Image fusion using multi-resolution decomposition and LMMSE filter

The subject of data fusion utilizing heterogeneous sensors has received significant attention in recent years. Each of the sensors provides a limited perspective of the desired information. A heterogeneous sensor environment combined with a procedure for synergistically combining data from each of the transducers can potentially lead to a more comprehensive and accurate estimate of the desired information. An example of a field that can profit from the application of data fusion techniques is the area of nondestructive evaluation (NDE);This dissertation is concerned with developing efficient image fusion techniques for NDE applications. This dissertation begins with a brief description of several NDE imaging techniques with a special emphasis on eddy current and ultrasonic inspection methods. Signal degradation mechanisms associated with each NDE imaging method are described together with a discussion on methods to compensate or reduce the degradation effects;This dissertation then presents several image fusion methods beginning with those employing multilayer perceptron and radial basis function neural networks. This dissertation also introduces an optimal approach for fusing images derived from a heterogeneous sensor environment. The method uses a linear minimum mean square error (LMMSE) filter to fuse multiple images. The validity of the approach is evaluated using a pair of eddy current and ultrasonic NDE images;Finally the dissertation presents image fusion methods using multi-resolution decomposition techniques using both Fourier as well as two-dimensional wavelet transforms to decompose NDE images and reconstruct the fused image

Contribuciones en el área de sondas y algoritmos aplicadas a la detección de discontinuidades, metrología de distancia y clasificación de materiales con técnicas no destructivas basadas en corrientes inducidas

Se presenta esta tesis doctoral sobre sondas y algoritmos de procesado de datos de técnicas de ensayos no destructivas basadas en corrientes inducidas. El objetivo ha sido proponer (i) sondas para la detección de discontinuidades, y la metrología de espesor de recubrimiento no conductor, y (ii) redes neuronales para tratar los datos base mono y multifrecuencia para la clasificación de piezas con diferente temple. Del Ensayo 1, la Sonda 1 inductiva ha proporcionado mejores resultados que la Sonda 2 con sensor Hall en (i) respuesta frecuencial, (ii) detección de agujeros y (iii) predicción de espesor. Del Ensayo 2, la Sonda 3 inductiva y las redes neuronales han proporcionado mejores resultados con el procesado multifrecuencia en cuanto a (iv) tasa de acierto, (v) carga computacional; y (vi) tiempo de ejecución. Los resultados sugieren utilizar las sondas inductivas puras y redes con procesado multifrecuencia para la resolución de los problemas inversos presentados.Departamento de Teoría de la Señal, Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione

NDE data fusion using phenomenological approaches

Data fusion techniques are beginning to attract considerable attention. In the NDE context, such techniques can be used to combine information from two or more NDE test methods to improve the probability of detection and enhance characterization results. An example of such an application involves the fusion of eddy current and ultrasonic NDE data. The eddy current phenomena relies on the diffusion process to propagate energy. Ultrasonic phenomena, in contrast, rely on wave propagation. The manner in which the energy interacts with the material under test is fundamentally different. It can therefore be argued that each test method provides a different perspective and consequently approaches that allow data from the two test methodologies to be fused have the potential for offering an improved understanding of the condition of the material;This dissertation presents an incremental step towards the development of a very novel phenomenological approach to data fusion. The method involves mapping of the wave field to a diffusion field using Q-transforms. The transformed and diffusion fields are then combined to synthesize the fused image. A systematic study of the issues involved in fusion and the registration of the data was conducted. The study was accomplished by developing and using a two-dimensional analytical model that includes both the diffusion and wave propagation contributions. The ultrasonic tests were simulated using an existing finite element model. The dissertation presents results obtained by transforming the ultrasonic data into the diffusion domain. The effect of Q-transform properties, especially its time shift property, on data registration is analyzed. A modified version of the Q-transform is also presented to overcome the problems associated with large differences in the values of the underlying partial differential equation coefficients. Theoretical results obtained using the approach together with a discussion on additional work that needs to be undertaken are presented

Learning defects from aircraft NDT data

Non-destructive evaluation of aircraft production is optimised and digitalised with Industry 4.0. The aircraft structures produced using fibre metal laminate are traditionally inspected using water-coupled ultrasound scans and manually evaluated. This article proposes Machine Learning models to examine the defects in ultrasonic scans of A380 aircraft components. The proposed approach includes embedded image feature extraction methods and classifiers to learn defects in the scan images. The proposed algorithm is evaluated by benchmarking embedded classifiers and further promoted to research with an industry-based certification process. The HoG-Linear SVM classifier has outperformed SURF-Decision Fine Tree in detecting potential defects. The certification process uses the Probability of Detection function, substantiating that the HoG-Linear SVM classifier detects minor defects. The experimental trials prove that the proposed method will be helpful to examiners in the quality control and assurance of aircraft production, thus leading to significant contributions to non-destructive evaluation 4.0

