Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

Trap-State Dynamics in Visible-Light-Emitting ZnO:MgO Nanocrystals

Oleate-capped ZnO:MgO nanocrystals have been synthesized that are soluble in nonpolar solvents and which emit strongly in the visible region (450−600 nm) on excitation by UV radiation. The visible emission involves recombination of trap states of the nanocrystalline ZnO core and has a higher quantum yield than the band gap UV exciton emission. The spectrally resolved dynamics of the trap states have been investigated by time-resolved emission spectroscopy. The time-evolution of the photoluminescence spectra show that there are, in fact, two features in the visible emission whose relative importance and efficiencies vary with time. These features originate from recombination involving trapped electrons and holes, respectively, and with efficiencies that depend on the occupancy of the trap density of states.

Vector evaluated particle swarm optimization (VEPSO) for multi-objective design optimization of composite structures

We present a generic method/model for multi-objective design optimization of laminated composite components, based on vector evaluated particle swarm optimization (VEPSO) algorithm. VEPSO is a novel, co-evolutionary multi-objective variant of the popular particle swarm optimization algorithm (PSO). In the current work a modified version of VEPSO algorithm for discrete variables has been developed and implemented successfully for the, multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are - the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria; failure mechanism based failure criteria, Maximum stress failure criteria and the Tsai-Wu failure criteria. The optimization method is validated for a number of different loading configurations - uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences, as well fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. (C) 2007 Elsevier Ltd. All rights reserved.

Magnetic trapping of Yb in the metastable P-3(2) state

We report magnetic trapping of Yb in the excited P-3(2) state. This state, with a lifetime of 15 s, could play an important role in studies ranging from optical clocks and quantum computation to the search for a permanent electric dipole moment. Yb atoms are first cooled and trapped in the ground state in a 399-nm magneto-optic trap. The cold atoms are then pumped into the excited state by driving the S-1(0) -> P-3(1) -> S-3(1) transition. Atoms in the P-3(2) state are magnetically trapped in a spherical quadrupole field with an axial gradient of 110 G/cm. We trap up to 10(6) atoms with a lifetime of 1.5 s.

Mixed pixel analysis for flood mapping using extended support vector machine

This paper addresses the challenges of flood mapping using multispectral images. Quantitative flood mapping is critical for flood damage assessment and management. Remote sensing images obtained from various satellite or airborne sensors provide valuable data for this application, from which the information on the extent of flood can be extracted. However the great challenge involved in the data interpretation is to achieve more reliable flood extent mapping including both the fully inundated areas and the 'wet' areas where trees and houses are partly covered by water. This is a typical combined pure pixel and mixed pixel problem. In this paper, an extended Support Vector Machines method for spectral unmixing developed recently has been applied to generate an integrated map showing both pure pixels (fully inundated areas) and mixed pixels (trees and houses partly covered by water). The outputs were compared with the conventional mean based linear spectral mixture model, and better performance was demonstrated with a subset of Landsat ETM+ data recorded at the Daly River Basin, NT, Australia, on 3rd March, 2008, after a flood event.

Vector Evaluated Particle Swarm Optimization (VEPSO) of Supersonic Ejector for Hydrogen Fuel Cells

Fuel cells are emerging as alternate green power producers for both large power production and for use in automobiles. Hydrogen is seen as the best option as a fuel; however, hydrogen fuel cells require recirculation of unspent hydrogen. A supersonic ejector is an apt device for recirculation in the operating regimes of a hydrogen fuel cell. Optimal ejectors have to be designed to achieve best performances. The use of the vector evaluated particle swarm optimization technique to optimize supersonic ejectors with a focus on its application for hydrogen recirculation in fuel cells is presented here. Two parameters, compression ratio and efficiency, have been identified as the objective functions to be optimized. Their relation to operating and design parameters of ejector is obtained by control volume based analysis using a constant area mixing approximation. The independent parameters considered are the area ratio and the exit Mach number of the nozzle. The optimization is carried out at a particularentrainment ratio and results in a set of nondominated solutions, the Pareto front. A set of such curves can be used for choosing the optimal design parameters of the ejector.

