Studies all transport and magnetic properties of nano particle doped MgB2 superconductor for technological applications

This thesis Entitled Studies on transport and magnetic properties of nano particle doped mgb2 superconductor for technological applications.The thesis ahead focuses on the establishment of enhanced superconducting properties in bulk MgB2 via nano particle doping and its conversion into mono/multifilamentary wires. Further, an attempt has also been made to develop prototypes of MgB2 coil and conduction cooled current lead for technological applications. The thesis is configured into 6 chapters. The opening chapter gives an idea on the phenomenon of superconductivity, the various types of superconductors and its applications in different fields. The second chapter is an introduction on MgB2 superconductor and its relevance which includes crystal and electronic structure, superconducting mechanism, basic superconducting properties along with its present international status. The third chapter provides details on the preparation and characterization techniques followed through out the study on MgB2. Fourth chapter discusses the effect of processing temperature and chemical doping using nano sized dopants on the superconducting properties of MgB2• Fifth chapter deals with the optimization of processing parameters and novel preparation techniques for wire fabrication. Sixth chapter furnishes the preparation of multifilamentary wires with various filament configurations, their electromechanical properties and it also incorporates the development of an MgB2 coil and a general purpose conduction cooled current lead.

Design and synthesis of efficient mac architectures for high speed decimal processor

Most of the commercial and financial data are stored in decimal fonn. Recently, support for decimal arithmetic has received increased attention due to the growing importance in financial analysis, banking, tax calculation, currency conversion, insurance, telephone billing and accounting. Performing decimal arithmetic with systems that do not support decimal computations may give a result with representation error, conversion error, and/or rounding error. In this world of precision, such errors are no more tolerable. The errors can be eliminated and better accuracy can be achieved if decimal computations are done using Decimal Floating Point (DFP) units. But the floating-point arithmetic units in today's general-purpose microprocessors are based on the binary number system, and the decimal computations are done using binary arithmetic. Only few common decimal numbers can be exactly represented in Binary Floating Point (BF P). ln many; cases, the law requires that results generated from financial calculations performed on a computer should exactly match with manual calculations. Currently many applications involving fractional decimal data perform decimal computations either in software or with a combination of software and hardware. The performance can be dramatically improved by complete hardware DFP units and this leads to the design of processors that include DF P hardware.VLSI implementations using same modular building blocks can decrease system design and manufacturing cost. A multiplexer realization is a natural choice from the viewpoint of cost and speed.This thesis focuses on the design and synthesis of efficient decimal MAC (Multiply ACeumulate) architecture for high speed decimal processors based on IEEE Standard for Floating-point Arithmetic (IEEE 754-2008). The research goal is to design and synthesize deeimal'MAC architectures to achieve higher performance.Efficient design methods and architectures are developed for a high performance DFP MAC unit as part of this research.

Molecular breakdown of different forms of natural rubber and its influence on properties of their mixes and vulcanizates

The forms of natural rubber studied were sheet [RSS 4 and RSS 5], ISNR 20 and EBC. In the case of the latter two forms samples from estate and nonestate sectors were included. The samples were collected from different locations at specified intervals, for a particular period. The effect of the extent of mastication on raw rubber properties as well as the properties of the compounds and vulcanizates also studied. The consistency in raw rubber properties and breakdown behavior of skim rubber were studied by collecting samples periodically from selected processing units. The effect of incorporation of skim with ISNR 20 has also been investigated

Teaching an Old Robot New Tricks: Learning Novel Tasks via Interaction with People and Things

As AI has begun to reach out beyond its symbolic, objectivist roots into the embodied, experientialist realm, many projects are exploring different aspects of creating machines which interact with and respond to the world as humans do. Techniques for visual processing, object recognition, emotional response, gesture production and recognition, etc., are necessary components of a complete humanoid robot. However, most projects invariably concentrate on developing a few of these individual components, neglecting the issue of how all of these pieces would eventually fit together. The focus of the work in this dissertation is on creating a framework into which such specific competencies can be embedded, in a way that they can interact with each other and build layers of new functionality. To be of any practical value, such a framework must satisfy the real-world constraints of functioning in real-time with noisy sensors and actuators. The humanoid robot Cog provides an unapologetically adequate platform from which to take on such a challenge. This work makes three contributions to embodied AI. First, it offers a general-purpose architecture for developing behavior-based systems distributed over networks of PC's. Second, it provides a motor-control system that simulates several biological features which impact the development of motor behavior. Third, it develops a framework for a system which enables a robot to learn new behaviors via interacting with itself and the outside world. A few basic functional modules are built into this framework, enough to demonstrate the robot learning some very simple behaviors taught by a human trainer. A primary motivation for this project is the notion that it is practically impossible to build an "intelligent" machine unless it is designed partly to build itself. This work is a proof-of-concept of such an approach to integrating multiple perceptual and motor systems into a complete learning agent.

