Search

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.

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.

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.

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.

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.

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.

In this paper, we propose a novel multivariate multimodal ear recognition method which exploits correlation between left and right ear modality of an individual for his/her identification using joint sparse representation and its variant, joint dynamic sparse representation based classification approach. To make the problem much more robust against outliers that might be resulted from illumination variation or noises due to inaccurate measurements or from partial occlusion due to hair or ornaments — especially for female subjects, we employ a novel weighted multivariate regression scheme under joint sparse as well as joint dynamic sparse penalization. That particular scheme learns a set of weights iteratively for each and every residual corresponding to each observation and subsequently, during the time of classification, gives lesser weight to elements detected as outliers such that they are not able to participate for query set representation. To further improve accuracy of the system, the proposed method is kernelized to tackle non-linearity infusion made by pose variations and occlusions. In the end, extensive experimentations are carried out over a novel database developed in our laboratory to compare performance of the proposed method to several competitive, state-of-the-art methods in order to check suitability of the proposed classification method for various real life applications.

This paper proposes a novel method, Multi-Size patch based Collaborative Representation based Classification (CRC) strategy by Enhanced Ensemble Learning, for palm dorsa vein pattern (PDVP) based human recognition employing thermal imaging. This thermal PDVP imaging based human recognition methodology has been specifically employed to encounter the challenging crisis of intrusion posed by imposters. To address the Small Sample Size (SSS) problem, intrinsic to many biometric applications, each image is transformed into multiple patches, leading to an increase in the total number of samples. In a bid to make the classification strategy less sensitive to the choice of patch-size, the present paper proposes a new enhanced ensemble learning for the patch based CRC (PCRC) algorithm, where margin maximization is carried out using exponential squared loss minimization. This work also proposes how this loss minimization can be achieved by a stochastic optimization algorithm and solves this problem using artificial bee colony (ABC) algorithm. In this context, a new ABC variant, called modified interactive artificial bee colony (MI-ABC) algorithm, has also been proposed, which has been demonstrated to outperform the basic ABC and its several modern variants. The proposed methodology has been implemented on a wellstructured real database, developed in our laboratory using real subjects, and the results obtained in implementation phase clearly demonstrate that our proposed method could outperform its several competitors and achieve substantially high classification rates, for the problem under consideration.

It has been reported that Matrixmetalloproteinase-2 (MMP-2) is involved in the pathogenesis of cancer. The over expression of MMP-2 is associated with the progression of malignancy of several types of carcinoma. Human saliva is a biological fluid with several advantages for non-invasive diagnosis and prognosis of diseases. The aim of this study was to detect MMPs expression and activity in biological fluids (saliva, urineetc.) derived from breast cancer patients. Here, our results showed that the activity of MMP-2 was higher at the time before the surgery than after the saliva collected from the same patients. Therefore, we suggested that the highly active form of MMP-2 presented in saliva could be used as a novel potential biomarker for non-invasive diagnosis of breast cancer.

This work presents a new idea for human behavior recognition based on dictionary learning algorithm and collaborative representation-based classification approach. In this paper, we have proposed an l1 – ls-based KSVD algorithm for learning a dictionary and collaborative representation is used in the classification phase for this problem. The performance of our proposed idea for human behavior recognition problem establishes the superiority of our new idea.

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.

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.

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.

Variance in pose during data acquisition poses a serious challenge for any biometric system which uses the human face as a physiological biometric feature. In this paper, we present an enhanced patchwise fractal dimension based feature extraction technique for the purpose of pose-invariant face recognition. We have presented an improved version of the Differential Box Counting (DBC) based fractal dimension computation technique which is used for feature extraction of thermal images of the human face. A Far-Infrared (FIR) imaging based human face database, called the JU-FIR-F1: FIR Face Database, was developed in the Electrical Instrumentation and Measurement Laboratory, Electrical Engineering Department, Jadavpur University, Kolkata, India for testing the accuracy, stability, and robustness of our proposed feature extraction methodology. We have included the results obtained through extensive experimentation to elaborate the superiority of our proposed algorithm over its other well-known counterparts.

