We present tools for a network topology based characterization of heterogeneity in multiagent systems, thereby providing a framework for the analysis and design of heterogeneous multiagent networks from a network structure view-point. In heterogeneous networks, agents with a diverse set of resources coordinate with each other. Coordination among different agents and the structure of the underlying network topology have significant impacts on the overall behavior and functionality of the system. Using constructs from graph theory, a qualitative as well as a quantitative analysis is performed to examine an inter-relationship between the network topology and the distribution of agents with various capabilities in heterogeneous networks. Our goal is to allow agents maximally exploit heterogeneous resources available within the network through local interactions, thus exploring a promise heterogeneous networks hold to accomplish complicated tasks by leveraging upon the assorted capabilities of agents. For a reliable operations of such systems, the issue of security against intrusions and malicious agents is also addressed. We provide a scheme to secure a network against a sequence of intruder attacks through a set of heterogeneous guards. Moreover, robustness of networked systems against noise corruption and structural changes in the underlying network topology is also examined.

This thesis focuses on decentralized deadbeat output regulation of discrete-time nonlinear plants that are composed of multiple agents. These agents interact, via scalar-valued signals, in a known structured way represented with a graph. This work is motivated by applications where it is infeasible and/or undesirable to introduce control action within each plant agent; instead, control agents are introduced to interact with certain plant agents, where each control agent focuses on regulating a specific plant agent, called its target. Then, two analyses are carried out to determine if regulation is achieved: targeting analysis is used to determine if control laws can be found to regulate all target agents, then growing analysis is used to determine the effect of those control laws on non-target plant agents. The strength of this novel approach is the intuitively-appealing notion of each control agent focusing on the regulation of just one plant agent. This work goes beyond previous research by generalizing the class of allowable plant dynamics, considering not only arbitrary propagation times through plant agents, but also allowing for non-symmetrical influence between the agents. Moreover, new necessary and sufficient algebraic conditions are derived to determine when targeting succeeds. The main contribution of this work, however, is the development of new easily-verifiable conditions necessary for targeting and/or growing to succeed. These new conditions are valuable due to their simplicity and scalability to large systems. They concern the positioning of control agents and targets as well as the propagation time of signals through the plant, and they help significantly with design decisions. Various graph structures (such as queues, grids, spiders, rings, etc.) are considered and for each, these conditions are used to develop a control scheme with the minimum number of control agents needed.

Submitted by Maike Costa (maiksebas@gmail.com) on 2016-04-29T13:19:13Z No. of bitstreams: 1 arquivo total.pdf: 1827584 bytes, checksum: 8e2c4699b20e87ffce14b44d0467564c (MD5) / Made available in DSpace on 2016-04-29T13:19:13Z (GMT). No. of bitstreams: 1 arquivo total.pdf: 1827584 bytes, checksum: 8e2c4699b20e87ffce14b44d0467564c (MD5) Previous issue date: 2016-02-02 / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / To reduce waste and increase the generation of value for customers, more and more construction companies have sought to apply the Lean Construction. However, there is no unanimous formalization of the implementation process and companies lack information about their real capacity to go through a lean transition. As stressed by the literature, one of the key factors for the success of any lean implementation is to assess the readiness of the organization. This paper assumes that this assessment should consider the technical, human and cultural dimensions and their interrelationships. Seeking ways to evaluate, an important tool based on graph theory and matrix algebra, identified in the literature as Graph Theoretic Approach (GTA), has been applied in different contexts, with desirable properties such as the ability to model interactions between criteria and generating hierarchical models for solving complex decision-making problems. Thus, this study aims to develop a readiness assessment method for implementation of Lean Construction using the GTA as a tool for its operationalization. As steps involved in building the method, it was carried out a literature review, in which it was possible to identify the readiness factors and sub-factors considered in the evaluation, and the parameterization of the model, through the establishment of inter-relations between the selected factors and sub-factors. The developed method was tested through the application in construction