Joint Transceiving and Reflecting Design for Intelligent Reflecting Surface Aided Wireless Power Transfer

In an intelligent reflecting surface (IRS) aided wireless power transfer (WPT) system, a practical architecture of an energy receiver (ER) is proposed, which includes multiple receive antennas, an analog energy combiner, a power splitter and multiple energy harvesters. In order to maximise the output direct-current (DC) power, the transmit beamformer of the transmitter, the passive beamformer of the IRS, the energy combiner, and the power splitter of the ER are jointly optimised. The optimisation problem is equivalently divided into two sub-problems, which independently maximises the input RF power and the output DC power of the energy harvesters, respectively. A successive linear approximation (SLA) based algorithm with a low complexity is proposed to maximise the input RF power to the energy harvesters, which converges to a Karush-Kuhn-Tucker (KKT) point. We also propose an improved greedy randomized adaptive search procedure (I-GRASP) based algorithm having better performance to maximise the input RF power. Furthermore, the optimal power splitter for maximising the output DC power of the energy harvesters is derived in closed-form. The numerical results are provided to verify the performance advantage of the IRS-aided WPT and to demonstrate that conceiving the optimised energy combiner achieves better WPT performance than the deterministic counterpart

Hardware Aspects of Montgomery Modular Multiplication

This chapter compares Peter Montgomery\u27s modular multiplication method with traditional techniques for suitability on hardware platforms. It also covers systolic array implementations and side channel leakage

Acoustic power distribution techniques for wireless sensor networks

Recent advancements in wireless power transfer technologies can solve several residual problems concerning the maintenance of wireless sensor networks. Among these, air-based acoustic systems are still less exploited, with considerable potential for powering sensor nodes. This thesis aims to understand the significant parameters for acoustic power transfer in air, comprehend the losses, and quantify the limitations in terms of distance, alignment, frequency, and power transfer efficiency. This research outlines the basic concepts and equations overlooking sound wave propagation, system losses, and safety regulations to understand the prospects and limitations of acoustic power transfer. First, a theoretical model was established to define the diffraction and attenuation losses in the system. Different off-the-shelf transducers were experimentally investigated, showing that the FUS-40E transducer is most appropriate for this work. Subsequently, different load-matching techniques are analysed to identify the optimum method to deliver power. The analytical results were experimentally validated, and complex impedance matching increased the bandwidth from 1.5 to 4 and the power transfer efficiency from 0.02% to 0.43%. Subsequently, a detailed 3D profiling of the acoustic system in the far-field region was provided, analysing the receiver sensitivity to disturbances in separation distance, receiver orientation and alignment. The measured effects of misalignment between the transducers are provided as a design graph, correlating the output power as a function of separation distance, offset, loading methods and operating frequency. Finally, a two-stage wireless power network is designed, where energy packets are inductively delivered to a cluster of nodes by a recharge vehicle and later acoustically distributed to devices within the cluster. A novel dynamic recharge scheduling algorithm that combines weighted genetic clustering with nearest neighbour search is developed to jointly minimise vehicle travel distance and power transfer losses. The efficacy and performance of the algorithm are evaluated in simulation using experimentally derived traces that presented 90% throughput for large, dense networks.Open Acces

Encapsulating soldered electronic components for electronically functional yarn

E-Textiles are fabrics with embedded electronic functions that can be used in many fields, such as clothing, medicine, furniture, safety and many others. The integration of electronics with textiles requires a flexible structure that keeps the garment flexible to ensure the textile retains its physical characteristics and feel. E-textiles in wearable applications are subject to human activities. The integrated electronic components are vulnerable to these stresses, such as bending, torsion, and tensile. These forces can potentially damage the components or their interconnection. Electronics can be integrated into textiles in one of three approaches described as generations of E-Textiles. The Thesis will introduce the three generations of E-textiles through a comprehensive literature review in chapter 2. It will then discuss developing the electronically functional yarn (EFY) as a third generation in E-textiles and garments. The production process of this yarn has three main steps: soldering the semi-conductor on the copper wire, encapsulating it within a micro pod of resin, and covering the micro pod within the filament of the yarn. A detailed study of the encapsulation process and the unit's design is then introduced in the Thesis, where a novel method for packaging electronics using a UV-curable resin was introduced. The design process for the automated encapsulation of soldered semi-conductor has been investigated in Chapter 3 of this Thesis. The novel approach has been evaluated on various electronics and then extended to thin Kapton strips with embedded electronics. The resulting EFY then can be later used in woven or knitted textile. Finite element analysis (FEA) of the soldered semi-conductor on the wire is presented in chapter 4. FEA simulations are used to evaluate the mechanical performance of different electronics and how stresses are distributed after adding the resin and creating the micro pod. This FEA investigation of the materials and micro pod dimensions will understand the packaging method's reliability. The final part of the Thesis included further development added to the design of the encapsulation unit and the electronically functional yarn manufacturing. Developing a reliable, repeatable, and automated electronic packaging method for electronics embedded in the electronically functional yarn (EFY) was achieved in this project. The results were promising for further research

