3,219 research outputs found
The anatomy of the Gunn laser
A monopolar GaAs Fabry–Pérot cavity laser based on the Gunn effect is studied both experimentally and theoretically. The light emission occurs via the band-to-band recombination of impact-ionized excess carriers in the propagating space-charge (Gunn) domains. Electroluminescence spectrum from the cleaved end-facet emission of devices with Ga1−xAlxAs (x = 0.32) waveguides shows clearly a preferential mode at a wavelength around 840 nm at T = 95 K. The threshold laser gain is assessed by using an impact ionization coefficient resulting from excess carriers inside the high-field domain
Energy and momentum relaxation dynamics of hot holes in modulation doped GaInNAs/GaAs quantum wells
We present the studies of energy and momentum relaxation dynamics of nonequilibrium holes in GaxIn1−xNyAs1−y/GaAs quantum well modulation doped with Be. Experimental results show that the real-space transfer (RST) of hot holes occurs via thermionic emission from the high-mobility GaInNAs quantum wells into the low-mobility GaAs barriers at a threshold electric field of F ∼ 6 kV/cm at T = 13 K. At this field the hole drift velocity saturates at vd ∼ 1×107 cm/s. A slight increase in the field above the threshold leads to the impact ionization of acceptors in the barriers by the nonequilibrium holes. We observe and model theoretically a negative differential mobility effect induced by RST that occurs at an electric field of F ∼ 7 kV/cm. The observed current surge at electric fields above 7 kV/cm is attributed to the hole multiplication induced by shallow impurity breakdown in the GaAs barrier and impact ionization in the high-field domain regime associated with the packet of RST of holes in the well
Covariance-domain Dictionary Learning for Overcomplete EEG Source Identification
We propose an algorithm targeting the identification of more sources than
channels for electroencephalography (EEG). Our overcomplete source
identification algorithm, Cov-DL, leverages dictionary learning methods applied
in the covariance-domain. Assuming that EEG sources are uncorrelated within
moving time-windows and the scalp mixing is linear, the forward problem can be
transferred to the covariance domain which has higher dimensionality than the
original EEG channel domain. This allows for learning the overcomplete mixing
matrix that generates the scalp EEG even when there may be more sources than
sensors active at any time segment, i.e. when there are non-sparse sources.
This is contrary to straight-forward dictionary learning methods that are based
on the assumption of sparsity, which is not a satisfied condition in the case
of low-density EEG systems. We present two different learning strategies for
Cov-DL, determined by the size of the target mixing matrix. We demonstrate that
Cov-DL outperforms existing overcomplete ICA algorithms under various scenarios
of EEG simulations and real EEG experiments
GDP nowcasting using high frequency asset price, commodity price and banking data
Ankara : The Department of Economics, İhsan Doğramacı Bilkent University, 2011.Thesis (Master's) -- Bilkent University, 2011.Includes bibliographical references leaves 18.Knowing the current state of the economy is important especially when we consider
that GDP information comes with a lag of quarter. From this perspective,
employing high frequency variables in GDP nowcasting may contribute to our
knowledge of economic conditions, since they are timelier compared to GDP.
This paper deals with nowcasting US GDP using an expectation maximization
algorithm in a Kalman Ölter estimation, which includes asset prices, commodity
prices and banking data as explanatory variables together with real variables
and price indices. As a result of the estimations, asset prices and other high frequency
variables are found useful in nowcasting US GDP contrary to previous
studies. Model predictions beat the traditional methods with the medium size
model, which includes Öfteen variables, yielding the best nowcast results. Finally,
this paper also proposes a new route for achieving better nowcast results
by changing system speciÖcations of the state variables.Balkan, BinnurM.S
Identity Politics (POLS 53) Syllabus
Where do our identities come from and why do they matter for social and political life? Do we have the freedom to choose our own identities or are they ascribed to us by others? And to what extent do our identities dictate what we can do, think, know, say, or feel? This class explores how categories like class, race, gender, ethnicity, nation, religion, and sexuality impact politics and struggles for power around the world
Testing the Reliability of Mathematical Models
The mathematical models are extensively used in the engineering fields. It is a very powerful tool to
design, analyze and test the systems without actually building them, which of course reduces time
spent on the designing. The question was raised, is it actually appropriate to use the mathematical
models for testing purposes, how precise is the outcome and what kind of information about the
system the mathematical model gives. In order to investigate this situation, testing the reliability of
the mathematical models was proposed. The purpose of this project is to scrutinize the mathematical
models, investigate the pros and cons, the criteria and limitations that a mathematical model have
and of course the errors introduces if those criteria and limitations are violated. The scope of the
project covers general mathematical models for sake of investigating how they are used and their
limitations. Also, models related to electrical and electronics field are studied to quantify the errors.
As this project is mostly research based, the theories behind mathematical models are investigated
and considered and then MATLABprogramming is used to illustrate how models are used and their
outcomes for given inputs for different situations. The results of the MATLAB simulations show the
quantity and percentage of the errors and are given in form of table later in the chapters. Considering
what has been studied and the results of the simulations, mathematical models do not ideally
represent real hfe models, although if carefully designed the errors could be reduced to acceptable
limits and a close approximation of the real life model could be obtained. As for educational
purposes the little percentage of the error does not carry any significance mathematical models can
be used to enhanceunderstanding of the student. If nature of the application requires high precision,
extra care must be taken while modelling and designing
Superconductivity in heavily compensated Mg-doped InN
We report superconductivity in Mg-doped InN grown by molecular beam epitaxy. Superconductivity phase transition temperature occurs Tc = 3.97 K as determined by magnetoresistance and Hall resistance measurements. The two-dimensional (2D) carrier density of the measured sample is n2D = 9×1014 cm−2 corresponding to a three-dimensional (3D) electron density of n3D = 1.8×1019 cm−3 which is within the range of values between Mott transition and the superconductivity to metal transition. We propose a plausible mechanism to explain the existence of the superconductivity in terms of a uniform distribution of superconducting InN nanoparticles or nanosized indium dots forming microscopic Josephson junctions in the heavily compensated insulating bulk InN matrix
- …