companies. As a result from the application, it was obtained the classification of two companies which showed insufficient and partial readiness levels. From the case studies, it was concluded that the proposed method is suitable for its purpose, meeting the criteria of feasibility, usability and utility. The method results allow to provide a diagnostic of the current situation of construction companies, which serves as a driver for change initiatives towards the Lean Construction. / Visando reduzir desperdícios e aumentar a geração de valor para seus clientes finais, cada vez mais empresas construtoras tem buscado aplicar a Construção Enxuta. O que se constata é que não há uma formalização unânime do processo de implementação e que as empresas carecem de informações sobre sua real capacidade para passar por um processo de transição enxuta. Como ressalta a literatura, um dos fatores chave para o sucesso de qualquer implementação enxuta consiste em avaliar a prontidão da organização. Esta pesquisa parte do pressuposto que essa avaliação deve considerar as dimensões de ordem técnica, humana e cultural e suas inter-relações. Na busca de meios para a avaliação, uma importante ferramenta baseada na teoria dos grafos e álgebra matricial, identificada na literatura como Graph Theoretic Approach (GTA), vem sendo aplicada em diversos contextos, apresentando propriedades desejáveis como a capacidade de modelar interações de critérios e de gerar modelos hierárquicos para resolução de problemas de tomada de decisões complexas. Desta forma, este trabalho tem como objetivo desenvolver um método de avaliação de prontidão para implementação da Construção Enxuta utilizando a GTA como ferramenta para sua operacionalização. Como etapas inerentes à construção do método foi realizada uma revisão bibliográfica, na qual foi possível identificar os fatores e os subfatores de prontidão considerados na avaliação, e a parametrização do modelo, por meio do estabelecimento das inter-relações entre os fatores e os subfatores selecionados. O método desenvolvido foi testado por meio da aplicação em empresas construtoras. Como resultado da aplicação, obteve-se a classificação de duas empresas avaliadas as quais apresentaram níveis insuficientes e parciais de prontidão. A partir dos estudos de caso, foi possível concluir que o método proposto é adequado aos fins a que se destina, atendendo aos critérios de viabilidade, usabilidade e utilidade. Os seus resultados permitem fornecer um diagnóstico da situação atual das construtoras, o qual serve de balizador para iniciativas de mudança em direção à Construção Enxuta.

Submitted by ANA KARLA PEREIRA RODRIGUES (anakarla_@hotmail.com) on 2017-10-03T11:36:30Z No. of bitstreams: 1 arquivototal.pdf: 3049650 bytes, checksum: 3c12ba8f608227bcf644c23dbded37d2 (MD5) / Made available in DSpace on 2017-10-03T11:36:30Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 3049650 bytes, checksum: 3c12ba8f608227bcf644c23dbded37d2 (MD5) Previous issue date: 2017-02-22 / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / The formation of business arrangements became a strategy adopted by companies to acquire combined benefits that could not be generated individually. Thus, collaboration emerges as a fundamental characteristic in setting and maintaining interorganizational relationships. Local productive arrangements (APLs), known in the international literature as clusters or industrial districts, are widely discussed in several aspects, for instance, their taxonomy. However, there are research gaps that demand new investigations, such as the collaboration between firms in this type of arrangement. There are papers that discuss the benefits that companies can achieve from collaborative interorganizational relationships, but, in addition, it is necessary to create mechanisms to assess the collaboration level in order to assist managers in decision-making. Therefore, this work aims to develop a model to assess the collaboration in companies participating in APLs, testing it through case studies in five companies of a furniture APL in the state of Paraíba. The proposed model used a support tool named Graph Theoretic Approach (GTA), which was combined with the Delphi technique applied to experts in order to define the interrelationships between attributes. The attributes from literature were classified as assessment factors and sub-factors, by the following hierarchical relationships: governance factor (constituted by sub-factors public policies, institutions support and risks and rewards sharing); geographical proximity factor (constituted by sub-factors tangible resources sharing, information sharing and knowledge sharing); and trust factor (constituted by sub-factors informal links, long-term relationship and reputation). Through qualitative-quantitative analysis, it was identified that, in general, the evaluated companies are in the average level of collaboration, according to the classification scale that was developed. The application of the model contributes to encouraging companies to develop collaborative practices for factors with lower levels. As a result, the proposed model can be considered appropriate for its purposes, allowing a general