Automatic control theory, predominantly the concept of feedback, has played a fundamental role to the development of automation [1].

Autonomous mobile robot navigation has been regarded as a popular research area for quite sometimes now, which has still several open problems. There have been many attempts to solve the problems related to mobile robot navigation in both map-based and mapless environments [1, 2, 3, 4, 5, 6, 7, 8, 9]. In mobile robot navigation, simultaneous localization and mapping (SLAM) technique has also been popularly researched and several probable solutions to the problem have been proposed by the researchers [5, 6].

The present paper utilizes covariance matrix adaptation (CMA), an evolution strategy, in conjunction with H∞-based robust control law to design a stable adaptive fuzzy controller for a class of non-linear systems. The objective of the design is to develop a self-adaptive optimal/near optimal fuzzy controller, with guaranteed stability and satisfactory robust transient performance. The global search capability of CMA and H∞-based tuning, that provide a fast adaptation utilizing local search method, is employed in tandem with this proposed design methodology. The hybrid control strategy is implemented for benchmark simulation case study, and the results demonstrate the usefulness of the proposed approach.

This paper presents a Pose Invariant Face Recognition algorithm for pose-variance in face databases, which is one of the toughest challenges of any face recognition based biometrics, using a novel feature extraction technique. The feature extraction of the raw images is based upon a novel patch-wise self-similarity measure within an image. The algorithm has been tested upon a Far-infrared (FIR) imaging based Face database called the JU-FIR-F1: FIR Face Database that has been developed in the Electrical Instrumentation and Measurement Laboratory, Electrical Engineering Department, Jadavpur University, Kolkata, India. The results obtained through extensive experimentation clearly demonstrate the superiority of the proposed method over the existing algorithms.

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.

The present paper proposes a supervised learning based automated human facial emotion recognition strategy with a feature selection scheme employing a novel variation of the gravitational search algorithm GSA. The initial feature set is generated from the facial images by using the 2-D discrete cosine transform DCT and then the proposed modified binary quantum GSA with differential mutation MBQGSA-DM is utilized to select a sub-set of features with high discriminative power. This is achieved by minimising the cost function formulated as the ratio of the within class and interclass distances. The overall system performs its final classification task based on selected feature inputs, utilising a back propagation based artificial neural network ANN. Extensive experimental evaluations are carried out utilising a standard, benchmark emotion database, that is, Japanese Female Facial Expresssion JAFFE database and the results clearly indicate that the proposed method outperforms several existing techniques, already known in literature for solving similar problems. Further validation has also been carried out on a facial expression database developed at Jadavpur University, Kolkata, India and the results obtained further strengthen the notion of superiority of the proposed method.

Hybrid stable adaptive fuzzy controllers based on the Lyapunov theory and HS algorithm are implemented.Real-life controls of the temperature of an air heater process at different cross sections of air flow duct are implemented.Heat transfer dynamics of the process in the presence of fixed and randomly variable disturbances are tested.The same controllers designed for "no transportation delay" case are also utilized to control the temperature with the incorporation of transportation delay in the process. The present paper proposes systematic designs of stable adaptive fuzzy logic controllers (AFLCs), employing hybridizations of Lyapunov strategy based approach (LSBA) and a contemporary stochastic optimization technique, for controlling the temperature of a thermal process in an air-heater system with transport-delay and in the presence of disturbances. Harmony search (HS) algorithm has been considered here as a candidate stochastic optimization technique utilized in conjunction with the Lyapunov theory to develop the hybrid models in this work. The objective of this work is to design stable adaptive fuzzy controllers which can provide high degree of automation, guarantee asymptotic stability and also achieve satisfactory transient performance by simultaneous adaptations of both the fuzzy controller structure and its free parameters. The results obtained from real-life experiments aptly demonstrate the usefulness of the proposed approach.

