Sharif Digital Repository

In this paper, we suggest to use a steepest descent algorithm for learning a parametric dictionary in which the structure or atom functions are known in advance. The structure of the atoms allows us to find a steepest descent direction of parameters instead of the steepest descent direction of the dictionary itself. We also use a thresholded version of Smoothed- ℓ0 (SL0) algorithm for sparse representation step in our proposed method. Our simulation results show that using atom structure similar to the Gabor functions and learning the parameters of these Gabor-like atoms yield better representations of our noisy speech signal than non parametric dictionary learning methods like K-SVD, in... 

In this paper, an algebraic rule for tuning the integer realizations of fractional-order PI controllers is developed, with an integral square error performance index, which outperforms that of an optimal ordinary PI controller. To this end, the PIλ control structure is used in conjunction with a third-order integer approximating filter to provide a three parameter fixed-structure extension of the ordinary PI controller. Next, the extra degree of freedom in setting the order of integration λ is leveraged to introduce a steepest descent direction in the extended controller parameter space. It is then stated that shifting the parameters of an ordinary PI controller along the proposed descent... 

In this study, the authors propose a novel Filon-type integration method that uses error-controllable and frequency-independent closed-form expression to evaluate highly oscillatory physical optics (PO) surface integrals on smooth conducting surfaces. In this method, the phase and amplitude functions of PO integrals for a general parametric surface and incident wave are approximated by second order polynomials, first. Although there are some complicated closed-form expressions for these integrals in the literature, the authors present simple and computationally efficient closed-form expressions for them. For this end, the parametric domain of each resulted integral is meshed by triangular... 

In this paper, we propose a frequency-independent method for calculation of scattered electromagnetic (EM) fields by the human body in millimeter-wave (MMW) band. The surface of the body is accurately modeled by non-uniform rational B-spline (NURBS) surfaces and is illuminated by a plane wave. The scattered EM fields are formulated by physical optics (PO) approximation. This approximation yields PO surface highly oscillatory integrals that are solved by numerical steepest descent path (NSDP) method, frequency-independently. This paper provides an efficient and accurate method for considering human body in indoor propagation, surveillance and medical imaging, and detection of human body by... 

This letter proposes a dictionary learning algorithm for blind one bit compressed sensing. In the blind one bit compressed sensing framework, the original signal to be reconstructed from one bit linear random measurements is sparse in an unknown domain. In this context, the multiplication of measurement matrix A and sparse domain matrix φ, i.e., D = Aφ, should be learned. Hence, we use dictionary learning to train this matrix. Towards that end, an appropriate continuous convex cost function is suggested for one bit compressed sensing and a simple steepest-descent method is exploited to learn the rows of the matrix D. Experimental results show the effectiveness of the proposed algorithm... 

This letter proposes a dictionary learning algorithm for solving the grid mismatch problem in direction of arrival (DOA) estimation from both the array sensor data and from the sign of the array sensor data. Discretization of the grid in the sparsity-based DOA estimation algorithms is a problem, which leads to a bias error. To compensate this bias error, a dictionary learning technique is suggested, which is based on minimizing a suitable cost function. We also propose an algorithm for the estimation of DOA from the sign of the measurements. It extends the iterative method with adaptive thresholding algorithm to the 1-b compressed sensing framework. Simulation results show the effectiveness... 

Microburst wind shear is a potential hazard for aviation safety, especially in some crucial phases of flight like take-off and landing. Optimal trajectories could be useful for suitable retrieval of transport aircrafts encountering microburst. These types of trajectories are investigated in this paper for landing phase in two scenarios of escape and landing utilizing the most recent realistic analytical model of the microburst. In this regard, a set of complete six degrees of freedom aircraft's equations of motion is taken in a variation formulation of this problem. This approach is particularly useful for determination of optimal escape or approach trajectories constrained with respect to... 

Wannier functions are real space basis functions which are not unique due to an arbitrary phase factor in the Bloch envelope function. We exploit this property to optimize a spread functional numerically using an accelerated steepest descent, and obtain the maximally confined set of Wannier functions, which are obtained for the first time in the area of planar plasmonics and conducting interfaces  

In this paper, we suggest to use a modified version of Smoothed- ℓ0 (SL0) algorithm in the sparse representation step of iterative dictionary learning algorithms. In addition, we use a steepest descent for updating the non unit columnnorm dictionary instead of unit column-norm dictionary. Moreover, to do the dictionary learning task more blindly, we estimate the average number of active atoms in the sparse representation of the training signals, while previous algorithms assumed that it is known in advance. Our simulation results show the advantages of our method over K-SVD in terms of complexity and performance. ©2009 IEEE  

This letter proposes a sparse diffusion algorithm for 1-bit compressed sensing (CS) in wireless sensor networks, and the algorithm is inherently robust against impulsive noise. The approach exploits the diffusion strategy from distributed learning in the 1-bit CS framework. To estimate a common sparse vector cooperatively from only the sign of measurements, a steepest descent method that minimizes the suitable global and local convex cost functions is used. A diffusion strategy is suggested for distributive learning of the sparse vector. A new application of the proposed algorithm to sparse channel estimation is also introduced. The proposed sparse diffusion algorithm is compared with both... 

The equilibrium signal-setting problem is stated and subsequently formulated as a continuous equilibrium network design problem. The bilevel formulation is nonconvex and therefore cannot be solved for global optima by using descent solution algorithms. Therefore, a lower bound using a system optimal flow pattern is proposed that will be quite tight in both uncongested and highly congested network traffic situations. A solution algorithm based on the standard steepest-descent method is proposed for the lower-bound problem. Performance of the solution algorithm on a network problem is reported  

In this paper, a new algorithm for source recovery in under-determined Sparse Component Analysis (SCA) or atomic decomposition on over-complete dictionaries is presented in the noisy case. The algorithm is essentially a method for obtaining sufficiently sparse solutions of under-determined systems of linear equations with additive Gaussian noise. The method is based on iterative Expectation-Maximization of a Maximum A Posteriori estimation of sources (EM-MAP) and a new steepest-descent method is introduced for the optimization in the Mstep. The solution obtained by the proposed algorithm is compared to the minimum ℓ1-norm solution achieved by Linear Programming (LP). It is experimentally...