Microprocessor-Based Field-Oriented Control of A CSI-Fed Induction Motor Drive

This paper describes the method of field orientation of the stator current vector with respect to the stator, mutual, and rotor flux vectors, for the control of an induction motor fed from a current source inverter (CSI). A control scheme using this principle is described for orienting the stator current with respect to the rotor flux, as this gives natural decoupling between the current coordinates. A dedicated microcomputer system developed for implementing this scheme has been described. The experimental results are also presented.

MPC controlled multirotor with suspended slung load: System architecture and visual load detection

There is an increased interest in the use of Unmanned Aerial Vehicles for load transportation from environmental remote sensing to construction and parcel delivery. One of the main challenges is accurate control of the load position and trajectory. This paper presents an assessment of real flight trials for the control of an autonomous multi-rotor with a suspended slung load using only visual feedback to determine the load position. This method uses an onboard camera to take advantage of a common visual marker detection algorithm to robustly detect the load location. The load position is calculated using an onboard processor, and transmitted over a wireless network to a ground station integrating MATLAB/SIMULINK and Robotic Operating System (ROS) and a Model Predictive Controller (MPC) to control both the load and the UAV. To evaluate the system performance, the position of the load determined by the visual detection system in real flight is compared with data received by a motion tracking system. The multi-rotor position tracking performance is also analyzed by conducting flight trials using perfect load position data and data obtained only from the visual system. Results show very accurate estimation of the load position (~5% Offset) using only the visual system and demonstrate that the need for an external motion tracking system is not needed for this task.

Evaluation of visual stress symptoms in Age-matched Dyslexic, Meares–Irlen syndrome (MIS) and normal adults

AIM To examine the prevalence of dyslexia and Meares–Irlen syndrome (MIS) among female students and determine their level of visual stress in comparison with normal subjects. METHODS A random sample of 450 female medical students of King Saud University Riyadh (age range, 18 - 30 years) responded to a wide range of questions designed to accomplish the aims of this study. The detailed questionnaire consisted of 54 questions with twelve questions enquiring on ocular history and demography of participants while 42 questions were on visual symptoms. Items were categorized into; critical and non-critical questions (CQ and NCQ) and were rated on four point Likert scale. Based on the responses obtained, the subjects were grouped into normal (control), dyslexic with or without MIS (Group 1) and subjects with MIS only (Group 2). Responses were analysed as averages and mean scores were calculated and compared between-groups using one way analysis of variance to evaluate total (TVSS = NCQ + CQ), critical and non-critical visual stress scores. The relationship between categorical variables such as age, handedness and condition were assessed with Chi- Square test. RESULTS The completion rate was 96.8% and majority of the respondents (92%) were normal readers, 2% dyslexic and 6% had MIS. They were age-matched. More than half of the participants had visited an eye care practitioner in the last 2yrs. About 13% were recommended eye exercises and one participant experienced pattern glare. Hand preference was not associated with any condition but Group 1 subjects (3/9, 33%) were significantly more likely to be diagnosed of lazy eye than Group 2 (2/27, 7%) and control (27/414, 5%) subjects. The mean ± SD of TVSS responses were 63 ± 14 but it was 44 ± 9 for CQ and 19 ± 5 for NCQ. Responses from all three variables were normally distributed but the CQ responses were on the average more positive (82%) in Group 2 and less positive (46%) in Group 1 than control. With NCQ, the responses were equally less positive in Group 1 and 2 than control. Group 2 subjects showed significantly higher TVSS (P = 0.002), NCQ (P = 0.006) and CQ (P = 0.008) visual stress scores than control but no difference between Group 1 and control subjects, was observed for all scores (P > 0.05, for all comparisons). CONCLUSION The prevalence of dyslexia and MIS among Saudi female students was 2 and 6%, respectively. Critical questions performed best for assessing visual stress symptoms in dyslexic and MIS subjects. Generally, students with MIS were more sensitive to visual stress than normal students but dyslexics were more likely to present with a lazy eye than MIS and normal readers.

