Microring stereo sensor model using Kerr–Vernier effect for bio-cell sensor and communication

In this paper, a micro-stereo sensor is proposed using two-identical Panda-ring resonators, which are coupled by jointed drop ports. When light from the identical coherent sources is fed into the system via the input ports, the coupling outputs are obtained at the drop port at the resonant condition. These are mixed signals in the form of stereo signals. By using different input power between the right and left systems, the phase difference generated by the Kerr-Effect in the non-linear medium leads to the shift in the coupling outputs. The shift in the center wavelength is the primary measurement of interest along with coupling crosstalk signals that are also visible at the output. The measurement self-calibration of the two channels is confirmed by the mixed channel signals. In the manipulation, the crosstalk signals can be used to interpret the cross-communication of bio-cells. The crosstalk results have shown the optical crosstalks of ∼2.0 and ∼2.5 dB are calculated and obtained, respectively. The stereo sensor sensitivity of ∼5.70 nmW−1is noted

Artificial intelligence for advanced manufacturing quality

100 p.This Thesis addresses the challenge of AI-based image quality control systems applied to manufacturing industry, aiming to improve this field through the use of advanced techniques for data acquisition and processing, in order to obtain robust, reliable and optimal systems. This Thesis presents contributions onthe use of complex data acquisition techniques, the application and design of specialised neural networks for the defect detection, and the integration and validation of these systems in production processes. It has been developed in the context of several applied research projects that provided a practical feedback of the usefulness of the proposed computational advances as well as real life data for experimental validation

Image-based 3-D reconstruction of constrained environments

Nuclear power plays a important role to the United Kingdom electricity generation infrastructure, providing a reliable baseload of low carbon electricity. The Advanced Gas-cooled Reactor (AGR) design makes up approximately 50% of the existing fleet, however, many of the operating reactors have exceeding their original design lifetimes.To ensure safe reactor operation, engineers perform periodic in-core visual inspections of reactor components to monitor the structural health of the core as it ages. However, current inspection mechanisms deployed provide limited structural information about the fuel channel or defects.;This thesis investigates the suitability of image-based 3-D reconstruction techniques to acquire 3-D structural geometry to enable improved diagnostic and prognostic abilities for inspection engineers. The application of image-based 3-D reconstruction to in-core inspection footage highlights significant challenges, most predominantly that the image saliency proves insuffcient for general reconstruction frameworks. The contribution of the thesis is threefold. Firstly, a novel semi-dense matching scheme which exploits sparse and dense image correspondence in combination with a novel intra-image region strength approach to improve the stability of the correspondence between images.;This results in a percentage increase of 138.53% of correct feature matches over similar state-of-the-art image matching paradigms. Secondly, a bespoke incremental Structure-from-Motion (SfM) framework called the Constrained Homogeneous SfM (CH-SfM) which is able to derive structure from deficient feature spaces and constrained environments. Thirdly, the application of the CH-SfM framework to remote visual inspection footage gathered within AGR fuel channels, outperforming other state-of-the-art reconstruction approaches and extracting representative 3-D structural geometry of orientational scans and fully circumferential reconstructions.;This is demonstrated on in-core and laboratory footage, achieving an approximate 3-D point density of 2.785 - 23.8025NX/cm² for real in-core inspection footage and high quality laboratory footage respectively. The demonstrated novelties have applicability to other constrained or feature-poor environments, with future work looking to producing fully dense, photo-realistic 3-D reconstructions.Nuclear power plays a important role to the United Kingdom electricity generation infrastructure, providing a reliable baseload of low carbon electricity. The Advanced Gas-cooled Reactor (AGR) design makes up approximately 50% of the existing fleet, however, many of the operating reactors have exceeding their original design lifetimes.To ensure safe reactor operation, engineers perform periodic in-core visual inspections of reactor components to monitor the structural health of the core as it ages. However, current inspection mechanisms deployed provide limited structural information about the fuel channel or defects.;This thesis investigates the suitability of image-based 3-D reconstruction techniques to acquire 3-D structural geometry to enable improved diagnostic and prognostic abilities for inspection engineers. The application of image-based 3-D reconstruction to in-core inspection footage highlights significant challenges, most predominantly that the image saliency proves insuffcient for general reconstruction frameworks. The contribution of the thesis is threefold. Firstly, a novel semi-dense matching scheme which exploits sparse and dense image correspondence in combination with a novel intra-image region strength approach to improve the stability of the correspondence between images.;This results in a percentage increase of 138.53% of correct feature matches over similar state-of-the-art image matching paradigms. Secondly, a bespoke incremental Structure-from-Motion (SfM) framework called the Constrained Homogeneous SfM (CH-SfM) which is able to derive structure from deficient feature spaces and constrained environments. Thirdly, the application of the CH-SfM framework to remote visual inspection footage gathered within AGR fuel channels, outperforming other state-of-the-art reconstruction approaches and extracting representative 3-D structural geometry of orientational scans and fully circumferential reconstructions.;This is demonstrated on in-core and laboratory footage, achieving an approximate 3-D point density of 2.785 - 23.8025NX/cm² for real in-core inspection footage and high quality laboratory footage respectively. The demonstrated novelties have applicability to other constrained or feature-poor environments, with future work looking to producing fully dense, photo-realistic 3-D reconstructions

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Aerial Robotics for Inspection and Maintenance

Aerial robots with perception, navigation, and manipulation capabilities are extending the range of applications of drones, allowing the integration of different sensor devices and robotic manipulators to perform inspection and maintenance operations on infrastructures such as power lines, bridges, viaducts, or walls, involving typically physical interactions on flight. New research and technological challenges arise from applications demanding the benefits of aerial robots, particularly in outdoor environments. This book collects eleven papers from different research groups from Spain, Croatia, Italy, Japan, the USA, the Netherlands, and Denmark, focused on the design, development, and experimental validation of methods and technologies for inspection and maintenance using aerial robots

Advanced Knowledge Application in Practice

The integration and interdependency of the world economy leads towards the creation of a global market that offers more opportunities, but is also more complex and competitive than ever before. Therefore widespread research activity is necessary if one is to remain successful on the market. This book is the result of research and development activities from a number of researchers worldwide, covering concrete fields of research

UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection