A novel 15N tracer approach for the quantification of N2 and N2O emissions from soil incubations in a completely automated laboratory set up

The microbial mediated production of nitrous oxide (N2O) and its reduction to dinitrogen (N2) via denitrification represents a loss of nitrogen (N) from fertilised agro-ecosystems to the atmosphere. Although denitrification has received great interest by biogeochemists in the last decades, the magnitude of N2lossesand related N2:N2O ratios from soils still are largely unknown due to methodical constraints. We present a novel 15N tracer approach, based on a previous developed tracer method to study denitrification in pure bacterial cultures which was modified for the use on soil incubations in a completely automated laboratory set up. The method uses a background air in the incubation vessels that is replaced with a helium-oxygen gas mixture with a 50-fold reduced N2 background (2 % v/v). This method allows for a direct and sensitive quantification of the N2 and N2O emissions from the soil with isotope-ratio mass spectrometry after 15N labelling of denitrification N substrates and minimises the sensitivity to the intrusion of atmospheric N2 at the same time. The incubation set up was used to determine the influence of different soil moisture levels on N2 and N2O emissions from a sub-tropical pasture soil in Queensland/Australia. The soil was labelled with an equivalent of 50 μg-N per gram dry soil by broadcast application of KNO3solution (4 at.% 15N) and incubated for 3 days at 80% and 100% water filled pore space (WFPS), respectively. The headspace of the incubation vessel was sampled automatically over 12hrs each day and 3 samples (0, 6, and 12 hrs after incubation start) of headspace gas analysed for N2 and N2O with an isotope-ratio mass spectrometer (DELTA V Plus, Thermo Fisher Scientific, Bremen, Germany(. In addition, the soil was analysed for 15N NO3- and NH4+ using the 15N diffusion method, which enabled us to obtain a complete N balance. The method proved to be highly sensitive for N2 and N2O emissions detecting N2O emissions ranging from 20 to 627 μN kg-1soil-1hr-1and N2 emissions ranging from 4.2 to 43 μN kg-1soil-1hr-1for the different treatments. The main end-product of denitrification was N2O for both water contents with N2 accounting for 9% and 13% of the total denitrification losses at 80% and 100%WFPS, respectively. Between 95-100% of the added 15N fertiliser could be recovered. Gross nitrification over the 3 days amounted to 8.6 μN g-1 soil-1 and 4.7 μN g-1 soil-1, denitrification to 4.1 μN g-1 soil-1 and 11.8 μN g-1 soil-1at 80% and 100%WFPS, respectively. The results confirm that the tested method allows for a direct and highly sensitive detection of N2 and N2O fluxes from soils and hence offers a sensitive tool to study denitrification and N turnover in terrestrial agro-ecosystems.

Automated fit assessment of intramedullary nail designs during insertion

Modern intramedullary nails, which are utilised for the treatment of bone fractures, need to be designed to fit the anatomy of the patient population. Traditional and recent semi-automated approaches for quantifying the anatomical fit between bones and nail designs suffer from various drawbacks. This thesis proposed an automated comprehensive nail design validation method. The developed software tool was utilised to quantify the anatomical fit of four commercial nail designs. Furthermore, the thesis demonstrated the existence of a bone-nail specific nail entry point. The developed method is of great benefit for the implant manufacturing industry as a nail design validation tool.

Automated categorisation of patent claims that reference human genome sequences

Debates on gene patents have necessitated the analysis of patents that disclose and reference human sequences. In this study, we built an automated classifier that assigns sequences to one of nine predefined categories according to their functional roles in patent claims by applying natural language processing and supervised learning techniques. To improve its correctness, we experimented with various feature mappings, resulting in the maximal accuracy of 79%.

