Understanding the indoor environment through mining sensory data - A case study

A wireless sensor network (WSN) is a group of sensors linked by wireless medium to perform distributed sensing tasks. WSNs have attracted a wide interest from academia and industry alike due to their diversity of applications, including home automation, smart environment, and emergency services, in various buildings. The primary goal of a WSN is to collect data sensed by sensors. These data are characteristic of being heavily noisy, exhibiting temporal and spatial correlation. In order to extract useful information from such data, as this paper will demonstrate, people need to utilise various techniques to analyse the data. Data mining is a process in which a wide spectrum of data analysis methods is used. It is applied in the paper to analyse data collected from WSNs monitoring an indoor environment in a building. A case study is given to demonstrate how data mining can be used to optimise the use of the office space in a building.

On the variability of the surface environment response to synoptic forcing over complex terrain: a multivariate data analysis approach

Synoptic climatology relates the atmospheric circulation with the surface environment. The aim of this study is to examine the variability of the surface meteorological patterns, which are developing under different synoptic scale categories over a suburban area with complex topography. Multivariate Data Analysis techniques were performed to a data set with surface meteorological elements. Three principal components related to the thermodynamic status of the surface environment and the two components of the wind speed were found. The variability of the surface flows was related with atmospheric circulation categories by applying Correspondence Analysis. Similar surface thermodynamic fields develop under cyclonic categories, which are contrasted with the anti-cyclonic category. A strong, steady wind flow characterized by high shear values develops under the cyclonic Closed Low and the anticyclonic H–L categories, in contrast to the variable weak flow under the anticyclonic Open Anticyclone category.

Validation of Canopy Height Profile methodology for small-footprint full-waveform airborne LiDAR data in a discontinuous canopy environment

A Canopy Height Profile (CHP) procedure presented in Harding et al. (2001) for large footprint LiDAR data was tested in a closed canopy environment as a way of extracting vertical foliage profiles from LiDAR raw-waveform. In this study, an adaptation of this method to small-footprint data has been shown, tested and validated in an Australian sparse canopy forest at plot- and site-level. Further, the methodology itself has been enhanced by implementing a dataset-adjusted reflectance ratio calculation according to Armston et al. (2013) in the processing chain, and tested against a fixed ratio of 0.5 estimated for the laser wavelength of 1550nm. As a by-product of the methodology, effective leaf area index (LAIe) estimates were derived and compared to hemispherical photography-derived values. To assess the influence of LiDAR aggregation area size on the estimates in a sparse canopy environment, LiDAR CHPs and LAIes were generated by aggregating waveforms to plot- and site-level footprints (plot/site-aggregated) as well as in 5m grids (grid-processed). LiDAR profiles were then compared to leaf biomass field profiles generated based on field tree measurements. The correlation between field and LiDAR profiles was very high, with a mean R2 of 0.75 at plot-level and 0.86 at site-level for 55 plots and the corresponding 11 sites. Gridding had almost no impact on the correlation between LiDAR and field profiles (only marginally improvement), nor did the dataset-adjusted reflectance ratio. However, gridding and the dataset-adjusted reflectance ratio were found to improve the correlation between raw-waveform LiDAR and hemispherical photography LAIe estimates, yielding the highest correlations of 0.61 at plot-level and of 0.83 at site-level. This proved the validity of the approach and superiority of dataset-adjusted reflectance ratio of Armston et al. (2013) over a fixed ratio of 0.5 for LAIe estimation, as well as showed the adequacy of small-footprint LiDAR data for LAIe estimation in discontinuous canopy forests.

Data assimilation: From photon counts to Earth System forecasts

Data assimilation – the set of techniques whereby information from observing systems and models is combined optimally – is rapidly becoming prominent in endeavours to exploit Earth Observation for Earth sciences, including climate prediction. This paper explains the broad principles of data assimilation, outlining different approaches (optimal interpolation, three-dimensional and four-dimensional variational methods, the Kalman Filter), together with the approximations that are often necessary to make them practicable. After pointing out a variety of benefits of data assimilation, the paper then outlines some practical applications of the exploitation of Earth Observation by data assimilation in the areas of operational oceanography, chemical weather forecasting and carbon cycle modelling. Finally, some challenges for the future are noted.

