BabelFuse data fusion unit with precision wireless clock synchronisation

A significant issue encountered when fusing data received from multiple sensors is the accuracy of the timestamp associated with each piece of data. This is particularly important in applications such as Simultaneous Localisation and Mapping (SLAM) where vehicle velocity forms an important part of the mapping algorithms; on fastmoving vehicles, even millisecond inconsistencies in data timestamping can produce errors which need to be compensated for. The timestamping problem is compounded in a robot swarm environment due to the use of non-deterministic readily-available hardware (such as 802.11-based wireless) and inaccurate clock synchronisation protocols (such as Network Time Protocol (NTP)). As a result, the synchronisation of the clocks between robots can be out by tens-to-hundreds of milliseconds making correlation of data difficult and preventing the possibility of the units performing synchronised actions such as triggering cameras or intricate swarm manoeuvres. In this thesis, a complete data fusion unit is designed, implemented and tested. The unit, named BabelFuse, is able to accept sensor data from a number of low-speed communication buses (such as RS232, RS485 and CAN Bus) and also timestamp events that occur on General Purpose Input/Output (GPIO) pins referencing a submillisecondaccurate wirelessly-distributed "global" clock signal. In addition to its timestamping capabilities, it can also be used to trigger an attached camera at a predefined start time and frame rate. This functionality enables the creation of a wirelessly-synchronised distributed image acquisition system over a large geographic area; a real world application for this functionality is the creation of a platform to facilitate wirelessly-distributed 3D stereoscopic vision. A ‘best-practice’ design methodology is adopted within the project to ensure the final system operates according to its requirements. Initially, requirements are generated from which a high-level architecture is distilled. This architecture is then converted into a hardware specification and low-level design, which is then manufactured. The manufactured hardware is then verified to ensure it operates as designed and firmware and Linux Operating System (OS) drivers are written to provide the features and connectivity required of the system. Finally, integration testing is performed to ensure the unit functions as per its requirements. The BabelFuse System comprises of a single Grand Master unit which is responsible for maintaining the absolute value of the "global" clock. Slave nodes then determine their local clock o.set from that of the Grand Master via synchronisation events which occur multiple times per-second. The mechanism used for synchronising the clocks between the boards wirelessly makes use of specific hardware and a firmware protocol based on elements of the IEEE-1588 Precision Time Protocol (PTP). With the key requirement of the system being submillisecond-accurate clock synchronisation (as a basis for timestamping and camera triggering), automated testing is carried out to monitor the o.sets between each Slave and the Grand Master over time. A common strobe pulse is also sent to each unit for timestamping; the correlation between the timestamps of the di.erent units is used to validate the clock o.set results. Analysis of the automated test results show that the BabelFuse units are almost threemagnitudes more accurate than their requirement; clocks of the Slave and Grand Master units do not di.er by more than three microseconds over a running time of six hours and the mean clock o.set of Slaves to the Grand Master is less-than one microsecond. The common strobe pulse used to verify the clock o.set data yields a positive result with a maximum variation between units of less-than two microseconds and a mean value of less-than one microsecond. The camera triggering functionality is verified by connecting the trigger pulse output of each board to a four-channel digital oscilloscope and setting each unit to output a 100Hz periodic pulse with a common start time. The resulting waveform shows a maximum variation between the rising-edges of the pulses of approximately 39¥ìs, well below its target of 1ms.

