7683416
Digital filter design using multiple Pareto fronts
Evolutionary approaches have been used in a large variety of design domains, from aircraft engineering to the designs of analog filters. Many of these approaches use measures to improve the variety of solutions in the population. In this paper, clustering and Pareto optimisation are combined into a single evolutionary design algorithm. The objective of this is to prevent the system from converging prematurely to a local minimum and to encourage a number of different designs that fulfil the design criteria. Our approach is demonstrated in the domain of digital filter design. Using a polar coordinate based pole-zero representation, two different lowpass filter design problems are explored. The results are compared to designs created by a human expert. The results demonstrate that the evolutionary process is able to create designs that are competitive with those created using a conventional design process by a human expert. They also demonstrate that each evolutionary run can produce a number of different designs with similar fitness values, but very different characteristics.
16705590
A New Asynchronous Parallel Evolutionary Algorithm for Function Optimization
This paper introduces a new asynchronous parallel evolutionary algorithm (APEA) based on the island model for solving function optimization problems. Our fully distributed APEA overlaps the communication and computation efficiently and is inherently fault-tolerant in a large-scale distributed computing environment. For the scalable BUMP problem, our APEA algorithm achieves the best solution for the 50-dimension problem, and is the first algorithm of which we are aware that can solve the 1,000,000- dimension problem. For other benchmark problems, our APEA finds the best solution to G7 in fewer time steps than [16,17], and finds a better solution to G10 than [17].
1575051
Digital filter design using multiple pareto fronts
Evolutionary approaches have been used in a large variety of design domains, from aircraft engineering to the designs of analog filters. Many of these approaches use measures to improve the variety of solutions in the population. One such measure is clustering. In this paper, clustering and Pareto optimisation are combined into a single evolutionary design algorithm. The population is split into a number of clusters, and parent and offspring selection, as well as fitness calculation, are performed on a per-cluster basis. The objective of this is to prevent the system from converging prematurely to a local minimum and to encourage a number of different designs that fulfil the design criteria. Our approach is demonstrated in the domain of digital filter design. Using a polar coordinate based pole-zero representation, two different lowpass filter design problems are explored. The results are compared to designs created by a human expert. They demonstrate that the evolutionary process is able to create designs that are competitive with those created using a conventional design process by a human expert. They also demonstrate that each evolutionary run can produce a number of different designs with similar fitness values, but very different characteristics.
227525
Integrated RFID data modeling: an approach for querying physical objects in pervasive computing
Radio frequency identiﬂcation(RFID) technology uses radio-frequency waves to transfer data between readers and mov-able tagged objects without line of sight. RFID holds thepromise of real-time identifying, locating, tracking and mon-itoring physical objects, and can be used for a wide range ofpervasive computing applications. To achieve these goals,RFID data have to be collected, transformed and expres-sively modeled as their virtual counterparts in the virtualworld. RFID data, however, have their own unique char-acteristics { including aggregation, location, temporal andhistory-oriented { which have to be fully considered and inte-grated into the data model. The diversity of RFID applica-tions pose further challenges to a generalized framework forRFID data modeling. In this paper, we explore the funda-mental characteristics of RFID data and classify RFID ap-plications into a set of typical scenarios. We then propose ageneralized RFID data modeling framework with constructsfor each typical scenario. These constructs can be combinedto model most RFID applications in real world.
16813176
Programming the Bi-CGSTAB matrix solver for HPC and benchmarking IBM SP3 and alpha ES40
The panel method, or the boundary element method, has been widely used for calculating lifting flows in which forces on wing surfaces are primarily of interest. In panel method for a wind generation system with a number of propfans or for an oil platform motion control assembly with a number of thrusters/propellers, calling time for a matrix solver is the key factor for computing efficiency. This paper discusses the code development of an iterative matrix solver in MPI via C and benchmark analysis for a shared and a distributed memory UNIX machine, respectively.
15348035
Using neural network function approximation for optimal design of continuous-state parallel-series systems
This paper presents a novel continuous-state system model for optimal design of parallel-series systems when both cost and reliability are considered. The advantage of a continuous-state system model is that it represents realities more accurately than discrete-state system models. However, using conventional optimization algorithms to solve the optimal design problem for continuous-state systems becomes very complex. Under general cases, it is impossible to obtain an explicit expression of the objective function to be optimized. In this paper, we propose a neural network (NN) approach to approximate the objective function. Once the approximate optimization model is obtained with the NN approach, the subsequent optimization methods and procedures are the same and straightforward. A 2-stage example is given to compare the analytical approach with the proposed NN approach. A complicated 4-stage example is given to illustrate that it is easy to use the NN approach while it is too difficult to solve the problem analytically.
15212270
Recovery from Malicious Transactions
Preventive measures sometimes fail to deflect malicious attacks. We adopt an information warfare perspective, which assumes success by the attacker in achieving partial, but not complete, damage. In particular, we work in the database context and consider recovery from malicious but committed transactions. Traditional recovery mechanisms do not address this problem, except for complete rollbacks, which undo the work of benign transactions as well as malicious ones, and compensating transactions, whose utility depends on application semantics. Recovery is complicated by the presence of benign transactions that depend, directly or indirectly, on the malicious transactions. We present algorithms to restore only the damaged part of the database. We identify the information that needs to be maintained for such algorithms. The initial algorithms repair damage to quiescent databases; subsequent algorithms increase availability by allowing new transactions to execute concurrently with the repair process. Also, via a study of benchmarks, we show practical examples of how offline analysis can efficiently provide the necessary data to repair the damage of malicious transactions.
15077137
Modeling nonlinearity in dilution design microarray data
MOTIVATION
Dilution design (Mixed tissue RNA) has been utilized by some researchers to evaluate and assess the performance of multiple microarray platforms. Current microarray data analysis approaches assume that the quantified signal intensities are linearly related to the expression of the corresponding genes in the sample. However, there are sources of nonlinearity in microarray expression measurements. Such nonlinearity study in the expressions of the RNA mixtures provides a new way to analyze gene expression data, and we argue that the nonlinearity can reveal novel information for microarray data analysis. Therefore, we proposed a statistical model, called proportion model, which is based on the linear regression analysis. To approximately quantify the nonlinearity in the dilution design, a new calibration, beta ratio (BR) was derived from the proportion model. Furthermore, a new adjusted fold change (adj-FC) was proposed to predict the true FC without nonlinearity, in particular for large FC.


RESULTS
We applied our method to one microarray dilution dataset. The experimental results indicated that, to some extent, there are global biases comparing with the linear assumption for the significant genes. Further analysis of those highly expressed genes with significant nonlinearity revealed some promising results, e.g. 'poison' effect was discovered for some genes in RNA mixtures. The adj-FCs of those genes with 'poison' effect, indicate that the nonlinearity can be also caused by the inherent feature of the genes besides signal noise and technical variation. Moreover, when percentage of overlapping genes (POG) was used as a cross-platform consistency measure, adj-FC outperformed simple fold change to show that Affymetrix and Illumina platforms are consistent.


AVAILABILITY
The R codes which implements all described methods, and some Supplementary material, are freely available from http://www.utdallas.edu/~ying.liu/BetaRatio.htm