Gas identification with tin oxide sensor array and self-organizing maps: adaptive correction of sensor drifts

Low-cost tin oxide gas sensors are inherently nonspecific. In addition, they have several undesirable characteristics such as slow response, nonlinearities, and long-term drifts. This paper shows that the combination of a gas-sensor array together with self-organizing maps (SOM's) permit success in gas classification problems. The system is able to determine the gas present in an atmosphere with error rates lower than 3%. Correction of the sensor's drift with an adaptive SOM has also been investigated

Synchronizability of EEG-Based Functional Networks in Early Alzheimer's Disease.

Recently graph theory and complex networks have been widely used as a mean to model functionality of the brain. Among different neuroimaging techniques available for constructing the brain functional networks, electroencephalography (EEG) with its high temporal resolution is a useful instrument of the analysis of functional interdependencies between different brain regions. Alzheimer's disease (AD) is a neurodegenerative disease, which leads to substantial cognitive decline, and eventually, dementia in aged people. To achieve a deeper insight into the behavior of functional cerebral networks in AD, here we study their synchronizability in 17 newly diagnosed AD patients compared to 17 healthy control subjects at no-task, eyes-closed condition. The cross-correlation of artifact-free EEGs was used to construct brain functional networks. The extracted networks were then tested for their synchronization properties by calculating the eigenratio of the Laplacian matrix of the connection graph, i.e., the largest eigenvalue divided by the second smallest one. In AD patients, we found an increase in the eigenratio, i.e., a decrease in the synchronizability of brain networks across delta, alpha, beta, and gamma EEG frequencies within the wide range of network costs. The finding indicates the destruction of functional brain networks in early AD.

Assessing the Functionality and Biocompatibility of a Wireless Contact Lens Sensor for 24 hour-intraocular Pressure Monitoring in Volunteers: Preliminary Results

Purpose: In vitro studies in porcine eyes have demonstrated a good correlation between induced intraocular pressure variations and corneal curvature changes, using a contact lens with an embedded microfabricated strain gauge. Continuous 24 hour-intraocular pressure (IOP) monitoring to detect large diurnal fluctuation is currently an unmet clinical need. The aims of this study is to evaluate precision of signal transmission and biocompatibility of 24 hour contact lens sensor wear (SENSIMED Triggerfish®) in humans. Methods: After full eye examination in 10 healthy volunteers, a 8.7 mm radius contact lens sensor and an orbital bandage containing a loop antenna were applied and connected to a portable recorder. Best corrected visual acuity and position, lubrication status and mobility of the sensor were assessed after 5 and 30 minutes, 4, 7 and 24 hours. Subjective comfort was scored and activities documented in a logbook. After sensor removal full eye examination was repeated, and the registration signal studied. Results: The comfort score was high and did not fluctuate significantly, except at the 7 hour-visit. The mobility of the contact lens was minimal but its lubrication remained good. Best corrected visual acuity was significantly reduced during the sensor wear and immediately after its removal. Three patients developed mild corneal staining. In all but one participant we obtained a registration IOP curve with visible ocular pulse amplitude. Conclusions: This 24 hour-trial confirmed the functionality and biocompatibility of SENSIMED Triggerfish® wireless contact lens sensor for IOP-fluctuation monitoring in volunteers. Further studies with a range of different contact lens sensor radii are indicated.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Models of social networks based on social distance attachment

We propose a class of models of social network formation based on a mathematical abstraction of the concept of social distance. Social distance attachment is represented by the tendency of peers to establish acquaintances via a decreasing function of the relative distance in a representative social space. We derive analytical results (corroborated by extensive numerical simulations), showing that the model reproduces the main statistical characteristics of real social networks: large clustering coefficient, positive degree correlations, and the emergence of a hierarchy of communities. The model is confronted with the social network formed by people that shares confidential information using the Pretty Good Privacy (PGP) encryption algorithm, the so-called web of trust of PGP.

