" Nonlinear Signal Processing of Electroencephalogram .' Application in the Study of Neurodynamics. "

Interfacings of various subjects generate new field ofstudy and research that help in advancing human knowledge. One of the latest of such fields is Neurotechnology, which is an effective amalgamation of neuroscience, physics, biomedical engineering and computational methods. Neurotechnology provides a platform to interact physicist; neurologist and engineers to break methodology and terminology related barriers. Advancements in Computational capability, wider scope of applications in nonlinear dynamics and chaos in complex systems enhanced study of neurodynamics. However there is a need for an effective dialogue among physicists, neurologists and engineers. Application of computer based technology in the field of medicine through signal and image processing, creation of clinical databases for helping clinicians etc are widely acknowledged. Such synergic effects between widely separated disciplines may help in enhancing the effectiveness of existing diagnostic methods. One of the recent methods in this direction is analysis of electroencephalogram with the help of methods in nonlinear dynamics. This thesis is an effort to understand the functional aspects of human brain by studying electroencephalogram. The algorithms and other related methods developed in the present work can be interfaced with a digital EEG machine to unfold the information hidden in the signal. Ultimately this can be used as a diagnostic tool.

SPATIAL CLUSTERING ALGORITHMS-AN OVERVIEW.

An Overview of known spatial clustering algorithms The space of interest can be the two-dimensional abstraction of the surface of the earth or a man-made space like the layout of a VLSI design, a volume containing a model of the human brain, or another 3d-space representing the arrangement of chains of protein molecules. The data consists of geometric information and can be either discrete or continuous. The explicit location and extension of spatial objects define implicit relations of spatial neighborhood (such as topological, distance and direction relations) which are used by spatial data mining algorithms. Therefore, spatial data mining algorithms are required for spatial characterization and spatial trend analysis. Spatial data mining or knowledge discovery in spatial databases differs from regular data mining in analogous with the differences between non-spatial data and spatial data. The attributes of a spatial object stored in a database may be affected by the attributes of the spatial neighbors of that object. In addition, spatial location, and implicit information about the location of an object, may be exactly the information that can be extracted through spatial data mining

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576

Agri-environmental evaluation of traditional and alternative corn production systems in Chiapas, Mexico

This study evaluates the effects of environmental variables on traditional and alternative agroecosystems in three Ejidos (communal lands) in the Chiapas rainforest in Mexico. The tests occurred within two seasonal agricultural cycles. In spring-summer, experiments were performed with the traditional slash, fell and burn (S-F-B) system, no-burn systems and rotating systems with Mucuna deeringiana Bort., and in the autumn-winter agricultural cycle, three no-burn systems were compared to evaluate the effect of alternative sowing with corn (no-burn and topological modification of sowing). The results show a high floristic diversity in the study area (S_S = 4 - 23%), with no significant differences among the systems evaluated. In the first cycle, the analysis of the agronomical variables of the corn indicated better properties in the fallowing systems, with an average yield of 1950 kg ha^‑1, but there was variation related to the number of years left fallow. In the second cycle, the yields were positive for the alternative technology (average yield 3100 kg ha^‑1). The traditional S-F-B systems had reduced pests and increased organic matter and soil phosphorous content. These results are the consequence of fallow periods and adaptation to the environment; thus, this practice in the Chiapas rainforest constitutes an ethnocultural reality, which is unlikely to change in the near future if the agrosystems are managed based on historical principles.

Semi-algebraic methods for symbolic analysis of complex reaction networks

The identification of chemical mechanism that can exhibit oscillatory phenomena in reaction networks are currently of intense interest. In particular, the parametric question of the existence of Hopf bifurcations has gained increasing popularity due to its relation to the oscillatory behavior around the fixed points. However, the detection of oscillations in high-dimensional systems and systems with constraints by the available symbolic methods has proven to be difficult. The development of new efficient methods are therefore required to tackle the complexity caused by the high-dimensionality and non-linearity of these systems. In this thesis, we mainly present efficient algorithmic methods to detect Hopf bifurcation fixed points in (bio)-chemical reaction networks with symbolic rate constants, thereby yielding information about their oscillatory behavior of the networks. The methods use the representations of the systems on convex coordinates that arise from stoichiometric network analysis. One of the methods called HoCoQ reduces the problem of determining the existence of Hopf bifurcation fixed points to a first-order formula over the ordered field of the reals that can then be solved using computational-logic packages. The second method called HoCaT uses ideas from tropical geometry to formulate a more efficient method that is incomplete in theory but worked very well for the attempted high-dimensional models involving more than 20 chemical species. The instability of reaction networks may lead to the oscillatory behaviour. Therefore, we investigate some criterions for their stability using convex coordinates and quantifier elimination techniques. We also study Muldowney's extension of the classical Bendixson-Dulac criterion for excluding periodic orbits to higher dimensions for polynomial vector fields and we discuss the use of simple conservation constraints and the use of parametric constraints for describing simple convex polytopes on which periodic orbits can be excluded by Muldowney's criteria. All developed algorithms have been integrated into a common software framework called PoCaB (platform to explore bio- chemical reaction networks by algebraic methods) allowing for automated computation workflows from the problem descriptions. PoCaB also contains a database for the algebraic entities computed from the models of chemical reaction networks.

