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

Digital Filter Design Using Improved Artificial Bee Colony Algorithms

Digital filters are often used in digital signal processing applications. The design objective of a digital filter is to find the optimal set of filter coefficients, which satisfies the desired specifications of magnitude and group delay responses. Evolutionary algorithms are population-based meta-heuristic algorithms inspired by the biological behaviors of species. Compared to gradient-based optimization algorithms such as steepest descent and Newton’s like methods, these bio-inspired algorithms have the advantages of not getting stuck at local optima and being independent of the starting point in the solution space. The limitations of evolutionary algorithms include the presence of control parameters, problem specific tuning procedure, premature convergence and slower convergence rate. The artificial bee colony (ABC) algorithm is a swarm-based search meta-heuristic algorithm inspired by the foraging behaviors of honey bee colonies, with the benefit of a relatively fewer control parameters. In its original form, the ABC algorithm has certain limitations such as low convergence rate, and insufficient balance between exploration and exploitation in the search equations. In this dissertation, an ABC-AMR algorithm is proposed by incorporating an adaptive modification rate (AMR) into the original ABC algorithm to increase convergence rate by adjusting the balance between exploration and exploitation in the search equations through an adaptive determination of the number of parameters to be updated in every iteration. A constrained ABC-AMR algorithm is also developed for solving constrained optimization problems.There are many real-world problems requiring simultaneous optimizations of more than one conflicting objectives. Multiobjective (MO) optimization produces a set of feasible solutions called the Pareto front instead of a single optimum solution. For multiobjective optimization, if a decision maker’s preferences can be incorporated during the optimization process, the search process can be confined to the region of interest instead of searching the entire region. In this dissertation, two algorithms are developed for such incorporation. The first one is a reference-point-based MOABC algorithm in which a decision maker’s preferences are included in the optimization process as the reference point. The second one is a physical-programming-based MOABC algorithm in which physical programming is used for setting the region of interest of a decision maker. In this dissertation, the four developed algorithms are applied to solve digital filter design problems. The ABC-AMR algorithm is used to design Types 3 and 4 linear phase FIR differentiators, and the results are compared to those obtained by the original ABC algorithm, three improved ABC algorithms, and the Parks-McClellan algorithm. The constrained ABC-AMR algorithm is applied to the design of sparse Type 1 linear phase FIR filters of filter orders 60, 70 and 80, and the results are compared to three state-of-the-art design methods. The reference-point-based multiobjective ABC algorithm is used to design of asymmetric lowpass, highpass, bandpass and bandstop FIR filters, and the results are compared to those obtained by the preference-based multiobjective differential evolution algorithm. The physical-programming-based multiobjective ABC algorithm is used to design IIR lowpass, highpass and bandpass filters, and the results are compared to three state-of-the-art design methods. Based on the obtained design results, the four design algorithms are shown to be competitive as compared to the state-of-the-art design methods

Advanced Electromyogram Signal Processing with an Emphasis on Simplified, Near-Optimal Whitening

Estimates of the time-varying standard deviation of the surface EMG signal (EMGσ) are extensively used in the field of EMG-torque estimation. The use of a whitening filter can substantially improve the accuracy of EMGσ estimation by removing the signal correlation and increasing the statistical bandwidth. However, a subject-specific whitening filter which is calibrated to each subject, is quite complex and inconvenient. To solve this problem, we first calibrated a 60th-order “Universal” FIR whitening filter by using the ensemble mean of the inverse of the square root of the power spectral density (PSD) of the noise-free EMG signal. Pre-existing data from elbow contraction of 64 subjects, providing 512 recording trials were used. The test error on an EMG-torque task based on the “Universal” FIR whitening filter had a mean error of 4.80% maximum voluntary contraction (MVC) with a standard deviation of 2.03% MVC. Meanwhile the subject-specific whitening filter had performance of 4.84±1.98% MVC (both have a whitening band limit at 600 Hz). These two methods had no statistical difference. Furthermore, a 2nd-order IIR whitening filter was designed based on the magnitude response of the “Universal” FIR whitening filter, via the differential evolution algorithm. The performance of this IIR whitening filter was very similar to the FIR filter, with a performance of 4.81±2.12% MVC. A statistical test showed that these two methods had no significant difference either. Additionally, a complete theory of EMG in additive measured noise contraction modeling is described. Results show that subtracting the variance of whitened noise by computing the root difference of the square (RDS) is the correct way to remove noise from the EMG signal

