Message passing for task redistribution on sparse graphs

The problem of resource allocation in sparse graphs with real variables is studied using methods of statistical physics. An efficient distributed algorithm is devised on the basis of insight gained from the analysis and is examined using numerical simulations, showing excellent performance and full agreement with the theoretical results.

Cortical oscillatory activity associated with the perception of illusory and real visual contours

We used magnetoencephalography (MEG) to examine the nature of oscillatory brain rhythms when passively viewing both illusory and real visual contours. Three stimuli were employed: a Kanizsa triangle; a Kanizsa triangle with a real triangular contour superimposed; and a control figure in which the corner elements used to form the Kanizsa triangle were rotated to negate the formation of illusory contours. The MEG data were analysed using synthetic aperture magnetometry (SAM) to enable the spatial localisation of task-related oscillatory power changes within specific frequency bands, and the time-course of activity within given locations-of-interest was determined by calculating time-frequency plots using a Morlet wavelet transform. In contrast to earlier studies, we did not find increases in gamma activity (> 30 Hz) to illusory shapes, but instead a decrease in 10–30 Hz activity approximately 200 ms after stimulus presentation. The reduction in oscillatory activity was primarily evident within extrastriate areas, including the lateral occipital complex (LOC). Importantly, this same pattern of results was evident for each stimulus type. Our results further highlight the importance of the LOC and a network of posterior brain regions in processing visual contours, be they illusory or real in nature. The similarity of the results for both real and illusory contours, however, leads us to conclude that the broadband (< 30 Hz) decrease in power we observed is more likely to reflect general changes in visual attention than neural computations specific to processing visual contours.

The design of a real-time nowcasting system for localised weather

National meteorological offices are largely concerned with synoptic-scale forecasting where weather predictions are produced for a whole country for 24 hours ahead. In practice, many local organisations (such as emergency services, construction industries, forestry, farming, and sports) require only local short-term, bespoke, weather predictions and warnings. This thesis shows that the less-demanding requirements do not require exceptional computing power and can be met by a modern, desk-top system which monitors site-specific ground conditions (such as temperature, pressure, wind speed and direction, etc) augmented with above ground information from satellite images to produce `nowcasts'. The emphasis in this thesis has been towards the design of such a real-time system for nowcasting. Local site-specific conditions are monitored using a custom-built, stand alone, Motorola 6809 based sub-system. Above ground information is received from the METEOSAT 4 geo-stationary satellite using a sub-system based on a commercially available equipment. The information is ephemeral and must be captured in real-time. The real-time nowcasting system for localised weather handles the data as a transparent task using the limited capabilities of the PC system. Ground data produces a time series of measurements at a specific location which represents the past-to-present atmospheric conditions of the particular site from which much information can be extracted. The novel approach adopted in this thesis is one of constructing stochastic models based on the AutoRegressive Integrated Moving Average (ARIMA) technique. The satellite images contain features (such as cloud formations) which evolve dynamically and may be subject to movement, growth, distortion, bifurcation, superposition, or elimination between images. The process of extracting a weather feature, following its motion and predicting its future evolution involves algorithms for normalisation, partitioning, filtering, image enhancement, and correlation of multi-dimensional signals in different domains. To limit the processing requirements, the analysis in this thesis concentrates on an `area of interest'. By this rationale, only a small fraction of the total image needs to be processed, leading to a major saving in time. The thesis also proposes an extention to an existing manual cloud classification technique for its implementation in automatically classifying a cloud feature over the `area of interest' for nowcasting using the multi-dimensional signals.

Deep hole drilling with twist drills: aspects of the CNC process and its real time monitoring and adaptive control

Deep hole drilling is one of the most complicated metal cutting processes and one of the most difficult to perform on CNC machine-tools or machining centres under conditions of limited manpower or unmanned operation. This research work investigates aspects of the deep hole drilling process with small diameter twist drills and presents a prototype system for real time process monitoring and adaptive control; two main research objectives are fulfilled in particular : First objective is the experimental investigation of the mechanics of the deep hole drilling process, using twist drills without internal coolant supply, in the range of diarneters Ø 2.4 to Ø4.5 mm and working length up to 40 diameters. The definition of the problems associated with the low strength of these tools and the study of mechanisms of catastrophic failure which manifest themselves well before and along with the classic mechanism of tool wear. The relationships between drilling thrust and torque with the depth of penetration and the various machining conditions are also investigated and the experimental evidence suggests that the process is inherently unstable at depths beyond a few diameters. Second objective is the design and implementation of a system for intelligent CNC deep hole drilling, the main task of which is to ensure integrity of the process and the safety of the tool and the workpiece. This task is achieved by means of interfacing the CNC system of the machine tool to an external computer which performs the following functions: On-line monitoring of the drilling thrust and torque, adaptive control of feed rate, spindle speed and tool penetration (Z-axis), indirect monitoring of tool wear by pattern recognition of variations of the drilling thrust with cumulative cutting time and drilled depth, operation as a data base for tools and workpieces and finally issuing of alarms and diagnostic messages.

