User-defined Value Research Articles

Efficiently identifying critical nodes in large complex networks

The critical node detection problem (CNDP) aims to fragment a graph G=(V,E) by removing a set of vertices R with cardinality |R|≤k, such that the residual graph has minimum pairwise connectivity for user-defined value k. Existing optimization algorithms are incapable of finding a good set R in graphs with many thousands or millions of vertices due to the associated computational cost. Hence, there exists a need for a time- and space-efficient approach for evaluating the impact of removing any v∈V in the context of the CNDP. In this paper, we propose an algorithm based on a modified depth-first search that requires O(k(|V|+|E|)) time complexity. We employ the method within in a greedy algorithm for quickly identifying R. Our experimental results consider small- (≤250 nodes) and medium-sized (≤25,000 nodes) networks, where it is possible to compare to known optimal solutions or results obtained by other heuristics. Additionally, we show results using six real-world networks. The proposed algorithm can be easily extended to vertex- and edge-weighted variants of the CNDP.

Comparison of silver and molybdenum microfocus X-ray sources for single-crystal structure determination.

The quality of diffraction data obtained using silver and molybdenum microsources has been compared for six model compounds with a wide range of absorption factors. The experiments were performed on two 30 W air-cooled Incoatec IµS microfocus sources with multilayer optics mounted on a Bruker D8 goniometer with a SMART APEX II CCD detector. All data were analysed, processed and refined using standard Bruker software. The results show that Ag Kα radiation can be beneficial when heavy elements are involved. A numerical absorption correction based on the positions and indices of the crystal faces is shown to be of limited use for the highly focused microsource beams, presumably because the assumption that the crystal is completely bathed in a (top-hat profile) beam of uniform intensity is no longer valid. Fortunately the empirical corrections implemented in SADABS, although originally intended as a correction for absorption, also correct rather well for the variations in the effective volume of the crystal irradiated. In three of the cases studied (two Ag and one Mo) the final SHELXL R1 against all data after application of empirical corrections implemented in SADABS was below 1%. Since such corrections are designed to optimize the agreement of the intensities of equivalent reflections with different paths through the crystal but the same Bragg 2θ angles, a further correction is required for the 2θ dependence of the absorption. For this, SADABS uses the transmission factor of a spherical crystal with a user-defined value of μr (where μ is the linear absorption coefficient and r is the effective radius of the crystal); the best results are obtained when r is biased towards the smallest crystal dimension. The results presented here suggest that the IUCr publication requirement that a numerical absorption correction must be applied for strongly absorbing crystals is in need of revision.

A new computational approach to cracks quantification from 2D image analysis: Application to micro-cracks description in rocks

In this paper we propose a crack quantification method based on 2D image analysis. This technique is applied to a gray level Scanning Electron Microscope (SEM) images, segmented and converted in Black and White (B/W) images using the Trainable Segmentation plugin of Fiji. Resulting images are processed using a novel Matlab script composed of three different algorithms: the separation algorithm, the filtering and quantification algorithm and the orientation one. Initially the input image is enhanced via 5 morphological processes. The resulting lattice is “cut” into single cracks using 1 pixel-wide bisector lines originated from every node. Cracks are labeled using the connected-component method, then the script computes geometrical parameters, such as width, length, area, aspect ratio and orientation. A filtering is performed using a user-defined value of aspect ratio, followed by a statistical analysis of remaining cracks. In the last part of this paper we discuss about the efficiency of this script, introducing an example of analysis of two datasets with different dimension and resolution; these analyses are performed using a notebook and a high-end professional desktop solution, in order to simulate different working environments.

