EFFECTIVE NETWORKS FOR REAL-TIME DISTRIBUTED PROCESSING

This paper applies the concepts and methods of complex networks to the development of models and simulations of master-slave distributed real-time systems by introducing an upper bound in the allowable delivery time of the packets with computation results. Two representative interconnection models are taken into account: Uniformly random and scale free (Barabasi-Albert), including the presence of background traffic of packets. The obtained results include the identification of the uniformly random interconnectivity scheme as being largely more efficient than the scale-free counterpart. Also, increased latency tolerance of the application provides no help under congestion.

A multivariate approach for processing magnetization effects in triggered event-related functional magnetic resonance imaging time series

Triggered event-related functional magnetic resonance imaging requires sparse intervals of temporally resolved functional data acquisitions, whose initiation corresponds to the occurrence of an event, typically an epileptic spike in the electroencephalographic trace. However, conventional fMRI time series are greatly affected by non-steady-state magnetization effects, which obscure initial blood oxygen level-dependent (BOLD) signals. Here, conventional echo-planar imaging and a post-processing solution based on principal component analysis were employed to remove the dominant eigenimages of the time series, to filter out the global signal changes induced by magnetization decay and to recover BOLD signals starting with the first functional volume. This approach was compared with a physical solution using radiofrequency preparation, which nullifies magnetization effects. As an application of the method, the detectability of the initial transient BOLD response in the auditory cortex, which is elicited by the onset of acoustic scanner noise, was used to demonstrate that post-processing-based removal of magnetization effects allows to detect brain activity patterns identical with those obtained using the radiofrequency preparation. Using the auditory responses as an ideal experimental model of triggered brain activity, our results suggest that reducing the initial magnetization effects by removing a few principal components from fMRI data may be potentially useful in the analysis of triggered event-related echo-planar time series. The implications of this study are discussed with special caution to remaining technical limitations and the additional neurophysiological issues of the triggered acquisition.

Increased sensitivity in mapping task demand in visuospatial processing using reaction-time-dependent hemodynamic response predictors in rapid event-related fMRI

Searching for the neural correlates of visuospatial processing using functional magnetic resonance imaging (fMRI) is usually done in an event-related framework of cognitive subtraction, applying a paradigm comprising visuospatial cognitive components and a corresponding control task. Besides methodological caveats of the cognitive subtraction approach, the standard general linear model with fixed hemodynamic response predictors bears the risk of being underspecified. It does not take into account the variability of the blood oxygen level-dependent signal response due to variable task demand and performance on the level of each single trial. This underspecification may result in reduced sensitivity regarding the identification of task-related brain regions. In a rapid event-related fMRI study, we used an extended general linear model including single-trial reaction-time-dependent hemodynamic response predictors for the analysis of an angle discrimination task. In addition to the already known regions in superior and inferior parietal lobule, mapping the reaction-time-dependent hemodynamic response predictor revealed a more specific network including task demand-dependent regions not being detectable using the cognitive subtraction method, such as bilateral caudate nucleus and insula, right inferior frontal gyrus and left precentral gyrus.

Moving along the mental time line influences the processing of future related words

Abstract concepts like numbers or time are thought to be represented in the more concrete domain of space and the sensorimotor system. For example, thinking of past or future events has a physical manifestation in backward or forward body sway, respectively. In the present study, we investigated the reverse effect: can passive whole-body motion influence the processing of temporal information? Participants were asked to categorize verbal stimuli to the concepts future or past while they were displaced forward and backward (Experiment 1), or upward and downward (Experiment 2). The results showed that future related verbal stimuli were categorized faster during forward as compared to backward motion. This finding supports the view that temporal events are represented along a mental time line and that the sensorimotor system is linked to that representation. We showed that body motion is not just an epiphenomenon of temporal thoughts. Passive whole-body motion can influence higher-order temporal cognition.

Area-Efficient Linear Regression Architecture for Real-Time Signal Processing on FPGAs

Linear regression is a technique widely used in digital signal processing. It consists on finding the linear function that better fits a given set of samples. This paper proposes different hardware architectures for the implementation of the linear regression method on FPGAs, specially targeting area restrictive systems. It saves area at the cost of constraining the lengths of the input signal to some fixed values. We have implemented the proposed scheme in an Automatic Modulation Classifier, meeting the hard real-time constraints this kind of systems have.

