Design of interpolative sigma-delta modulators via semi-indefinite programming

This correspondence considers the optimized design of interpolative sigma delta modulators (SDMs). The first optimization problem is to determine the denominator coefficients. The objective of the optimization problem is to minimize the passband energy of the denominator of the loop filter transfer function (excluding the dc poles) subject to the continuous constraint of this function defined in the frequency domain. The second optimization problem is to determine the numerator coefficients in which the cost function is to minimize the stopband ripple energy of the loop filter subject to the stability condition of the noise transfer function (NTF) and signal transfer function (STF). These two optimization problems are actually quadratic semi-infinite programming (SIP) problems. By employing the dual-parameterization method, global optimal solutions that satisfy the corresponding continuous constraints are guaranteed if the filter length is long enough. The advantages of this formulation are the guarantee of the stability of the transfer functions, applicability to design of rational infinite-impulse-response (IIR) filters without imposing specific filter structures, and the avoidance of iterative design of numerator and denominator coefficients. Our simulation results show that this design yields a significant improvement in the signal-to-noise ratio (SNR) and have a larger stability range, compared with the existing designs

Digital Filter Design Using Improved Teaching-Learning-Based Optimization

Digital filters are an important part of digital signal processing systems. Digital filters are divided into finite impulse response (FIR) digital filters and infinite impulse response (IIR) digital filters according to the length of their impulse responses. An FIR digital filter is easier to implement than an IIR digital filter because of its linear phase and stability properties. In terms of the stability of an IIR digital filter, the poles generated in the denominator are subject to stability constraints. In addition, a digital filter can be categorized as one-dimensional or multi-dimensional digital filters according to the dimensions of the signal to be processed. However, for the design of IIR digital filters, traditional design methods have the disadvantages of easy to fall into a local optimum and slow convergence. The Teaching-Learning-Based optimization (TLBO) algorithm has been proven beneficial in a wide range of engineering applications. To this end, this dissertation focusses on using TLBO and its improved algorithms to design five types of digital filters, which include linear phase FIR digital filters, multiobjective general FIR digital filters, multiobjective IIR digital filters, two-dimensional (2-D) linear phase FIR digital filters, and 2-D nonlinear phase FIR digital filters. Among them, linear phase FIR digital filters, 2-D linear phase FIR digital filters, and 2-D nonlinear phase FIR digital filters use single-objective type of TLBO algorithms to optimize; multiobjective general FIR digital filters use multiobjective non-dominated TLBO (MOTLBO) algorithm to optimize; and multiobjective IIR digital filters use MOTLBO with Euclidean distance to optimize. The design results of the five types of filter designs are compared to those obtained by other state-of-the-art design methods. In this dissertation, two major improvements are proposed to enhance the performance of the standard TLBO algorithm. The first improvement is to apply a gradient-based learning to replace the TLBO learner phase to reduce approximation error(s) and CPU time without sacrificing design accuracy for linear phase FIR digital filter design. The second improvement is to incorporate Manhattan distance to simplify the procedure of the multiobjective non-dominated TLBO (MOTLBO) algorithm for general FIR digital filter design. The design results obtained by the two improvements have demonstrated their efficiency and effectiveness

A FPGA/DSP design for real-time fracture detection using low transient pulse

This work presents the hardware and software architecture for the detection of fractures and edges in materials. While the detection method is based on the novel concept of Low Transient Pulse (LTP), the overall system implementation is based on two digital microelectronics technologies widely used for signal processing: Digital Signal Processor (DSP) and Field Programmable Gate Array (FPGA). Under the proposed architecture, the DSP carries out the analysis of the received baseband signal at a lower rate and hence can be used for large number of signal channels. The FPGA\u27s master clock runs at a higher frequency (62.5MHz) for the generation of LTP signal and to demodulate the passband ultrasonic signals sampled at 1MHz which interrupts the DSP at every 1 [Is. This research elaborates on designing a Quadrature Amplitude Modulator - demodulator (QAM) on the FPGA for the received signal from the ultrasound and edge detection on the DSP processor to detect the presence of edges/fractures on a test Sawbone plate. In this work, the LTP technology is applied to determine the location of the Sawbone plate edges based on the reflected signals to the receivers. This signal is then passed through a QAM to get the maxima (peaks) at the received signal to study the parameters in the DSP. This work successfully demonstrates the feasibility of modular programming approach across the two platforms. The dual time scale platform readily accommodates higher temporal resolution needed for the generation of Low Transient Pulses and the processing of real time baseband signals on the DSP for various test conditions

A robust and scalable implementation of the Parks-McClellan algorithm for designing FIR filters

Preliminary version accepted for publicationInternational audienceWith a long history dating back to the beginning of the 1970s, the Parks-McClellan algorithm is probably the most well-known approach for designing finite impulse response filters. Despite being a standard routine in many signal processing packages, it is possible to find practical design specifications where existing codes fail to work. Our goal is twofold. We first examine and present solutions for the practical difficulties related to weighted minimax polynomial approximation problems on multi-interval domains (i.e., the general setting under which the Parks-McClellan algorithm operates). Using these ideas, we then describe a robust implementation of this algorithm. It routinely outperforms existing minimax filter design routines

Linear Phase FIR Low Pass Filter Design Based on Firefly Algorithm

In this paper, a linear phase Low Pass FIR filter is designed and proposed based on Firefly algorithm. We exploit the exploitation and exploration mechanism with a local search routine to improve the convergence and get higher speed computation. The optimum FIR filters are designed based on the Firefly method for which the finite word length is used to represent coefficients. Furthermore, Particle Swarm Optimization (PSO) and Differential Evolution algorithm (DE) will be used to show the solution. The results will be compared with PSO and DE methods. Firefly algorithm and Parks–McClellan (PM) algorithm are also compared in this paper thoroughly. The design goal is successfully achieved in all design examples using the Firefly algorithm. They are compared with that obtained by using the PSO and the DE algorithm. For the problem at hand, the simulation results show that the Firefly algorithm outperforms the PSO and DE methods in some of the presented design examples. It also performs well in a portion of the exhibited design examples particularly in speed and quality