Robotics, Vision and Control : Fundamental Algorithms in MATLAB

The practice of robotics and computer vision each involve the application of computational algorithms to data. The research community has developed a very large body of algorithms but for a newcomer to the field this can be quite daunting. For more than 10 years the author has maintained two open-source MATLAB® Toolboxes, one for robotics and one for vision. They provide implementations of many important algorithms and allow users to work with real problems, not just trivial examples. This new book makes the fundamental algorithms of robotics, vision and control accessible to all. It weaves together theory, algorithms and examples in a narrative that covers robotics and computer vision separately and together. Using the latest versions of the Toolboxes the author shows how complex problems can be decomposed and solved using just a few simple lines of code. The topics covered are guided by real problems observed by the author over many years as a practitioner of both robotics and computer vision. It is written in a light but informative style, it is easy to read and absorb, and includes over 1000 MATLAB® and Simulink® examples and figures. The book is a real walk through the fundamentals of mobile robots, navigation, localization, arm-robot kinematics, dynamics and joint level control, then camera models, image processing, feature extraction and multi-view geometry, and finally bringing it all together with an extensive discussion of visual servo systems.

Scheduling Trains with Priorities : a no-wait blocking parallel-machine job-shop scheduling model

The paper investigates train scheduling problems when prioritised trains and non-prioritised trains are simultaneously traversed in a single-line rail network. In this case, no-wait conditions arise because the prioritised trains such as express passenger trains should traverse continuously without any interruption. In comparison, non-prioritised trains such as freight trains are allowed to enter the next section immediately if possible or to remain in a section until the next section on the routing becomes available, which is thought of as a relaxation of no-wait conditions. With thorough analysis of the structural properties of the No-Wait Blocking Parallel-Machine Job-Shop-Scheduling (NWBPMJSS) problem that is originated in this research, an innovative generic constructive algorithm (called NWBPMJSS_Liu-Kozan) is proposed to construct the feasible train timetable in terms of a given order of trains. In particular, the proposed NWBPMJSS_Liu-Kozan constructive algorithm comprises several recursively-used sub-algorithms (i.e. Best-Starting-Time-Determination Procedure, Blocking-Time-Determination Procedure, Conflict-Checking Procedure, Conflict-Eliminating Procedure, Tune-up Procedure and Fine-tune Procedure) to guarantee feasibility by satisfying the blocking, no-wait, deadlock-free and conflict-free constraints. A two-stage hybrid heuristic algorithm (NWBPMJSS_Liu-Kozan-BIH) is developed by combining the NWBPMJSS_Liu-Kozan constructive algorithm and the Best-Insertion-Heuristic (BIH) algorithm to find the preferable train schedule in an efficient and economical way. Extensive computational experiments show that the proposed methodology is promising because it can be applied as a standard and fundamental toolbox for identifying, analysing, modelling and solving real-world scheduling problems.

Optimising a coal rail network under capacity constraints

This research deals with an innovative methodology for optimising the coal train scheduling problem. Based on our previously published work, generic solution techniques are developed by utilising a “toolbox” of standard well-solved standard scheduling problems. According to our analysis, the coal train scheduling problem can be basically modelled a Blocking Parallel-Machine Job-Shop Scheduling (BPMJSS) problem with some minor constraints. To construct the feasible train schedules, an innovative constructive algorithm called the SLEK algorithm is proposed. To optimise the train schedule, a three-stage hybrid algorithm called the SLEK-BIH-TS algorithm is developed based on the definition of a sophisticated neighbourhood structure under the mechanism of the Best-Insertion-Heuristic (BIH) algorithm and Tabu Search (TS) metaheuristic algorithm. A case study is performed for optimising a complex real-world coal rail system in Australia. A method to calculate the lower bound of the makespan is proposed to evaluate results. The results indicate that the proposed methodology is promising to find the optimal or near-optimal feasible train timetables of a coal rail system under network and terminal capacity constraints.

