Real-time Microphone Array Processing for Sound-field Analysis and Perceptually Motivated Reproduction

This thesis details real-time implementations of sound-field analysis and perceptually motivated reproduction methods for visualisation and auralisation purposes. For the former, various methods for visualising the relative distribution of sound energy from one point in space are investigated and contrasted; including a novel reformulation of the cross-pattern coherence (CroPaC) algorithm, which integrates a new side-lobe suppression technique. Whereas for auralisation applications, listening tests were conducted to compare ambisonics reproduction with a novel headphone formulation of the directional audio coding (DirAC) method. The results indicate that the side-lobe suppressed CroPaC method offers greater spatial selectivity in reverberant conditions compared with other popular approaches, and that the new DirAC formulation yields higher perceived spatial accuracy when compared to the ambisonics method

Plenoptic Signal Processing for Robust Vision in Field Robotics

This thesis proposes the use of plenoptic cameras for improving the robustness and simplicity of machine vision in field robotics applications. Dust, rain, fog, snow, murky water and insufficient light can cause even the most sophisticated vision systems to fail. Plenoptic cameras offer an appealing alternative to conventional imagery by gathering significantly more light over a wider depth of field, and capturing a rich 4D light field structure that encodes textural and geometric information. The key contributions of this work lie in exploring the properties of plenoptic signals and developing algorithms for exploiting them. It lays the groundwork for the deployment of plenoptic cameras in field robotics by establishing a decoding, calibration and rectification scheme appropriate to compact, lenslet-based devices. Next, the frequency-domain shape of plenoptic signals is elaborated and exploited by constructing a filter which focuses over a wide depth of field rather than at a single depth. This filter is shown to reject noise, improving contrast in low light and through attenuating media, while mitigating occluders such as snow, rain and underwater particulate matter. Next, a closed-form generalization of optical flow is presented which directly estimates camera motion from first-order derivatives. An elegant adaptation of this "plenoptic flow" to lenslet-based imagery is demonstrated, as well as a simple, additive method for rendering novel views. Finally, the isolation of dynamic elements from a static background is considered, a task complicated by the non-uniform apparent motion caused by a mobile camera. Two elegant closed-form solutions are presented dealing with monocular time-series and light field image pairs. This work emphasizes non-iterative, noise-tolerant, closed-form, linear methods with predictable and constant runtimes, making them suitable for real-time embedded implementation in field robotics applications

Plenoptic Signal Processing for Robust Vision in Field Robotics

This thesis proposes the use of plenoptic cameras for improving the robustness and simplicity of machine vision in field robotics applications. Dust, rain, fog, snow, murky water and insufficient light can cause even the most sophisticated vision systems to fail. Plenoptic cameras offer an appealing alternative to conventional imagery by gathering significantly more light over a wider depth of field, and capturing a rich 4D light field structure that encodes textural and geometric information. The key contributions of this work lie in exploring the properties of plenoptic signals and developing algorithms for exploiting them. It lays the groundwork for the deployment of plenoptic cameras in field robotics by establishing a decoding, calibration and rectification scheme appropriate to compact, lenslet-based devices. Next, the frequency-domain shape of plenoptic signals is elaborated and exploited by constructing a filter which focuses over a wide depth of field rather than at a single depth. This filter is shown to reject noise, improving contrast in low light and through attenuating media, while mitigating occluders such as snow, rain and underwater particulate matter. Next, a closed-form generalization of optical flow is presented which directly estimates camera motion from first-order derivatives. An elegant adaptation of this "plenoptic flow" to lenslet-based imagery is demonstrated, as well as a simple, additive method for rendering novel views. Finally, the isolation of dynamic elements from a static background is considered, a task complicated by the non-uniform apparent motion caused by a mobile camera. Two elegant closed-form solutions are presented dealing with monocular time-series and light field image pairs. This work emphasizes non-iterative, noise-tolerant, closed-form, linear methods with predictable and constant runtimes, making them suitable for real-time embedded implementation in field robotics applications

A Novel Approach for Continuous Speech Tracking and Dynamic Time Warping. Adaptive Framing Based Continuous Speech Similarity Measure and Dynamic Time Warping using Kalman Filter and Dynamic State Model

Dynamic speech properties such as time warping, silence removal and background noise interference are the most challenging issues in continuous speech signal matching. Among all of them, the time warped speech signal matching is of great interest and has been a tough challenge for the researchers. An adaptive framing based continuous speech tracking and similarity measurement approach is introduced in this work following a comprehensive research conducted in the diverse areas of speech processing. A dynamic state model is introduced based on system of linear motion equations which models the input (test) speech signal frame as a unidirectional moving object along the template speech signal. The most similar corresponding frame position in the template speech is estimated which is fused with a feature based similarity observation and the noise variances using a Kalman filter. The Kalman filter provides the final estimated frame position in the template speech at current time which is further used for prediction of a new frame size for the next step. In addition, a keyword spotting approach is proposed by introducing wavelet decomposition based dynamic noise filter and combination of beliefs. The Dempster’s theory of belief combination is deployed for the first time in relation to keyword spotting task. Performances for both; speech tracking and keyword spotting approaches are evaluated using the statistical metrics and gold standards for the binary classification. Experimental results proved the superiority of the proposed approaches over the existing methods.The appendices files are not available online

