Representing 3D shape and location

The 3D shape of an object and its 3D location have traditionally thought of as very separate entities, although both can be described within a single 3D coordinate frame. Here, 3D shape and location are considered as two aspects of a view-based approach to representing depth, avoiding the use of 3D coordinate frames.

Disparity-driven vs blur-driven models of accommodation and convergence in binocular vision and intermittent strabismus

Background. Current models of concomitant, intermittent strabismus, heterophoria, convergence and accommodation anomalies are either theoretically complex or incomplete. We propose an alternative and more practical way to conceptualize clinical patterns. Methods. In each of three hypothetical scenarios (normal; high AC/A and low CA/C ratios; low AC/A and high CA/C ratios) there can be a disparity-biased or blur-biased “style”, despite identical ratios. We calculated a disparity bias index (DBI) to reflect these biases. We suggest how clinical patterns fit these scenarios and provide early objective data from small illustrative clinical groups. Results. Normal adults and children showed disparity bias (adult DBI 0.43 (95%CI 0.50-0.36), child DBI 0.20 (95%CI 0.31-0.07) (p=0.001). Accommodative esotropes showed less disparity-bias (DBI 0.03). In the high AC/A and low CA/C scenario, early presbyopes had mean DBI of 0.17 (95%CI 0.28-0.06), compared to DBI of -0.31 in convergence excess esotropes. In the low AC/A and high CA/C scenario near exotropes had mean DBI of 0.27, while we predict that non-strabismic, non-amblyopic hyperopes with good vision without spectacles will show lower DBIs. Disparity bias ranged between 1.25 and -1.67. Conclusions. Establishing disparity or blur bias, together with knowing whether convergence to target demand exceeds accommodation or vice versa explains clinical patterns more effectively than AC/A and CA/C ratios alone. Excessive bias or inflexibility in near-cue use increases risk of clinical problems. We suggest clinicians look carefully at details of accommodation and convergence changes induced by lenses, dissociation and prisms and use these to plan treatment in relation to the model.

Only connect: an annotated bibliography reflecting the breadth and diversity of systems thinking

The field of systems thinking is both broad and diverse. This paper tries to provide assistance to outsiders wishing to find out what systems thinking is and also to insiders interested in exploring areas of the systems movement other than their own. A selection of books, papers and articles is given. Each has a full reference and a brief annotation, this being an account of, and a critical comment on, its content. The selection does not aim to be definitive or authoritative and obviously displays the predilections of the authors. However, the hope is that it will convey a sense of the intellectual and practical endeavours that, to the authors, constitute systems thinking and that it may aid the exploration of the range of holistic ideas that people have found useful in thinking about and acting in the world.

Comparative analysis of performance and stability among composite cross populations, variety mixtures and pure lines of winter wheat in organic and conventional cropping systems

This study investigated the effects of increased genetic diversity in winter wheat (Triticum aestivum L.), either from hybridization across genotypes or from physical mixing of lines, on grain yield, grain quality, and yield stability in different cropping environments. Sets of pure lines (no diversity), chosen for high yielding ability or high quality, were compared with line mixtures (intermediate level of diversity), and lines crossed with each other in composite cross populations (CCPn, high diversity). Additional populations containing male sterility genes (CCPms) to increase outcrossing rates were also tested. Grain yield, grain protein content, and protein yield were measured at four sites (two organically-managed and two conventionally-managed) over three years, using seed harvested locally in each preceding year. CCPn and mixtures out-yielded the mean of the parents by 2.4% and 3.6%, respectively. These yield differences were consistent across genetic backgrounds but partly inconsistent across cropping environments and years. Yield stability measured by environmental variance was higher in CCPn and CCPms than the mean of the parents. An index of yield reliability tended to be higher in CCPn, CCPms and mixtures than the mean of the parents. Lin and Binns’ superiority values of yield and protein yield were consistently and significantly lower (i.e. better) in the CCPs than in the mean of the parents, but not different between CCPs and mixtures. However, CCPs showed greater early ground cover and plant height than mixtures. When compared with the (locally non-predictable) best-yielding pure line, CCPs and mixtures exhibited lower mean yield and somewhat lower yield reliability but comparable superiority values. Thus, establishing CCPs from smaller sets of high-performing parent lines might optimize their yielding ability. On the whole, the results demonstrate that using increased within-crop genetic diversity can produce wheat crops with improved yield stability and good yield reliability across variable and unpredictable cropping environments.