A multi-objective genetic optimization for spectrum sensing in cognitive radio

Cognitive radio (CR) has emerged as a promising solution to the problem of spectrum underutilization. In CR, spectrum sensing is a key feature. It enables the cognitive user or secondary user (SU) to detect spectrum holes and ensure non-interference to primary communication. Spectrum sensing has its own challenges, such as discovery of opportunities for transmission and sensing overhead. High sensing overhead may impair spectral efficiency as the radio is mostly used for detecting primary users (PUs), rather than transmitting data. On the other hand, a less frequent sensing may result in interference to PU, due to the delay in the detection of the PÚs reappearance and can lead to loss of transmission opportunities. Thus, it is of paramount importance to optimize the sensing periods for each primary channel in order to maximize the number of transmission opportunities and reduce the sensing overhead incurred. This paper extends our previous letter (Balieiro, Yoshioka, Dias, Cavalcanti, & Cordeiro, 2013) and presents a detailed description of our adaptive sensing optimization scheme for CR Networks based on a multi-objective genetic algorithm (GA) formulation. Our scheme aims at maximizing the spectrum opportunities as well as keeping the sensing overhead always within a user-defined maximum value. The simulation results show that the proposed scheme outperforms the schemes described in the literature, while keeping the sensing overhead within a target value. In addition, it provides different levels of protection to PU communication through the configuration of threshold for sensing overhead.

Framework for surveillance of instant messages

Instant messengers IMs and social networking sites SNS such as Facebook may contain harmful and suspicious messages, which is of national security concerns. Organised crimes have adopted online chatting technique to send these suspicious messages as these systems have all the facilities and could serve as platform to spread across their information widely through socio-engineered and general text messages. A solution to this problem is to detect suspicious messages from the typed messages. In this paper, we proposed a suspicious message detection system SMDs to detect suspicious messages. SMDs framework makes use of databases where instant messages are stored and an ontology information extraction technique which is able to detect suspicious messages using probabilistic models. The objective of SMDs framework is to trace the identified criminals by browsing their profile details available from their e-mail account, where suspicious messages are discovered during online chat. Experimental analysis is evaluated using the user generated content UGC testbed which consist of suspicious messages for eight different test cases with user-defined threshold value tested using SMDs. The results obtained shows high precision rate compared to the existing state-of-the-art systems.

Universal fingerprinting chip server.

The Virtual Hybridization approach predicts the most probable hybridization sites across a target nucleic acid of known sequence, including both perfect and mismatched pairings. Potential hybridization sites, having a user-defined minimum number of bases that are paired with the oligonucleotide probe, are first identified. Then free energy values are evaluated for each potential hybridization site, and if it has a calculated free energy of equal or higher negative value than a user-defined free energy cut-off value, it is considered as a site of high probability of hybridization. The Universal Fingerprinting Chip Applications Server contains the software for visualizing predicted hybridization patterns, which yields a simulated hybridization fingerprint that can be compared with experimentally derived fingerprints or with a virtual fingerprint arising from a different sample.Availability http://bioinformatica.homelinux.org/UFCVH/

Discrete Wavelet Transform and Radial Basis Neural Network for Semiconductor Wet-Etching Fabrication Flow-Rate Analysis

This paper presents research that uses discrete wavelet transform (DWT) and radial basis neural network for automatic classification. The flow rate of a wet-etching fabrication facility for a single wafer can be analyzed automatically. The electrical signal of a flow meter is collected and decomposed by means of DWT. The signal power of the coefficients processed by the DWT is fed into the radial basis neural network for initial classification. A digital filter for post signal processing and a user-defined threshold value are applied; calculations for successful identification rate take place at the final step. The research results are applicable to automatic identification functions for in situ fabrication monitoring.

Numeric-based XML labeling schema by generalized dynamic method

Most efficient indeces and query techniques over XML (extensible markup language) data are based on a certain labeling scheme, which can quickly determine ancestor-descendant and parent-child relationship between two nodes. The current basic labeling schemes such as containment scheme and prefix scheme cannot avoid relabeling when XML documents are updated. After analyzing the essence of existing dynamic XML labels such as compact dynamic binary string (CDBS) and vector encoding, this paper gives a common unifying framework for the numeric-based generalized dynamic label, which can be implemented into a variety of dynamic labels according to the different user-defined value comparison methods. This paper also proposes a novel dynamic labeling scheme called radical sign label. Extensive experiments show that the radical sign label performs well for the initialization, insertion and query operations, and especially for skewed insertion where the storage cost of the radical sign label is better than that of former methods.

