Wearable antennas for medical monitoring systems

A wearable silver nano particle inkjet printed antenna suitable for wireless biomedical sensing is presented. The performance is evaluated on a synthetic variable layered phantom test-bed, representative of human tissue for operation in the 868/915 MHz, and 2400 MHz industrial, scientific and medical frequency bands. Antenna radiation efficiency measurements on the phantom were compared with antennas prototyped with copper. Total radiation efficiencies up to ???6.5 dB are reported, with less than 0.5 dB difference in performance between copper and silver nano particle variants, showing promising application for low-cost disposable wireless sensing.

Field investigation of contactless displacement measurement using computer vision systems for civil engineering applications

Much of the bridge stock on major transport links in North America and Europe was constructed in the 1950s and 1960s and has since deteriorated or is carrying loads far in excess of the original design loads. Structural Health Monitoring Systems (SHM) can provide valuable information on the bridge capacity but the application of such systems is currently limited by access and bridge type. This paper investigates the use of computer vision systems for SHM. A series of field tests have been carried out to test the accuracy of displacement measurements using contactless methods. A video image of each test was processed using a modified version of the optical flow tracking method to track displacement. These results have been validated with an established measurement method using linear variable differential transformers (LVDTs). The results obtained from the algorithm provided an accurate comparison with the validation measurements. The calculated displacements agree within 2% of the verified LVDT measurements, a number of post processing methods were then applied to attempt to reduce this error.

Laboratory investigation of contactless displacement measurement using computer vision systems

Much of the bridge stock on major transport links in North America and Europe was constructed in the 1950’s and 1960’s and has since deteriorated or is carrying loads far in excess of the original design loads. Structural Health Monitoring Systems (SHM) can provide valuable information on the bridge capacity but the application of such systems is currently limited by access and system cost. This paper investigates the development of a low cost portable SHM system using commercially available cameras and computer vision techniques. A series of laboratory tests have been carried out to test the accuracy of displacement measurements using contactless methods. The results from each of the tests have been validated with established measurement methods, such as linear variable differential transformers (LVDTs). A video image of each test was processed using two different digital image correlation programs. The results obtained from the digital image correlation methods provided an accurate comparison with the validation measurements. The calculated displacements agree within 4% of the verified measurements LVDT measurements in most cases confirming the suitability full camera based SHM systems

Field and Laboratory Tests of Flow-Proportional Passive Samplers for Determining Average Phosphorus and Nitrogen Concentration in Rivers

Flow responsive passive samplers offer considerable potential in nutrient monitoring in catchments; bridging the gap between the intermittency of grab sampling and the high cost of automated monitoring systems. A commercially available passive sampler was evaluated in a number of river systems encapsulating a gradient in storm response, combinations of diffuse and point source pressures, and levels of phosphorus and nitrogen concentrations. Phosphorus and nitrogen are sequestered to a resin matrix in a permeable cartridge positioned in line with streamflow. A salt tracer dissolves in proportion to advective flow through the cartridge. Multiple deployments of different cartridge types were undertaken and the recovery of P and N compared with the flow-weighted mean concentration (FWMC) from high-resolution bank-side analysers at each site. Results from the passive samplers were variable and largely underestimated the FWMC derived from the bank-side analysers. Laboratory tests using ambient river samples indicated good replication of advective throughflow using pumped water, although this appeared not to be a good analogue of river conditions where flow divergence was possible. Laboratory tests also showed good nutrient retention but not elution and these issues appeared to combine to limit the utility in ambient river systems at the small catchment scale.

Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.

Dynamic, in situ optical, magnetic and magneto-optical monitoring of the growth of Co/Au and Pd/Co/Au multilayer systems

The growth of magnetron sputtered Co/Au and Pd/Co/Au superlattices on Au and Pd buffer layers, deposited onto glass substrates, has been monitored optically and magneto-optically in real time, using rotating analyser ellipsometry and Kerr polarimetry, at a wavelength of 633 nm. The magneto-optical traces, combined with ex situ and in situ hysteresis loops, provide a detailed and informative fingerprint of the optical and magnetic properties of the films as they evolve during growth. For Co/Au, oscillations in the polar magneto-optical effect developed during the deposition of An overlayers on Co and these may be attributed to quantum well states. However, the hysteresis measurements show that the magnetic field required to maintain saturation magnetization throughout the experiment was larger than available in situ, introducing a degree of confusion concerning the interpretation of the data. This problem was overcome by the incorporation of Pd layers into the Co/Au structure, thereby eliminating variation in magnetic orientation during growth of the Au layers as a contributory factor to the observations.

Monitoring of Corrosion in Structural Reinforcing Bars: performance comparison using in-situ fibre optic and electric wire strain gauge systems

Abstract This work addresses the problems of effective in situ measurement of the initiation or the rate of steel corrosion in reinforced concrete structures through the use of optical fiber sensor systems. By undertaking a series of tests over prolonged periods, coupled with acceleration of corrosion, the performance of fiber Bragg grating-based sensor systems attached to high-tensile steel reinforcement bars (ldquorebarsrdquo), and cast into concrete blocks was determined, and the results compared with those from conventional strain gauges where appropriate. The results show the benefits in the use of optical fiber networks under these circumstances and their ability to deliver data when conventional sensors failed.

Statistical-based monitoring of multivariate non-Gaussian systems

The monitoring of multivariate systems that exhibit non-Gaussian behavior is addressed. Existing work advocates the use of independent component analysis (ICA) to extract the underlying non-Gaussian data structure. Since some of the source signals may be Gaussian, the use of principal component analysis (PCA) is proposed to capture the Gaussian and non-Gaussian source signals. A subsequent application of ICA then allows the extraction of non-Gaussian components from the retained principal components (PCs). A further contribution is the utilization of a support vector data description to determine a confidence limit for the non-Gaussian components. Finally, a statistical test is developed for determining how many non-Gaussian components are encapsulated within the retained PCs, and associated monitoring statistics are defined. The utility of the proposed scheme is demonstrated by a simulation example, and the analysis of recorded data from an industrial melter.