There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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Table Of Integrals Series And Products

Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

Artificial intelligence for fault diagnosis of rotating machinery: A review

A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data

http://www.infotech.monash.edu.au/assets/documents/srlab/search-and-tracking-algorithms-for-swarms-of-robots.pdf

Search and tracking algorithms for swarms of robots

Recent literature has revealed that the decision boundary of a Support Vector Machine (SVM) classifier skews towards the minority class for imbalanced data, resulting in high misclassification rate for minority samples. In this paper, we present a novel strategy for SVM in class imbalanced scenario. In particular, we focus on orienting the trained decision boundary of SVM so that a good margin between the decision boundary and each of the classes is maintained, and also classification performance is improved for imbalanced data. In contrast to existing strategies that introduce additional parameters, the values of which are determined through empirical search involving multiple SVM training, our strategy corrects the skew of the learned SVM model automatically irrespective of the choice of learning parameters without multiple SVM training. We compare our strategy with SVM and SMOTE, a widely accepted strategy for imbalanced data, applied to SVM on five well known imbalanced datasets. Our strategy demonstrates improved classification performance for imbalanced data and is less sensitive to the selection of SVM learning parameters.

z-SVM: an SVM for improved classification of imbalanced data

Accurate identification of cerebral palsy (CP) gait is important for diagnosis as well as for proper evaluation of the treatment outcomes. This paper explores the use of support vector machines (SVM) for automated detection and classification of children with CP using two basic temporal-spatial gait parameters (stride length and cadence) as input features. Application of the SVM method to a children's dataset (68 normal healthy and 88 with spastic diplegia form of CP) and testing on tenfold cross-validation scheme demonstrated that an SVM classifier was able to classify the children groups with an overall accuracy of 83.33% [sensitivity 82.95%, specificity 83.82%, area under the receiver operating curve (AUC-ROC=0.88)]. Classification accuracy improved significantly when the gait parameters were normalized by the individual leg length and age, leading to an overall accuracy of 96.80% (sensitivity 94.32%, specificity 100%, AUC-DROC area=0.9924). This accuracy result was, respectively, 3.21% and 1.93% higher when compared to an linear discriminant analysis and an multilayer-perceptron-based classifier. SVM classifier also attains considerably higher ROC area than the other two classifiers. Among the four SVM kernel functions (linear, polynomial, radial basis, and analysis of variance spline) studied, the polynomial and radial basis kernel performed comparably and outperformed the others. Classifier's performance as functions of regularization and kernel parameters was also investigated. The enhanced classification accuracy of the SVM using only two easily obtainable basic gait parameters makes it attractive for identifying CP children as well as for evaluating the effectiveness of various treatment methods and rehabilitation techniques

Support Vector Machines and Other Pattern Recognition Approaches to the Diagnosis of Cerebral Palsy Gait

In today's global economy, accuracy in forecasting the foreign exchange rate or at least predicting the trend correctly is of crucial importance for any future investment The use of computational intelligence based techniques for forecasting has been proved extremely successful in recent times In this paper, we developed and investigated three artificial neural network (ANN) based forecasting model using standard backpropagation (SBP), scaled conjugate gradient (SCG) and backpropagation with Baysian regularization (BPR) for Australian foreign exchange to predict six different currencies against Australian dollar Five moving average technical indicators are used to build the models These models were evaluated on five performance metrics and a comparison was made with traditional ARIMA model All the ANN based models outperform ARIMA model It is found that SCG based model performs best when measured on the two most commonly used metrics and shows competitive results when compared with BPR based model on other three metrics Experimental results demonstrate that ANN based model can closely forecast the forex market

Joarder Kamruzzaman

Papers

A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data

Search and tracking algorithms for swarms of robots

z-SVM: an SVM for improved classification of imbalanced data

Support Vector Machines and Other Pattern Recognition Approaches to the Diagnosis of Cerebral Palsy Gait

Forecasting of currency exchange rates using ANN: a case study