A Prospective Study of Prevalence of Carotid Artery Disease in Patients with Coronary Artery Disease and its Correlation with Traditional Atherosclerotic Risk Factors in Central India

Introduction: Early atherosclerosis mainly involves carotid artery, which leads to increased carotid artery intima media thickness (CIMT).The potential value of CIMT improving the predictive capacity of traditional risk factors of CAD is an understudied and underutilized issue. Because of increasing availability of highly sensitive ultrasonography probes and for a noninvasive procedures, we can predict coronary artery disease (CAD) more precisely in patients having multiple traditional risk factors so it may reduce morbidity and mortality due to CAD and elevated CIMT can be used as surrogate marker of underlying CAD.Methods: This study enrolled 250 admitted patients as a case of CAD. The patients were assessed by detailed history taking, thorough clinical examination, measurement of CIMT, blood sugar and lipid level.Results: Carotid artery disease was present in 88 (35%) of 250 CAD patients. All modifiable cardiovascular risk factors were statistically significantly high in patients of CAD with carotid artery disease. In obese, diabetic, hypertensive, dyslipidemia and smoker patients, carotid artery disease was present in 55% (P = 0.00), 41% (P = 0.00), 43% (P = 0.007), 47% (P = 0.002) and 43% (P = 0.003) respectively. CAD patients who had 1 risk factor, 29% were associated with carotid artery disease. Comparison of single risk factor with patients who had no risk factor, there was non-significant correlation for carotid artery disease. CAD patients who had 2, 3, 4 and 5 risk factors, carotid artery disease was present 24 (32%) (p = 0.02), 15 (55%) (P = 0.0003), 17 (61%) (P = 0.00006) and 6 (67%) (P = 0.0008).Conclusion: elevated CIMT can be used as one of the important risk factor for early diagnosis of CAD and to reduce morbidity and mortality due to CAD

Nonlinear State Estimation Algorithms and their Applications

State estimation is a process of estimating the unmeasured or noisy states using the measured outputs and control inputs along with process and measurement models. The extended Kalman filter (EKF) has been an important approach for nonlinear state estimation over the last five decades. However, EKFs are only suitable for ‘mild’ nonlinearities where the first-order approximations of the nonlinear functions are available and they also require evaluation of state and measurement Jacobians at every iteration. This thesis presents a few linear and nonlinear state estimation methods and their applications. To start with, we investigate the use of the linear H∞ filter, which can deal with non-Gaussian noises, in a control application. The efficacy of the linear H∞ filter based sliding mode controller is verified on a quadruple tank system. The main tools for nonlinear state estimation are cubature Kalman filter (CKF) and its variants. A solution to simultaneous localisation and mapping (SLAM) problem using CKF is proposed. The effectiveness of the nonlinear CKF-SLAM over EKF- and UKF-SLAM is demonstrated. We propose a couple of new nonlinear state estimation algorithms, namely, cubature information filters (CIFs) and cubature H∞ filters (CH∞Fs), and their square root versions. The CIF is derived from an extended information filter and a CKF. The CIF is further extended for use in multi-sensor state estimation and its square root version is derived using a unitary transformation. For non-linear and non-Gaussian systems, we fuse an extended H∞ filter and CKF to form CH∞F which has the desirable features of both CKF and an extended H∞ filter. Further, we derive a square root CH∞F using a J-unitary transformation for numerical stability. The efficacies of the proposed algorithms are evaluated on simulation examples

Cubature H∞ information filter

This paper presents a state estimation algorithm referred to as a cubature H∞ information filter (CH∞IF) for nonlinear systems. The proposed algorithm is developed from a cubature Kalman filter, an H∞ filter and an extended information filter. The CH ∞IF is a derivative free filter, where the information state vector and information matrix are propagated rather than the state vector and error covariance matrix. Furthermore, the CH∞IF is extended for multi-sensor state estimation. The efficacy of the CH∞IF is demonstrated by a simulation example of a permanent magnet synchronous motor in the presence of Gaussian and non-Gaussian noises

A cubature H∞filter and its square-root version

In this paper, we present a nonlinear state estimation algorithm based on the fusion of an extended H∞ (EH∞F) and a cubature Kalman filter (CKF); the resulting estimator is called a cubature H∞ filter. The recently developed CKF is a Gaussian approximation of a Bayesian filter and its performance over non-Gaussian noises may degrade. In contrast, the H∞ filter is capable of estimating the states of linear systems with non-Gaussian noises and the extended H∞ filter (EH∞F) can estimate the states of non-linear and non-Gaussian systems. Similar to the H∞ filter, an EH∞F also does not make any assumptions about the statistics of the process or measurement noise, but it does require Jacobians during the state estimation of nonlinear systems, which degrade the overall performance when the nonlinearities are severe. The cubature H∞ filter is developed to have the desirable features of both CKF and EH∞F. For numerical accuracy, a square-root version of the cubature H∞ filter is developed using J-unitary transformation. The efficacy of the square-root cubature H∞ filter is verified on continuous stirred tank reactor and permanent magnet synchronous motor examples

Square root cubature information filter

Nonlinear state estimation plays a major role in many real-life applications. Recently, some sigma-point filters, such as the unscented Kalman filter, the particle filter, or the cubature Kalman filter have been proposed as promising substitutes for the conventional extended Kalman filter. For multisensor fusion, the information form of the Kalman filter is preferred over standard covariance filters due to its simpler measurement update stage. This paper presents a new state estimation algorithm called the square root cubature information filter (SRCIF) for nonlinear systems. The cubature information filter is first derived from an extended information filter and a recently developed cubature Kalman filter. For numerical accuracy, its square root version is then developed. Unlike the extended Kalman or extended information filters, the proposed filter does not require the evaluation of Jacobians during state estimation. The proposed approach is further extended for use in multisensor state estimation. The efficacy of the SRCIF is demonstrated by a simulation example of a permanent magnet synchronous motor

Cubature Kalman Filter Based Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is the process of simultaneously building a map and localizing in it, and can be used for autonomous navigation. SLAM deals with estimation of vehicle states and landmarks. Most SLAM algorithms are based on extended Kalman filters (EKFs). However, the use of EKF for SLAM is not the best choice, as it works only for `mild' nonlinear environments owing to the assumption of first order Taylor series approximations of process and observation models. A few researchers has also proposed the use of other estimation techniques like particle filters, unscented Kalman filters (UKFs), etc for SLAM. In this paper, we propose the use of a cubature Kalman filter (CKF) for the estimation of the SLAM augmented state vector. The proposed SLAM is derivative less SLAM. A comparison of CKF SLAM and UKF SLAM is given through numerical simulations

SLAM using EKF, EH∞ and mixed EH2/H∞ filter

The process of simultaneously building the map and locating a vehicle is known as Simultaneous Localization and Mapping (SLAM) and can be used for autonomous navigation. The estimation of vehicle states and landmarks plays an important role in SLAM. Most of the SLAM algorithms are based on extended Kalman filters (EKFs). However, EKF's are not the best choice for SLAM as they suffer from the assumption of Gaussian noise statistics and linearization errors, which can degrade the performance. H∞ filter is one of the alternative of Kalman filter. This paper investigates three SLAM algorithms: (i) EKF SLAM (ii) extended H∞(EH∞) SLAM and (iii) mixed extended H2/H∞(EH2/H∞) SLAM. A comparison of the three algorithms is given through numerical simulations