The lived experience of diagnosis delivery in motor neurone disease: A sociological-phenomenological study

Relatively few previous studies of individuals receiving a diagnosis of Motor Neurone Disease within the UK health care system have employed qualitative approaches to examine the diagnostic journey from a patient perspective. A qualitative sociological study was undertaken, involving interviews with 42 participants diagnosed with MND, to provide insight into their experiences of undergoing testing and receiving a diagnosis. Adopting a sociological-phenomenological perspective, this article examines key themes that emerged from participant accounts surrounding the lived experience of the diagnostic journey. The key themes that emerged were: The diagnostic quest; living with uncertainty; hearing bad news; communication difficulties; and a reified body of medical interest. In general, doctor-patient communication both at pre and post diagnosis was experienced as highly stressful, distressing and profoundly upsetting. Participants reported such distress as being due to the mode of delivery and communication strategies used by health professionals. We therefore suggest that professional training needs to emphasize the importance to health professionals of fostering greater levels of tact, sensitivity and empathy towards patients diagnosed with devastating, life-limiting illnesses such as MND.

Complementary forages – integration at a whole-farm level

A high proportion of the Australian and New Zealand dairy industry is based on a relatively simple, low input and low cost pasture feedbase. These factors enable this type of production system to remain internationally competitive. However, a key limitation of pasture-based dairy systems is periodic imbalances between herd intake requirements and pasture DM production, caused by strong seasonality and high inter-annual variation in feed supply. This disparity can be moderated to a certain degree through the strategic management of the herd through altering calving dates and stocking rates, and the feedbase by conserving excess forage and irrigating to flatten seasonal forage availability. Australasian dairy systems are experiencing emerging market and environmental challenges, which includes increased competition for land and water resources, decreasing terms of trade, a changing and variable climate, an increasing environmental focus that requires improved nutrient and water-use efficiency and lower greenhouse gas emissions. The integration of complementary forages has long been viewed as a means to manipulate the home-grown feed supply, to improve the nutritive value and DM intake of the diet, and to increase the efficiency of inputs utilised. Only recently has integrating complementary forages at the whole-farm system level received the significant attention and investment required to examine their potential benefit. Recent whole-of-farm research undertaken in both Australia and New Zealand has highlighted the importance of understanding the challenges of the current feedbase and the level of complementarity between forage types required to improve profit, manage risk and/or alleviate/mitigate against adverse outcomes. This paper reviews the most recent systems-level research into complementary forages, discusses approaches to modelling their integration at the whole-farm level and highlights the potential of complementary forages to address the major challenges currently facing pasture-based dairy systems.

Relational accountability in Indigenizing visual research for participatory communication

This article argues that an indigenous approach to communication research allows us to re-think academic approaches of engaging in and evaluating participatory communication research. It takes as its case study the Komuniti Tok Piksa project undertaken in the Highlands of Papua New Guinea. The project explores ways in which visual methods when paired with a community action approach embedded within an indigenous framework can be used to facilitate social change through meaningful participation. It involves communities to narrate their experiences in regard to HIV and AIDS and assists them in designing and recording their own messages. Local researchers are trained in using visual tools to facilitate this engagement with the communities.

A Bayesian latent class safety performance function for identifying motor vehicle crash blackspots

The current state of the practice in Blackspot Identification (BSI) utilizes safety performance functions based on total crash counts to identify transport system sites with potentially high crash risk. This paper postulates that total crash count variation over a transport network is a result of multiple distinct crash generating processes including geometric characteristics of the road, spatial features of the surrounding environment, and driver behaviour factors. However, these multiple sources are ignored in current modelling methodologies in both trying to explain or predict crash frequencies across sites. Instead, current practice employs models that imply that a single underlying crash generating process exists. The model mis-specification may lead to correlating crashes with the incorrect sources of contributing factors (e.g. concluding a crash is predominately caused by a geometric feature when it is a behavioural issue), which may ultimately lead to inefficient use of public funds and misidentification of true blackspots. This study aims to propose a latent class model consistent with a multiple crash process theory, and to investigate the influence this model has on correctly identifying crash blackspots. We first present the theoretical and corresponding methodological approach in which a Bayesian Latent Class (BLC) model is estimated assuming that crashes arise from two distinct risk generating processes including engineering and unobserved spatial factors. The Bayesian model is used to incorporate prior information about the contribution of each underlying process to the total crash count. The methodology is applied to the state-controlled roads in Queensland, Australia and the results are compared to an Empirical Bayesian Negative Binomial (EB-NB) model. A comparison of goodness of fit measures illustrates significantly improved performance of the proposed model compared to the NB model. The detection of blackspots was also improved when compared to the EB-NB model. In addition, modelling crashes as the result of two fundamentally separate underlying processes reveals more detailed information about unobserved crash causes.