Detection and chemical profiling of medicine counterfeits by Raman spectroscopy and chemometrics

Raman spectroscopy combined with chemometrics has recently become a widespread technique for the analysis of pharmaceutical solid forms. The application presented in this paper is the investigation of counterfeit medicines. This increasingly serious issue involves networks that are an integral part of industrialized organized crime. Efficient analytical tools are consequently required to fight against it. Quick and reliable authentication means are needed to allow the deployment of measures from the company and the authorities. For this purpose a method in two steps has been implemented here. The first step enables the identification of pharmaceutical tablets and capsules and the detection of their counterfeits. A nonlinear classification method, the Support Vector Machines (SVM), is computed together with a correlation with the database and the detection of Active Pharmaceutical Ingredient (API) peaks in the suspect product. If a counterfeit is detected, the second step allows its chemical profiling among former counterfeits in a forensic intelligence perspective. For this second step a classification based on Principal Component Analysis (PCA) and correlation distance measurements is applied to the Raman spectra of the counterfeits.

Input diffusion and the evolution of production networks

What determines which inputs are initially considered and eventually adopted in the productionof new or improved goods? Why are some inputs much more prominent than others? We modelthe evolution of input linkages as a process where new producers first search for potentially usefulinputs and then decide which ones to adopt. A new product initially draws a set of 'essentialsuppliers'. The search stage is then confined to the network neighborhood of the latter, i.e., to theinputs used by the essential suppliers. The adoption decision is driven by a tradeoff between thebenefits accruing from input variety and the costs of input adoption. This has important implicationsfor the number of forward linkages that a product (input variety) develops over time. Inputdiffusion is fostered by network centrality ? an input that is initially represented in many networkneighborhoods is subsequently more likely to be adopted. This mechanism also delivers a powerlaw distribution of forward linkages. Our predictions continue to hold when varieties are aggregatedinto sectors. We can thus test them, using detailed sectoral US input-output tables. We showthat initial network proximity of a sector in 1967 significantly increases the likelihood of adoptionthroughout the subsequent four decades. The same is true for rapid productivity growth in aninput-producing sector. Our empirical results highlight two conditions for new products to becomecentral nodes: initial network proximity to prospective adopters, and technological progress thatreduces their relative price. Semiconductors met both conditions.

Real-time monitoring of protein conformational changes using a nano-mechanical sensor.

Proteins can switch between different conformations in response to stimuli, such as pH or temperature variations, or to the binding of ligands. Such plasticity and its kinetics can have a crucial functional role, and their characterization has taken center stage in protein research. As an example, Topoisomerases are particularly interesting enzymes capable of managing tangled and supercoiled double-stranded DNA, thus facilitating many physiological processes. In this work, we describe the use of a cantilever-based nanomotion sensor to characterize the dynamics of human topoisomerase II (Topo II) enzymes and their response to different kinds of ligands, such as ATP, which enhance the conformational dynamics. The sensitivity and time resolution of this sensor allow determining quantitatively the correlation between the ATP concentration and the rate of Topo II conformational changes. Furthermore, we show how to rationalize the experimental results in a comprehensive model that takes into account both the physics of the cantilever and the dynamics of the ATPase cycle of the enzyme, shedding light on the kinetics of the process. Finally, we study the effect of aclarubicin, an anticancer drug, demonstrating that it affects directly the Topo II molecule inhibiting its conformational changes. These results pave the way to a new way of studying the intrinsic dynamics of proteins and of protein complexes allowing new applications ranging from fundamental proteomics to drug discovery and development and possibly to clinical practice.

