Detection of concealed weapons in X-ray images using fuzzy K-NN

ABSTRAC

Taming Chaotic Circuits

Control algorithms that exploit chaotic behavior can vastly improve the performance of many practical and useful systems. The program Perfect Moment is built around a collection of such techniques. It autonomously explores a dynamical system's behavior, using rules embodying theorems and definitions from nonlinear dynamics to zero in on interesting and useful parameter ranges and state-space regions. It then constructs a reference trajectory based on that information and causes the system to follow it. This program and its results are illustrated with several examples, among them the phase-locked loop, where sections of chaotic attractors are used to increase the capture range of the circuit

Ring Dark Solitons in Toroidal Bose-Einstein Condensates

In this Thesis, we study various aspects of ring dark solitons (RDSs) in quasi-two-dimensional toroidally trapped Bose-Einstein condensates, focussing on atomic realisations thereof. Unlike the well-known planar dark solitons, exact analytic expressions for RDSs are not known. We address this problem by presenting exact localized soliton-like solutions to the radial Gross-Pitaevskii equation. To date, RDSs have not been experimentally observed in cold atomic gases, either. To this end, we propose two protocols for their creation in experiments. It is also currently well known that in dimensions higher than one, (ring) dark solitons are susceptible, in general, to an irreversible decay into vortex-antivortex pairs through the snake instability. We show that the snake instability is caused by an unbalanced quantum pressure across the soliton's notch, linking the instability to the Bogoliubov-de Gennes spectrum. In particular, if the angular symmetry is maintained (or the toroidal trapping is restrictive enough), we show that the RDS is stable (long-lived with a lifetime of order seconds) in two dimensions. Furthermore, when the decay does take place, we show that the snake instability can in fact be reversible, and predict a previously unknown revival phenomenon for the original (many-)RDS system: the soliton structure is recovered and all the point-phase singularities (i.e. vortices) disappear. Eventually, however, the decay leads to an example of quantum turbulence; a quantum example of the laminar-to-turbulent type of transition.Tässä työssä käsitellään pimeitä rengassolitoneja litteissä kaksiulotteisissa atomisissa Bosen-Einsteinin kondensaateissa. Toisin kuin suorat pimeät solitonit, pimeälle rengassolitonille ei ole tiedossa analyyttista kaavaa. Väitöskirjan ensimmäisessä julkaisussa esitellään muun muassa uusia eksakteja rengassolitonin kaltaisia ratkaisuja Grossin-Pitaevskiin yhtälölle. Väitöskirjassa kehitetään ja esitellään myös kaksi kokeellista menetelmää pimeiden rengassolitonien luomiseen laboratoriossa. Pimeiden rengassolitonien hajoamista ja sitä seuraavaa vorteksi-antivorteksiparien fysiikkaa tutkitaan väitöskirjan loppupuolella. Osoitetaan, että pimeän solitonin hajoaminen ei olekaan peruuttamatonta kuten aiemmin on luultu, vaan se on mahdollista, mikäli atomiloukun muoto valitaan sopivasti.Siirretty Doriast

Frequency domain model-based intracranial pressure estimation

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 73-75).Elevation of intracranial pressure (ICP), the pressure of the fluid surrounding the brain, can require urgent medical attention. Current methods for determining ICP are invasive, require neurosurgical expertise, and can lead to infection. ICP measurement is therefore limited to the sickest patients, though many others could potentially benefit from availability of this vital sign. We present a frequency-domain approach to ICP estimation using a simple lumped, linear time-invariant model of cerebrovascular dynamics. Preliminary results from 28 records of patients with severe traumatic brain injury are presented and discussed. Suggestions for future work to improve the estimation algorithm are proposed.by Irena T. Hwang.M.Eng

Variable viewpoint reality : a protoype for realtime 3D reconstruction

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (p. 70-72).by Owen W. Ozier.M.Eng

Solar system applications of Mie theory and of radiative transfer of polarized light

A theory of the multiple scattering of polarized light is discussed using the doubling method of van de Hulst. The concept of the Stokes parameters is derived and used to develop the form of the scattering phase matrix of a single particle. The diffuse reflection and transmission matrices of a single scattering plane parallel atmosphere are expressed as a function of the phase matrix, and the symmetry properties of these matrices are examined. Four matrices are required to describe scattering and transmission. The scattering matrix that results from the addition of two identical layers is derived. Using the doubling method, the scattering and transmission matrices of layers of arbitrary optical thickness can be derived. The doubling equations are then rewritten in terms of their Fourier components. Computation time is reduced since each Fourier component doubles independently. Computation time is also reduced through the use of symmetry properties

Optimal selection of stocks using computational intelligence methods

Master of Science in Engineering - EngineeringVarious methods, mostly statistical in nature have been introduced for stock market modelling and prediction. These methods are, however, complex and difficult to manipulate. Computational intelligence facilitates this approach of predicting stocks due to its ability to accurately and intuitively learn complex patterns and characterise these patterns as simple equations. In this research, a methodology that uses neural networks and Bayesian framework to model stocks is developed. The NASDAQ all-share index was used as test data. A methodology to optimise the input time-window for stock prediction using neural networks was also devised. Polynomial approximation and reformulated Bayesian frameworks methodologies were investigated and implemented. A neural network based algorithm was then designed. The performance of this final algorithm was measured based on accuracy. The effect of simultaneous use of diverse neural network engines is also investigated. The test result and accuracy measurements are presented in the final part of this thesis. Key words: Neural Networks, Bayesian framework and Markov Chain Monte Carl