Schauder – Zur Dekonstruktion eines Spürens

Zusammenfassung: Ziel der Arbeit ist den Sinn von Schauder philosophisch zu erhellen. An einem Einzelphänomen, wie es so bisher nicht behandelt wurde, wird zugleich das Geflecht von Affekt/Emotion/Gefühl, gestützt auf Hegel, Heidegger, Husserl, Freud und Lacan, in immer neuen Ansätzen kritisch reflektiert und Zug um Zug mit Derrida dekonstruiert. In einem Textverfahren, das sich auch grafisch durch gegenübergestellte Kolumnen auszeichnet, werden heterogene Ansätze zum Sinn von Schauder mit dekonstruktivistischen Einsichten konfrontiert. Die Ansätze, Schauder über Datum, Begriff, Phänomen oder Strukturelement bestimmen zu wollen, durchdringen sich dabei mit denjenigen, die sich einer solchen Bestimmung entziehen (Hegels Negativität, Heideggers Seinsentzug oder Lacans Signifikantenmangel). Am Fokus Schauder, an dem sich das Fiktive einer mit sich selbst identischen Präsenz besonders eindringlich darstellt, werden so spezifische Aporien der Metaphysik der Präsenz entfaltet und die Geschlossenheit logozentristischer Systeme in die Bewegung einer anderen Öffnung und Schließung im Sinn der Schrift bzw. des allgemeinen Textes transformiert. Neben der différance, dem Entzug der Metapher, dem Supplement und dem Gespenstischen stützt sich die Arbeit auf die Iterabilität, bei der im selben Zug die Identität des Sinns gestiftet und zerstreut wird (Dissemination). Im Kapitel Piloerection werden Ambivalenzen und Paradoxien des Schauders am Beispiel von computergestützten empirisch-psychologischen Studien aufgezeigt. Im Kapitel Atopologie des Schauders prädikative, propositionale und topologische Bedingungen zum Sinn von Schauder analysiert und dekonstruiert. Ebenso, im Folgekapitel Etymon, etymologische und technisch-mediale Bedingungen. Im Schlußkapitel Maß, Anmaß, Unmaß der Empfindung des Schauders wird am Beispiel der konkreten Beiträge zum Schauder von Aristoteles, Kant, Fechner, Otto, Klages, Lorenz und Adorno aufgezeigt, dass (1) ein Schauder nicht von einem Außen aus an-gemessen werden kann, (2) sich im Schauder die metaphysische Opposition von Fiktion und Realität in einer Unentscheidbarkeit zerstreut, (3) dass trotz der Heterogenität der Ansätze in diesen Beiträgen eine Komplizenschaft zum Ausdruck kommt: ein Begehren nach Präsenz, das durch den Ausschluß des Anderen zugleich die Gewalt des Einen produziert, (4) dass der Signifikant Schauder, der selbst in Abwesenheit eines Referenten, eines bestimmten Signifikats, einer aktuellen Bedeutungs­intention, eines Sen­ders oder Empfängers funktioniert, als verändertes Zu­rückbleiben eines differenzieller Zeichens betrachtet werden muss, als Effekt von Spuren, die sich nur in ihrem eige­nen Auslöschen ereignen. Die Arbeit schließt mit dem Vorschlag, Spüren jenseits von Arché, Telos oder Eschaton, jenseits eines Phallogozentrismus in der derridaschen Spur zu denken. Nicht zuletzt über diese Pfropfung, wie sie im Französischen [trace] so nicht möglich ist, schließt sie als deutschsprachiger Beitrag an sein Werk an.

A Distributed Model for Mobile Robot Environment-Learning and Navigation

A distributed method for mobile robot navigation, spatial learning, and path planning is presented. It is implemented on a sonar-based physical robot, Toto, consisting of three competence layers: 1) Low-level navigation: a collection of reflex-like rules resulting in emergent boundary-tracing. 2) Landmark detection: dynamically extracts landmarks from the robot's motion. 3) Map learning: constructs a distributed map of landmarks. The parallel implementation allows for localization in constant time. Spreading of activation computes both topological and physical shortest paths in linear time. The main issues addressed are: distributed, procedural, and qualitative representation and computation, emergent behaviors, dynamic landmarks, minimized communication.

Understanding Subsystems in Biology through Dimensionality Reduction, Graph Partitioning and Analytical Modeling

Biological systems exhibit rich and complex behavior through the orchestrated interplay of a large array of components. It is hypothesized that separable subsystems with some degree of functional autonomy exist; deciphering their independent behavior and functionality would greatly facilitate understanding the system as a whole. Discovering and analyzing such subsystems are hence pivotal problems in the quest to gain a quantitative understanding of complex biological systems. In this work, using approaches from machine learning, physics and graph theory, methods for the identification and analysis of such subsystems were developed. A novel methodology, based on a recent machine learning algorithm known as non-negative matrix factorization (NMF), was developed to discover such subsystems in a set of large-scale gene expression data. This set of subsystems was then used to predict functional relationships between genes, and this approach was shown to score significantly higher than conventional methods when benchmarking them against existing databases. Moreover, a mathematical treatment was developed to treat simple network subsystems based only on their topology (independent of particular parameter values). Application to a problem of experimental interest demonstrated the need for extentions to the conventional model to fully explain the experimental data. Finally, the notion of a subsystem was evaluated from a topological perspective. A number of different protein networks were examined to analyze their topological properties with respect to separability, seeking to find separable subsystems. These networks were shown to exhibit separability in a nonintuitive fashion, while the separable subsystems were of strong biological significance. It was demonstrated that the separability property found was not due to incomplete or biased data, but is likely to reflect biological structure.

Transport of Sub-micron Aerosols in Bifurcations

The convective-diffusive transport of sub-micron aerosols in an oscillatory laminar flow within a 2-D single bifurcation is studied, using order-of-magnitude analysis and numerical simulation using a commercial software (FEMLAB®). Based on the similarity between momentum and mass transfer equations, various transient mass transport regimes are classified and scaled according to Strouhal and beta numbers. Results show that the mass transfer rate is highest at the carinal ridge and there is a phase-shift in diffusive transport time if the beta number is greater than one. It is also shown that diffusive mass transfer becomes independent of the oscillating outer flow if the Strouhal number is greater than one.

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