Real-time image processing for crop/weed discrimination in maize fields

This paper presents a computer vision system that successfully discriminates between weed patches and crop rows under uncontrolled lighting in real-time. The system consists of two independent subsystems, a fast image processing delivering results in real-time (Fast Image Processing, FIP), and a slower and more accurate processing (Robust Crop Row Detection, RCRD) that is used to correct the first subsystem's mistakes. This combination produces a system that achieves very good results under a wide variety of conditions. Tested on several maize videos taken of different fields and during different years, the system successfully detects an average of 95% of weeds and 80% of crops under different illumination, soil humidity and weed/crop growth conditions. Moreover, the system has been shown to produce acceptable results even under very difficult conditions, such as in the presence of dramatic sowing errors or abrupt camera movements. The computer vision system has been developed for integration into a treatment system because the ideal setup for any weed sprayer system would include a tool that could provide information on the weeds and crops present at each point in real-time, while the tractor mounting the spraying bar is moving

A Dynamically Adaptable Image Processing Application Trading Off Between High Performance, Consumption and Dependability in Real Time

As embedded systems evolve, problems inherent to technology become important limitations. In less than ten years, chips will exceed the maximum allowed power consumption affecting performance, since, even though the resources available per chip are increasing, frequency of operation has stalled. Besides, as the level of integration is increased, it is difficult to keep defect density under control, so new fault tolerant techniques are required. In this demo work, a new dynamically adaptable virtual architecture (ARTICo3) to allow dynamic and context-aware use of resources is implemented in a high performance Wireless Sensor node (HiReCookie) to perform an image processing application.

Tabulating equipment: card processing operations; standard time values for determining operational effectiveness for planning estimates and for budget preparation.

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Morningness-eveningness orientation, optimal time-of-day and attitude change: Evidence for the systematic processing of a persuasive communication

This study investigates the effects of morningness-eveningness orientation and time-of-day on persuasion. In an attitude change paradigm, 120 female participants read a persuasive message that consisted of six counter-attitudinal arguments (anti-voluntary euthanasia) either in the morning (8:30 a.m.) or in the evening (7:00 p.m.). Attitude change was assessed by measuring attitudes towards the target issue before and after exposure to the message. Message processing was assessed by thought-listing and message recall tasks. Self-reported mood and arousal were monitored throughout. Participants were classified into M- and E-types according to their scores on the Horne and Ostberg (1976) MEQ questionnaire. When tested at their respective optimal time-of-day (i.e., morning for M-types/evening for E-types), M- and E-types reported higher energetic arousal, greater agreement with the message, greater message-congruent thinking, and a propensity for superior message recall compared to M- and E-types tested at their nonoptimal time-of-day (i.e., evening for M-types/morning for E-types). The attitude change in those tested at their optimal time-of-day was mediated by the level of message-congruent thinking. Results are interpreted in terms of the Elaboration Likelihood Model of persuasion. (c) 2005 Elsevier Ltd. All rights reserved.

Variable time-scale information processing

With the extensive use of pulse modulation methods in telecommunications, much work has been done in the search for a better utilisation of the transmission channel.The present research is an extension of these investigations. A new modulation method, 'Variable Time-Scale Information Processing', (VTSIP), is proposed.The basic principles of this system have been established, and the main advantages and disadvantages investigated. With the proposed system, comparison circuits detect the instants at which the input signal voltage crosses predetermined amplitude levels.The time intervals between these occurrences are measured digitally and the results are temporarily stored, before being transmitted.After reception, an inverse process enables the original signal to be reconstituted.The advantage of this system is that the irregularities in the rate of information contained in the input signal are smoothed out before transmission, allowing the use of a smaller transmission bandwidth. A disadvantage of the system is the time delay necessarily introduced by the storage process.Another disadvantage is a type of distortion caused by the finite store capacity.A simulation of the system has been made using a standard speech signal, to make some assessment of this distortion. It is concluded that the new system should be an improvement on existing pulse transmission systems, allowing the use of a smaller transmission bandwidth, but introducing a time delay.