An Accurate Grid -based PAM Clustering Method for Large Dataset

is the procedure to group similar objects together. Several algorithms have been proposed for clustering. Among them, the K-means clustering method has less time complexity. But it is sensitive to extreme values and would cause less accurate clustering of the dataset. However, K- medoids method does not have such limitations. But this method uses user-defined value for K. Therefore, if the number of clusters is not chosen correctly, it will not provide the natural number of clusters and hence the accuracy will be minimized. In this paper, we propose a grid based clustering method that has higher accuracy than the existing K-medoids algorithm. Our proposed Grid Multi-dimensional K-medoids (GMK) algorithm uses the concept of cluster validity index and it is shown from the experimental results that the new proposed method has higher accuracy than the existing K- medoids method. The object space is quantized into a number of cells, and the distance between the intra cluster objects decrease which contributes to the higher accuracy of the proposed method. Therefore, the proposed approach has higher accuracy and provides natural clustering method which scales well for large dataset.

Tradeoff between energy and throughput for online deadline scheduling

Abstract We consider dynamic speed scaling on a single processor and study the tradeoff between throughput and energy for deadline scheduling. Specifically, we assume each job is associated with a user-defined value (or importance) and a deadline. We allow a scheduling algorithm to discard some of the jobs (i.e., not finishing them by their deadlines), and the objective is to minimize the total energy usage plus the total value of jobs discarded. We give new online algorithms under both the unbounded-speed and bounded-speed models. When the maximum speed is unbounded, we give an O(1)-competitive algorithm. This algorithm relies on a key notion called the profitable speed, which is the maximum speed beyond which processing a job costs more energy than the value of the job. When the processor has a bounded maximum speed T, we show that no O(1)-competitive algorithm can exist and more precisely, the competitive ratio grows with the penalty ratio of the input, which is defined as the ratio between the maximum profitable speed of a job to the maximum speed T. On the positive side, we give an algorithm with a competitive ratio whose dependency on the penalty ratio almost matches the lower bound.

Probabilistic visual secret sharing schemes for grey-scale images and color images

Visual secret sharing (VSS) scheme is an encryption technique that utilizes the human visual system in recovering the secret image and does not require any cryptographic computation. Pixel expansion has been a major issue of VSS schemes. A number of probabilistic VSS schemes with minimum pixel expansion have been proposed for binary secret images. This paper presents a general probabilistic ( k, n)-VSS scheme for grey-scale images and another scheme for color images. With our schemes, the pixel expansion can be set to a user-defined value. When this value is 1, there is no pixel expansion at all. The quality of reconstructed secret images, measured by average contrast (or average relative difference), is equivalent to the contrast of existing deterministic VSS schemes. Previous probabilistic VSS schemes for black-and-white images can be viewed as special cases in the schemes proposed here.

Algorithms for partitioning of large routing networks

Partitioning of large networks is vital for decentralized management and control.This paper presents two algorithms called ‘Hierarchical Recursive Progression-1’ (HRP-1) and ‘Hierarchical Recursive Progression-2’ (HRP-2) for partitioning of large networks into subnetworks of limited size with very few interconnections between them. In other words, we are trying to maximize the internal nodes and minimize the external connections of the subnetworks. The restriction on the size and the external connections is obtained by comparison against a user-defined value for the size of the subnetwork and for external connections via a term called density. The density of a subnetwork is defined as the ratio of the number of external connections and the size of the subnetwork. The two algorithms presented in the paper are based on the principle of subnetwork clustering. At the start of the algorithms,the number of subnetworks is equal to the total number of nodes of the network with each subnetwork containing a single node. Later, subnetworks are merged at various runs of the algorithm to form new subnetworks using connectivity,density and size criteria. The algorithms terminate when all the subnetworks satisfy a user-defined size and density limit. The difference between the algorithms HRP-1 and HRP-2 lies in the definition of density of subnetworks and the way through which the subnetworks are grouped at consecutive iterations of the algorithm. Note that the number of nodes inside the subnetworks never violates the size limit, thereby ensuring even distribution of load on the partitions obtained. Finally, the two algorithms are compared and tested on randomly generated graphs and a part of Paris road Network.

