Architectures for maximum-sequence-length digital delta-sigma modulators

In this paper, we extend the idea developed in some of our earlier works of using output feedback to make the quantization step in a digital delta-sigma modulator (DDSM) appear prime. This maximizes the cycle lengths for constant inputs, spreading the quantization error over the maximum number of frequency terms, and consequently, minimizing the power per tone. We show how this concept can be applied to multibit higher order error-feedback modulators (EFMs). In addition, we show that the idea can be implemented in a class of single-quantizer DDSMs (SQ-DDSM) where STF (z) = z(-L) and NTF (z) = (1 - Z(-1))(L)

Mathematical analysis of prime modulus quantizer MASH digital delta-sigma modulator

A MASH digital delta-sigma modulator (DDSM) is analyzed mathematically. It incorporates first-order error feedback modulators (EFM) which include prime modulus quantizers to guarantee a minimum sequence length M. The purpose of this analysis is to calculate the exact sequence length of the aforementioned MASH DDSM. We show that the sequence length for an lth-order member of this modulator family is M for all constant inputs, and for all initial conditions, where M is the sequence length of the constituent first-order prime modulus quantizer EFMs.

A hybrid HS-CSS algorithm for simultaneous analysis, design and optimization of trusses via force method

In this paper, a hybrid heuristic method is developed using the harmony search (HS) and charged system search (CSS), called HS-CSS. In this algorithm the use of HS improves the exploitation property of the standard CSS. An energy formulation of the force method is developed and the analysis, design and optimization are performed simultaneously using the standard CSS and HS-CSS. New goal functions are introduced for minimization, and the CSS and the HS-CSS are employed for continuous optimization. An efficient method is introduced using the CSS and HS-CSS for designing structures having members of prescribed stress ratios. Finally, the minimum weight design of truss structures is formulated using the CSS and HS-CSS algorithms and applied to some benchmark problems from literature

Prediction of the Spectrum of a Digital Delta–Sigma Modulator Followed by a Polynomial Nonlinearity

This paper presents a mathematical analysis of the power spectral density of the output of a nonlinear block driven by a digital delta-sigma modulator. The nonlinearity is a memoryless third-order polynomial with real coefficients. The analysis yields expressions that predict the noise floor caused by the nonlinearity when the input is constant

A Physics-based Metaheuristic Algorithm Based on Doppler Effect Phenomenon and Mean Euclidian Distance Threshold

Doppler Effect (DE) is a physical phenomenon observed by Doppler, an Austrian mathematician, in 1842. In recent years, the mathematical formulation of this phenomenon has been used to improve the frequency equation of the standard Bat Algorithm (BA) developed by Yang in 2010. In this paper, we use the mathematical formulation of DE with some idealized rules to update the observer velocity existing in the Doppler equation. Thus, a new physics-based Metaheuristic (MH) optimizer is developed. In the proposed algorithm, the observers’ velocities as the algorithm’s search agents are updated based on the DE equation. A new mechanism named Mean Euclidian Distance Threshold (MEDT) is introduced to enhance the quality of the observers. The proposed MEDT mechanism is also employed to avoid the locally optimum solutions and increase the convergence rate of the presented optimizer. Since the proposed algorithm simultaneously utilizes the DE equation and MEDT mechanism, it is called the Doppler Effect-Mean Euclidian Distance Threshold (DE-MEDT) metaheuristic algorithm. The proposed DE-MEDT algorithm’s efficiency is evaluated by solving well-known unconstrained and constrained optimization problems. In the unconstrained optimization problems, 23 well-known optimization functions are used to assess the exploratory, exploitative, and convergence behaviors of the DE-MEDT algorithm

Boundary Strategy for Optimization-based Structural Damage Detection Problem using Metaheuristic Algorithms