Management of Exceptions on Access Control Policies

Peer-reviewed

Sensor Fusion of Proprioception, Force and Vision in Estimation and Robot

Sensor-based robot control allows manipulation in dynamic environments with uncertainties. Vision is a versatile low-cost sensory modality, but low sample rate, high sensor delay and uncertain measurements limit its usability, especially in strongly dynamic environments. Force is a complementary sensory modality allowing accurate measurements of local object shape when a tooltip is in contact with the object. In multimodal sensor fusion, several sensors measuring different modalities are combined to give a more accurate estimate of the environment. As force and vision are fundamentally different sensory modalities not sharing a common representation, combining the information from these sensors is not straightforward. In this thesis, methods for fusing proprioception, force and vision together are proposed. Making assumptions of object shape and modeling the uncertainties of the sensors, the measurements can be fused together in an extended Kalman filter. The fusion of force and visual measurements makes it possible to estimate the pose of a moving target with an end-effector mounted moving camera at high rate and accuracy. The proposed approach takes the latency of the vision system into account explicitly, to provide high sample rate estimates. The estimates also allow a smooth transition from vision-based motion control to force control. The velocity of the end-effector can be controlled by estimating the distance to the target by vision and determining the velocity profile giving rapid approach and minimal force overshoot. Experiments with a 5-degree-of-freedom parallel hydraulic manipulator and a 6-degree-of-freedom serial manipulator show that integration of several sensor modalities can increase the accuracy of the measurements significantly.

Use of neural networks in robot positioning of large flexible redundant manipulators

Deflection compensation of flexible boom structures in robot positioning is usually done using tables containing the magnitude of the deflection with inverse kinematics solutions of a rigid structure. The number of table values increases greatly if the working area of the boom is large and the required positioning accuracy is high. The inverse kinematics problems are very nonlinear, and if the structure is redundant, in some cases it cannot be solved in a closed form. If the structural flexibility of the manipulator arms is taken into account, the problem is almost impossible to solve using analytical methods. Neural networks offer a possibility to approximate any linear or nonlinear function. This study presents four different methods of using neural networks in the static deflection compensation and inverse kinematics solution of a flexible hydraulically driven manipulator. The training information required for training neural networks is obtained by employing a simulation model that includes elasticity characteristics. The functionality of the presented methods is tested based on the simulated and measured results of positioning accuracy. The simulated positioning accuracy is tested in 25 separate coordinate points. For each point, the positioning is tested with five different mass loads. The mean positioning error of a manipulator decreased from 31.9 mm to 4.1 mm in the test points. This accuracy enables the use of flexible manipulators in the positioning of larger objects. The measured positioning accuracy is tested in 9 separate points using three different mass loads. The mean positioning error decreased from 10.6 mm to 4.7 mm and the maximum error from 27.5 mm to 11.0 mm.

The effects of social networks on work-team effectiveness

This is a study of team social networks, their antecedents and outcomes. In focusing attention on the structural configuration of the team this research contributes to a new wave of thinking concerning group social capital. The research site was a random sample of Finnish work organisations. The data consisted of 499 employees in 76 teams representing 48 different organisations. A systematic literature review and quantitative methods were used in conducting the research: the former primarily to establish the current theoretical position on the relationships among the variables and the latter to test these relationships. Social network analysis was the primary method used in identifying the social-network relations among the work-team members. The first and key contribution of this study is that it relates the structuralnetwork properties of work teams to behavioural outcomes, attitudinal outcomes and, ultimately, team performance. Moreover, it shows that addressing attitudinal outcomes is also important in terms of team performance; attitudinal outcomes (team identity) mediated the relationship between the team’s performance and its social network. The second contribution is that it examines the possible antecedents of the social structure. It is thus one response to Salancik’s (1995) call for a network theory in that it explains why certain network characteristics exist. Itdemonstrates that irrespective of whether or not a team is heterogeneous in terms of age or gender, educational diversity may protect it from centralisation. However, heterogeneity in terms of gender turned out to have a negative impact on density. Thirdly, given the observation that the benefits of (team) networks are typically theorised and modelled without reference to the nature of the relationships comprising the structure, the study directly tested whether team knowledge mediated the effects of instrumental and expressive network relationships on team performance. Furthermore, with its focus on expressive networks that link the workplace to a more informal world, which have been rather neglected in previous research, it enhances knowledge of teams andnetworks. The results indicate that knowledge sharing fully mediates the influence of complementarities between dense and fragmented instrumental network relationships, thus providing empirical validation of the implicit understanding that networks transfer knowledge. Fourthly, the study findings suggest that an optimal configuration of the work-team social-network structure combines both bridging and bonding social relationships.

