Despite numerous advantages, mobile phones cause serious health issues to people due to electromagnetic radiation. Various head models already exist to study the impact of radiation on a human head. The accuracy of the measurement of power absorbed by different layers of a head should be high. A new head model with six layers is proposed in this paper. Parameters such as dielectric constant, conductivity and mass density of different tissue layers skin, fat, bone, Dura, cerebrospinal fluid (CSF), and brain are extracted from the Federal Communications Commission (FCC) database. To study the impact of radiation in the proposed model, standard planar inverted F-antennas (PIFA) capable to radiate at 1.7 GHz and 2.4 GHz are used. Simulations are performed using ANSYS Electromagnetics Suite. The analysis shows that the specific absorption rate (SAR) in the brain layer decreased in the proposed model when compared to the existing model.

Image segmentation is an important pre-processing step towards higher level tasks such as object recognition, computer vision or image compression. Most of the existing segmentation algorithms deal with grayscale images only. But in the modern world, color images are extensively used in many situations. A new approach for color image segmentation is presented in this paper. There are many ways to deal with image segmentation problem and in these techniques; a particular class of algorithms traces their origin from region-based methods. These algorithms group homogeneous pixels, which are connected to primitive regions. They are easy to implement and are promising. Therefore, here one of the most efficient region-based segmentation algorithms is explained. The color image is quantized adaptively, using a wavelet transform. Then the region growing process is adopted. As preprocess, before actual region merging, small regions are eliminated by merging them with neighbor regions depending upon color similarity. After this, homogeneous regions are merged to get segmented output.

We introduce a rule base fuzzy technique on decomposed wavelet coefficients, to improve the wavelet edge representation. Our algorithm mitigates ‘incorrect’ responses, due primarily to the symmetries of directional derivative filters. Since the Discrete Wavelet Transform (DWT) coefficients are revealed from two dimensional symmetric filter banks and undermine some gradient information. These wavelet coefficients are prearranged into ‘if-then’ rule structure of a fuzzy inference system, to improve the wavelet edge representation.

This paper presents a new combination of Decomposition Filters (DF) and Hyperbolic Tangent (HBT) based simplified directional operators for edge detection (ED). Conventional separable algorithms are limited in capturing the geometric features and non-separable filters eliminate these restrictions. The high-frequency band of the DF of proposed method achieves the edge information. A set of edge operators in the form of simple non-separable patterns for different scales, and orientations are applied on the edge information to capture linear edge structures and directions. While testing with ten natural images, this algorithm has improved performances in terms of reduced Mean Squared Error (MSE) in the reconstruction estimation measure. The performance measure compares the Simplified Gabor Wavelet (SGW) based ED.