Wave-based sensor, actuator and optimizer

Programa doutoral em Sistemas Avançados de Engenharia para a Indústria (AESI)A presente tese explora a utilização de ondas para abordar dois desafios significativos na indústria automóvel. O primeiro desafio consiste no desenvolvimento de um sistema de cancelamento ativo de ruído (ANC) que possa reduzir os ruídos não estacionários no compartimento de passageiros de um veículo. O segundo desafio é criar uma metodologia de conceção ótima para sensores de posição indutivos capazes de medir deslocamentos lineares, rotacionais e angulares. Para abordar o primeiro desafio, foi desenvolvido de um sistema ANC onde wavelets foram combinadas com um banco de filtros adaptativos. O sistema foi implementado em uma FPGA, e testes demonstraram que o sistema pode reduzir o ruído não estacionário em um ambiente acústico aberto e não controlado em 9 dB. O segundo desafio foi abordado através de uma metodologia que combina um algoritmo genético com um método numérico rápido para otimizar um sensor de posição indutivo. O método numérico foi usado para simular o campo eletromagnético associado à geometria do sensor, permitindo a maximização da corrente induzida nas bobinas recetoras e a minimização da não-linearidade no sensor. A minimização da não-linearidade foi conseguida através do desenho (layout) das bobinas que compõem o sensor. Sendo este otimizado no espaço de Fourier através da adição de harmónicos apropriados na geometria. As melhores geometrias otimizadas apresentaram uma não-linearidade inferior a 0,01% e a 0,25% da escala total para os sensores de posição angular e linear, respetivamente, sem calibração por software. O sistema ANC proposto tem o potencial de melhorar o conforto dos ocupantes do veículo, reduzindo o ruído indesejado dentro do compartimento de passageiros. Isso poderia reduzir o uso de materiais de isolamento acústico no veículo, levando a um veículo mais leve e, em última análise, a uma redução no consumo de energia. A metodologia desenvolvida para sensores de posição indutivos contribui para o estado da arte de sensores de posição eficientes e económicos, o que é crucial para os requisitos complexos da indústria automóvel. Essas contribuições têm implicações para o desenho de sistemas automotivos, com requisitos de desempenho e considerações ambientais e económicas.This thesis explores the use of waves to tackle two major engineering challenges in the automotive industry. The first challenge is the development of an Active Noise Cancelling (ANC) system that can effectively reduce non-stationary noise inside a vehicle’s passenger compartment. The second challenge is the optimization of an inductive position sensor design methodology capable of measuring linear, rotational, and angular displacements. To address the first challenge, this work designs an ANC system that employs wavelets combined with a bank of adaptive filters. The system was implemented in an FPGA, and field tests demonstrate its ability to reduce non-stationary noise in an open and uncontrolled acoustic environment by 9 dB. The second challenge was tackled by proposing a new approach that combines a genetic algorithm with a fast and lightweight numerical method to optimize the geometry of an inductive position sensor. The numerical method is used to simulate the sensor’s electromagnetic field, allowing for the maximization of induced current on the receiver coils while minimizing the sensor’s non-linearity. The non-linearity minimization was achieved through its unique sensor’s coils design optimized in the Fourier space by adding the appropriate harmonics to the coils’ geometry. The best optimized geometries exhibited a non-linearity of less than 0.01% and 0.25% of the full scale for the angular and linear position sensors, respectively. Both results were achieved without the need for signal calibration or post-processing manipulation. The proposed ANC system has the potential to enhance the comfort of vehicle occupants by reducing unwanted noise inside the passenger compartment. Moreover, it has the potential to reduce the use of acoustic insulation materials in the vehicle, leading to a lighter vehicle and ultimately reducing energy consumption. The developed methodology for inductive position sensors represents a state-of-the-art contribution to efficient and cost-effective position sensor design, which is crucial for meeting the complex requirements of the automotive industry.I would like to thank the Fundação para a Ciência e Tecnologia (FCT) and Bosch Car Multimedia for funding my PhD (grant PD/BDE/142901/2018)

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Deep penetration magnetoquasistatic sensors