A Novel Long-Range Passive UHF RFID System over Twisted-pair Cable

Radio Frequency Identification (RFID) is one of the most representative, rapidly growing, and highly extendable technologies, which uses electromagnetic waves in accordance with specific communications standards and regulations to identify, track, or even localise desired objects. However, due to its high cost, limited read range, and uncertain reliability, its adoption still lags, especially in large-scale organisations. Even though an RFID distributed antenna system (DAS) can greatly improve the detection range and read rate of a single reader when system uses different combinations of antenna states with frequency and phase hopping, the lossy and heavy coaxial cables between reader and antennas still limits the system coverage and design flexibility for wide-area passive UHF RFID applications. In order to develop a cost-efficient and flexibly-installed passive RFID DAS, a novel large-range passive UHF RFID system over twisted-pair cable is proposed in this dissertation. This new system consists of one baseband central controller and one antenna subsystem, connected by a commonly used twisted-pair cable. It is shown that transmitting/receiving low frequency baseband signals over a twisted-pair cable can significantly reduce cable attenuation and extend the communication distance. A simulation is conducted to demonstrate that frequency and phase hopping can also be remotely controlled to fit this system structure by slightly varying the frequency or phase of the input reference signal of the frequency synthesis system. The features of twisted-pair cable in terms of its low cost, light weight, and bend radius greatly improve the design and installation flexibility of an RFID system. The implemented system is designed based on the ISO 18000-6C and EPC Class 1 Generation 2 standards, and can operate according to FCC (902-928 MHz) and ETSI (865-868MHz) regulations. The results of the measurement show the reader can achieve a sensitivity of - 94.5 dBm over 30 m Cat5e cable, and its sensitivity can still remain at around -94.2 dBm over 150 m Cat5e cable. The experimental results of tag detection show that the passive tags can be successfully detected over a 6 m wireless range following a 300 m of twisted-pair cable between the central controller and antenna. This detection range cannot be achieved by existing commercial RFID systems. Since the transmission and reception in a RFID system are simultaneous, finite isolation of the circulator/directional coupler and environmentally dependent reflection ratio of the antenna lead to serious leakage problems. Leakage can directly cause sensitivity degradation due to saturation of the RF components. A fast leakage suppression block is developed in efforts to solve this problem. Measurements show that this new canceller can deliver an average suppression of 36.9 dB, and this excellent performance remains when the system uses frequency hopping. With help of an improved scanning algorithm, this canceller can find its optimal status within 38 ms, and this settling time is short enough for most commercial RFID readers. By reducing the number of voltage samples taken, the convergence time can be further improved. To fully investigate this new passive UHF RFID system value, a comparison study between the new system and a commercial system is conducted. This new automatic passive UHF RFID system is confirmed to deliver high performance long-range passive tag detection. Particular advantages are shown in the fast tag read rate and capability of uplink SNR improvement. This novel system is also superior to conventional RFID systems in terms of link distance, link cost, and installation flexibility

