signal to Noise Ratio (PSNR) and Execution Time (ET) with help of shrinkage function. Programming these using MATLAB R2014a by exploring its wavelet transform, Contourlet transform, image processing and signal processing toolboxes and the values are presented in tabular forms and discussed in the section 6. In this paper the block matching haar discrete cosine transform is proposed for denoising of images (especially for those images possessing detailed textures) that works through haar transform and discrete cosine transform outstrips the basic transform discrete wavelet transform and semi translation invariant contourlet transform. For the images corrupted by Gaussian noise and denoised by the proposed transform outstrips the basic transform “Discrete Wavelet Transform by PSNR = 6.71 dB, ET = 25.89 sec” and “Semi Translation Invariant Contourlet Transforms by PSNR = 5.49 dB, ET = 5.89 sec”. For the images corrupted by Salt and Pepper noise and denoised by the proposed transform outstrips the basic transform “Discrete Wavelet Transform by PSNR = 21.15 dB, ET = 0.27 sec” and “Semi Translation Invariant Contourlet Transforms by PSNR = 20.05 dB, ET = 5.80 sec”. Application/Improvements: In this paper Block Matching Haar Discrete cosine transform is proposed to overcome the limitations of wavelet transforms and Contourlet transforms, hence to attain the trade-off between high peak signal to noise ratio and less execution time. Results and Discussion section illustrates the efficacy of the proposed transform in terms of peak signal to noise ratio, execution time and visual quality of images.

Keywords

Bi-variate Shrinkage and Image Denoising, Block Matching Transforms, Contourlet Transforms, Wavelet Transforms.

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Mondal Ramkrishna ¹, P. Satyanarayana ²

Affiliations
1 AMRI Hospital Dhakuria, Gariahat Road, Kolkata, West Bengal, IN
2 Dept of Medial Administration, Yashoda Hospital, Secunderabad, Telengana, IN

Abstract

Background: The key to minimization and effective management of biomedical waste (BMW) is segregation (separation) and identification of the waste. The most appropriate way of identifying the categories of healthcare waste is by sorting the waste into color-coded plastic bags or containers.[2, 3] Since early recovery of patients and health of clinical staff directly depends on a clean and hygienic environment, excellent hygiene practices in health care facilities are the prerequisite for good medical waste management [8]. Objective: Aim of this study is to see effectiveness of segregation practiced in the hospital and establish a monitoring system to monitor deficiencies in segregation. Methods: This cross sectional study conducted during the period of June to August 2012 in a tertiary care Hospital. Eighty Two (82) areas were identified as BMW generation area. As per the norms segregation was done in four different color coded containers. So, total segregation containers will be 82X4=328. Any mix up or wrong segregation will be noted in the register daily by the Infection Control Nurse. A Monthly report based on BMW Register was developed. Results: The index named as Bio-Medical Waste Segregation Deficiency index (BMWSD index). calculated as number of deficiencies found in a area/areas divided by the possible number of deficiencies can occur in that area/areas. Daily data then entered into the Monthly report and monthly BMWSD index was made. Though this is a continuous process, for this study three months data (June, July and August 2012) were taken for analysis. Discussion: In this Cross sectional study, it was found that there was a over all deterioration in segregation of BMW when compare to June, July and August reports. The areas with high BMWSD index or where segregation is poor will be reflected through this monitoring system. Conclusion: This BMWSD index can be used as an indicator of Infection control Practice. This report is to identify the deficiency areas in the entire hospital in BMW segregation, which can be used for analysis and planning for better BMW Management like, training of the staffs of particular units, modification in methods etc.

Keywords

Biomedical Waste Management, Waste Segregation, BMW Color Coded System, Monitoring System, BMW Segregation Matrix.

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