Polycaprolactone-based scaffold plus recombinant human bone morphogenic protein rhBMP-2) in a sheep thoracic spine fusion model

Adolescent idiopathic scoliosis is a complex three dimensional deformity affecting 2-3% of the general population. The resulting spinal deformity consists of coronal curvature, hypokyphosis of the thoracic spine and vertebral rotation in the axial plane with posterior elements turned into the curve concavity. The potential for curve progression is heightened during the adolescent growth spurt. Success of scoliosis deformity correction depends on solid bony fusion between adjacent vertebrae after the intervertebral (IV) discs have been surgically cleared and the disc spaces filled with graft material. Recently a bioactive and resorbable scaffold fabricated from medical grade polycaprolactone has been developed for bone regeneration at load bearing sites. Combined with rhBMP-2, this has been shown to be successful in acting as a bone graft substitute in a porcine lumbar interbody fusion model when compared to autologous bone graft alone. The study aimed to establish a large animal thoracic spine interbody fusion model, develop spine biodegradable scaffolds (PCL) in combination with biologics (rhBMP-2) and to establish a platform for research into spine tissue engineering constructs. Preliminary results demonstrate higher grades of radiologically evident bony fusion across all levels when comparing fusion scores between the 3 and 6 month postop groups at the PCL CaP coated scaffold level, which is observed to be a similar grade to autograft, while no fusion is seen at the scaffold only level. Results to date suggest that the combination of rhBMP-2 and scaffold engineering actively promotes bone formation, laying the basis of a viable tissue engineered constructs.

Biomechanical performance of polycaprolactone (PCL)-based scaffold with rhBMP-2 in a sheep thoracic spine fusion model

Adolescent idiopathic scoliosis is a complex three dimensional deformity affecting 2-3% of the general population. Resulting spine deformities include progressive coronal curvature, hypokyphosis, or frank lordosis in the thoracic spine and vertebral rotation in the axial plane with posterior elements turned into the curve concavity. The potential for curve progression is heightened during the adolescent growth spurt. Success of scoliosis deformity correction depends on solid bony fusion between adjacent vertebrae after the intervertebral discs have been surgically cleared and the disc spaces filled with graft material. Problems with bone graft harvest site morbidity as well as limited bone availability have led to the search for bone graft substitutes. Recently, a bioactive and resorbable scaffold fabricated from medical grade polycaprolactone (PCL) has been developed for bone regeneration at load bearing sites. Combined with recombinant human bone morphogenic protein–2 (rhBMP-2), this has been shown to be successful in acting as a bone graft substitute in acting as a bone graft substitute in a porcine lumbar interbody fusion model when compared to autologous bone graft. This in vivo sheep study intends to evaluate the suitability of a custom designed medical grade PCL scaffold in combination with rhBMP-2 as a bone graft substitute in the setting of mini–thoracotomy surgery as a platform for ongoing research to benefit patients with adolescent idiopathic scoliosis.

Towards brain-based sensor fusion for navigating robots

Current state of the art robot mapping and navigation systems produce impressive performance under a narrow range of robot platform, sensor and environmental conditions, in contrast to animals such as rats that produce “good enough” maps that enable them to function under an incredible range of situations. In this paper we present a rat-inspired featureless sensor-fusion system that assesses the usefulness of multiple sensor modalities based on their utility and coherence for place recognition during a navigation task, without knowledge as to the type of sensor. We demonstrate the system on a Pioneer robot in indoor and outdoor environments with abrupt lighting changes. Through dynamic weighting of the sensors, the system is able to perform correct place recognition and mapping where the static sensor weighting approach fails.

Measuring a destination’s brand equity between 2003 and 2012 using the consumer-based brand equity (CBBE) hierarchy

Given the increasing investments being made in brand development by destination marketing organisations (DMO) since the 1990s, including rebranding and repositioning, more research is needed to enhance understanding of how to effectively monitor destination brand performance over time. This paper reports the results of a study of brand performance of a competitive set of destinations, in their most important market, between 2003 and 2012. Brand performance was measured from the perspective of consumer perceptions, based on the concept of consumer-based brand equity (CBBE). A structured questionnaire was administered to different samples in 2003, 2007 and 2012. The results indicated minimal changes in perceptions of the five destinations over the 10 year period. Due to the commonality of challenges faced by DMOs worldwide, it is suggested the CBBE hierarchy provides destination marketers with a practical tool for evaluating brand performance over time; in terms of measures of effectiveness of past marketing communications, as well as indicators of future performance. In addition, and importantly, CBBE also provides transparent accountability measures for stakeholders. While the topic of destination image has been one of the most popular in the tourism literature, there has been a paucity of research published in relation to the temporal aspect of consumer perceptions. This is a rare investigation into the measurement of perceptions of destinations over a 10 year period.

