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Fast-Match Algorithm is a fast and randomized template matching algorithm based on affine transformations. However, it is not suitable for color images as they are converted into greyscale images, and hence the color information is lost. An improved algorithm named Color Fast Affine Template Matching (CFAsT-Match), was thus proposed which utilizes all the three color channels (R, G, B). Our previous works have demonstrated how CFAsT-Match can be effectively used in shoe detection problem, wherein a robot can successfully track a target person from consecutive frames captured by an onboard camera. This paper proposes a new technique which extends the template matching algorithm using the Fuzzy Density based Clustering Algorithm instead of the traditional DBSCAN Algorithm. Three variants of Fuzzy based DBSCAN algorithms have been implemented here and a novel similarity measure called Fuzzy-based-colour-sum-of-absolute-differences (FCSAD) has been proposed. Experimental results firmly demonstrate that Fuzzy DBSCAN based CFAsT-Match algorithms can comfortably outperform recently proposed crisp DBSCAN based CFAsT-Match algorithms for shoe detection in human-robot coexisting environments. [ABSTRACT FROM AUTHOR]

In this paper, a novel human localization approach called Human Position Estimation based on Filtered Sonar Scan Matching (HPE-FSSM) is proposed for the purposes of estimating the trajectory of a walking person in human-robot coexisting environments, by matching consecutive pairs of sonar scans obtained with a mobile robot. The work proposes a novel concept of removing large amounts of noise and spurious readings present in two consecutive raw scans by using a spatial clustering method called DENCLUE (DENsity-based CLUstEring). Moreover, an Edge Feature Based Leg Recognition (EFBLR) algorithm is developed, which, in both scans, extracts the data points characterizing the human legs. Finally, a Likelihood field (LF) based scan matching technique is implemented to estimate the roto-translation of leg pair between the refined scans. This procedure is repeatedly implemented to estimate human position, required for following. Extensive experiments carried out in different environments establish the supremacy of the proposed algorithm, compared to other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The present work shows how a novel solution using regularization concept based K-SVD dictionary learning (DL) can be proposed for passive infrared (PIR) sensor based ambient assisted living (AAL) technologies. In this work, the AAL system focuses on detecting any human movement in specific directions in an unmanned environment. The regularization concept punishes solutions with large values of sparse representation coefficients and this work has successfully implemented regularized K-SVD (RK-SVD) and regularized approximated K-SVD (RAK-SVD) algorithms for a low cost hardware-software based intelligent AAL system, indigenously developed in our laboratory. This work also proposes modified versions of both algorithms (named MRK-SVD and MRAK-SVD algorithms) where novel methods of adapting the regularization parameter have been introduced. Extensive experimentations established that these approaches could significantly improve upon performances of DL based state-of-the-art approaches known, e.g. using multiple cluster pursuit (MCP) approaches, with MRAK-SVD algorithm emerging as the best alternative. [ABSTRACT FROM AUTHOR]

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]

The design and implementation of a real-time object motion sensing and trajectory replicating system, dependent on the image-based visual servoing capabilities of an electrohydraulic eye-in-hand parallel manipulator has been described in this paper. The tracking system consists of a monocular vision apparatus affixed to the top platform of a six-cylinder parallel manipulator constructed with industry grade double-acting single rod hydraulic actuators and budget proportional valves. The control algorithm is an amalgamation of three principles: machine vision based object tracking, image based visual servoing and actuator-level feedback control utilizing the inverse kinematics of the parallel manipulator. The real-time experiments are performed on a laboratory scale parallel manipulator setup with the aid of a virtual tracking object rendered on a computer monitor. The results for a few reference trajectories reveal that accurate tracking of target motions can be achieved with visual servoing algorithms designed for parallel manipulators in real-time. [ABSTRACT FROM AUTHOR]