The Public Service Media and Public Service Internet Manifesto

This book presents the collectively authored Public Service Media and Public Service Internet Manifesto and accompanying materials.The Internet and the media landscape are broken. The dominant commercial Internet platforms endanger democracy. They have created a communications landscape overwhelmed by surveillance, advertising, fake news, hate speech, conspiracy theories, and algorithmic politics. Commercial Internet platforms have harmed citizens, users, everyday life, and society. Democracy and digital democracy require Public Service Media. A democracy-enhancing Internet requires Public Service Media becoming Public Service Internet platforms – an Internet of the public, by the public, and for the public; an Internet that advances instead of threatens democracy and the public sphere. The Public Service Internet is based on Internet platforms operated by a variety of Public Service Media, taking the public service remit into the digital age. The Public Service Internet provides opportunities for public debate, participation, and the advancement of social cohesion. Accompanying the Manifesto are materials that informed its creation: Christian Fuchs’ report of the results of the Public Service Media/Internet Survey, the written version of Graham Murdock’s online talk on public service media today, and a summary of an ecomitee.com discussion of the Manifesto’s foundations

High-throughput phenotyping of plant leaf morphological, physiological, and biochemical traits on multiple scales using optical sensing

Acquisition of plant phenotypic information facilitates plant breeding, sheds light on gene action, and can be applied to optimize the quality of agricultural and forestry products. Because leaves often show the fastest responses to external environmental stimuli, leaf phenotypic traits are indicators of plant growth, health, and stress levels. Combination of new imaging sensors, image processing, and data analytics permits measurement over the full life span of plants at high temporal resolution and at several organizational levels from organs to individual plants to field populations of plants. We review the optical sensors and associated data analytics used for measuring morphological, physiological, and biochemical traits of plant leaves on multiple scales. We summarize the characteristics, advantages and limitations of optical sensing and data-processing methods applied in various plant phenotyping scenarios. Finally, we discuss the future prospects of plant leaf phenotyping research. This review aims to help researchers choose appropriate optical sensors and data processing methods to acquire plant leaf phenotypes rapidly, accurately, and cost-effectively

Aeronautical engineering: A continuing bibliography with indexes (supplement 318)

This bibliography lists 217 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1995. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Novel Approaches for Structural Health Monitoring

The thirty-plus years of progress in the field of structural health monitoring (SHM) have left a paramount impact on our everyday lives. Be it for the monitoring of fixed- and rotary-wing aircrafts, for the preservation of the cultural and architectural heritage, or for the predictive maintenance of long-span bridges or wind farms, SHM has shaped the framework of many engineering fields. Given the current state of quantitative and principled methodologies, it is nowadays possible to rapidly and consistently evaluate the structural safety of industrial machines, modern concrete buildings, historical masonry complexes, etc., to test their capability and to serve their intended purpose. However, old unsolved problematics as well as new challenges exist. Furthermore, unprecedented conditions, such as stricter safety requirements and ageing civil infrastructure, pose new challenges for confrontation. Therefore, this Special Issue gathers the main contributions of academics and practitioners in civil, aerospace, and mechanical engineering to provide a common ground for structural health monitoring in dealing with old and new aspects of this ever-growing research field

Wireless Monitoring Systems for Long-Term Reliability Assessment of Bridge Structures based on Compressed Sensing and Data-Driven Interrogation Methods.

The state of the nation’s highway bridges has garnered significant public attention due to large inventories of aging assets and insufficient funds for repair. Current management methods are based on visual inspections that have many known limitations including reliance on surface evidence of deterioration and subjectivity introduced by trained inspectors. To address the limitations of current inspection practice, structural health monitoring (SHM) systems can be used to provide quantitative measures of structural behavior and an objective basis for condition assessment. SHM systems are intended to be a cost effective monitoring technology that also automates the processing of data to characterize damage and provide decision information to asset managers. Unfortunately, this realization of SHM systems does not currently exist. In order for SHM to be realized as a decision support tool for bridge owners engaged in performance- and risk-based asset management, technological hurdles must still be overcome. This thesis focuses on advancing wireless SHM systems. An innovative wireless monitoring system was designed for permanent deployment on bridges in cold northern climates which pose an added challenge as the potential for solar harvesting is reduced and battery charging is slowed. First, efforts advancing energy efficient usage strategies for WSNs were made. With WSN energy consumption proportional to the amount of data transmitted, data reduction strategies are prioritized. A novel data compression paradigm termed compressed sensing is advanced for embedment in a wireless sensor microcontroller. In addition, fatigue monitoring algorithms are embedded for local data processing leading to dramatic data reductions. In the second part of the thesis, a radical top-down design strategy (in contrast to global vibration strategies) for a monitoring system is explored to target specific damage concerns of bridge owners. Data-driven algorithmic approaches are created for statistical performance characterization of long-term bridge response. Statistical process control and reliability index monitoring are advanced as a scalable and autonomous means of transforming data into information relevant to bridge risk management. Validation of the wireless monitoring system architecture is made using the Telegraph Road Bridge (Monroe, Michigan), a multi-girder short-span highway bridge that represents a major fraction of the U.S. national inventory.PhDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116749/1/ocosean_1.pd