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

Uses of Complex Wavelets in Deep Convolutional Neural Networks

Image understanding has long been a goal for computer vision. It has proved to be an exceptionally difficult task due to the large amounts of variability that are inherent to objects in a scene. Recent advances in supervised learning methods, particularly convolutional neural networks (CNNs), have pushed forth the frontier of what we have been able to train computers to do. Despite their successes, the mechanics of how these networks are able to recognize objects are little understood, and the networks themselves are often very difficult and time-consuming to train. It is very important that we improve our current approaches in every way possible. A CNN is built from connecting many learned convolutional layers in series. These convolutional layers are fairly crude in terms of signal processing - they are arbitrary taps of a finite impulse response filter, learned through stochastic gradient descent from random initial conditions. We believe that if we reformulate the problem, we may achieve many insights and benefits in training CNNs. Noting that modern CNNs are mostly viewed from and analyzed in the spatial domain, this thesis aims to view the convolutional layers in the frequency domain (viewing things in the frequency domain has proved useful in the past for denoising, filter design, compression and many other tasks). In particular, we use complex wavelets (rather than the Fourier transform or the discrete wavelet transform) as basis functions to reformulate image understanding with deep networks. In this thesis, we explore the most popular and well-developed form of using complex wavelets in deep learning, the ScatterNet from Stephane Mallat. We explore its current limitations by building a DeScatterNet and found that while it has many nice properties, it may not be sensitive to the most appropriate shapes for understanding natural images. We then develop a locally invariant convolutional layer, a combination of a complex wavelet transform, a modulus operation, and a learned mixing. To do this, we derive backpropagation equations and allow gradients to flow back through the (previously fixed) ScatterNet front end. Connecting several such locally invariant layers allows us to build learnable ScatterNet, a more flexible and general form of the ScatterNet (while still maintaining its desired properties). We show that the learnable ScatterNet can provide significant improvements over the regular ScatterNet when being used as a front end for a learning system. Additionally, we show that the locally invariant convolutional layer can directly replace convolutional layers in a deep CNN (and not just at the front-end). The locally invariant convolutional layers naturally downsample the input (because of the complex modulus) while increasing the channel dimension (because of the multiple wavelet orientations used). This is an operation that often happens in a CNN by a combination of a pooling and convolutional layer. It was at these locations in a CNN where the learnable ScatterNet performed best, implying it may be useful as learnable pooling layer. Finally, we develop a system to learn complex weights that act directly on the wavelet coefficients of signals, in place of a convolutional layer. We call this layer the wavelet gain layer and show it can be used alongside convolutional layers. The network designer may then choose to learn in the pixel or wavelet domains. This layer shows a lot of promise and affords more control over what regions of the frequency space we want our layer to learn from. Our experiments show that it can improve on learning in the pixel domain for early layers of a CNN

Digital Signal Processing (Second Edition)

This book provides an account of the mathematical background, computational methods and software engineering associated with digital signal processing. The aim has been to provide the reader with the mathematical methods required for signal analysis which are then used to develop models and algorithms for processing digital signals and finally to encourage the reader to design software solutions for Digital Signal Processing (DSP). In this way, the reader is invited to develop a small DSP library that can then be expanded further with a focus on his/her research interests and applications. There are of course many excellent books and software systems available on this subject area. However, in many of these publications, the relationship between the mathematical methods associated with signal analysis and the software available for processing data is not always clear. Either the publications concentrate on mathematical aspects that are not focused on practical programming solutions or elaborate on the software development of solutions in terms of working ‘black-boxes’ without covering the mathematical background and analysis associated with the design of these software solutions. Thus, this book has been written with the aim of giving the reader a technical overview of the mathematics and software associated with the ‘art’ of developing numerical algorithms and designing software solutions for DSP, all of which is built on firm mathematical foundations. For this reason, the work is, by necessity, rather lengthy and covers a wide range of subjects compounded in four principal parts. Part I provides the mathematical background for the analysis of signals, Part II considers the computational techniques (principally those associated with linear algebra and the linear eigenvalue problem) required for array processing and associated analysis (error analysis for example). Part III introduces the reader to the essential elements of software engineering using the C programming language, tailored to those features that are used for developing C functions or modules for building a DSP library. The material associated with parts I, II and III is then used to build up a DSP system by defining a number of ‘problems’ and then addressing the solutions in terms of presenting an appropriate mathematical model, undertaking the necessary analysis, developing an appropriate algorithm and then coding the solution in C. This material forms the basis for part IV of this work. In most chapters, a series of tutorial problems is given for the reader to attempt with answers provided in Appendix A. These problems include theoretical, computational and programming exercises. Part II of this work is relatively long and arguably contains too much material on the computational methods for linear algebra. However, this material and the complementary material on vector and matrix norms forms the computational basis for many methods of digital signal processing. Moreover, this important and widely researched subject area forms the foundations, not only of digital signal processing and control engineering for example, but also of numerical analysis in general. The material presented in this book is based on the lecture notes and supplementary material developed by the author for an advanced Masters course ‘Digital Signal Processing’ which was first established at Cranfield University, Bedford in 1990 and modified when the author moved to De Montfort University, Leicester in 1994. The programmes are still operating at these universities and the material has been used by some 700++ graduates since its establishment and development in the early 1990s. The material was enhanced and developed further when the author moved to the Department of Electronic and Electrical Engineering at Loughborough University in 2003 and now forms part of the Department’s post-graduate programmes in Communication Systems Engineering. The original Masters programme included a taught component covering a period of six months based on two semesters, each Semester being composed of four modules. The material in this work covers the first Semester and its four parts reflect the four modules delivered. The material delivered in the second Semester is published as a companion volume to this work entitled Digital Image Processing, Horwood Publishing, 2005 which covers the mathematical modelling of imaging systems and the techniques that have been developed to process and analyse the data such systems provide. Since the publication of the first edition of this work in 2003, a number of minor changes and some additions have been made. The material on programming and software engineering in Chapters 11 and 12 has been extended. This includes some additions and further solved and supplementary questions which are included throughout the text. Nevertheless, it is worth pointing out, that while every effort has been made by the author and publisher to provide a work that is error free, it is inevitable that typing errors and various ‘bugs’ will occur. If so, and in particular, if the reader starts to suffer from a lack of comprehension over certain aspects of the material (due to errors or otherwise) then he/she should not assume that there is something wrong with themselves, but with the author