Space-Vector-Based Hybrid Pulsewidth Modulation Techniques for Reduced Harmonic Distortion and Switching Loss

Novel switching sequences can be employed in spacevector-based pulsewidth modulation (PWM) of voltage source inverters. Differentswitching sequences are evaluated and compared in terms of inverter switching loss. A hybrid PWM technique named minimum switching loss PWM is proposed, which reduces the inverter switching loss compared to conventional space vector PWM (CSVPWM) and discontinuous PWM techniques at a given average switching frequency. Further, four space-vector-based hybrid PWM techniques are proposed that reduce line current distortion as well as switching loss in motor drives, compared to CSVPWM. Theoretical and experimental results are presented.

Conformational restrictions of peptides via backbone modification: Solution and crystal-state analysis of Boc-L-Pro-DZPhe-Gly-NH2

An N-alpha-protected model tripeptide amide containing, in the central position, an alpha,beta-dehydrophenylalanine (Z-configurational isomer), Boc-L-Pro-DELTA-Z-Phe-Gly-NH2 (Boc, tert-butyloxycarbonyl), has been synthesized by solution methods and fully characterized. IR absorption and H-1 NMR studies provided evidence for the occurrence of a significant population of a conformer containing two consecutive, intramolecularly H-bonded (type II-III') beta-bends in solution. However, an X-ray diffraction analysis clearly indicates that only the type-II beta-bend structure survives in the crystal state.

Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

The Finnish Competition State and Entrepreneurial Policies in the Helsinki Region

The thesis examines the intensification and characteristics of a policy that emphasises economic competitiveness in Finland during the 1990s and early 2000s. This accentuation of economic objectives is studied at the level of national policy-making as well as at the regional level through the policies and strategies of cities and three universities in the Helsinki region. By combining the analysis of state policies, urban strategies and university activities, the study illustrates the pervasiveness of the objective of economic competitiveness and growth across these levels and sheds light on the features and contradictions of these policies on a broad scale. The thesis is composed of five research articles and a summary article. At the level of national policies, the central focus of the thesis is on the growing role of science and technology policy as a state means to promote structural economic change and its transformation towards a broader, yet ambivalent concept of innovation policy. This shift brings forward a tension between an increasing emphasis on economic aspects – innovations and competitiveness – as well as the expanding scope of issues across a wide range of policy sectors that are being subsumed under this market- and economy oriented framework. Related to science and technology policy, attention is paid to adjustments in university policy in which there has been increasing pressure for efficiency, rationalisation and commercialisation of academic activities. Furthermore, political efforts to build an information society through the application of information and communication technologies are analysed with particular attention to the balance between economic and social objectives. Finally, changes in state regional policy priorities and the tendency towards competitiveness are addressed. At the regional level, the focus of the thesis is on the policies of the cities in Finland’s capital region as well as strategies of three universities operating in the region, namely the University of Helsinki, Helsinki University of technology and Helsinki School of Economics. As regards the urban level, the main focus is on the changes and characteristics of the urban economic development policy of the City of Helsinki. With respect to the universities, the thesis examines their attempts to commercialise research and thus bring academic research closer to economic interests, and pays particular attention to the contradictions of commercialisation. Related to the universities, the activities of three intermediary organisations that the universities have established in order to increase cooperation with industry are analysed. These organisations are the Helsinki Science Park, Otaniemi International Innovation Centre and LTT Research Ltd. The summary article provides a synthesis of the material presented in the five original articles and relates the results of the articles to a broader discussion concerning the emergence of competition states and entrepreneurial cities and regions. The main points of reference are Bob Jessop’s and Neil Brenner’s theses on state and urban-regional restructuring. The empirical results and considerations from Finland and the Helsinki region are used to comment on, specify and criticise specific parts of the two theses.

