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.

Distributed space-time block coding with incremental relay: performance improvement under imperfect synchronisation

This paper addresses the impact of imperfect synchronisation on D-STBC when combined with incremental relay. To suppress such an impact, a novel detection scheme is proposed, which retains the two key features of the STBC principle: simplicity (i.e. linear computational complexity), and optimality (i.e. maximum likelihood). These two features make the new detector very suitable for low power wireless networks (e.g. sensor networks).

Knowledge-elicitation and data-mining: fusing human and industrial plant information

Knowledge-elicitation is a common technique used to produce rules about the operation of a plant from the knowledge that is available from human expertise. Similarly, data-mining is becoming a popular technique to extract rules from the data available from the operation of a plant. In the work reported here knowledge was required to enable the supervisory control of an aluminium hot strip mill by the determination of mill set-points. A method was developed to fuse knowledge-elicitation and data-mining to incorporate the best aspects of each technique, whilst avoiding known problems. Utilisation of the knowledge was through an expert system, which determined schedules of set-points and provided information to human operators. The results show that the method proposed in this paper was effective in producing rules for the on-line control of a complex industrial process. (C) 2005 Elsevier Ltd. All rights reserved.

Fusing data-mining and knowledge elicitation for the supervisory control of an aluminium plate mill

Knowledge-elicitation is a common technique used to produce rules about the operation of a plant from the knowledge that is available from human expertise. Similarly, data-mining is becoming a popular technique to extract rules from the data available from the operation of a plant. In the work reported here knowledge was required to enable the supervisory control of an aluminium hot strip mill by the determination of mill set-points. A method was developed to fuse knowledge-elicitation and data-mining to incorporate the best aspects of each technique, whilst avoiding known problems. Utilisation of the knowledge was through an expert system, which determined schedules of set-points and provided information to human operators. The results show that the method proposed in this paper was effective in producing rules for the on-line control of a complex industrial process.

Mining gene networks with application to GAW15 problem 1

The genetic analysis workshop 15 (GAW15) problem 1 contained baseline expression levels of 8793 genes in immortalised B cells from 194 individuals in 14 Centre d’Etude du Polymorphisme Humane (CEPH) Utah pedigrees. Previous analysis of the data showed linkage and association and evidence of substantial individual variations. In particular, correlation was examined on expression levels of 31 genes and 25 target genes corresponding to two master regulatory regions. In this analysis, we apply Bayesian network analysis to gain further insight into these findings. We identify strong dependences and therefore provide additional insight into the underlying relationships between the genes involved. More generally, the approach is expected to be applicable for integrated analysis of genes on biological pathways.

Child labour in African Artisanal Mining Communities: experiences from Northern Ghana

The issue of child labour in the artisanal and small-scale mining (ASM) economy is attracting significant attention worldwide. This article critically examines this ‘problem’ in the context of sub-Saharan Africa, where a lack of formal sector employment opportunities and/or the need to provide financial support to their impoverished families has led tens of thousands of children to take up work in this industry. The article begins by engaging with the main debates on child labour in an attempt to explain why young boys and girls elect to pursue arduous work in ASM camps across the region. The remainder of the article uses the Ghana experience to further articulate the challenges associated with eradicating child labour at ASM camps, drawing upon recent fieldwork undertaken in Talensi-Nabdam District, Upper East Region. Overall, the issue of child labour in African ASM communities has been diagnosed far too superficially, and until donor agencies and host governments fully come to grips with the underlying causes of the poverty responsible for its existence, it will continue to burgeon.

Effect of incremental levels of fish oil in the diet on ruminal lipid metabolism in growing steers

Based on the potential benefits to human health, there is interest in developing sustainable nutritional strategies to enhance the concentration of long-chain n-3 fatty acids in ruminant-derived foods. Four Aberdeen Angus steers fitted with rumen and duodenal cannulae were used in a 4 × 4 Latin square experiment with 21 d experimental periods to examine the potential of fish oil (FO) in the diet to enhance the supply of 20 : 5n-3 and 22 : 6n-3 available for absorption in growing cattle. Treatments consisted of total mixed rations based on maize silage fed at a rate of 85 g DM/kg live weight0·75/d containing 0, 8, 16 and 24 g FO/kg diet DM. Supplements of FO reduced linearly (P < 0·01) DM intake and shifted (P < 0·01) rumen fermentation towards propionate at the expense of acetate and butyrate. FO in the diet enhanced linearly (P < 0·05) the flow of trans-16 : 1, trans-18 : 1, trans-18 : 2, 20 : 5n-3 and 22 : 6n-3, and decreased linearly (P < 0·05) 18 : 0 and 18 : 3n-3 at the duodenum. Increases in the flow of trans-18 : 1 were isomer dependent and were determined primarily by higher amounts of trans-11 reaching the duodenum. In conclusion, FO alters ruminal lipid metabolism of growing cattle in a dose-dependent manner consistent with an inhibition of ruminal biohydrogenation, and enhances the amount of long-chain n-3 fatty acids at the duodenum, but the increases are marginal due to extensive biohydrogenation in the rumen.

