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In this paper, a wideband printed polygon-shaped monopole antenna has been designed using microstrip line feeding technique which provides dual-notch band characteristics (2.98–3.19 GHz) and (3.62–5.00 GHz) by the use of slots geometry in both the patch and the ground plane. The results of the antenna have been compared both with and without slots in both planes. The initial antenna without DGS and slots in the patch was made to work in the frequency range from 2.56–5.98 GHz having impedance bandwidth of about 80.09%. The proposed antenna can be made usable for multi-band applications such as WLAN (2.4/3.2/5.2/5.8 GHz) and Wi-MAX (3.5 and 5.5 GHz) applications providing fractional bandwidth (FBW) of 85.36% (2.33–5.80 GHz) and maximum peak gain of 5.65 dBi at 3.50 GHz. The value of return loss obtained is about 53.36 dB at 2.56 GHz. Prototype of the final antenna is fabricated and the results are verified with the simulated ones. [ABSTRACT FROM AUTHOR]

Multimodal Magnetic Resonance Imaging (MRI) is an imaging technique widely used in the diagnosis and treatment planning of patients. Lesion segmentation of brain MRI is one of the most important image analysis task in medical imaging. In this paper, a new method for the supervised segmentation of the lesion in brain MRI using Grammatical Bee Colony (GBC) is proposed. The segmentation process is adversely affected by the presence of noises and intensity inhomogeneities in the Magnetic Resonance (MR) images. Therefore, noises are removed from the images and intensity inhomogeneities are corrected in the pre-processing steps. A set of stationary wavelet features are extracted from the co-registered 1-weighted (-), 2-weighted (-) and Fluid-Attenuated Inversion Recovery (FLAIR) images after skull stripping. A classifier is evolved using the GBC to classify the tissues as healthy tissues or lesions. The GBC classifier is trained with extracted features. The trained classifier is used to segment the test Magnetic Resonance (MR) image into healthy tissues or lesion regions. Finally, the connected component labeling algorithm is used to extract the lesions from the segmented images in the post-processing step. Effectiveness of the proposed method is tested by identifying the brain lesions from a set of MR images. [ABSTRACT FROM AUTHOR]