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Institution

Technische Universität Ilmenau

EducationIlmenau, Thüringen, Germany
About: Technische Universität Ilmenau is a education organization based out in Ilmenau, Thüringen, Germany. It is known for research contribution in the topics: MIMO & Turbulence. The organization has 4054 authors who have published 9056 publications receiving 124311 citations. The organization is also known as: TU Ilmenau & Ilmenau University of Technology.
Topics: MIMO, Turbulence, Magnetic field, Silicon, Convection


Papers
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Journal ArticleDOI
TL;DR: While the proposed algorithms are suboptimal, they lead to simpler transmitter and receiver structures and allow for a reasonable tradeoff between performance and complexity.
Abstract: The use of space-division multiple access (SDMA) in the downlink of a multiuser multiple-input, multiple-output (MIMO) wireless communications network can provide a substantial gain in system throughput. The challenge in such multiuser systems is designing transmit vectors while considering the co-channel interference of other users. Typical optimization problems of interest include the capacity problem - maximizing the sum information rate subject to a power constraint-or the power control problem-minimizing transmitted power such that a certain quality-of-service metric for each user is met. Neither of these problems possess closed-form solutions for the general multiuser MIMO channel, but the imposition of certain constraints can lead to closed-form solutions. This paper presents two such constrained solutions. The first, referred to as "block-diagonalization," is a generalization of channel inversion when there are multiple antennas at each receiver. It is easily adapted to optimize for either maximum transmission rate or minimum power and approaches the optimal solution at high SNR. The second, known as "successive optimization," is an alternative method for solving the power minimization problem one user at a time, and it yields superior results in some (e.g., low SNR) situations. Both of these algorithms are limited to cases where the transmitter has more antennas than all receive antennas combined. In order to accommodate more general scenarios, we also propose a framework for coordinated transmitter-receiver processing that generalizes the two algorithms to cases involving more receive than transmit antennas. While the proposed algorithms are suboptimal, they lead to simpler transmitter and receiver structures and allow for a reasonable tradeoff between performance and complexity.

3,291 citations

Journal ArticleDOI
TL;DR: This work demonstrates the successfully synergistic regulations of both structural and electronic benefits by controllable disorder engineering and simultaneous oxygen incorporation in MoS2 catalysts, leading to the dramatically enhanced HER activity.
Abstract: Molybdenum disulfide (MoS2) has emerged as a promising electrocatalyst for catalyzing protons to hydrogen via the so-called hydrogen evolution reaction (HER). In order to enhance the HER activity, tremendous effort has been made to engineer MoS2 catalysts with either more active sites or higher conductivity. However, at present, synergistically structural and electronic modulations for HER still remain challenging. In this work, we demonstrate the successfully synergistic regulations of both structural and electronic benefits by controllable disorder engineering and simultaneous oxygen incorporation in MoS2 catalysts, leading to the dramatically enhanced HER activity. The disordered structure can offer abundant unsaturated sulfur atoms as active sites for HER, while the oxygen incorporation can effectively regulate the electronic structure and further improve the intrinsic conductivity. By means of controllable disorder engineering and oxygen incorporation, an optimized catalyst with a moderate degree of ...

2,001 citations

Journal ArticleDOI
TL;DR: MNE-Python as discussed by the authors is an open-source software package that provides state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions.
Abstract: Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne.

1,723 citations

Journal ArticleDOI
TL;DR: Detailed information about the MNE package is given and typical use cases are described while also warning about potential caveats in analysis.

1,447 citations

Journal ArticleDOI
TL;DR: A series of spinel-structured nanosheets with oxygen deficiencies and ultrathin thicknesses designed to increase the reactivity and the number of active sites of the catalysts were taken as an excellent platform for promoting the water oxidation process and should provide a new pathway for the design of advanced OER catalysts.
Abstract: Electrochemical water splitting is a clean technology for H2 fuels, but greatly hindered by the slow kinetics of the oxygen evolution reaction (OER). Herein, a series of spinel-structured nanosheets with oxygen deficiencies and ultrathin thicknesses were designed to increase the reactivity and the number of active sites of the catalysts, which were then taken as an excellent platform for promoting the water oxidation process. Theoretical investigations showed that the oxygen vacancies confined in the ultrathin nanosheet could lower the adsorption energy of H2O, leading to increased OER efficiency. As expected, the NiCo2O4 ultrathin nanosheets rich in oxygen vacancies exhibited a large current density of 285 mA cm−2 at 0.8 V and a small overpotential of 0.32 V, both of which are superior to the corresponding values of bulk samples or samples with few oxygen deficiencies and even higher than those of most reported non-precious-metal catalysts. This work should provide a new pathway for the design of advanced OER catalysts.

1,164 citations


Authors

Showing all 4111 results

NameH-indexPapersCitations
Andreas Richter11076948262
Aldo R. Boccaccini103123454155
Axel Scherer9073643939
Michael M. Wolf6732216168
Ute Kaiser6653320225
Oliver Ambacher6484826256
Andreas Schober6434516791
Yong Lei6326113721
Jie Xiong6257613235
Matthias Hein5757413109
Yang Xu5661313940
Thomas Dietrich5518515546
Richard Berndt5531911501
Ulrich Starke5321911999
Ulrich Schurr511639408
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202331
202269
2021478
2020598
2019618
2018614