Quasi-optimal assimilation of satellite retrievals for incremental 4D-Var

An interface between satellite retrievals and the incremental version of the four-dimensional variational assimilation scheme is developed, making full use of the information content of satellite measurements. In this paper, expressions for the function that calculates simulated observations from model states (called “observation operator”), together with its tangent linear version and adjoint, are derived. Results from our work can be used for implementing a quasi-optimal assimilation of satellite retrievals (e.g., of atmospheric trace gases) in operational meteorological centres.

Video activity extraction and reporting with incremental unsupervised learning

The present work presents a new method for activity extraction and reporting from video based on the aggregation of fuzzy relations. Trajectory clustering is first employed mainly to discover the points of entry and exit of mobiles appearing in the scene. In a second step, proximity relations between resulting clusters of detected mobiles and contextual elements from the scene are modeled employing fuzzy relations. These can then be aggregated employing typical soft-computing algebra. A clustering algorithm based on the transitive closure calculation of the fuzzy relations allows building the structure of the scene and characterises the ongoing different activities of the scene. Discovered activity zones can be reported as activity maps with different granularities thanks to the analysis of the transitive closure matrix. Taking advantage of the soft relation properties, activity zones and related activities can be labeled in a more human-like language. We present results obtained on real videos corresponding to apron monitoring in the Toulouse airport in France.

A 'Kimberley protest': diamond mining, export sanctions, and poverty in Awatia, Ghana

The temporary suspension of diamond exports in Ghana in 2006 and 2007 is arguably the most significant move to address mounting criticisms of the Kimberley Process Certification Scheme (KPCS), an international initiative aimed at stemming the flow of rough diamonds used to finance wars. The ban, which took effect in November 2006, was much praised, particularly in civil society circles, where it continues to be seen as a genuine effort to prevent the smuggling of ‘conflict diamonds’. At the time, Ghana was accused of harbouring stones originating from rebel-held territories in neighbouring Côte d’Ivoire. No evidence was found in support of the case that it was a repository for ‘conflict diamonds’, however, and exports resumed early in March 2007. This article examines the context for the accusations of Ghana’s implication in the smuggling of illicit diamonds, and draws on recent fieldwork to explain how the suspension has affected Akwatia, the country’s main diamondiferous area. The actions taken raise important questions about how suspected violators – particularly smaller diamond-producing nations – of the KPCS should be handled, and underscore how global compacts can have a host of negative repercussions at the village level.

Livelihood diversification and the expansion of artisanal mining in rural Tanzania: drivers and policy implications

This paper provides an extended analysis of livelihood diversification in rural Tanzania, with special emphasis on artisanal and small-scale mining (ASM). Over the past decade, this sector of industry, which is labour-intensive and comprises an array of rudimentary and semi-mechanized operations, has become an indispensable economic activity throughout Sub-Saharan Africa, providing employment to a host of redundant public sector workers, retrenched large-scale mine labourers and poor farmers. In many of the region’s rural areas, it is overtaking subsistence agriculture as the primary industry. Such a pattern appears to be unfolding within the Morogoro and Mbeya regions of southern Tanzania, where findings from recent research suggest that a growing number of smallholder farmers are turning to ASM for employment and financial support. It is imperative that national rural development programmes take this trend into account and provide support to these people.

Data-mining chess databases

This is a report on the data-mining of two chess databases, the objective being to compare their sub-7-man content with perfect play as documented in Nalimov endgame tables. Van der Heijden’s ENDGAME STUDY DATABASE IV is a definitive collection of 76,132 studies in which White should have an essentially unique route to the stipulated goal. Chessbase’s BIG DATABASE 2010 holds some 4.5 million games. Insight gained into both database content and data-mining has led to some delightful surprises and created a further agenda.