A Field Guide to Genetic Programming

xiv, 233 p. : il. ; 23 cm.Libro ElectrónicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction -- Representation, initialisation and operators in Tree-based GP -- Getting ready to run genetic programming -- Example genetic programming run -- Alternative initialisations and operators in Tree-based GP -- Modular, grammatical and developmental Tree-based GP -- Linear and graph genetic programming -- Probalistic genetic programming -- Multi-objective genetic programming -- Fast and distributed genetic programming -- GP theory and its applications -- Applications -- Troubleshooting GP -- Conclusions.Contents xi 1 Introduction 1.1 Genetic Programming in a Nutshell 1.2 Getting Started 1.3 Prerequisites 1.4 Overview of this Field Guide I Basics 2 Representation, Initialisation and GP 2.1 Representation 2.2 Initialising the Population 2.3 Selection 2.4 Recombination and Mutation Operators in Tree-based 3 Getting Ready to Run Genetic Programming 19 3.1 Step 1: Terminal Set 19 3.2 Step 2: Function Set 20 3.2.1 Closure 21 3.2.2 Sufficiency 23 3.2.3 Evolving Structures other than Programs 23 3.3 Step 3: Fitness Function 24 3.4 Step 4: GP Parameters 26 3.5 Step 5: Termination and solution designation 27 4 Example Genetic Programming Run 4.1 Preparatory Steps 29 4.2 Step-by-Step Sample Run 31 4.2.1 Initialisation 31 4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming 5 Alternative Initialisations and Operators in 5.1 Constructing the Initial Population 5.1.1 Uniform Initialisation 5.1.2 Initialisation may Affect Bloat 5.1.3 Seeding 5.2 GP Mutation 5.2.1 Is Mutation Necessary? 5.2.2 Mutation Cookbook 5.3 GP Crossover 5.4 Other Techniques 32 5.5 Tree-based GP 39 6 Modular, Grammatical and Developmental Tree-based GP 47 6.1 Evolving Modular and Hierarchical Structures 47 6.1.1 Automatically Defined Functions 48 6.1.2 Program Architecture and Architecture-Altering 50 6.2 Constraining Structures 51 6.2.1 Enforcing Particular Structures 52 6.2.2 Strongly Typed GP 52 6.2.3 Grammar-based Constraints 53 6.2.4 Constraints and Bias 55 6.3 Developmental Genetic Programming 57 6.4 Strongly Typed Autoconstructive GP with PushGP 59 7 Linear and Graph Genetic Programming 61 7.1 Linear Genetic Programming 61 7.1.1 Motivations 61 7.1.2 Linear GP Representations 62 7.1.3 Linear GP Operators 64 7.2 Graph-Based Genetic Programming 65 7.2.1 Parallel Distributed GP (PDGP) 65 7.2.2 PADO 67 7.2.3 Cartesian GP 67 7.2.4 Evolving Parallel Programs using Indirect Encodings 68 8 Probabilistic Genetic Programming 8.1 Estimation of Distribution Algorithms 69 8.2 Pure EDA GP 71 8.3 Mixing Grammars and Probabilities 74 9 Multi-objective Genetic Programming 75 9.1 Combining Multiple Objectives into a Scalar Fitness Function 75 9.2 Keeping the Objectives Separate 76 9.2.1 Multi-objective Bloat and Complexity Control 77 9.2.2 Other Objectives 78 9.2.3 Non-Pareto Criteria 80 9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80 9.4 Multi-objective Optimisation via Operator Bias 81 10 Fast and Distributed Genetic Programming 83 10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83 10.2 Reducing Cost of Fitness with Caches 86 10.3 Parallel and Distributed GP are Not Equivalent 88 10.4 Running GP on Parallel Hardware 89 10.4.1 Master–slave GP 89 10.4.2 GP Running on GPUs 90 10.4.3 GP on FPGAs 92 10.4.4 Sub-machine-code GP 93 10.5 Geographically Distributed GP 93 11 GP Theory and its Applications 97 11.1 Mathematical Models 98 11.2 Search Spaces 99 11.3 Bloat 101 11.3.1 Bloat in Theory 101 11.3.2 Bloat Control in Practice 104 III Practical Genetic Programming 12 Applications 12.1 Where GP has Done Well 12.2 Curve Fitting, Data Modelling and Symbolic Regression 12.3 Human Competitive Results – the Humies 12.4 Image and Signal Processing 12.5 Financial Trading, Time Series, and Economic Modelling 12.6 Industrial Process Control 12.7 Medicine, Biology and Bioinformatics 12.8 GP to Create Searchers and Solvers – Hyper-heuristics xiii 12.9 Entertainment and Computer Games 127 12.10The Arts 127 12.11Compression 128 13 Troubleshooting GP 13.1 Is there a Bug in the Code? 13.2 Can you Trust your Results? 13.3 There are No Silver Bullets 13.4 Small Changes can have Big Effects 13.5 Big Changes can have No Effect 13.6 Study your Populations 13.7 Encourage Diversity 13.8 Embrace Approximation 13.9 Control Bloat 13.10 Checkpoint Results 13.11 Report Well 13.12 Convince your Customers 14 Conclusions Tricks of the Trade A Resources A.1 Key Books A.2 Key Journals A.3 Key International Meetings A.4 GP Implementations A.5 On-Line Resources 145 B TinyGP 151 B.1 Overview of TinyGP 151 B.2 Input Data Files for TinyGP 153 B.3 Source Code 154 B.4 Compiling and Running TinyGP 162 Bibliography 167 Inde