An automated emergency landing system for fixed-wing aircraft: Planning and control

A number of hurdles must be overcome in order to integrate unmanned aircraft into civilian airspace for routine operations. The ability of the aircraft to land safely in an emergency is essential to reduce the risk to people, infrastructure and aircraft. To date, few field-demonstrated systems have been presented that show online re-planning and repeatability from failure to touchdown. This paper presents the development of the Guidance, Navigation and Control (GNC) component of an Automated Emergency Landing System (AELS) intended to address this gap, suited to a variety of fixed-wing aircraft. Field-tested on both a fixed-wing UAV and Cessna 172R during repeated emergency landing experiments, a trochoid-based path planner computes feasible trajectories and a simplified control system executes the required manoeuvres to guide the aircraft towards touchdown on a predefined landing site. This is achieved in zero-thrust conditions with engine forced to idle to simulate failure. During an autonomous landing, the controller uses airspeed, inertial and GPS data to track motion and maintains essential flight parameters to guarantee flyability, while the planner monitors glide ratio and re-plans to ensure approach at correct altitude. Simulations show reliability of the system in a variety of wind conditions and its repeated ability to land within the boundary of a predefined landing site. Results from field-tests for the two aircraft demonstrate the effectiveness of the proposed GNC system in live operation. Results show that the system is capable of guiding the aircraft to close proximity of a predefined keyhole in nearly 100% of cases.

Artificial Neural Network Based Automatic Emergency Landing Site Selection for UAVs and Highly Automated Aircraft

In this report an artificial neural network (ANN) based automated emergency landing site selection system for unmanned aerial vehicle (UAV) and general aviation (GA) is described. The system aims increase safety of UAV operation by emulating pilot decision making in emergency landing scenarios using an ANN to select a safe landing site from available candidates. The strength of an ANN to model complex input relationships makes it a perfect system to handle the multicriteria decision making (MCDM) process of emergency landing site selection. The ANN operates by identifying the more favorable of two landing sites when provided with an input vector derived from both landing site's parameters, the aircraft's current state and wind measurements. The system consists of a feed forward ANN, a pre-processor class which produces ANN input vectors and a class in charge of creating a ranking of landing site candidates using the ANN. The system was successfully implemented in C++ using the FANN C++ library and ROS. Results obtained from ANN training and simulations using randomly generated landing sites by a site detection simulator data verify the feasibility of an ANN based automated emergency landing site selection system.

Automated topometric graph generation from floor plan analysis

The world is rich with information such as signage and maps to assist humans to navigate. We present a method to extract topological spatial information from a generic bitmap floor plan and build a topometric graph that can be used by a mobile robot for tasks such as path planning and guided exploration. The algorithm first detects and extracts text in an image of the floor plan. Using the locations of the extracted text, flood fill is used to find the rooms and hallways. Doors are found by matching SURF features and these form the connections between rooms, which are the edges of the topological graph. Our system is able to automatically detect doors and differentiate between hallways and rooms, which is important for effective navigation. We show that our method can extract a topometric graph from a floor plan and is robust against ambiguous cases most commonly seen in floor plans including elevators and stairwells.

Using automated supplementation systems to meet growth targets for grazing sheep

Remote drafting technology now available for sheep allows targeted supplementation of individuals within a grazing flock. This paper reports results of three experiments. Experiment 1 examined the weight change of Merino wethers allowed access to either lupin grain or whole cottonseed 0, 1, 2 or 7 days/week for 6 weeks. Experiment 2 examined the weight change of Merino wethers allowed access to either lupins or a sorghum + cottonseed meal (CSM) supplement 0, 2, 4 or 7 days/week for 8 weeks. Experiment 3 investigated the relationship between five allocations of trough space at the supplement self-feeders (5–50 cm/sheep) and the weight change of Merino wethers allowed access to lupins 1 day/week for 8 weeks. In all experiments, the Merino wethers had free access as a single group to drinking water and low quality hay in a large group pen and were allowed access to supplement once per day on their scheduled days of access. No water was available in the areas containing supplement, but one-way flow gates allowed animals to return to the group pen in their own time. There was a linear response in growth rate to increased frequency of access to lupins in Experiments 1 and 2, with each additional day of access increasing liveweight gain by 26 and 21 g/day, respectively. Similarly, the response to the sorghum + CSM supplement was linear, although significantly lower (P < 0.05), at 12 g/day. Providing access to whole cottonseed resulted in no significant change in growth rate compared with the control animals. In Experiment 3, decreasing trough space from 50 to 5 cm/sheep had no effect on sheep liveweight change. It was concluded that the relationships developed here, for growth response to increased frequency of access to lupins or a sorghum + CSM supplement, could be used to indicate the most appropriate frequency of access to supplement, through a remote drafting unit, to achieve sheep weight change targets. Also, that a trough space of 5 cm/sheep appears adequate in this supplementation system.