People-following in a leader-follower scheme is considered as a major contemporary research problem in the domain of human-robot coexistence. The problem essentially requires human detection and tracking, and, in recent times, vision sensing based shoe detection has evolved as an effective mechanism for human detection. The problem gets more challenging when the environment becomes affected by photometric conditions like varying illumination, shadows, specularities, etc. Recently, a state-of-the-art fast image template matching algorithm, called photometric-invariant CFAsT-match (PICFAsT-match), has been specifically proposed for shoe detection based people-following in those challenging environments. In this approach, for determining the best shoe matching result corresponding to a frame, each single transformation included in a number of specified grids of general affine transformations is evaluated. However, in order to achieve a high speed real-life implementation of PICFAsT-match, every transformation is assessed by considering a handful number of pixels sampled randomly from the images. Research beyond PICFAsT-match reveals that the matching accuracy significantly relies on the quality of the random sampling. In this paper, we propose an improved variant of PICFAsT-match, called chaos based PICFAsT-match (CBPICFAsT-match), where a novel approach for this process of random sampling of pixels has been developed. This strategy is based on the state-time histories of multiple fractional order chaotic systems. Experimental results aptly demonstrate the superiority of the proposed algorithm, when compared to competing state-of-the-art algorithms, successfully employed for the said shoe detection purpose in human-robot coexisting environments. [ABSTRACT FROM AUTHOR]

In human–robot coexisting environment, one of the primary objectives is to equip robots with the facilities of detecting and following a human being in front, using various sensors. Within this genre, developing algorithms for vision sensor-based shoe detection and subsequently following is considered an active problem. Considering that the shoe poses, during the pursuit, undergo different transformations, this paper presents how the vision sensor based shoe detection problem can be treated as analogous to template matching under general conditions. This paper first shows how a popular fast randomized template-matching algorithm, called FAsT-Match algorithm, and its contemporary variant for color images, called CFAsT-Match algorithm, can be implemented in real robots with success for detecting shoes in subsequent frames. Then, this paper proposes a new density-based clustering method, called ordering points to identify the clustering structure (OPTICS), based template-matching algorithm that is specifically developed to overcome several implementation problems associated with the FAsT-Match and CFAsT-Match algorithms, in real life. Experimental results and performance evaluations demonstrate the superiority of the proposed OPTICS-based algorithm for shoe detection during human tracking in human–robot coexisting environments. [ABSTRACT FROM AUTHOR]

Human-tracking problems are considered as one of the major problems in a human–robot coexisting environment. Such systems often employ a sensor fusion mechanism involving a vision sensing-based system along with ultrasonic or IR sensing systems. In this paper, we consider a very important sub-problem, where vision sensing is employed in such a human–robot coexisting environment for shoe detection purposes, by approximating the target person’s positions in successive frames captured by the onboard camera of a robot, during human tracking. This paper considers a more challenging scenario of shoe detection under photometric changes and proposes a novel variant of the CFAsT-match algorithm, called photometric-invariant CFAsT-match (PICFAsT-match) algorithm. Both the CFAsT-match and PICFAsT-match have been implemented using the DBSCAN algorithm, a density-based clustering approach. Performance evaluations carried out for real-life tracking of human shoes show the supremacy of the proposed PICFAsT-match algorithm. [ABSTRACT FROM AUTHOR]

The experimental current–voltage ( ${I}_{D}$ – ${V}_{G}$ ) and capacitance–voltage ( ${C}$ – ${V}_{G}$ ) characteristics of the bottom-gate/top-contact ZnO thin-film transistors (TFTs) are analyzed accounting for quantum confinement and interface state effects. All the measurements are performed on the same TFTs composed of a 45-nm-thick nanocrystalline ZnO channel, indium tin oxide gate electrode, and a 21-nm-thick Al2O3 gate insulator. Interface state density (Dit) derived from the combined high–low frequency capacitance method reveals a large Dit near the conduction band edge. The TFT characteristics are simulated using a Schrodinger–Poisson model and compared to a semiclassical model. The Schrodinger–Poisson model calculates a lower C in accumulation by correctly accounting for the peak carrier concentration being several nanometers away from the ZnO/Al2O3 interface. The fit error in ${C}$ – ${V}_{G}$ in strong accumulation is only 1.2% compared to 4% in the semiclassical simulations. All simulations are performed using 21 nm of Al2O3. An effective mobility ( $\mu _{\mathrm{ eff}}$ ) that increases linearly with gate voltage is derived from the ratio of the experimental ${I}_{D}$ and the simulated mobile charge per unit area of the channel. Ignoring quantum confinement overestimates the channel charge and hence underestimates $\mu _{\mathrm{ eff}}$ by 40%. An additional Dit profile, at the top interface of the ZnO, is proposed in our analysis to explain the ${I}_{D}$ – ${V}_{G}$ characteristics in the subthreshold region. [ABSTRACT FROM AUTHOR]