A context-fusion framework for enhancing context-awareness on mobile application

Mobile devices and smartphones have become a significant communication channel for everyday life. The sensing capabilities of mobile devices are expanding rapidly, and sensors embedded in these devices are cheaper and more powerful than before. It is evident that mobile devices have become the most suitable candidates to sense contextual information without needing extra tools. However, current research shows only a limited number of sensors are being explored and investigated. As a result, it still needs to be clarified what forms of contextual information extracted from mo- bile sensors are useful. Therefore, this research investigates the context sensing using current mobile sensors, the study follows experimental methods and sensor data is evaluated and synthesised, in order to deduce the value of various sensors and combinations of sensor for the use in context-aware mobile applications. This study aims to develop a context fusion framework that will enhance the context-awareness on mobile applications, as well as exploring innovative techniques for context sensing on smartphone devices.

Establishment and characterisation of an open mini-thoracotomy surgical approach to an ovine thoracic spine fusion model

Background A large animal model is required for assessment of minimally invasive, tissue engineering based approaches to thoracic spine fusion, with relevance to deformity correction surgery for human adolescent idiopathic scoliosis. Here we develop a novel open mini–thoracotomy approach in an ovine model of thoracic interbody fusion which allows assessment of various fusion constructs, with a focus on novel, tissue engineering based interventions. Methods The open mini-thoracotomy surgical approach was developed through a series of mock surgeries, and then applied in a live sheep study. Customized scaffolds were manufactured to conform with intervertebral disc space clearances required of the study. Twelve male Merino sheep aged 4 to 6 years and weighing 35 – 45 kg underwent the abovementioned procedure and were divided into two groups of six sheep at survival timelines of 6 and 12 months. Each sheep underwent a 3-level discectomy (T6/7, T8/9 and T10/11) with randomly allocated implantation of a different graft substitute at each of the three levels; (i) polycaprolactone (PCL) based scaffold plus 0.54μg rhBMP-2, (ii) PCL-based scaffold alone or (iii) autograft. The sheep were closely monitored post- operatively for signs of pain (i.e. gait abnormalities/ teeth gnawing/ social isolation). Fusion assessments were conducted post-sacrifice using Computed Tomography and hard-tissue histology. All scientific work was undertaken in accordance with the study protocol has been approved by the Institute's committee on animal research. Results. All twelve sheep were successfully operated on and reached the allotted survival timelines, thereby demonstrating the feasibility of the surgical procedure and post-operative care. There were no significant complications and during the post-operative period the animals did not exhibit marked signs of distress according to the described assessment criteria. Computed Tomographic scanning demonstrated higher fusion grades in the rhBMP-2 plus PCL-based scaffold group in comparison to either PCL-based scaffold alone or autograft. These results were supported by histological evaluation of the respective groups. Conclusion. This novel open mini-thoracotomy surgical approach to the ovine thoracic spine represents a safe surgical method which can reproducibly form the platform for research into various spine tissue engineered constructs (TEC) and their fusion promoting properties.