Automated estimation of fatigue crack length and closure/opening stress

An experimental technique is proposed for the estimation of crack length as well as crack closure/opening stress during fatigue crack growth. A specially designed, single cantilever, crack opening displacement gauge is used to monitor these variables during fatigue crack propagation testing. The technique was experimentally validated through electronfractography.

Towards automated content analysis of discussion transcripts: A cognitive presence case

In this paper, we present the results of an exploratory study that examined the problem of automating content analysis of student online discussion transcripts. We looked at the problem of coding discussion transcripts for the levels of cognitive presence, one of the three main constructs in the Community of Inquiry (CoI) model of distance education. Using Coh-Metrix and LIWC features, together with a set of custom features developed to capture discussion context, we developed a random forest classification system that achieved 70.3% classification accuracy and 0.63 Cohen's kappa, which is significantly higher than values reported in the previous studies. Besides improvement in classification accuracy, the developed system is also less sensitive to overfitting as it uses only 205 classification features, which is around 100 times less features than in similar systems based on bag-of-words features. We also provide an overview of the classification features most indicative of the different phases of cognitive presence that gives an additional insights into the nature of cognitive presence learning cycle. Overall, our results show great potential of the proposed approach, with an added benefit of providing further characterization of the cognitive presence coding scheme.

Designing against boredom in automated driving

I draw on four years of experience in mobility and transport research. I was part of a research project with Siemens, for which we identified global trends in urban mobility and explored future business opportunities through scenario planning methods. Some of the proposed solutions for personal and public transport included driverless vehicles. In collaboration with BMW Design I explored the potential of new materials for automotive user interfaces...

Infrared thermal imaging for automated detection of diabetic foot complications

Background Although thermal imaging can be a valuable technology in the prevention and management of diabetic foot disease, it is not yet widely used in clinical practice. Technological advancement in infrared imaging increases its application range. The aim was to explore the first steps in the applicability of high-resolution infrared thermal imaging for noninvasive automated detection of signs of diabetic foot disease. Methods The plantar foot surfaces of 15 diabetes patients were imaged with an infrared camera (resolution, 1.2 mm/pixel): 5 patients had no visible signs of foot complications, 5 patients had local complications (e.g., abundant callus or neuropathic ulcer), and 5 patients had difuse complications (e.g., Charcot foot, infected ulcer, or critical ischemia). Foot temperature was calculated as mean temperature across pixels for the whole foot and for specified regions of interest (ROIs). Results No diferences in mean temperature >1.5 °C between the ipsilateral and the contralateral foot were found in patients without complications. In patients with local complications, mean temperatures of the ipsilateral and the contralateral foot were similar, but temperature at the ROI was >2 °C higher compared with the corresponding region in the contralateral foot and to the mean of the whole ipsilateral foot. In patients with difuse complications, mean temperature diferences of >3 °C between ipsilateral and contralateral foot were found. Conclusions With an algorithm based on parameters that can be captured and analyzed with a high-resolution infrared camera and a computer, it is possible to detect signs of diabetic foot disease and to discriminate between no, local, or difuse diabetic foot complications. As such, an intelligent telemedicine monitoring system for noninvasive automated detection of signs of diabetic foot disease is one step closer. Future studies are essential to confirm and extend these promising early findings.