Spectral unmixing with estimated adaptive endmember index using Extended Support Vector Machine

The most difficult operation in the flood inundation mapping using optical flood images is to separate fully inundated areas from the ‘wet’ areas where trees and houses are partly covered by water. This can be referred as a typical problem the presence of mixed pixels in the images. A number of automatic information extraction image classification algorithms have been developed over the years for flood mapping using optical remote sensing images. Most classification algorithms generally, help in selecting a pixel in a particular class label with the greatest likelihood. However, these hard classification methods often fail to generate a reliable flood inundation mapping because the presence of mixed pixels in the images. To solve the mixed pixel problem advanced image processing techniques are adopted and Linear Spectral unmixing method is one of the most popular soft classification technique used for mixed pixel analysis. The good performance of linear spectral unmixing depends on two important issues, those are, the method of selecting endmembers and the method to model the endmembers for unmixing. This paper presents an improvement in the adaptive selection of endmember subset for each pixel in spectral unmixing method for reliable flood mapping. Using a fixed set of endmembers for spectral unmixing all pixels in an entire image might cause over estimation of the endmember spectra residing in a mixed pixel and hence cause reducing the performance level of spectral unmixing. Compared to this, application of estimated adaptive subset of endmembers for each pixel can decrease the residual error in unmixing results and provide a reliable output. In this current paper, it has also been proved that this proposed method can improve the accuracy of conventional linear unmixing methods and also easy to apply. Three different linear spectral unmixing methods were applied to test the improvement in unmixing results. Experiments were conducted in three different sets of Landsat-5 TM images of three different flood events in Australia to examine the method on different flooding conditions and achieved satisfactory outcomes in flood mapping.

Impact of riparian land use on stream insects of Kudremukh National Park, Karnataka state, India

The impact of riparian land use on the stream insect communities was studied at Kudremukh National Park located within Western Ghats, a tropical biodiversity hotspot in India. The diversity and community composition of stream insects varied across streams with different riparian land use types. The rarefied family and generic richness was highest in streams with natural semi evergreen forests as riparian vegetation. However, when the streams had human habitations and areca nut plantations as riparian land use type, the rarefied richness was higher than that of streams with natural evergreen forests and grasslands. The streams with scrub lands and iron ore mining as the riparian land use had the lowest rarefied richness. Within a landscape, the streams with the natural riparian vegetation had similar community composition. However, streams with natural grasslands as the riparian vegetation, had low diversity and the community composition was similar to those of paddy fields. We discuss how stream insect assemblages differ due to varied riparian land use patterns, reflecting fundamental alterations in the functioning of stream ecosystems. This understanding is vital to conserve, manage and restore tropical riverine ecosystems.

Sequential split vector quantization of LSF parameters using conditional PDF

A better performing product code vector quantization (VQ) method is proposed for coding the line spectrum frequency (LSF) parameters; the method is referred to as sequential split vector quantization (SeSVQ). The split sub-vectors of the full LSF vector are quantized in sequence and thus uses conditional distribution derived from the previous quantized sub-vectors. Unlike the traditional split vector quantization (SVQ) method, SeSVQ exploits the inter sub-vector correlation and thus provides improved rate-distortion performance, but at the expense of higher memory. We investigate the quantization performance of SeSVQ over traditional SVQ and transform domain split VQ (TrSVQ) methods. Compared to SVQ, SeSVQ saves 1 bit and nearly 3 bits, for telephone-band and wide-band speech coding applications respectively.

Support vector machine approach for the switching transient peak overvoltages during line energization

At the time of restoration transmission line switching is one of the major causes, which creates transient overvoltages. Though detailed Electro Magnetic Transient studies are carried out extensively for the planning and design of transmission systems, such studies are not common in a day-today operation of power systems. However it is important for the operator to ensure during restoration of supply that peak overvoltages resulting from the switching operations are well within safe limits. This paper presents a support vector machine approach to classify the various cases of line energization in the category of safe or unsafe based upon the peak value of overvoltage at the receiving end of line. Operator can define the threshold value of voltage to assign the data pattern in either of the class. For illustration of proposed approach the power system used for switching transient peak overvoltages tests is a 400 kV equivalent system of an Indian southern gri