Using dispersion measures for determining block-size in motion estimation

Video compression techniques remove temporal redundancy among frames and enable high compression efficiency in coding systems. Reduction of temporal redundancy is achieved by motion compensation. In turn, motion compensation requires motion estimation. Block matching is perhaps the most reliable and robust technique for motion estimation in video coding. However, block matching is computational expensive. Different approaches have been proposed in order to improve block matching motion estimation accuracy and efficiency. In this paper a block-matching strategy for motion estimation is introduced. In the proposed approach the size of matching block is adapted according to the variability of the matching areas. That is, the block-size is constrained by variations of the image intensity. The variability is assessed using two variability measures: the variance and the mean absolute deviation. Results of computer experiments aimed at validating the performance of the proposed approach are also reported

The Toowoomba adult trauma triage tool

Since the introduction of the Australasian Triage Scale (ATS) there has been considerable variation in its application. Improved uniformity in the application of the ATS by triage nurses is required. A reproducible, reliable and valid method to classify the illness acuity of Emergency Department patients so that a triage category 3 by one nurse means the same as a triage category 3 by another, not only in the same ED but also in another institution would be of considerable value to emergency nurses. This has been the driving motivation behind developing the Toowoomba Adult Trauma Triage Tool (TATTT). It is hoped the TATTT will support emergency nurses working in this challenging area by promoting standardisation and decreasing subjectivity in the triage decision process

Providing Dynamic TXOP for QoS Support of Video Transmission in IEEE 802.11e WLANs

The IEEE 802.11e standard introduced by IEEE 802.11 Task Group E (TGe) enhances the Quality of Service (QoS) by means of HCF Controlled Channel Access (HCCA). The scheduler of HCCA allocates Transmission Opportunities (TXOPs) to QoS-enabled Station (QSTA) based on their TS Specifications (TSPECs) negotiated at the traffic setup time so that it is only efficient for Constant Bit Rate (CBR) applications. However, Variable Bit Rate (VBR) traffics are not efficiently supported as they exhibit nondeterministic profile during the time. In this paper, we present a dynamic TXOP assignment Scheduling Algorithm for supporting the video traffics transmission over IEEE 802.11e wireless networks. This algorithm uses a piggybacked information about the size of the subsequent video frames of the uplink traffic to assist the Hybrid Coordinator accurately assign the TXOP according to the fast changes in the VBR profile. The proposed scheduling algorithm has been evaluated using simulation with different variability level video streams. The simulation results show that the proposed algorithm reduces the delay experienced by VBR traffic streams comparable to HCCA scheduler due to the accurate assignment of the TXOP which preserve the channel time for transmission.Comment: arXiv admin note: substantial text overlap with arXiv:1602.0369

Fidelity Assessment in Community Programs: An Approach to Validating Simplified Methodology.

Fidelity to intervention protocol is linked to best outcomes for individuals with autism spectrum disorder (ASD; see Boyd & Corley [Autism 5(4):430-441, 2001]; Pellecchia et al. [J Autism Dev Disord 45(9):2917-2927, 2015]); however, fidelity measurement tools that are both accurate and feasible for community use are often not available. In this paper we explore methods for validated simplification of fidelity assessment procedures toward the goal of increased use in clinical practice. Video recordings (n = 36) of therapists working with children with ASD were coded using three variations of fidelity assessment methodology (trial-by-trial, 5-point Likert Scale, and 3-point Likert Scale), and the results were compared for exact agreement, mastery criterion agreement, and overall reliability. The results indicated overall a very high percentage of exact agreement (mean 99.44%, range 94.4-100%) and excellent reliability (mean Krippendorff's alpha [Kα] 1.0) between the trial-by-trial and 5-point Likert Scale across all components; however, the 3-point method may be viewed as being the more feasible strategy within community programs

Keeping an eye on the violinist: motor experts show superior timing consistency in a visual perception task

Common coding theory states that perception and action may reciprocally induce each other. Consequently, motor expertise should map onto perceptual consistency in specific tasks such as predicting the exact timing of a musical entry. To test this hypothesis, ten string musicians (motor experts), ten non-string musicians (visual experts), and ten non-musicians were asked to watch progressively occluded video recordings of a first violinist indicating entries to fellow members of a string quartet. Participants synchronised with the perceived timing of the musical entries. Results revealed significant effects of motor expertise on perception. Compared to visual experts and non-musicians, string players not only responded more accurately, but also with less timing variability. These findings provide evidence that motor experts’ consistency in movement execution—a key characteristic of expert motor performance—is mirrored in lower variability in perceptual judgements, indicating close links between action competence and perception

Decoding the Encoding of Functional Brain Networks: an fMRI Classification Comparison of Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Sparse Coding Algorithms

Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet mathematical constraints such as sparse coding and positivity both provide alternate biologically-plausible frameworks for generating brain networks. Non-negative Matrix Factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms ($L1$ Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks for different constraints are used as basis functions to encode the observed functional activity at a given time point. These encodings are decoded using machine learning to compare both the algorithms and their assumptions, using the time series weights to predict whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. For classifying cognitive activity, the sparse coding algorithm of $L1$ Regularized Learning consistently outperformed 4 variations of ICA across different numbers of networks and noise levels (p$<$0.001). The NMF algorithms, which suppressed negative BOLD signal, had the poorest accuracy. Within each algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (p$<$0.001). The success of sparse coding algorithms may suggest that algorithms which enforce sparse coding, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA