Employing data fusion & diversity in the applications of adaptive signal processing

The paradigm of adaptive signal processing is a simple yet powerful method for the class of system identification problems. The classical approaches consider standard one-dimensional signals whereby the model can be formulated by flat-view matrix/vector framework. Nevertheless, the rapidly increasing availability of large-scale multisensor/multinode measurement technology has render no longer sufficient the traditional way of representing the data. To this end, the author, who from this point onward shall be referred to as `we', `us', and `our' to signify the author myself and other supporting contributors i.e. my supervisor, my colleagues and other overseas academics specializing in the specific pieces of research endeavor throughout this thesis, has applied the adaptive filtering framework to problems that employ the techniques of data diversity and fusion which includes quaternions, tensors and graphs. At the first glance, all these structures share one common important feature: invertible isomorphism. In other words, they are algebraically one-to-one related in real vector space. Furthermore, it is our continual course of research that affords a segue of all these three data types. Firstly, we proposed novel quaternion-valued adaptive algorithms named the n-moment widely linear quaternion least mean squares (WL-QLMS) and c-moment WL-LMS. Both are as fast as the recursive-least-squares method but more numerically robust thanks to the lack of matrix inversion. Secondly, the adaptive filtering method is applied to a more complex task: the online tensor dictionary learning named online multilinear dictionary learning (OMDL). The OMDL is partly inspired by the derivation of the c-moment WL-LMS due to its parsimonious formulae. In addition, the sequential higher-order compressed sensing (HO-CS) is also developed to couple with the OMDL to maximally utilize the learned dictionary for the best possible compression. Lastly, we consider graph random processes which actually are multivariate random processes with spatiotemporal (or vertex-time) relationship. Similar to tensor dictionary, one of the main challenges in graph signal processing is sparsity constraint in the graph topology, a challenging issue for online methods. We introduced a novel splitting gradient projection into this adaptive graph filtering to successfully achieve sparse topology. Extensive experiments were conducted to support the analysis of all the algorithms proposed in this thesis, as well as pointing out potentials, limitations and as-yet-unaddressed issues in these research endeavor.Open Acces

A computational model of visual attention.

Visual attention is a process by which the Human Visual System (HVS) selects most important information from a scene. Visual attention models are computational or mathematical models developed to predict this information. The performance of the state-of-the-art visual attention models is limited in terms of prediction accuracy and computational complexity. In spite of significant amount of active research in this area, modelling visual attention is still an open research challenge. This thesis proposes a novel computational model of visual attention that achieves higher prediction accuracy with low computational complexity. A new bottom-up visual attention model based on in-focus regions is proposed. To develop the model, an image dataset is created by capturing images with in-focus and out-of-focus regions. The Discrete Cosine Transform (DCT) spectrum of these images is investigated qualitatively and quantitatively to discover the key frequency coefficients that correspond to the in-focus regions. The model detects these key coefficients by formulating a novel relation between the in-focus and out-of-focus regions in the frequency domain. These frequency coefficients are used to detect the salient in-focus regions. The simulation results show that this attention model achieves good prediction accuracy with low complexity. The prediction accuracy of the proposed in-focus visual attention model is further improved by incorporating sensitivity of the HVS towards the image centre and the human faces. Moreover, the computational complexity is further reduced by using Integer Cosine Transform (ICT). The model is parameter tuned using the hill climbing approach to optimise the accuracy. The performance has been analysed qualitatively and quantitatively using two large image datasets with eye tracking fixation ground truth. The results show that the model achieves higher prediction accuracy with a lower computational complexity compared to the state-of-the-art visual attention models. The proposed model is useful in predicting human fixations in computationally constrained environments. Mainly it is useful in applications such as perceptual video coding, image quality assessment, object recognition and image segmentation

A multisensor SLAM for dense maps of large scale environments under poor lighting conditions