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 193-198).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.This research effort extends the capabilities of existing model-based spatially periodic quasistatic-field sensors. The research developed three significant improvements in the field of nondestructive evaluation. The impact of each is detailed below: 1. The design of a distributed current drive magneto resistive magnetometer that matches the model response sufficiently to perform air calibration and absolute property measurement. Replacing the secondary winding with a magnetoresistive sensor allows the magnetometer to be operated at frequencies much lower than ordinarily possible, including static (DC) operation, which enables deep penetration defect imaging. Low frequencies are needed for deep probing of metals, where the depth of penetration is otherwise limited by the skin depth due to the shielding effect of induced eddy currents. The capability to perform such imaging without dependence on calibration standards has both substantial cost, ease of use, and technological benefits. The absolute property measurement capability is important because it provides a robust comparison for manufacturing quality control and monitoring of aging processes. Air calibration also alleviates the dependence on calibration standards that can be difficult to maintain. 2. The development and validation of cylindrical geometry models for inductive and capacitive sensors. The development of cylindrical geometry models enable the design of families of circularly symmetric magnetometers and dielectrometers with the "model-based" methodology, which requires close agreement between actual sensor response and simulated response. These kinds of sensors are needed in applications where the components being tested have circular symmetry, e.g. cracks near fasteners, or if it is important to measure the spatial average of an anisotropic property. 3. The development of accurate and efficient two-dimensional inverse interpolation and grid look-up techniques to determine electromagnetic and geometric properties. The ability to perform accurate and efficient grid interpolation is important for all sensors that follow the model-based principle, but it is particularly important for the complex shaped grids used with the magnetometers and dielectrometers in this thesis. A prototype sensor that incorporates all new features, i.e. a circularly symmetric magnetometer with a distributed current drive that uses a magnetoresistive secondary element, was designed, built, and tested. The primary winding is designed to have no net dipole moment, which improves repeatability by reducing the influence of distant objects. It can also support operation at two distinct effective spatial wavelengths. A circuit is designed that places the magnetoresistive sensor in a feedback configuration with a secondary winding to provide the necessary biasing and to ensure a linear transfer characteristic. Efficient FFT-based methods are developed to model magnetometers with a distributed current drive for both Cartesian and cylindrical geometry sensors. Results from measurements with a prototype circular dielectrometer that agree with the model-based analysis are also presented. In addition to the main contributions described so far, this work also includes other related enhancements to the time and space periodic-field sensor models, such as incorporating motion in the models to account for moving media effects. This development is important in low frequency scanning applications. Some improvements of the existing semi-analytical collocation point models for the standard Cartesian magnetometers and dielectrometers are also presented.by Yanko Sheiretov.Ph.D

An electromagnetic imaging system for metallic object detection and classification

PhD ThesisElectromagnetic imaging currently plays a vital role in various disciplines, from engineering to medical applications and is based upon the characteristics of electromagnetic fields and their interaction with the properties of materials. The detection and characterisation of metallic objects which pose a threat to safety is of great interest in relation to public and homeland security worldwide. Inspections are conducted under the prerequisite that is divested of all metallic objects. These inspection conditions are problematic in terms of the disruption of the movement of people and produce a soft target for terrorist attack. Thus, there is a need for a new generation of detection systems and information technologies which can provide an enhanced characterisation and discrimination capabilities. This thesis proposes an automatic metallic object detection and classification system. Two related topics have been addressed: to design and implement a new metallic object detection system; and to develop an appropriate signal processing algorithm to classify the targeted signatures. The new detection system uses an array of sensors in conjunction with pulsed excitation. The contributions of this research can be summarised as follows: (1) investigating the possibility of using magneto-resistance sensors for metallic object detection; (2) evaluating the proposed system by generating a database consisting of 12 real handguns with more than 20 objects used in daily life; (3) extracted features from the system outcomes using four feature categories referring to the objects’ shape, material composition, time-frequency signal analysis and transient pulse response; and (4) applying two classification methods to classify the objects into threats and non-threats, giving a successful classification rate of more than 92% using the feature combination and classification framework of the new system. The study concludes that novel magnetic field imaging system and their signal outputs can be used to detect, identify and classify metallic objects. In comparison with conventional induction-based walk-through metal detectors, the magneto-resistance sensor array-based system shows great potential for object identification and discrimination. This novel system design and signal processing achievement may be able to produce significant improvements in automatic threat object detection and classification applications.Iraqi Cultural Attaché, Londo

Investigating magnetisation dynamics in magnetic thin films and micro-structures

Data storage is the fulcrum on which technology advances. As the rate of consumption of data increases there is growing demand from the storage industry for faster, more efficient storage solutions which is capable of storing more information per unit area. Non-volatile magnetic memory based on spin-electronics is one of the key proponents for addressing this demand. In order to develop such a memory it is necessary to attain a better understanding of the magnetic properties on which this is based. This thesis aims to develop a tool to study the magnetisation dynamics of the free layer in a Pseudo Spin-Valve (PSV) of the form Co/Cu/NiFe utilising both the Giant Magneto-Resistance (GMR) and the Anisotropic Magneto-Resistance (AMR) effects. The key difference in the resistance responses of each of these effects is exploited here to showcase a proof of concept device which can be used as another method to aid in the study of magnetisation dynamics. GMR’s resistance response is dependent on the relative orientations of magnetisation between the free layer and the pinned layer. The AMR’s resistance response however, depends on the relative orientations of the magnetisation and the current direction. However, the resistance response of AMR is independent of magnetisation parallel or anti-parallel to current. This independence and the dependence of GMR on the relative magnetisations in each layer are used in combination to study the evolution of the magnetisation in a free layer PSV. The magneto-resistance of the Co/Cu/NiFe spin-valve was measured in as deposited samples and samples deposited in the presence of an in-plane field. The magneto-resistance was compared to a PSV with an insertion of a Co layer at the Cu/NiFe interface. An L-shaped device was designed and patterned which could be used to study the magnetisation reversal in the NiFe free layer. This was done by tuning the RKKY coupling between the Co and NiFe, shape anisotropy of the patterned micro-wire and the Anisotropic Magneto-Resistance ratio of the NiFe in the pseudo-spin-valve