Augmenting public participation : enhancing planning outcomes through the use of social media and web 2.0

Social media and web 2.0 tools offer opportunities to devise novel participation strategies that can engage previously difficult to reach as well as new segments of society in urban planning. This paper examines participatory planning in the four local government areas of Brisbane City Council, Gold Coast City Council, Redland City Council, and Toowoomba Regional Council, all situated in South East Queensland, Australia. The paper discusses how social media and web 2.0 tools can deliver a more engaging planning experience to citizens, and investigates local government’s current use and receptiveness to social media tools for plan making and community engagement. The study’s research informed the development of criteria to assess the level of participation reached through the current use of social media and web 2.0 in the four local government areas. This resulted in an adaptation of the International Association for Public Participation (IAP2) Toolbox to integrate these new tools which is being presented to encourage further discussion and evaluation by planning professionals.

Business research in virtual worlds : possibilities and practicalities

Purpose – It is predicted that virtual business and related research possibilities will expand significantly. In this context, the aim of this paper is to use insights from a virtual research project to present a theoretically-informed toolbox of practical suggestions to guide the conduct of virtual world business research. Design/methodology/approach – Archival evidence is presented, and data from a study conducted in Second Lifew in 2007 is interpreted through Llewellyn’s framework (physical, structural, agential, cultural and mental dimensions). Findings – With the burgeoning of virtual business applications, appropriate systems that encompass the dynamics of both the real and the virtual will need to be developed by and for accountants, auditors and business professionals. Researchers of virtual business activities will need to adapt to the physical, structural, agential, cultural and mental dimensions unique to virtual worlds. Research limitations/implications – While based on reflections from a single study in Second Life, this paper identifies possibilities for future virtual research on issues of accountability and accounting relating to virtual worlds. Practical implications – The practical toolbox will assist virtual researchers to deal with the possibilities and practicalities of conducting research in virtual worlds. Originality/value – Despite the proliferation of virtual worlds, predictions of virtual business applications, and consequent accountability and accounting implications, there is a paucity of academic literature on conducting business research in virtual settings. This prescient paper develops a conceptual framework to guide the conduct of research in virtual worlds, and identifies the unique opportunities and challenges they present.

Outdoor worker sun protection project : a comprehensive applied research project to demonstrate the effectiveness of sun protection measures which influence high risk outdoor workers in Queensland to adopt sun safe behaviour practices 2010 – 2013

The QUT Outdoor Worker Sun Protection (OWSP) project undertook a comprehensive applied health promotion project to demonstrate the effectiveness of sun protection measures which influence high risk outdoor workers in Queensland to adopt sun safe behaviours. The three year project (2010-2013) was driven by two key concepts: 1) The hierarchy of control, which is used to address risks in the workplace, advocates for six control measures that need to be considered in order of priority (refer to Section 3.4.2); and 2) the Ottawa Charter which recommends five action means to achieve health promotion (refer to Section 2.1). The project framework was underpinned by a participatory action research approach that valued peoples’ input, took advantage of existing skills and resources, and stimulated innovation (refer to Section 4.2). Fourteen workplaces (small and large) with a majority outdoor workforce were recruited across regional Queensland (Darling Downs, Northwest, Mackay and Cairns) from four industries types: 1) building and construction, 2) rural and farming, 3) local government, and 4) public sector. A workplace champion was identified at each workplace and was supported (through resource provision, regular contact and site visits) over a 14 to 18 month intervention period to make sun safety a priority in their workplace. Employees and employers were independently assessed for pre- and postintervention sun protection behaviours. As part of the intervention, an individualised sun safety action plan was developed in conjunction with each workplace to guide changes across six key strategy areas including: 1) Policy (e.g., adopt sun safety practices during all company events); 2) Structural and environmental (e.g., shade on worksites; eliminate or minimise reflective surfaces); 3) Personal protective equipment (PPE) (e.g., trial different types of sunscreens, or wide-brimmed hats); 4) Education and awareness (e.g., include sun safety in inductions and toolbox talks; send reminder emails or text messages to workers);5) Role modelling (e.g., by managers, supervisors, workplace champions and mentors); and 6) Skin examinations (e.g., allow time off work for skin checks). The participatory action process revealed that there was no “one size fits all” approach to sun safety in the workplace; a comprehensive, tailored approach was fundamental. This included providing workplaces with information, resources, skills, know how, incentives and practical help. For example, workplaces engaged in farming complete differing seasonal tasks across the year and needed to prepare for optimal sun safety of their workers during less labour intensive times. In some construction workplaces, long pants were considered a trip hazard and could not be used as part of a PPE strategy. Culture change was difficult to achieve and workplace champions needed guidance on the steps to facilitate this (e.g., influencing leaders through peer support, mentoring and role modelling). With the assistance of the project team the majority of workplaces were able to successfully implement the sun safety strategies contained within their action plans, up skilling them in the evidence for sun safety, how to overcome barriers, how to negotiate with all relevant parties and assess success. The most important enablers to the implementation of a successful action plan were a pro-active workplace champion, strong employee engagement, supportive management, the use of highly visual educational resources, and external support (provided by the project team through regular contact either directly through phone calls or indirectly through emails and e-newsletters). Identified barriers included a lack of time, the multiple roles of workplace champions, (especially among smaller workplaces), competing issues leading to a lack of priority for sun safety, the culture of outdoor workers, and costs or budgeting constraints. The level of sun safety awareness, knowledge, and sun protective behaviours reported by the workers increased between pre-and post-intervention. Of the nine sun protective behaviours that were assessed, the largest changes reported included a 26% increase in workers who “usually or always” wore a broad-brimmed hat, a 20% increase in the use of natural shade, a 19% increase in workers wearing long-sleeved collared shirts, and a 16% increase in workers wearing long trousers.

HeatWave : a handheld 3D thermography system for energy auditing

Energy auditing is an effective but costly approach for reducing the long-term energy consumption of buildings. When well-executed, energy loss can be quickly identified in the building structure and its subsystems. This then presents opportunities for improving energy efficiency. We present a low-cost, portable technology called "HeatWave" which allows non-experts to generate detailed 3D surface temperature models for energy auditing. This handheld 3D thermography system consists of two commercially available imaging sensors and a set of software algorithms which can be run on a laptop. The 3D model can be visualized in real-time by the operator so that they can monitor their degree of coverage as the sensors are used to capture data. In addition, results can be analyzed offline using the proposed "Spectra" multispectral visualization toolbox. The presence of surface temperature data in the generated 3D model enables the operator to easily identify and measure thermal irregularities such as thermal bridges, insulation leaks, moisture build-up and HVAC faults. Moreover, 3D models generated from subsequent audits of the same environment can be automatically compared to detect temporal changes in conditions and energy use over time.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Resection-intersection bundle adjustment revisited

Bundle adjustment is one of the essential components of the computer vision toolbox. This paper revisits the resection-intersection approach, which has previously been shown to have inferior convergence properties. Modifications are proposed that greatly improve the performance of this method, resulting in a fast and accurate approach. Firstly, a linear triangulation step is added to the intersection stage, yielding higher accuracy and improved convergence rate. Secondly, the effect of parameter updates is tracked in order to reduce wasteful computation; only variables coupled to significantly changing variables are updated. This leads to significant improvements in computation time, at the cost of a small, controllable increase in error. Loop closures are handled effectively without the need for additional network modelling. The proposed approach is shown experimentally to yield comparable accuracy to a full sparse bundle adjustment (20% error increase) while computation time scales much better with the number of variables. Experiments on a progressive reconstruction system show the proposed method to be more efficient by a factor of 65 to 177, and 4.5 times more accurate (increasing over time) than a localised sparse bundle adjustment approach.

Practical aspects of frequency-domain identification of dynamic models of marine structures from hydrodynamic data

The motion response of marine structures in waves can be studied using finite-dimensional linear-time-invariant approximating models. These models, obtained using system identification with data computed by hydrodynamic codes, find application in offshore training simulators, hardware-in-the-loop simulators for positioning control testing, and also in initial designs of wave-energy conversion devices. Different proposals have appeared in the literature to address the identification problem in both time and frequency domains, and recent work has highlighted the superiority of the frequency-domain methods. This paper summarises practical frequency-domain estimation algorithms that use constraints on model structure and parameters to refine the search of approximating parametric models. Practical issues associated with the identification are discussed, including the influence of radiation model accuracy in force-to-motion models, which are usually the ultimate modelling objective. The illustration examples in the paper are obtained using a freely available MATLAB toolbox developed by the authors, which implements the estimation algorithms described.

