On including quality in applied automatic gait recognition

Many gait recognition approaches use silhouette data. Imperfections in silhouette extraction have a negative effect on the performance of a gait recognition system. In this paper we extend quality metrics for gait recognition and evaluate new ways of using quality to improve a recognition system. We demonstrate use of quality to improve silhouette data and select gait cycles of best quality. The potential of the new approaches has been demonstrated experimentally on a challenging dataset, showing how recognition capability can be dramatically improved. Our practical study also shows that acquiring samples of adequate quality in arbitrary environments is difficult and that including quality analysis can improve performance markedly

Joint Spectrum Sensing and Resource Allocation for OFDM-based Transmission with a Cognitive Relay

In this paper, we investigate the joint spectrum sensing and resource allocation problem to maximize throughput capacity of an OFDM-based cognitive radio link with a cognitive relay. By applying a cognitive relay that uses decode and forward (D&F), we achieve more reliable communications, generating less interference (by needing less transmit power) and more diversity gain. In order to account for imperfections in spectrum sensing, the proposed schemes jointly modify energy detector thresholds and allocates transmit powers to all cognitive radio (CR) subcarriers, while simultaneously assigning subcarrier pairs for secondary users (SU) and the cognitive relay. This problem is cast as a constrained optimization problem with constraints on (1) interference introduced by the SU and the cognitive relay to the PUs; (2) miss-detection and false alarm probabilities and (3) subcarrier pairing for transmission on the SU transmitter and the cognitive relay and (4) minimum Quality of Service (QoS) for each CR subcarrier. We propose one optimal and two sub-optimal schemes all of which are compared to other schemes in the literature. Simulation results show that the proposed schemes achieve significantly higher throughput than other schemes in the literature for different relay situations.Comment: EAI Endorsed Transactions on Wireless Spectrum 14(1): e4 Published 13th Apr 201

The relationship of social support and quality of life with the level of stress in pregnant women using the PATH model

Background: Lack of adequate social support, stress, and generally poor quality of life during pregnancy leads to adverse pregnancy outcomes for both the mother and the baby. Objectives: This study aimed to investigate the relationship of social support and quality of life with level of stress during pregnancy. Materials and Methods: This was a descriptive-correlative study conducted on 210 pregnant women (meeting study criteria), attending Shahriar Social Services Hospital during 2012. Purposive convenient sampling was used. Study subjects completed questionnaires of obstetrics and demographics, VAUX social support, World Health Organization quality of life, and stress during pregnancy. Data were analyzed with SPSS-19 and Lisrel 8.8, utilizing statistical path analysis. Results: The final path model fitted well (CF1 = 1, RMSEA = 0.00) and showed that direct quality of life paths with β = -0.2, and indirect social support with β = -0.088 had the most effects on reduction of stress during pregnancy. Conclusion: Social support indirectly and quality of life directly affect stress during pregnancy. Thus, health officials should attempt to establish measures to further enhance social support and quality of life of pregnant women to reduce stress and its consequences during this time. © 2013, Iranian Red Crescent Medical Journal

Using a taxonomy of behaviour change techniques to define key components of Stop Delirium! a complex intervention to prevent delirium in care homes

Objective: This paper aims to describe Behaviour Change Techniques (BCTs) used within a multi-component intervention to prevent delirium in older people living in care homes, called Stop Delirium! Methods: The Behaviour Change Technique Taxonomy version 1 (BCTTv1) was used to code and characterise the ‘key ingredients’ within Stop Delirium!. Four sources of information were examined to identify BCTs used: intervention manual and toolkit; the delirium resource box; and contemporaneous written logs recorded by staff delivering the intervention in two feasibility studies. Details of BCTs used in each part of the intervention and whom they were targeting were recorded, as well as the frequency of each identified BCT. Results: 31.2% of all BCTs described in the BCTTv1 were used in the Stop Delirium! intervention. The majority of BCTs focused on changing care home staff behaviour through enhanced education, training and empowerment. ‘Social support (practical)’ was the most frequently occurring BCT. Conclusion: The large number of different BCTs identified within the Stop Delirium! intervention reflects the complexities of multicomponent interventions. The prominence of social support and empowerment further emphasises the group and organisational effort required to improve delirium care. By explicitly identifying and describing the BCTs used in Stop Delirium!, can enhance standardisation and replicability, and promote intervention fidelity for future trial evaluation and implementation of a multicomponent intervention to prevent delirium in long-term care

On graphs whose star sets are (co-)cliques

AbstractIn this paper we study graphs all of whose star sets induce cliques or co-cliques. We show that the star sets of every tree for each eigenvalue are independent sets. Among other results it is shown that each star set of a connected graph G with three distinct eigenvalues induces a clique if and only if G=K1,2 or K2,…,2. It is also proved that stars are the only graphs with three distinct eigenvalues having a star partition with independent star sets

Rotation invariant texture descriptors based on Gaussian Markov random fields for classification

Local Parameter Histograms (LPH) based on Gaussian–Markov random fields (GMRFs) have been successfully used in effective texture discrimination. LPH features represent the normalized histograms of locally estimated GMRF parameters via local linear regression. However, these features are not rotation invariant. In this paper two techniques to design rotation invariant LPH texture descriptors are discussed namely, Rotation Invariant LPH (RI-LPH) and the Isotropic LPH (I-LPH) descriptors. Extensive texture classification experiments using traditional GMRF features, LPH features, RI-LPH and I-LPH features are performed. Furthermore comparisons to the current state-of-the-art texture features are made. Classification results demonstrate that LPH, RI-LPH and I-LPH features achieve significantly better accuracies compared to the traditional GMRF features. RI-LPH descriptors give the highest classification rates and offer the best texture discriminative competency. RI-LPH and I-LPH features maintain higher accuracies in rotation invariant texture classification providing successful rotational invariance