An iterated tabu search algorithm for the design of fir filters

« RÉSUMÉ : Les systèmes modernes de télécommunication sans fils occupent une place majeure dans la société actuelle. Dans les dernières années, la complexité des outils qui en découlent n'a cessé d'augmenter car, en plus de prendre en charge les tâches basiques de communication vocale, ceux-ci doivent également supporter une quantité croissante de modules et d'applications parallèles (connexion internet, capture vidéo, guidage par satellite, etc.). En conséquence, l'évolution rapide subie par ces outils qui, dans la majorité des cas, sont alimentés par batteries, a singulièrement accru l'importance du rôle joué par la consommation énergétique, et a ainsi fait de l'efficacité énergétique et de l'informatique éco-responsable des caractéristiques essentielles dans les développements récents de la micro-éléctronique. Afin d'offrir une solution à ces problèmes énergétiques, une partie des recherches s'est focalisée sur la conception de filtres numériques efficaces. Les filtres numériques sont la pierre angulaire de tous les systèmes de traitement de signal numérique. Chaque filtre est implanté par un circuit intégré, qui, lui-même, est composé d'une liste d'éléments de base incluant des additionneurs, des multiplicateurs, des inverseurs, etc. La piste principale suivie par les chercheurs dans le but de réduire la quantité d'énergie consommée par les filtres numériques propose de remplacer les multiplicateurs dans les circuits par des éléments moins énergivores, tels que des additionneurs, des décaleurs et des inverseurs. L'objectif des méthodes introduites dans ce sens consiste généralement à remplacer les multiplicateurs tout en utilisant le moins d'additionneurs possible. En effet, en l'absence de multiplicateurs dans les circuits, les additionneurs deviennent l'élément le plus demandant en ressource énergétique. Dans les faits, la quantité d'additionneurs contenue dans un circuit sans multiplicateurs, aussi connue comme son coût en additionneurs, est communément utilisée afin d'estimer sa consommation énergétique. Nos travaux se concentrent sur la conception de filtres numériques sans multiplicateurs énergétiquement efficaces. Ils se décomposent en deux contributions majeures: un nouveau modèle de représentation efficace des circuits intégrés, et un algorithme innovateur destiné à la conception de filtres numériques efficaces. Dans un premier temps, notre modélisation des circuits sous la forme de graphes pondérés a l'avantage d'offrir une représentation concise des circuits intégrés, tout en annulant la symétrie présente dans les modèles de représentation actuels.Dans un second temps, notre métaheuristique, qui combine à la fois une recherche tabou et une recherche tabou itérée, offre un contrôle direct du niveau d'énergie consommée par le circuit qu'elle construit, en fixant la quantité d'additionneurs qu'il contient avant le démarrage du processus de conception. En outre, contrairement aux méthodes existantes, notre approche ne se réfère à aucune architecture spécifique afin de concevoir un circuit. Ce degré de liberté permet à notre méthode d'atteindre une optimisation plus globale de la structure du circuit en comparaison des autres méthodes et, ainsi, de posséder un contrôle plus précis de sa consommation énergétique. L'algorithme proposé est testé sur un jeu de données contenant plus de 700 filtres de complexité variée. Les résultats obtenus démontrent les performances élevées de notre approche car, en se basant sur le coût en additionneurs, dans plus de 99% des cas, notre méthode conçoit des filtres numériques avec un niveau de consommation énergétique total équivalent au niveau induit uniquement par l'architecture à laquelle les méthodes actuelles se réfèrent. En parallèle, notre méthode fournit également un meilleur contrôle de la longueur de mot interne dans les circuits, qui représente un autre aspect crucial de leur efficacité énergétique. La comparaison avec l'algorithme Heuristic cumulative benefit (Hcub) qui, à ce jour, est la méthode la plus performante montre que les filtres construits par notre algorithme utilisent 55% moins d'additionneurs que Hcub, tout en réduisant la taille de ces additionneurs de 33%. Ces améliorations sont obtenues au simple coût d'une augmentation de 17% du nombre de délais dans les circuits. Cependant, la consommation énergétique d'un délai étant de l'ordre de 20% de celle d'un additionneur, si l'on considère le nombre et la taille des additionneurs ainsi que la quantité de délais inclus dans nos circuits afin d'estimer leur consommation énergétique, on peut s'attendre à une économie globale de l'ordre de 65% en comparaison de la meilleure méthode actuelle.»