Vision-based estimation of airborne target pseudobearing rate using hidden Markov model filters

The problem of estimating pseudobearing rate information of an airborne target based on measurements from a vision sensor is considered. Novel image speed and heading angle estimators are presented that exploit image morphology, hidden Markov model (HMM) filtering, and relative entropy rate (RER) concepts to allow pseudobearing rate information to be determined before (or whilst) the target track is being estimated from vision information.

Automated aerial inspection guidance with improved turn planning

Aerial inspection of pipelines, powerlines, and other large linear infrastructure networks has emerged in a number of civilian remote sensing applications. Challenges relate to automating inspection flight for under-actuated aircraft with LiDAR/camera sensor constraints whilst subjected to wind disturbances. This paper presents new improved turn planning strategies with guidance suitable for automation of linear infrastructure inspection able to reduce inspection flight distance by including wind information. Simulation and experimental flight tests confirmed the flight distance saving, and the proposed guidance strategies exhibited good tracking performance in a range of wind conditions.

Creating gold nanoprisms directly on quartz crystal microbalance electrodes for mercury vapor sensing

A novel electrochemical route is used to form highly {111}-oriented and size-controlled Au nanoprisms directly onto the electrodes of quartz crystal microbalances (QCMs) which are subsequently used as mercury vapor sensors. The Au nanoprism loaded QCM sensors exhibited excellent response–concentration linearity with a response enhancement of up to ~ 800% over a non-modified sensor at an operating temperature of 28 °C. The increased surface area and atomic-scale features (step/defect sites) introduced during the growth of nanoprisms are thought to play a significant role in enhancing the sensing properties of the Au nanoprisms toward Hg vapor. The sensors are shown to have excellent Hg sensing capabilities in the concentration range of 0.123–1.27 ppmv (1.02–10.55 mg m − 3), with a detection limit of 2.4 ppbv (0.02 mg m − 3) toward Hg vapor when operating at 28 °C, and 17 ppbv (0.15 mg m − 3) at 89 °C, making them potentially useful for air monitoring applications or for monitoring the efficiency of Hg emission control systems in industries such as mining and waste incineration. The developed sensors exhibited excellent reversible behavior (sensor recovery) within 1 h periods, and crucially were also observed to have high selectivity toward Hg vapor in the presence of ethanol, ammonia and humidity, and excellent long-term stability over a 33 day operating period.

Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the a mission should be aborted due to mechanical or other failure. On-board cameras provide information that can be used in the determination of potential landing sites, which are continually updated and ranked to prevent injury and minimize damage. Pulse Coupled Neural Networks have been used for the detection of features in images that assist in the classification of vegetation and can be used to minimize damage to the aerial vehicle. However, a significant drawback in the use of PCNNs is that they are computationally expensive and have been more suited to off-line applications on conventional computing architectures. As heterogeneous computing architectures are becoming more common, an OpenCL implementation of a PCNN feature generator is presented and its performance is compared across OpenCL kernels designed for CPU, GPU and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images obtained during unmanned aerial vehicle trials to determine the plausibility for real-time feature detection.

HMM relative entropy rate concepts for vision-based aircraft manoeuvre detection

Machine vision is emerging as a viable sensing approach for mid-air collision avoidance (particularly for small to medium aircraft such as unmanned aerial vehicles). In this paper, using relative entropy rate concepts, we propose and investigate a new change detection approach that uses hidden Markov model filters to sequentially detect aircraft manoeuvres from morphologically processed image sequences. Experiments using simulated and airborne image sequences illustrate the performance of our proposed algorithm in comparison to other sequential change detection approaches applied to this application.

Practical Modbus flooding attack and detection

The Modicon Communication Bus (Modbus) protocol is one of the most commonly used protocols in industrial control systems. Modbus was not designed to provide security. This paper confirms that the Modbus protocol is vulnerable to flooding attacks. These attacks involve injection of commands that result in disrupting the normal operation of the control system. This paper describes a set of experiments that shows that an anomaly-based change detection algorithm and signature-based Snort threshold module are capable of detecting Modbus flooding attacks. In comparing these intrusion detection techniques, we find that the signature-based detection requires a carefully selected threshold value, and that the anomaly-based change detection algorithm may have a short delay before detecting the attacks depending on the parameters used. In addition, we also generate a network traffic dataset of flooding attacks on the Modbus control system protocol.

