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One of the most critical issues of Wireless Sensor Networks (WSNs) is the deployment of a limited number of sensors in order to achieve maximum coverage on a terrain. The optimal sensor deployment which enables one to minimize the consumed energy, communication time and manpower for the maintenance of the network has attracted interest with the increased number of studies conducted on the subject in the last decade. Most of the studies in the literature today are proposed for two dimensional (2D) surfaces; however, real world sensor deployments often arise on three dimensional (3D) environments. In this paper, a guided wavelet transform (WT) based deployment strategy (WTDS) for 3D terrains, in which the sensor movements are carried out within the mutation phase of the genetic algorithms (GAs) is proposed. The proposed algorithm aims to maximize the Quality of Coverage (QoC) of a WSN via deploying a limited number of sensors on a 3D surface by utilizing a probabilistic sensing model and the Bresenham's line of sight (LOS) algorithm. In addition, the method followed in this paper is novel to the literature and the performance of the proposed algorithm is compared with the Delaunay Triangulation (DT) method as well as a standard genetic algorithm based method and the results reveal that the proposed method is a more powerful and more successful method for sensor deployment on 3D terrains.

A new wavelet-based image enhancement algorithm is proposed to improve performance of face detection in non-uniform lighting environment with high dynamic range. Wavelet transform is used for dimension reduction so that dynamic range compression with local contrast enhancement algorithm is applied only to the approximation coefficients. The normalized approximation coefficients are transformed using a hyperbolic sine curve which achieves dynamic range compression. Contrast enhancement is realized by tuning the magnitude of each coefficient with respect to its surroundings. The detail coefficients are also modified to prevent the edge deformation. Experimental results on the proposed algorithm show improvement on the performance of the Viola-Jones face detector when compared to other prominent enhancement techniques.Index Terms-Image Enhancement, dynamic range compression, local contrast enhancement, face detection.

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