----------«ABSTRACT : In today's modern society, we rely on wireless telecommunication devices that use applications and modules to perform many different tasks and are growing in their complexity day by day. Consequently, the fast evolution of these devices, which, most of the time, are battery-powered, drastically increased the importance of their energy consumption and made energy efficiency and green computing essential features of recent developments in microelectronics. To deal with the related issues, many researchers have focused their attention to designing energy-efficient digital filters, which are essential building blocks of all digital signal processing systems. Any digital filter is implemented by an integrated circuit composed by a list of basic elements, including adders, multipliers, shifts, etc. One of the paths that researchers have followed in order to decrease the amount of energy used by the integrated circuits was to replace the multipliers in the circuit structure with less energy-consuming elements such as adders, shifts and inverters. The goal of these methods is usually to perform the replacement of multipliers while using the least amount of adders, as, for multiplierless circuits, adders become the most energy-consuming elements. In fact, the quantity of adders contained in a multiplierless circuit, also known as its adder cost, is commonly used as an estimate of its power consumption. In our research we focus on energy-efficient multiplierless filters. Our work has two main contributions: a new model to efficiently represent integrated circuits, and an innovative algorithm to design efficient digital filters. On one hand, the main advantage of our new graph-based model is that it is able to represent any integrated circuit in a concise form, while avoiding symmetry in the representation. On the other hand, our metaheuristic, that combines both a tabu search and an iterated tabu search, offers a direct control of the level of energy consumed by the circuits it constructs, by fixing the number of adders that they contain. Besides, unlike other existing methods used for designing multiplierless filters, our approach does not refer to any specific architecture in the corresponding circuit structure. This degree of freedom allows our method to have a more globalized view on the optimization of circuit structure compared to the other methods, and thus, a better control on its power consumption. The proposed algorithm is tested on a benchmark containing more than 700 filters of different orders of complexity. The obtained results demonstrate the high accuracy of the proposed approach as, based on the adder cost estimation, in more than $99\%$ of the cases our method designs integrated circuits with a level of energy consumption equivalent to those implied only by the most accurate circuit architectures from which existing algorithms build their circuits, and absolutely no deviation from the desired filtering specifications. In parallel, our method also provides a better control of the internal wordlength in the circuits, which is another crucial point to improve the energy-efficiency. The comparison to the current state-of-the-art algorithm Heuristic cumulative benefit (Hcub) when designing all the benchmark filters shows that filters constructed with our algorithm are using 55% less adders than Hcub, while decreasing their size by 33%. This improvement can be reached at the cost of an increase of 17% in the number of delays in the circuits. However, by considering the number and the size of adders used in the circuit as well as the quantity of delays it contains as an estimate of the power consumed by the circuit, assuming that the energy consumption of a delay is in the order of 20% of the consumption of an adder, we can approximately expect an overall energy saving of 65% in our circuits compared to the best current method

