Cramer-Rao bound analysis of location estimation in multi-hop wireless sensor networks

Location estimation is important for wireless sensor network (WSN) applications. In this paper we propose a Cramer-Rao Bound (CRB) based analytical approach for two centralized multi-hop localization algorithms to get insights into the error performance and its sensitivity to the distance measurement error, anchor node density and placement. The location estimation performance is compared with four distributed multi-hop localization algorithms by simulation to evaluate the efficiency of the proposed analytical approach. The numerical results demonstrate the complex tradeoff between the centralized and distributed localization algorithms on accuracy, complexity and communication overhead. Based on this analysis, an efficient and scalable performance evaluation tool can be designed for localization algorithms in large scale WSNs, where simulation-based evaluation approaches are impractical. © 2013 IEEE.

Spatial aspects of MRSA epidemiology:A case study using stochastic simulation, kernel estimation and SaTScan

The identification of disease clusters in space or space-time is of vital importance for public health policy and action. In the case of methicillin-resistant Staphylococcus aureus (MRSA), it is particularly important to distinguish between community and health care-associated infections, and to identify reservoirs of infection. 832 cases of MRSA in the West Midlands (UK) were tested for clustering and evidence of community transmission, after being geo-located to the centroids of UK unit postcodes (postal areas roughly equivalent to Zip+4 zip code areas). An age-stratified analysis was also carried out at the coarser spatial resolution of UK Census Output Areas. Stochastic simulation and kernel density estimation were combined to identify significant local clusters of MRSA (p<0.025), which were supported by SaTScan spatial and spatio-temporal scan. In order to investigate local sampling effort, a spatial 'random labelling' approach was used, with MRSA as cases and MSSA (methicillin-sensitive S. aureus) as controls. Heavy sampling in general was a response to MRSA outbreaks, which in turn appeared to be associated with medical care environments. The significance of clusters identified by kernel estimation was independently supported by information on the locations and client groups of nursing homes, and by preliminary molecular typing of isolates. In the absence of occupational/ lifestyle data on patients, the assumption was made that an individual's location and consequent risk is adequately represented by their residential postcode. The problems of this assumption are discussed, with recommendations for future data collection.

Estimation of ocular volume from axial length

Background/aims - To determine which biometric parameters provide optimum predictive power for ocular volume. Methods - Sixty-seven adult subjects were scanned with a Siemens 3-T MRI scanner. Mean spherical error (MSE) (D) was measured with a Shin-Nippon autorefractor and a Zeiss IOLMaster used to measure (mm) axial length (AL), anterior chamber depth (ACD) and corneal radius (CR). Total ocular volume (TOV) was calculated from T2-weighted MRIs (voxel size 1.0 mm3) using an automatic voxel counting and shading algorithm. Each MR slice was subsequently edited manually in the axial, sagittal and coronal plane, the latter enabling location of the posterior pole of the crystalline lens and partitioning of TOV into anterior (AV) and posterior volume (PV) regions. Results - Mean values (±SD) for MSE (D), AL (mm), ACD (mm) and CR (mm) were −2.62±3.83, 24.51±1.47, 3.55±0.34 and 7.75±0.28, respectively. Mean values (±SD) for TOV, AV and PV (mm3) were 8168.21±1141.86, 1099.40±139.24 and 7068.82±1134.05, respectively. TOV showed significant correlation with MSE, AL, PV (all p<0.001), CR (p=0.043) and ACD (p=0.024). Bar CR, the correlations were shown to be wholly attributable to variation in PV. Multiple linear regression indicated that the combination of AL and CR provided optimum R2 values of 79.4% for TOV. Conclusion - Clinically useful estimations of ocular volume can be obtained from measurement of AL and CR.