Lost in translation (and rotation) : rapid extrinsic calibration for 2D and 3D LIDARs

This paper describes a novel method for determining the extrinsic calibration parameters between 2D and 3D LIDAR sensors with respect to a vehicle base frame. To recover the calibration parameters we attempt to optimize the quality of a 3D point cloud produced by the vehicle as it traverses an unknown, unmodified environment. The point cloud quality metric is derived from Rényi Quadratic Entropy and quantifies the compactness of the point distribution using only a single tuning parameter. We also present a fast approximate method to reduce the computational requirements of the entropy evaluation, allowing unsupervised calibration in vast environments with millions of points. The algorithm is analyzed using real world data gathered in many locations, showing robust calibration performance and substantial speed improvements from the approximations.

Embedding human expert cognition and real-time trajectory planning in autonomous UAS

This thesis presents a new approach to compute and optimize feasible three dimensional (3D) flight trajectories using aspects of Human Decision Making (HDM) strategies, for fixed wing Unmanned Aircraft (UA) operating in low altitude environments in the presence of real time planning deadlines. The underlying trajectory generation strategy involves the application of Manoeuvre Automaton (MA) theory to create sets of candidate flight manoeuvres which implicitly incorporate platform dynamic constraints. Feasible trajectories are formed through the concatenation of predefined flight manoeuvres in an optimized manner. During typical UAS operations, multiple objectives may exist, therefore the use of multi-objective optimization can potentially allow for convergence to a solution which better reflects overall mission requirements and HDM preferences. A GUI interface was developed to allow for knowledge capture from a human expert during simulated mission scenarios. The expert decision data captured is converted into value functions and corresponding criteria weightings using UTilite Additive (UTA) theory. The inclusion of preferences elicited from HDM decision data within an Automated Decision System (ADS) allows for the generation of trajectories which more closely represent the candidate HDM’s decision strategies. A novel Computationally Adaptive Trajectory Decision optimization System (CATDS) has been developed and implemented in simulation to dynamically manage, calculate and schedule system execution parameters to ensure that the trajectory solution search can generate a feasible solution, if one exists, within a given length of time. The inclusion of the CATDS potentially increases overall mission efficiency and may allow for the implementation of the system on different UAS platforms with varying onboard computational capabilities. These approaches have been demonstrated in simulation using a fixed wing UAS operating in low altitude environments with obstacles present.

A simulated annealing algorithm for energy efficient virtual machine placement

Improving energy efficiency has become increasingly important in data centers in recent years to reduce the rapidly growing tremendous amounts of electricity consumption. The power dissipation of the physical servers is the root cause of power usage of other systems, such as cooling systems. Many efforts have been made to make data centers more energy efficient. One of them is to minimize the total power consumption of these servers in a data center through virtual machine consolidation, which is implemented by virtual machine placement. The placement problem is often modeled as a bin packing problem. Due to the NP-hard nature of the problem, heuristic solutions such as First Fit and Best Fit algorithms have been often used and have generally good results. However, their performance leaves room for further improvement. In this paper we propose a Simulated Annealing based algorithm, which aims at further improvement from any feasible placement. This is the first published attempt of using SA to solve the VM placement problem to optimize the power consumption. Experimental results show that this SA algorithm can generate better results, saving up to 25 percentage more energy than First Fit Decreasing in an acceptable time frame.

Modeling resource management in the building design process by information constraint Petri nets

The effect of resource management on the building design process directly influences the development cycle time and success of construction projects. This paper presents the information constraint net (ICN) to represent the complex information constraint relations among design activities involved in the building design process. An algorithm is developed to transform the information constraints throughout the ICN into a Petri net model. A resource management model is developed using the ICN to simulate and optimize resource allocation in the design process. An example is provided to justify the proposed model through a simulation analysis of the CPN Tools platform in the detailed structural design. The result demonstrates that the proposed approach can obtain the resource management and optimization needed for shortening the development cycle and optimal allocation of resources.