The power of hands-on exercises in SCADA cyber security education

For decades Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS) have used computers to monitor and control physical processes in many critical industries, including electricity generation, gas pipelines, water distribution, waste treatment, communications and transportation. Increasingly these systems are interconnected with corporate networks via the Internet, making them vulnerable and exposed to the same risks as those experiencing cyber-attacks on a conventional network. Very often SCADA networks services are viewed as a specialty subject, more relevant to engineers than standard IT personnel. Educators from two Australian universities have recognised these cultural issues and highlighted the gap between specialists with SCADA systems engineering skills and the specialists in network security with IT background. This paper describes a learning approach designed to help students to bridge this gap, gain theoretical knowledge of SCADA systems' vulnerabilities to cyber-attacks via experiential learning and acquire practical skills through actively participating in hands-on exercises.

Autonomous forced landing system for light general aviation aircraft in unknown environments

This paper presents a system which enhances the capabilities of a light general aviation aircraft to land autonomously in case of an unscheduled event such as engine failure. The proposed system will not only increase the level of autonomy for the general aviation aircraft industry but also increase the level of dependability. Safe autonomous landing in case of an engine failure with a certain level of reliability is the primary focus of our work as both safety and reliability are attributes of dependability. The system is designed for a light general aviation aircraft but can be extended for dependable unmanned aircraft systems. The underlying system components are computationally efficient and provides continuous situation assessment in case of an emergency landing. The proposed system is undergoing an evaluation phase using an experimental platform (Cessna 172R) in real world scenarios.

Novelty-based visual obstacle detection in agriculture

This paper describes a novel obstacle detection system for autonomous robots in agricultural field environments that uses a novelty detector to inform stereo matching. Stereo vision alone erroneously detects obstacles in environments with ambiguous appearance and ground plane such as in broad-acre crop fields with harvested crop residue. The novelty detector estimates the probability density in image descriptor space and incorporates image-space positional understanding to identify potential regions for obstacle detection using dense stereo matching. The results demonstrate that the system is able to detect obstacles typical to a farm at day and night. This system was successfully used as the sole means of obstacle detection for an autonomous robot performing a long term two hour coverage task travelling 8.5 km.

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

The Brain Research Institute (BRI) uses various types of indirect measurements, including EEG and fMRI, to understand and assess brain activity and function. As well as the recovery of generic information about brain function, research also focuses on the utilisation of such data and understanding to study the initiation, dynamics, spread and suppression of epileptic seizures. To assist with the future focussing of this aspect of their research, the BRI asked the MISG 2010 participants to examine how the available EEG and fMRI data and current knowledge about epilepsy should be analysed and interpreted to yield an enhanced understanding about brain activity occurring before, at commencement of, during, and after a seizure. Though the deliberations of the study group were wide ranging in terms of the related matters considered and discussed, considerable progress was made with the following three aspects. (1) The science behind brain activity investigations depends crucially on the quality of the analysis and interpretation of, as well as the recovery of information from, EEG and fMRI measurements. A number of specific methodologies were discussed and formalised, including independent component analysis, principal component analysis, profile monitoring and change point analysis (hidden Markov modelling, time series analysis, discontinuity identification). (2) Even though EEG measurements accurately and very sensitively record the onset of an epileptic event or seizure, they are, from the perspective of understanding the internal initiation and localisation, of limited utility. They only record neuronal activity in the cortical (surface layer) neurons of the brain, which is a direct reflection of the type of electrical activity they have been designed to record. Because fMRI records, through the monitoring of blood flow activity, the location of localised brain activity within the brain, the possibility of combining fMRI measurements with EEG, as a joint inversion activity, was discussed and examined in detail. (3) A major goal for the BRI is to improve understanding about ``when'' (at what time) an epileptic seizure actually commenced before it is identified on an eeg recording, ``where'' the source of this initiation is located in the brain, and ``what'' is the initiator. Because of the general agreement in the literature that, in one way or another, epileptic events and seizures represent abnormal synchronisations of localised and/or global brain activity the modelling of synchronisations was examined in some detail. References C. M. Michel, G. Thut, S. Morand, A. Khateb, A. J. Pegna, R. Grave de Peralta, S. Gonzalez, M. Seeck and T. Landis, Electric source imaging of human brain functions, Brain Res. 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Dynamic positioning of marine craft using a port-Hamiltonian framework

Dynamic positioning of marine craft refers to the use of the propulsion system to regulate the vessel position and heading. This type of motion control is commonly used in the offshore industry for surface vessels, and it is also used for some underwater vehicles. In this paper, we use a port-Hamiltonian framework to design a novel nonlinear set-point-regulation controller with integral action. The controller handles input saturation and guarantees internal stability, rejection of unknown constant disturbances, and (integral-)input-to-state stability.