Review of computer vision in intelligent environment design

This paper discusses and compares the use of vision based and non-vision based technologies in developing intelligent environments. By reviewing the related projects that use vision based techniques in intelligent environment design, the achieved functions, technical issues and drawbacks of those projects are discussed and summarized, and the potential solutions for future improvement are proposed, which leads to the prospective direction of my PhD research.

Variational data assimilation for parameter estimation: application to a simple morphodynamic model

Data assimilation is a sophisticated mathematical technique for combining observational data with model predictions to produce state and parameter estimates that most accurately approximate the current and future states of the true system. The technique is commonly used in atmospheric and oceanic modelling, combining empirical observations with model predictions to produce more accurate and well-calibrated forecasts. Here, we consider a novel application within a coastal environment and describe how the method can also be used to deliver improved estimates of uncertain morphodynamic model parameters. This is achieved using a technique known as state augmentation. Earlier applications of state augmentation have typically employed the 4D-Var, Kalman filter or ensemble Kalman filter assimilation schemes. Our new method is based on a computationally inexpensive 3D-Var scheme, where the specification of the error covariance matrices is crucial for success. A simple 1D model of bed-form propagation is used to demonstrate the method. The scheme is capable of recovering near-perfect parameter values and, therefore, improves the capability of our model to predict future bathymetry. Such positive results suggest the potential for application to more complex morphodynamic models.

Preparation of consistent soil data sets for modelling purposes: Secondary SOTER data for four case study areas

The common GIS-based approach to regional analyses of soil organic carbon (SOC) stocks and changes is to define geographic layers for which unique sets of driving variables are derived, which include land use, climate, and soils. These GIS layers, with their associated attribute data, can then be fed into a range of empirical and dynamic models. Common methodologies for collating and formatting regional data sets on land use, climate, and soils were adopted for the project Assessment of Soil Organic Carbon Stocks and Changes at National Scale (GEFSOC). This permitted the development of a uniform protocol for handling the various input for the dynamic GEFSOC Modelling System. Consistent soil data sets for Amazon-Brazil, the Indo-Gangetic Plains (IGP) of India, Jordan and Kenya, the case study areas considered in the GEFSOC project, were prepared using methodologies developed for the World Soils and Terrain Database (SOTER). The approach involved three main stages: (1) compiling new soil geographic and attribute data in SOTER format; (2) using expert estimates and common sense to fill selected gaps in the measured or primary data; (3) using a scheme of taxonomy-based pedotransfer rules and expert-rules to derive soil parameter estimates for similar soil units with missing soil analytical data. The most appropriate approach varied from country to country, depending largely on the overall accessibility and quality of the primary soil data available in the case study areas. The secondary SOTER data sets discussed here are appropriate for a wide range of environmental applications at national scale. These include agro-ecological zoning, land evaluation, modelling of soil C stocks and changes, and studies of soil vulnerability to pollution. Estimates of national-scale stocks of SOC, calculated using SOTER methods, are presented as a first example of database application. Independent estimates of SOC stocks are needed to evaluate the outcome of the GEFSOC Modelling System for current conditions of land use and climate. (C) 2007 Elsevier B.V. All rights reserved.