The ambient assisted living (AAL) technology aims to provide more safety and self-sufficiency, while permitting older persons to live self-dependently in their homes. Monitoring of activities of daily living (ADL) is one of the key ideas of AAL. This becomes an interesting research idea in modern world, where condition monitoring of various ADLs and their automatic classification is a big challenge. This paper proposes a new approach for activity recognition of motion primitives relying on the sparse representation of signals, where signals are represented using a sparse combination of atoms from an over-complete dictionary. This paper intends to investigate the suitability of applying dictionary learning algorithms like K-singular value decomposition (K-SVD), which is usually used to construct an over-complete dictionary, for the effective progress of the ADL monitoring system. This paper proposes to formulate the classification approach by using SRC classifiers, based on the dictionaries learned using K-SVD algorithm. We have validated our proposed approach on a publicly available ADL data set of wrist-worn accelerometer sensor for activity recognition. Performance evaluations demonstrate that the proposed method outperforms several other competing methods. [ABSTRACT FROM PUBLISHER]

With the advancement in biometric recognition techniques, Palm Dorsa Subcutaneous Vein Pattern (PDSVP) [1] has emerged as a reliable and promising physiological biometric feature. Consequently, PDSVP has been extensively used as a biometric feature in recent research works on biometrics. However, the procedure of automatic data acquisition and PDSVP extraction is quite challenging. This article shows how this problem can be addressed in practice where a real life biometric identification system has been developed utilising near infrared (NIR) imaging of PDSVP of humans, where no fixed setup is employed for data acquisition. We highlight, in this article, three key ideas: automatic data acquisition, PDSVP extraction and, subsequently, biometric person identification using sparse representation based classification. Finally, we highlight the research challenges associated with implementing a Biometric Identification System (BIS) using PDSVP as a physiological feature. [ABSTRACT FROM PUBLISHER]

An adaptive fuzzy-bias sliding-mode controller has been designed by adding biasing to an existing controller for motion tracking by an electrohydraulic system. The system has a proportional valve with large deadband and a cylinder with jump in static friction and nonlinear but continuous dynamic friction. While the biasing controller takes care of the discontinuous features, the fuzzy controller approximates the other system nonlinearities. The sliding-mode controller compensates the approximations. By a number of real-time experiments, the appropriate choices for the number of fuzzy rule base and some free parameters have been established. Then, starting with the same set of initial parameters for tracking step and sinusoidal demands of different frequencies, the responses have demonstrated sufficient adaptability of the controller. [ABSTRACT FROM PUBLISHER]

This paper describes the development of a real-time system for the recognition of a real human subject using the palm dorsa subcutaneous vein pattern (PDSVP) as a physiological biometric feature. The system has been developed using a low-cost, single board computer, called the Raspberry Pi Model B, in conjunction with an infrared sensitive camera, called the Raspberry Pi No Infrared camera, and other components. The camera is sensitive to near infrared (NIR) radiations and this acquisition property has been used to acquire the pattern of vascular structure present in the subcutaneous layer of the dorsum of the human palm. Moreover, an automatic two-axis pan-tilt mechanism has been developed on which the camera is mounted. This is a completely novel mechanism that has been developed so that the data acquisition is independent of the position where the palm dorsum is positioned, as an automatic palm dorsum self-locating strategy is developed using the two-axis pan-tilt mechanism. Now, the NIR images of the PDSVP acquired, in the aforementioned methodology, do not represent the vein pattern with appreciable clarity and discernibility. Therefore, each image acquired undergoes few steps of image preprocessing, to extract the vein pattern, before they are subjected to testing conditions or they are incorporated into the training database. The recognition strategy has been developed using the collaborative representation-based classification. In this paper, we have emphasized upon the most severe case of small sample size, which is single sample per person-based training data set creation. The proposed method is tested on a well-structured database, of NIR images of the PDSVP, JU-NIR-V1: NIR Vein Database, developed in the Electrical Instrumentation and Measurement Laboratory, Electrical Engineering Department, Jadavpur University, Kolkata, India. Subsequently, through extensive experimentation it has been proven that the proposed strategy attains substantially high and stable recognition rate. Moreover, the performance of the recognition strategy is highly robust even in the presence of artifacts, such as angular displacement and scaling, that corrupt the NIR images acquired during data acquisition. [ABSTRACT FROM PUBLISHER]