Advanced numerical characterization of silicon with defect by nanoindentation

Nano silicon is widely used as the essential element of complementary metal–oxide–semiconductor (CMOS) and solar cells. It is recognized that today, large portion of world economy is built on electronics products and related services. Due to the accessible fossil fuel running out quickly, there are increasing numbers of researches on the nano silicon solar cells. The further improvement of higher performance nano silicon components requires characterizing the material properties of nano silicon. Specially, when the manufacturing process scales down to the nano level, the advanced components become more and more sensitive to the various defects induced by the manufacturing process. It is known that defects in mono-crystalline silicon have significant influence on its properties under nanoindentation. However, the cost involved in the practical nanoindentation as well as the complexity of preparing the specimen with controlled defects slow down the further research on mechanical characterization of defected silicon by experiment. Therefore, in current study, the molecular dynamics (MD) simulations are employed to investigate the mono-crystalline silicon properties with different pre-existing defects, especially cavities, under nanoindentation. Parametric studies including specimen size and loading rate, are firstly conducted to optimize computational efficiency. The optimized testing parameters are utilized for all simulation in defects study. Based on the validated model, different pre-existing defects are introduced to the silicon substrate, and then a group of nanoindentation simulations of these defected substrates are carried out. The simulation results are carefully investigated and compared with the perfect Silicon substrate which used as benchmark. It is found that pre-existing cavities in the silicon substrate obviously influence the mechanical properties. Furthermore, pre-existing cavities can absorb part of the strain energy during loading, and then release during unloading, which possibly causes less plastic deformation to the substrate. However, when the pre-existing cavities is close enough to the deformation zone or big enough to exceed the bearable stress of the crystal structure around the spherical cavity, the larger plastic deformation occurs which leads the collapse of the structure. Meanwhile, the influence exerted on the mechanical properties of silicon substrate depends on the location and size of the cavity. Substrate with larger cavity size or closer cavity position to the top surface, usually exhibits larger reduction on Young’s modulus and hardness.

Physical activity in the lives of those living with lymphoedema following cancer treatment

Background Despite recent evidence demonstrating that exercise neither increases risk of nor exacerbates lymphoedema, lymphoedema prevention and management advice cautions against ‘repetitive use’ or ‘overuse’ of the affected arm. It is plausible that this advice creates a barrier to participation in exercise and, more generally, physical activity (any daily activity [PA]). This study explored the relationship between lymphoedema and PA among people following cancer treatment. Methods Social constructionist grounded theory guided study design, development of interview questions and the qualitative data analysis approach undertaken. Data were collected via focus groups and telephone interviews. Results Five focus groups (n=16 participants) and 13 telephone interviews were completed. Participants (women n=26, men n=3) were aged 39-80 years and were experiencing mild to severe lymphoedema following treatment for a variety of cancers. Participants varied in how they defined PA. Its perceived importance was mostly associated with the ability to partake in daily activities, with only some participants highlighting its importance for lymphoedema management or more general health benefits. Most participants’ PA decreased after diagnosis, a consequence of confusion around appropriate PA and fear that PA could worsen lymphoedema symptoms. Conclusions Lymphoedema guidelines need to be more clear and specific when discussing the role of PA and exercise in the prevention and management of lymphoedema. It may be more appropriate to discuss ways to optimize safety when engaging in specific tasks rather than to highlight the need for avoidance of participating in certain activities.

Multiphysics modelling of convective drying of food materials

Currently, 1.3 billion tonnes of food is lost annually due to lack of proper processing and preservation method. Drying is one of the easiest and oldest methods of food processing which can contribute to reduce that huge losses, combat hunger and promote food security. Drying increase shelf life, reduce weight and volume of food thus minimize packing, storage, and transportation cost and enable storage of food under ambient environment. However, drying is a complex process which involves combination of heat and mass transfer and physical property change and shrinkage of the food material. Modelling of this process is essential to optimize the drying kinetics and improve energy efficiency of the process. Since material properties varies with moisture content, the models should not consider constant materials properties, constant diffusion .The objective of this paper is to develop a multiphysics based mathematical model to simulate coupled heat and mass transfer during convective drying of fruit considering variable material properties. This model can be used predict the temperature and moisture distribution inside the food during drying. Effect of different drying air temperature and drying air velocity on drying kinetics has been demonstrated. The governing equations of heat and mass transfer were solved with Comsol Multiphysics 4.3.

Spatio temporal feature evaluation for action recognition

Spatio-Temporal interest points are the most popular feature representation in the field of action recognition. A variety of methods have been proposed to detect and describe local patches in video with several techniques reporting state of the art performance for action recognition. However, the reported results are obtained under different experimental settings with different datasets, making it difficult to compare the various approaches. As a result of this, we seek to comprehensively evaluate state of the art spatio- temporal features under a common evaluation framework with popular benchmark datasets (KTH, Weizmann) and more challenging datasets such as Hollywood2. The purpose of this work is to provide guidance for researchers, when selecting features for different applications with different environmental conditions. In this work we evaluate four popular descriptors (HOG, HOF, HOG/HOF, HOG3D) using a popular bag of visual features representation, and Support Vector Machines (SVM)for classification. Moreover, we provide an in-depth analysis of local feature descriptors and optimize the codebook sizes for different datasets with different descriptors. In this paper, we demonstrate that motion based features offer better performance than those that rely solely on spatial information, while features that combine both types of data are more consistent across a variety of conditions, but typically require a larger codebook for optimal performance.

