Sharif Digital Repository

one of the main problems of radar identification in electronic warfare support system is analysis of pulse repetition interval (PRI) modulation. In this paper a robust method of recognizing the PRI modulation type of radar signals is proposed to improve robustness of previous recognition methods as well as to reduce the complexity. Simulation results show that the proposed method achieves much better error recognition rate in case of missing and spurious pulses than other methods such as autocorrelation based techniques and the Noone method  

We present a competitive algorithm to minimize a locally Lipschitz function constrained with locally Lipschitz constraints. The approach is to use an ℓ1 nonsmooth penalty function. The method generates second order descent directions to minimize the ℓ1 penalty function. We introduce a new criterion to decide upon acceptability of a Goldstein subdifferential approximation. We show that the new criterion leads to an improvement of the Goldstein subdifferential approximation, as introduced by Mahdavi-Amiri and Yousefpour. Also, making use of our proposed line search strategy, the method always moves on differentiable points. Furthermore, the method has an adaptive behaviour in the sense that,... 

Situational Method Engineering (SME) is a branch of software engineering which helps develop bespoke methodologies to fit the specific characteristics of the software project at hand. As in software development, SME too involves rigorous Requirements Engineering (RE), so much so that if requirements elicitation and definition is mishandled in any way, methodology development will most likely fail as a result. In this paper, we propose a Feature-driven methodology for SME; in this SME methodology, the requirements of the target methodology are captured as Features. First introduced in the agile FDD (Feature-Driven Development) methodology, Features are fully object-oriented and provide all... 

We construct a uniform approximation for generalized Hessian matrix of an SC1 function. Using the discrete gradient and the extended second order derivative, we define the discrete Hessian matrix.We construct a sequence of sets, where each set is composed of discrete Hessian matrices. We first show some new properties of SC1 functions. Then, we prove that for SC1 functions the sequence of the set of discrete Hessian matrices is uniformly convergent to the generalized Hessian matrix  

We present a competitive algorithm to minimize a locally Lipschitz function constrained with locally Lipschitz constraints. The approach is to use an ℓ1 nonsmooth penalty function. The method generates second order descent directions to minimize the ℓ1 penalty function. We introduce a new criterion to decide upon acceptability of a Goldstein subdifferential approximation. We show that the new criterion leads to an improvement of the Goldstein subdifferential approximation, as introduced by Mahdavi-Amiri and Yousefpour. Also, making use of our proposed line search strategy, the method always moves on differentiable points. Furthermore, the method has an adaptive behaviour in the sense that,... 

In a multi-robot system, a number of autonomous robots would sense, communicate, and decide to move within a given domain to achieve a common goal. In the pursuit-evasion problem, a polygonal region is given and a robot called a pursuer tries to find some mobile targets called evaders. The goal of this problem is to design a motion strategy for the pursuer such that it can detect all the evaders. In this paper, we consider a new variant of the pursuit-evasion problem in which the robots (pursuers) each moves back and forth along an orthogonal line segment inside a simple orthogonal polygon P. We assume that P includes unpredictable, moving evaders that have bounded speed. We propose the... 

We present an algorithm for minimizing locally Lipschitz functions being continuously differentiable in an open dense subset of Rn. The function may be nonsmooth and/or nonconvex. The method makes use of a gradient sampling method along with a conjugate gradient scheme. To find search directions, we make use of a sequence of positive definite approximate Hessians based on conjugate gradient matrices. The algorithm benefits from a restart procedure to improve upon poor search directions or to make sure that the approximate Hessians remain bounded. The global convergence of the algorithm is established. An implementation of the algorithm is executed on a collection of well-known test problems.... 

Fractional-pixel accuracy Motion Estimation (ME) has been shown to result in higher quality reconstructed image sequences in hybrid video coding systems. However, the higher quality is achieved by notably increased Motion Field (MF) bitrate and more complex computations. In this paper, new half-pixel block matching ME algorithms are proposed to improve the rate-distortion characteristics of low bitrate video communications. The proposed methods tend to decrease the required video bandwidth, while improving the motion compensation quality. The key idea is to put a deeper focus on the search origin of the ME process, based on center-bias characteristics of low bitrate video MFs. To employ the... 

Motion estimation at fractional-pixel accuracy results in higher quality video sequences. However, the higher quality is achieved by more computations and increased required motion field bitrates. In this paper, new half-pixel accuracy block matching-based motion estimation algorithms are proposed to improve the rate-distortion characteristics of low-bitrate video communications. The proposed methods especially tend to decrease the required video bandwidth, with no degradation in quality of reconstructed image sequences. The key idea is to put a deeper focus on the origin (zero motion) based on the center-bias characteristics of low bitrate video motion fields. The experimental results show... 

Motion estimation and compensation is an essential part of existing video coding systems. Compared to the widely used block-matching algorithms (BMA), the mesh-based motion estimation (MME) produces better subjective and objective qualified image sequences in many cases, especially at low bitrates. However, the iterative refinement step of the MME is computationally too expensive. In this paper, we propose a fast MME scheme, which adjusts its interpolation patterns to the motion features of image sequences to achieve higher quality reconstructed results. Experimental results show notable rate-distortion improvement over both the BMA and the conventional non-iterative MME schemes, especially... 

Stem cell therapy has been introduced as an emerging approach for injured tissue regeneration. This chapter addresses developing regenerative medicine techniques for controlling stem cell behavior. Recent studies have been reviewed and novel approaches have been divided into four main categories: 3D bioprinting, lithography, microfluidics, and electrospinning. Moreover, the impact of applied biophysical and/or biochemical cues to the designed scaffold on controlling stem cell activity has been discussed. The potential of using stem cells for various soft and hard tissue regenerations has been explored in different bioengineered scaffolds and the applied techniques for controlling stem cell... 

The Wedderburn rank reduction formula and the Abaffy-Broyden-Spedicato (ABS) algorithms are powerful methods for developing matrix factorizations and many fundamental numerical linear algebra processes such as Gram-Schmidt, conjugate direction and Lanczos methods. We present a rank reduction formula for transforming the rows and columns of A, extending the Wedderburn rank reduction formula and the ABS approach. By repeatedly applying the formula to reduce the rank, an extended rank reducing process is derived. The biconjugation process associated with the Wedderburn rank reduction process and the scaled extended ABS class of algorithms are shown to be in our proposed rank reducing process,... 

We present a structured algorithm for solving constrained nonlinear least squares problems, and establish its local two-step Q-superlinear convergence. The approach is based on an adaptive structured scheme due to Mahdavi-Amiri and Bartels of the exact penalty method of Coleman and Conn for nonlinearly constrained optimization problems. The structured adaptation also makes use of the ideas of Nocedal and Overton for handling quasi-Newton updates of projected Hessians. We discuss the comparative results of the testing of our programs and three nonlinear programming codes from KNITRO on some randomly generated test problems due to Bartels and Mahdavi-Amiri. The results indeed confirm the...