An anticipative reactive control strategy to deal with unforeseen obstacles during a multi-sensor-based navigation task

We address the problem of the rangefinder-based avoidance of unforeseen static obstacles during a visual navigation task. We extend previous strategies which are efficient in most cases but remain still hampered by some drawbacks (e.g., risks of collisions or of local minima in some particular cases, etc.). The key idea is to complete the control strategy by adding a controller providing the robot some anticipative skills to guarantee non collision and by defining more general transition conditions to deal with local minima. Simulation results show the proposed strategy efficiency.

Multipurpose nanoporous alumina-carbon nanowall bi-dimensional nano-hybrid platform via catalyzed and catalyst-free plasma CVD

Simple, rapid, plasma-assisted synthesis of large-area arrays of vertically-aligned carbon nanowalls on highly-porous, transparent bare and gold-coated alumina membranes with the two pore sizes is reported. It is demonstrated that the complex patterns of vertically aligned nanowalls can nucleate and form different morphologies in the low-temperature plasmas. The process is stable, and the twofold change in the gas flow (10 and 20 sccm) does not noticeably influence the morphology of the nanowall pattern. Application of a thin (5 nm) gold layer to nanoporous membrane prior to the nanowall growth allows controlling the network morphology.

Design and flight testing of an integrated solar powered UAV and WSN for greenhouse gas monitoring emissions in agricultural farms

There is an increased interest in measuring the amount of greenhouse gases produced by farming practices . This paper describes an integrated solar powered Unmanned Air Vehicles (UAV) and Wireless Sensor Network (WSN) gas sensing system for greenhouse gas emissions in agricultural lands. The system uses a generic gas sensing system for CH4 and CO2 concentrations using metal oxide (MoX) and non-dispersive infrared sensors, and a new solar cell encapsulation method to power the unmanned aerial system (UAS)as well as a data management platform to store, analyze and share the information with operators and external users. The system was successfully field tested at ground and low altitudes, collecting, storing and transmitting data in real time to a central node for analysis and 3D mapping. The system can be used in a wide range of outdoor applications at a relatively low operational cost. In particular, agricultural environments are increasingly subject to emissions mitigation policies. Accurate measurements of CH4 and CO2 with its temporal and spatial variability can provide farm managers key information to plan agricultural practices. A video of the bench and flight test performed can be seen in the following link: https://www.youtube.com/watch?v=Bwas7stYIxQ

Remote Sensing and Data Collection from Solar Powered Wireless Sensor Network using an Unmanned Aerial Vehicle System

Sensor networks for environmental monitoring present enormous benefits to the community and society as a whole. Currently there is a need for low cost, compact, solar powered sensors suitable for deployment in rural areas. The purpose of this research is to develop both a ground based wireless sensor network and data collection using unmanned aerial vehicles. The ground based sensor system is capable of measuring environmental data such as temperature or air quality using cost effective low power sensors. The sensor will be configured such that its data is stored on an ATMega16 microcontroller which will have the capability of communicating with a UAV flying overhead using UAV communication protocols. The data is then either sent to the ground in real time or stored on the UAV using a microcontroller until it lands or is close enough to enable the transmission of data to the ground station.

LiDAR Augmented Optimal Path Planning for Unmanned Aerial Vehicle during Remote Sensing at Low Altitude and Network Sensor Data Acquisition

This technical report describes a Light Detection and Ranging (LiDAR) augmented optimal path planning at low level flight methodology for remote sensing and sampling Unmanned Aerial Vehicles (UAV). The UAV is used to perform remote air sampling and data acquisition from a network of sensors on the ground. The data that contains information on the terrain is in the form of a 3D point clouds maps is processed by the algorithms to find an optimal path. The results show that the method and algorithm are able to use the LiDAR data to avoid obstacles when planning a path from a start to a target point. The report compares the performance of the method as the resolution of the LIDAR map is increased and when a Digital Elevation Model (DEM) is included. From a practical point of view, the optimal path plan is loaded and works seemingly with the UAV ground station and also shows the UAV ground station software augmented with more accurate LIDAR data.