Designs of Digital Filters and Neural Networks using Firefly Algorithm

Firefly algorithm is an evolutionary algorithm that can be used to solve complex multi-parameter problems in less time. The algorithm was applied to design digital filters of different orders as well as to determine the parameters of complex neural network designs. Digital filters have several applications in the fields of control systems, aerospace, telecommunication, medical equipment and applications, digital appliances, audio recognition processes etc. An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, processes information and can be simulated using a computer to perform certain specific tasks like clustering, classification, and pattern recognition etc. The results of the designs using Firefly algorithm was compared to the state of the art algorithms and found that the digital filter designs produce results close to the Parks McClellan method which shows the algorithm’s capability of handling complex problems. Also, for the neural network designs, Firefly algorithm was able to efficiently optimize a number of parameter values. The performance of the algorithm was tested by introducing various input noise levels to the training inputs of the neural network designs and it produced the desired output with negligible error in a time-efficient manner. Overall, Firefly algorithm was found to be competitive in solving the complex design optimization problems like other popular optimization algorithms such as Differential Evolution, Particle Swarm Optimization and Genetic Algorithm. It provides a number of adjustable parameters which can be tuned according to the specified problem so that it can be applied to a number of optimization problems and is capable of producing quality results in a reasonable amount of time

Frequency Domain Design of Fractional Order PID Controller for AVR System Using Chaotic Multi-objective Optimization

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.A fractional order (FO) PID or FOPID controller is designed for an Automatic Voltage Regulator (AVR) system with the consideration of contradictory performance objectives. An improved evolutionary Non-dominated Sorting Genetic Algorithm (NSGA-II), augmented with a chaotic Henon map is used for the multi-objective optimization based design procedure. The Henon map as the random number generator outperforms the original NSGA-II algorithm and its Logistic map assisted version for obtaining a better design trade-off with an FOPID controller. The Pareto fronts showing the trade-offs between the different design objectives have also been shown for both the FOPID controller and the conventional PID controller to enunciate the relative merits and demerits of each. The design is done in frequency domain and hence stability and robustness of the design is automatically guaranteed unlike the other time domain optimization based controller design methods

