acm - an acm publication

2007 - October

  • Economic recognition of innovation

    Globalization has benefited the economies of member countries of the Organization for Economic Cooperation and Development (OECD) by helping their businesses stay profitable through cost-effective outsourcing of mostly garden-variety tasks and some knowledge-based activities. With time, the latter will account for the lion's share of work outsourced and emerging export houses will also tend to cater more to their own domestic markets because of their expanding infrastructure and growing manpower possessing advanced skills. This will result in a leveled playing field coaxing developed countries to adopt widespread innovations to maintain their high perch in the economic pecking order. Such large-scale creativity can be managed better if it could be gauged with an appropriate measure. This work propounds a new economic measure called the Gross Domestic Innovation (GDI) to quantify innovations in OECD countries. It will supplement universal measures such as the Gross Domestic Product (GDP), productivity and numbers concerning employment. Apart from the methodology for its estimation, the impact of GDI on the various facets of a vibrant economy is discussed and inter alia, the role of GDI in fighting inflation and alleviating the negative influences of globalization is stressed. Also, a tentative analysis on the economies of U.S., Japan, Germany and China is presented to illustrate the concept.

  • When it comes to E-learning
    E-learning is fast becoming a major learning and skills delivery method within larger companies as a staff development tool. Survey shows that among American colleges and universities in 2002, 11% of students took an online course, 97 % of public institutions offered at least one online or blended course, 49% offered an online degree program, and 67% considering e-learning a critical long-term strategy for their institution. The questions about e-learning have become "how", "why" and "with what outcomes.
  • Computational foundations of image interpolation algorithms

    Image interpolation is an important image processing operation applied in diverse areas ranging from computer graphics, rendering, editing, medical image reconstruction, to online image viewing. Image interpolation techniques are referred in literature by many terminologies, such as image resizing, image resampling, digital zooming, image magnification or enhancement, etc. Basically, an image interpolation algorithm is used to convert an image from one resolution (dimension) to another resolution without loosing the visual content in the picture. Image interpolation algorithms can be grouped in two categories, non-adaptive and adaptive. The computational logic of an adaptive image interpolation technique is mostly dependent upon the intrinsic image features and contents of the input image whereas computational logic of a non-adaptive image interpolation technique is fixed irrespective of the input image features. In this paper, we review the progress of both non-adaptive and adaptive image interpolation techniques. We also proposed a new algorithm for image interpolation in discrete wavelet transform domain and shown its efficacy. We describe the underlying computational foundations of all these algorithms and their implementation techniques. We present some experimental results to show the impact of these algorithms in terms of image quality metrics and computational requirements for implementation.

  • RKPianGraphSort: a graph based sorting algorithm

    Sorting is a well-known problem frequently used in many aspects of the world of computational applications. Sorting means arranging a set of records (or a list of keys) in some (increasing or decreasing) order. In this paper, we propose a graph based comparison sorting algorithm, designated as RKPianGraphSort, that takes time Θ(n2) in the worst-case, where n is the number of records in the given list to be sorted.