Fast heuristics for the Steiner tree problem with revenues, budget and hop constraints

This article describes and compares three heuristics for a variant of the Steiner tree problem with revenues, which includes budget and hop constraints. First, a greedy method which obtains good approximations in short computational times is proposed. This initial solution is then improved by means of a destroy-and-repair method or a tabu search algorithm. Computational results compare the three methods in terms of accuracy and speed. (C) 2007 Elsevier B.V. All rights reserved.

FAST, PARALLEL AND SECURE CRYPTOGRAPHY ALGORITHM USING LORENZ`S ATTRACTOR

A novel cryptography method based on the Lorenz`s attractor chaotic system is presented. The proposed algorithm is secure and fast, making it practical for general use. We introduce the chaotic operation mode, which provides an interaction among the password, message and a chaotic system. It ensures that the algorithm yields a secure codification, even if the nature of the chaotic system is known. The algorithm has been implemented in two versions: one sequential and slow and the other, parallel and fast. Our algorithm assures the integrity of the ciphertext (we know if it has been altered, which is not assured by traditional algorithms) and consequently its authenticity. Numerical experiments are presented, discussed and show the behavior of the method in terms of security and performance. The fast version of the algorithm has a performance comparable to AES, a popular cryptography program used commercially nowadays, but it is more secure, which makes it immediately suitable for general purpose cryptography applications. An internet page has been set up, which enables the readers to test the algorithm and also to try to break into the cipher.

Development of a fast capillary electrophoresis method for the determination of propranolol-Total analysis time reduction strategies

The aim of this study was to develop a fast capillary electrophoresis method for the determination of propranolol in pharmaceutical preparations. In the method development the pH and constituents of the background electrolyte were selected using the effective mobility versus pH curves. Benzylamine was used as the internal standard. The background electrolyte was composed of 60 mmol L(-1) tris(hydroxymethyl)aminomethane and 30 mmol L(-1) 2-hydroxyisobutyric acid,at pH 8.1. Separation was conducted in a fused-silica capillary (32 cm total length and 8.5 cm effective length, 50 mu m I.D.) with a short-end injection configuration and direct UV detection at 214 nm. The run time was only 14 s. Three different strategies were studied in order to develop a fast CE method with low total analysis time for propranolol analysis: low flush time (Lflush) 35 runs/h, without flush (Wflush) 52 runs/h, and Invert (switched polarity) 45 runs/h. Since the three strategies developed are statistically equivalent, Mush was selected due to the higher analytical frequency in comparison with the other methods. A few figures of merit of the proposed method include: good linearity (R(2) > 0.9999); limit of detection of 0.5 mg L(-1): inter-day precision better than 1.03% (n = 9) and recovery in the range of 95.1-104.5%. (C) 2009 Elsevier B.V. All rights reserved.

Fast Acquisition of (13)C NMR Spectra using the Steady-state Free Precession Sequence

In this paper, we show that the steady-state free precession sequence can be used to acquire (13)C high-resolution nuclear magnetic resonance spectra and applied to qualitative analysis. The analysis of brucine sample using this sequence with 60 degrees flip angle and time interval between pulses equal to 300 ms (acquisition time, 299.7 ms; recycle delay, 300 ms) resulted in spectrum with twofold enhancement in signal-to-noise ratio, when compared to standard (13)C sequence. This gain was better when a much shorter time interval between pulses (100 ms) was applied. The result obtained was more than fivefold enhancement in signal-to-noise ratio, equivalent to more than 20-fold reduction in total data recording time. However, this short time interval between pulses produces a spectrum with severe phase and truncation anomalies. We demonstrated that these anomalies can be minimized by applying an appropriate apodization function and plotting the spectrum in the magnitude mode.

KClone: A Proposed Approach to Fast Precise Code Clone Detection

In all applications of clone detection it is important to have precise and efficient clone identification algorithms. This paper proposes and outlines a new algorithm, KClone for clone detection that incorporates a novel combination of lexical and local dependence analysis to achieve precision, while retaining speed. The paper also reports on the initial results of a case study using an implementation of KClone with which we have been experimenting. The results indi- cate the ability of KClone to find types-1,2, and 3 clones compared to token-based and PDG-based techniques. The paper also reports results of an initial empirical study of the performance of KClone compared to CCFinderX.

