The Unscented Kalman Filter (UKF) is a well-known nonlinear state estimation method. It shows superior performance at nonlinear estimation compared to the Extended Kalman Filter (EKF). This paper is devoted to an investigation between UKF and EKF with different feedback control modes in vehicle navigation. Theoretical formulation, simulation and field tests have been carried out to compare the performance of UKF and EKF. The simulation and test results demonstrate that the estimated state of a UKF relies on the measurements and is less sensitive to historical model information. The results also indicate that UKF has benefits for prototype model design due to avoidance of calculation of a Jacobian matrix. EKF, however, is more computationally efficient and more stable.

Comparison of Unscented and Extended Kalman Filters with Application in Vehicle Navigation

The mechanical behaviour of bamboo is greatly influenced by its transverse properties, which are not easily measured by experiment. This study develops a framework and the computational tools required to evaluate the material and mechanical properties of bamboo in its full-culm form. A numerical model of bamboo as a transversely isotropic material with functionally graded material properties in the radial direction is developed. The random field method was introduced as a means of quantifying the measured uncertainty of bamboo with respect to the mechanical characterisation of its full-culm state. Four increasingly complex approaches to model circumferential compression tests of bamboo are presented: a theoretical evaluation using Castigliano’s theorem; an orthotropic model neglecting the graded nature of the culm wall; and, two models—one discrete and one continuum-based that define a transversely isotropic graded material. Output from each model is compared, calibrated and validated with experimental results. While the models developed were robust, their application has drawn into question the fundamental hypothesis that the functionally graded behaviour of bamboo can be captured using the rule of mixtures.

Modelling full-culm bamboo as a naturally varying functionally graded material

In this work we provide a framework for dynamic secret sharing and present the first dynamic and verifiable hierarchical secret sharing scheme based on Birkhoff interpolation. Since the scheme is dynamic it allows, without reconstructing the message distributed, to add and remove shareholders, to renew shares, and to modify the conditions for accessing the message. Furthermore, each shareholder can verify its share received during these algorithms protecting itself against malicious dealers and shareholders. While these algorithms were already available for classical Lagrange interpolation based secret sharing, corresponding techniques for Birkhoff interpolation based schemes were missing. Note that Birkhoff interpolation is currently the only technique available that allows to construct hierarchical secret sharing schemes that are efficient and allow to provide shares of equal size for all shareholder in the hierarchy. Thus, our scheme is an important contribution to hierarchical secret sharing.

Dynamic and Verifiable Hierarchical Secret Sharing.

Dynamic and Verifiable Hierarchical Secret Sharing

An enormous volume of data, known as Big Data, of varied properties, is continuously being generating from several sources. For efficient and consequential use of this huge amount of data, automated and correct categorization is very important. The precise categorization can find the correlations, hidden patterns, and other valuable insights. The process of categorization of mixed heterogeneous data is known as data classification and is done based on some predefined features. Various algorithms and techniques are proposed for Big Data classification. This chapter attempts to discuss various technicalities of Big Data classification, comprehensively. To start with, the basics of Big Data classifications such as need, types, patterns, phases, approaches, etc. are explained aptly. Different classification techniques, including traditional, evolutionary, and advanced machine learning technique, are discussed with suitable examples, along with citing their advantages and disadvantages. Finally, a survey of various open-source and commercial libraries, platforms, and tools for Big Data classification is presented.

Big Data classification: techniques and tools

Decomposition of matrix is a vital part of many scientific and engineering applications. It is a technique that breaks down a square numeric matrix into two different square matrices and is a basis for efficiently solving a system of equations, which in turn is the basis for inverting a matrix. An inverting matrix is a part of many important algorithms. Matrix factorizations have wide applications in numerical linear algebra, in solving linear systems, computing inertia, and rank estimation is an important consideration. This paper presents review of all the matrix decomposition techniques used in signal processing applications on the basis of their computational complexity, advantages and disadvantages. Various Decomposition techniques such as LU Decomposition, QR decomposition , Cholesky decomposition are discussed here.

Monika Agarwal

Papers

Review of Matrix Decomposition Techniques for Signal Processing Applications