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Signal processing with Fourier analysis, novel algorithms and applications

Fourier analysis is the study of the way general functions may be represented or approximated by sums of simpler trigonometric functions, also analogously known as sinusoidal modeling. The original idea of Fourier had a profound impact on mathematical analysis, physics and engineering because it diagonalizes time-invariant convolution operators. In the past signal processing was a topic that stayed almost exclusively in electrical engineering, where only the experts could cancel noise, compress and reconstruct signals. Nowadays it is almost ubiquitous, as everyone now deals with modern digital signals. Medical imaging, wireless communications and power systems of the future will experience more data processing conditions and wider range of applications requirements than the systems of today. Such systems will require more powerful, efficient and flexible signal processing algorithms that are well designed to handle such needs. No matter how advanced our hardware technology becomes we will still need intelligent and efficient algorithms to address the growing demands in signal processing. In this thesis, we investigate novel techniques to solve a suite of four fundamental problems in signal processing that have a wide range of applications. The relevant equations, literature of signal processing applications, analysis and final numerical algorithms/methods to solve them using Fourier analysis are discussed for different applications in the electrical engineering/computer science. The first four chapters cover the following topics of central importance in the field of signal processing: • Fast Phasor Estimation using Adaptive Signal Processing (Chapter 2) • Frequency Estimation from Nonuniform Samples (Chapter 3) • 2D Polar and 3D Spherical Polar Nonuniform Discrete Fourier Transform (Chapter 4) • Robust 3D registration using Spherical Polar Discrete Fourier Transform and Spherical Harmonics (Chapter 5) Even though each of these four methods discussed may seem completely disparate, the underlying motivation for more efficient processing by exploiting the Fourier domain signal structure remains the same. The main contribution of this thesis is the innovation in the analysis, synthesis, discretization of certain well known problems like phasor estimation, frequency estimation, computations of a particular non-uniform Fourier transform and signal registration on the transformed domain. We conduct propositions and evaluations of certain applications relevant algorithms such as, frequency estimation algorithm using non-uniform sampling, polar and spherical polar Fourier transform. The techniques proposed are also useful in the field of computer vision and medical imaging. From a practical perspective, the proposed algorithms are shown to improve the existing solutions in the respective fields where they are applied/evaluated. The formulation and final proposition is shown to have a variety of benefits. Future work with potentials in medical imaging, directional wavelets, volume rendering, video/3D object classifications, high dimensional registration are also discussed in the final chapter. Finally, in the spirit of reproducible research we release the implementation of these algorithms to the public using Github

Designing sound : procedural audio research based on the book by Andy Farnell

In procedural media, data normally acquired by measuring something, commonly described as sampling, is replaced by a set of computational rules (procedure) that defines the typical structure and/or behaviour of that thing. Here, a general approach to sound as a definable process, rather than a recording, is developed. By analysis of their physical and perceptual qualities, natural objects or processes that produce sound are modelled by digital Sounding Objects for use in arts and entertainments. This Thesis discusses different aspects of Procedural Audio introducing several new approaches and solutions to this emerging field of Sound Design.Em Media Procedimental, os dados os dados normalmente adquiridos através da medição de algo habitualmente designado como amostragem, são substituídos por um conjunto de regras computacionais (procedimento) que definem a estrutura típica, ou comportamento, desse elemento. Neste caso é desenvolvida uma abordagem ao som definível como um procedimento em vez de uma gravação. Através da análise das suas características físicas e perceptuais , objetos naturais ou processos que produzem som, são modelados como objetos sonoros digitais para utilização nas Artes e Entretenimento. Nesta Tese são discutidos diferentes aspectos de Áudio Procedimental, sendo introduzidas várias novas abordagens e soluções para o campo emergente do Design Sonoro

