Nearest neighbor imputation using spatial-temporal correlations in wireless sensor networks.

Missing data is common in Wireless Sensor Networks (WSNs), especially with multi-hop communications. There are many reasons for this phenomenon, such as unstable wireless communications, synchronization issues, and unreliable sensors. Unfortunately, missing data creates a number of problems for WSNs. First, since most sensor nodes in the network are battery-powered, it is too expensive to have the nodes retransmit missing data across the network. Data re-transmission may also cause time delays when detecting abnormal changes in an environment. Furthermore, localized reasoning techniques on sensor nodes (such as machine learning algorithms to classify states of the environment) are generally not robust enough to handle missing data. Since sensor data collected by a WSN is generally correlated in time and space, we illustrate how replacing missing sensor values with spatially and temporally correlated sensor values can significantly improve the network's performance. However, our studies show that it is important to determine which nodes are spatially and temporally correlated with each other. Simple techniques based on Euclidean distance are not sufficient for complex environmental deployments. Thus, we have developed a novel Nearest Neighbor (NN) imputation method that estimates missing data in WSNs by learning spatial and temporal correlations between sensor nodes. To improve the search time, we utilize a kd-tree data structure, which is a non-parametric, data-driven binary search tree. Instead of using traditional mean and variance of each dimension for kd-tree construction, and Euclidean distance for kd-tree search, we use weighted variances and weighted Euclidean distances based on measured percentages of missing data. We have evaluated this approach through experiments on sensor data from a volcano dataset collected by a network of Crossbow motes, as well as experiments using sensor data from a highway traffic monitoring application. Our experimental results show that our proposed 𝒦-NN imputation method has a competitive accuracy with state-of-the-art Expectation-Maximization (EM) techniques, while using much simpler computational techniques, thus making it suitable for use in resource-constrained WSNs.

Monitoring the Environmental Impact of TiO2 Nanoparticles Using a Plant-Based Sensor Network.

The increased manufacturing of nanoparticles for use in cosmetics, foods, and clothing necessitates the need for an effective system to monitor and evaluate the potential environmental impact of these nanoparticles. The goal of this research was to develop a plant-based sensor network for characterizing, monitoring, and understanding the environmental impact of TiO2 nanoparticles. The network consisted of potted Arabidopsis thaliana with a surrounding water supply, which was monitored by cameras attached to a laptop computer running a machine learning algorithm. Using the proposed plant sensor network, we were able to examine the toxicity of TiO2 nanoparticles in two systems: algae and terrestrial plants. Increased terrestrial plant growth was observed upon introduction of the nanoparticles, whereas algal growth decreased significantly. The proposed system can be further automated for high-throughput screening of nanoparticle toxicity in the environment at multiple trophic levels. The proposed plant-based sensor network could be used for more accurate characterization of the environmental impact of nanomaterials.

Reality of pay rise gives little scope for celebration.

Following our recent 1% pay rise, I opened my payslip with excitement, only to be sorely disappointed.

A RAPD-based comparison of thermophilic bacilli from milk powders.

The similarity of strains of thermophilic Geobacillus stearothermophilus (formerly Bacillus stearothermophilus), Anoxybacillus flavithermus (formerly Bacillus flavothermus), Bacillus licheniformis and Bacillus subtilis isolated from separate milk powder production runs from multiple factories was examined using a random amplified polymorphic DNA (RAPD) protocol. As a result of the analysis of the RAPD fingerprints and data relating to general growth and biochemical tests, over 98% of the 1470 isolates examined (grown at 55 degrees C) were assigned to the species G. stearothermophilus, A. flavithermus, B. licheniformis and B. subtilis. The G. stearothermophilus isolates were identified as being nearly identical to G. stearothermophilus (DSMZ 22; equivalent to ATCC 12980), or G. stearothermophilus var. calidolactis (DSMZ 1550). Three groups of isolates were found to be related to A. flavithermus (DSMZ 2641) by partial small ribosomal subunit (16S) sequence comparisons and shown to be interrelated by RAPD analyses with multiple primer sets. The thermophilic isolates of B. licheniformis were positively identified by comparison with type strains of B. licheniformis DSMZ 13 and DSMZ 8785. All of the B. subtilis strains shared bands in their RAPD profiles and were similar to a common B. subtilis type strain (DSMZ 10 and DSMZ 347). Overall, the most common and prevalent group of strains (group A) was demonstrated to be closely related to G. stearothermophilus (DSMZ 22).