Biological performance of a polycaprolactone-based scaffold plus recombinant human morphogenetic protein-2 (rhBMP-2) in an ovine thoracic interbody fusion model

Introduction. We develop a sheep thoracic spine interbody fusion model to study the suitability of polycaprolactone-based scaffold and recombinant human bone morphogenetic protein-2 (rhBMP-2) as a bone graft substitute within the thoracic spine. The surgical approach is a mini- open thoracotomy with relevance to minimally invasive deformity correction surgery for adolescent idiopathic scoliosis. To date there are no studies examining the use of this biodegradable implant in combination with biologics in a sheep thoracic spine model. Methods. In the present study, six sheep underwent a 3-level (T6/7, T8/9 and T10/11) discectomy with randomly allocated implantation of a different graft substitute at each of the three levels; (i) calcium phosphate (CaP) coated polycaprolactone based scaffold plus 0.54µg rhBMP-2, (ii) CaP coated PCL- based scaffold alone or (iii) autograft (mulched rib head). Fusion was assessed at six months post-surgery. Results. Computed Tomographic scanning demonstrated higher fusion grades in the rhBMP-2 plus PCL- based scaffold group in comparison to either PCL-based scaffold alone or autograft. These results were supported by histological evaluations of the respective groups. Biomechanical testing revealed significantly higher stiffness for the rhBMP-2 plus PCL- based scaffold group in all loading directions in comparison to the other two groups. Conclusions. The results of this study demonstrate that rhBMP-2 plus PCL-based scaffold is a viable bone graft substitute, providing an optimal environment for thoracic interbody spinal fusion in a large animal model.

Classifier selection using sequential error ratio criterion for multi-instance and multi-sample fusion

Classifier selection is a problem encountered by multi-biometric systems that aim to improve performance through fusion of decisions. A particular decision fusion architecture that combines multiple instances (n classifiers) and multiple samples (m attempts at each classifier) has been proposed in previous work to achieve controlled trade-off between false alarms and false rejects. Although analysis on text-dependent speaker verification has demonstrated better performance for fusion of decisions with favourable dependence compared to statistically independent decisions, the performance is not always optimal. Given a pool of instances, best performance with this architecture is obtained for certain combination of instances. Heuristic rules and diversity measures have been commonly used for classifier selection but it is shown that optimal performance is achieved for the `best combination performance' rule. As the search complexity for this rule increases exponentially with the addition of classifiers, a measure - the sequential error ratio (SER) - is proposed in this work that is specifically adapted to the characteristics of sequential fusion architecture. The proposed measure can be used to select a classifier that is most likely to produce a correct decision at each stage. Error rates for fusion of text-dependent HMM based speaker models using SER are compared with other classifier selection methodologies. SER is shown to achieve near optimal performance for sequential fusion of multiple instances with or without the use of multiple samples. The methodology applies to multiple speech utterances for telephone or internet based access control and to other systems such as multiple finger print and multiple handwriting sample based identity verification systems.

Multi-level information fusion for environment aware robotic navigation

This thesis develops the hardware and software framework for an integrated navigation system. Dynamic data fusion algorithms are used to develop a system with a high level of resistance to the typical problems that affect standard navigation systems.

Sequential decision fusion of multibiometrics applied to text-dependent speaker verification for controlled errors

Reliability of the performance of biometric identity verification systems remains a significant challenge. Individual biometric samples of the same person (identity class) are not identical at each presentation and performance degradation arises from intra-class variability and inter-class similarity. These limitations lead to false accepts and false rejects that are dependent. It is therefore difficult to reduce the rate of one type of error without increasing the other. The focus of this dissertation is to investigate a method based on classifier fusion techniques to better control the trade-off between the verification errors using text-dependent speaker verification as the test platform. A sequential classifier fusion architecture that integrates multi-instance and multisample fusion schemes is proposed. This fusion method enables a controlled trade-off between false alarms and false rejects. For statistically independent classifier decisions, analytical expressions for each type of verification error are derived using base classifier performances. As this assumption may not be always valid, these expressions are modified to incorporate the correlation between statistically dependent decisions from clients and impostors. The architecture is empirically evaluated by applying the proposed architecture for text dependent speaker verification using the Hidden Markov Model based digit dependent speaker models in each stage with multiple attempts for each digit utterance. The trade-off between the verification errors is controlled using the parameters, number of decision stages (instances) and the number of attempts at each decision stage (samples), fine-tuned on evaluation/tune set. The statistical validation of the derived expressions for error estimates is evaluated on test data. The performance of the sequential method is further demonstrated to depend on the order of the combination of digits (instances) and the nature of repetitive attempts (samples). The false rejection and false acceptance rates for proposed fusion are estimated using the base classifier performances, the variance in correlation between classifier decisions and the sequence of classifiers with favourable dependence selected using the 'Sequential Error Ratio' criteria. The error rates are better estimated by incorporating user-dependent (such as speaker-dependent thresholds and speaker-specific digit combinations) and class-dependent (such as clientimpostor dependent favourable combinations and class-error based threshold estimation) information. The proposed architecture is desirable in most of the speaker verification applications such as remote authentication, telephone and internet shopping applications. The tuning of parameters - the number of instances and samples - serve both the security and user convenience requirements of speaker-specific verification. The architecture investigated here is applicable to verification using other biometric modalities such as handwriting, fingerprints and key strokes.