Antispin thrust allocation for marine vessels

In extreme weather conditions, thrusters on ships and rigs may be subject to severe thrust losses caused by ventilation and in-and-out-of-water events. When a thruster ventilates, air is sucked down from the surface and into the propeller. In more severe cases, parts of or even the whole propeller can be out of the water. These losses vary rapidly with time and cause increased wear and tear in addition to reduced thruster performance. In this paper, a thrust allocation strategy is proposed to reduce the effects of thrust losses and to reduce the possibility of multiple ventilation events. This thrust allocation strategy is named antispin thrust allocation, based on the analogous behavior of antispin wheel control of cars. The proposed thrust allocation strategy is important for improving the life span of the propulsion system and the accuracy of positioning for vessels conducting station keeping in terms of dynamic positioning or thruster-assisted position mooring. Application of this strategy can result in an increase of operational time and, thus, increased profitability. The performance of the proposed allocation strategy is demonstrated with experiments on a model ship.

Hybrid frequency–time domain models for dynamic response analysis of marine structures

Time-domain models of marine structures based on frequency domain data are usually built upon the Cummins equation. This type of model is a vector integro-differential equation which involves convolution terms. These convolution terms are not convenient for analysis and design of motion control systems. In addition, these models are not efficient with respect to simulation time, and ease of implementation in standard simulation packages. For these reasons, different methods have been proposed in the literature as approximate alternative representations of the convolutions. Because the convolution is a linear operation, different approaches can be followed to obtain an approximately equivalent linear system in the form of either transfer function or state-space models. This process involves the use of system identification, and several options are available depending on how the identification problem is posed. This raises the question whether one method is better than the others. This paper therefore has three objectives. The first objective is to revisit some of the methods for replacing the convolutions, which have been reported in different areas of analysis of marine systems: hydrodynamics, wave energy conversion, and motion control systems. The second objective is to compare the different methods in terms of complexity and performance. For this purpose, a model for the response in the vertical plane of a modern containership is considered. The third objective is to describe the implementation of the resulting model in the standard simulation environment Matlab/Simulink.

Characterising the spatial dynamics of sympatric Aedes aegypti and Aedes albopictus populations in the Philippines

Entomological surveillance and control are essential to the management of dengue fever (DF). Hence, understanding the spatial and temporal patterns of DF vectors, Aedes (Stegomyia) aegypti (L.) and Ae. (Stegomyia) albopictus (Skuse), is paramount. In the Philippines, resources are limited and entomological surveillance and control are generally commenced during epidemics, when transmission is difficult to control. Recent improvements in spatial epidemiological tools and methods offer opportunities to explore more efficient DF surveillance and control solutions: however, there are few examples in the literature from resource-poor settings. The objectives of this study were to: (i) explore spatial patterns of Aedes populations and (ii) predict areas of high and low vector density to inform DF control in San Jose village, Muntinlupa city, Philippines. Fortnightly, adult female Aedes mosquitoes were collected from 50 double-sticky ovitraps (SOs) located in San Jose village for the period June-November 2011. Spatial clustering analysis was performed to identify high and low density clusters of Ae. aegypti and Ae. albopictus mosquitoes. Spatial autocorrelation was assessed by examination of semivariograms, and ordinary kriging was undertaken to create a smoothed surface of predicted vector density in the study area. Our results show that both Ae. aegypti and Ae. albopictus were present in San Jose village during the study period. However, one Aedes species was dominant in a given geographic area at a time, suggesting differing habitat preferences and interspecies competition between vectors. Density maps provide information to direct entomological control activities and advocate the development of geographically enhanced surveillance and control systems to improve DF management in the Philippines.

Recursive actuator fault detection and diagnosis for emergency landing of UASs

This paper presents a practical recursive fault detection and diagnosis (FDD) scheme for online identification of actuator faults for unmanned aerial systems (UASs) based on the unscented Kalman filtering (UKF) method. The proposed FDD algorithm aims to monitor health status of actuators and provide indication of actuator faults with reliability, offering necessary information for the design of fault-tolerant flight control systems to compensate for side-effects and improve fail-safe capability when actuator faults occur. The fault detection is conducted by designing separate UKFs to detect aileron and elevator faults using a nonlinear six degree-of-freedom (DOF) UAS model. The fault diagnosis is achieved by isolating true faults by using the Bayesian Classifier (BC) method together with a decision criterion to avoid false alarms. High-fidelity simulations with and without measurement noise are conducted with practical constraints considered for typical actuator fault scenarios, and the proposed FDD exhibits consistent effectiveness in identifying occurrence of actuator faults, verifying its suitability for integration into the design of fault-tolerant flight control systems for emergency landing of UASs.