Assessment of Mycobacterium tuberculosis transmission in Oxfordshire, UK, 2007-12, with whole pathogen genome sequences: an observational study.

BACKGROUND: Patients born outside the UK have contributed to a 20% rise in the UK's tuberculosis incidence since 2000, but their effect on domestic transmission is not known. Here we use whole-genome sequencing to investigate the epidemiology of tuberculosis transmission in an unselected population over 6 years. METHODS: We identified all residents with Oxfordshire postcodes with a Mycobacterium tuberculosis culture or a clinical diagnosis of tuberculosis between Jan 1, 2007, and Dec 31, 2012, using local databases and checking against the national Enhanced Tuberculosis Surveillance database. We used Illumina technology to sequence all available M tuberculosis cultures from identified cases. Sequences were clustered by genetic relatedness and compared retrospectively with contact investigations. The first patient diagnosed in each cluster was defined as the index case, with links to subsequent cases assigned first by use of any epidemiological linkage, then by genetic distance, and then by timing of diagnosis. FINDINGS: Although we identified 384 patients with a diagnosis of tuberculosis, country of birth was known for 380 and we sequenced isolates from 247 of 269 cases with culture-confirmed disease. 39 cases were genomically linked within 13 clusters, implying 26 local transmission events. Only 11 of 26 possible transmissions had been previously identified through contact tracing. Of seven genomically confirmed household clusters, five contained additional genomic links to epidemiologically unidentified non-household members. 255 (67%) patients were born in a country with high tuberculosis incidence, conferring a local incidence of 109 cases per 100,000 population per year in Oxfordshire, compared with 3·5 cases per 100,000 per year for those born in low-incidence countries. However, patients born in the low-incidence countries, predominantly UK, were more likely to have pulmonary disease (adjusted odds ratio 1·8 [95% CI 1·2-2·9]; p=0·009), social risk factors (4·4 [2·0-9·4]; p<0·0001), and be part of a local transmission cluster (4·8 [1·6-14·8]; p=0·006). INTERPRETATION: Although inward migration has contributed to the overall tuberculosis incidence, our findings suggest that most patients born in high-incidence countries reactivate latent infection acquired abroad and are not involved in local onward transmission. Systematic screening of new entrants could further improve tuberculosis control, but it is important that health care remains accessible to all individuals, especially high-risk groups, if tuberculosis control is not to be jeopardised. FUNDING: UK Clinical Research Collaboration (Wellcome Trust, Medical Research Council, National Institute for Health Research [NIHR]), and NIHR Oxford Biomedical Research Centre.

Real-time multiple human perception with color-depth cameras on a mobile robot.

The ability to perceive humans is an essential requirement for safe and efficient human-robot interaction. In real-world applications, the need for a robot to interact in real time with multiple humans in a dynamic, 3-D environment presents a significant challenge. The recent availability of commercial color-depth cameras allow for the creation of a system that makes use of the depth dimension, thus enabling a robot to observe its environment and perceive in the 3-D space. Here we present a system for 3-D multiple human perception in real time from a moving robot equipped with a color-depth camera and a consumer-grade computer. Our approach reduces computation time to achieve real-time performance through a unique combination of new ideas and established techniques. We remove the ground and ceiling planes from the 3-D point cloud input to separate candidate point clusters. We introduce the novel information concept, depth of interest, which we use to identify candidates for detection, and that avoids the computationally expensive scanning-window methods of other approaches. We utilize a cascade of detectors to distinguish humans from objects, in which we make intelligent reuse of intermediary features in successive detectors to improve computation. Because of the high computational cost of some methods, we represent our candidate tracking algorithm with a decision directed acyclic graph, which allows us to use the most computationally intense techniques only where necessary. We detail the successful implementation of our novel approach on a mobile robot and examine its performance in scenarios with real-world challenges, including occlusion, robot motion, nonupright humans, humans leaving and reentering the field of view (i.e., the reidentification challenge), human-object and human-human interaction. We conclude with the observation that the incorporation of the depth information, together with the use of modern techniques in new ways, we are able to create an accurate system for real-time 3-D perception of humans by a mobile robot.

Standard uniform colours could be a big help in cases of mistaken identity.

I was interested to read your news story (August 19) on the new uniforms being introduced at the Heart of England NHS Foundation Trust as part of its Start of Our Journey to World Class Nursing Care initiative.