Search

This paper proposes an iterative strategy employing the linear programming (LP) method for the design of an all-pass variable-phase (AP-VP) digital filter in the minimax error sense. Mathematically, designing an AP-VP digital filter belongs to a non-linear optimisation problem. The novel LP design method first reduces this non-linear minimisation problem to a linear minimisation problem and then solves the linearised optimisation problem iteratively via using the LP method. The iterative LP-solving scheme is able to reduce the largest design errors significantly and thus can get a considerable accuracy improvement as compared to the noniterative one. We will utilise three design examples to show that this iterative scheme can get much better design results (much smaller maximum errors) than the noniterative method in the literature. Therefore, this iterative LP design strategy can significantly enhance the performance of the AP-VP digital filter. [ABSTRACT FROM AUTHOR]

Stability is one of the most concerned issues in designing a recursive variable digital filter (VDF). This is because the coefficients of a recursive VDF constantly vary in the tuning process, and updating the coefficients may incur instability. Thus, an appropriate measure needs to be taken for ensuring its stability. This paper presents a new coefficient transformation (CT) method for transforming the coefficients of a recursive transfer-function denominator into a set of new coefficients. From the viewpoint of conventional constant-coefficient filter (constant filter) design, the new coefficients can take arbitrary values without incurring instability. For designing a stable VDF, we apply this CT to the variable case and approximate each transformed coefficient as a distinct polynomial in the tuning parameter. Thus, we can change the filter coefficients by changing the value of the tuning parameter, and thus tune the magnitude response. Thanks to the proposed CT, updating the filter coefficients will never incur instability. This is the core part of the CT-based design approach. In this paper, we utilise a weighting function to ignore the transition-band errors and thus enhance the design accuracy of important frequency bands (passband and stopband). Moreover, the polynomials use different degrees so as to reduce the VDF complexity. Two design examples (lowpass VDF and bandpass VDF) are provided for verifying the design accuracy and checking the stability. [ABSTRACT FROM PUBLISHER]

An interpolation kernel is required in a wide variety of signal processing applications such as image interpolation and timing adjustment in digital communications. This article presents a general-form interpolation kernel called 3-3-3 interpolation kernel and derives its frequency response in a closed-form by using a simple derivation method. This closed-form formula is preliminary to designing various 3-3-3 interpolation kernels subject to a set of design constraints. The 3-3-3 interpolation kernel is formed through utilising the third-degree piecewise polynomials, and it is an even-symmetric function. Thus, it will suffice to consider only its right-hand side when deriving its frequency response. Since the right-hand side of the interpolation kernel contains three piecewise polynomials of the third degree, i.e. the degrees of the three piecewise polynomials are (3,3,3), we call it the 3-3-3 interpolation kernel. Once the general-form frequency-response formula is derived, we can systematically formulate the design of various 3-3-3 interpolation kernels subject to a set of design constraints, which are targeted for different interpolation applications. Therefore, the closed-form frequency-response expression is preliminary to the optimal design of various 3-3-3 interpolation kernels. We will use an example to show the optimal design of a 3-3-3 interpolation kernel based on the closed-form frequency-response expression. [ABSTRACT FROM PUBLISHER]

In this article, we propose a new frequency-domain weighted-least-squares method for designing high-accuracy interpolator (interpolation kernel) that reduces the number of the first-order derivative continuity constraints. By reducing the number of the first-order derivative constraints, we can increase the degree of freedom in the design, and thus have more flexibility to get more accurate design results. The interpolator consists of 6 piecewise polynomials of the third degree (cubic), and it is performed in the frequency-domain through minimising the weighted integrated-squared-error of the spectrum (frequency response). The weighting function is adjusted so as to ignore some insignificant frequency bands and put more emphasis on the important frequency bands. By imposing the continuity constraints on the interpolator itself as well as the reduced first-order derivative constraints at the contacting points, we get three free parameters of the interpolator. These three parameters are then optimised in such a way that the weighted integrated-squared-error of the frequency response is minimised. We will utilise a narrow-band example to demonstrate the performance improvement over other existing interpolators. [ABSTRACT FROM AUTHOR]