Integration of natural hazards, risk and climate change into spatial planning practices

Most countries of Europe, as well as many countries in other parts of the world, are experiencing an increased impact of natural hazards. It is often speculated, but not yet proven, that climate change might influence the frequency and magnitude of certain hydro-meteorological natural hazards. What has certainly been observed is a sharp increase in financial losses caused by natural hazards worldwide. Eventhough Europe appears to be a space that is not affected by natural hazards to such catastrophic extents as other parts of the world are, the damages experienced here are certainly increasing too. Natural hazards, climate change and, in particular, risks have therefore recently been put high on the political agenda of the EU. In the search for appropriate instruments for mitigating impacts of natural hazards and climate change, as well as risks, the integration of these factors into spatial planning practices is constantly receiving higher attention. The focus of most approaches lies on single hazards and climate change mitigation strategies. The current paradigm shift of climate change mitigation to adaptation is used as a basis to draw conclusions and recommendations on what concepts could be further incorporated into spatial planning practices. Especially multi-hazard approaches are discussed as an important approach that should be developed further. One focal point is the definition and applicability of the terms natural hazard, vulnerability and risk in spatial planning practices. Especially vulnerability and risk concepts are so many-fold and complicated that their application in spatial planning has to be analysed most carefully. The PhD thesis is based on six published articles that describe the results of European research projects, which have elaborated strategies and tools for integrated communication and assessment practices on natural hazards and climate change impacts. The papers describe approaches on local, regional and European level, both from theoretical and practical perspectives. Based on these, passed, current and future potential spatial planning applications are reviewed and discussed. In conclusion it is recommended to shift from single hazard assessments to multi-hazard approaches, integrating potential climate change impacts. Vulnerability concepts should play a stronger role than present, and adaptation to natural hazards and climate change should be more emphasized in relation to mitigation. It is outlined that the integration of risk concepts in planning is rather complicated and would need very careful assessment to ensure applicability. Future spatial planning practices should also consider to be more interdisciplinary, i.e. to integrate as many stakeholders and experts as possible to ensure the sustainability of investments.

Regularized numerical integration of multibody dynamics with the generalized alpha method*

This paper discusses the consistent regularization property of the generalized α method when applied as an integrator to an initial value high index and singular differential-algebraic equation model of a multibody system. The regularization comes from within the discretization itself and the discretization remains consistent over the range of values the regularization parameter may take. The regularization involves increase of the smallest singular values of the ill-conditioned Jacobian of the discretization and is different from Baumgarte and similar techniques which tend to be inconsistent for poor choice of regularization parameter. This regularization also helps where pre-conditioning the Jacobian by scaling is of limited effect, for example, when the scleronomic constraints contain multiple closed loops or singular configuration or when high index path constraints are present. The feed-forward control in Kane's equation models is additionally considered in the numerical examples to illustrate the effect of regularization. The discretization presented in this work is adopted to the first order DAE system (unlike the original method which is intended for second order systems) for its A-stability and same order of accuracy for positions and velocities.

A model for visual recognition of alphanumerics

In this paper we propose a hypothetical scheme for recognizing the alphanumerics. The scheme is based on the known physiological structure of the visual cortex and the concept of a short Lino extractor nouron (SLEN). We assumo four basic typca of such units for extracting vertical, horizontal, right and left inclined straight line segments. The patterns reconstructed from the scheme show perfect agreement with the test patterns. The model indicates that the recognition of letters T and H requires extraction of the largest number of features.

A Probabilistic Approach to the Primary Visual Cortex

What can the statistical structure of natural images teach us about the human brain? Even though the visual cortex is one of the most studied parts of the brain, surprisingly little is known about how exactly images are processed to leave us with a coherent percept of the world around us, so we can recognize a friend or drive on a crowded street without any effort. By constructing probabilistic models of natural images, the goal of this thesis is to understand the structure of the stimulus that is the raison d etre for the visual system. Following the hypothesis that the optimal processing has to be matched to the structure of that stimulus, we attempt to derive computational principles, features that the visual system should compute, and properties that cells in the visual system should have. Starting from machine learning techniques such as principal component analysis and independent component analysis we construct a variety of sta- tistical models to discover structure in natural images that can be linked to receptive field properties of neurons in primary visual cortex such as simple and complex cells. We show that by representing images with phase invariant, complex cell-like units, a better statistical description of the vi- sual environment is obtained than with linear simple cell units, and that complex cell pooling can be learned by estimating both layers of a two-layer model of natural images. We investigate how a simplified model of the processing in the retina, where adaptation and contrast normalization take place, is connected to the nat- ural stimulus statistics. Analyzing the effect that retinal gain control has on later cortical processing, we propose a novel method to perform gain control in a data-driven way. Finally we show how models like those pre- sented here can be extended to capture whole visual scenes rather than just small image patches. By using a Markov random field approach we can model images of arbitrary size, while still being able to estimate the model parameters from the data.