evaluation of the collaboration in companies that work in APLs. / A formação de arranjos empresarias tornou-se uma estratégia adotada pelas empresas para adquirirem benefícios conjuntos que não poderiam ser gerados individualmente. Neste sentido, a colaboração surge como uma característica fundamental no estabelecimento e na manutenção dos relacionamentos interorganizacionais. Os arranjos produtivos locais (APLs), conhecidos na literatura internacional como clusters ou industrial districts, são amplamente discutidos na academia em relação a diversos aspectos, como, por exemplo, sua taxonomia. No entanto, existem lacunas de pesquisa que apontam para novas investigações, como é o caso da colaboração entre empresas nesse tipo de arranjo. Há pesquisas que discutem sobre os benefícios que as empresas podem obter a partir das relações interorganizacionais colaborativas, mas, além disso, é necessário criar mecanismos de avaliação da colaboração que possam auxiliar os gestores na tomada de decisão. Dessa forma, este trabalho tem como principal objetivo desenvolver um modelo para avaliar a colaboração em empresas participantes de APLs, aplicando-o por meio de estudos de caso em cinco empresas do APL de móveis do Estado da Paraíba. Para o desenvolvimento do modelo, utilizou-se uma ferramenta denominada de graph theoretic approach (GTA), cuja definição das inter-relações entre os atributos de avaliação se deu por meio da aplicação da técnica Delphi, com pesquisadores especialistas sobre a temática. Os atributos levantados na literatura foram denominados de fatores e subfatores de avaliação, com as seguintes relações hierárquicas: fator governança (composto pelos subfatores suporte de políticas públicas, suporte de instituições de apoio e compartilhamento de riscos e recompensas); fator proximidade geográfica (composto pelos subfatores compartilhamento de recursos tangíveis, compartilhamento de informações e compartilhamento de conhecimento); e fator confiança (composto pelos subfatores vínculos informais, relacionamento de longo prazo e reputação). Através da análise quali-quantitativa sobre o tema investigado, verificou-se que, em geral, as empresas avaliadas encontram-se no nível médio de colaboração, de acordo com a escala de classificação adotada. A aplicação do modelo contribui para incentivar as empresas a desenvolverem práticas colaborativas para os fatores com menores índices. Foi possível concluir que o modelo proposto é adequado para os seus devidos fins, estabelecendo uma avaliação geral da colaboração em empresas que atuam em APLs.

No description available.

One of the most prevalent inherited diseases is cystic fibrosis. This disease is caused by a mutation in a membrane protein, the cystic fibrosis transmembrane conductance regulator (CFTR). CFTR is known to function as a chloride channel that regulates the viscosity of mucus that lines the ducts of a number of organs. Generally, most of the prevalent mutations of CFTR are located in one of two nucleotide binding domains, namely, the nucleotide binding domain 1 (NBD1). However, some mutations in nucleotide binding domain 2 (NBD2) can equally cause cystic fibrosis. In this work, a hierarchical graph is built for NBD2. Using this model for NBD2, we examine the consequence of single point mutations on NBD2. We collate the wildtype structure with eight of the most prevalent mutations and observe how the NBD2 is affected by each of these mutations.

In this thesis, novel approaches for dense depth field estimation and object segmentation from mono, stereo and multiple views are presented. In the first stage, a novel graph-theoretic color segmentation algorithm is proposed, in which the popular Normalized Cuts 59H[6] segmentation algorithm is improved with some modifications on its graph structure. Segmentation is obtained by the recursive partitioning of the weighted graph. The simulation results for the comparison of the proposed segmentation scheme with some well-known segmentation methods, such as Recursive Shortest Spanning Tree 60H[3] and Mean-Shift 61H[4] and the conventional Normalized Cuts, show clear improvements over these traditional methods. The proposed region-based approach is also utilized during the dense depth map estimation step, based on a novel modified plane- and angle-sweeping strategy. In the proposed dense depth estimation technique, the whole scene is assumed to be region-wise planar and 3D models of these plane patches are estimated by a greedy-search algorithm that also considers visibility constraint. In order to refine the depth maps and relax the planarity assumption of the scene, at the final step, two refinement techniques that are based on region splitting and pixel-based optimization via Belief Propagation 62H[32] are also applied. Finally, the image segmentation algorithm is extended to object segmentation in multi-view video with the additional depth and optical flow information. Optical flow estimation is obtained via two different methods, KLT tracker and region-based block matching and the comparisons between these methods are performed. The experimental results indicate an improvement for the segmentation performance by the usage of depth and motion information.