A relatively new technology, palmprint recognition, has evolved drastically over the recent years. It is currently considered to be one of the potential research topics due to the escalating need of biometric authentication based security systems. This paper presents a method of authentication of hand images by relaxed collaborative representation (RCR) model of classification. It makes use of the similarity as well as uniqueness of the associated features. By computing the radon transform of the images, the feature vectors of the images are computed. The coding of each feature vector is done over the dictionary that is linked with RCR and in order to compute the resemblance among features the inconsistency of coding vectors is reduced. The main aim is to develop an efficient system whose results can be implemented on any database.

Image segmentation is a very important part of image pre-processing and its application towards computer vision.

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.

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 synthesizervector 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 reinforcementbased 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 machinebased 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 ELMpolynomial-based reinforcements for such measurements is aptly demonstrated.

Wind power is considered one of the most rapidly growing sources of electricity generation all over the world. This study proposes a new approach for wind power prediction. The proposed method is derived by integrating the kernel principal component analysis (KPCA) method with the evolutionary optimised general regression neural network (GRNN) and local prediction framework. Local prediction uses only a set of K nearest neighbours in the reconstructed embedded space with considering the more relevant historical instances. In the evolutionary optimised local GRNN (EOLGRNN), the kernel bandwidth (smooth parameter) that controls the smoothness of the approximation is coded in a chromosome and determined by the optimisation using evolutionary algorithm. In the proposed method, KPCA is used in the first stage to extract features and obtain kernel principal components which used to construct the phase space of the time series of input. Then, EOLGRNN is employed in the second stage to solve the wind power prediction problem. The proposed method is evaluated using real world dataset. The results show that the proposed method provides a much better prediction performance in comparison with other published methods employing the same data.

The present work shows how a new genre of problem solution techniques called compressive sensing (CI) can be utilized for solving power quality problems. Specifically speaking, this work undertakes the power system transient recognition problem and solves it using sparse representation based classification (SRC) technique, a popular, recently emerging technique in CI. The method employs SRC based classification in combination with a feature extraction procedure, carried out using dual tree complex wavelet transform (DTCWT). The proposed method uses statistical characteristics to extract features from the DTCWT coefficients obtained from each signal and then these extracted features are used as input arrays for the SRC based classifier. The PQ disturbance events considered in this work include four different transient conditions, namely, transients due to capacitor switching, transformer inrush currents, transients due to motor switching and transients due to short circuit faults. The proposed algorithm could achieve perfect classification accuracy of the order of 100% and could comfortably outperform several similar contemporary methods known for identical power system transient classification problems.

The present paper proposes a dual-tree complex wavelet transform DTCWT based approach for recognition of power system transients. Several researchers, all over the world, have so far attempted to solve the problems of recognition of power system transients, hybridizing transform-based techniques with popular computational intelligence based tools, for example, using wavelet transform and S-transform for feature extraction, followed by artificial neural networks ANN or fuzzy logic-based classifiers. The proposed method of hybridizing DTCWT-based feature extraction with ANN-based classification could efficiently detect several commonly occurring power quality PQ disturbance events. The PQ disturbance events considered include four different transient conditions, namely transients due to capacitor switching, transformer inrush currents, transients due to motor switching and transients due to short circuit faults. A detailed performance comparison with several contemporary, competing methods existing in the literatures for similar problems aptly demonstrates the suitability of the proposed method.

This paper presents a novel stereo matching algorithm that utilizes the disparity variations of each pixel by cost formulation in a cooperative manner to solve several stereo disambiguation problems. A completely novel Stereo Orthogonal Feature-mapping transform (SOFT) has been proposed to compute the local as well as semi-global stereo matching cost that involves the extraction of the color information of the stereo images to make the process illumination invariant and improve the robustness and stability against noise. The computation of the simultaneous left and right disparity helps in removal of occlusion regions as outliers. The stereo matching is achieved only by a constraint based optimization of the cost function relying on the pixel wise matching costs. Our algorithm was verified on the Middlebury and KITTI datasets and the performance is compared to the classical as well as the state-of the art algorithms. The results clearly demonstrate that the proposed approach yields higher accuracy and incisiveness in reconstruction at the cost of lesser computational complexity.

