Ali Muhammad Ali Rushdi

Bio: Ali Muhammad Ali Rushdi is an academic researcher from King Abdulaziz University. The author has contributed to research in topics: Karnaugh map & Reliability (statistics). The author has an hindex of 24, co-authored 152 publications receiving 1625 citations. Previous affiliations of Ali Muhammad Ali Rushdi include University of Illinois at Urbana–Champaign.

Symbolic Reliability Analysis with the Aid of Variable-Entered Karnaugh Maps

Abstract: A method for finding the symbolic reliability of a moderately complex system is presented. The method uses Bayesian decomposition to reduce the system into simpler series-parallel subsystems. The successes or failures of these subsystems are found by inspection and then recast into disjoint sum-of-product forms with the aid of the Karnaugh map. Subsequently, an almost minimal disjoint expression for the system success or failure is obtained with the aid of a variable-entered Karnaugh map (VEKM). This VEKM method is illustrated by applying it to some examples recently solved in the literature. The main advantage of the method is the pictorial insight it provides to the reliability analyst.

Indexes of a telecommunication network

Abstract: Two recently proposed performance indexes for telecommunication networks are shown to be the s-t and overall versions of the same measure, namely, the mean normalized network capacity. The network capacity is a pseudo-switching-function of the branch successes, and hence it means value is readily obtainable from its sum-of-products expression. Three manual techniques of conventional reliability analysis are adapted for the computation of the proposed performance indexes: a map procedure, reduction rules, and a generalized cutset procedure. Four tutorial examples illustrate these techniques and demonstrate their computational advantages over the state-enumeration technique. >

How to Hand-Check a Symbolic Reliability Expression

Abstract: Checking symbolic reliability expressions is very useful for detecting faults in hand derivations and for debugging computer programs. This checking can be achieved in a systematic way, though it may be a formidable task. Three exhaustive tests are given. These tests apply to unreliability and reliability expressions for noncoherent as well as coherent systems, and to cases when both nodes and branches are unreliable, or when the system has a flow constraint. Further properties of reliability expressions derived through various methods are discussed. All the tests and other pertinent results are proved and illustrated by examples.

Linear systems

Abstract: Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

Tools and Algorithms for the Construction and Analysis of Systems. Proc. TACAS 2009

Abstract: ing WS1S Systems to Verify Parameterized Networks . . . . . . . . . . . . 188 Kai Baukus, Saddek Bensalem, Yassine Lakhnech and Karsten Stahl FMona: A Tool for Expressing Validation Techniques over Infinite State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 J.-P. Bodeveix and M. Filali Transitive Closures of Regular Relations for Verifying Infinite-State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Bengt Jonsson and Marcus Nilsson Diagnostic and Test Generation Using Static Analysis to Improve Automatic Test Generation . . . . . . . . . . . . . 235 Marius Bozga, Jean-Claude Fernandez and Lucian Ghirvu Efficient Diagnostic Generation for Boolean Equation Systems . . . . . . . . . . . . 251 Radu Mateescu Efficient Model-Checking Compositional State Space Generation with Partial Order Reductions for Asynchronous Communicating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Jean-Pierre Krimm and Laurent Mounier Checking for CFFD-Preorder with Tester Processes . . . . . . . . . . . . . . . . . . . . . . . 283 Juhana Helovuo and Antti Valmari Fair Bisimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Thomas A. Henzinger and Sriram K. Rajamani Integrating Low Level Symmetries into Reachability Analysis . . . . . . . . . . . . . 315 Karsten Schmidt Model-Checking Tools Model Checking Support for the ASM High-Level Language . . . . . . . . . . . . . . 331 Giuseppe Del Castillo and Kirsten Winter Table of

Shooting and bouncing rays: calculating the RCS of an arbitrarily shaped cavity

Abstract: A ray-shooting approach is presented for calculating the interior radar cross section (RCS) from a partially open cavity. In the problem considered, a dense grid of rays is launched into the cavity through the opening. The rays bounce from the cavity walls based on the laws of geometrical optics and eventually exit the cavity via the aperture. The ray-bouncing method is based on tracking a large number of rays launched into the cavity through the opening and determining the geometrical optics field associated with each ray by taking into consideration: (1) the geometrical divergence factor, (2) polarization, and (3) material loading of the cavity walls. A physical optics scheme is then applied to compute the backscattered field from the exit rays. This method is so simple in concept that there is virtually no restriction on the shape or material loading of the cavity. Numerical results obtained by this method are compared with those for the modal analysis for a circular cylinder terminated by a PEC plate. RCS results for an S-bend circular cylinder generated on the Cray X-MP supercomputer show significant RCS reduction. Some of the limitations and possible extensions of this technique are discussed. >

Flows in Networks

Abstract: In this chapter, you will see how the simplex method simplifies when it is applied to a class of optimization problems that are known as “network flow models.” You will also see that if a network flow model has “integer-valued data,” the simplex method finds an optimal solution that is integer-valued.