It is estimated that about 35-40% of adults in the U.S. suffer from insufficient sleep. Chronic sleep deprivation has become a prevalent phenomenon because of contemporary lifestyle and work-related factors. Sleep deprivation can reduce the capabilities and efficiency of attentional performance by impairing perception, increasing effort to maintain concentration, as well as introducing vision disturbance. Thus, it is important to understand the neural mechanisms behind how chronic sleep deprivation impairs sustained attention. In recent years, more attention has been paid to the study of the integration between anatomically distributed and functionally connected brain regions. Functional connectivity has been widely used to characterize brain functional integration, which measures the statistical dependency between neurophysiological events of the human brain. Further, evidence from recent studies has shown the non-stationary nature of brain functional connectivity, which may reveal more information about the human brain. Thus, the objective of this thesis is to investigate the effects of chronic sleep deprivation on sustained attention from the perspective of dynamic functional connectivity. A modified spatial cueing paradigm was used to assess human sustained attention in rested wakefulness and chronic sleep deprivation conditions. Partial least squares approach was applied to distinguish brain functional connectivity for the experimental conditions. With the integration of a sliding-window approach, dynamic patterns of brain functional connectivity were identified in two experimental conditions. The brain was modeled as a series of dynamic functional networks in each experimental condition. Graph theoretic analysis was performed to investigate the dynamic properties of brain functional networks, using network measures of clustering coefficient and characteristics path length. In the chronic sleep deprivation condition, a compensation mechanism between highly clustered organization and ineffective adaptability of brain functional networks was observed. Specifically, a highly clustered organization of brain functional networks was illustrated with a large clustering coefficient. This organization suggested that brain utilizes more connections to maintain attention in the chronic sleep deprivation condition. A smaller impact of clustering coefficient variation on characteristics path lengths indicated an ineffective adaptability of brain functional networks in the chronic sleep deprivation condition. In the rested wakefulness condition, brain functional networks showed the small-world topology in general, with the average small-world topology index larger than one. Small-world topology was identified as an optimal network structure with the balance between local information processing and global integration. Given the fluctuating values of the index over time, small-world brain networks were observed in most cases, indicating an effective adaptability of the human brain to maintain the dominance of small-world networks in the rested wakefulness condition. On the contrary, given that the average small-world topology index was smaller than one, brain functional networks generally exhibited random network structure. From the perspective of dynamic functional networks, even though there were few cases showing small-world brain networks, brain functional networks failed to maintain the dominance of small-world topology in the chronic sleep deprivation condition. In conclusion, to the best of our knowledge this thesis was the first to investigate the effects of chronic sleep deprivation on sustained attention from the perspective of dynamic brain functional connectivity. A compensation mechanism between highly clustered organization and ineffective adaptability of brain functional networks was observed in the chronic sleep deprivation condition. Furthermore, chronic sleep deprivation impaired sustained attention by reducing the effectiveness of brain functional networks' adaptability, resulting in the disrupted dominance of small-world brain networks.

Physiological signals, electroencephalogram (EEG) and heart rate variability (HRV), are generated by complex self-regulating systems. These signals are extremely inhomogeneous and nonstationary, and fluctuate in an irregular and highly complex manner. These fluctuations are due to underlying dynamics of the system. The synchronous neural activity measured as scalp EEG indicates underlying neural dynamics of the brain. Hence, quantitative EEG analysis has become a very useful tool in interpreting results from physiological experiments. The analysis of HRV provides valuable information to assess the autonomous nervous system (ANS). The HRV can be significantly affected by physiological state changes and many disease states. Hence, HRV analysis is becoming a major experimental and diagnostic tool. In this thesis, we focus on the study of EEG and HRV time series using tools from nonlinear time series analysis with special emphasis on its implications in detecting physiological state changes such as, in diseases like epileptic seizure and schizophrenia, and in altered states of consciousness as in sleep and meditation. The proposed nonlinear techniques are used in discriminating different physiological states from control states. Artifact processing of EEG signal Interferences (artifacts) from various sources unavoidably contaminate EEG recordings. In quantitative analysis, results can differ significantly by these artifacts, which may lead to wrong interpretation of the results. In this part of the thesis, we have devised methods to minimize ocular and muscle artifacts in EEG. The artifact correction methods are based on blind source