High-Redshift Galaxies in Cold Dark Matter Models

We use hydrodynamic cosmological simulations to predict the star formation properties of high-redshift galaxies (z=2-6) in five variants of the inflationary cold dark matter scenario, paying particular attention to z=3, the redshift of the largest "Lyman-break galaxy" (LBG) samples. Because we link the star formation timescale to the local gas density, the rate at which a galaxy forms stars is governed mainly by the rate at which it accretes cooled gas from the surrounding medium. At z=3, star formation in most of the simulated galaxies is steady on 200 Myr timescales, and the instantaneous star formation rate (SFR) is correlated with total stellar mass. However, there is enough scatter in this correlation that a sample selected above a given SFR threshold may contain galaxies with a fairly wide range of masses. The redshift history and global density of star formation in the simulations depend mainly on the amplitude of mass fluctuations in the underlying cosmological model. The three models whose mass fluctuation amplitudes agree with recent analyses of the Lyman-alpha forest also reproduce the observed luminosity function of LBGs reasonably well, though the dynamic range of the comparison is small and the theoretical and observational uncertainties are large. The models with higher and lower amplitudes appear to predict too much and too little star formation, respectively, though they are not clearly ruled out. The intermediate amplitude models predict SFR ~ 30-40 Msun/yr for galaxies with a surface density 1 per arcmin^2 per unit redshift at z=3. They predict much higher surface densities at lower SFR, and significant numbers of galaxies with SFR > 10 Msun/yr at z >= 5.Comment: Submitted to ApJ. 31 pages including 10 ps figures. Full resolution version of Fig 2 available at http://www.astronomy.ohio-state.edu/~dhw/Sph/zgal.fig2.ps.g

Ly{\alpha} emission from galaxies in the Epoch of Reionization

The intrinsic strength of the Ly$\alpha$ line in young, star-forming systems makes it a special tool for studying high-redshift galaxies. However, interpreting observations remains challenging due to the complex radiative transfer involved. Here, we combine state-of-the-art hydrodynamical simulations of 'Althaea', a prototypical Lyman Break Galaxy (LBG, stellar mass $M_{\star}$$\simeq$ $10^{10}{\rm M}_{\odot})$ at $z=7.2$, with detailed radiative transfer computations of dust/continuum, [CII] 158 $\mu$m, and Ly$\alpha$ to clarify the relation between the galaxy properties and its Ly$\alpha$ emission. Althaea exhibits low ($f_\alpha< 1\%$) Ly$\alpha$ escape fractions and Equivalent Widths, EW $\lesssim 6$ Angstrom for the simulated lines of sight, with a large scatter. The correlation between escape fraction and inclination is weak, as a result of the rather chaotic structure of high-redshift galaxies. Low $f_\alpha$ values persist even if we artificially remove neutral gas around star forming regions to mimick the presence of HII regions. The high attenuation is primarily caused by dust clumps co-located with young stellar clusters. We can turn Althaea into a Lyman Alpha Emitter (LAE) only if we artificially remove dust from the clumps, yielding EWs up to $22$ Angstrom. Our study suggests that the LBG-LAE duty-cycle required by recent clustering measurements poses the challenging problem of a dynamically changing dust attenuation. Finally, we find an anti-correlation between the magnitude of Ly$\alpha$-[CII] line velocity shift and Ly$\alpha$ luminosity.Comment: published in MNRA

Improving the Performance of K-Means for Color Quantization

Color quantization is an important operation with many applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much respect in the color quantization literature because of its high computational requirements and sensitivity to initialization. In this paper, we investigate the performance of k-means as a color quantizer. We implement fast and exact variants of k-means with several initialization schemes and then compare the resulting quantizers to some of the most popular quantizers in the literature. Experiments on a diverse set of images demonstrate that an efficient implementation of k-means with an appropriate initialization strategy can in fact serve as a very effective color quantizer.Comment: 26 pages, 4 figures, 13 table

Path planning for autonomous buses based on optimal control

This thesis presents an algorithm to generate trajectories for an autonomous bus approaching a bus stop. The path planning algorithm is formulated as an Optimal Control Problem (OCP) which is solved by means of nonlinear programming (NLP) using the direct multiple shooting method. This method has shown to be a good choice for solving nonlinear Boundary Value Problems (BVP) like this one -where there are constraints such as the limits of the road, the model dynamics or passengers comfort- due to its highly accurate solution and faster convergence and stability than other methods like direct single shooting methods. It uses a kinematic bicycle model with a coordinate transformation which uses the vehicle position along the path as independent variable instead of using time which permits the definition of the constraints independently of the vehicle’s speed. The OCP is solved in MATLAB using CasADi, a symbolic tool for solving nonlinear optimization problems that provides high level interfaces to make the problem writing easier, in addition of having better performance than similar tools. The proposed algorithm is evaluated in multiple scenarios like different kinds of bus stops and paths inside confined areas, giving as a result a trajectory that meets with the imposed constraints successfully. Experimental tests on a real autonomous bus are carried out, resulting in a smooth bus stop manoeuvre that the passengers evaluated as fully acceptable.Outgoin

Text-Independent, Open-Set Speaker Recognition

Speaker recognition, like other biometric personal identification techniques, depends upon a person\u27s intrinsic characteristics. A realistically viable system must be capable of dealing with the open-set task. This effort attacks the open-set task, identifying the best features to use, and proposes the use of a fuzzy classifier followed by hypothesis testing as a model for text-independent, open-set speaker recognition. Using the TIMIT corpus and Rome Laboratory\u27s GREENFLAG tactical communications corpus, this thesis demonstrates that the proposed system succeeded in open-set speaker recognition. Considering the fact that extremely short utterances were used to train the system (compared to other closed-set speaker identification work), this system attained reasonable open-set classification error rates as low as 23% for TIMIT and 26% for GREENFLAG. Feature analysis identified the filtered linear prediction cepstral coefficients with or without the normalized log energy or pitch appended as a robust feature set (based on the 17 feature sets considered), well suited for clean speech and speech degraded by tactical communications channels

Speaker recognition utilizing distributed DCT-II based Mel frequency cepstral coefficients and fuzzy vector quantization

In this paper, a new and novel Automatic Speaker Recognition (ASR) system is presented. The new ASR system includes novel feature extraction and vector classification steps utilizing distributed Discrete Cosine Transform (DCT-II) based Mel Frequency Cepstral Coef?cients (MFCC) and Fuzzy Vector Quantization (FVQ). The ASR algorithm utilizes an approach based on MFCC to identify dynamic features that are used for Speaker Recognition (SR)