Characterisation of fatigue damage in composites using an Acoustic Emission Parameter Correction Technique

In industrial applications of composite materials, accurate characterisation of damage is vital. Acoustic Emission (AE) can be utilised to achieve this, however, in large-scale complex geometry components, traditional AE approaches have limitations. In this study a large carbon fibre specimen was used to generate different damage mechanisms under fatigue loading. The Delta T Mapping technique was used to locate damage and signal features were corrected using the Parameter Correction Technique (PCT). A comparison between results obtained using traditional signal features and those obtained using PCT is given. The results are validated using C-scanning and computed tomography. Matrix cracking and delamination were successfully identified using the PCT approach and improved location accuracy was achieved

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Deep Learning Methods for Estimation of Elasticity and Backscatter Quantitative Ultrasound

Ultrasound (US) imaging is increasingly attracting the attention of both academic and industrial researchers due to being a real-time and nonionizing imaging modality. It is also less expensive and more portable compared to other medical imaging techniques. However, the granular appearance hinders the interpretation of US images, hindering its wider adoption. This granular appearance (also referred to as speckles) arises from the backscattered echo from microstructural components smaller than the ultrasound wavelength, which are called scatterers. While significant effort has been undertaken to reduce the appearance of speckles, they contain scatterer properties that are highly correlated with the microstructure of the tissue that can be employed to diagnose different types of disease. There are many properties that can be extracted from speckles that are clinically valuable, such as the elasticity and organization of scatterers. Analyzing the motion of scatterers in the presence of an internal or external force can be used to obtain the elastic properties of the tissue. The technique is called elastography and has been widely used to characterize the tissue. Estimating the scatterer organization (scatterer number density and coherent to diffuse scattering power) is also crucial as it provides information about tissue microstructure and potentially aids in disease diagnosis and treatment monitoring. This thesis proposes several deep learning-based methods to facilitate and improve the estimation of speckle motion and scatterer properties, potentially simplifying the interpretation of US images. In particular, we propose new methods for displacement estimation in Chapters 2 to 6 and introduce novel techniques in Chapters 7 to 11 to quantify scatterers’ number density and organization

Review of photoacoustic imaging plus X

Photoacoustic imaging (PAI) is a novel modality in biomedical imaging technology that combines the rich optical contrast with the deep penetration of ultrasound. To date, PAI technology has found applications in various biomedical fields. In this review, we present an overview of the emerging research frontiers on PAI plus other advanced technologies, named as PAI plus X, which includes but not limited to PAI plus treatment, PAI plus new circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus novel ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities. We will discuss each technology's current state, technical advantages, and prospects for application, reported mostly in recent three years. Lastly, we discuss and summarize the challenges and potential future work in PAI plus X area

Speckle Detection in Ultrasonic Images Using Unsupervised Clustering Techniques

Research for the improvement of the quality of clinical ultrasound images has been a topic of interest for researchers and physicians. One of the challenges is the presence of speckle artifacts. This dissertation reviews the speckle phenomena in such images, and develops algorithms to better identify this artifact in sonographic images. Speckle artifact is categorized into two groups: partially developed speckles and fully developed speckles (FDS). This concept has been used, along with the classification techniques, to segment the ultrasound images into patches and classify the patches in the image as FDS or non-FDS. The proposed algorithms and the results of the experiments have been validated using simulation, phantom and real data that were created for the purposes of this study or taken from other research groups. Current speckle detection methods do not optimize statistical features and they are not based on machine learning techniques. For the first time this work introduces a novel method for searching and extracting the best features for optimizing speckle detection rate using statistical machine learning and ensemble classification. Potential applications include strain imaging by tracking speckle displacement, elastography, speckle tracking and suppression applications, and needle-tracking applications.Ph.D., Biomedical Engineering -- Drexel University, 201

Structural damage monitoring based on machine learning and bio-inspired computing