Soil N2O and CO2 emissions from cotton in Australia under varying irrigation management

Irrigation is known to stimulate soil microbial carbon and nitrogen turnover and potentially the emissions of nitrous oxide (N2O) and carbon dioxide (CO2). We conducted a study to evaluate the effect of three different irrigation intensities on soil N2O and CO2 fluxes and to determine if irrigation management can be used to mitigate N2O emissions from irrigated cotton on black vertisols in South-Eastern Queensland, Australia. Fluxes were measured over the entire 2009/2010 cotton growing season with a fully automated chamber system that measured emissions on a sub-daily basis. Irrigation intensity had a significant effect on CO2 emission. More frequent irrigation stimulated soil respiration and seasonal CO2 fluxes ranged from 2.7 to 4.1 Mg-C ha−1 for the treatments with the lowest and highest irrigation frequency, respectively. N2O emission happened episodic with highest emissions when heavy rainfall or irrigation coincided with elevated soil mineral N levels and seasonal emissions ranged from 0.80 to 1.07 kg N2O-N ha−1 for the different treatments. Emission factors (EF = proportion of N fertilizer emitted as N2O) over the cotton cropping season, uncorrected for background emissions, ranged from 0.40 to 0.53 % of total N applied for the different treatments. There was no significant effect of the different irrigation treatments on soil N2O fluxes because highest emission happened in all treatments following heavy rainfall caused by a series of summer thunderstorms which overrode the effect of the irrigation treatment. However, higher irrigation intensity increased the cotton yield and therefore reduced the N2O intensity (N2O emission per lint yield) of this cropping system. Our data suggest that there is only limited scope to reduce absolute N2O emissions by different irrigation intensities in irrigated cotton systems with summer dominated rainfall. However, the significant impact of the irrigation treatments on the N2O intensity clearly shows that irrigation can easily be used to optimize the N2O intensity of such a system.

Embedding human expert cognition into autonomous UAS trajectory planning

This paper presents a new approach for the inclusion of human expert cognition into autonomous trajectory planning for unmanned aerial systems (UASs) operating in low-altitude environments. During typical UAS operations, multiple objectives may exist; therefore, the use of multicriteria decision aid techniques can potentially allow for convergence to trajectory solutions which better reflect overall mission requirements. In that context, additive multiattribute value theory has been applied to optimize trajectories with respect to multiple objectives. A graphical user interface was developed to allow for knowledge capture from a human decision maker (HDM) through simulated decision scenarios. The expert decision data gathered are converted into value functions and corresponding criteria weightings using utility additive theory. The inclusion of preferences elicited from HDM data within an automated decision system allows for the generation of trajectories which more closely represent the candidate HDM decision preferences. This approach has been demonstrated in this paper through simulation using a fixed-wing UAS operating in low-altitude environments.

Effects of scaffold architecture on cranial bone healing

Scaffolds for bone tissue engineering should be designed to optimize cell migration, enhance new bone formation and give mechanical support. In the present study, we used polycaprolactone-tricalciumphosphate (PCL/TCP) scaffolds with two different fibre lay down patterns which were coated with hydroxyapatite and gelatine as an approach for optimizing bone regeneration in a critical sized calvarial defect. After 12 weeks bone regeneration was quantified using microCT analysis, biomechanical testing and histological evaluation. Notably, the experimental groups containing coated scaffolds showed lower bone formation and lower biomechanical properties within the defect compared to the uncoated scaffolds. Surprisingly, the different lay down pattern of the fibres resulted in different bone formation and biomechanical properties; namely 0/60/120° scaffolds revealed lower bone formation and biomechanical properties compared to the 0/90° scaffolds in all the experimental groups. The different architecture of the scaffold fibres may have an effect on nutrition supply as well as the attachment of the newly formed matrix to the scaffold. Therefore, future bone regeneration strategies utilising scaffolds should consider scaffold architecture as an important factor during the scaffold optimisation stages in order to move closer to a clinical application.