In-Motes EYE:a real time application for automobiles in wireless sensor networks

Wireless sensor networks have been identified as one of the key technologies for the 21st century. In order to overcome their limitations such as fault tolerance and conservation of energy, we propose a middleware solution, In-Motes. In-Motes stands as a fault tolerant platform for deploying and monitoring applications in real time offers a number of possibilities for the end user giving him in parallel the freedom to experiment with various parameters, in an effort the deployed applications to run in an energy efficient manner inside the network. The proposed scheme is evaluated through the In-Motes EYE application, aiming to test its merits under real time conditions. In-Motes EYE application which is an agent based real time In-Motes application developed for sensing acceleration variations in an environment. The application was tested in a prototype area, road alike, for a period of four months.

A near real-time water surface detection method based on HSV transformation of MODIS multi-Spectral time series data

In the face of global population growth and the uneven distribution of water supply, a better knowledge of the spatial and temporal distribution of surface water resources is critical. Remote sensing provides a synoptic view of ongoing processes, which addresses the intricate nature of water surfaces and allows an assessment of the pressures placed on aquatic ecosystems. However, the main challenge in identifying water surfaces from remotely sensed data is the high variability of spectral signatures, both in space and time. In the last 10 years only a few operational methods have been proposed to map or monitor surface water at continental or global scale, and each of them show limitations. The objective of this study is to develop and demonstrate the adequacy of a generic multi-temporal and multi-spectral image analysis method to detect water surfaces automatically, and to monitor them in near-real-time. The proposed approach, based on a transformation of the RGB color space into HSV, provides dynamic information at the continental scale. The validation of the algorithm showed very few omission errors and no commission errors. It demonstrates the ability of the proposed algorithm to perform as effectively as human interpretation of the images. The validation of the permanent water surface product with an independent dataset derived from high resolution imagery, showed an accuracy of 91.5% and few commission errors. Potential applications of the proposed method have been identified and discussed. The methodology that has been developed 27 is generic: it can be applied to sensors with similar bands with good reliability, and minimal effort. Moreover, this experiment at continental scale showed that the methodology is efficient for a large range of environmental conditions. Additional preliminary tests over other continents indicate that the proposed methodology could also be applied at the global scale without too many difficulties

The multiple pheromone ant clustering algorithm and its application to real world domains

The Multiple Pheromone Ant Clustering Algorithm (MPACA) models the collective behaviour of ants to find clusters in data and to assign objects to the most appropriate class. It is an ant colony optimisation approach that uses pheromones to mark paths linking objects that are similar and potentially members of the same cluster or class. Its novelty is in the way it uses separate pheromones for each descriptive attribute of the object rather than a single pheromone representing the whole object. Ants that encounter other ants frequently enough can combine the attribute values they are detecting, which enables the MPACA to learn influential variable interactions. This paper applies the model to real-world data from two domains. One is logistics, focusing on resource allocation rather than the more traditional vehicle-routing problem. The other is mental-health risk assessment. The task for the MPACA in each domain was to predict class membership where the classes for the logistics domain were the levels of demand on haulage company resources and the mental-health classes were levels of suicide risk. Results on these noisy real-world data were promising, demonstrating the ability of the MPACA to find patterns in the data with accuracy comparable to more traditional linear regression models. © 2013 Polish Information Processing Society.

Mobility changes in older age: neuropsychological, neurophysiological and cognitive predictors of successful adaptation in a real world scenario