Deconstructing and reconstructing the capability hierarchy

In this article we develop a hierarchical framework of ordinary capabilities, dynamic functional capabilities, and dynamic learning capabilities. These three levels of capabilities differ across four interdependent internal dimensions of predominant resources, routine patterning, learning, and strategic intent. The levels are also influenced by external environmental velocity. This framework progresses the ongoing debate surrounding the capability hierarchy and offers a novel view of capabilities. We also provide direction regarding how the framework can lead future research toward a validated measurable model that contributes to solving the definitional and associated measurement debates around ordinary and dynamic capabilities.

Brain-inspired sensor fusion for navigating robots

Current state of the art robot mapping and navigation systems produce impressive performance under a narrow range of robot platform, sensor and environmental conditions, in contrast to animals such as rats that produce “good enough” maps that enable them to function under an incredible range of situations. In this paper we present a rat-inspired featureless sensor-fusion system that assesses the usefulness of multiple sensor modalities based on their utility and coherence for place recognition, without knowledge as to the type of sensor. We demonstrate the system on a Pioneer robot in indoor and outdoor environments with abrupt lighting changes. Through dynamic weighting of the sensors, the system is able to perform correct place recognition and mapping where the static sensor weighting approach fails.

Decision fusion from parts and samples for robust iris recognition

Fusion techniques can be used in biometrics to achieve higher accuracy. When biometric systems are in operation and the threat level changes, controlling the trade-off between detection error rates can reduce the impact of an attack. In a fused system, varying a single threshold does not allow this to be achieved, but systematic adjustment of a set of parameters does. In this paper, fused decisions from a multi-part, multi-sample sequential architecture are investigated for that purpose in an iris recognition system. A specific implementation of the multi-part architecture is proposed and the effect of the number of parts and samples in the resultant detection error rate is analysed. The effectiveness of the proposed architecture is then evaluated under two specific cases of obfuscation attack: miosis and mydriasis. Results show that robustness to such obfuscation attacks is achieved, since lower error rates than in the case of the non-fused base system are obtained.

Laser-radar data fusion with Gaussian Process Implicit Surfaces

This work considers the problem of building high-fidelity 3D representations of the environment from sensor data acquired by mobile robots. Multi-sensor data fusion allows for more complete and accurate representations, and for more reliable perception, especially when different sensing modalities are used. In this paper, we propose a thorough experimental analysis of the performance of 3D surface reconstruction from laser and mm-wave radar data using Gaussian Process Implicit Surfaces (GPIS), in a realistic field robotics scenario. We first analyse the performance of GPIS using raw laser data alone and raw radar data alone, respectively, with different choices of covariance matrices and different resolutions of the input data. We then evaluate and compare the performance of two different GPIS fusion approaches. The first, state-of-the-art approach directly fuses raw data from laser and radar. The alternative approach proposed in this paper first computes an initial estimate of the surface from each single source of data, and then fuses these two estimates. We show that this method outperforms the state of the art, especially in situations where the sensors react differently to the targets they perceive.