La experiencia de los receptores en el trasplante de órganos y tejidos

El trasplante de órganos y/o tejidos es considerado como una opción terapéutica viable para el tratamiento tanto de enfermedades crónicas o en estadios terminales, como de afectaciones no vitales, pero que generen una disminución en la calidad de vida percibida por el paciente. Este procedimiento, de carácter multidimensional, está compuesto por 3 actores principales: el donante, el órgano/tejido, y el receptor. Si bien un porcentaje significativo de investigaciones y planes de intervención han girado en torno a la dimensión biológica del trasplante, y a la promoción de la donación; el interés por la experiencia psicosocial y la calidad de vida de los receptores en este proceso ha aumentado durante la última década. En relación con esto, la presente monografía se plantea como objetivo general la exploración de la experiencia y los significados construidos por los pacientes trasplantados, a través de una revisión sistemática de la literatura sobre esta temática. Para ello, se plantearon unos objetivos específicos derivados del general, se seleccionaron términos o palabras claves por cada uno de estos, y se realizó una búsqueda en 5 bases de datos para revistas indexadas: Ebsco Host (Academic Search; y Psychology and Behavioral Sciences Collection); Proquest; Pubmed; y Science Direct. A partir de los resultados, se establece que si bien la vivencia de los receptores ha comenzado a ser investigada, aún es necesaria una mayor exploración sobre la experiencia de estos pacientes; exploración que carecería de objetivo si no se hiciera a través de las narrativas o testimonios de los mismos receptores

A customizable multi-agent system for distributed data mining

We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances.

Toward construction of an inexpensive brain computer interface for goal oriented applications

The paper describes the implementation of an offline, low-cost Brain Computer Interface (BCI) alternative to more expensive commercial models. Using inexpensive general purpose clinical EEG acquisition hardware (Truscan32, Deymed Diagnostic) as the base unit, a synchronisation module was constructed to allow the EEG hardware to be operated precisely in time to allow for recording of automatically time stamped EEG signals. The synchronising module allows the EEG recordings to be aligned in stimulus time locked fashion for further processing by the classifier to establish the class of the stimulus, sample by sample. This allows for the acquisition of signals from the subject’s brain for the goal oriented BCI application based on the oddball paradigm. An appropriate graphical user interface (GUI) was constructed and implemented as the method to elicit the required responses (in this case Event Related Potentials or ERPs) from the subject.

Vidibus: a low-cost, modular bus system for real-time video processing

The advantages of standard bus systems have been appreciated for many years. The ability to connect only those modules required to perform a given task has both technical and commercial advantages over a system with a fixed architecture which cannot be easily expanded or updated. Although such bus standards have proliferated in the microprocessor field, a general purpose low-cost standard for digital video processing has yet to gain acceptance. The paper describes the likely requirements of such a system, and discusses three currently available commercial systems. A new bus specification known as Vidibus, developed to fulfil these requirements, is presented. Results from applications already implemented using this real-time bus system are also given.

The effects of word exposure frequency and elaboration of word processing on incidental L2 vocabulary acquisition through reading

Research on incidental second language (L2) vocabulary acquisition through reading has claimed that repeated encounters with unfamiliar words and the relative elaboration of processing these words facilitate word learning. However, so far both variables have been investigated in isolation. To help close this research gap, the current study investigates the differential effects of the variables ‘word exposure frequency’ and ‘elaboration of word processing’ on the initial word learning and subsequent word retention of advanced learners of L2 English. Whereas results showed equal effects for both variables on initial word learning, subsequent word retention was more contingent on elaborate processing of form–meaning relationships than on word frequency. These results, together with those of the studies reviewed, suggest that processing words again after reading (input–output cycles) is superior to reading-only tasks. The findings have significant implications for adaptation and development of teaching materials that enhance L2 vocabulary learning.

