Observations of the High Redshift Universe

(Abridged) In these lectures aimed for non-specialists, I review progress in understanding how galaxies form and evolve. Both the star formation history and assembly of stellar mass can be empirically traced from redshifts z~6 to the present, but how the various distant populations inter-relate and how stellar assembly is regulated by feedback and environmental processes remains unclear. I also discuss how these studies are being extended to locate and characterize the earlier sources beyond z~6. Did early star-forming galaxies contribute significantly to the reionization process and over what period did this occur? Neither theory nor observations are well-developed in this frontier topic but the first results presented here provide important guidance on how we will use more powerful future facilities.Comment: To appear in `First Light in Universe', Saas-Fee Advanced Course 36, Swiss Soc. Astrophys. Astron. in press. 115 pages, 64 figures (see http://www.astro.caltech.edu/~rse/saas-fee.pdf for hi-res figs.) For lecture ppt files see http://obswww.unige.ch/saas-fee/preannouncement/course_pres/overview_f.htm

Detection of rain in acoustic recordings of the environment

Environmental monitoring has become increasingly important due to the significant impact of human activities and climate change on biodiversity. Environmental sound sources such as rain and insect vocalizations are a rich and underexploited source of information in environmental audio recordings. This paper is concerned with the classification of rain within acoustic sensor re-cordings. We present the novel application of a set of features for classifying environmental acoustics: acoustic entropy, the acoustic complexity index, spectral cover, and background noise. In order to improve the performance of the rain classification system we automatically classify segments of environmental recordings into the classes of heavy rain or non-rain. A decision tree classifier is experientially compared with other classifiers. The experimental results show that our system is effective in classifying segments of environmental audio recordings with an accuracy of 93% for the binary classification of heavy rain/non-rain