Genotype x environment interaction in the uptake of Cs and Sr from soils by plants

The soil-plant transfer factors for Cs and Sr were analyzed in relationship to soil properties, crops, and varieties of crops. Two crops and two varieties of each crop: lettuce (Lactuca sativa L.), cv. Salad Bowl Green and cv. Lobjoits Green Cos, and radish (Raphanus sativus L.), cv. French Breakfast 3 and cv. Scarlet Globe, were grown on five different soils amended with Cs and Sr to give concentrations of 1 mg kg(-1) and 50 mg kg(-1) of each element. Soil-plant transfer coefficients ranged between 0.12-19.10 (Cs) and 1.48-146.10 (Sr) for lettuce and 0.09-13.24 (Cs) and 2.99-93.00 (Sr) for radish. Uptake of Cs and Sr by plants depended on both plant and soil properties. There were significant (P less than or equal to 0.05) differences between soil-plant transfer factors for each plant type at the two soil concentrations. At each soil concentration about 60% of the variance in the uptake of the Cs and Sr was due to soil properties. For a given concentration of Cs or Sr in soil, the most important factor effecting soil-plant transfer of these elements was the soil properties rather than the crops or varieties of crops. Therefore, for the varieties considered here, soil-plant transfer of Cs and Sr would be best regulated through the management of soil properties. At each concentration of Cs and Sr, the main soil properties effecting the uptake of Cs and Sr by lettuce and radish were the concentrations of K and Ca, pH and CEC. Together with the concentrations of contaminants in soils, they explained about 80% of total data variance, and were the best predictors for soil-plant transfer. The different varieties of lettuce and radish gave different responses in soil-plant transfer of Cs and Sr in different soil conditions, i.e. genotype x environment interaction caused about 30% of the variability in the uptake of Cs and Sr by plants. This means that a plant variety with a low soil-plant transfer of Cs and Sr in one soil could have an increased soil-plant transfer factor in other soils. The broad implications of this work are that in contaminated agricultural lands still used for plant growing, contaminant-excluding crop varieties may not be a reliable method for decreasing contaminant transfer to foodstuffs. Modification of soil properties would be a more reliable technique. This is particularly relevant to agricultural soils in the former USSR still affected by fallout from the Chernobyl disaster.

Studies on particulate matter (PM10) and its precursors over urban environment of Reading, UK

The extent to which airborne particles penetrate into the human respiratory system is determined mainly by their size, with possible health effects. The research over the scientific evidence of the role of airborne particles in adverse health effects has been intensified in recent years. In the present study, seasonal variations of PM10 and its relation with anthropogenic activities have been studied by using the data from UK National Air Quality Archive over Reading, UK. The diurnal variation of PM10 shows a morning peak during 7:00-10:00 LT and an evening peak during 19:00-22:00 LT. 3 The variation between 12:00 and 17:00 LT remains more or less steady for PM10 with the minimum value of similar to 16 mu g m(-3). PM10 and black smoke (BS) concentrations during weekdays were found to be high compared to weekends. A reduction in the concentration of PM10 has been found during the Christmas holidays compared to normal days during December. Seasonal variations of PM10 showed high values during spring compared to other seasons. A linear relationship has been found between PM10 and NO, during March, July, November and December suggesting that most of the PM10 is due to local traffic exhaust emissions. PM10 and SO2 concentrations showed positive correlation with the correlation coefficient of R-2 = 0.65 over the study area. Seasonal variations of SO2 and NOx showed high concentrations during winter and low concentrations during spring. Fraction of BS in PM10 has been found to be 50% during 2004 over the study area. (C) 2005 Elsevier Ltd. All rights reserved.

Data assimilation for marine monitoring and prediction: The MERCATOR operational assimilation systems and the MERSEA developments