separation (BSS) techniques such as singular value decomposition (SVD), algorithm for multiple signal extraction (AMUSE), canonical correlation analysis (CCA), information maximization (INFOMAX) independent component analysis (ICA) and joint approximate diagonalization of eigen-matrices (JADE) ICA. We have proposed a method to simulate clean and artifact corrupted EEG data based on the BSS methods. In order to enhance the performance of BSS methods, a technique called wavelet-filtered component inclusion method has been introduced. In addition, second-order statistics (SOS) and higher-order statistics (HOS) based BSS methods have been studied considering less number of EEG channels; and performance comparison of these methods has also been made. We have also addressed the problem of simultaneous correction of ocular and muscle artifacts in EEG recordings using the BSS methods. Irrespective of the BSS methods, the component elimination method has introduced high spectral error in all the bands after reconstruction of clean EEG. However, the wavelet filtered component inclusion method has retained almost all spectral powers of EEG channels in theta, alpha, and beta bands after ocular artifact minimization. When the number of EEG channels is very less, the enhanced CCA (SOS BSS) has given superior artifact minimization results than HOS BSS methods, especially in delta band. The component elimination method is used in muscle artifact minimization, and hence the SVD method cannot be used for this purpose since it leads to large spectral distortion of reconstructed EEG. The AMUSE and CCA methods have given comparable performance in muscle artifact minimization. In addition, the JADE method has introduced less mean spectral error compared to other methods. The CCA method has shown superior performance in simultaneous minimization of ocular and muscle artifacts, and AMUSE and JADE methods have given comparable results. Furthermore, the less computation time of wavelet enhanced SOS BSS methods make them very useful in real clinical environments. Fractal characterization of time series In biomedical signal analysis, fractal dimension (FD) is used as a quantitative measure to estimate complexity of physiological signals. Such analysis helps to study physiological processes of underlying systems. The FD can also be used to study dynamics of transitions between different states of systems like brain and ANS, in various physiological and pathological states. In this part, we have proposed a method to estimate FD of time series, called multiresolution box-counting (MRBC) method. A modification of this method resulted in multiresolution length (MRL) method. The estimation performance of the proposed methods is compared with that of Katz, Sevcik, and Higuchi methods, by simulating mathematically defined fractal signals, and also the computation time is compared between the methods. The MRBC and MRL methods have given comparable performance to that of Higuchi method, in estimating FD of waveforms, with the advantage of less computational time. In addition, various properties of the FD are studied and discussed in connection with classical signal processing concepts such as amplitude, frequency, sampling frequency, effect of noise, band width, correlation, etc. The FD value of signals has increased with number of harmonics, noise variance, band-width, and mid-band frequency, and decreased with degree of correlation in AR signal. An analogy between Katz FD and smoothed Teager energy operator has also been made. Application of fractal analysis to EEG and HRV time series The fluctuation of EEG potentials normally depends upon degree of alertness, and varies in amplitude and frequency. Hence, the EEG is an important clinical tool for studying sleep and sleep related disorders, epileptic seizures, schizophrenia, and meditation. In this part of the thesis, we have used FD which gives signal complexity, and detrended fluctuation analysis (DFA) which gives multiscale exponent of time series to quantify EEG. We have extended the concept of FD to multiscale FD to compute complexity of time series at multiple scales. The main applications of the proposed method are epileptic seizure detection, sleep stage detection, schizophrenia EEG analysis, and analysis of heart rate variability during meditation. For seizure detection, we have used intracranial EEG recordings with seizure-free and seizure intervals. In sleep EEG analysis, whole-night sleep EEG is used and results are compared with the manually scored hypnogram. The schizophrenia symptom is further categorized into positive and negative symptoms and complexity is estimated using FD and DFA. We have also analyzed HRV data of Chi and Kundalini meditation using FD and DFA techniques. In all the applications considered, we have tested for statistical significance of the computed parameters, between the case of interest and corresponding control cases, to discriminate between the physiological states. The ocular artifact has reduced FD while muscle artifact increased FD of EEG. The FD of seizure EEG has shown high value compared to that of seizure-free EEG. In addition, the seizure-free EEG has more DFA exponent-1 than seizure EEG. The value of FD of EEG is decreased with deepening of sleep, wake state having high FD value. The FD of REM state sleep EEG showed value between that of wake and state-1. The DFA exponent-1 has increased with deepening of sleep state, having small value for wake state. The REM state has given exponent-1 value between wake and state-1. The schizophrenia subjects have shown lower FD value than healthy controls in all the EEG channels except the bilateral temporal and occipital regions. The positive symptom sub-group has shown