Dynamic clustering threshold reduces conformer ensemble size while maintaining a biologically relevant ensemble

Representing the 3D structures of ligands in virtual screenings via multi-conformer ensembles can be computationally intensive, especially for compounds with a large number of rotatable bonds. Thus, reducing the size of multi-conformer databases and the number of query conformers, while simultaneously reproducing the bioactive conformer with good accuracy, is of crucial interest. While clustering and RMSD filtering methods are employed in existing conformer generators, the novelty of this work is the inclusion of a clustering scheme (NMRCLUST) that does not require a user-defined cut-off value. This algorithm simultaneously optimizes the number and the average spread of the clusters. Here we describe and test four inter-dependent approaches for selecting computer-generated conformers, namely: OMEGA, NMRCLUST, RMS filtering and averaged-RMS filtering. The bioactive conformations of 65 selected ligands were extracted from the corresponding protein:ligand complexes from the Protein Data Bank, including eight ligands that adopted dissimilar bound conformations within different receptors. We show that NMRCLUST can be employed to further filter OMEGA-generated conformers while maintaining biological relevance of the ensemble. It was observed that NMRCLUST (containing on average 10 times fewer conformers per compound) performed nearly as well as OMEGA, and both outperformed RMS filtering and averaged-RMS filtering in terms of identifying the bioactive conformations with excellent and good matches (0.5 < RMSD < 1.0 Å). Furthermore, we propose thresholds for OMEGA root-mean square filtering depending on the number of rotors in a compound: 0.8, 1.0 and 1.4 for structures with low (1–4), medium (5–9) and high (10–15) numbers of rotatable bonds, respectively. The protocol employed is general and can be applied to reduce the number of conformers in multi-conformer compound collections and alleviate the complexity of downstream data processing in virtual screening experiments.Electronic supplementary materialThe online version of this article (doi:10.1007/s10822-010-9365-1) contains supplementary material, which is available to authorized users.

TaylUR 3, a multivariate arbitrary-order automatic differentiation package for Fortran 95

This new version of TaylUR is based on a completely new core, which now is able to compute the numerical values of all of a complex-valued function's partial derivatives up to an arbitrary order, including mixed partial derivatives.

The GNSS ambiguity ratio-test revisited: a better way of using it

Integer carrier phase ambiguity resolution is the key to fast and high-precision global navigation satellite system (GNSS) positioning and application. Apart from integer estimation, also acceptance tests are part of the ambiguity resolution process. A popular acceptance test is the so-called ratio-test. In this contribution we study the properties and the underlying concepts of the ratio-test. We discuss some misconceptions of the ratio-test and in particular show that the ratio-test is not a test for testing the correctness of the integer least-squares solution. We also show that the common usage of the ratio-test with a fixed critical value has shortcomings. Instead, the fixed failure rate approach is recommended. This approach is part of the more general theory of integer aperture estimation, and enables that the failure rate does not exceed a user-defined value. Results of the fixed failure-rate ratio-test and its improved performance are illustrated with a number of examples.

Porosity Measurement Method by X-Ray Computed Tomography

This paper describes a new nondestructive approach for porosity measurement developed in response to a need for a more accurate porosity measuring method for solid samples. The conventional methods in X-ray CT imaging are based on image segmentation where a threshold is applied at a user-defined value. As a result, the uncertainty in the porosity measurement is introduced. Therefore, the new method, called the grey level method, which reflects the phenomenon of image processing and computation of ration between the volume of voids and the total volume of the entire sample, was considered and improved in term of image noise and artifacts. The volumes of 2D CT image as a surface are achieved by means of integrating the surface with operations relating to image histogram. The porosity value is given from the curve of a porosity distribution. Subsequently, the properties of individual pores were measured and comparison with conventional destructive method of porosity computation was carried out. Also limitations of industrial X-ray CT as well as their accuracy are discussed.