This thesis describes the development and implementation of a multisensor large scale autonomous mapping system for surveying tasks in underground mines. The hazardous nature of the underground mining industry has resulted in a push towards autonomous solutions to the most dangerous operations, including surveying tasks. Many existing autonomous mapping techniques rely on approaches to the Simultaneous Localization and Mapping (SLAM) problem which are not suited to the extreme characteristics of active underground mining environments. Our proposed multisensor system has been designed from the outset to address the unique challenges associated with underground SLAM. The robustness, self-containment and portability of the system maximize the potential applications.The multisensor mapping solution proposed as a result of this work is based on a fusion of omnidirectional bearing-only vision-based localization and 3D laser point cloud registration. By combining these two SLAM techniques it is possible to achieve some of the advantages of both approaches – the real-time attributes of vision-based SLAM and the dense, high precision maps obtained through 3D lasers. The result is a viable autonomous mapping solution suitable for application in challenging underground mining environments.A further improvement to the robustness of the proposed multisensor SLAM system is a consequence of incorporating colour information into vision-based localization. Underground mining environments are often dominated by dynamic sources of illumination which can cause inconsistent feature motion during localization. Colour information is utilized to identify and remove features resulting from illumination artefacts and to improve the monochrome based feature matching between frames.Finally, the proposed multisensor mapping system is implemented and evaluated in both above ground and underground scenarios. The resulting large scale maps contained a maximum offset error of ±30mm for mapping tasks with lengths over 100m

Responding to human full-body gestures embedded in motion data streams.

 This research created a neural-network enabled artificially intelligent performing agent that was able to learn to dance and recognise movement through a rehearsal and performance process with a human dancer. The agent exhibited emergent dance behaviour and successfully engaged in a live, semi-improvised dance performance with the human dancer

Industrial Robot Collision Handling in Harsh Environments

The focus in this thesis is on robot collision handling systems, mainly collision detection and collision avoidance for industrial robots operating in harsh environments (e.g. potentially explosive atmospheres found in the oil and gas sector). Collision detection should prevent the robot from colliding and therefore avoid a potential accident. Collision avoidance builds on the concept of collision detection and aims at enabling the robot to find a collision free path circumventing the obstacle and leading to the goal position. The work has been done in collaboration with ABB Process Automation Division with focus on applications in oil and gas. One of the challenges in this work has been to contribute to safer use of industrial robots in potentially explosive environments. One of the main ideas is that a robot should be able to work together with a human as a robotic co-worker on for instance an oil rig. The robot should then perform heavy lifting and precision tasks, while the operator controls the steps of the operation through typically a hand-held interface. In such situations, when the human works alongside with the robot in potentially explosive environments, it is important that the robot has a way of handling collisions. The work in this thesis presents solutions for collision detection in paper A, B and C, thereafter solutions for collision avoidance are presented in paper D and E. Paper A approaches the problem of collision avoidance comparing an expert system and a hidden markov model (HMM) approach. An industrial robot equipped with a laser scanner is used to gather environment data on arbitrary set of points in the work cell. The two methods are used to detect obstacles within the work cell and shows a different set of strengths. The expert system shows an advantage in algorithm performance and the HMM method shows its strength in its ease of learning models of the environment. Paper B builds upon Paper A by incorporating a CAD model of the environment. The CAD model allows for a very fast setup of the expert system where no manual map creation is needed. The HMM can be trained based on the CAD model, which addresses the previous dependency on real sensor data for training purposes. Paper C compares two different world-model representation techniques, namely octrees and point clouds using both a graphics processing unit (GPU) and a central processing unit (CPU). The GPU showed its strength for uncompressed point clouds and high resolution point cloud models. However, if the resolution gets low enough, the CPU starts to outperform the GPU. This shows that parallel problems containing large data sets are suitable for GPU processing, but smaller parallel problems are still handled better by the CPU. In paper D, real-time collision avoidance is studied for a lightweight industrial robot using a development platform controller. A Microsoft Kinect sensor is used for capturing 3D depth data of the environment. The environment data is used together with an artificial potential fields method for generating virtual forces used for obstacle avoidance. The forces are projected onto the end-effector, preventing collision with the environment while moving towards the goal. Forces are also projected on to the elbow of the 7-Degree of freedom robot, which allows for nullspace movement. The algorithms for manipulating the sensor data and calculating virtual forces were developed for the GPU, this resulted in fast algorithms and is the enabling factor for real-time collision avoidance. Finally, paper E builds on the work in paper D by providing a framework for using the algorithms on a standard industrial controller and robot with minimal modifications. Further, algorithms were specifically developed for the robot controller to handle reactive movement. In addition, a full collision avoidance system for an end-user application which is very simple to implement is presented. The work described in this thesis presents solutions for collision detection and collision avoidance for safer use of robots. The work is also a step towards making businesses more competitive by enabling easy integration of collision handling for industrial robots

Medical image synthesis using generative adversarial networks: towards photo-realistic image synthesis

This proposed work addresses the photo-realism for synthetic images. We introduced a modified generative adversarial network: StencilGAN. It is a perceptually-aware generative adversarial network that synthesizes images based on overlaid labelled masks. This technique can be a prominent solution for the scarcity of the resources in the healthcare sector