Palmprint identification has emerged as one of the most popular and promising biometric modalities for personal identity verification due to its ease of acquisition, non-invasive procedure, high user acceptance and reliability. This paper proposes the development of a new method for palmprint based biometric authentication which utilizes the textural information available on the palmprint by employing the Dual Tree Complex Wavelet Transform (DTCWT). The method proposes to construct a region of interest (ROI) for the scanned color images of the palm, and then determine a histogram of the two dimensional image. This enables to utilize a feature extraction module, implemented using the one-dimensional (1D) Dual Tree Complex Wavelet Transform (DTCWT) on the histogram signal. The DTCWT is an improvement over the discrete wavelet transform (DWT) as it provides nearly shift invariant performance, reduced aliasing and directional wavelets in higher dimensions. Backpropagation neural-network (BPNN) based binary classifiers are developed for authentication utilizing the features extracted. The system is developed on the basis of several scanned color images of palms of individuals in real-life, in our laboratory. The experimental results obtained from the data have demonstrated the utility of the proposed system, by exhibiting an overall mean accuracy as high as 98.35%.

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.

The present paper describes a novel method of implementation of a stochastic optimization technique for the face recognition problem. The method proposed divides the original images into patches in space, and seeks a non-linear functional mapping using second-order Volterra kernels. The artificial bee colony optimization technique, a modern stochastic optimization algorithm, is used to derive optimal Volterra kernels during training to simultaneously maximize inter-class distances and minimize intra-class distances in the feature space. During testing, a voting procedure is used in conjunction with a nearest neighbor classifier to decide to which class each individual patch belongs. Finally, the aggregate classification results of all patches in an image are used to determine the overall recognition outcome for the given image. The utility of the proposed scheme is aptly demonstrated by implementing it on two popular benchmark face recognition datasets, and comparing the effectiveness of the proposed approach vis-a-vis other statistical learning procedures in facial recognition and also several other methods developed so far. The effectiveness of the artificial bee colony optimization technique and its Levy-mutated variation in optimizing Volterra kernels is conclusively proven in this paper by significantly outperforming many popular contemporary algorithms.

This study aimed to detect the comparative expression and activity of matrix metalloproteinase-9 (MMP-9) and its correlation with known pathological parameters such as estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (HER2) in 81 malignant breast tumors and adjacent normal breast tissues and in blood sera of these patients from different clinical TNM stages (ductal carcinoma in situ to T4) of breast cancer. MMP-9 was highly expressed in node-positive tumors and the preoperative blood serum of patients, but MMP-9 activity was appreciably inhibited in blood serum samples collected after surgery. The mature form of MMP-9 (84 kD) was expressed only in clinical stage III tumors (T2-4). Appreciable reduction of tissue inhibitor of metalloproteinase 1, phosphorylation of epidermal growth factor receptor, and translocation of nuclear factor-κΒ suggested their possible role in MMP-9 activation in HER2-positive breast cancer Overexpression and activation of MMP-9 predicted a higher stage of hormone-sensitive ductal breast carcinoma. Downregulation of the endogenous inhibitor of MMP-9, tissue inhibitor of metalloproteinase 1, and translocation of the transcription factor nuclear factor-κΒ in tumors may have an appreciable role in the overexpression of MMP-9. However, MMP-9 activation was not correlated with expression of estrogen and progesterone receptors. Evaluation of MMP-9 expression may provide valuable information about breast cancer treatment.

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.

