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

Invariance transformations for processing NDE signals

The ultimate objective in nondestructive evaluation (NDE) is the characterization of materials, on the basis of information in the response from energy/material interactions. This is commonly referred to as the inverse problem. Inverse problems are in general ill-posed and full analytical solutions to these problems are seldom tractable. Pragmatic approaches for solving them employ a constrained search technique by limiting the space of all possible solutions. A more modest goal is therefore to use the received signal for characterizing defects in objects in terms of the location, size and shape. However, the NDE signal received by the sensors is influenced not only by the defect, but also by the operational parameters associated with the experiment. This dissertation deals with the subject of invariant pattern recognition techniques that render NDE signals insensitive to operational variables, while at the same time, preserve or enhance defect related information. Such techniques are comprised of invariance transformations that operate on the raw signals prior to interpretation using subsequent defect characterization schemes. Invariance transformations are studied in the context of the magnetostatic flux leakage (MFL) inspection technique, which is the method of choice for inspecting natural gas transmission pipelines buried underground;The magnetic flux leakage signal received by the scanning device is very sensitive to a number of operational parameters. Factors that have a major impact on the signal include those caused by variations in the permeability of the pipe-wall material and the velocity of the inspection tool. This study describes novel approaches to compensate for the effects of these variables;Two types of invariance schemes, feature selection and signal compensation, are studied. In the feature selection approach, the invariance transformation is recast as a problem in interpolation of scattered, multi-dimensional data. A variety of interpolation techniques are explored, the most powerful among them being feed-forward neural networks. The second parametric variation is compensated by using restoration filters. The filter kernels are derived using a constrained, stochastic least square optimization technique or by adaptive methods. Both linear and non-linear filters are studied as tools for signal compensation;Results showing the successful application of these invariance transformations to real and simulated MFL data are presented

An Extended Review on Fabric Defects and Its Detection Techniques

In Textile Industry, Quality of the Fabric is the main important factor. At the initial stage, it is very essential to identify and avoid the fabrics faults/defects and hence human perception consumes lot of time and cost to reveal the fabrics faults. Now-a-days Automated Inspection Systems are very useful to decrease the fault prediction time and gives best visualizing clarity- based on computer vision and image processing techniques. This paper made an extended review about the quality parameters in the fiber-to-fabric process, fabrics defects detection terminologies applied on major three clusters of fabric defects knitting, woven and sewing fabric defects. And this paper also explains about the statistical performance measures which are used to analyze the defect detection process. Also, comparison among the methods proposed in the field of fabric defect detection

Optical-Resolution Photoacoustic Microscopy

Optical microscopy, providing valuable biomedical insights at the cellular and organelle levels, has been widely recognized as an enabling technology. Mainstream optical microscopy technologies, including single-/multi-photon fluorescence microscopy and OCT, have demonstrated extraordinary sensitivities to fluorescence and optical scattering contrasts, respectively. However, the optical absorption contrast of biological tissues, which encodes essential physiological/pathological information, has not yet been fully assessable. The emergence of biomedical photoacoustics has led to a new branch of optical microscopy--OR-PAM. As a valuable complement to existing optical microscopy technologies, OR-PAM detects optical absorption contrasts with exquisite sensitivity: i.e., 100%). Combining OR-PAM with fluorescence microscopy or optical-scattering-based OCT: or both) provides comprehensive optical properties of biological tissues. Moreover, OR-PAM encodes optical absorption into acoustic waves, in contrast to the pure optical processes in fluorescence microscopy and OCT, and thus provides background-free detection. The acoustic detection in OR-PAM mitigates the impacts of optical scattering on signal degradation and naturally eliminates possible interferences: i.e., crosstalks) between excitation and detection, which is a common problem in fluorescence microscopy due to the overlap between the excitation and fluorescence spectra and imperfect extinction of the filter. Unique for high-resolution imaging of optical absorption, OR-PAM has demonstrated broad biomedical applications in fields such as neurology, ophthalmology, vascular biology, and dermatology. My doctoral research focuses on developments and biomedical applications of OR-PAM. The first part of my dissertation discusses the development of three generations of OR-PAM towards high-resolution, high-sensitivity, high-speed, and wide FOV in vivo imaging. In this section, I provide a comprehensive description of OR-PAM, including the principle, system design, system configuration, experimental procedures, laser safety, functional imaging scheme, and example biomedical applications at a variety of in vivo anatomical sites: i.e., skins, eyes and brains). The second part of my dissertation focuses on the application of OR-PAM in vascular biology, with an emphasis on neovascularization. In this section, I demonstrate longitudinal OR-PAM monitoring of the morphological: i.e., vessel diameter, length, tortuosity and volume) and functional: i.e., sO2) changes of angiogenic microenvironment at the capillary level, in both a non-disease TetON-HIF-1 transgenic mouse model and a cancer xenograft model in mouse ear. The last part of my dissertation focuses on the application of OR-PAM in neurology, with an emphasis on cortical stimulation, Alzheimer\u27s disease, and ischemic stroke. In this section, I use label-free OR-PAM for both acute monitoring of microvascular responses to direct electrical stimulations of the mouse somatosensory cortex through a cranial opening and longitudinal monitoring of the morphological and functional changes of cortical vasculature in a transient middle cerebral artery occlusion mouse model. I also explore the potential of OR-PAM for transcranial monitoring of amyloid plaque growth in an AD mouse model

