Understanding and improving the identification of concurrently presented earcons

The use of sound to communicate information as part of a user interface has been an active research area for several years. Research has shown that sound can be concurrently presented to users to increase the bandwidth and rate of data presentation. However, when sounds are concurrently presented, they may interfere with each other, such that determining the data encoded in the sound becomes difficult. Modifications to the sounds can help to avoid such interference, but due to the nature of the sounds the impact of the modifications may be constrained. This thesis investigates such interaction with concurrently presented earcons. One experiment investigates how the identification of earcons is affected by the number concurrently presented. It was found that increasing the number of earcons concurrently presented lead to a significant decrease in the proportion of earcons and their attributes successfully identified by participants. With identification falling from 70% correct for one presented earcon to 30% for four concurrently presented earcons. A second experiment identified how modifications to the design and presentation of concurrently presented earcons affected their identification. It was found that presenting each earcon with a unique timbre as well as introducing an onsettoonset delay of at least 300ms caused a significant improvement in earcon identification, and the timbre encoded attribute of earcons. However overall identification levels remained low at around 30%. Two further experiments investigated the impact of spatialisation on concurrent earcon identification. They showed that spatial presentation of earcons which did not incorporate the findings of the previous experiment significantly improved identification of earcons and the register encoded earcon attribute, over earcons that were not spatially presented but did incorporate the findings of the previous experiment. Another experiment showed that spatial presentation of earcons which incorporated the unique timbre and 300ms onsettoonset modifications significantly improved the identification of the timbre encoded earcon attribute, although overall identification remained low. These four experiments yielded a set of guidelines for concurrent earcon presentation. Due to the nature of those experiments however, a further experiment was conducted to determine the impact of the guidelines on more ecologically valid tasks. A set of modified and unmodified earcons which represented entries in a mobile diary system were compared. Overall task accuracy remained low, although participants rated the modified earcons to require significantly less subjective workload

Simulation Studies Relating to Rudder Roll Stabilization of a Container Ship Using Neural Networks

International audienceRRS (Rudder Roll Stabilization) of Ships is a difficult problem because of its associated non-linear dynamics, coupling effects and complex control requirements. This paper proposes a solution of this stabilization problem that is based on an ANN (Artificial Neural Network) controller. The controller has been trained using supervised learning. The simulation studies have been carried out using MATLAB and a non-linear model of a container ship. It has been demonstrated that the proposed controller regulates heading and also controls roll angle very successfully

Ultrasonic Auger for Narrow-Gauge Borehole Drilling

An ultrasonic auger was designed, manufactured, and tested in glass microspheres to investigate force and torque reducing effects upon application of high-powered ultrasonic vibration. It was found that whilst vibration had no effect on overhead force during rotary augering, the torque was reduced by up to 30%, depending on amplitude of vibration

FDIR for a Biologically Inspired Trenchless Drilling Device

Failure Detection, Isolation and Recovery (FDIR) of autonomous systems working in hazardous conditions is essential. Methods of detection and recovery without intervention are required. This work describes the failure modes currently identified with an autonomous biologically inspired trenchless drilling robotic system. Inverse Simulation is used for detecting failures and is demonstrated on a simulation model of the robotic system. Results from the experiments, show that Inverse Simulation can be used to detect and identify system failures

Modelling and Control of a Biologically Inspired Trenchless Drilling Device

This work presents the methods used and initial findings of the control of the model for an autonomous trenchless drilling device, with bioinspired worm-like locomotion. The model is validated using Inverse Simulation. The initial control is detailed with data from the simulation and experimental device

Simulation studies relating to rudder roll stabilization of a container ship using neural networks

RRS (Rudder Roll Stabilization) of Ships is a difficult problem because of its associated non-linear dynamics, coupling effects and complex control requirements. This paper proposes a solution of this stabilization problem that is based on an ANN (Artificial Neural Network) controller. The controller has been trained using supervised learning. The simulation studies have been carried out using MATLAB and a non-linear model of a container ship. It has been demonstrated that the proposed controller regulates heading and also controls roll angle very successfully

Understanding concurrent earcons: applying auditory scene analysis principles to concurrent earcon recognition

Two investigations into the identification of concurrently presented, structured sounds, called earcons were carried out. One of the experiments investigated how varying the number of concurrently presented earcons affected their identification. It was found that varying the number had a significant effect on the proportion of earcons identified. Reducing the number of concurrently presented earcons lead to a general increase in the proportion of presented earcons successfully identified. The second experiment investigated how modifying the earcons and their presentation, using techniques influenced by auditory scene analysis, affected earcon identification. It was found that both modifying the earcons such that each was presented with a unique timbre, and altering their presentation such that there was a 300 ms onset-to-onset time delay between each earcon were found to significantly increase identification. Guidelines were drawn from this work to assist future interface designers when incorporating concurrently presented earcons

Simulation Studies Relating to Rudder Roll Stabilization of a Container Ship Using Neural Networks

RRS (Rudder Roll Stabilization) of Ships is a difficult problem because of its associated non-linear dynamics, coupling effects and complex control requirements. This paper proposes a solution of this stabilization problem that is based on an ANN (Artificial Neural Network) controller. The controller has been trained using supervised learning. The simulation studies have been carried out using MATLAB and a non-linear model of a container ship. It has been demonstrated that the proposed controller regulates heading and also controls roll angle very successfully