2 resultados para Aha! Moment

em Cochin University of Science


Relevância:

20.00% 20.00%

Publicador:

Resumo:

Ferrofluids belonging to the series, Ni x Fe1-x Fe2O4 and Zn x Fe1-x Fe2O4, were synthesized using cold co-precipitation. Liquid films of these ferrofluids were prepared by encapsulating the ferrofluids in between two optically smooth and ultrasonically cleaned glass plates. Magnetic field induced laser transmission through these ferrofluid films has been investigated. Magnetic field values can be calibrated in terms of output laser power in the low field region in which the variation is linear. This set up can be used as a cheap optical gaussmeter in the low field regime. Using the same set-up, the saturation magnetization of the sample used can also be calculated with a sample that is pre-characterized. Hence both magnetization of the sample, as well as applied magnetic field can be sensed and calculated with a precalibrated sample.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

This paper reports a novel region-based shape descriptor based on orthogonal Legendre moments. The preprocessing steps for invariance improvement of the proposed Improved Legendre Moment Descriptor (ILMD) are discussed. The performance of the ILMD is compared to the MPEG-7 approved region shape descriptor, angular radial transformation descriptor (ARTD), and the widely used Zernike moment descriptor (ZMD). Set B of the MPEG-7 CE-1 contour database and all the datasets of the MPEG-7 CE-2 region database were used for experimental validation. The average normalized modified retrieval rate (ANMRR) and precision- recall pair were employed for benchmarking the performance of the candidate descriptors. The ILMD has lower ANMRR values than ARTD for most of the datasets, and ARTD has a lower value compared to ZMD. This indicates that overall performance of the ILMD is better than that of ARTD and ZMD. This result is confirmed by the precision-recall test where ILMD was found to have better precision rates for most of the datasets tested. Besides retrieval accuracy, ILMD is more compact than ARTD and ZMD. The descriptor proposed is useful as a generic shape descriptor for content-based image retrieval (CBIR) applications