Beuth University of Applied Sciences Berlin

About: Beuth University of Applied Sciences Berlin is a education organization based out in Berlin, Germany. It is known for research contribution in the topics: Computer science & Competence (human resources). The organization has 445 authors who have published 806 publications receiving 9243 citations.

Monolithic perovskite/silicon tandem solar cell with >29% efficiency by enhanced hole extraction

Abstract: Tandem solar cells that pair silicon with a metal halide perovskite are a promising option for surpassing the single-cell efficiency limit. We report a monolithic perovskite/silicon tandem with a certified power conversion efficiency of 29.15%. The perovskite absorber, with a bandgap of 1.68 electron volts, remained phase-stable under illumination through a combination of fast hole extraction and minimized nonradiative recombination at the hole-selective interface. These features were made possible by a self-assembled, methyl-substituted carbazole monolayer as the hole-selective layer in the perovskite cell. The accelerated hole extraction was linked to a low ideality factor of 1.26 and single-junction fill factors of up to 84%, while enabling a tandem open-circuit voltage of as high as 1.92 volts. In air, without encapsulation, a tandem retained 95% of its initial efficiency after 300 hours of operation.

The reverb challenge: Acommon evaluation framework for dereverberation and recognition of reverberant speech

Abstract: Recently, substantial progress has been made in the field of reverberant speech signal processing, including both single- and multichannel dereverberation techniques, and automatic speech recognition (ASR) techniques robust to reverberation. To evaluate state-of-the-art algorithms and obtain new insights regarding potential future research directions, we propose a common evaluation framework including datasets, tasks, and evaluation metrics for both speech enhancement and ASR techniques. The proposed framework will be used as a common basis for the REVERB (REverberant Voice Enhancement and Recognition Benchmark) challenge. This paper describes the rationale behind the challenge, and provides a detailed description of the evaluation framework and benchmark results.

Analyzing undergraduate students\' performance using educational data mining

Abstract: The tremendous growth in electronic data of universities creates the need to have some meaningful information extracted from these large volumes of data The advancement in the data mining field makes it possible to mine educational data in order to improve the quality of the educational processes This study, thus, uses data mining methods to study the performance of undergraduate students Two aspects of students' performance have been focused upon First, predicting students' academic achievement at the end of a four-year study programme Second, studying typical progressions and combining them with prediction results Two important groups of students have been identified: the low and high achieving students The results indicate that by focusing on a small number of courses that are indicators of particularly good or poor performance, it is possible to provide timely warning and support to low achieving students, and advice and opportunities to high performing students