Video coding for compression and content-based functionality

The lifetime of this research project has seen two dramatic developments in the area of digital video coding. The first has been the progress of compression research leading to a factor of two improvement over existing standards, much wider deployment possibilities and the development of the new international ITU-T Recommendation H.263. The second has been a radical change in the approach to video content production with the introduction of the content-based coding concept and the addition of scene composition information to the encoded bit-stream. Content-based coding is central to the latest international standards efforts from the ISO/IEC MPEG working group. This thesis reports on extensions to existing compression techniques exploiting a priori knowledge about scene content. Existing, standardised, block-based compression coding techniques were extended with work on arithmetic entropy coding and intra-block prediction. These both form part of the H.263 and MPEG-4 specifications respectively. Object-based coding techniques were developed within a collaborative simulation model, known as SIMOC, then extended with ideas on grid motion vector modelling and vector accuracy confidence estimation. An improved confidence measure for encouraging motion smoothness is proposed. Object-based coding ideas, with those from other model and layer-based coding approaches, influenced the development of content-based coding within MPEG-4. This standard made considerable progress in this newly adopted content based video coding field defining normative techniques for arbitrary shape and texture coding. The means to generate this information, the analysis problem, for the content to be coded was intentionally not specified. Further research work in this area concentrated on video segmentation and analysis techniques to exploit the benefits of content based coding for generic frame based video. The work reported here introduces the use of a clustering algorithm on raw data features for providing initial segmentation of video data and subsequent tracking of those image regions through video sequences. Collaborative video analysis frameworks from COST 21 l qual and MPEG-4, combining results from many other segmentation schemes, are also introduced

New advances in vehicular technology and automotive engineering

An automobile was seen as a simple accessory of luxury in the early years of the past century. Therefore, it was an expensive asset which none of the common citizen could afford. It was necessary to pass a long period and waiting for Henry Ford to establish the first plants with the series fabrication. This new industrial paradigm makes easy to the common American to acquire an automobile, either for running away or for working purposes. Since that date, the automotive research grown exponentially to the levels observed in the actuality. Now, the automobiles are indispensable goods; saying with other words, the automobile is a first necessity article in a wide number of aspects of living: for workers to allow them to move from their homes into their workplaces, for transportation of students, for allowing the domestic women in their home tasks, for ambulances to carry people with decease to the hospitals, for transportation of materials, and so on, the list don’t ends. The new goal pursued by the automotive industry is to provide electric vehicles at low cost and with high reliability. This commitment is justified by the oil’s peak extraction on 50s of this century and also by the necessity to reduce the emissions of CO2 to the atmosphere, as well as to reduce the needs of this even more valuable natural resource. In order to achieve this task and to improve the regular cars based on oil, the automotive industry is even more concerned on doing applied research on technology and on fundamental research of new materials. The most important idea to retain from the previous introduction is to clarify the minds of the potential readers for the direct and indirect penetration of the vehicles and the vehicular industry in the today’s life. In this sequence of ideas, this book tries not only to fill a gap by presenting fresh subjects related to the vehicular technology and to the automotive engineering but to provide guidelines for future research. This book account with valuable contributions from worldwide experts of automotive’s field. The amount and type of contributions were judiciously selected to cover a broad range of research. The reader can found the most recent and cutting-edge sources of information divided in four major groups: electronics (power, communications, optics, batteries, alternators and sensors), mechanics (suspension control, torque converters, deformation analysis, structural monitoring), materials (nanotechnology, nanocomposites, lubrificants, biodegradable, composites, structural monitoring) and manufacturing (supply chains). We are sure that you will enjoy this book and will profit with the technical and scientific contents. To finish, we are thankful to all of those who contributed to this book and who made it possible.info:eu-repo/semantics/publishedVersio

Semi-automatic liquid filling system using NodeMCU as an integrated Iot Learning tool

Computer programming and IoT are the key skills required in Industrial Revolution 4.0 (IR4.0). The industry demand is very high and therefore related students in this field should grasp adequate knowledge and skill in college or university prior to employment. However, learning technology related subject without applying it to an actual hardware can pose difficulty to relate the theoretical knowledge to problems in real application. It is proven that learning through hands-on activities is more effective and promotes deeper understanding of the subject matter (He et al. in Integrating Internet of Things (IoT) into STEM undergraduate education: Case study of a modern technology infused courseware for embedded system course. Erie, PA, USA, pp 1–9 (2016)). Thus, to fulfill the learning requirement, an integrated learning tool that combines learning of computer programming and IoT control for an industrial liquid filling system model is developed and tested. The integrated learning tool uses NodeMCU, Blynk app and smartphone to enable the IoT application. The system set-up is pre-designed for semi-automation liquid filling process to enhance hands-on learning experience but can be easily programmed for full automation. Overall, it is a user and cost friendly learning tool that can be developed by academic staff to aid learning of IoT and computer programming in related education levels and field