Electrical Engineering

Researchers undertook a cross-sectional study to examine socioeconomic disparities, unintended pregnancies, and decisions about induced abortions among ever-married women in the urban area of Sivas, Turkey. The data for the present study were gathered from a randomly-selected, household-based probability sample of 1,264 ever-married Turkish women. Unintended pregnancies accounted for 46.2% of total pregnancies and, of these, 30% ended in induced abortion. The proportion of induced abortion among all pregnancies was 21.7%. Multiple logistic regression analyses with adjusted odds ratios (aOR) showed that being aged less than 35 years (aOR = 2.14, p < 0.001), having less than a high school education (aOR = 2.18, p < 0.001), being unemployed (aOR = 2.77, p < 0.001), having more than three children (aOR = 1.54, p = 0.006), and having lower income (aOR = 2.11, p < 0.001) were associated with unintended pregnancies. Among women with unintended pregnancy, having more than three children (aOR = 3.06, p < 0.001), lower income (aOR = 3.39, p < 0.001), and age less than 35 years (aOR = 2.57, p < 0.001) were associated with induced abortion. These findings suggest that lower socioeconomic status was associated with induced abortion among women facing an unintended pregnancy. Women who experience unintended pregnancies, who have lower socioeconomic status and education level, should be the target group for midwives and other relevant healthcare providers for educational efforts regarding family planning and contraception.

We propose a new set partitioning in hierarchical trees (SPIHT) based mesh compression framework. The 3D mesh is first transformed to 2D images on a regular grid structure. Then, this image-like representation is wavelet transformed and SPIHT is applied on the wavelet domain data. The method is progressive because the resolution of the reconstructed mesh can be changed by varying the length of the one-dimensional data stream created by SPIHT algorithm. Nearly perfect reconstruction is possible if all of the data stream is received

Two compression frameworks that are based on a set partitioning in hierarchical trees (SPIHT) and JPEG2000 methods are proposed. The 3D mesh is first transformed to 2D images on a regular grid structure. Then, this image-like representation is wavelet transformed employing an adaptive predictor that takes advantage of the connectivity information of mesh vertices. Then SPIHT or JPEG2000 is applied on the wavelet domain data. The SPIHT based method is progressive because the resolution of the reconstructed mesh can be changed by varying the length of the one-dimensional data stream created by SPIHT algorithm. The results of the SPIHT based algorithm is observed to be superior to JPEG2000 based mesh coder and MPEG-3DGC in rate-distortion.

In the last few years, there is great increase in capture and representation of real 3-Dimensonal scenes using 3D animation models. The 3D signals are then compressed, transmitted to the client side and reconstructed for the user view. Each step mentioned here opened a new subject in the field of signal processing. While processing these models, using the model as a whole is not the best approach. Therefore clustering the model vertices became a very common method. For example, it is very common to use motion based clustering in animation compression. In this paper a new dynamic model clustering algorithm is proposed. Animation vertices are first put through PCA and partitioned into their eigenvalues and eigenvectors. The eigenvectors found using the proposed method can be called eigentrajectories. Then the dot product of the these eigentrajectories with the trajectories of the animation vertice are found. These coefficients are used to cluster the animation model. The results and the comparisons with a similar approach show that the proposed algorithm is successful.

We propose a new connectivity-guided adaptive wavelet transform based mesh compression framework. The 3D mesh is first transformed to 2D images on a regular grid structure by performing orthogonal projections onto the image plane. Then, this image-like representation is wavelet transformed using a lifting structure employing an adaptive predictor that takes advantage of the connectivity information of mesh vertices. Then the wavelet domain data is encoded using "Set Partitioning In Hierarchical Trees" (SPIHT) method or JPEG2000. The SPIHT approach is progressive because the resolution of the reconstructed mesh can be changed by varying the length of the ID data stream created by the algorithm. In JPEG2000 based approach, quantization of the coefficients determines the quality of the reconstruction. The results of the SPIHT based algorithm is observed to be superior to JPEG200 based mesh coder and MPEG-3DGC in rate-distortion.