During the past 15 years, a number of initiatives have been undertaken at national level to develop ocean forecasting systems operating at regional and/or global scales. The co-ordination between these efforts has been organized internationally through the Global Ocean Data Assimilation Experiment (GODAE). The French MERCATOR project is one of the leading participants in GODAE. The MERCATOR systems routinely assimilate a variety of observations such as multi-satellite altimeter data, sea-surface temperature and in situ temperature and salinity profiles, focusing on high-resolution scales of the ocean dynamics. The assimilation strategy in MERCATOR is based on a hierarchy of methods of increasing sophistication including optimal interpolation, Kalman filtering and variational methods, which are progressively deployed through the Syst`eme d’Assimilation MERCATOR (SAM) series. SAM-1 is based on a reduced-order optimal interpolation which can be operated using ‘altimetry-only’ or ‘multi-data’ set-ups; it relies on the concept of separability, assuming that the correlations can be separated into a product of horizontal and vertical contributions. The second release, SAM-2, is being developed to include new features from the singular evolutive extended Kalman (SEEK) filter, such as three-dimensional, multivariate error modes and adaptivity schemes. The third one, SAM-3, considers variational methods such as the incremental four-dimensional variational algorithm. Most operational forecasting systems evaluated during GODAE are based on least-squares statistical estimation assuming Gaussian errors. In the framework of the EU MERSEA (Marine EnviRonment and Security for the European Area) project, research is being conducted to prepare the next-generation operational ocean monitoring and forecasting systems. The research effort will explore nonlinear assimilation formulations to overcome limitations of the current systems. This paper provides an overview of the developments conducted in MERSEA with the SEEK filter, the Ensemble Kalman filter and the sequential importance re-sampling filter.

The use of wireless data communication and body sensing devices to evaluate occupants' comfort in buildings

Physiological parameters measured by an embedded body sensor system were demonstrated to respond to changes of the air temperature in an office environment. The thermal parameters were monitored with the use of a wireless sensor system that made possible to turn any existing room into a field laboratory. Two human subjects were monitored over daily activities and at various steady-state thermal conditions when the air temperature of the room was altered from 22-23°C to 25-28°C. The subjects indicated their thermal feeling on questionnaires. The measured skin temperature was distributed close to the calculated mean skin temperature corresponding to the given activity level. The variation of Galvanic Skin Response (GSR) reflected the evaporative heat loss through the body surfaces and indicated whether sweating occurred on the subjects. Further investigations are needed to fully evaluate the influence of thermal and other factors on the output given by the investigated body sensor system.

Effects of genotype and environment on free amino acid levels in wheat grain: implications for acrylamide formation during processing

Acrylamide forms from free asparagine and reducing sugars during cooking, with asparagine concentration being the key parameter determining the formation in foods produced from wheat flour. In this study free amino acid concentrations were measured in the grain of varieties Spark and Rialto and four doubled haploid lines from a Spark x Rialto mapping population. The parental and doubled haploid lines had differing levels of total free amino acids and free asparagine in the grain, with one line consistently being lower than either parent for both of these factors. Sulfur deprivation led to huge increases in the concentrations of free asparagine and glutamine, and canonical variate analysis showed clear separation of the grain samples as a result of treatment (environment, E) and genotype (G) and provided evidence of G x E interactions. Low grain sulfur and high free asparagine concentration were closely associated with increased risk of acrylamide formation. G, E, and G x E effects were also evident in grain from six varieties of wheat grown at field locations around the United Kingdom in 2006 and 2007. The data indicate that progress in reducing the risk of acrylamide formation in processed wheat products could be made immediately through the selection and cultivation of low grain asparagme varieties and that further genetically driven improvements should be achievable. However, genotypes that are selected should also be tested under a range of environmental conditions.

VERA: virtual environment for research in archaeology

The VERA (Virtual Environment for Research in Archaeology) project is based on a research excavation of part of the large Roman town at Silchester, which aims to trace the site's development from its origins before the Roman conquest to its abandonment in the fifth century A.D. [1]. The VERA project aims to investigate how archaeologists use Information Technology (IT) in the context of a field excavation, and also for post-excavation analysis. VERA is a two-year project funded by the JISC VRE 2 programme that involves researchers from the University of Reading, University College London, and York Archaeological Trust. The overall aim of the project is to assess and introduce new tools and technologies that can aid the archaeological processes of gathering, recording and later analysis of data on the finds and artefacts discovered. The researchers involved in the project have a mix of skills, ranging from those related to archaeology, and computer science, though to ones involving usability and user assessment. This paper reports on the status of the research and development work undertaken in the project so far; this includes addressing various programming hurdles, on-site experiments and experiences, and the outcomes of usability and assessment studies.