Brief cognitive behavioural therapy for extreme shape concern: an evaluation

Objectives. This study was designed to evaluate a new brief cognitive-behavioural intervention to reduce concerns about body shape. Design. Women with high levels of shape concern (N = 50) were randomly assigned to cognitive behaviour therapy or applied relaxation (AR). Baseline assessments were made and then women received their treatment immediately after this assessment, ('immediate' treatment) or 5 weeks after this assessment, during which time no treatment was given ('delayed' treatment, DT). Methods. Shape concern and related cognitions and emotions were assessed at baseline, post-treatment and at 4 and 12 week follow-up (FU). Results. Immediate treatment was superior to DT in reducing shape concerns, and this difference was maintained at 4 week FU. The cognitive behavioural intervention was more effective than AR in changing shape concern and this difference was largely maintained for 3 months. Conclusions. These initial findings support the further investigation of this brief intervention.

The spatial and temporal shape of oculomotor inhibition

Selecting a stimulus as the target for a goal-directed movement involves inhibiting other competing possible responses. Inhibition has generally proved hard to study behaviorally, because it results in no measurable output. The effect of distractors on the shape of oculomotor and manual trajectories provide evidence of such inhibition. Individual saccades may deviate initially either towards, or away from, a competing distractor - the direction and extent of this deviation depends upon saccade latency, target predictability and the target to distractor separation. The experiment reported here used these effects to show how inhibition of distractor locations develops over time. Distractors could be presented at various distances from unpredictable and predictable targets in two separate experiments. The deviation of saccade trajectories was compared between trials with and without distractors. Inhibition was measured by saccade trajectory deviation. Inhibition was found to increase as the distractor distance from target decreased but was found to increase with saccade latency at all distractor distances (albeit to different peaks). Surprisingly, no differences were found between unpredictable and predictable targets perhaps because our saccade latencies were generally long (similar to 260-280 ms.). We conclude that oculomotor inhibition of saccades to possible target objects involves the same mechanisms for all distractor distances and target types. (C) 2009 Elsevier Ltd. All rights reserved.

A longitudinal study of perceptual grouping by proximity, luminance and shape in infants at two, four and six months

Grouping by luminance and shape similarity has previously been demonstrated in neonates and at 4 months, respectively. By contrast, grouping by proximity has hitherto not been investigated in infancy. This is also the first study to chart the developmental emergence of perceptual grouping longitudinally. Sixty-one infants were presented with a matrix of local stimuli grouped horizontally or vertically by luminance, shape or proximity at 2, 4, and 6 months. Infants were exposed to each set of stimuli for three presentation durations. Grouping was demonstrated for luminance similarity at the earliest testing age, 2 months, by shape similarity at 4 months, but was not observed for grouping by proximity. Grouping by shape similarity showed a distinctive pattern of grouping ability across exposure durations, which reflected familiarity preferences followed by novelty preferences. This remained stable across age. No link was found between the emergence of perceptual grouping ability and the exposure duration required to elicit grouping. We conclude by stressing the importance of longitudinal studies of infant development in furthering our understanding of human cognition, rather than relying on assumptions from the adult endstate.

A fuzzy multi-criteria decision approach for enhanced auto-tracking of seismic events

The main activity carried out by the geophysicist when interpreting seismic data, in terms of both importance and time spent is tracking (or picking) seismic events. in practice, this activity turns out to be rather challenging, particularly when the targeted event is interrupted by discontinuities such as geological faults or exhibits lateral changes in seismic character. In recent years, several automated schemes, known as auto-trackers, have been developed to assist the interpreter in this tedious and time-consuming task. The automatic tracking tool available in modem interpretation software packages often employs artificial neural networks (ANN's) to identify seismic picks belonging to target events through a pattern recognition process. The ability of ANNs to track horizons across discontinuities largely depends on how reliably data patterns characterise these horizons. While seismic attributes are commonly used to characterise amplitude peaks forming a seismic horizon, some researchers in the field claim that inherent seismic information is lost in the attribute extraction process and advocate instead the use of raw data (amplitude samples). This paper investigates the performance of ANNs using either characterisation methods, and demonstrates how the complementarity of both seismic attributes and raw data can be exploited in conjunction with other geological information in a fuzzy inference system (FIS) to achieve an enhanced auto-tracking performance.

Efficient fuzzy control of a rotary inverted pendulum based on LQR mapping

This paper develops fuzzy methods for control of the rotary inverted pendulum, an underactuated mechanical system. Two control laws are presented, one for swing up and another for the stabilization. The pendulum is swung up from the vertical down stable position to the upward unstable position in a controlled trajectory. The rules for the swing up are heuristically written such that each swing results in greater energy build up. The stabilization is achieved by mapping a stabilizing LQR control law to two fuzzy inference engines, which reduces the computational load compared with using a single fuzzy inference engine. The robustness of the balancing control is tested by attaching a bottle of water at the tip of the pendulum.

Autonomous intelligent cruise control using a novel multiple-controller framework incorporating fuzzy-logic-based switching and tuning

This paper presents a novel intelligent multiple-controller framework incorporating a fuzzy-logic-based switching and tuning supervisor along with a generalised learning model (GLM) for an autonomous cruise control application. The proposed methodology combines the benefits of a conventional proportional-integral-derivative (PID) controller, and a PID structure-based (simultaneous) zero and pole placement controller. The switching decision between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using a fuzzy-logic-based supervisor, operating at the highest level of the system. The supervisor is also employed to adaptively tune the parameters of the multiple controllers in order to achieve the desired closed-loop system performance. The intelligent multiple-controller framework is applied to the autonomous cruise control problem in order to maintain a desired vehicle speed by controlling the throttle plate angle in an electronic throttle control (ETC) system. Sample simulation results using a validated nonlinear vehicle model are used to demonstrate the effectiveness of the multiple-controller with respect to adaptively tracking the desired vehicle speed changes and achieving the desired speed of response, whilst penalising excessive control action. Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.

A novel shape feature for fast region-based pedestrian recognition

A new class of shape features for region classification and high-level recognition is introduced. The novel Randomised Region Ray (RRR) features can be used to train binary decision trees for object category classification using an abstract representation of the scene. In particular we address the problem of human detection using an over segmented input image. We therefore do not rely on pixel values for training, instead we design and train specialised classifiers on the sparse set of semantic regions which compose the image. Thanks to the abstract nature of the input, the trained classifier has the potential to be fast and applicable to extreme imagery conditions. We demonstrate and evaluate its performance in people detection using a pedestrian dataset.