Investigation of structural parameter dependence of confinement losses in PCF–FBG sensor for oil and gas sensing applications.

Photonic crystal fibre (PCF)–fibre bragg grating (FBG) integration opens up new possibilities in multi-parameter fibre-optic sensing, owing to their active control over light characteristics and mode confinements. Their integration results in a mismatch in their mode field diameters (MFDs), which in turn causes various types of losses such as confinement loss, scattering loss, etc. This paper primarily investigates the effect of geometrical parameters on fibre parameters such as confinement loss and MFD, which plays a significant role in long distance fibre-optic remote sensing. Liquid crystal PCFs (LCPCFs) are utilized in the sensor configuration, exploiting their optical properties for photonic bandgap based tighter mode confinements and wavelength tunability. Furthermore, the LCPCF–FBG combo enables multi-parameter fibre-optic sensing which can be effectively utilized in oil and gas sensing applications. Theoretical study conducted on the fibre sensor revealed that confinement loss and MFD can be reduced by properly optimizing their structural parameters

Dynamic localization plan for underwater mobile sensor nodes using fuzzy decision support system.

Underwater mobile sensor node localization is a key enabling technology for several subsea missions. A novel scalable underwater localization scheme, called Best Suitable Localization Algorithm (BLSA), is proposed to dynamically fuse multiple position estimates of sensor nodes using fuzzy logic, aiming at improving localization accuracy and availability along the whole trajectory in missions. Numerical simulation has been conducted to demonstrate significant improvement in localization accuracy and availability by using the proposed fuzzy inference system. The proposed method provides a costeffective localization system by harnessing all available localization methods on-board

A Fuzzy Cooperative Localisation Framework for Underwater Robotic Swarms

This article proposes a holistic localisation framework for underwater robotic swarms to dynamically fuse multiple position estimates of an autonomous underwater vehicle while using fuzzy decision support system. A number of underwater localisation methods have been proposed in the literature for wireless sensor networks. The proposed navigation framework harnesses the established localisation methods in order to provide navigation aids in the absence of acoustic exteroceptive sensors navigation aid (i.e., ultra-short base line) and it can be extended to accommodate newly developed localisation methods by expanding the fuzzy rule base. Simplicity, flexibility, and scalability are the main three advantages that are inherent in the proposed localisation framework when compared to other traditional and commonly adopted underwater localisation methods, such as the Extended Kalman Filter. A physics-based simulation platform that considers environment’s hydrodynamics, industrial grade inertial measurement unit, and underwater acoustic communications characteristics is implemented in order to validate the proposed localisation framework on a swarm size of 150 autonomous underwater vehicles. The proposed fuzzy-based localisation algorithm improves the entire swarm mean localisation error and standard deviation by 16.53% and 35.17%, respectively, when compared to the Extended Kalman Filter based localisation with round-robin scheduling

Graphics-processing-unit-based acceleration of electromagnetic transients simulation.

This paper presents a novel parallelization approach to speedup EMT simulation, using GPU-based computing. This paper extends earlier published works in the area, by exploiting additional parallelism to accelerate EMT simulation. A 2D-parallel matrix-vector multiplication is used that is faster than previous 1D-methods. Also this paper implements a simpler GPU-specific sparsity technique to further speed up the simulations as available CPU-based sparse techniques are not suitable for GPUs. Additionally, as an extension to previous works, this paper demonstrates modelling of a power electronic subsystem. A low granularity system, i.e. one with a large cluster of busses connected to others with a few transmission lines is considered, as is also a high granularity where a small cluster of busses is connected to other clusters thereby requiring more interconnecting transmission lines. Computation times for GPU-based computing are compared with the computation times for sequential implementations on the CPU. The paper shows two surprising differences of GPU simulation in comparison with CPU simulation. Firstly, the inclusion of sparsity only makes minor reductions in the GPU-based simulation time. Secondly excessive granularity, even though it appears to increase the number of parallel computable subsystems, significantly slows down the GPU-based simulation

Extremely random forest based automatic tonic-clonic seizure detection using spectral analysis on electroencephalography data.

Machine learning proliferates society and has begun changing medicine. This report covers an investigation into how Extremely Random Forests combined with Fast Fourier Transform feature extraction performed on two-dimensional time-series Epileptic Seizure data from the Bonn/UCI dataset. It found that robust classification can take place with lower channel counts, achieving 99.81% recall, 98.8% precision and 99.35% accuracy, outperforming previous works carried into this scenario

Position referenced force augmentation in teleoperated hydraulic manipulators operating under delayed and lossy networks: a pilot study.