Development of multi-rotor localised surveillance using multi-spectral sensors for plant biosecurity

This report describes a proof of concept for multi-rotor localised surveillance using a multi-spectral sensor for plant biosecurity applications. A literature review was conducted on previous applications using airborne multispectral imaging for plant biosecurity purposes. A ready built platform was purchased and modified in order to fit and provide suitable clearance for a Tetracam Mini-MCA multispectral camera. The appropriate risk management documents were developed allowing the platform and the multi-spectral camera to be tested extensively. However, due to technical difficulties with the platform the Mini- MCA was not mounted to the platform. Once a suitable platform is developed, future extensions can be conducted into the suitability of the Mini-MCA for airborne surveillance of Australian crops.

Elliptical summary randomisation for sensor-based human activity recognition

Many conventional statistical machine learning al- gorithms generalise poorly if distribution bias ex- ists in the datasets. For example, distribution bias arises in the context of domain generalisation, where knowledge acquired from multiple source domains need to be used in a previously unseen target domains. We propose Elliptical Summary Randomisation (ESRand), an efficient domain generalisation approach that comprises of a randomised kernel and elliptical data summarisation. ESRand learns a domain interdependent projection to a la- tent subspace that minimises the existing biases to the data while maintaining the functional relationship between domains. In the latent subspace, ellipsoidal summaries replace the samples to enhance the generalisation by further removing bias and noise in the data. Moreover, the summarisation enables large-scale data processing by significantly reducing the size of the data. Through comprehensive analysis, we show that our subspace-based approach outperforms state-of-the-art results on several activity recognition benchmark datasets, while keeping the computational complexity significantly low.

Lesbian, gay, bisexual, trans, and queer visibility through selfies: Comparing platform mediators across Ruby Rose’s Instagram and Vine presence

This article investigates the relationship between social media platforms and the production and dissemination of selfies in light of its implications for the visibility of lesbian, gay, bisexual, trans, and queer (LGBTQ) people. Applying an Actor Network Theory lens, two popular visual media apps, Instagram and Vine, are examined through a comparative walkthrough method. This reveals platform elements, or mediators, that can influence the conversational capacity of selfies in terms of the following: range, the variety of discourses addressed within a selfie; reach, circulation within and across publics; and salience, the strength and clarity of discourses communicated through a selfie. These mediators are illustrated through LGBTQ celebrity Ruby Rose’s Instagram selfies and Vine videos. Instagram’s use expectations encourage selfies focused on mainstream discourses of normative beauty and conspicuous consumption with an emphasis on appearance, extending through features constraining selfies’ reach and salience. In contrast, Vine’s broader use expectations enable a variety of discourses to be communicated across publics with an emphasis on creative, first-person sharing. These findings are reflected in Rose’s Instagram selfies, which mute alternative discourses of gender and sexuality through desexualized and aesthetically appealing self-representations, while Vines display her personal side, enabling both LGBTQ and heterosexual, cisgender people to identify with her without minimizing non-normative aspects of her gender and sexuality. These findings demonstrate the relevance of platforms in shaping selfies’ conversational capacity, as mediators can influence whether selfies feature in conversations reinforcing dominant discourses or in counterpublic conversations, contributing to everyday activism that challenges normative gender and sexual discourses.

Developing an Unmanned Aerial Vehicle (UAV) multi-sensor payload carrier for air quality monitoring

Emissions of gases and particles from sea-faring ships have been shown to impact on the atmospheric chemistry and climate. To efficiently monitor and report these emissions found from a ship’s plume, the concept of using a multi-rotor or UAV to hover inside or near the exhaust of the ship to actively record the data in real time is being developed. However, for the required sensors obtain the data; their sensors must face into the airflow of the ships plume. This report presents an approach to have sensors able to read in the chemicals and particles emitted from the ship without affecting the flight dynamics of the multi-rotor UAV by building a sealed chamber in which a pump can take in the surrounding air (outside the downwash effect of the multi-rotor) where the sensors are placed and can analyse the gases safely. Results show that the system is small, lightweight and air-sealed and ready for flight test.