Collaborative design learning for traditional crafts industry

Traditional craft industries need assistance with being transformed into creative industries; as such a transformation will support them to face the future competitive global market. Assistance such as advisory programs should serve long-term benefit for crafts industries as well as optimize self-help potential. Advisory programs using participatory methods will enable craftspeople and stakeholders to reveal resources and potencies, such as socio-cultural value, tradition and other kind of heritages, to generate new innovative ideas of craft design in a sustainable way.

Drying kinetics of two grape varieties of Italy

Two varieties of grapes, white grape and red grape grown in the Campania region of Italy were selected for the study of drying characteristics. Comparisons were made with treated and untreated grapes under constant drying condition of 50o C in a conventional drying system. This temperature was selected to represent farm drying conditions. Grapes were purchased from a local market from the same supplier to maintain the same size of grapes and same properties. An abrasive physical treatment was used as pretreatment. The drying curves were constructed and drying kinetics was calculated using several commonly available models. It was found that treated samples show better drying characteristics than untreated samples. The objective of this study is to obtain drying kinetics which can be used to optimize the drying operations in grape drying.

Pixel value to electron density conversion for a Megavoltage Cone Beam CT System

Introduction: The motivation for developing megavoltage (and kilovoltage) cone beam CT (MV CBCT) capabilities in the radiotherapy treatment room was primarily based on the need to improve patient set-up accuracy. There has recently been an interest in using the cone beam CT data for treatment planning. Accurate treatment planning, however, requires knowledge of the electron density of the tissues receiving radiation in order to calculate dose distributions. This is obtained from CT, utilising a conversion between CT number and electron density of various tissues. The use of MV CBCT has particular advantages compared to treatment planning with kilovoltage CT in the presence of high atomic number materials and requires the conversion of pixel values from the image sets to electron density. Therefore, a study was undertaken to characterise the pixel value to electron density relationship for the Siemens MV CBCT system, MVision, and determine the effect, if any, of differing the number of monitor units used for acquisition. If a significant difference with number of monitor units was seen then pixel value to ED conversions may be required for each of the clinical settings. The calibration of the MV CT images for electron density offers the possibility for a daily recalculation of the dose distribution and the introduction of new adaptive radiotherapy treatment strategies. Methods: A Gammex Electron Density CT Phantom was imaged with the MVCB CT system. The pixel value for each of the sixteen inserts, which ranged from 0.292 to 1.707 relative electron density to the background solid water, was determined by taking the mean value from within a region of interest centred on the insert, over 5 slices within the centre of the phantom. These results were averaged and plotted against the relative electron densities of each insert with a linear least squares fit was preformed. This procedure was performed for images acquired with 5, 8, 15 and 60 monitor units. Results: The linear relationship between MVCT pixel value and ED was demonstrated for all monitor unit settings and over a range of electron densities. The number of monitor units utilised was found to have no significant impact on this relationship. Discussion: It was found that the number of MU utilised does not significantly alter the pixel value obtained for different ED materials. However, to ensure the most accurate and reproducible MV to ED calibration, one MU setting should be chosen and used routinely. To ensure accuracy for the clinical situation this MU setting should correspond to that which is used clinically. If more than one MU setting is used clinically then an average of the CT values acquired with different numbers of MU could be utilized without loss in accuracy. Conclusions: No significant differences have been shown between the pixel value to ED conversion for the Siemens MV CT cone beam unit with change in monitor units. Thus as single conversion curve could be utilised for MV CT treatment planning. To fully utilise MV CT imaging for radiotherapy treatment planning further work will be undertaken to ensure all corrections have been made and dose calculations verified. These dose calculations may be either for treatment planning purposes or for reconstructing the delivered dose distribution from transit dosimetry measurements made using electronic portal imaging devices. This will potentially allow the cumulative dose distribution to be determined through the patient’s multi-fraction treatment and adaptive treatment strategies developed to optimize the tumour response.

A hybrid genetic algorithm for the minimum interconnection cut problem

In the real world there are many problems in network of networks (NoNs) that can be abstracted to a so-called minimum interconnection cut problem, which is fundamentally different from those classical minimum cut problems in graph theory. Thus, it is desirable to propose an efficient and effective algorithm for the minimum interconnection cut problem. In this paper we formulate the problem in graph theory, transform it into a multi-objective and multi-constraint combinatorial optimization problem, and propose a hybrid genetic algorithm (HGA) for the problem. The HGA is a penalty-based genetic algorithm (GA) that incorporates an effective heuristic procedure to locally optimize the individuals in the population of the GA. The HGA has been implemented and evaluated by experiments. Experimental results have shown that the HGA is effective and efficient.