Nonlinear container ship model for the study of parametric roll resonance

Parametric roll is a critical phenomenon for ships, whose onset may cause roll oscillations up to +-40 degrees, leading to very dangerous situations and possibly capsizing. Container ships have been shown to be particularly prone to parametric roll resonance when they are sailing in moderate to heavy head seas. A Matlab/Simulink parametric roll benchmark model for a large container ship has been implemented and validated against a wide set of experimental data. The model is a part of a Matlab/Simulink Toolbox (MSS, 2007). The benchmark implements a 3rd-order nonlinear model where the dynamics of roll is strongly coupled with the heave and pitch dynamics. The implemented model has shown good accuracy in predicting the container ship motions, both in the vertical plane and in the transversal one. Parametric roll has been reproduced for all the data sets in which it happened, and the model provides realistic results which are in good agreement with the model tank experiments.

A motor controller using field oriented control and Hall effect rotor position sensors : simulation and implementation

A field oriented control (FOC) algorithm is simulated and implemented for use with a permanent magnet synchronous motor (PMSM). Rotor position is sensed using Hall effect switches on the stator because other hardware position sensors attached to the rotor may not be desirable or cost effective for certain applications. This places a limit on the resolution of position sensing – only a few Hall effect switches can be placed. In this simulation, three sensors are used and the position information is obtained at higher resolution by estimating it from the rotor dynamics, as shown in literature previously. This study compares the performance of the method with an incremental encoder using simulations. The FOC algorithm is implemented using Digital Motor Control (DMC) and IQ Texas Instruments libraries from a Simulink toolbox called Embedded Coder, and downloaded into a TI microcontroller (TMS320F28335) known as the Piccolo via Code Composer Studio (CCS).

Interactions of iodoperfluorobenzene compounds with gold nanoparticles

Understanding the interactions of small molecules with gold nanoparticles is important for controlling their surface chemistry and, hence, how they can be used in specific applications. The interaction of iodoperfluorobenzene compounds with gold nanoparticles was investigated by UV-Vis difference spectroscopy, surface enhanced Raman spectroscopy (SERS) and Synchrotron X-ray photoelectron spectroscopy (XPS). Results from UV-Vis difference spectroscopy demonstrated that iodoperfluorobenzene compounds undergo charge transfer complexation with gold nanoparticles. SERS of the small molecule–gold nanoparticle adducts provided further evidence for formation of charge transfer complexes, while Synchrotron X-ray photoelectron spectroscopy provided evidence of the binding mechanism. Demonstration of interactions of iodoperfluorobenzene compounds with gold nanoparticles further expands the molecular toolbox that is available for functionalising gold nanoparticles and has significant potential for expanding the scope for generation of hybrid halogen bonded materials.

Southern criminology

Issues of vital criminological research and policy significance abound in the global South, with important implications for South/North relations and for global security and justice. Having a theoretical framework capable of appreciating the significance of this global dynamic will contribute to criminology being able to better understand the challenges of the present and the future. We employ southern theory in a reflexive (and not a reductive) way to elucidate the power relations embedded in the hierarchal production of criminological knowledge that privileges theories, assumptions and methods based largely on empirical specificities of the global North. Our purpose is not to dismiss the conceptual and empirical advances in criminology, but to more usefully de-colonize and democratize the toolbox of available criminological concepts, theories and methods. As a way of illustrating how southern criminology might usefully contribute to better informed responses to global justice and security, this article examines three distinct projects that could be developed under such a rubric. These include, firstly, certain forms and patterns of crime specific to the global periphery; secondly, the distinctive patterns of gender and crime in the global south shaped by diverse cultural, social, religious and political factors and lastly the distinctive historical and contemporary penalities of the global south and their historical links with colonialism and empire building.