We love to read — a collaborative endeavor to build the foundation for lifelong readers

This article presents a model of a reading motivation project for a group of fourth grade students. The project incorporates strategies shown to promote engagement in literacy: opportunities for choice, reflection and social interaction. It features the use of metacognitive activities where students set weekly goals and reflect upon how they are growing as readers

System Model Bias Processing Approach for Regional Coordinated States Information Involved Filtering

In the Kalman filtering applications, the conventional dynamic model which connects the states information of two consecutive epochs by state transition matrix is usually predefined and assumed to be invariant. Aiming to improve the adaptability and accuracy of dynamic model, we propose multiple historical states involved filtering algorithm. An autoregressive model is used as the dynamic model which is subsequently combined with observation model for deriving the optimal window-recursive filter formulae in the sense of minimum mean square error principle. The corresponding test statistics characteristics of system residuals are discussed in details. The test statistics of regional predicted residuals are then constructed in a time-window for model bias testing with two hypotheses, that is, the null and alternative hypotheses. Based on the innovations test statistics, we develop a model bias processing procedure including bias detection, location identification, and state correction. Finally, the minimum detectable bias and bias-tonoise ratio are both computed for evaluating the internal and external reliability of overall system, respectively

The Influence of Irrelevant Visual Distractors on Eye Movement Control in Chinese Children with Autism Spectrum Disorder: Evidence from the Remote Distractor Paradigm

The current study examined eye movement control in autistic (ASD) children. Simple targets were presented in isolation, or with central, parafoveal, or peripheral distractors synchronously. Sixteen children with ASD (47-81 months) and nineteen age and IQ matched typically developing children (TD) were instructed to look to the target as accurately and quickly as possible. Both groups showed high proportions (40%) of saccadic errors towards parafoveal and peripheral distractors. For correctly executed eye movements to the targets, centrally presented distractors produced the longest latencies (time taken to initiate eye movements), followed by parafoveal and peripheral distractor conditions. Central distractors had a greater effect in the ASD group, indicating evidence for potential atypical voluntary attentional control in ASD children

Generalized Linear Quaternion Complementary Filter for Attitude Estimation from Multi-Sensor Observations: An Optimization Approach

International audienceFocusing on generalized sensor combinations, this paper deals with attitude estimation problem using a linear complementary filter. The quaternion observation model is obtained via a gradient descent algorithm (GDA). An additive measurement model is then established according to derived results. The filter is named as the generalized complementary filter (GCF) where the observation model is simplified to its limit as a linear one that is quite different from previous-reported brute-force computation results. Moreover, we prove that representative derivative-based optimization algorithms are essentially equivalent to each other. Derivations are given to establish the state model based on the quaternion kinematic equation. The proposed algorithm is validated under several experimental conditions involving free-living environment, harsh external field disturbances and aerial flight test aided by robotic vision. Using the specially designed experimental devices, data acquisition and algorithm computations are performed to give comparisons on accuracy, robustness, time-consumption and etc. with representative methods. The results show that not only the proposed filter can give fast, accurate and stable estimates in terms of various sensor combinations, but it also produces robust attitude estimation in the presence of harsh situations e.g. irregular magnetic distortion. Note to Practitioners-Multi-sensor attitude estimation is a crucial technique in robotic devices. Many existing methods focus on the orientation fusion of specific sensor combinations. In this paper we make the problem more abstract. The results given in this paper are very general and can significantly decrease the space consumption and computation burden without losing the original estimation accuracy. Such performance will be of benefit to robotic platforms requiring flexible and easy-to-tune attitude estimation in the future