Surgical Instruments based on flexible micro-electronics

This dissertation explores strategies to create micro-scale tools with integrated electronic and mechanical functionalities. Recently developed approaches to control the shape of flexible micro-structures are employed to fabricate micro-electronic instruments that embed components for sensing and actuation, aiming to expand the toolkit of minimally invasive surgery. This thesis proposes two distinct types of devices that might expand the boundaries of modern surgical interventions and enable new bio-medical applications. First, an electronically integrated micro-catheter is developed. Electronic components for sensing and actuation are embedded into the catheter wall through an alternative fabrication paradigm that takes advantage of a self-rolling polymeric thin-film system. With a diameter of only 0.1 mm, the catheter is capable of delivering fluids in a highly targeted fashion, comprises actuated opposing digits for the efficient manipulation of microscopic objects, and a magnetic sensor for navigation. Employing a specially conceived approach for position tracking, navigation with a high resolution below 0.1 mm is achieved. The fundamental functionalities and mechanical properties of this instrument are evaluated in artificial model environments and ex vivo tissues. The second development explores reshapeable micro-electronic devices. These systems integrate conductive polymer actuators and strain or magnetic sensors to adjust their shape through feedback-driven closed loop control and mechanically interact with their environment. Due to their inherent flexibility and integrated sensory capabilities, these devices are well suited to interface with and manipulate sensitive biological tissues, as demonstrated with an ex vivo nerve bundle, and may facilitate new interventions in neural surgery.:List of Abbreviations 1 Introduction 1.1 Motivation 1.2 Objectives and structure of this dissertation 2 Background 2.1 Tools for minimally invasive surgery 2.1.1 Catheters 2.1.2 Tools for robotic micro-surgery 2.1.3 Flexible electronics for smart surgical tools 2.2 Platforms for shapeable electronics 2.2.1 Shapeable polymer composites 2.2.2 Shapeable electronics 2.2.3 Soft actuators and manipulators 2.3 Sensors for position and shape feedback 2.3.1 Magnetic sensors for position and orientation measurements 2.3.2 Strain gauge sensors 3 Materials and Methods 3.1 Materials for shapeable electronics 3.1.1 Metal-organic sacrificial layer 3.1.2 Polyimide as reinforcing material 3.1.3 Swelling hydrogel for self assembly 3.1.4 Polypyrrole for flexible micro actuators 3.2 Device fabrication techniques 3.2.1 Photolithography 3.2.2 Electron beam deposition 3.2.3 Sputter deposition 3.2.4 Atomic layer deposition 3.2.5 Electro-polymerization of polypyrrole 3.3 Device characterization techniques 3.3.1 Kerr magnetometry 3.3.2 Electro-magnetic characterization of sensors 3.3.3 Electro-chemical analysis of polypyrrole 3.3.4 Preparation of model environments and materials 3.4 Sensor signal evaluation and processing 3.4.1 Signal processing 3.4.2 Cross correlation for phase analysis 3.4.3 PID feedback control 4 Electronically Integrated Self Assembled Micro Catheters 4.1 Design and Fabrication 4.1.1 Fabrication and self assembly 4.1.2 Features and design considerations 4.1.3 Electronic and fluidic connections 4.2 Integrated features and functionalities 4.2.1 Fluidic transport 4.2.2 Bending stability 4.2.3 Actuated micro manipulator 4.3 Magnetic position tracking 4.3.1 Integrated magnetic sensor 4.3.2 Position control with sensor feedback 4.3.3 Introduction of magnetic phase encoded tracking 4.3.4 Experimental realization 4.3.5 Simultaneous magnetic and ultrasound tracking 4.3.6 Discussion, limitations, and perspectives 5 Reshapeable Micro Electronic Devices 5.1 Design and fabrication 5.1.1 Estimation of optimal fabrication parameters 5.1.2 Device Fabrication 5.1.3 Control electronics and software 5.2 Performance of Actuators 5.2.1 Blocking force, speed, and durability 5.2.2 Curvature 5.3 Orientation control with magnetic sensors 5.3.1 Magnetic sensors on actuated device 5.3.2 Reference magnetic field 5.3.3 Feedback control 5.4 Shape control with integrated strain sensors 5.4.1 Strain gauge curvature sensors 5.4.2 Feedback control 5.4.3 Obstacle detection 5.5 Heterogenous integration with active electronics 5.5.1 Fabrication and properties of active matrices 5.5.2 Fabrication and operation of PPy actuators 5.5.3 Site selective actuation 6 Discussion and Outlook 6.1 Integrated self assembled catheters 6.1.1 Outlook 6.2 Reshapeable micro electronic devices 6.2.1 Outlook 7 Conclusion Appendix A1 Processing parameters for polymer stack layers A2 Derivation of magnetic phase profile in 3D Bibliography List of Figures and Tables Acknowledgements Theses List of Publication

Recent Trends in Communication Networks

In recent years there has been many developments in communication technology. This has greatly enhanced the computing power of small handheld resource-constrained mobile devices. Different generations of communication technology have evolved. This had led to new research for communication of large volumes of data in different transmission media and the design of different communication protocols. Another direction of research concerns the secure and error-free communication between the sender and receiver despite the risk of the presence of an eavesdropper. For the communication requirement of a huge amount of multimedia streaming data, a lot of research has been carried out in the design of proper overlay networks. The book addresses new research techniques that have evolved to handle these challenges