The present paper proposes a new strategy namely Boundary Strategy (BS) in the process of optimization-based damage detection using metaheuristic algorithms. This strategy gradually neutralizes the effects of structural elements that are healthy in the optimization process. BS causes the optimization method to find the optimum solution better than conventional methods that do not use the proposed BS. This technique improves both aspects of the accuracy and convergence speed of the algorithms in identifying and quantifying the damage. To evaluate the performance of the developed strategy, a new damage-sensitive cost function, which is defined based on vibration data of the structure, is optimized utilizing the Shuffled Shepherd Optimization Algorithm (SSOA). Different examples including truss, beam, and frame are investigated numerically in order to indicate the applicability of the proposed technique. The proposed approach is also applied to other well-known optimization algorithms including TLBO, GWO, and MFO. The obtained results illustrate that the proposed method improves the performance of the utilized algorithms in identifying and quantifying of the damaged elements, even for noise-contaminated data

Modified Dolphin Monitoring Operator for Weight Optimization of Frame Structures

In this article, a modified dolphin monitoring (MDM) operatoris introduced and used to improve the performance of the collidingbodies optimization (CBO) algorithm for optimal designof steel structures (CBO-MDM). The performance of the CBO,enhanced colliding bodies optimization (ECBO) and CBOMDMare compared through three well-established structuralbenchmarks. The optimized designs obtained by thesealgorithms are compared, and the results show that the performanceof CBO-MDM is superior to those of the other twoalgorithms. The MDM is found to be a suitable tool to enhancethe performance of the CBO algorithm

Performance of the Modified Dolphin Monitoring Operator for Weight Optimization of Skeletal Structures

In this study, the Modified Dolphin Monitoring (MDM) operator is used to enhance the performance of some metaheuristic algorithms. The MDM is a recently presented operator that controls the population dispersion in each iteration. Algorithms are selected from some well-established algorithms. Here, this operator is applied on Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Vibrating Particles System (VPS), Enhanced Vibrating Particles System (EVPS), Colliding Bodied Optimization (CBO) and Harmony Search (HS) and the performance of these algorithms are evaluated with and without this operator on three well-known structural optimization problems. The results show the performance of this operator on these algorithms for the best, the worst, average and average weight of the first quarter of answers

Layout Optimization of Planar Braced Frames Using Modified Dolphin Monitoring Operator

Determining the optimum placement of braces in steel frames has always been one of the most challenging issues in structural engineering. In this paper, the size and placement of the X-braces in planar frame structures is determined in a way that the total weight of the braced frames becomes minimum, while satisfying the design requirements and constraints. Variables of the optimization contain the cross sections for beams, columns, and X-braces as well as the placement of these braces in the frames. Attempt has also been made to consider all the constraints of an actual design problem. One of the other objectives of this study is to investigate the effect of including or excluding some of the constraints affecting the optimization of the planar frame design. For this purpose, the Colliding Bodies Optimization (CBO) and CBO-MDM algorithms have been utilized. Modified Dolphin Monitoring (MDM) operator is recently developed for improving the performance of the metaheuristic algorithms. Here, this operator is utilized to enhance the performance of the CBO algorithm to optimize the weight of the frames. For additional comparison of the results, the particle swarm optimization (PSO) algorithm and imperialist competitive algorithm (ICA) are used

Optimal Design of Steel Curved Roof Frames by Enhanced Vibrating Particles System Algorithm

The paper presents an optimal design of steel curved roof frames with its roof being part of a circular arc. The elements of frames are tapered I-section members. In the objective function for optimization, two factors affecting the weight of frames are considered simultaneously. First, the roof slope angle as an effective variable on the values of the structural loading and second, the cross-section of members that are considered as continuous and discrete variables, respectively. In the range of 3 to 70 degrees, the optimum range of roof slope angles for steel curved roof frames, as well as precise value of the best roof slope angle, will be reported. Enhanced Vibrating Particles System (EVPS) algorithm is utilized for the optimal design of steel curved roof frames with tapered members. The performance and efficiency of the EVPS algorithm is compared with six other recently developed optimization algorithms including VPS, GWO, HS, SSA, ECBO and GOA algorithms. The effectiveness and performance of EVPS algorithm is proven. Frames design are performed using ANSI/AISC 360-05 specifications which strength, displacement and stability constraints are imposed on the frames