In the last few years, due to the global warming and draught related to it, there is an increase in the number of forest fires. Forest fire detection is mainly done by people but there exists some automated systems in this field too. Besides the detection of the forest fires, effective fire extinguishing has an important role in fire fighting. If the spread of the fire can be predicted from the starting, early intervene can be achieved and fire can be extinguished swiftly. Using the fire propagation simulator explained here it is aimed, to predict the fire development beforehand and to visulalize this predictions on a 3D-GIS environment.

In this article, the goal is to show that it is possible to filter nonuniformly sampled signals according to specs defined in the Fourier domain. In many practical applications, it is necessary to filter irregularly sampled data including seismic signal processing, synthetic aperture radar (SAR) imaging systems, three-dimensional (3-D) meshes, and digital terrain models [1], [2].

In most compressive sensing problems l1 norm is used during the signal reconstruction process. In this article the use of entropy functional is proposed to approximate the l1 norm. A modified version of the entropy functional is continuous, differentiable and convex. Therefore, it is possible to construct globally convergent iterative algorithms using Bregman's row action D-projection method for compressive sensing applications. Simulation examples are presented.

In this paper, an Entropy functional based online Adaptive Decision Fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several sub-algorithms each of which yielding its own decision as a real number centered around zero, representing the confidence level of that particular sub-algorithm. Decision values are linearly combined with weights which are updated online according to an active fusion method based on performing entropic projections onto convex sets describing sub-algorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video based wildfire detection system is developed to evaluate the performance of the algorithm in handling the problems where data arrives sequentially. In this case, the oracle is the security guard of the forest lookout tower verifying the decision of the combined algorithm. Simulation results are presented. The EADF framework is also tested with a standard dataset.

In this paper, an adaptive color transform for image compression is introduced. In each block of the image, coefficients of the color transform are determined from the previously compressed neighboring blocks using weighted sums of the RGB pixel values, making the transform block-specific. There is no need to transmit or store the transform coefficients because they are estimated from previous blocks. The compression efficiency of the transform is demonstrated using the JPEG image coding scheme. In general, the suggested transformation results in better peak signal-to-noise ratio (PSNR) values for a given compression level.

In this paper a new Digital Elevation Map (DEM) image compression algorithm is proposed. DEM image can be threated as a grayscale image, whose pixel values are the elevation values of the map points. The grayscale DEM image is compressed using an adaptive wavelet based image compression algorithm. The method, which is an extension of the progressive mesh compression takes advantage of the multiresolution property of the wavelets while coding the map images. This makes it possible to decode different resolutions of the map from the encoded bit stream providing a multiresolution display of a given map. Experimental results are presented.

ABSTRACT: Beyond taking precautionary measures to avoid a forest fire, early warning and immediate response to a fire breakout are the only ways to avoid great losses and environmental and cultural heritage damages. To this end, this paper aims to present a computer vision based algorithm for wildfire detection and a 3D fire propagation estimation system.

abstract We propose a new Set Partitioning In Hierarchical Trees (SPIHT) based mesh compression framework. The 3D mesh is first transformed to 2D images on a regular grid structure. Then, this image-like representation is wavelet transformed and SPIHT is applied on the wavelet domain data. The method is progressive because the resolution of the reconstructed mesh can be changed by varying the length of the 1D data stream created by SPIHT algorithm.

Abstract In this paper, an entropy-functional-based online adaptive decision fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular subalgorithm.

ABSTRACT The paper presents a novel automatic early warning system to remotely monitor areas of archaeological and cultural interest from the risk of fire. Since these areas have been treasured and tended for very long periods of time, they are usually surrounded by old and valuable vegetation or situated close to forest regions, which exposes them to an increased risk of fire.

In this thesis, signal and image processing algorithms based on sparsity and interval convex programming are developed for inverse problems. Inverse signal processing problems are solved by minimizing the ℓ1 norm or the Total Variation (TV) based cost functions in the literature. A modified entropy functional approximating the absolute value function is defined. This functional is also used to approximate the ℓ1 norm, which is the most widely used cost function in sparse signal processing problems.