Sparse signal representation for complex-valued imaging

We propose a sparse signal representation-based method for complex-valued imaging. Many coherent imaging systems such as synthetic aperture radar (SAR) have an inherent random phase, complex-valued nature. On the other hand sparse signal representation, which has mostly been exploited in real-valued problems, has many capabilities such as superresolution and feature enhancement for various reconstruction and recognition tasks. For complex-valued problems, the key challenge is how to choose the dictionary and the representation scheme for effective sparse representation. We propose a mathematical framework and an associated optimization algorithm for a sparse signal representation-based imaging method that can deal with these issues. Simulation results show that this method offers improved results compared to existing powerful imaging techniques

Multiple feature-enhanced synthetic aperture radar imaging

Non-quadratic regularization based image formation is a recently proposed framework for feature-enhanced radar imaging. Specific image formation techniques in this framework have so far focused on enhancing one type of feature, such as strong point scatterers, or smooth regions. However, many scenes contain a number of such features. We develop an image formation technique that simultaneously enhances multiple types of features by posing the problem as one of sparse signal representation based on overcomplete dictionaries. Due to the complex-valued nature of the reflectivities in SAR, our new approach is designed to sparsely represent the magnitude of the complex-valued scattered field in terms of multiple features, which turns the image reconstruction problem into a joint optimization problem over the representation of the magnitude and the phase of the underlying field reflectivities. We formulate the mathematical framework needed for this method and propose an iterative solution for the corresponding joint optimization problem. We demonstrate the effectiveness of this approach on various SAR images

Multiple feature-enhanced SAR imaging using sparsity in combined dictionaries

Nonquadratic regularization-based image formation is a recently proposed framework for feature-enhanced radar imaging. Specific image formation techniques in this framework have so far focused on enhancing one type of feature, such as strong point scatterers, or smooth regions. However, many scenes contain a number of such feature types. We develop an image formation technique that simultaneously enhances multiple types of features by posing the problem as one of sparse representation based on combined dictionaries. This method is developed based on the sparse representation of the magnitude of the scattered complex-valued field, composed of appropriate dictionaries associated with different types of features. The multiple feature-enhanced reconstructed image is then obtained through a joint optimization problem over the combined representation of the magnitude and the phase of the underlying field reflectivities

Sparse representation-based SAR imaging

There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying scene. Based on the observation that typical underlying scenes usually exhibit sparsity in terms of such features, we develop an image formation method which formulates the SAR imaging problem as a sparse signal representation problem. Sparse signal representation, which has mostly been exploited in real-valued problems, has many capabilities such as superresolution and feature enhancement for various reconstruction and recognition tasks. However, for problems of complex-valued nature, such as SAR, a key challenge is how to choose the dictionary and the representation scheme for effective sparse representation. Since we are usually interested in features of the magnitude of the SAR reflectivity field, our new approach is designed to sparsely represent the magnitude of the complex-valued scattered field. This turns the image reconstruction problem into a joint optimization problem over the representation of magnitude and phase of the underlying field reflectivities. We develop the mathematical framework for this method and propose an iterative solution for the corresponding joint optimization problem. Our experimental results demonstrate the superiority of this method over previous approaches in terms of both producing high quality SAR images as well as exhibiting robustness to uncertain or limited data

Sparse representation-based synthetic aperture radar imaging

There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying scene. Based on the observation that typical underlying scenes usually exhibit sparsity in terms of such features, we develop an image formation method which formulates the SAR imaging problem as a sparse signal representation problem. Sparse signal representation, which has mostly been exploited in real-valued problems, has many capabilities such as superresolution and feature enhancement for various reconstruction and recognition tasks. However, for problems of complex-valued nature, such as SAR, a key challenge is how to choose the dictionary and the representation scheme for effective sparse representation. Since we are usually interested in features of the magnitude of the SAR reflectivity field, our new approach is designed to sparsely represent the magnitude of the complex-valued scattered field. This turns the image reconstruction problem into a joint optimization problem over the representation of magnitude and phase of the underlying field reflectivities. We develop the mathematical framework for this method and propose an iterative solution for the corresponding joint optimization problem. Our experimental results demonstrate the superiority of this method over previous approaches in terms of both producing high quality SAR images as well as exhibiting robustness to uncertain or limited data

