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