Programming by Demonstration on Riemannian Manifolds

This thesis presents a Riemannian approach to Programming by Demonstration (PbD). It generalizes an existing PbD method from Euclidean manifolds to Riemannian manifolds. In this abstract, we review the objectives, methods and contributions of the presented approach. OBJECTIVES PbD aims at providing a user-friendly method for skill transfer between human and robot. It enables a user to teach a robot new tasks using few demonstrations. In order to surpass simple record-and-replay, methods for PbD need to \u2018understand\u2019 what to imitate; they need to extract the functional goals of a task from the demonstration data. This is typically achieved through the application of statisticalmethods. The variety of data encountered in robotics is large. Typical manipulation tasks involve position, orientation, stiffness, force and torque data. These data are not solely Euclidean. Instead, they originate from a variety of manifolds, curved spaces that are only locally Euclidean. Elementary operations, such as summation, are not defined on manifolds. Consequently, standard statistical methods are not well suited to analyze demonstration data that originate fromnon-Euclidean manifolds. In order to effectively extract what-to-imitate, methods for PbD should take into account the underlying geometry of the demonstration manifold; they should be geometry-aware. Successful task execution does not solely depend on the control of individual task variables. By controlling variables individually, a task might fail when one is perturbed and the others do not respond. Task execution also relies on couplings among task variables. These couplings describe functional relations which are often called synergies. In order to understand what-to-imitate, PbDmethods should be able to extract and encode synergies; they should be synergetic. In unstructured environments, it is unlikely that tasks are found in the same scenario twice. The circumstances under which a task is executed\u2014the task context\u2014are more likely to differ each time it is executed. Task context does not only vary during task execution, it also varies while learning and recognizing tasks. To be effective, a robot should be able to learn, recognize and synthesize skills in a variety of familiar and unfamiliar contexts; this can be achieved when its skill representation is context-adaptive. THE RIEMANNIAN APPROACH In this thesis, we present a skill representation that is geometry-aware, synergetic and context-adaptive. The presented method is probabilistic; it assumes that demonstrations are samples from an unknown probability distribution. This distribution is approximated using a Riemannian GaussianMixtureModel (GMM). Instead of using the \u2018standard\u2019 Euclidean Gaussian, we rely on the Riemannian Gaussian\u2014 a distribution akin the Gaussian, but defined on a Riemannian manifold. A Riev mannian manifold is a manifold\u2014a curved space which is locally Euclidean\u2014that provides a notion of distance. This notion is essential for statistical methods as such methods rely on a distance measure. Examples of Riemannian manifolds in robotics are: the Euclidean spacewhich is used for spatial data, forces or torques; the spherical manifolds, which can be used for orientation data defined as unit quaternions; and Symmetric Positive Definite (SPD) manifolds, which can be used to represent stiffness and manipulability. The Riemannian Gaussian is intrinsically geometry-aware. Its definition is based on the geometry of the manifold, and therefore takes into account the manifold curvature. In robotics, the manifold structure is often known beforehand. In the case of PbD, it follows from the structure of the demonstration data. Like the Gaussian distribution, the Riemannian Gaussian is defined by a mean and covariance. The covariance describes the variance and correlation among the state variables. These can be interpreted as local functional couplings among state variables: synergies. This makes the Riemannian Gaussian synergetic. Furthermore, information encoded in multiple Riemannian Gaussians can be fused using the Riemannian product of Gaussians. This feature allows us to construct a probabilistic context-adaptive task representation. CONTRIBUTIONS In particular, this thesis presents a generalization of existing methods of PbD, namely GMM-GMR and TP-GMM. This generalization involves the definition ofMaximum Likelihood Estimate (MLE), Gaussian conditioning and Gaussian product for the Riemannian Gaussian, and the definition of ExpectationMaximization (EM) and GaussianMixture Regression (GMR) for the Riemannian GMM. In this generalization, we contributed by proposing to use parallel transport for Gaussian conditioning. Furthermore, we presented a unified approach to solve the aforementioned operations using aGauss-Newton algorithm. We demonstrated how synergies, encoded in a Riemannian Gaussian, can be transformed into synergetic control policies using standard methods for LinearQuadratic Regulator (LQR). This is achieved by formulating the LQR problem in a (Euclidean) tangent space of the Riemannian manifold. Finally, we demonstrated how the contextadaptive Task-Parameterized Gaussian Mixture Model (TP-GMM) can be used for context inference\u2014the ability to extract context from demonstration data of known tasks. Our approach is the first attempt of context inference in the light of TP-GMM. Although effective, we showed that it requires further improvements in terms of speed and reliability. The efficacy of the Riemannian approach is demonstrated in a variety of scenarios. In shared control, the Riemannian Gaussian is used to represent control intentions of a human operator and an assistive system. Doing so, the properties of the Gaussian can be employed to mix their control intentions. This yields shared-control systems that continuously re-evaluate and assign control authority based on input confidence. The context-adaptive TP-GMMis demonstrated in a Pick & Place task with changing pick and place locations, a box-taping task with changing box sizes, and a trajectory tracking task typically found in industr