Heterogeneous multi-sensor data fusion using geometric transformations and Parzen windows for the nondestructive evaluation of gas transmission pipelines

The natural gas transmission pipeline network in the United States is a key component of the nation\u27s energy supply infrastructure and extends for over 280,000 miles and has an average age of over 60 years. The integrity of the pipeline is maintained by periodic inline inspections using magnetic or ultrasonic pigs. Defect characterization algorithms developed using current pigging data are hampered by the fact that single inspection techniques (either magnetic or ultrasonic) do not yield sufficient information for accurately and repeatably characterizing defects. This thesis demonstrates that defect characterization algorithms using multiple inspection techniques can accomplish this task. In particular, it is shown that the varying depth of a surface breaking pipeline defect can be precisely determined using a combination of multiple inspection methods. Also the precise location of such defects can be predicted using dissimilar interrogation methods. A judicious combination of signal and image processing strategies, including geometric transformations, radial basis function approximations and Parzen windows density estimations, have been used to fuse data from both homogeneous and heterogeneous sensors. Application results using data from laboratory experiments demonstrate the consistency and efficacy of the proposed approach

NASA Tech Briefs Index, 1977, volume 2, numbers 1-4

Announcements of new technology derived from the research and development activities of NASA are presented. Abstracts, and indexes for subject, personal author, originating center, and Tech Brief number are presented for 1977

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

Modelling and optimising micro-nozzle resin injection repair of impacted composites using CFD

Resin injection repair is identified to have a gap of knowledge and rigour in the modelling and execution of the process. We outline the strategy of our proposed predictive modelling strategy of ‘reconstruction-simulation-injection’ to simulate real cases to improve repair outcomes. We model the damage zone using Darcy’s law and determine permeability using two methods applied on the Kozeny-Carman equation. We then discuss how we evaluate porosity and detail two proposed methods on reconstructing the porosity field. We verify the model through simulation, and demonstrate verification using a novel comprehensive 2D porosity liquid-ideal gas phase flow model after deriving the analytical solution, which is a contribution of our work. Next, we apply the now-established model to reconstruct real damage cases using the two methods and compare them. We also calibrate the permeability parameter for the model by comparison to a simulation accuracy index, and also calibrate an ultrasonic scanning parameter to minimise reconstruction artefacts as well as the sensitivity of the reconstructed geometry characteristics to scan parameter variations. Then, we validate the model by simulating real repair cases and comparing them to the experimental outcomes, achieving simulation accuracy indices of about 70% or more. We demonstrate the application of the resin injection model by applying resin injection in a proof-of-concept simulation and use it for a case study, and examine the importance of hole configuration, vacuum usage as well as resin flow behaviour between inlet and outlet holes in the context of a given damage area geometry. It is important to maximise the total length of resin flow paths available, through carefully placing inlet and outlet holes, to allow resin to infiltrate the damage zone as much as possible. Vacuum increases the minimum achievable filling, and it is still invariably better to use vacuum with an optimal hole placement, instead of one or the other. In a second case study, we improve the predicted outcome by the model after intentionally changing the hole configuration to maximise resin infiltration, demonstrating that filling can be improved by placing holes intelligently (e.g. by using gathered information on the damage area, together with knowledge of how resin would flow). Using this, we conduct an optimisation study of the resin injection model by first setting up the optimisation strategy and carefully determining the methodology. The optimisation procedure is verified by using one and two degree-of-freedom optimisation cases, with known optima. Then, the optimisation strategy is applied to reconstructed repair cases to demonstrate and assess the efficacy of the optimisation procedure, with average reductions in unfilled volumes of approximately 28% compared to initial configurations.Open Acces

Deep-Learning-Based Algorithm for the Removal of Electromagnetic Interference Noise in Photoacoustic Endoscopic Image Processing

Despite all the expectations for photoacoustic endoscopy (PAE), there are still several technical issues that must be resolved before the technique can be successfully translated into clinics. Among these, electromagnetic interference (EMI) noise, in addition to the limited signal-to-noise ratio (SNR), have hindered the rapid development of related technologies. Unlike endoscopic ultrasound, in which the SNR can be increased by simply applying a higher pulsing voltage, there is a fundamental limitation in leveraging the SNR of PAE signals because they are mostly determined by the optical pulse energy applied, which must be within the safety limits. Moreover, a typical PAE hardware situation requires a wide separation between the ultrasonic sensor and the amplifier, meaning that it is not easy to build an ideal PAE system that would be unaffected by EMI noise. With the intention of expediting the progress of related research, in this study, we investigated the feasibility of deep-learning-based EMI noise removal involved in PAE image processing. In particular, we selected four fully convolutional neural network architectures, U-Net, Segnet, FCN-16s, and FCN-8s, and observed that a modified U-Net architecture outperformed the other architectures in the EMI noise removal. Classical filter methods were also compared to confirm the superiority of the deep-learning-based approach. Still, it was by the U-Net architecture that we were able to successfully produce a denoised 3D vasculature map that could even depict the mesh-like capillary networks distributed in the wall of a rat colorectum. As the development of a low-cost laser diode or LED-based photoacoustic tomography (PAT) system is now emerging as one of the important topics in PAT, we expect that the presented AI strategy for the removal of EMI noise could be broadly applicable to many areas of PAT, in which the ability to apply a hardware-based prevention method is limited and thus EMI noise appears more prominently due to poor SNR