Position error between motions of the master and slave end-effectors is inevitable as it originates from hard-to-avoid imperfections in controller design and model uncertainty. Moreover, when a slave manipulator is controlled through a delayed and lossy communication channel, the error between the desired motion originating from the master device and the actual movement of the slave manipulator end-effector is further exacerbated. This paper introduces a force feedback scheme to alleviate this problem by simply guiding the operator to slow down the haptic device motion and, in turn, allows the slave manipulator to follow the desired trajectory closely. Using this scheme, the master haptic device generates a force, which is proportional to the position error at the slave end-effector, and opposite to the operator's intended motion at the master site. Indeed, this force is a signal or cue to the operator for reducing the hand speed when position error, due to delayed and lossy network, appears at the slave site. Effectiveness of the proposed scheme is validated by performing experiments on a hydraulic telemanipulator setup developed for performing live-line maintenance. Experiments are conducted when the system operates under both dedicated and wireless networks. Results show that the scheme performs well in reducing the position error between the haptic device and the slave end-effector. Specifically, by utilizing the proposed force, the mean position error, for the case presented here, reduces by at least 92% as compared to the condition without the proposed force augmentation scheme. The scheme is easy to implement, as the only required on-line measurement is the angular displacement of the slave manipulator joints

Pipeline leakage detection and characterisation with adaptive surrogate modelling using particle swarm optimisation.

Pipelines are often subject to leakage due to ageing, corrosion, and weld defects, and it is difficult to avoid as the sources of leakages are diverse. Several studies have demonstrated the applicability of the machine learning model for the timely prediction of pipeline leakage. However, most of these studies rely on a large training data set for training accurate models. The cost of collecting experimental data for model training is huge, while simulation data is computationally expensive and time-consuming. To tackle this problem, the present study proposes a novel data sampling optimisation method, named adaptive particle swarm optimisation (PSO) assisted surrogate model, which was used to train the machine learning models with a limited dataset and achieved good accuracy. The proposed model incorporates the population density of training data samples and model prediction fitness to determine new data samples for improved model fitting accuracy. The proposed method is applied to 3-D pipeline leakage detection and characterisation. The result shows that the predicted leak sizes and location match the actual leakage. The significance of this study is two-fold: the practical application allows for pipeline leak prediction with limited training samples and provides a general framework for computational efficiency improvement using adaptive surrogate modelling in various real-life applications

A pilot study on aeronautical surveillance system for drone delivery using heterogeneous software defined radio framework.

This paper presents a heterogeneous computing framework to interface single board computers (SBC) to (i) distinct type of computing nodes, (ii) distinct operating systems, and (iii) distinct software applications for aeronautical surveillance system for drone delivery. The implementation platform selected is the Beagle Bone Black (BBB) having the operating system (OS) Linux Ubuntu 14. The computing nodes the BBB interfaces to are: (i) a personal laptop (MacBook Pro), (ii) a virtual machine, and (iii) two servers with distinct OSs. The software applications the BBB interfaces to are: (i) Gqrx, (ii) GNURadio, (iii) Google Earth, (iv) systems took kit (STK), and (v) Matlab. This heterogeneous computing framework, with the potential for incorporating specialized processing and networking capabilities, allows scalability for system integration to existing surveillance system for manned aircrafts. The proposed system successfully decodes the location of aircraft in real-time

Sliding mode control with disturbance estimation for underwater robot.

This paper proposes a sliding mode control with a disturbance estimation for an underwater robot. The mobility performance of an underwater robot is influenced by modeling error, observation noise, and several disturbances such as ocean current and tidal current. Therefore, a robust control system is needed for precise motion control of an underwater robot. This paper uses a sliding mode control, which is one of the robust control methods. In a sliding mode control, chattering tends to occur, if the switching gain is set to a high value. On the other hand, it is desirable to set the switching gain high from the viewpoint of robustness. Therefore, there is a trade-off between the switching gain and robustness. In the proposed method, the disturbance is estimated in real-time, and this estimated value is added to the control input. Most of the disturbances are compensated by this estimated value, and the sliding mode control is used for the rest of the disturbances. As a result, the robust control system is achieved by using the proposed method, even if the switching gain is set to a low value. The validity of the proposed method was confirmed from the simulation and experimental results