Performance assessment of non-self-regulating controllers in a cogeneration power plant

This work details a novel method for assessing the performance of a PI (proportional-integral) feedback controller when the process displays non-self-regulating dynamic behavior. By applying an intuitive process control-based pattern recognition method to the autocorrelation function of the process measurement signal, the controller’s disturbance rejection performance can automatically be categorized. Stochastic data collected over days or weeks is analyzed to compute an index descriptive of current controller performance. If the control response has drifted from a user-defined target value, the analysis further provides a guide for tuning adjustments to restore desired performance. Significant aspects of this approach are that no plant disruption or process knowledge is required for evaluation. Classic examples of non-self-regulating behavior include certain liquid level control loops and pressure control loops which are prevalent in cogeneration power plants. In this work, we detail how the performance assessment method was used to improve performance of such controllers in the University of Connecticut’s power plant.

Collocated H∞ Control of a Cantilevered Beam using an Analytical Upper-bound Approach

This article examines the collocated H∞ control of vector second-order structural systems using an analytical upper-bound method. The structural system considered in this work is that of a cantilevered aluminum beam with a collocated pair of piezoceramic patches to serve as actuators and sensors. An explicit expression that bounds the H∞ norm of collocated structural systems is applied for analysis and control synthesis, resulting in significant computational advantages compared to standard H ∞ analysis and control methods. The proposed approximation method is used to design a static output feedback controller that guarantees a closed-loop H∞ norm less than any user-defined value. This method is validated using a finite element representation of the beam, and then verified experimentally.

Unfolding for Color Volume Dataset Using the Difference of Segmented Contours

Objective: Unfolding is a rendering method to visualize organs at a glance by virtually incising them. Although conventional methods exploit gray-scale volume datasets such as CT or MR images, we use the Visible Korean Human dataset preserving actual color. This can be helpful for the study of anatomical knowledge. Segmented images of Visible Korean Human dataset store the boundary of organs. Since medical experts manually perform the segmentation from anatomical color images, it is very time-consuming. In general, therefore, some images selectively sampled with interval from entire color images are segmented. When we generate a segment volume dataset with the selected images, final results are deteriorated due to lack of segmentation information for missed images. In this paper, we solve this problem by generating intermediate images without performing a manual segmentation. Methods: Firstly, after comparing differences of organ’s contours in between two consecutive segmented images, we represent the differences as a user-defined value in the intermediate images. This procedure is repeated for all pairs of manually segmented images to reconstruct entire volume data consist of manually segmented images and their intermediate images. In rendering stage, we perform the radial volume ray casting along with the central path of target organ. If a ray reaches to a region having the user-defined values, we advance over the region without compositions to the boundary of that region. Then the color composition is begun by performing backtracking, since the advanced region is regarded to the thickness of it. Results: As a result, we can produce high quality unfolding images for the stomach, colon, bronchus, and artery of the Visible Korea Human dataset. Conclusion: Since our approach can be applied to virtual dissection including actual human colors, it is helpful for the endoscopy and anatomy studies.

GEPS: the Gene Expression Pattern Scanner

Gene Expression Pattern Scanner (GEPS) is a web-based server to provide interactive pattern analysis of user-submitted microarray data for facilitating their further interpretation. Putative gene expression patterns such as correlated expression, similar expression and specific expression are determined globally and systematically using geometric comparison and correlation analysis methods. These patterns can be visualized via linear plot with quantitative measures. User-defined threshold value is allowed to customize the format of the pattern search results. For better understanding of gene expression, patterns derived from 329 205 non-redundant gene expression records from the GNF SymAltas and the Gene Expression Omnibus are also provided. These profiles cover 24 277 human genes in 79 tissues, 32 905 mouse genes in 61 tissues and 4201 rat genes in 44 tissues. GEPS is available at .