Towards improving cluster-based feature selection with a simplified silhouette filter

This paper proposes a filter-based algorithm for feature selection. The filter is based on the partitioning of the set of features into clusters. The number of clusters, and consequently the cardinality of the subset of selected features, is automatically estimated from data. The computational complexity of the proposed algorithm is also investigated. A variant of this filter that considers feature-class correlations is also proposed for classification problems. Empirical results involving ten datasets illustrate the performance of the developed algorithm, which in general has obtained competitive results in terms of classification accuracy when compared to state of the art algorithms that find clusters of features. We show that, if computational efficiency is an important issue, then the proposed filter May be preferred over their counterparts, thus becoming eligible to join a pool of feature selection algorithms to be used in practice. As an additional contribution of this work, a theoretical framework is used to formally analyze some properties of feature selection methods that rely on finding clusters of features. (C) 2011 Elsevier Inc. All rights reserved.

Wavelet-based cepstrum calculation

In this paper we present a new wavelet-based algorithm for low-cost computation of the cepstrum. It can be used for real time precise pitch determination in automatic speech and speaker recognition systems. Many wavelet families are examined to determine the one that works best. The results confirm the efficacy and accuracy of the proposed technique for pitch extraction. (C) 2008 Elsevier B.V. All rights reserved.

The cost of recovery protocols in web-based database systems

The cost of recovery protocols is important with respect to system performance during normal operation and failure in terms of overhead, and time taken to recover failed transactions. The cost of recovery protocols for web database systems has not been addressed much. In this paper, we present a quantitative study of cost of recovery protocols. For this purpose, we use an experiment setup to evaluate the performance of two recovery algorithms, namely the, two-phase commit algorithm and log-based algorithm. Our work is a step towards building reliable protocols for web database systems.

A new blind signal separation algorithm for instantaneous MIMO system

We address the problem of adaptive blind source separation (BSS) from instantaneous multi-input multi-output (MIMO) channels. In this paper, we propose a new constant modulus (CM)-based algorithm which employ nonlinear function as the de-correlation term. Moreover, it is shown by theoretical analysis that the proposed algorithm has less mean square error (MSE), i.e., better separation performance, in steady state than the cross-correlation and constant modulus algorithm (CC-CMA). Numerical simulations show the effectiveness of the proposed result.

An algorithm for logit network loading problem by topological sorting

This paper presents a topological sorting based algorithm for logit network loading problem to exclude all cycles by removing certain links from loops. The new algorithm calculates the link weights and flows according to topological order. It produces the theoretical results for networks without loops. Numerical examples show that the new algorithm can reduce errors introduced by the strict definition of 'reasonable route' in Dial's algorithm.

Adaptive data based neural network leader-follower control of multi-agent networks

In this paper, we propose a data based neural network leader-follower control for multi-agent networks where each agent is described by a class of high-order uncertain nonlinear systems with input perturbation. The control laws are developed using multiple-surface sliding control technique. In particular, novel set of sliding variables are proposed to guarantee leader-follower consensus on the sliding surfaces. Novel switching is proposed to overcome the unavailability of instantaneous control output from the neighbor. By utilizing RBF neural network and Fourier series to approximate the unknown functions, leader-follower consensus can be reached, under the condition that the dynamic equations of all agents are unknown. An O(n) data based algorithm is developed, using only the network’s measurable input/output data to generate the distributed virtual control laws. Simulation results demonstrate the effectiveness of the approach.

Observer-based controller design of time-delay systems with an interval time-varying delay

This paper considers the problem of designing an observer-based output feedback controller to exponentially stabilize a class of linear systems with an interval time-varying delay in the state vector. The delay is assumed to vary within an interval with known lower and upper bounds. The time-varying delay is not required to be differentiable, nor should its lower bound be zero. By constructing a set of Lyapunov–Krasovskii functionals and utilizing the Newton–Leibniz formula, a delay-dependent stabilizability condition which is expressed in terms of Linear Matrix Inequalities (LMIs) is derived to ensure the closed-loop system is exponentially stable with a prescribed α-convergence rate. The design of an observerbased output feedback controller can be carried out in a systematic and computationally efficient manner via the use of an LMI-based algorithm. A numerical example is given to illustrate the design procedure.

Contextually Learnt Detection of Unusual Motion-Based Behaviour in Crowded Public Spaces

In this paper we are interested in analyzing behaviour in crowded publicplaces at the level of holistic motion. Our aim is to learn, without user input, strong scene priors or labelled data, the scope of ‘‘normal behaviour’’ for a particular scene and thus alert to novelty in unseen footage. The first contribution is a low-level motion model based on what we term tracklet primitives, which are scenespecific elementary motions. We propose a clustering-based algorithm for tracklet estimation from local approximations to tracks of appearance features. This is followed by two methods for motion novelty inference from tracklet primitives: (a) an approach based on a non-hierarchial ensemble of Markov chains as a means of capturing behavioural characteristics at different scales, and (b) a more flexible alternative which exhibits a higher generalizing power by accounting for constraints introduced by intentionality and goal-oriented planning of human motion in a particular scene. Evaluated on a 2 h long video of a busy city marketplace, both algorithms are shown to be successful at inferring unusual behaviour, the latter model achieving better performance for novelties at a larger spatial scale.

A hierarchical PCA-based anomaly detection model

A hierarchical intrusion detection model is proposed to detect both anomaly and misuse attacks. In order to further speed up the training and testing, PCA-based feature extraction algorithm is used to reduce the dimensionality of the data. A PCA-based algorithm is used to filter normal data out in the upper level. The experiment results show that PCA can reduce noise in the original data set and the PCA-based algorithm can reach the desirable performance.

