Wide bandgap perovskites are emerging as suitable candidates for the technology of tandem solar cells. Understanding their optical properties is a prerequisite for improving the corresponding solar cells’ efficiencies. In this thesis, we employ various steady-state spectroscopies to reveal the optical properties of two wide bandgap perovskites: FA0.83Cs0.17Pb(I0.7Br0.3)3 or PVK1 and FA0.83Cs0.17Pb(I0.5Br0.5)3 or PVK2. The optical properties of interest are the semiconductors’ absorption spectra, the sub-bandgap absorption features, the bandgap energy, the Urbach energy, and the excitonic binding energy. We find that the sub-bandgap absorption can be characterized by a single exponential function. We also find that the Urbach energies and the excitonic binding energies are below the thermal energy at room temperature, which signals that PVK1 and PVK2 are excellent nominees for photovoltaic absorbers. Finally, the bandgap energy is red shifted due to excitonic effects as revealed by the Elliot model.

In automatic speech recognition systems (ASRs), training is a critical phase to the system?s success. Communication media, either analog (such as analog landline phones) or digital (VoIP) distort the speaker?s speech signal often in very complex ways: linear distortion occurs in all channels, either in the magnitude or phase spectrum. Non-linear but time-invariant distortion will always appear in all real systems. In digital systems we also have network effects which will produce packet losses and delays and repeated packets. Finally, one cannot really assert what path a signal will take, and so having error or distortion in between is almost a certainty. The channel introduces an acoustical mismatch between the speaker's signal and the trained data in the ASR, which results in poor recognition performance. The approach so far, has been to try to undo the havoc produced by the channels, i.e. compensate for the channel's behavior. In this thesis, we try to characterize the effects of different transmission media and use that as an inexpensive and repeatable way to train ASR systems.