Real-time 3D magnetometer calibration for embedded systems based on ellipsoid fitting

Magnetometers are massively diffused in a variety of instruments for different applications. However, these sensors are affected by non-idealities, especially bias and hard/soft-iron interference. Even though a number of calibration approaches could be adopted to compensate for magnetic interferences, their implementation on resources-constrained platforms is still problematic due to the highly complex, computationally intensive mathematical operations involved. The present work demonstrates the possibility to develop a fast and efficient 3D magnetometer calibration for real-time embedded systems with limited system resources. A number of techniques and approaches are discussed to mitigate the computational burden. Results confirm that this is achievable by preserving the same level of accuracy reached with more computationally intensive approaches. Results are also promising regarding the adoption of the discussed method on low-power real-life systems in several applications

Kinematic State Estimation using Multiple DGPS/MEMS-IMU Sensors

Animals have evolved over billions of years and understanding these complex and intertwined systems have potential to advance the technology in the field of sports science, robotics and more. As such, a gait analysis using Motion Capture (MOCAP) technology is the subject of a number of research and development projects aimed at obtaining quantitative measurements. Existing MOCAP technology has limited the majority of studies to the analysis of the steady-state locomotion in a controlled (indoor) laboratory environment. MOCAP systems such as the optical, non-optical acoustic and non-optical magnetic MOCAP systems require predefined capture volume and controlled environmental conditions whilst the non-optical mechanical MOCAP system impedes the motion of the subject. Although the non-optical inertial MOCAP system allows MOCAP in an outdoor environment, it suffers from measurement noise and drift and lacks global trajectory information. The accuracy of these MOCAP systems are known to decrease during the tracking of the transient locomotion. Quantifying the manoeuvrability of animals in their natural habitat to answer the question “Why are animals so manoeuvrable?” remains a challenge. This research aims to develop an outdoor MOCAP system that will allow tracking of the steady-state as well as the transient locomotion of an animal in its natural habitat outside a controlled laboratory condition. A number of researchers have developed novel MOCAP systems with the same aim of creating an outdoor MOCAP system that is aimed at tracking the motion outside a controlled laboratory (indoor) environment with unlimited capture volume. These novel MOCAP systems are either not validated against the commercial MOCAP systems or do not have comparable sub-millimetre accuracy as the commercial MOCAP systems. The developed DGPS/MEMS-IMU multi-receiver fusion MOCAP system was assessed to have global trajectory accuracy of _0:0394m, relative limb position accuracy of _0:006497m. To conclude the research, several recommendations are made to improve the developed MOCAP system and to prepare for a field-testing with a wild animal from a family of a terrestrial megafauna

Applications in Electronics Pervading Industry, Environment and Society

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs

Robust computational intelligence techniques for visual information processing

The third part is exclusively dedicated to the super-resolution of Magnetic Resonance Images. In one of these works, an algorithm based on the random shifting technique is developed. Besides, we studied noise removal and resolution enhancement simultaneously. To end, the cost function of deep networks has been modified by different combinations of norms in order to improve their training. Finally, the general conclusions of the research are presented and discussed, as well as the possible future research lines that are able to make use of the results obtained in this Ph.D. thesis.This Ph.D. thesis is about image processing by computational intelligence techniques. Firstly, a general overview of this book is carried out, where the motivation, the hypothesis, the objectives, and the methodology employed are described. The use and analysis of different mathematical norms will be our goal. After that, state of the art focused on the applications of the image processing proposals is presented. In addition, the fundamentals of the image modalities, with particular attention to magnetic resonance, and the learning techniques used in this research, mainly based on neural networks, are summarized. To end up, the mathematical framework on which this work is based on, ₚ-norms, is defined. Three different parts associated with image processing techniques follow. The first non-introductory part of this book collects the developments which are about image segmentation. Two of them are applications for video surveillance tasks and try to model the background of a scenario using a specific camera. The other work is centered on the medical field, where the goal of segmenting diabetic wounds of a very heterogeneous dataset is addressed. The second part is focused on the optimization and implementation of new models for curve and surface fitting in two and three dimensions, respectively. The first work presents a parabola fitting algorithm based on the measurement of the distances of the interior and exterior points to the focus and the directrix. The second work changes to an ellipse shape, and it ensembles the information of multiple fitting methods. Last, the ellipsoid problem is addressed in a similar way to the parabola

Flight Control System Unit for Small UAV Aircraft

katedra měřen

Improving RF Localization Through Measurement and Manipulation of the Channel Impulse Response

For over twenty years, global navigation satellite systems like GPS have provided an invaluable navigation, tracking, and time synchronization service that is used by people, wildlife, and machinery. Unfortunately, the coverage and accuracy of GPS is diminished or lost when brought indoors since GPS signals experience attenuation and distortion after passing through and reflecting off of building materials. This disparity in coverage coupled with growing demands for indoor positioning, navigation, and tracking has led to a plethora of research in localization technologies. To date, however, no single system has emerged as a clear solution to the indoor localization and navigation problem because the myriad of potential applications have widely varying performance requirements and design constraints that no system satisfies. Fortunately, recently-introduced commercial ultra-wideband RF hardware offers excellent ranging accuracy in difficult indoor settings, but these systems lack the robustness and simplicity needed for many indoor applications. We claim that an asymmetric design that separates transmit and receive functions can enable many of the envisioned applications not currently realizable with an integrated design. This separation of functionality allows for a flexible architecture which is more robust to the in-band interference and heavy multipath commonly found in indoor environments. In this dissertation, we explore the size, weight, accuracy, and power requirements imposed on tracked objects (tags) for three broadly representative applications and propose the design of fixed-location infrastructure (anchors) that accurately and robustly estimate a tag’s location, while minimizing deployment complexity and adhering to a unified system architecture. Enabled applications range from 3D tracking of small, fast-moving micro-quadrotors to 2D personal navigation across indoor maps to tracking objects that remain stationary for long periods of time with near-zero energy cost. Each application requires careful measurement of the ultra-wideband channel impulse response, and an augmented narrowband receiver is proposed to perform these measurements. The key design principle is to offload implementation complexity to static infrastructure where an increase in cost and complexity can be more easily absorbed and amortized. Finally, with an eye towards the future, we explore how the increasingly crowded RF spectrum impacts current ultra-wideband system design, and propose an alternative architecture that enables improved coexistence of narrowband and ultra-wideband transmissions.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138642/1/bpkempke_1.pd

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Low-Cost Sensors and Biological Signals

Many sensors are currently available at prices lower than USD 100 and cover a wide range of biological signals: motion, muscle activity, heart rate, etc. Such low-cost sensors have metrological features allowing them to be used in everyday life and clinical applications, where gold-standard material is both too expensive and time-consuming to be used. The selected papers present current applications of low-cost sensors in domains such as physiotherapy, rehabilitation, and affective technologies. The results cover various aspects of low-cost sensor technology from hardware design to software optimization

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens