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We propose an input delay neural network (IDNN) based time series prediction algorithm for compressing electrocardiogram (ECG) signals. Our algorithm has been tested and successfully compared vis-à-vis other popular techniques for its compression efficiency and reconstruction capability.

Development of sensor(s)-based, signal processing-aided, low-cost ambient-assisted living tools (AALs), essentially for assisting elderly people, home automation, and remote monitoring purposes, has become an important research domain. Within this domain, developing intelligent systems for human movement recognition in specific directions has become a very important problem statement. This article shows how a sophisticated, low-cost, integrated system can be developed using an indigenously developed hardware–software combine. The solution employs around four pyroelectric infrared (PIR) sensor based hardware systems coupled with a novel dictionary learning algorithm. The work successfully carries out the recently proposed multiple cluster pursuit (MCP)-algorithm-based dictionary learning for the human detection problem and then proposes a new variant of MCP algorithm, called modified MCP algorithm, for this purpose. Extensive real-life performance evaluations have been performed to demonstrate the suitability of MCP and the modified MCP algorithms for the problem under consideration. [ABSTRACT FROM AUTHOR]

In this work ear recognition of a moving person with the help of a single fixed-in-position video camera is investigated, a novel problem undertaken to the best of our knowledge and belief. The challenges associated with this work are that during data capturing process each moving subject appears in unconstrained views together with random noise, motion blur and different level of illumination. Secondly, the collection of large number of redundant samples for each subject makes the training database bulky, which eventually increases classification time. So, in this paper, we introduce a novel, reduced time, metaface dictionary learning approach which by employing Frobenius norm based regularization reduces redundancy of training database with much lesser time as compared to other available database learning methods. In general, ear samples with random poses, severe motion blur and different levels of illumination loose significant class specific information and hence inflict severe nonlinearity to the system. The simple and well accounted solution for the above problem is kernel framework which makes samples of different classes linearly separable by elevating them to higher dimensions. In our proposed solution, we have used novel l 2 -norm regularized affine hull based kernel collaborative representation based classification scheme, which represents each query set as an affine hull and then collaboratively represents this hull over the linear span of gallery sets of all classes in the high dimensional space. Finally, the query set is assigned to that particular class which gives least representation or residual error among all the available classes. Results of extensive experimentations carried out over an indigenously developed database in our laboratory (named ERVIDJU) aptly demonstrate that our proposed method is a valid biometric identifier and it can produce superior performance compared to several other contemporary algorithms, developed for similar purposes. [ABSTRACT FROM AUTHOR]

The active contour models have been popularly employed for image segmentation for almost a decade now. Among these active contour models, the level set based Chan and Vese algorithm is a popular region-based model that inherently utilizes intensity homogeneity in each region under consideration. However, the Chan and Vese model often suffers from the possibility of getting trapped in a local minimum, if the contour is not properly initialized. This problem assumes greater importance in the context of medical images where the intensity variations may assume large varieties of local and global profiles. In this work we propose a robust version of the Chan and Vese algorithm which is expected to achieve satisfactory segmentation performance, irrespective of the initial choice of the contour. This work formulates the fitting energy minimization problem to be solved using a metaheuristic optimization algorithm and makes a successful implementation of our algorithm using particle swarm optimization (PSO) technique. Our algorithm has been developed for two-phase level set implementation of the Chan and Vese model and it has been successfully utilized for both scalar-valued and vector-valued images. Extensive experimentations utilizing different varieties of medical images demonstrate how our proposed method could significantly improve upon the quality of segmentation performance achieved by Chan and Vese algorithm with varied initializations of contours. [ABSTRACT FROM AUTHOR]

In this paper the harmony search (HS) algorithm and Lyapunov theory are hybridized together to design a stable adaptive fuzzy tracking control strategy for vision-based navigation of autonomous mobile robots. The proposed variant of HS algorithm, with complete dynamic harmony memory (named here as DyHS algorithm), is utilized to design two self-adaptive fuzzy controllers, for $$x$$-direction and $$y$$-direction movements of a mobile robot. These fuzzy controllers are optimized, both in their structures and free parameters, such that they can guarantee desired stability and simultaneously they can provide satisfactory tracking performance for the vision-based navigation of mobile robots. In addition, the concurrent and preferential combinations of global-search capability, utilizing DyHS algorithm, and Lyapunov theory-based local search method, are employed simultaneously to provide a high degree of automation in the controller design process. The proposed schemes have been implemented in both simulation and real-life experiments. The results demonstrate the usefulness of the proposed design strategy and shows overall comparable performances, when compared with two other competing stochastic optimization algorithms, namely, genetic algorithm and particle swarm optimization. [ABSTRACT FROM AUTHOR]

