Hussein I. Shahein

Chameleon ART: a non-optimization based analog design migration framework

Presented in this paper is a tool that automatically migrates analog designs from one process to another while keeping circuit and layout topologies. A netlist migration engine recalculates the new device dimensions in the target technology followed by a layout migration engine that compacts the design according to the new process design rules. The overall framework preserves design intelligence embedded in the original IP such as symmetry, hierarchy, placement and routing. The circuit migration engine, being very fast, can retarget large analog blocks in only a few minutes while giving same or better performance of the original design. The migration of 3 different circuits is presented to validate the overall methodology. These circuits have been fabricated and measured

Enhanced SVM versus Several Approaches in SAR Target Recognition

This paper presents a comparative study between different automatic target recognition (ATR) approaches in the application of synthetic aperture radar (SAR) target recognition. Four different categories of approaches are investigated and compared. The first is distribution-based where a statistical data model is assumed for the SAR image data. The second category contains one approach that is based upon principal component analysis (PCA). The third category employs different neural network architectures. The last category utilizes support vector machines (SVM). It contains the classical SVM implementation and an enhanced implementation proposed elsewhere by the authors in which the traditional Euclidean kernel is replaced by a new one that is more suitable for the application in question. Experimental results are presented. It is shown that the enhanced SVM approach outperforms all other investigated approaches in both the classification performance and the confuser rejection

An integer-coded evolutionary approach for mixture maximum likelihood clustering

This paper outlines an algorithm for solving the maximum mixture likelihood clustering problem using an integer-coded genetic algorithm (IGA-ML) where a fixed length chromosome encodes the object-to-cluster assignment. The main advantage of the outlined algorithm (IGA-ML) compared with other known algorithms, such as the k-means technique, is that it can successfully discover the correct number of clusters, in addition to carrying out the partitioning process. The algorithm implements a post-fixing sorting mechanism that drastically reduces the searched solution space by eliminating duplicate solutions that appear after applying the genetic operations. Simulation results show the effectiveness of the algorithm especially with the case of overlapping clusters.

Analog Layout Retargeting

This chapter focuses on analog layout process retargeting. Unlike automatic placement and routing tools, retargeting starts with an input layout in 6 a given process. The main target is to conserve most of the layout physical intelli- gence while migrating it to another given technology. This is usually achieved by adapting existing layout compaction techniques borrowed from the digital world. Historically, layout compaction used to rely on fast constraint-graph operations. More recently, linear programming has been introduced to support hierarchy in ad- dition to complex analog constraints. This chapter introduces a novel graph-based simplex algorithm that combines the efficiency of graph-based methods together with the generality of linear programming ones. It also allows symmetry, hierar-chy, and cell replacement support to be integrated seamlessly without any artificial modification of the algorithm. For simple layout constraints, the algorithm com- plexity tends to be as linear as graph-based techniques, while for the most complex constraints and objective function it tends to that of the simplex method.

A Novel General Graph-Based Simplex Algorithm Applied to IC Layout Compaction and Migration

In this paper, a novel method for solving the IC layout compaction problem is introduced. The solution supports all kinds of linear constraints and linear optimization functions. Graph-based techniques are employed so that all matrix operations in linear programming are replaced by much faster graph operations. The proposed algorithm outperforms all studied compaction methods by combining the generality of linear programming and the efficiency of graph-based methods. Correct and optimal migrated layouts were produced with significant improvement in performance.

New Spatial FCM Approach with Application to SAR Target Clustering

This paper develops a new fuzzy clustering approach that is suitable for image processing applications. The developed approach is based upon the classical fuzzy c-means (FCM) and referred to as the spatial FCM (SFCM). Its effectiveness is due to two mechanisms. The first is the replacement of the Euclidean distance traditionally used to measure similarity between input images and clusters prototypes by a novel similarity measure that considers spatial relationships between image pixels and thus becomes less sensitive to image perturbations. The second SFCM mechanism for effectiveness is the addition of a similarity penalty term to FCMs objective function. The aim is to encourage clustering similar images into same clusters. The SFCM is compared to the FCM and some of its variants in the difficult application of synthetic aperture radar (SAR) target clustering. It is shown that the SFCM consistently yields better performance

SVM Enhancement with Application to SAR Imagery Classification

This paper investigates enhancing the performance of support vector machines (SVMs) in the application of synthetic aperture radar (SAR) imagery classification. The approach is to replace the conventional Euclidean distance in the SVM kernel with a new similarity measure that is less sensitive to perturbations. Same-target SAR images show perturbations, in part due to the presence of speckle and in part due to small variations in radar depression angle and target orientation. It is expected that SVMs with the proposed new kernel will outperform those with the conventional Euclidean kernel. Experimental results are presented to validate this expectation for both batch and iterative implementations of SVMs. The paper also argues that the proposed approach is well-founded theoretically by demonstrating that the new kernel is still a Mercer kernel

Recard: using recommendation cards approach for building trust in peer-to-peer networks

The peer-to-peer applications have recently seen an enormous success and spread over the Internet community which showed a dramatic change in the current client-server paradigm; that caused the appearance of some new concepts and protocols. One of the main new concepts introduced is the user anonymity which is in spite of being considered one of the main characteristics of the peer-to-peer paradigm it has introduced a serious security flaw due to the missing of trust between the participants in the system. This paper proposes an approach for peer-to-peer security, where the system participants can establish a trust relationship between each others based on their reputation gained by the participation in the system. The proposed technique relays on the concept of the recommendation cards. This paper discusses this technique and how to apply it to a peer-to-peer file sharing application.

Towards graph based parallel sparse solver for circuit simulation problems

In this paper, a novel technique based on graph theory is introduced to solve sparse linear systems. The proposed technique enhances the ability to build sparse parallel solvers for circuit simulation matrices. Thus the proposed technique improves the performance of circuit simulation algorithms. The new technique represents sparse linear system as a signal flow graph. Then it divides the graph into separate strongly connected components SCC. SCCs relations are represented and used to enhance the parallelism of the solver.

Gabor wavelet based automatic coin classsification

We present an automatic coin classifier mainly depending on visual features. Our multistage system starts out by segmentation using circular Hough Transform, features extraction by two complementary cues and finally classification by simple nearest neighbor measure. Our features extraction process relies on rotation invariant edge orientation followed by Gabor wavelet convolution. Testing on the publicly available portion of a benchmark European coins database, we can correctly classify 93.5% and 98% of the coins using single face and double faces images respectively. We also show that our correct classification rate can reach 99.8% when adding the coin thickness measurement (which is available for this database).