Development of an optical redox chemical sensor for nitrite determination

An optical chemical sensor for the determination of nitrite based on incorporating methyltrioctylammonium chloride as an anionic exchanger on the triacetylcellulose polymer has been reported. The response of the sensor is based on the redox reaction between nitrite in aqueous solution and iodide adsorbed on sensing membrane using anion exchange phenomena. The sensing membrane reversibly responses to nitrite ion over the range of 6.52×10-6 - 8.70×10-5 mol L-1 with a detection limit of 6.05×10-7 mol L-1 (0.03 µg mL-1) and response time of 6 min. The relative standard deviation for eight replicate measurements of 8.70×10-6 and 4.34×10-5 mol L-1 of nitrite was 4.4 and 2.5 %, respectively. The sensor was successfully applied for determination of nitrite in food, saliva and water samples.

Monitoring of praseodymium(III) ions in aqueous solutions, soil and sediment samples by a PVC membrane sensor based on a furan-triazole derivative

A furan-triazole derivative has been explored as an ionophore for preparation of a highly selective Pr(III) membrane sensor. The proposed sensor exhibits a Nernstian response for Pr(III) activity over a wide concentration range with a detection limit of 5.2×10-8 M. Its response is independent of pH of the solution in the range 3.0-8.8 and offers the advantages of fast response time. To investigate the analytical applicability of the sensor, it was applied successfully as an indicator electrode in potentiometric titration of Pr(III) solution and also in the direct and indirect determination of trace Pr(III) ions in some samples.

Development of research networks : the case of social capital

The aim of this study is to explore how a new concept appears inscientific discussion and research, how it diffuses to other fields and out of the scientific communities, and how the networks are formed around the concept. Text and terminology take the interest of a reader in the digital environment. Texts create networks where the terminology used is dependent on the ideas, viewsand paradigms of the field. This study is based mainly on bibliographic data. Materials for bibliometric studies have been collected from different databases. The databases are also evaluated and their quality and coverage are discussed. The thesauri of those databases that have been selected for a more in depth study have also been evaluated. The material selected has been used to study how long and in which ways an innovative publication, which can be seen as a milestone in a specific field, influences the research. The concept that has been chosen as a topic for this research is Social Capital, because it has been a popular concept in different scientific fields as well as in everyday speech and the media. It seemed to be a `fashion concept´ that appeared in different situations at the Millennium. The growth and diffusion of social capital publications has been studied. The terms connected with social capital in different fields and different stages of the development have also been analyzed. The methods that have been used in this study are growth and diffusion analysis, content analysis, citation analysis, coword analysis and cocitation analysis. One method that can be used tounderstand and to interpret results of these bibliometric studies is to interview some key persons, who are known to have a gatekeeper position in the diffusion of the concept. Thematic interviews with some Finnish researchers and specialists that have influenced the diffusion of social capital into Finnish scientificand social discussions provide background information. iv The Milestone Publications on social capital have been chosen and studied. They give answers to the question "What is Social Capital?" By comparing citations to Milestone Publications with the growth of all social capital publications in a database, we can drawconclusions about the point at which social capital became generally approved `tacit knowledge´. The contribution of the present study lies foremost in understanding the development of network structures around a new concept that has diffused in scientific communities and also outside them. The network means both networks of researchers, networks of publications and networks of concepts that describe the research field. The emphasis has been on the digital environment and onthe socalled information society that we are now living in, but in this transitional stage, the printed publications are still important and widely used in social sciences and humanities. The network formation is affected by social relations and informal contacts that push new ideas. This study also gives new information about using different research methods, like bibliometric methods supported by interviews and content analyses. It is evident that interpretation of bibliometric maps presupposes qualitative information and understanding of the phenomena under study.