Learning Nonlinear Visual Processing from Natural Images

The paradigm of computational vision hypothesizes that any visual function -- such as the recognition of your grandparent -- can be replicated by computational processing of the visual input. What are these computations that the brain performs? What should or could they be? Working on the latter question, this dissertation takes the statistical approach, where the suitable computations are attempted to be learned from the natural visual data itself. In particular, we empirically study the computational processing that emerges from the statistical properties of the visual world and the constraints and objectives specified for the learning process. This thesis consists of an introduction and 7 peer-reviewed publications, where the purpose of the introduction is to illustrate the area of study to a reader who is not familiar with computational vision research. In the scope of the introduction, we will briefly overview the primary challenges to visual processing, as well as recall some of the current opinions on visual processing in the early visual systems of animals. Next, we describe the methodology we have used in our research, and discuss the presented results. We have included some additional remarks, speculations and conclusions to this discussion that were not featured in the original publications. We present the following results in the publications of this thesis. First, we empirically demonstrate that luminance and contrast are strongly dependent in natural images, contradicting previous theories suggesting that luminance and contrast were processed separately in natural systems due to their independence in the visual data. Second, we show that simple cell -like receptive fields of the primary visual cortex can be learned in the nonlinear contrast domain by maximization of independence. Further, we provide first-time reports of the emergence of conjunctive (corner-detecting) and subtractive (opponent orientation) processing due to nonlinear projection pursuit with simple objective functions related to sparseness and response energy optimization. Then, we show that attempting to extract independent components of nonlinear histogram statistics of a biologically plausible representation leads to projection directions that appear to differentiate between visual contexts. Such processing might be applicable for priming, \ie the selection and tuning of later visual processing. We continue by showing that a different kind of thresholded low-frequency priming can be learned and used to make object detection faster with little loss in accuracy. Finally, we show that in a computational object detection setting, nonlinearly gain-controlled visual features of medium complexity can be acquired sequentially as images are encountered and discarded. We present two online algorithms to perform this feature selection, and propose the idea that for artificial systems, some processing mechanisms could be selectable from the environment without optimizing the mechanisms themselves. In summary, this thesis explores learning visual processing on several levels. The learning can be understood as interplay of input data, model structures, learning objectives, and estimation algorithms. The presented work adds to the growing body of evidence showing that statistical methods can be used to acquire intuitively meaningful visual processing mechanisms. The work also presents some predictions and ideas regarding biological visual processing.

VisArchive: a time and relevance based visual interface for searching, browsing, and exploring project archives

Background Project archives are becoming increasingly large and complex. On construction projects in particular, the increasing amount of information and the increasing complexity of its structure make searching and exploring information in the project archive challenging and time-consuming. Methods This research investigates a query-driven approach that represents new forms of contextual information to help users understand the set of documents resulting from queries of construction project archives. Specifically, this research extends query-driven interface research by representing three types of contextual information: (1) the temporal context is represented in the form of a timeline to show when each document was created; (2) the search-relevance context shows exactly which of the entered keywords matched each document; and (3) the usage context shows which project participants have accessed or modified a file. Results We implemented and tested these ideas within a prototype query-driven interface we call VisArchive. VisArchive employs a combination of multi-scale and multi-dimensional timelines, color-coded stacked bar charts, additional supporting visual cues and filters to support searching and exploring historical project archives. The timeline-based interface integrates three interactive timelines as focus + context visualizations. Conclusions The feasibility of using these visual design principles is tested in two types of project archives: searching construction project archives of an educational building project and tracking of software defects in the Mozilla Thunderbird project. These case studies demonstrate the applicability, usefulness and generality of the design principles implemented.