An investigation into feature construction to assist word sense disambiguation

Identifying the correct sense of a word in context is crucial for many tasks in natural language processing (machine translation is an example). State-of-the art methods for Word Sense Disambiguation (WSD) build models using hand-crafted features that usually capturing shallow linguistic information. Complex background knowledge, such as semantic relationships, are typically either not used, or used in specialised manner, due to the limitations of the feature-based modelling techniques used. On the other hand, empirical results from the use of Inductive Logic Programming (ILP) systems have repeatedly shown that they can use diverse sources of background knowledge when constructing models. In this paper, we investigate whether this ability of ILP systems could be used to improve the predictive accuracy of models for WSD. Specifically, we examine the use of a general-purpose ILP system as a method to construct a set of features using semantic, syntactic and lexical information. This feature-set is then used by a common modelling technique in the field (a support vector machine) to construct a classifier for predicting the sense of a word. In our investigation we examine one-shot and incremental approaches to feature-set construction applied to monolingual and bilingual WSD tasks. The monolingual tasks use 32 verbs and 85 verbs and nouns (in English) from the SENSEVAL-3 and SemEval-2007 benchmarks; while the bilingual WSD task consists of 7 highly ambiguous verbs in translating from English to Portuguese. The results are encouraging: the ILP-assisted models show substantial improvements over those that simply use shallow features. In addition, incremental feature-set construction appears to identify smaller and better sets of features. Taken together, the results suggest that the use of ILP with diverse sources of background knowledge provide a way for making substantial progress in the field of WSD.

A multi-GPU algorithm for large-scale neuronal networks

Large-scale simulations of parts of the brain using detailed neuronal models to improve our understanding of brain functions are becoming a reality with the usage of supercomputers and large clusters. However, the high acquisition and maintenance cost of these computers, including the physical space, air conditioning, and electrical power, limits the number of simulations of this kind that scientists can perform. Modern commodity graphical cards, based on the CUDA platform, contain graphical processing units (GPUs) composed of hundreds of processors that can simultaneously execute thousands of threads and thus constitute a low-cost solution for many high-performance computing applications. In this work, we present a CUDA algorithm that enables the execution, on multiple GPUs, of simulations of large-scale networks composed of biologically realistic Hodgkin-Huxley neurons. The algorithm represents each neuron as a CUDA thread, which solves the set of coupled differential equations that model each neuron. Communication among neurons located in different GPUs is coordinated by the CPU. We obtained speedups of 40 for the simulation of 200k neurons that received random external input and speedups of 9 for a network with 200k neurons and 20M neuronal connections, in a single computer with two graphic boards with two GPUs each, when compared with a modern quad-core CPU. Copyright (C) 2010 John Wiley & Sons, Ltd.

Agents and the Grid: Service Discovery

The Grid is a large-scale computer system that is capable of coordinating resources that are not subject to centralised control, whilst using standard, open, general-purpose protocols and interfaces, and delivering non-trivial qualities of service. In this chapter, we argue that Grid applications very strongly suggest the use of agent-based computing, and we review key uses of agent technologies in Grids: user agents, able to customize and personalise data; agent communication languages offering a generic and portable communication medium; and negotiation allowing multiple distributed entities to reach service level agreements. In the second part of the chapter, we focus on Grid service discovery, which we have identified as a prime candidate for use of agent technologies: we show that Grid-services need to be located via personalised, semantic-rich discovery processes, which must rely on the storage of arbitrary metadata about services that originates from both service providers and service users. We present UDDI-MT, an extension to the standard UDDI service directory approach that supports the storage of such metadata via a tunnelling technique that ties the metadata store to the original UDDI directory. The outcome is a flexible service registry which is compatible with existing standards and also provides metadata-enhanced service discovery.

BDI4JADE: a BDI layer on top of JADE

Several agent platforms that implement the belief-desire-intention (BDI) architecture have been proposed. Even though most of them are implemented based on existing general purpose programming languages, e.g. Java, agents are either programmed in a new programming language or Domain-specific Language expressed in XML. As a consequence, this prevents the use of advanced features of the underlying programming language and the integration with existing libraries and frameworks, which are essential for the development of enterprise applications. Due to these limitations of BDI agent platforms, we have implemented the BDI4JADE, which is presented in this paper. It is implemented as a BDI layer on top of JADE, a well accepted agent platform.

Light Coverage on Colby College Campus

Outdoor lighting is an essential component for nighttime safety on college campuses. Outdoor lighting on Colby College campus is not uniform, leaving some areas with minimal light. The purpose of this project is to analytically evaluate if and where major walkways on Colby College have inadequate lighting. While we were unable to define inadequate lighting, we found that most paths on campus do appear to be well lit, while in general open spaces on campus have the lowest light levels.