Change escalation processes and complex adaptive systems: from incremental reconfigurations to discontinuous restructuring

This study examines when “incremental” change is likely to trigger “discontinuous” change, using the lens of complex adaptive systems theory. Going beyond the simulations and case studies through which complex adaptive systems have been approached so far, we study the relationship between incremental organizational reconfigurations and discontinuous organizational restructurings using a large-scale database of U.S. Fortune 50 industrial corporations. We develop two types of escalation process in organizations: accumulation and perturbation. Under ordinary conditions, it is perturbation rather than the accumulation that is more likely to trigger subsequent discontinuous change. Consistent with complex adaptive systems theory, organizations are more sensitive to both accumulation and perturbation in conditions of heightened disequilibrium. Contrary to expectations, highly interconnected organizations are not more liable to discontinuous change. We conclude with implications for further research, especially the need to attend to the potential role of managerial design and coping when transferring complex adaptive systems theory from natural systems to organizational systems.

The prediction of Northern Hemisphere tropical cyclone extended life cycles by the ECMWF Ensemble and Deterministic Prediction Systems. Part I: tropical cyclone stage

This study has explored the prediction errors of tropical cyclones (TCs) in the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) for the Northern Hemisphere summer period for five recent years. Results for the EPS are contrasted with those for the higher-resolution deterministic forecasts. Various metrics of location and intensity errors are considered and contrasted for verification based on IBTrACS and the numerical weather prediction (NWP) analysis (NWPa). Motivated by the aim of exploring extended TC life cycles, location and intensity measures are introduced based on lower-tropospheric vorticity, which is contrasted with traditional verification metrics. Results show that location errors are almost identical when verified against IBTrACS or the NWPa. However, intensity in the form of the mean sea level pressure (MSLP) minima and 10-m wind speed maxima is significantly underpredicted relative to IBTrACS. Using the NWPa for verification results in much better consistency between the different intensity error metrics and indicates that the lower-tropospheric vorticity provides a good indication of vortex strength, with error results showing similar relationships to those based on MSLP and 10-m wind speeds for the different forecast types. The interannual variation in forecast errors are discussed in relation to changes in the forecast and NWPa system and variations in forecast errors between different ocean basins are discussed in terms of the propagation characteristics of the TCs.

A new adaptive multiple modelling approach for non-linear and non-stationary systems

This paper proposes a novel adaptive multiple modelling algorithm for non-linear and non-stationary systems. This simple modelling paradigm comprises K candidate sub-models which are all linear. With data available in an online fashion, the performance of all candidate sub-models are monitored based on the most recent data window, and M best sub-models are selected from the K candidates. The weight coefficients of the selected sub-model are adapted via the recursive least square (RLS) algorithm, while the coefficients of the remaining sub-models are unchanged. These M model predictions are then optimally combined to produce the multi-model output. We propose to minimise the mean square error based on a recent data window, and apply the sum to one constraint to the combination parameters, leading to a closed-form solution, so that maximal computational efficiency can be achieved. In addition, at each time step, the model prediction is chosen from either the resultant multiple model or the best sub-model, whichever is the best. Simulation results are given in comparison with some typical alternatives, including the linear RLS algorithm and a number of online non-linear approaches, in terms of modelling performance and time consumption.

Application of a hybrid tracking algorithm to motion analysis

An algorithm for tracking multiple feature positions in a dynamic image sequence is presented. This is achieved using a combination of two trajectory-based methods, with the resulting hybrid algorithm exhibiting the advantages of both. An optimizing exchange algorithm is described which enables short feature paths to be tracked without prior knowledge of the motion being studied. The resulting partial trajectories are then used to initialize a fast predictor algorithm which is capable of rapidly tracking multiple feature paths. As this predictor algorithm becomes tuned to the feature positions being tracked, it is shown how the location of occluded or poorly detected features can be predicted. The results of applying this tracking algorithm to data obtained from real-world scenes are then presented.

Biologically-inspired robust motion segmentation using mutual information

This paper presents a neuroscience inspired information theoretic approach to motion segmentation. Robust motion segmentation represents a fundamental first stage in many surveillance tasks. As an alternative to widely adopted individual segmentation approaches, which are challenged in different ways by imagery exhibiting a wide range of environmental variation and irrelevant motion, this paper presents a new biologically-inspired approach which computes the multivariate mutual information between multiple complementary motion segmentation outputs. Performance evaluation across a range of datasets and against competing segmentation methods demonstrates robust performance.

