Analysis of IIR Filters by Interval Response

The classical theory of signal processing assumes that the designed IIR filters are continuous and have infinitely accurate coefficients. However, when developing filters for real-world digital signal processing tasks, it is necessary to take into account the finite precision of the coefficients representation, especially considering the fixed-point arithmetic. In this paper, we propose a new approach, which we call the interval size approach, making it easy to evaluate the actual digital filter response for various fixed-point arithmetic parameters. We provide Illustrative examples to demonstrate the frequency response bounds evolution as an order, cutoff frequency, and signal frequency are varying. We show that the interval size can be used as a well-suited accuracy factor of a digital filter

The choice between delta and shift operators for low-precision data representation

Low-precision data types for embedded applications reduce the power consumption and enhance the price-performance ratio. Inconsistence between the specified accuracy of a designed filter or controller and an imprecise data type can be overcome using the δ-operator, an alternative to the traditional discrete-time z-operator. Though in many cases it significantly increases accuracy, sometimes it shows no advantage over the shift operator. So the problem of choice between delta and shift operator arises. Therefore, a study on δ-operator applicability bounds is needed to solve this problem and provide δ-operator efficient practical use. In this paper we introduce a concept of the δ-operator applicability criterion. The discrete system implementation technique with discrete-time operator choice is given for the low-precision machine arithmetic

Integrate-and-Differentiate Approach to Nonlinear System Identification

In this paper, we consider a problem of parametric identification of a piece-wise linear mechanical system described by ordinary differential equations. We reconstruct the phase space of the investigated system from accelerometer data and perform parameter identification using iteratively reweighted least squares. Two key features of our study are as follows. First, we use a differentiated governing equation containing acceleration and velocity as the main independent variables instead of the conventional governing equation in velocity and position. Second, we modify the iteratively reweighted least squares method by including an auxiliary reclassification step into it. The application of this method allows us to improve the identification accuracy through the elimination of classification errors needed for parameter estimation of piece-wise linear differential equations. Simulation of the Duffing-like chaotic mechanical system and experimental study of an aluminum beam with asymmetric joint show that the proposed approach is more accurate than state-of-the-art solutions

Adaptive Chaotic Maps in Cryptography Applications

Chaotic cryptography is a promising area for the safe and fast transmission, processing, and storage of data. However, many developed chaos-based cryptographic primitives do not meet the size and composition of the keyspace and computational complexity. Another common problem of such algorithms is dynamic degradation caused by computer simulation with finite data representation and rounding of results of arithmetic operations. The known approaches to solving these problems are not universal, and it is difficult to extend them to many chaotic systems. This chapter describes discrete maps with adaptive symmetry, making it possible to overcome several disadvantages of existing chaos-based cryptographic algorithms simultaneously. The property of adaptive symmetry allows stretching, compressing, and rotating the phase space of such maps without significantly changing the bifurcation properties. Therefore, the synthesis of one-way piecewise functions based on adaptive maps with different symmetry coefficients supposes flexible control of the keyspace size and avoidance of dynamic degradation due to the embedded technique of perturbing the chaotic trajectory

Analysis of IIR Filters by Interval Response

The classical theory of signal processing assumes that the designed IIR filters are continuous and have infinitely accurate coefficients. However, when developing filters for real-world digital signal processing tasks, it is necessary to take into account the finite precision of the coefficients representation, especially considering the fixed-point arithmetic. In this paper, we propose a new approach, which we call the interval size approach, making it easy to evaluate the actual digital filter response for various fixed-point arithmetic parameters. We provide Illustrative examples to demonstrate the frequency response bounds evolution as an order, cutoff frequency, and signal frequency are varying. We show that the interval size can be used as a well-suited accuracy factor of a digital filter

Analysis of IIR Filters by Interval Response

The classical theory of signal processing assumes that the designed IIR filters are continuous and have infinitely accurate coefficients. However, when developing filters for real-world digital signal processing tasks, it is necessary to take into account the finite precision of the coefficients representation, especially considering the fixed-point arithmetic. In this paper, we propose a new approach, which we call the interval size approach, making it easy to evaluate the actual digital filter response for various fixed-point arithmetic parameters. We provide Illustrative examples to demonstrate the frequency response bounds evolution as an order, cutoff frequency, and signal frequency are varying. We show that the interval size can be used as a well-suited accuracy factor of a digital filter

Integrate-and-Differentiate Approach to Nonlinear System Identification

In this paper, we consider a problem of parametric identification of a piece-wise linear mechanical system described by ordinary differential equations. We reconstruct the phase space of the investigated system from accelerometer data and perform parameter identification using iteratively reweighted least squares. Two key features of our study are as follows. First, we use a differentiated governing equation containing acceleration and velocity as the main independent variables instead of the conventional governing equation in velocity and position. Second, we modify the iteratively reweighted least squares method by including an auxiliary reclassification step into it. The application of this method allows us to improve the identification accuracy through the elimination of classification errors needed for parameter estimation of piece-wise linear differential equations. Simulation of the Duffing-like chaotic mechanical system and experimental study of an aluminum beam with asymmetric joint show that the proposed approach is more accurate than state-of-the-art solutions

Integrate-and-Differentiate Approach to Nonlinear System Identification

In this paper, we consider a problem of parametric identification of a piece-wise linear mechanical system described by ordinary differential equations. We reconstruct the phase space of the investigated system from accelerometer data and perform parameter identification using iteratively reweighted least squares. Two key features of our study are as follows. First, we use a differentiated governing equation containing acceleration and velocity as the main independent variables instead of the conventional governing equation in velocity and position. Second, we modify the iteratively reweighted least squares method by including an auxiliary reclassification step into it. The application of this method allows us to improve the identification accuracy through the elimination of classification errors needed for parameter estimation of piece-wise linear differential equations. Simulation of the Duffing-like chaotic mechanical system and experimental study of an aluminum beam with asymmetric joint show that the proposed approach is more accurate than state-of-the-art solutions

Study of two-memcapacitor circuit model with semi-explicit ODE solver

This article discusses software tools for studying non-linear dynamical systems. For a detailed analysis of the behavior of chaotic systems stepsize-parameter diagrams are introduced. A new self-adjoint semi-explicit algorithm for the numerical integration of differential equations is described. Two modifications of the proposed method are represented. A two-memcapacitor circuit is selected as a test dynamical system. Symmetry, accuracy and performance analysis of semi-explicit extrapolation ODE solver are considered in a series of computational experiments. Phase space of the two-memcapacitor circuit model, stepsize-parameter diagrams and dynamical maps are given as experimental findings

Adaptive Chaotic Maps in Cryptography Applications

Chaotic cryptography is a promising area for the safe and fast transmission, processing, and storage of data. However, many developed chaos-based cryptographic primitives do not meet the size and composition of the keyspace and computational complexity. Another common problem of such algorithms is dynamic degradation caused by computer simulation with finite data representation and rounding of results of arithmetic operations. The known approaches to solving these problems are not universal, and it is difficult to extend them to many chaotic systems. This chapter describes discrete maps with adaptive symmetry, making it possible to overcome several disadvantages of existing chaos-based cryptographic algorithms simultaneously. The property of adaptive symmetry allows stretching, compressing, and rotating the phase space of such maps without significantly changing the bifurcation properties. Therefore, the synthesis of one-way piecewise functions based on adaptive maps with different symmetry coefficients supposes flexible control of the keyspace size and avoidance of dynamic degradation due to the embedded technique of perturbing the chaotic trajectory