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Proposing a measurement matrix is one of the researchers' concerns for compressive sensing-based signal processing. A deterministic measurement matrix with zero entries reduces the long data collecting time. In this paper a deterministic measurement matrix based on priory information about k-space in Ground Penetrating Radar (GPR) using Synthetic Aperture Radar (SAR) is proposed. Algorithm which produces...
The hyperspectral imaging system is a powerful tool in the field of remote sensing. The extremely high dimensionality of these data sets, make the analysis of HSIs a complicated task. So for good learning, it is important to select some important features. Recently in [7] a sparse feature selection method based on regularized regression model proposed to select heterogeneous important features. But...
Copy-move is a simple and effective operation for creating digital image forgeries, where an area of an image is copied and pasted to a different location in that image. Generally, a forger uses some affine transformations to make the changes visually intact. Most existing copy-move detection methods are not effective when copied regions are under geometrical distortions. In this paper, a new copy-move...
Due to the rapid growth in e-business and electronic payment systems, Fraud is rising in banking transactions associated with credit cards. This paper intends to develop a credit card fraud detection (CCFD) model based on Artificial Neural Networks (ANN) and Meta Cost procedure to reduce risk reputation and risk of loss. ANN strategy have been used for credit card fraud prevention and detection. Because...
In this paper we present a novel method for real time hardware implementation of Central Pattern Generators (CPGs) for bipedal robot walking. We introduce a closed form solution for Matsuoka CPG model which is a widely applied parametric neuron-based method for walking pattern generation. Existing parameter tuning methods including trial and error, optimization methods like genetic algorithms or etc...
Many disorders can be diagnosed by analysis of gene expression microarrays and this can save lots of lives. However, as gene expression data have high dimensions, establishing a method to identify the genes related to the target disease still remains a challenge, because it should provide a well-grounded prediction about the disease status. To this end, the best subset of genes should be distinguished...
Positioning in mobile smartphones is done by variety of available applications which often use GPS modules equipped in them. Most of these approaches have a high energy consumption because of those GPS modules and decreases battery life. As the period of GPS signal updating reduces, the precision of positioning decreases, but energy consumption decreases either. By fusing the GPS and INS data, positioning...
This paper introduces a new detector for digital watermarking based on dual-tree complex wavelet transform (DT-CWT). The DT-CWT benefits from the high directionality and shift invariance, which ensure the high efficiency of proposed method. The watermark is additively embedded in the magnitude components of DT-CWT coefficients. Also, the watermark detection is formulated as a binary hypothesis test...
Photoacoustic imaging is an emerging modality which is being developed due to its high capabilities. At the other hand, one of the most interesting areas of research is brain imaging. Photoacoustic imaging has been used in brain imaging due to its capability of both functional and anatomical imaging. Photoacoustic spectral analysis is one of the last research areas in this field. But, as our point...
Risk refers to a set of events that lead to loss but risk from the tax perspective refers to the taxpayers' behaviors that may lead to negligence from the public property by the taxpayers due to tax evasion. Such actions cause unusual volatilities in the amounts envisaged in the government budgeting. The fiscal and financial transactions outside the scope of the precautionary bound and failure to...
In this paper, performance of relative and absolute simultaneous localization and mapping (SLAM) are studied in dealing with open and closed loop paths. SLAM techniques based on absolute landmark and robot positions (AMF) has global look at the environment. This property is used to error reduction in closed loop paths. However, in open loop path, its error value increases and performance decreases...
A new vision-based approach to accomplish automatic detection and speed measuring of vehicles is proposed in this paper. In the proposed method, a cascade classifier, based on Haar features, is trained on frontal view of vehicles and deployed for vehicle detection. A fast and accurate foreground segmentation algorithm is proposed to distinguish moving vehicles from the background and prune detection...
Assumption of normally distributed residuals is one of the big challenges in the generalized linear models (GLM). Recently, generalized Gaussian distribution (GGD) is used widely to analyze and model heavy-tail signals. Consequently, investigations for robust estimation of regression coefficients have led us to introduce GG-GLM, which models the GLM residuals using GGD. This model can deal with broad...
Optimum-path forest (OPF) is a novel supervised graph-based classifier which reduces the classification problem into partitioning of vertices in a graph derived from the data samples. One of the main processes in OPF is identifying the optimum set of key samples named prototypes. This process is based on creating a minimum spanning tree on a complete weighted graph which is derived from the training...
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