This present paper describes the development of a capacitance measurement system in the picofarad region. The system uses an universal serial bus port-based arrangement in conjunction with an indigenously developed Programmable Intelligent Computer microcontroller-based frequency synthesizer-vector voltmeter that can be used to measure the voltage in vector form and the capacitance can be determined using circuit solution technique. An intelligent two-layered, hierarchical reinforcement-based instrumentation scheme is proposed that can be integrated along with the original measurements to significantly improve the system performance. In layer 1, an extreme learning machine-based supervised phase reinforcement scheme is employed to improve the accuracy of the voltage measurement. Subsequently, in layer 2, local polynomial-based reinforcements are employed to improve both the resistive and reactive part measurements in the unknown capacitance. Three variants of ELM-based reinforcements are implemented for capacitance measurements in the range 100–10 000 pF and the utility of the hybrid ELM-polynomial-based reinforcements for such measurements is aptly demonstrated. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel methodology for autonomous mobile robot navigation utilizing the concept of tracking control. Vision-based path planning and subsequent tracking are performed by utilizing proposed stable adaptive state feedback fuzzy tracking controllers designed using the Lyapunov theory and particle-swarm-optimization (PSO)-based hybrid approaches. The objective is to design two self-adaptive fuzzy controllers, for -direction and -direction movements, optimizing both its structures and free parameters, such that the designed controllers can guarantee desired stability and, simultaneously, can provide satisfactory tracking performance for the vision-based navigation of mobile robot. The design methodology for the controllers simultaneously utilizes the global search capability of PSO and Lyapunov-theory-based local search method, thus providing a high degree of automation. Two different variants of hybrid approaches have been employed in this work. The proposed schemes have been implemented in both simulation and experimentations with a real robot, and the results demonstrate the usefulness of the proposed concept. [ABSTRACT FROM AUTHOR]

In this paper, a new variant of particle swarm optimization (PSO), called random spatial lbest PSO model, is proposed and implemented for designing a newly devised stable adaptive hybrid fuzzy controller. The newly developed concurrent hybrid strategy for designing fuzzy controllers utilizes the conventional Lyapunov theory and the proposed PSO-based stochastic approach. The objective is to design a self-adaptive fuzzy controller online, optimizing both its structures and free parameters such that the designed controller can guarantee the desired stability and simultaneously provide satisfactory transients performance. The global version and two different lbest variants of PSO schemes and the proposed random spatial lbest model of PSO are employed for three popular, challenging, and nonlinear processes, and the proposed controller emerges as the superior algorithm in terms of tracking performance overall. These results aptly demonstrate the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents the development of an intelligent fuzzy-based compensation scheme, utilizing reinforcement learning methodology, which can be used to compensate the reading of an unknown voltage, in vector form. This compensator is implemented online, in real time, with an indigenously developed microcontroller-based scheme that can be used both as a frequency synthesizer and as a vector voltmeter. This frequency synthesizer/vector voltmeter is developed using a direct digital synthesis method for the frequency synthesizer and a synchronous detection technique for the vector voltmeter. The developed fuzzy compensator has been tested in both offline and online modes, and in both cases, it has been found to significantly improve the accuracy of the measurement compared to those obtained with an uncompensated vector voltmeter. It has been shown that the final compensated measurements are in close agreement with the true unknown voltages under measurement. [ABSTRACT FROM AUTHOR]

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]

