4 resultados para Maryland Institution for the Instruction of the Blind

em Indian Institute of Science - Bangalore - Índia


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We have carried out a three-part study comparing the research performance of Indian institutions with that of other international institutions. In the first part, the publication profiles of various Indian institutions were examined and ranked based on the h-index and p-index. We found that the institutions of national importance contributed the highest in terms of publications and citations per institution. In the second part of the study, we looked at the publication profiles of various Indian institutions in the high-impact journals and compared these profiles against that of the top Asian and US universities. We found that the number of papers in these journals from India was miniscule compared to the US universities. Recognizing that the publication profiles of various institutions depend on the field/departments, we studied the publication profiles of many science and engineering departments at the Indian Institute of Science (IISc), Bangalore, the Indian Institutes of Technology, as well as top Indian universities. Because the number of faculty in each department varies widely, we have computed the publications and citations per faculty per year for each department. We have also compared this with other departments in various Asian and US universities. We found that the top Indian institution based on various parameters in various disciplines was IISc, but overall even the top Indian institutions do not compare favourably with the top US or Asian universities.

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In this paper, we present robust semi-blind (SB) algorithms for the estimation of beamforming vectors for multiple-input multiple-output wireless communication. The transmitted symbol block is assumed to comprise of a known sequence of training (pilot) symbols followed by information bearing blind (unknown) data symbols. Analytical expressions are derived for the robust SB estimators of the MIMO receive and transmit beamforming vectors. These robust SB estimators employ a preliminary estimate obtained from the pilot symbol sequence and leverage the second-order statistical information from the blind data symbols. We employ the theory of Lagrangian duality to derive the robust estimate of the receive beamforming vector by maximizing an inner product, while constraining the channel estimate to lie in a confidence sphere centered at the initial pilot estimate. Two different schemes are then proposed for computing the robust estimate of the MIMO transmit beamforming vector. Simulation results presented in the end illustrate the superior performance of the robust SB estimators.

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Engineering education quality embraces the activities through which a technical institution satisfies itself that the quality of education it provides and standards it has set are appropriate and are being maintained. There is a need to develop a standardised approach to most aspects of quality assurance for engineering programmes which is sufficiently well defined to be accepted for all assessments.We have designed a Technical Educational Quality Assurance and Assessment (TEQ-AA) System, which makes use of the information on the web and analyzes the standards of the institution. With the standards as anchors for definition, the institution is clearer about its present in order to plan better for its future and enhancing the level of educational quality.The system has been tested and implemented on the technical educational Institutions in the Karnataka State which usually host their web pages for commercially advertising their technical education programs and their Institution objectives, policies, etc., for commercialization and for better reach-out to the students and faculty. This helps in assisting the students in selecting an institution for study and to assist in employment.

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We construct a quantum random walk algorithm, based on the Dirac operator instead of the Laplacian. The algorithm explores multiple evolutionary branches by superposition of states, and does not require the coin toss instruction of classical randomised algorithms. We use this algorithm to search for a marked vertex on a hypercubic lattice in arbitrary dimensions. Our numerical and analytical results match the scaling behaviour of earlier algorithms that use a coin toss instruction.