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

Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs.

Computational Intelligence in Wireless Sensor Networks: A Survey

We present a new weighted voting classification ensemble method, called WAVE, that uses two weight vectors: a weight vector of classifiers and a weight vector of instances. The instance weight vector assigns higher weights to observations that are hard to classify. The weight vector of classifiers puts larger weights on classifiers that perform better on hard-to-classify instances. One weight vector is designed to be calculated in conjunction with the other through an iterative procedure. That is, the instances of higher weights play a more important role in determining the weights of classifiers, and vice versa. We proved that the iterated weight vectors converge to the optimal weights which can be directly calculated from the performance matrix of classifiers in an ensemble. The final prediction of the ensemble is obtained by voting using the optimal weight vector of classifiers. To compare the performance between a simple majority voting and the proposed weighted voting, we applied both of the voting methods to bootstrap aggregation and investigated the performance on 28 datasets. The result shows that the proposed weighted voting performs significantly better than the simple majority voting in general.

A weight-adjusted voting algorithm for ensembles of classifiers

This paper introduces Learn++, an ensemble of classifiers based algorithm originally developed for incremental learning, and now adapted for information/data fusion applications. Recognizing the conceptual similarity between incremental learning and data fusion, Learn++ follows an alternative approach to data fusion, i.e., sequentially generating an ensemble of classifiers that specifically seek the most discriminating information from each data set. It was observed that Learn++ based data fusion consistently outperforms a similarly configured ensemble classifier trained on any of the individual data sources across several applications. Furthermore, even if the classifiers trained on individual data sources are fine tuned for the given problem, Learn++ can still achieve a statistically significant improvement by combining them, if the additional data sets carry complementary information. The algorithm can also identify-albeit indirectly-those data sets that do not carry such additional information. Finally, it was shown that the algorithm can consecutively learn both the supplementary novel information coming from additional data of the same source, and the complementary information coming from new data sources without requiring access to any of the previously seen data

/pdf/an-ensemble-based-incremental-learning-approach-to-data-3mohdsvrmk.pdf

An Ensemble-Based Incremental Learning Approach to Data Fusion

This paper presents a new approach to prioritize “Large-scale Data” of patients with chronic heart diseases by using body sensors and communication technology during disasters and peak seasons. An evaluation matrix is used for emergency evaluation and large-scale data scoring of patients with chronic heart diseases in telemedicine environment. However, one major problem in the emergency evaluation of these patients is establishing a reasonable threshold for patients with the most and least critical conditions. This threshold can be used to detect the highest and lowest priority levels when all the scores of patients are identical during disasters and peak seasons. A practical study was performed on 500 patients with chronic heart diseases and different symptoms, and their emergency levels were evaluated based on four main measurements: electrocardiogram, oxygen saturation sensor, blood pressure monitoring, and non-sensory measurement tool, namely, text frame. Data alignment was conducted for the raw data and decision-making matrix by converting each extracted feature into an integer. This integer represents their state in the triage level based on medical guidelines to determine the features from different sources in a platform. The patients were then scored based on a decision matrix by using multi-criteria decision-making techniques, namely, integrated multi-layer for analytic hierarchy process (MLAHP) and technique for order performance by similarity to ideal solution (TOPSIS). For subjective validation, cardiologists were consulted to confirm the ranking results. For objective validation, mean ± standard deviation was computed to check the accuracy of the systematic ranking. This study provides scenarios and checklist benchmarking to evaluate the proposed and existing prioritization methods. Experimental results revealed the following. (1) The integration of TOPSIS and MLAHP effectively and systematically solved the patient settings on triage and prioritization problems. (2) In subjective validation, the first five patients assigned to the doctors were the most urgent cases that required the highest priority, whereas the last five patients were the least urgent cases and were given the lowest priority. In objective validation, scores significantly differed between the groups, indicating that the ranking results were identical. (3) For the first, second, and third scenarios, the proposed method exhibited an advantage over the benchmark method with percentages of 40%, 60%, and 100%, respectively. In conclusion, patients with the most and least urgent cases received the highest and lowest priority levels, respectively.

Based on Real Time Remote Health Monitoring Systems: A New Approach for Prioritization "Large Scales Data" Patients with Chronic Heart Diseases Using Body Sensors and Communication Technology.

Multi-sensor fusion: an Evolutionary algorithm approach

Two modifications of Genetic algorithm (GA) are proposed that employ gradient analysis of the fitness function and are integrated with the main genetic procedure. Combination of the relative weighted error factor and adaptive size of the mutation pool accelerates convergence of the iterative process and indicates When the global optimum um solution is found. Local gradient correction of the initial pool during interactions refines the search procedure. Computational experiments show that both modifications can increase efficiency of GA when they are applied to an image registration problem.

Gradient-based genetic algorithms in image registration

The modified versions of the basic genetic operations - reproduction, crossover and mutation - in evolutionary algorithm are proposed in relation to 2D grayscale image registration problem. Two modifications of the reproduction phase include deletion of clones and genes with the same or similar parameter values, and local correction of the reproduction pool. Local correction is implemented as two consecutive stages - random search and local refinement. The RC-crossover is introduced that takes advantage of the best genes of the population while avoiding a direct replacement of the worse parameter values with their better counterparts. Mutation with memory aims to explore all poorly represented areas of the search space in order to eliminate the possibility of overlooking a better (or the best) solution. Computational experiments show that proposed modifications can improve convergence of evolutionary procedure when they are applied to 2D grayscale image registration problem.

Using local correction and mutation with memory to improve convergence of evolutionary algorithm in image registration

This article focuses on the efficiency problems associated with the use of local search in the hybrid evolutionary algorithm. A two-phase cyclic local search is proposed that alternates the random search and the downhill simplex method (DSM), and helps prevent the algorithm from converging to a sub-optimal solution in multidimensional optimization. The algorithm utilizes a novel micro-model of image local response, in order to reduce the number of fitness evaluations during the local DSM search, with the application to the global optimization problem arising in electronic imaging. The problem is stated as the search for the feasible transformation parameters that minimize the difference between two images. Image local response is defined as the variation of the fitness function that occurs because of a small variation of the parameters, and is computed over a small pixel area. The computed response coefficients specify a contraction transformation applied to the vector of the regular DSM coefficients that...

Reducing the computational cost of local search in the hybrid evolutionary algorithm with application to electronic imaging

Image registration is formulated as a nonlinear optimization problem of finding an affine transformation minimizing the difference between images. A particular scheme of the hybrid evolutionary algorithm is used to solve the problem. The reproduction phase of the algorithm is enhanced with a two-phase operation of local search and correction performed on the subset of the best chromosomes in the reproduction pool. In order to reduce the computational cost of the correction, a mechanism of the adaptive control of the local search is designed, based on a micro model of the local image response. The mechanism correlates the step size of the search with the local properties of the gray level surface at different points of an image. To reduce the number of evaluations of the local image response required by the control mechanism, all participating image points are evaluated and classified at once in the preprocessing stage. A self-organizing neural network is employed to classify different points according to their response values, and to build an adaptive, compact response map of the entire image. During the execution of the main algorithm, this map is used as a lookup table, to retrieve the appropriate response values for the points participating in the local search.

Improving local search with neural network in image registration with the hybrid evolutionary algorithm

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