This paper proposes the development of a new approach for water sample authentication, in real life, using a pulse-voltametry-method-based electronic tongue instrumentation system. The system is developed as a parallel combination of several neural network classifiers, each dedicated to authenticate a specific category of water sample, and can be extended for more categories of water sample authentication. The system employs a slantlet-transform (ST)-based feature extraction module and two popular variants of neural networks for classification. The proposed system hybridizes ST with two variants of backpropagation-neural-network-based binary classifiers to develop an automated authentication tool. ST is regarded as an improved version of orthogonal discrete wavelet transform that can provide improved time localization with simultaneous achievement of shorter supports for the filters. This proposed system, implemented in a laboratory environment for various water samples available in India, showed encouraging average authentication percentage accuracy, on the order of over 80% for most water categories and even producing accuracy results exceeding 90%, for several categories. [ABSTRACT FROM AUTHOR]

This paper proposes a new system for biometry-based human authentication, where postural signal information is utilized to identify a person. The system employs a novel approach where four types of temporal postural signals are acquired for each person to develop an authentication database, and for each posture, both signals in the x- and y -directions are utilized for the purpose of authentication. The proposed system utilizes S-transform, which is a joint time–frequency representation tool, to determine the characteristic features for each human posture. Based on these characteristic features, a radial basis function network (RBFN) system is developed for the purpose of specific authentication. The RBFN authentication system is developed by training it to employ extended Kalman filtering (EKF). The EKF-trained RBFN authentication system could produce overall authentication accuracy on the order of 94%–95% and could outperform similar authentication systems developed, which employ two very popular variants of backpropagation neural networks (BPNNs) and a variant of radial basis neural network (RBNN). [ABSTRACT FROM PUBLISHER]

This brief proposes hybrid stable adaptive fuzzy controller design procedures utilizing the conventional Lyapunov theory and, the relatively newly devised harmony search (HS) algorithm-based stochastic approach. The objective is to design a self-adaptive fuzzy controller, optimizing both its structures and free parameters, such that the designed controller can guarantee desired stability and simultaneously it can provide satisfactory performance with a high degree of automation in the design process. Two different variants of the hybrid controller are proposed in this work. These variants are implemented for a benchmark simulation case study and real-life experimentation. The results obtained demonstrate the usefulness of the proposed approach. [ABSTRACT FROM PUBLISHER]

The value of resistors generally changes with electrical current. For precision analog circuit layout, it is important to predict those resistance variations. For that purpose, we derive for the first time a simple analytic 3-D model to describe thermal resistance and self-heating, which we show as the dominant contribution to resistor nonlinearity for a wide range of resistors. The model shows good agreement with numerical simulations. Measured voltage coefficients of metal and polysilicon resistors can be fit to the model over a wide range of geometries. Finally, we show how thermal resistance can be reduced by placing metal bar structures underneath the resistor and apply the model to quantify the improvement. [ABSTRACT FROM AUTHOR]

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]

This paper proposes a new approach for designing stable adaptive fuzzy controllers, which employs a hybridization of a conventional Lyapunov-theory-based approach and a particle swarm optimization (PSO) based stochastic optimization approach. The objective is to design a self-adaptive fuzzy controller, optimizing both its structures and free parameters, such that the designed controller can guarantee desired stability and can simultaneously provide satisfactory performance. The design methodology for the controller simultaneously utilizes the good features of PSO (capable of providing good global search capability, required to provide a high degree of automation) and Lyapunov-based tuning (providing fast adaptation utilizing a local search method). Three different variants of the hybrid controller are proposed in this paper. These variants are implemented for benchmark simulation case studies and real-life experimentation, and their results demonstrate the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

Designing the control strategy for a flexible robotic arm has long been considered a complex problem as it requires stabilizing the vibration simultaneously with the primary objective of position control. A stable state-feedback fuzzy controller is proposed here for such a flexible arm. The controller is designed on the basis of a neuro-fuzzy state-space model that is successfully trained using the experimental data acquired from a real robotic arm. The complex problem of solving stability conditions is taken care of by recasting them in the form of linear matrix inequalities and then solving them using a popular interior-point-based method. This asymptotically stable fuzzy controller is further augmented to provide enhanced transient performance along with maintaining the excellent steady-state performance shown by the stable control strategy. The controller hence designed has been successfully implemented for a real robotic arm to operate over a long angular range of 1800 with several payload conditions and, for situations where the system is operated for a long range and with a large variation in payload conditions, it could successfully outperform the recently proposed proportional derivative and strain controller. [ABSTRACT FROM AUTHOR]