Target integration and the attentional blink

If people monitor a visual stimulus stream for targets they often miss the second (T2) if it appears soon after the first (T1)-the attentional blink. There is one exception: T2 is often not missed if it appears right after T1, i.e., at lag 1. This lag-l sparing is commonly attributed to the possibility that T1 processing opens an attentional gate, which may be so sluggish that an early T2 can slip in before it closes. We investigated why the gate may close and exclude further stimuli from processing. We compared a control approach, which assumes that gate closing is exogenously triggered by the appearance of nontargets, and an integration approach, which assumes that gate closing is under endogenous control. As predicted by the latter but not the former, T2 performance and target reversals were strongly affected by the temporal distance between T1 and T2, whereas the presence or the absence of a nontarget intervening between T1 and T2 had little impact. (c) 2005 Elsevier B.V. All rights reserved.

Linear hashtable motion estimation algorithm for distributed video processing

This paper presents a parallel Linear Hashtable Motion Estimation Algorithm (LHMEA). Most parallel video compression algorithms focus on Group of Picture (GOP). Based on LHMEA we proposed earlier [1][2], we developed a parallel motion estimation algorithm focus inside of frame. We divide each reference frames into equally sized regions. These regions are going to be processed in parallel to increase the encoding speed significantly. The theory and practice speed up of parallel LHMEA according to the number of PCs in the cluster are compared and discussed. Motion Vectors (MV) are generated from the first-pass LHMEA and used as predictors for second-pass Hexagonal Search (HEXBS) motion estimation, which only searches a small number of Macroblocks (MBs). We evaluated distributed parallel implementation of LHMEA of TPA for real time video compression.

Evolving features-algorithms knowledge map to support NIDS data intelligence and learning loop architecting – a generalised approach to NIDS pattern feature space knowledge processing

This paper describes a proposed new approach to the Computer Network Security Intrusion Detection Systems (NIDS) application domain knowledge processing focused on a topic map technology-enabled representation of features of the threat pattern space as well as the knowledge of situated efficacy of alternative candidate algorithms for pattern recognition within the NIDS domain. Thus an integrative knowledge representation framework for virtualisation, data intelligence and learning loop architecting in the NIDS domain is described together with specific aspects of its deployment.