This study focuses on the issue of position tracking of a budgeted electrohydraulic system with the aid of marker based visual sensing of the actuator rod position using a single camera. The system comprises of a single-rod cylinder having non-linear friction characteristics and industry grade proportional valve possessing a large-deadband. A controller has been developed based on an input linearized system model involving the combination of a sliding mode and a deadband controller. An adaptive strategy for the controller parameters relying on a Lyapunov function has been introduced to handle the nonlinearities and uncertainties involved in the system. The controller demonstrated satisfactory performance for the realtime experiments conducted on the laboratory scale system.

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.

The present paper proposes development of a transmission loss allocation scheme in a deregulated environment using fuzzy memberships and supervised neural networks. This method can be effectively utilized in online applications where game theory based solutions, which otherwise produce acceptable results, cannot be utilized for prohibitive computation load. We propose a fuzzy membership based approach to filter data from a global database and create a local relevant database, for each transaction detail online, each time. A neural network is trained for each such local database formed and utilized for estimating loss allocations among players, for the transaction detail under consideration. The proposed method has been employed for an IEEE 14 bus system and the results of our proposed method have been shown to be sufficiently accurate, when compared to results obtained by using game theoretic approach.

The present paper proposes a new formulation for the optimum distributed generator (DG) placement problem which considers a hybrid combination of technical factors, like minimization of the line loss, reduction in the voltage sag problem, etc., and economical factors, like installation and maintenance cost of the DGs. The new formulation proposed is inspired by the idea that the optimum placement of the DGs can help in reducing and mitigating voltage dips in low voltage distribution networks. The problem is configured as a multi-objective, constrained optimization problem, where the optimal number of DGs, along with their sizes and bus locations, are simultaneously obtained. This problem has been solved using genetic algorithm, a traditionally popular stochastic optimization algorithm. A few benchmark systems radial and networked (like 34-bus radial distribution system, 30 bus loop distribution system and IEEE 14 bus system) are considered as the case study where the effectiveness of the proposed algorithm is aptly demonstrated.

Aim: To study Expression and Phosphorylation status of Focal Adhesion Kinase (FAK) in Human Breast Cancer tissue. To study the relation of FAK with standard clinicopathological parameters of Human Breast Cancer. Methods: Tissue collection, Protein extraction, RNA isolation, Western Blot, Immunohistochemistry, RT-PCR, ELISA, Statistical analysis. Results: All the four techniques showed upregulated expression, phosphorylation (Tyr-397) and processing of FAK in human breast cancer tissue compared to the adjacent non-tumor tissue of the same patient. Upregulation of FAK was found to be increased parallely with the advancement of cancer. Localisation of FAK was found to be membrano-cytoplasmic. FAK is upregulated both in protein and mRNA level. Expression and phosphorylation of FAK is increased specifically in the tumor regions compared to the surrounding non-tumor region. Upregulation of FAK was frequently found in ER-positive and PR-positive but Her2/neu-negative breast cancer cases. Conclusion: FAK has crucial role in development and progression of human breast cancer. FAK may be considered as an indicator of human breast cancer progression. FAK processing may be considered as an indicator of invasive potential of breast cancer. FAK may be considered as a clinical indicator of human breast cancer development and progression.

The present paper describes a novel adaptive cross correlation technique of face recognition using closed loop discriminator estimation for face detection. All possible variations in the human face can be obtained by scaling and three types of head roll in different planes. We show here that face recognition system comprises face detection and face verification. Feature selection schemes like eigenfaces, local binary pattern (LBP) and speeded up robust features are implemented to extract features with high discriminative power, thereby performing face verification. Experiments revealed that this method strikes a balance between accurate face recognition and identification with sufficient speed of convergence. The algorithm proposed by this paper allows tracking of face against a wide scale range with sufficient immunity against like camera vibrations, sensor errors, illumination level fluctuation etc.

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.

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.

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.

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.

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.

Aims: To identify and estimate salivary VEGF of breast cancer patients using non invasive method. Methods: ELISA, Zymography, Immunoblot. Results: We observed that saliva of breast cancer patients express appreciably high VEGF compare to non breast cancer saliva samples. Conclusion: The findings indicate salivary VEGF as a potential breast cancer marker using non invasive method.