Abstract: In this paper a novel approach for channel equalization is presented, where a framework for Volterra system is used to model both the channel and the equalizer. We propose development of first-order and second-order Volterra equalizers using minimum mean square error (MMSE) approach and design these equalizers using swarm intelligence based stochastic optimization algorithm which is applied to adapt the equalizer coefficients to the time varying channel. This work proposes to use the artificial bee colony (ABC) algorithm, recently introduced for global optimization, simulating the intelligent foraging behavior of honey bee swarm in a simple, robust, and flexible manner. For comparative analysis, adaptive equalizers like least mean squares (LMSs) equalizer, recursive least squares (RLSs) equalizer and least mean p-Norm (LMP) equalizer and population based optimum equalizers employing PSO are also applied for identical problems and the superiority of the newly proposed algorithm is aptly demonstrated. [Copyright &y& Elsevier]

Abstract: This work describes the development of a computerized medical diagnostic tool for heart beat categorization. The main objective is to achieve an accurate, timely detection of cardiac arrhythmia for providing appropriate medical attention to a patient. The proposed scheme employs a feature extractor coupled with an Artificial Neural Network (ANN) classifier. The feature extractor is based on cross-correlation approach, utilizing the cross-spectral density information in frequency domain. The ANN classifier uses a Learning Vector Quantization (LVQ) scheme which classifies the ECG beats into three categories: normal beats, Premature Ventricular Contraction (PVC) beats and other beats. To demonstrate the generalization capability of the scheme, this classifier is developed utilizing a small training dataset and then tested with a large testing dataset. Our proposed scheme was employed for 40 benchmark ECG files of the MIT/BIH database. The system could produce classification accuracy as high as 95.24% and could outperform several competing algorithms. [Copyright &y& Elsevier]

Abstract: Nonlinear filtering techniques have recently become very popular in the field of signal processing. In this study we have considered the modeling of nonlinear systems using adaptive nonlinear Volterra filters and bilinear polynomial filters. The performance evaluation of these nonlinear filter models for the problem of nonlinear system identification has been carried out for several random input excitations and for measurement noise corrupted output signals. The coefficients of the two candidate filter models for are designed using several well known adaptive algorithms, such as least mean squares (LMS), recursive least squares (RLS), least mean p-norm (LMP), normalized LMP (NLMP), least mean absolute deviation (LMAD) and normalized LMAD (NLMAD) algorithms. Detailed simulation studies have been carried out for comparative analysis of Volterra model and bilinear polynomial filter, using these candidate adaptation algorithms, for system identification tasks and the superior solutions are determined. [Copyright &y& Elsevier]

Abstract: In this paper an Adaptive Bacterial Foraging is proposed for fuzzy entropy optimization when it is applied to the segmentation of gray images. The proposed algorithm represents the improved version of classical bacterial foraging algorithm which is a newly developed stochastic optimization tool. This optimization technique is applied for optimization of the fitness function which is fuzzy entropy. Classical bacterial foraging algorithm is improved by adaptively selecting the exploitation and exploration state in chemotaxis of E.coli. bacteria. The newly developed algorithm is applied on benchmark gray images and proved to be suitable for thresholding based image segmentation. [Copyright &y& Elsevier]

This paper studies the performance of a wireless sensor network (WSN) in the context of binary detection of a deterministic signal. This paper aims to develop a numerical solution for the optimal power allocation scheme via a variation of the biogeography-based optimization (BBO) algorithm, which is called the constrained BBO-DE algorithm. This new stochastic optimization algorithm is a hybridization of a very recently proposed stochastic optimization algorithm, i.e., the BBO algorithm, with another popular stochastic optimization algorithm called the differential evolution (DE) algorithm. The objective is to minimize the total power spent by the whole sensor network under a desired performance criterion, which is specified as the detection error probability. The proposed algorithm has been tested for several case studies, and its performances are compared with those of two constrained versions of the BBO and DE algorithms. [ABSTRACT FROM PUBLISHER]

Abstract: The present paper describes the development of an indirect adaptive fuzzy control scheme employing feedback linearizing technique. The scheme proposes the development of a fuzzy certainty equivalence controller for controlling non-linear plants. This controller is designed on the basis of plant parameters estimated online at each sampling instant using bacterial foraging optimization (BFO) technique, a stochastic optimization technique, popularly employed in recent times. The utility of the proposed scheme is aptly demonstrated by implementing it to control the level in a surge tank under a variety of reference input commands, where the fuzzy controller could significantly out-perform the corresponding classical feedback linearizing controller and PSO-based fuzzy controller. [Copyright &y& Elsevier]