Surveying the Energy Landscapes of Multistable Elastic Structures

Energy landscapes analysis is a versatile approach to study multistable systems by identifying the network of stable states and reconfiguration pathways. Thus far, it has primarily been used in microscale systems, such as studying chemical reaction rates and to characterise the behaviour of how protein fold. Here, however, we aim to utilise energy landscape techniques to study multistable elastic structures, in particular, complex 3D structures that have been buckled from 2D patterns, which are of interest for applications such as flexible electronics and microelectromechanical systems. To this end we have developed new energy landscape methods and software that are well suited to continuous, macroscale systems with many degrees of freedom. The first is the binary image transition state search method (BITSS), which offers greater efficiency for large scale systems compared to traditional transition state search methods, and it is well suited to complex, non-linear pathways. Next, a new software library is introduced that contains a variety of energy landscape methods and potentials which are parallelised to study large-scale continuous systems. This library can be flexibly used for any chosen application, and has been designed to be easily extensible for new methods and potentials. Furthermore, we exploit energy landscape analysis to tailor the stable states and reconfiguration paths of various reconfigurable buckled mesostructures. We establish stability phase diagrams and identify the corresponding available reconfiguration pathways by varying essential structural parameters. Furthermore, we identify how the introduction of creases affects the multistability of the structures, finding that a small number can increase the number of distinct states, but more creases can lead to a loss of multistability. Taken together, these results and methodology can be used to influence the design of new structures for a variety of different applications

Optimal design of morphing structures

Morphing structures change their geometric configuration to achieve a wide range of performance goals. For morphing aircraft these include alleviating drag, or altering aerofoil lift. The design of structures capable of realising these goals is a highly multidisciplinary problem. Optimally morphing a compliant structure involves finding the distribution of actuation which best achieves a desired configuration change. In this work, the location and magnitude of discrete actuators are optimised, to minimise both aerodynamic and geometric objective functions. A range of optimisation methods, including differential and stochastic techniques, has been implemented to search optimally the large, nonlinear, and often discontinuous design spaces associated with such problems. The optimal design of morphing systems is investigated through consideration of a morphing shock control bump and an adaptive leading edge. CFD is implemented to evaluate the aerodynamic performance of optimiser-controlled morphing structures. A bespoke grid-generation algorithm is developed, capable of producing a mesh for all possible geometries, with low levels of cell skewness and orthogonality at the fluid-structure boundaries. Structural compliance – a prerequisite for morphing – allows significant displacement of the structure to occur, but simultaneously enables the possibility of detrimental aeroelastic effects. Static aeroelasticity is catered for, at significant computational expense, via coupling of the structural and aerodynamic models within individual optimisation function evaluations. Morphing geometry is investigated to reduce computational design requirements, and provide an objective starting point for an aeroelastic optimisation. The requirements of morphing between aerodynamic shapes are evaluated using geometry-based objective functions. Displacements and curvatures are compared between an optimiser-controlled structure and the target morph, and the differences minimised to effect the required shape change. In addition to enabling optimal problem definition, these geometric objective functions allow conclusions on the feasibility of a morph to be drawn a priori