A systematic evaluation of field-based screening methods for the assessment of anterior cruciate ligament (ACL) injury risk

BACKGROUND: Laboratory-based measures provide an accurate method to identify risk factors for anterior cruciate ligament (ACL) injury; however, these methods are generally prohibitive to the wider community. Screening methods that can be completed in a field or clinical setting may be more applicable for wider community use. Examination of field-based screening methods for ACL injury risk can aid in identifying the most applicable method(s) for use in these settings. OBJECTIVE: The objective of this systematic review was to evaluate and compare field-based screening methods for ACL injury risk to determine their efficacy of use in wider community settings. DATA SOURCES: An electronic database search was conducted on the SPORTDiscus™, MEDLINE, AMED and CINAHL databases (January 1990-July 2015) using a combination of relevant keywords. A secondary search of the same databases, using relevant keywords from identified screening methods, was also undertaken. STUDY SELECTION: Studies identified as potentially relevant were independently examined by two reviewers for inclusion. Where consensus could not be reached, a third reviewer was consulted. Original research articles that examined screening methods for ACL injury risk that could be undertaken outside of a laboratory setting were included for review. STUDY APPRAISAL AND SYNTHESIS METHODS: Two reviewers independently assessed the quality of included studies. Included studies were categorized according to the screening method they examined. A description of each screening method, and data pertaining to the ability to prospectively identify ACL injuries, validity and reliability, recommendations for identifying 'at-risk' athletes, equipment and training required to complete screening, time taken to screen athletes, and applicability of the screening method across sports and athletes were extracted from relevant studies. RESULTS: Of 1077 citations from the initial search, a total of 25 articles were identified as potentially relevant, with 12 meeting all inclusion/exclusion criteria. From the secondary search, eight further studies met all criteria, resulting in 20 studies being included for review. Five ACL-screening methods-the Landing Error Scoring System (LESS), Clinic-Based Algorithm, Observational Screening of Dynamic Knee Valgus (OSDKV), 2D-Cam Method, and Tuck Jump Assessment-were identified. There was limited evidence supporting the use of field-based screening methods in predicting ACL injuries across a range of populations. Differences relating to the equipment and time required to complete screening methods were identified. LIMITATIONS: Only screening methods for ACL injury risk were included for review. Field-based screening methods developed for lower-limb injury risk in general may also incorporate, and be useful in, screening for ACL injury risk. CONCLUSIONS: Limited studies were available relating to the OSDKV and 2D-Cam Method. The LESS showed predictive validity in identifying ACL injuries, however only in a youth athlete population. The LESS also appears practical for community-wide use due to the minimal equipment and set-up/analysis time required. The Clinic-Based Algorithm may have predictive value for ACL injury risk as it identifies athletes who exhibit high frontal plane knee loads during a landing task, but requires extensive additional equipment and time, which may limit its application to wider community settings.

Aplicação do algoritmo de otimização por colônia de formigas sobre o problema do passeio do robô seletivo

This work seeks to propose and evaluate a change to the Ant Colony Optimization based on the results of experiments performed on the problem of Selective Ride Robot (PRS, a new problem, also proposed in this paper. Four metaheuristics are implemented, GRASP, VNS and two versions of Ant Colony Optimization, and their results are analyzed by running the algorithms over 32 instances created during this work. The metaheuristics also have their results compared to an exact approach. The results show that the algorithm implemented using the GRASP metaheuristic show good results. The version of the multicolony ant colony algorithm, proposed and evaluated in this work, shows the best results

Feature selection through gravitational search algorithm

In this paper we deal with the problem of feature selection by introducing a new approach based on Gravitational Search Algorithm (GSA). The proposed algorithm combines the optimization behavior of GSA together with the speed of Optimum-Path Forest (OPF) classifier in order to provide a fast and accurate framework for feature selection. Experiments on datasets obtained from a wide range of applications, such as vowel recognition, image classification and fraud detection in power distribution systems are conducted in order to asses the robustness of the proposed technique against Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and a Particle Swarm Optimization (PSO)-based algorithm for feature selection.

Roteamento de consultas em banco de dados peer-to-peer utilizando colônias de formigas e ontologias

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Reconfiguration of radial electric power distribution system via a scatter search algorithm

In this paper, to solve the reconfiguration problem of radial distribution systems a scatter search, which is a metaheuristic-based algorithm, is proposed. In the codification process of this algorithm a structure called node-depth representation is used. It then, via the operators and from the electrical power system point of view, results finding only radial topologies. In order to show the effectiveness, usefulness, and the efficiency of the proposed method, a commonly used test system, 135-bus, and a practical system, a part of Sao Paulo state's distribution network, 7052 bus, are conducted. Results confirm the efficiency of the proposed algorithm that can find high quality solutions satisfying all the physical and operational constraints of the problem.

A Hybrid Multimodal Non-Rigid Registration of MR Images Based on Diffeomorphic Demons

n this paper we present a novel hybrid approach for multimodal medical image registration based on diffeomorphic demons. Diffeomorphic demons have proven to be a robust and efficient way for intensity-based image registration. A very recent extension even allows to use mutual information (MI) as a similarity measure to registration multimodal images. However, due to the intensity correspondence uncertainty existing in some anatomical parts, it is difficult for a purely intensity-based algorithm to solve the registration problem. Therefore, we propose to combine the resulting transformations from both intensity-based and landmark-based methods for multimodal non-rigid registration based on diffeomorphic demons. Several experiments on different types of MR images were conducted, for which we show that a better anatomical correspondence between the images can be obtained using the hybrid approach than using either intensity information or landmarks alone.