Abstract: In this paper, we present a new variant of Particle Swarm Optimization (PSO) for image segmentation using optimal multi-level thresholding. Some objective functions which are very efficient for bi-level thresholding purpose are not suitable for multi-level thresholding due to the exponential growth of computational complexity. The present paper also proposes an iterative scheme that is practically more suitable for obtaining initial values of candidate multilevel thresholds. This self iterative scheme is proposed to find the suitable number of thresholds that should be used to segment an image. This iterative scheme is based on the well known Otsu’s method, which shows a linear growth of computational complexity. The thresholds resulting from the iterative scheme are taken as initial thresholds and the particles are created randomly around these thresholds, for the proposed PSO variant. The proposed PSO algorithm makes a new contribution in adapting ‘social’ and ‘momentum’ components of the velocity equation for particle move updates. The proposed segmentation method is employed for four benchmark images and the performances obtained outperform results obtained with well known methods, like Gaussian-smoothing method (Lim, Y. K., & Lee, S. U. (1990). On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern Recognition, 23, 935–952; Tsai, D. M. (1995). A fast thresholding selection procedure for multimodal and unimodal histograms. Pattern Recognition Letters, 16, 653–666), Symmetry-duality method (Yin, P. Y., & Chen, L. H. (1993). New method for multilevel thresholding using the symmetry and duality of the histogram. Journal of Electronics and Imaging, 2, 337–344), GA-based algorithm (Yin, P. -Y. (1999). A fast scheme for optimal thresholding using genetic algorithms. Signal Processing, 72, 85–95) and the basic PSO variant employing linearly decreasing inertia weight factor. [Copyright &y& Elsevier]

Abstract: The present work proposes the development of an automated medical diagnostic tool that can classify ECG beats. This is considered an important problem as accurate, timely detection of cardiac arrhythmia can help to provide proper medical attention to cure/reduce the ailment. The proposed scheme utilizes a cross-correlation based approach where the cross-spectral density information in frequency domain is used to extract suitable features. A least square support vector machine (LS-SVM) classifier is developed utilizing the features so that the ECG beats are classified into three categories: normal beats, PVC beats and other beats. This three-class classification scheme is developed utilizing a small training dataset and tested with an enormous testing dataset to show the generalization capability of the scheme. The scheme, when employed for 40 files in the MIT/BIH arrhythmia database, could produce high classification accuracy in the range 95.51–96.12% and could outperform several competing algorithms. [ABSTRACT FROM AUTHOR]

Abstract: The present paper deals with the optimal sizing of CMOS positive second-generation current conveyors (CCII+) employing an optimization algorithm. A contemporary non-gradient stochastic optimization algorithm, called bacterial foraging optimization (BFO) algorithm, has been employed to obtain the optimal physical dimensions of the constituent PMOS and NMOS transistors of the CCII+. The optimization problem has been cast as a bi-objective minimization problem, where we attempt to simultaneously minimize the parasitic X-port input resistance (R X ) and maximize the high end cut-off frequency of the current signal (f ci ). The results have been presented for a large selection of bias currents (I 0) and our proposed algorithm could largely outperform a similar algorithm, recently proposed, employing particle swarm optimization (PSO) algorithm and also the differential evolution (DE) algorithm. [ABSTRACT FROM AUTHOR]

Abstract: The present paper shows how a recently proposed modified Particle Swarm Optimization (PSO) algorithm, called Geese PSO algorithm, can be utilized to tune a fuzzy supervisor for an adaptive Extended Kalman filter (EKF) based approach to solve simultaneous localization and mapping (SLAM) problems for mobile robots or vehicles. This type of fuzzy based adaptive EKF approach for SLAM problems has recently been shown to be an effective approach to improve performance in those situations where correct a priori knowledge of process and/or sensor/measurement uncertainty statistics i.e. Q and/or R respectively, is not available. The newly proposed system in this work is demonstrated to provide better estimation and map-building performance in comparison with those fuzzy supervisors for the adaptive EKF algorithm, where the free parameters of the fuzzy systems are tuned using basic PSO based algorithm. The utility of the proposed approach is aptly demonstrated by employing it for several benchmark environment situations with various numbers of waypoints and landmarks, where the Geese PSO algorithm could tune the fuzzy supervisor better than the basic PSO based algorithm. [Copyright &y& Elsevier]