Technical and Economic Evaluation of Pinavia Interchange in Comparison with Roundabout Intersection by AIMSUN

Interchanges that are investigated in this research are roundabout interchange and pinavia interchange that are simulated in AIMSUN software using traffic data. The parameters that  are considered for each interchange are  traffic volume, pollutant emissions, fuel consumption, travel time, delay time ,construction cost, repair and maintenance cost, travel time cost , fuel consumption cost and safety , so that in technical evaluation traffic volume, pollutant emissions, fuel consumption, travel time, delay time are compared  by using two independent sample t – test that are used  for comparing of two group of data and It is assumed that the variances are equal . Then In economic evaluation construction cost, repair and maintenance cost, travel time cost , fuel consumption cost and safety are converted into cost by using axis produce way that based on this supposal that storage in exchange for an hour of travel time, increase an hour of production opportunities and construction cost, repair and maintenance cost calculated by executive plans and Related Regulations and finally each parameter is weighted by AHP and obtain the universal (total) cost. Finally due to the total cost of the resulting it can be seen that for twenty-year period pinavia interchange in compare with roundabout interchange has 49% more efficient

Multiple feature-enhanced SAR imaging using sparsity in combined dictionaries

ABSTRACT Non-quadratic regularization based image formation is a recently proposed framework for feature-enhanced radar imaging. Specific image formation techniques in this framework have so far focused on enhancing one type of feature, such as strong point scatterers, or smooth regions. However, many scenes contain a number of such feature types. We develop an image formation technique that simultaneously enhances multiple types of features by posing the problem as one of sparse signal representation based on combined dictionaries. Due to the complex-valued nature of the reflectivities in SAR, this method is developed based on the sparse representation of the magnitude of the scattered field , composed of appropriate dictionaries associated with different types of features. The multiple feature-enhanced reconstructed image is then obtained through a joint optimization problem over the combined representation of the magnitude and the phase of the underlying field reflectivities. We also present some considerations on the combined dictionary selection and propose an efficient combined dictionary for specific features of interest in a radar image. We demonstrate the effectiveness of this method through experimental results and quantify the quality of the reconstructed images based on a number of image quality metrics

Optimum range of angle tracking radars: a theoretical computing

In this paper, we determine an optimal range for angle tracking radars (ATRs) based on evaluating the standard deviation of all kinds of errors in a tracking system. In the past, this optimal range has often been computed by the simulation of the total error components; however, we are going to introduce a closed form for this computation which allows us to obtain the optimal range directly. Thus, for this purpose, we firstly solve an optimization problem to achieve the closed form of the optimal range (Ropt.) and then, we compute it by doing a simple simulation. The results show that both theoretical and simulation-based computations are similar to each other

Factors Associated with The Incidence of Coronary Heart Disease in The Mashad: A Cohort Study

Coronary heart disease (CHD) is the leading cause of morbidity and mortality globally, and specifically in Iran. Accurate assessments of Coronary heart disease (CHD) incidence is very necessary for public health. In current study we aimed to investigate the incidence of CHD and importance of several classical, modifiable and un-modifiable risk factors for CHD among an urban population in eastern Iran after 6 years of follow-up. Methods The population of MASHAD cohort study were followed up for 6 years, every 3 years in two step by phone and who reported symptoms of CVD were asked to attend for a cardiac examination, to estimate the incidence of CHD with 95% confidence interval (95% CI) as well multiple logistic regression analysis was performed to assess the association of several baseline characteristics with incidence of CHD event. Evaluation of goodness-of-fit was done using ROC analysis. CHD cases divided into four different classes which include: stable angina, unstable angina pectoris, myocardial infarction and sudden cardiac death. Results In the six years\u27 follow-up of Mashhad study, the incidence rate of all CHD event in men and women in 100,000 people-years with 95% confidence intervals were 1920 (810-3030) and 1160 (730-1590), respectively. The areas under ROC curve (AUC), based on multivariate predictors of CHD outcome, was 0.7825. Conclusion Our findings indicated that the incidence rate of coronary heart diseases in MASHAD cohort study increases with age as well as our final model designed, was able to predict approximately 78% of CHD events in Iranian population