Is speeding a "real" antisocial behavior? A comparison with other antisocial behaviors

The relationship between speed and crashes has been well established in the literature, with the consequence that speed reduction through enforced or other means should lead to a reduction in crashes. The extent to which the public regard speeding as a problem that requires enforcement is less clear. Analysis was conducted on public perceptions of antisocial behaviors including speeding traffic. The data was collected as part of the British Crime Survey, a face-to-face interview with UK residents on issues relating to crime. The antisocial behavior section required participants to state the degree to which they perceived 16 antisocial behaviors to be a problem in their area. Results revealed that speeding traffic was perceived as the greatest problem in local communities, regardless of whether respondents were male or female, young, middle aged, or old. The rating of speeding traffic as the greatest problem in the community was replicated in a second, smaller postal survey, where respondents also provided strong support for enforcement on residential roads, and indicated that traveling immediately above the speed limit on residential roads was unacceptable. Results are discussed in relation to practical implications for speed enforcement, and the prioritization of limited police resources. (c) 2006 Elsevier Ltd. All rights reserved.

Variable step LMS algorithm using the accumulated instantaneous error concept

The convergence speed of the standard Least Mean Square adaptive array may be degraded in mobile communication environments. Different conventional variable step size LMS algorithms were proposed to enhance the convergence speed while maintaining low steady state error. In this paper, a new variable step LMS algorithm, using the accumulated instantaneous error concept is proposed. In the proposed algorithm, the accumulated instantaneous error is used to update the step size parameter of standard LMS is varied. Simulation results show that the proposed algorithm is simpler and yields better performance than conventional variable step LMS.

Multivariable cluster analysis for high-speed industrial machinery

The overall operation and internal complexity of a particular production machinery can be depicted in terms of clusters of multidimensional points which describe the process states, the value in each point dimension representing a measured variable from the machinery. The paper describes a new cluster analysis technique for use with manufacturing processes, to illustrate how machine behaviour can be categorised and how regions of good and poor machine behaviour can be identified. The cluster algorithm presented is the novel mean-tracking algorithm, capable of locating N-dimensional clusters in a large data space in which a considerable amount of noise is present. Implementation of the algorithm on a real-world high-speed machinery application is described, with clusters being formed from machinery data to indicate machinery error regions and error-free regions. This analysis is seen to provide a promising step ahead in the field of multivariable control of manufacturing systems.

Default perception of high-speed motion

When human observers are exposed to even slight motion signals followed by brief visual transients—stimuli containing no detectable coherent motion signals—they perceive large and salient illusory jumps. This novel effect, which we call “high phi”, challenges well-entrenched assumptions about the perception of motion, namely the minimal-motion principle and the breakdown of coherent motion perception with steps above an upper limit. Our experiments with transients such as texture randomization or contrast reversal show that the magnitude of the jump depends on spatial frequency and transient duration, but not on the speed of the inducing motion signals, and the direction of the jump depends on the duration of the inducer. Jump magnitude is robust across jump directions and different types of transient. In addition, when a texture is actually displaced by a large step beyond dmax, a breakdown of coherent motion perception is expected, but in the presence of an inducer observers again perceive coherent displacements at or just above dmax. In sum, across a large variety of stimuli, we find that when incoherent motion noise is preceded by a small bias, instead of perceiving little or no motion, as suggested by the minimal-motion principle, observers perceive jumps whose amplitude closely follows their own dmax limits.