Visual detection of occluded crop: For automated harvesting

This paper presents a novel crop detection system applied to the challenging task of field sweet pepper (capsicum) detection. The field-grown sweet pepper crop presents several challenges for robotic systems such as the high degree of occlusion and the fact that the crop can have a similar colour to the background (green on green). To overcome these issues, we propose a two-stage system that performs per-pixel segmentation followed by region detection. The output of the segmentation is used to search for highly probable regions and declares these to be sweet pepper. We propose the novel use of the local binary pattern (LBP) to perform crop segmentation. This feature improves the accuracy of crop segmentation from an AUC of 0.10, for previously proposed features, to 0.56. Using the LBP feature as the basis for our two-stage algorithm, we are able to detect 69.2% of field grown sweet peppers in three sites. This is an impressive result given that the average detection accuracy of people viewing the same colour imagery is 66.8%.

Automated feature engineering for HTTP tunnel detection

Generating discriminative input features is a key requirement for achieving highly accurate classifiers. The process of generating features from raw data is known as feature engineering and it can take significant manual effort. In this paper we propose automated feature engineering to derive a suite of additional features from a given set of basic features with the aim of both improving classifier accuracy through discriminative features, and to assist data scientists through automation. Our implementation is specific to HTTP computer network traffic. To measure the effectiveness of our proposal, we compare the performance of a supervised machine learning classifier built with automated feature engineering versus one using human-guided features. The classifier addresses a problem in computer network security, namely the detection of HTTP tunnels. We use Bro to process network traffic into base features and then apply automated feature engineering to calculate a larger set of derived features. The derived features are calculated without favour to any base feature and include entropy, length and N-grams for all string features, and counts and averages over time for all numeric features. Feature selection is then used to find the most relevant subset of these features. Testing showed that both classifiers achieved a detection rate above 99.93% at a false positive rate below 0.01%. For our datasets, we conclude that automated feature engineering can provide the advantages of increasing classifier development speed and reducing development technical difficulties through the removal of manual feature engineering. These are achieved while also maintaining classification accuracy.

Automated inspection of aircraft parts using a modified ICP algorithm

The application of computer-aided inspection integrated with the coordinate measuring machine and laser scanners to inspect manufactured aircraft parts using robust registration of two-point datasets is a subject of active research in computational metrology. This paper presents a novel approach to automated inspection by matching shapes based on the modified iterative closest point (ICP) method to define a criterion for the acceptance or rejection of a part. This procedure improves upon existing methods by doing away with the following, viz., the need for constructing either a tessellated or smooth representation of the inspected part and requirements for an a priori knowledge of approximate registration and correspondence between the points representing the computer-aided design datasets and the part to be inspected. In addition, this procedure establishes a better measure for error between the two matched datasets. The use of localized region-based triangulation is proposed for tracking the error. The approach described improves the convergence of the ICP technique with a dramatic decrease in computational effort. Experimental results obtained by implementing this proposed approach using both synthetic and practical data show that the present method is efficient and robust. This method thereby validates the algorithm, and the examples demonstrate its potential to be used in engineering applications.

Automated Discovery of Structured Process Models: Discover Structured vs. Discover and Structure

This paper addresses the problem of discovering business process models from event logs. Existing approaches to this problem strike various tradeoffs between accuracy and understandability of the discovered models. With respect to the second criterion, empirical studies have shown that block-structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several automated process discovery methods generate block-structured models by construction. These approaches however intertwine the concern of producing accurate models with that of ensuring their structuredness, sometimes sacrificing the former to ensure the latter. In this paper we propose an alternative approach that separates these two concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic technique that discovers more accurate but sometimes unstructured (and even unsound) process models, and then transform the resulting model into a structured one. An experimental evaluation shows that our “discover and structure” approach outperforms traditional “discover structured” approaches with respect to a range of accuracy and complexity measures.