The suitability of the dendritic cell algorithm for robotic security applications

The implementation and running of physical security systems is costly and potentially hazardous for those employed to patrol areas of interest. From a technial perspective, the physical security problem can be seen as minimising the probability that intruders and other anomalous events will occur unobserved. A robotic solution is proposed using an artificial immune system, traditionally applied to software security, to identify threats and hazards: the dendritic cell algorithm. It is demonstrated that the migration from the software world to the hardware world is achievable for this algorithm and key properties of the resulting system are explored empirically and theoretically. It is found that the algorithm has a hitherto unknown frequency-dependent component, making it ideal for filtering out sensor noise. Weaknesses of the algorithm are also discovered, by mathematically phrasing the signal processing phase as a collection of linear classifiers. It is concluded that traditional machine learning approaches are likely to outperform the implemented system in its current form. However, it is also observed that the algorithm’s inherent filtering characteristics make modification, rather than rejection, the most beneficial course of action. Hybridising the dendritic cell algorithm with more traditional machine learning techniques, through the introduction of a training phase and using a non-linear classification phase is suggested as a possible future direction

The suitability of the dendritic cell algorithm for robotic security applications

The implementation and running of physical security systems is costly and potentially hazardous for those employed to patrol areas of interest. From a technial perspective, the physical security problem can be seen as minimising the probability that intruders and other anomalous events will occur unobserved. A robotic solution is proposed using an artificial immune system, traditionally applied to software security, to identify threats and hazards: the dendritic cell algorithm. It is demonstrated that the migration from the software world to the hardware world is achievable for this algorithm and key properties of the resulting system are explored empirically and theoretically. It is found that the algorithm has a hitherto unknown frequency-dependent component, making it ideal for filtering out sensor noise. Weaknesses of the algorithm are also discovered, by mathematically phrasing the signal processing phase as a collection of linear classifiers. It is concluded that traditional machine learning approaches are likely to outperform the implemented system in its current form. However, it is also observed that the algorithm’s inherent filtering characteristics make modification, rather than rejection, the most beneficial course of action. Hybridising the dendritic cell algorithm with more traditional machine learning techniques, through the introduction of a training phase and using a non-linear classification phase is suggested as a possible future direction

Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

A machine vision approach to rock fragmentation analysis

Bibliography: p. 217-223.[pp. i - iv missing] This thesis is concerned with the development of an instrument for the purpose of performing online measurement of rock size distribution using machine vision. This instrument has application in the gold mining industry where it could be used to measure the fragmentation of gold ore on a conveyor belt feed to an autogenous mill, for the purpose of controlling the mill. The gold ore can range in size from fine material (< 20mm) to very large rocks (0.5m). A machine vision approach is only capable of directly measuring the projected area of particles at the surface of the rock-stream. A volume distribution has to be estimated from this using a stereological method. These methods have been investigated previously and are typically error prone. They have not been investigated here. An investigation of lighting demonstrates that a diffuse lighting arrangement is suitable for this application. This would have two advantages: specular reflection from wet material is suppressed; and intensity values can be used to predict the orientation of the surface of the particles. A computational structure has been developed to identify and delineate rocks in an image for the purpose of measuring their areas. It is based on the human visual system in that it consists of a low-level preattentive vision stage and a higher-level stage of attention focusing. Multiscalar image processing techniques have also been integrated in order to improve the detection of rocks across a wide range of sizes. A performance advantage can be obtained in this way because all the algorithms can be better matched to the size of the objects being detected. Results have been obtained with an average true detection rate of 69 and a further close miss rate of 14 , with very few false alarms. The overall result is that the measured projected area distribution closely matches the true value for each test image

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

Three-Dimensional Geometry Inference of Convex and Non-Convex Rooms using Spatial Room Impulse Responses

This thesis presents research focused on the problem of geometry inference for both convex- and non-convex-shaped rooms, through the analysis of spatial room impulse responses. Current geometry inference methods are only applicable to convex-shaped rooms, requiring between 6--78 discretely spaced measurement positions, and are only accurate under certain conditions, such as a first-order reflection for each boundary being identifiable across all, or some subset of, these measurements. This thesis proposes that by using compact microphone arrays capable of capturing spatiotemporal information, boundary locations, and hence room shape for both convex and non-convex cases, can be inferred, using only a sufficient number of measurement positions to ensure each boundary has a first-order reflection attributable to, and identifiable in, at least one measurement. To support this, three research areas are explored. Firstly, the accuracy of direction-of-arrival estimation for reflections in binaural room impulse responses is explored, using a state-of-the-art methodology based on binaural model fronted neural networks. This establishes whether a two-microphone array can produce accurate enough direction-of-arrival estimates for geometry inference. Secondly, a spherical microphone array based spatiotemporal decomposition workflow for analysing reflections in room impulse responses is explored. This establishes that simultaneously arriving reflections can be individually detected, relaxing constraints on measurement positions. Finally, a geometry inference method applicable to both convex and more complex non-convex shaped rooms is proposed. Therefore, this research expands the possible scenarios in which geometry inference can be successfully applied at a level of accuracy comparable to existing work, through the use of commonly used compact microphone arrays. Based on these results, future improvements to this approach are presented and discussed in detail