Multihop connectivity-based algorithms have been receiving increased attention in recent times for localization in wireless sensor networks (WSNs). This paper proposes the development of a Fletcher-Reeves update-based conjugate gradient (CG) multilayered feedforward neural network for multihop connectivity-based localization of a large number of sensor nodes in a 2-D sensor network on the basis of information gathered from beacon nodes. The neural-network-based system employs a classification scheme where the location of a sensor is simultaneously estimated in both the x- and y-directions. The usefulness of the proposed scheme is demonstrated by employing the scheme for three case studies, with varied environments, where it could consistently show better performance than two popular recently proposed schemes. [ABSTRACT FROM AUTHOR]

Abstract: The present paper proposes the development of an adaptive neuro-fuzzy classifier which employs two relatively less explored and comparatively new problem solving domains in fuzzy systems. The relatively less explored field is the domain of the fuzzy linguistic hedges which has been employed here to define the flexible shapes of the fuzzy membership functions (MFs). To achieve finer and finer adaptation, and hence control, over the fuzzy MFs, each MF is composed of several piecewise MF sections and the shape of each such MF section is varied by applying a fuzzy linguistic operator on it. The system employs a Takagi–Sugeno based neuro-fuzzy system where the rule consequences are described by zero order elements. This proposed linguistic hedge based neuro-fuzzy classifier (LHBNFC) employs a relatively new field in the area of combinatorial metaheuristics, called particle swarm optimization (PSO), for its efficient learning. PSO has been employed in this scheme to simultaneously tune the shape of the fuzzy MFs as well as the rule consequences for the entire fuzzy rule base. The performance of the proposed system is demonstrated by implementing it for two classical benchmark data sets: (i) the iris data and (ii) the thyroid data. Performance comparison vis-à-vis other available algorithms shows the effectiveness of our proposed algorithm. [Copyright &y& Elsevier]

Abstract: Developing real-life solutions for implementation of the simultaneous localization and mapping (SLAM) algorithm for mobile robots has been well regarded as a complex problem for quite some time now. Our present work demonstrates a successful real implementation of extended Kalman filter (EKF) based SLAM algorithm for indoor environments, utilizing two web-cam based stereo-vision sensing mechanism. The vision-sensing mechanism is a successful development of a real algorithm for image feature identification in frames grabbed from continuously running videos on two cameras, tracking of these identified features in subsequent frames and incorporation of these landmarks in the map created, utilizing a 3D distance calculation module. The system has been successfully test-run in laboratory environments where the robot is commanded to navigate through some specified waypoints and create a map of its surrounding environment. Our experimentations showed that the estimated positions of the landmarks identified in the map created closely tallies with the actual positions of these landmarks in real-life. [Copyright &y& Elsevier]

Abstract: The present work describes the real-life implementation of a mobile robot navigation scheme where vision sensing is employed as primary sensor for path planning and IR sensors are employed as secondary sensors for actual navigation of the mobile robot with obstacle avoidance capability in a static or dynamic indoor environment. This two-layer based, goal-driven architecture utilizes a wireless camera in the first layer to acquire image and perform image processing, online, to determine subgoal, employing a shortest path algorithm, online. The subgoal information is then utilized in the second layer to navigate the robot utilizing IR sensors. Once the subgoal is reached, vision based path planning and IR guided navigation is reactivated. This sequential process is continued in an iterative fashion until the robot reaches the goal. The algorithm has been effectively tested for several real-life environments created in our laboratory and the results are found to be satisfactory. [Copyright &y& Elsevier]

Abstract: In this paper, a fuzzy model predictive control (FMPC) approach is introduced to design a control system for nonlinear processes. The proposed control strategy has been successfully employed for representative, benchmark chemical processes. Each nonlinear process system is described by fuzzy convolution models, which comprise a number of quasi-linear fuzzy implications (FIs). Each FI is employed to describe a fuzzy-set based relation between control input and model output. A quadratic optimization problem is then formulated, which minimizes the difference between the model predictions and the desired trajectory over a predefined predictive horizon and the requirement of control energy over a shorter control horizon. The present work proposes to solve this optimization problem by employing a contemporary population-based evolutionary optimization strategy, called the Bacterial Foraging Optimization (BFO) algorithm. The solution of this optimization problem is utilized to determine optimal controller parameters. The utility of the proposed controller is demonstrated by applying it to two non-linear chemical processes, where this controller could achieve better performances than those achieved by similar competing controller, under various operating conditions and design considerations. Further comparisons between various stochastic optimization algorithms have been reported and the efficacy of the proposed approach over similar optimization based algorithms has been concluded employing suitable performance indices. [Copyright &y& Elsevier]