Combining Human and Machine Brains - Practical systems in information & control

In this paper a look is taken at how the use of implant technology can be used to either increase the range of the abilities of a human and/or diminish the effects of a neural illness, such as Parkinson's Disease. The key element is the need for a clear interface linking the human brain directly with a computer. The area of interest here is the use of implant technology, particularly where a connection is made between technology and the human brain and/or nervous system. Pilot tests and experimentation are invariably carried out apriori to investigate the eventual possibilities before human subjects are themselves involved. Some of the more pertinent animal studies are discussed here. The paper goes on to describe human experimentation, in particular that carried out by the author himself, which led to him receiving a neural implant which linked his nervous system bi-directionally with the internet. With this in place neural signals were transmitted to various technological devices to directly control them. In particular, feedback to the brain was obtained from the fingertips of a robot hand and ultrasonic (extra) sensory input. A view is taken as to the prospects for the future, both in the near term as a therapeutic device and in the long term as a form of enhancement.

Modeling camera orientation and 3D structure from a sequence of images taken by a perambulating commercial video camera

In this paper we report the degree of reliability of image sequences taken by off-the-shelf TV cameras for modeling camera rotation and reconstructing 3D structure using computer vision techniques. This is done in spite of the fact that computer vision systems usually use imaging devices that are specifically designed for the human vision. Our scenario consists of a static scene and a mobile camera moving through the scene. The scene is any long axial building dominated by features along the three principal orientations and with at least one wall containing prominent repetitive planar features such as doors, windows bricks etc. The camera is an ordinary commercial camcorder moving along the axial axis of the scene and is allowed to rotate freely within the range +/- 10 degrees in all directions. This makes it possible that the camera be held by a walking unprofessional cameraman with normal gait, or to be mounted on a mobile robot. The system has been tested successfully on sequence of images of a variety of structured, but fairly cluttered scenes taken by different walking cameramen. The potential application areas of the system include medicine, robotics and photogrammetry.

Biologically motivated model for object detection and identification in real-world scenes

The classical computer vision methods can only weakly emulate some of the multi-level parallelisms in signal processing and information sharing that takes place in different parts of the primates’ visual system thus enabling it to accomplish many diverse functions of visual perception. One of the main functions of the primates’ vision is to detect and recognise objects in natural scenes despite all the linear and non-linear variations of the objects and their environment. The superior performance of the primates’ visual system compared to what machine vision systems have been able to achieve to date, motivates scientists and researchers to further explore this area in pursuit of more efficient vision systems inspired by natural models. In this paper building blocks for a hierarchical efficient object recognition model are proposed. Incorporating the attention-based processing would lead to a system that will process the visual data in a non-linear way focusing only on the regions of interest and hence reducing the time to achieve real-time performance. Further, it is suggested to modify the visual cortex model for recognizing objects by adding non-linearities in the ventral path consistent with earlier discoveries as reported by researchers in the neuro-physiology of vision.

Intelligent agents: are they feasible in swarm-array computing?

The work reported in this paper proposes 'Intelligent Agents', a Swarm-Array computing approach focused to apply autonomic computing concepts to parallel computing systems and build reliable systems for space applications. Swarm-array computing is a robotics a swarm robotics inspired novel computing approach considered as a path to achieve autonomy in parallel computing systems. In the intelligent agent approach, a task to be executed on parallel computing cores is considered as a swarm of autonomous agents. A task is carried to a computing core by carrier agents and can be seamlessly transferred between cores in the event of a predicted failure, thereby achieving self-* objectives of autonomic computing. The approach is validated on a multi-agent simulator.

Intelligent cores and intelligent agents: two approaches to achieve autonomy in swarm-array computing

How can a bridge be built between autonomic computing approaches and parallel computing systems? How can autonomic computing approaches be extended towards building reliable systems? How can existing technologies be merged to provide a solution for self-managing systems? The work reported in this paper aims to answer these questions by proposing Swarm-Array Computing, a novel technique inspired from swarm robotics and built on the foundations of autonomic and parallel computing paradigms. Two approaches based on intelligent cores and intelligent agents are proposed to achieve autonomy in parallel computing systems. The feasibility of the proposed approaches is validated on a multi-agent simulator.