The aims of this thesis were to investigate the neuropsychological, neurophysiological, and cognitive contributors to mobility changes with increasing age. In a series of studies with adults aged 45-88 years, unsafe pedestrian behaviour and falls were investigated in relation to i) cognitive functions (including response time variability, executive function, and visual attention tests), ii) mobility assessments (including gait and balance and using motion capture cameras), iii) motor initiation and pedestrian road crossing behavior (using a simulated pedestrian road scene), iv) neuronal and functional brain changes (using a computer based crossing task with magnetoencephalography), and v) quality of life questionnaires (including fear of falling and restricted range of travel). Older adults are more likely to be fatally injured at the far-side of the road compared to the near-side of the road, however, the underlying mobility and cognitive processes related to lane-specific (i.e. near-side or far-side) pedestrian crossing errors in older adults is currently unknown. The first study explored cognitive, motor initiation, and mobility predictors of unsafe pedestrian crossing behaviours. The purpose of the first study (Chapter 2) was to determine whether collisions at the near-side and far-side would be differentially predicted by mobility indices (such as walking speed and postural sway), motor initiation, and cognitive function (including spatial planning, visual attention, and within participant variability) with increasing age. The results suggest that near-side unsafe pedestrian crossing errors are related to processing speed, whereas far-side errors are related to spatial planning difficulties. Both near-side and far-side crossing errors were related to walking speed and motor initiation measures (specifically motor initiation variability). The salient mobility predictors of unsafe pedestrian crossings determined in the above study were examined in Chapter 3 in conjunction with the presence of a history of falls. The purpose of this study was to determine the extent to which walking speed (indicated as a salient predictor of unsafe crossings and start-up delay in Chapter 2), and previous falls can be predicted and explained by age-related changes in mobility and cognitive function changes (specifically within participant variability and spatial ability). 53.2% of walking speed variance was found to be predicted by self-rated mobility score, sit-to-stand time, motor initiation, and within participant variability. Although a significant model was not found to predict fall history variance, postural sway and attentional set shifting ability was found to be strongly related to the occurrence of falls within the last year. Next in Chapter 4, unsafe pedestrian crossing behaviour and pedestrian predictors (both mobility and cognitive measures) from Chapter 2 were explored in terms of increasing hemispheric laterality of attentional functions and inter-hemispheric oscillatory beta power changes associated with increasing age. Elevated beta (15-35 Hz) power in the motor cortex prior to movement, and reduced beta power post-movement has been linked to age-related changes in mobility. In addition, increasing recruitment of both hemispheres has been shown to occur and be beneficial to perform similarly to younger adults in cognitive tasks (Cabeza, Anderson, Locantore, & McIntosh, 2002). It has been hypothesised that changes in hemispheric neural beta power may explain the presence of more pedestrian errors at the farside of the road in older adults. The purpose of the study was to determine whether changes in age-related cortical oscillatory beta power and hemispheric laterality are linked to unsafe pedestrian behaviour in older adults. Results indicated that pedestrian errors at the near-side are linked to hemispheric bilateralisation, and neural overcompensation post-movement, 4 whereas far-side unsafe errors are linked to not employing neural compensation methods (hemispheric bilateralisation). Finally, in Chapter 5, fear of falling, life space mobility, and quality of life in old age were examined to determine their relationships with cognition, mobility (including fall history and pedestrian behaviour), and motor initiation. In addition to death and injury, mobility decline (such as pedestrian errors in Chapter 2, and falls in Chapter 3) and cognition can negatively affect quality of life and result in activity avoidance. Further, number of falls in Chapter 3 was not significantly linked to mobility and cognition alone, and may be further explained by a fear of falling. The objective of the above study (Study 2, Chapter 3) was to determine the role of mobility and cognition on fear of falling and life space mobility, and the impact on quality of life measures. Results indicated that missing safe pedestrian crossing gaps (potentially indicating crossing anxiety) and mobility decline were consistent predictors of fear of falling, reduced life space mobility, and quality of life variance. Social community (total number of close family and friends) was also linked to life space mobility and quality of life. Lower cognitive functions (particularly processing speed and reaction time) were found to predict variance in fear of falling and quality of life in old age. Overall, the findings indicated that mobility decline (particularly walking speed or walking difficulty), processing speed, and intra-individual variability in attention (including motor initiation variability) are salient predictors of participant safety (mainly pedestrian crossing errors) and wellbeing with increasing age. More research is required to produce a significant model to explain the number of falls.

Introducing a pilot data collection model for real-time evaluation of data redundancy

In order to reduce serious health incidents, individuals with high risks need to be identified as early as possible so that effective intervention and preventive care can be provided. This requires regular and efficient assessments of risk within communities that are the first point of contacts for individuals. Clinical Decision Support Systems CDSSs have been developed to help with the task of risk assessment, however such systems and their underpinning classification models are tailored towards those with clinical expertise. Communities where regular risk assessments are required lack such expertise. This paper presents the continuation of GRiST research team efforts to disseminate clinical expertise to communities. Based on our earlier published findings, this paper introduces the framework and skeleton for a data collection and risk classification model that evaluates data redundancy in real-time, detects the risk-informative data and guides the risk assessors towards collecting those data. By doing so, it enables non-experts within the communities to conduct reliable Mental Health risk triage.