Browsing by Author "Ashritha, K.S."

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    Activity theory based approach for requirements analysis of android Applications
    (2017) Ashritha, K.S.; Prajwala, T.M.; Chandrasekaran, K.
    This paper aims at providing a detailed explanation on the necessity of an alternative approach based on Activity theory for the requirements analysis of a restaurant automation application. In the recent past, android platform has turned out to be one of the most user-friendly platforms for the development of application software. Also, the number of devices on which android applications can be used outrun the other platforms. Automation is one such domain which takes advantage of this platform. One such scenario is the application of automation in restaurants. This helps in efficiently reducing manpower, improving efficiency, accuracy and quality of the system. However, the applications developed in this domain fail to meet all of an average customer�s requirements. Thus the requirements analysis phase is critical to the development of such applications. The traditional methods of requirements engineering do not ensure that a majority of the requirements are captured and hence turn out to be unsuitable for restaurant automation application. Thus, in this paper, the use of activity theory for requirements analysis has been proposed for capturing the non-functional requirements which play a major role in the evaluation of performance characteristics of the system. � Springer International Publishing AG 2017.
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    Activity theory based approach for requirements analysis of android Applications
    (Springer Verlag service@springer.de, 2017) Ashritha, K.S.; Prajwala, T.M.; Chandrasekaran, K.
    This paper aims at providing a detailed explanation on the necessity of an alternative approach based on Activity theory for the requirements analysis of a restaurant automation application. In the recent past, android platform has turned out to be one of the most user-friendly platforms for the development of application software. Also, the number of devices on which android applications can be used outrun the other platforms. Automation is one such domain which takes advantage of this platform. One such scenario is the application of automation in restaurants. This helps in efficiently reducing manpower, improving efficiency, accuracy and quality of the system. However, the applications developed in this domain fail to meet all of an average customer’s requirements. Thus the requirements analysis phase is critical to the development of such applications. The traditional methods of requirements engineering do not ensure that a majority of the requirements are captured and hence turn out to be unsuitable for restaurant automation application. Thus, in this paper, the use of activity theory for requirements analysis has been proposed for capturing the non-functional requirements which play a major role in the evaluation of performance characteristics of the system. © Springer International Publishing AG 2017.
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    Retinal-Layer Segmentation Using Dilated Convolutions
    (Springer Science and Business Media Deutschland GmbH, 2020) Guru Pradeep Reddy, T.; Ashritha, K.S.; Prajwala, T.M.; Girish, G.N.; Kothari, A.R.; Koolagudi, S.G.; Rajan, J.
    Visualization and analysis of Spectral Domain Optical Coherence Tomography (SD-OCT) cross-sectional scans has gained a lot of importance in the diagnosis of several retinal abnormalities. Quantitative analytic techniques like retinal thickness and volumetric analysis are performed on cross-sectional images of the retina for early diagnosis and prognosis of retinal diseases. However, segmentation of retinal layers from OCT images is a complicated task on account of certain factors like speckle noise, low image contrast and low signal-to-noise ratio amongst many others. Owing to the importance of retinal layer segmentation in diagnosing ophthalmic diseases, manual segmentation techniques have been proposed and adopted in clinical practice. Nonetheless, manual segmentations suffer from erroneous boundary detection issues. This paper thus proposes a fully automated semantic segmentation technique that uses an encoder–decoder architecture to accurately segment the prominent retinal layers. © 2020, Springer Nature Singapore Pte Ltd.