Characterisation of ion transport in sulfonate based ionomer systems containing lithium and quaternary ammonium cations

Two sulfonated ionomers based on poly(triethylmethyl ammonium 2-acrylamido-2-methyl-1-propane sulfonic acid) (PAMPS) and containing mixtures of Li⁺ and quaternary ammonium cations are characterised. The first system contains Li⁺ and the methyltriethyl ammonium cation (N1222) in a 1:9 molar ratio, and the ⁷Li NMR line widths showed that the Li⁺ ions are mobile in this system below the glass transition temperature (105°C) and are therefore decoupled from the polymer segmental motion. The conductivity in this system was measured as 10^-5 Scm^-1 at 130°C. A second PAMPS system containing Li⁺ and the dimethylbutylmethoxyethyl ammonium cation (N114(2O1)) in a 2:8 molar ratio showed much lower conductivities despite a significantly lower Tg (60°C), possibly due to associations between the Li⁺ and the ether group on the ammonium cation, or between the latter cations and the sulfonate groups.

Incorporating human perception with the motion washout filter using fuzzy logic control

Abstract—Nowadays, classical washout filters are extensively used in commercial motion simulators. Even though there are several advantages for classical washout filters, such as short processing time, simplicity and ease of adjustment, they have several shortcomings. The main disadvantage is the fixed scheme and parameters of the classical washout filter cause inflexibility of the structure and thus the resulting simulator fails to suit all circumstances. Moreover, it is a conservative approach and the platform cannot be fully exploited. The aim of this research is to present a fuzzy logic approach and take the human perception error into account in the classical motion cueing algorithm, in order to improve both the physical limits of restitution and realistic human sensations. The fuzzy compensator signal is applied to adjust the filtered signals on the longitudinal and rotational channels online, as well as the tilt coordination to minimize the vestibular sensation error below the human perception threshold. The results indicate that the proposed fuzzy logic controllers significantly minimize the drawbacks of having fixed parameters and conservativeness in the classical washout filter. In addition, the performance of motion cueing algorithm and human perception for most occasions is improved.

Detection of dynamic background due to swaying movements from motion features

Dynamically changing background (dynamic background) still presents a great challenge to many motion-based video surveillance systems. In the context of event detection, it is a major source of false alarms. There is a strong need from the security industry either to detect and suppress these false alarms, or dampen the effects of background changes, so as to increase the sensitivity to meaningful events of interest. In this paper, we restrict our focus to one of the most common causes of dynamic background changes: 1) that of swaying tree branches and 2) their shadows under windy conditions. Considering the ultimate goal in a video analytics pipeline, we formulate a new dynamic background detection problem as a signal processing alternative to the previously described but unreliable computer vision-based approaches. Within this new framework, we directly reduce the number of false alarms by testing if the detected events are due to characteristic background motions. In addition, we introduce a new data set suitable for the evaluation of dynamic background detection. It consists of real-world events detected by a commercial surveillance system from two static surveillance cameras. The research question we address is whether dynamic background can be detected reliably and efficiently using simple motion features and in the presence of similar but meaningful events, such as loitering. Inspired by the tree aerodynamics theory, we propose a novel method named local variation persistence (LVP), that captures the key characteristics of swaying motions. The method is posed as a convex optimization problem, whose variable is the local variation. We derive a computationally efficient algorithm for solving the optimization problem, the solution of which is then used to form a powerful detection statistic. On our newly collected data set, we demonstrate that the proposed LVP achieves excellent detection results and outperforms the best alternative adapted from existing art in the dynamic background literature.

Driver behaviour prediction for motion simulators using changepoint segmentation

Driving phenomenon is a repetitive process, that permits sequential learning under identifying the proper change periods. Sequential filtering is widely used for tracking and prediction of state dynamics. However, it suffers at abrupt changes, which cause sudden incremental prediction error. We provide a sequential filtering approach using online Bayesian detection of change points to decrease prediction error generally, and specifically at abrupt changes. The approach learns from optimally detected segments for identifying driving behaviour. Change points detection is done by the Pruned Exact Linear Time algorithm. Computational cost of our approach is bounded by the cost of the implemented sequential filter. This computational performance is suitable to the online nature of motion simulator's delay reduction. The approach was tested on a simulated driving scenario using Vortex by CM Labs. The state dimensions are simulated 2D space coordinates, and velocity. Particle filter was used for online sequential filtering. Prediction results show that change-point detection improves the quality of state estimation compared to traditional sequential filters, and is more suitable for predicting behavioural activities.

Predictive motion planning for AUVs subject to strong time-varying currents and forecasting uncertainties

This paper presents a novel path planning method for minimizing the energy consumption of an autonomous underwater vehicle subjected to time varying ocean disturbances and forecast model uncertainty. The algorithm determines 4-Dimensional path candidates using Nonlinear Robust Model Predictive Control (NRMPC) and solutions optimised using A∗-like algorithms. Vehicle performance limits are incorporated into the algorithm with disturbances represented as spatial and temporally varying ocean currents with a bounded uncertainty in their predictions. The proposed algorithm is demonstrated through simulations using a 4-Dimensional, spatially distributed time-series predictive ocean current model. Results show the combined NRMPC and A∗ approach is capable of generating energy-efficient paths which are resistant to both dynamic disturbances and ocean model uncertainty.

Motion/Force transmission analysis of axis-symmetric parallel mechanisms with closed-loop sub-chains

 This thesis presents several results regarding the kinematic performance analysis of axis-symmetric parallel mechanisms with closed-loop sub-chains. Screw theory based methods have been utilised to generate new indices, along with a formal procedure, enabling the systematic and complete singularity and motion/force transmission analysis of parallel mechanisms with these closed-loop sub-chains.

CCTV scene perspective distortion estimation from low-level motion features

Our aim is to estimate the perspective-effected geometric distortion of a scene from a video feed. In contrast to most related previous work, in this task we are constrained to use low-level spatiotemporally local motion features only. This particular challenge arises in many semiautomatic surveillance systems that alert a human operator to potential abnormalities in the scene. Low-level spatiotemporally local motion features are sparse (and thus require comparatively little storage space) and sufficiently powerful in the context of video abnormality detection to reduce the need for human intervention by more than 100-fold. This paper introduces three significant contributions. First, we describe a dense algorithm for perspective estimation, which uses motion features to estimate the perspective distortion at each image locus and then polls all such local estimates to arrive at the globally best estimate. Second, we also present an alternative coarse algorithm that subdivides the image frame into blocks and uses motion features to derive block-specific motion characteristics and constrain the relationships between these characteristics, with the perspective estimate emerging as a result of a global optimization scheme. Third, we report the results of an evaluation using nine large sets acquired using existing closed-circuit television cameras, not installed specifically for the purposes of this paper. Our findings demonstrate that both proposed methods are successful, their accuracy matching that of human labeling using complete visual data (by the constraints of the setup unavailable to our algorithms).