Path planning, modelling and simulation for energy optimised mobile robotics

This thesis is concerned with an investigation of a solution for mobile robotic platforms to minimize the usage of scarce energy that is available and is not wasted following traditionally planned paths for complex terrain environments. This therefore addresses the need to reduce the total energy cost during a field task or mission. A path planning algorithm is designed by creating a new approach of artificial potential field method that generates a planned path, utilising terrain map. The new approach has the capability of avoiding the local minimum problems which is one of the major problems of traditional potential field method. By solving such problems gives a reliable solution to establish a required path. Therefore the approach results in an energy efficient path of the terrain identified, instead obvious straight line of the terrain. A literature review is conducted which reviews the mainstream path planning algorithms with the applications in mobile robotic platforms was analysed. These path planning algorithms are compared for the purpose of energy optimized planning, which concludes the method of artificial potential field as the path planning algorithm which has the most potential and will be further investigated and improved in this research. The methodology of designing, modelling and simulating a mobile robotic platform is defined and presented for the purpose of energy optimized path planning requirement. The research is to clarify the needs, requirements, and specifications of the design. A complete set of models which include mechanical and electrical modelling, functional concept modelling, modelling of the system are established. Based on these models, an energy optimized path planning algorithm is designed. The modelling of force and the kinematics is established to validate and evaluate the result of the algorithm through simulations. Moreover a simulation environment is established which is constructed for multi perspective simulation. This also enables collaborative simulation using Simulink and ADAMS to for simulating a path generated by the path planning algorithm and assess the energy consumption of the driven and steering mechanism of an exemplar system called AgriRover. This simulation environment allows the capture of simulated result of the total energy consumption, therefore outlines the energy cost behaviour of the AgriRover. A total of two sets of paths was tested in the fields for validation, one being generated by the energy optimized path planning algorithm and the other following a straight path. During the field tests the total cost of energy was captured . Two sets of results are compared with each other and compared with the simulation. The comparison shows a 21.34% of the energy saving by deploying the path generated with the energy optimized path planning algorithm in the field test. This research made the following contribution to knowledge. A comparison and grading of mainstream path planning algorithms from energy optimisation perspective is undertaken using detailed evaluation criteria, including computational power required, extendibility, flexibility and more criteria that is relevant for the energy optimized planning purpose. These algorithms have not been compared from energy optimisation angle before, and the research for energy optimised planning under complex terrain environments have not been investigated. Addressing these knowledge gaps, a methodology of designing, modelling and simulating a mobile platform system is proposed to facilitate an energy optimized path planning. This , leads to a new approach of path planning algorithm that reduces unnecessary energy spend for climbing of the terrain, using the terrain data available. Such a methodology derives several novel methods: Namely, a method for avoiding local minimum problem for artificial potential field path planning using the approach of approximation; A method of achieving high expendability of the path planning algorithm, where this method is capable of generate a path through a large map in a short time; A novel method of multi perspective dynamic simulation, which is capable of simulating the behaviour of internal mechanism and the overall robotic mobile platform with the fully integrated control, The dynamic simulation enables prediction of energy consumption; Finally, a novel method of mathematically modelling and simplifying a steering mechanism for the wheel based mobile vehicle was further investigated.This thesis is concerned with an investigation of a solution for mobile robotic platforms to minimize the usage of scarce energy that is available and is not wasted following traditionally planned paths for complex terrain environments. This therefore addresses the need to reduce the total energy cost during a field task or mission. A path planning algorithm is designed by creating a new approach of artificial potential field method that generates a planned path, utilising terrain map. The new approach has the capability of avoiding the local minimum problems which is one of the major problems of traditional potential field method. By solving such problems gives a reliable solution to establish a required path. Therefore the approach results in an energy efficient path of the terrain identified, instead obvious straight line of the terrain. A literature review is conducted which reviews the mainstream path planning algorithms with the applications in mobile robotic platforms was analysed. These path planning algorithms are compared for the purpose of energy optimized planning, which concludes the method of artificial potential field as the path planning algorithm which has the most potential and will be further investigated and improved in this research. The methodology of designing, modelling and simulating a mobile robotic platform is defined and presented for the purpose of energy optimized path planning requirement. The research is to clarify the needs, requirements, and specifications of the design. A complete set of models which include mechanical and electrical modelling, functional concept modelling, modelling of the system are established. Based on these models, an energy optimized path planning algorithm is designed. The modelling of force and the kinematics is established to validate and evaluate the result of the algorithm through simulations. Moreover a simulation environment is established which is constructed for multi perspective simulation. This also enables collaborative simulation using Simulink and ADAMS to for simulating a path generated by the path planning algorithm and assess the energy consumption of the driven and steering mechanism of an exemplar system called AgriRover. This simulation environment allows the capture of simulated result of the total energy consumption, therefore outlines the energy cost behaviour of the AgriRover. A total of two sets of paths was tested in the fields for validation, one being generated by the energy optimized path planning algorithm and the other following a straight path. During the field tests the total cost of energy was captured . Two sets of results are compared with each other and compared with the simulation. The comparison shows a 21.34% of the energy saving by deploying the path generated with the energy optimized path planning algorithm in the field test. This research made the following contribution to knowledge. A comparison and grading of mainstream path planning algorithms from energy optimisation perspective is undertaken using detailed evaluation criteria, including computational power required, extendibility, flexibility and more criteria that is relevant for the energy optimized planning purpose. These algorithms have not been compared from energy optimisation angle before, and the research for energy optimised planning under complex terrain environments have not been investigated. Addressing these knowledge gaps, a methodology of designing, modelling and simulating a mobile platform system is proposed to facilitate an energy optimized path planning. This , leads to a new approach of path planning algorithm that reduces unnecessary energy spend for climbing of the terrain, using the terrain data available. Such a methodology derives several novel methods: Namely, a method for avoiding local minimum problem for artificial potential field path planning using the approach of approximation; A method of achieving high expendability of the path planning algorithm, where this method is capable of generate a path through a large map in a short time; A novel method of multi perspective dynamic simulation, which is capable of simulating the behaviour of internal mechanism and the overall robotic mobile platform with the fully integrated control, The dynamic simulation enables prediction of energy consumption; Finally, a novel method of mathematically modelling and simplifying a steering mechanism for the wheel based mobile vehicle was further investigated

Channel Equalization using GA Family

High speed data transmissions over communication channels distort the trans- mitted signals in both amplitude and phase due to presence of Inter Symbol Inter- ference (ISI). Other impairments like thermal noise, impulse noise and cross talk also cause further distortions to the received symbols. Adaptive equalization of the digital channels at the receiver removes/reduces the e®ects of such ISIs and attempts to recover the transmitted symbols. Basically an equalizer is an inverse ¯lter which is placed at the front end of the receiver. Its transfer function is inverse to the transfer function of the associated channel. The Least-Mean-Square (LMS), Recursive-Least-Square (RLS) and Multilayer perceptron (MLP) based adaptive equalizers aim to minimize the ISI present in the digital communication channel. These are gradient based learning algorithms and therefore there is possibility that during training of the equalizers, its weights do not reach to their optimum values due to ..