comparatively high FD values than healthy controls as well as overall schizophrenia sample in the bilateral tempero-parietal-occipital region. In addition, the positive symptom sub-group has shown significantly higher regional FD values than negative symptom sub-group especially in right temporal region. The overall schizophrenia samples as well as the positive and negative subgroup have shown least FD values in the bilateral frontal region. The values of DFA exponent-2 have shown significant high value in schizophrenia samples. In addition, the schizophrenia group has shown less DFA exponent-1 in bilateral temporal region than healthy control. The FD, multiscale FD, DFA exponents have shown significant performance in discriminating different physiological states from control states. The FD value of HRV time series during meditation is less compared to pre-meditation state in both Chi and Kundalini meditation. Irrespective of the type of meditation, meditation state has shown significantly high DFA exponent-1 than pre-meditation state, and significantly high DFA exponent-2 in pre-meditation state compared to meditation state. Functional connectivity analysis of brain during meditation In functionally related regions of the brain, even in those regions separated by substantial distances, the EEG fluctuations are synchronous, which is termed as functional connectivity. In this part, a novel application of functional connectivity analysis of brain using graph theoretic approach has been made on the EEG recorded from meditation practitioners. We have used 16 channel EEG data from subjects while performing Raja Yoga meditation. The pre-meditation condition is used as control state, against which meditation state is compared. For finding connectivity between EEG of various channels, we have computed pair-wise linear correlation and mutual information between the EEG channels, to form a connection matrix of size 16x16. Then, various graph parameters, such as average connection density, degree of nodes, characteristic path length, and cluster index, are computed from the connection matrix. The computed parameters are projected on to the scalp to get topographic head maps that give spatial variation of the parameter, and results are compared between meditation and pre-meditation states. The meditation state has shown low average connection density, less characteristic path length, and high average degree in fronto-central and central regions. Furthermore, high cluster index is shown in frontal and central regions than pre-meditation state. The parameters such as complexity, characteristic path length and average connection density are used as features in quadratic discriminant classifier to classify meditation and pre-meditation state, and have given good accuracy performance. Connectivity analysis using mutual information has given high average connection density in meditation state in theta, alpha and beta bands compared to pre-meditation state. The characteristic path length is high in delta, alpha and beta bands in meditation state. In addition, the meditation state has shown high degree and cluster index in theta and beta bands compared to pre-meditation state. Nonlinear dynamical characterization of HRV during meditation The cardiovascular system is influenced by internal dynamics as well as from various external factors, which makes the system more dynamic and nonlinear. In this part of the thesis, a novel application of using HRV data for studying Chi and Kundalini meditation has been made. The HRV time series are embedded into higher dimensional phase-space using Takens’ embedding theorem to reconstruct the attractor. After estimating the minimum embedding dimension to unfold the attractor dynamics, the complexity of the attractor is computed using correlation dimension, Lyapunov exponent, and nonlinearity scores. In all the analyses, the pre-meditation state is used as control state against which meditation state is compared. The statistical significance of the parameters estimated is tested to discriminate meditation state from control state. The HRV time series of both pre-meditation and meditation have shown similar minimum embedding dimensions in both Chi and Kundalini meditation. Irrespective of the type of meditation, the meditation state has shown high correlation dimension, largest Lyapunov exponent, and low nonlinearity score compared to pre-meditation state. Recurrent quantification analysis of HRV during meditation In this part, a novel application of recurrent quantification analysis (RQA) to HRV during meditation is studied. Here, the time series is embedded into a higher dimensional phase-space and Euclidean distance between the embedded vectors is calculated to form a distance matrix. The matrix is converted into binary matrix by applying a suitable threshold, and plotted as image to get recurrence plot. Various parameters are extracted from the recurrence plot such as percent recurrence rate, diagonal parameters (determinism, divergence, entropy, ratio), and vertical or horizontal parameters (laminarity, trapping time, maximal vertical line length). The procedure is applied to HRV data during meditation and pre-meditation (control) to discriminate between the states. The HRV of meditation state has shown more diagonal line structure whereas more black patches are observed in pre-meditation state. In addition, at low embedding dimensions, the meditation state has shown low recurrence rate, high determinism, low divergence, low entropy, high ratio, high laminarity, high trapping time, and less maximal vertical line length compared to pre-meditation state. These RQA parameters have shown superior performance in discriminating meditation state from control state.