Green Falcon IV: An Unmanned Aerial System and an integrated Wireless Sensor Network for remote sensing tasks

This report summarises the development of an Unmanned Aerial System and an integrated Wireless Sensor Network (WSN), suitable for the real world application in remote sensing tasks. Several aspects are discussed and analysed to provide improvements in flight duration, performance and mobility of the UAV, while ensuring the accuracy and range of data from the wireless sensor system.

Comparison of the sensitivity of different sensor technologies to imbalance severity in low speed wind turbines

Imbalance is not only a direct major cause of downtime in wind turbines, but also accelerates the degradation of neighbouring and downstream components (e.g. main bearing, generator). Along with detection, the imbalance quantification is also essential as some residual imbalance always exist even in a healthy turbine. Three different commonly used sensor technologies (vibration, acoustic emission and electrical measurements) are investigated in this work to verify their sensitivity to different imbalance grades. This study is based on data obtained by experimental tests performed on a small scale wind turbine drive train test-rig for different shaft speeds and imbalance levels. According to the analysis results, electrical measurements seem to be the most suitable for tracking the development of imbalance.

Nash equilibrium based semantic cache in mobile sensor grid database systems

Mobile applications are being increasingly deployed on a massive scale in various mobile sensor grid database systems. With limited resources from the mobile devices, how to process the huge number of queries from mobile users with distributed sensor grid databases becomes a critical problem for such mobile systems. While the fundamental semantic cache technique has been investigated for query optimization in sensor grid database systems, the problem is still difficult due to the fact that more realistic multi-dimensional constraints have not been considered in existing methods. To solve the problem, a new semantic cache scheme is presented in this paper for location-dependent data queries in distributed sensor grid database systems. It considers multi-dimensional constraints or factors in a unified cost model architecture, determines the parameters of the cost model in the scheme by using the concept of Nash equilibrium from game theory, and makes semantic cache decisions from the established cost model. The scenarios of three factors of semantic, time and locations are investigated as special cases, which improve existing methods. Experiments are conducted to demonstrate the semantic cache scheme presented in this paper for distributed sensor grid database systems.

Appendiceal sonography in children: A retrospective analysis as a platform for potential implementation of diagnostic categories

- Background Sonography is an important diagnostic tool in children with suspected appendicitis. Reported accuracy and appendiceal visualisation rates vary significantly, as does the management of equivocal ultrasound findings. The aim of this study was to audit appendiceal sonography at a tertiary children's hospital, and provide baseline data for a future prospective study. - Summary of work Records of children who underwent ultrasound studies for possible appendicitis between January 2008 and December 2010 were reviewed. Variables included patient demographics, sonographic appendix characteristics, and secondary signs. Descriptive statistics and analysis using ANOVA, Mann-Whitney U test, and ROC curves were performed. Mater Human Research Ethic Committee approval was granted. - Summary of results There were 457 eligible children. Using a dichotomous diagnostic model (including equivocal results), sensitivity was 89.6%, specificity 91.6%, and diagnostic yield of 40.7%. ROC curve analysis of a 6mm diameter cut-off was 0.88 AUC (95% CI 0.80 to 0.95). - Discussion and conclusions Sonography is an accurate test for acute appendicitis in children, with a high sensitivity and negative predictive value. A diameter of 6mm as an absolute cut-off in a binary model can lead to false findings. Results were compared with available literature. Recent publications propose categorising diameter1 and integrating secondary signs2 to improve accuracy and provide more meaningful results to clinicians. This study will be a benchmark for future studies with multiple diagnostic categorisation.