Application of fast and robust equalization in communication technology

In this paper, the authors explore the potential of several popular equalization techniques while overcoming their disadvantages. First, extensive literature survey on equalization is conducted. The focus is on popular linear equalization algorithms such as the conventional least-mean-square (LMS) algorithm , The recursive least-squares (RLS) algorithm, the filtered-X LMS algorithm and their development. To overcome the slow convergence problem while keeping the simplicity of the LMS based algorithms, an H2 optimal initialization is proposed.

Delay control and parallel admission algorithms for real-time anycast flow

An anycast flow is a flow that can be connected to any one of the members in a group of designated (replicated) servers (called anycast group). In this paper, we derive a set of formulas for calculating the end-to-end delay bound for the anycast flows and present novel admission control algorithms for anycast flows with real-time constraints. Given such an anycast group, our algorithms can effectively select the paths for anycast flows' admission and connection based on the least end-to-end delay bounds evaluated. We also present a parallel admission control algorithm that can effectively calculate the available paths with a short delay bound for different destinations in the anycast group so that a best path with the shortest delay bound can be chosen.

A class of modified variable step-size NLMS algorithms for system identification

This paper proposes a class of modified variable step-size normalized least mean square (VS NLMS) algorithms. The class of schemes are obtained from estimating the optimum step-size of NLMS that minimizes the mean square deviation (MSD). During the estimation, we consider the properties of the additive noise and the input excitation together. The developed class of VS NLMS algorithms have simple forms and give improved tradeoff of fast convergence rate and low misadjustment in system identification.

Application of fast and robust equalization in communication technology

This thesis explores the potential of several popular equalization techniques and proposes new approaches to overcome their disadvantages. A H2 optimal initialization has been proposed to overcome the slow convergence problem while keeping the simplicity algorithms. The effectiveness of the methods proposed in this thesis has been verified.

Shear-enhanced solution precipitation : a simple process to produce short polymeric nanofibers

The growing interest in polymeric nanofibers has been increasing the push for the development of simple and efficient nanofiber-preparation techniques. We herein describe how a conventional solution process is readapted to suit the needs for fast and efficient production of short polymeric nanofibers. Poly(ethylene-co-acrylic acid) (PEAA), a semi-crystalline polymer, was used as model. When a PEAA solution was injected into an alcoholic non-solvent while simultaneously applying high shear to the non-solvent system, PEAA nanofibers were obtained with average diameter as thin as 113 nm and length as short as 4.5 _m. The fiber diameter and length were also adjustable by varying the operating parameters. This one-step technique advances the currently available nanofabrication tools by adjusting a widely accepted concept to the nano-scale. It may constitute a viable method for large-scale production of short polymeric nanofibers.

Fast cross-validation of kernel fisher discriminant classifiers

Given n training examples, the training of a Kernel Fisher Discriminant (KFD) classifier corresponds to solving a linear system of dimension n. In cross-validating KFD, the training examples are split into 2 distinct subsets for a number of times (L) wherein a subset of m examples is used for validation and the other subset of(n - m) examples is used for training the classifier. In this case L linear systems of dimension (n - m) need to be solved. We propose a novel method for cross-validation of KFD in which instead of solving L linear systems of dimension (n - m), we compute the inverse of an n × n matrix and solve L linear systems of dimension 2m, thereby reducing the complexity when L is large and/or m is small. For typical 10-fold and leave-one-out cross-validations, the proposed algorithm is approximately 4 and (^4/9n) times respectively as efficient as the naive implementations. Simulations are provided to demonstrate the efficiency of the proposed algorithms.

Comparison between response surface models and artificial neural networks in hydrologic forecasting

Developing an efficient and accurate hydrologic forecasting model is crucial to managing water resources and flooding issues. In this study, response surface (RS) models including multiple linear regression (MLR), quadratic response surface (QRS), and nonlinear response surface (NRS) were applied to daily runoff (e.g., discharge and water level) prediction. Two catchments, one in southeast China and the other in western Canada, were used to demonstrate the applicability of the proposed models. Their performances were compared with artificial neural network (ANN) models, trained with the learning algorithms of the gradient descent with adaptive learning rate (ANN-GDA) and Levenberg-Marquardt (ANN-LM). The performances of both RS and ANN in relation to the lags used in the input data, the length of the training samples, long-term (monthly and yearly) predictions, and peak value predictions were also analyzed. The results indicate that the QRS and NRS were able to obtain equally good performance in runoff prediction, as compared with ANN-GDA and ANN-LM, but require lower computational efforts. The RS models bring practical benefits in their application to hydrologic forecasting, particularly in the cases of short-term flood forecasting (e.g., hourly) due to fast training capability, and could be considered as an alternative to ANN