For a few decades, systems for supervising structures have become increasingly irnportant. In origin, the strategies had as a goal only the detection of damages. Furthermore, now monitor­ing the civil or military structures permanently and offering sufficient and relevant information helping make the right decisions. The SHM is applicable, carrying out preventive or corrective maintenance decisions, reducing the possibility of accidents, and promoting the reduction of costs that more extensive repairs imply when the damage is detected early. The current work focused on three elements of diagnosis of structural damage: detection, classification, and loca­tion, either in metaltic or cornposite material structures, given their wide use in air, land, rnar­itime transport vehicles, aerospace, wind turbines, civil and military infrastructure. This work used the tools offered by machine leaming and bio-inspired computing. Given the right results to solve complex problems and recognizing pattems. It also involves changes in temperature since it is one of the parameters that influence real environments' structures. Information of a statistical nature applied to recognizing pattems and reducing the size of the information was used with tools such as PCA (principal component analysis), thanks to the experience obtained in works developed by the CoDAlab research group. The document is divided into five parts. The first includes a general description of the problem, the objecti.-es, and the results obtained, in addition to a brief theoretical introduction. Chapters 2, 3, and 4 include articles published in different joumals. Chapter 5 shows the results and conclusions. Other contributions, such as a book chapter and sorne papers presented at conferences, are included in appendix A. Finally, appendix B presents a multiplexing system used to develop the experiments carried out in this work.Desde hace algunas décadas los sistemas para supervisar estructuras han tenido cada vez más relevancia. En esta evolución se ha pasado de estrategias que tenían como meta sólo la detec­ción de fallas a otras que buscan monitorizar permanentemente las estructuras bien sean éstas civiles o militares, ofreciendo información suficiente y pertinente que incide positivamente en el momento de tomar buenas decisiones, dentro de las cuales cabe destacar por ejemplo, las ori­entadas a realizar mantenimientos preventivos o correctivos si es del caso, reduciendo la posi­bilidad de accidentes, además de propiciar la disminución de costos que implican las repara­ciones más extensas cuando el daño se logra detectar de manera temprana. El presente trabajo se enfocó en tres elementos de diagnóstico de daños en estructuras, siendo estos en particular la detección, clasificación y localización, bien sea en estructuras metálicas o de material com­puesto, dado su amplio uso en vehículos de transporte aéreo, terrestre, marítimo, aeroespacial, aerogeneradores, infraestructura civil y militar. Se utilizaron las herramientas que ofrecen el aprendizaje automático (machine leaming) y la computación bio-inspirada, dados los buenos resultados que han ofrecido en la solución de problemas complejos y el reconocimiento de pa­trones. Involucrando cambios de temperatura dado que es uno de los parámetros a los que se ven enfrentadas las estructuras en ambientes reales. Se utilizó información de naturaleza estadística aplicada al reconocimiento de patrones y reducción del tamaño de la información con herramientas como el PCA (análisis de componentes principales), gracias a la experiencia lograda en trabajos desarrollados por el grupo de investigación CoDAlab. El documento está dividido en cinco capítulos. En el primerio se incluye una descripción general del problema, los objetivos y los resultados obtenidos, además de un breve introduc­ción teórica. Los Capítulos 2,3 y 4 incluyen los artículos publicados en diferentes revistas. En el Capítulo 5 se realiza una presentación de los resultados y conclusiones. En el Anexo A se incluyen otras contribuciones tales como un capítulo de libro y algunos trabajos presentados en conferencias. Finalmente en el anexo B se presenta el diseño de un sistema de multipliexación utilizado en el desarrollo de los experimentos realizados en el presente trabajo.Postprint (published version

Target detection and classification using seismic signal processing in unattended ground sensor systems

This thesis studies the problem of target detection and classification in Unat-tended Ground Sensor (UGS) systems. One of the most challenging problems faced by target identification process is the design of a robust feature vector which is sta-ble and specific to a certain type of vehicle. UGS systems have been used to detect and classify a variety of vehicles. In these systems, acoustic and seismic signals are the most popularly used resources. This thesis studies recent development of target detection and classification techniques using seismic signals. Based on these studies, a new feature extraction algorithm. Spectral Statistics and Wavelet Coef-ficients Characterization (SSWCC), is proposed. This algorithm obtains a robust feature vector extracted from the spectrum, the power spectral density (BSD) and the wavelet coefficients of the signals. Shape statistics is used in both spectral and PSD analysis. These features not only describe the frequency distribution in the spectrum and PSD, but also shows the closeness of the magnitude of spectrum to the normal distribution. Furthermore, the wavelet coefficients are calculated to present the signal in the time-frequency domain. The energy and the distribution of the wavelet coefficients are used in feature extraction as well. After the features are obtained, principal component analysis (PGA) is used to reduce the dimension of the features and optimize the feature vector. Minimum-distance classifier and k-nearest neighbor (kNN) classifier are used to carry out the classification. Experimental results show that SSWCC provides a robust feature set for target identification. The overall performance level can reach as high as 90%

Developing of Ultrasound Experimental Methods using Machine Learning Algorithms for Application of Temperature Monitoring of Nano-Bio-Composites Extrusion

In industry fiber degradation during processing of biocomposite in the extruder is a problem that requires a reliable solution to save time and money wasted on producing damaged material. In this thesis, We try to focus on a practical solution that can monitor the change in temperature that causes fiber degradation and material damage to stop it when it occurs. Ultrasound can be used to detect the temperature change inside the material during the process of material extrusion. A monitoring approach for the extruder process has been developed using ultrasound system and the techniques of machine learning algorithms. A measurement cell was built to form a dataset of ultrasound signals at different temperatures for analysis. Machine learning algorithms were applied through machine-learning algorithm’s platform to classify the dataset based on the temperature. The dataset was classified with accuracy 97% into two categories representing over and below damage temperature (190oc) ultrasound signal. This approach could be used in industry to send an alarm or a temperature control signal when material damage is detected. Biocomposite is at the core of automotive industry material research and development concentration. Melt mixing process was used to mix biocomposite material with multi-walled carbon nanotubes (MWCNTs) for the purpose of enhancing mechanical and thermal properties of biocomposite. The resulting composite nano-bio- composite was tested via different types of thermal and mechanical tests to evaluate its performance relative to biocomposite. The developed material showed enhancement in mechanical and thermal properties that considered a high potential for applications in the future