Parallel Bayesian Active Learning using Dropout for Optimizing High-Speed Channel Equalization

3D virtual worlds are becoming widespread due to cheaper powerful computers, high-speed broadband connections and efforts towards their tighter integration with current 2D Web environments. Besides traditional gaming and entertainment applications, some serious propositions are starting to emerge for their use, particularly in education, where they are perceived as enablers of active learning, learning by doing, and knowledge construction through social interaction. However, there is still little understanding of how 3D virtual worlds can be designed and deployed effectively in the education domain, and many challenges remain. This chapter makes a contribution towards such an understanding by reporting on three notable case studies at the authors’ own institutions, which have pioneered the use of Second Life, a 3D virtual world, in higher education.

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High-speed channel equalization scheme for 100 Gbps system

2018 IEEE International Conference on Industrial Technology (ICIT) ◽

10.1109/icit.2018.8352390 ◽

2018 ◽

Cited By ~ 2

Author(s):

Karthik KrishneGowda ◽

Rolf Kraemer ◽

Andreas C. Wolf ◽

Eswara Rao Bammidi

Keyword(s):

High Speed ◽

Channel Equalization

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Invited tutorial: Channel equalization: Techniques for high-speed electrical links

2013 IEEE Workshop on Microelectronics and Electron Devices (WMED) ◽

10.1109/wmed.2013.6544497 ◽

2013 ◽

Author(s):

S. Palermo

Keyword(s):

High Speed ◽

Channel Equalization

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Closed loop bi-directional broadband Doppler compensation and channel equalization for single carrier high-speed underwater acoustic communication

OCEANS 2015 - MTS/IEEE Washington ◽

10.23919/oceans.2015.7401933 ◽

2015 ◽

Cited By ~ 1

Author(s):

Youwen Zhang ◽

Lu Liu ◽

Dajun Sun ◽

Xin Liu ◽

Hongyu Cui ◽

...

Keyword(s):

Acoustic Communication ◽

High Speed ◽

Closed Loop ◽

Channel Equalization ◽

Underwater Acoustic Communication ◽

Underwater Acoustic ◽

Single Carrier ◽

Doppler Compensation

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Numerical Study of Parallel Optoelectronic Reservoir Computing to Enhance Nonlinear Channel Equalization

Photonics ◽

10.3390/photonics8100406 ◽

2021 ◽

Vol 8 (10) ◽

pp. 406

Author(s):

Xingxing Feng ◽

Lu Zhang ◽

Xiaodan Pang ◽

Xiazhen Gu ◽

Xianbin Yu

Keyword(s):

Signal Processing ◽

High Power ◽

Communication Systems ◽

Power Efficiency ◽

High Speed ◽

Numerical Study ◽

Digital Signal ◽

Channel Equalization ◽

Optical Signal ◽

Reservoir Computing

Nonlinear impairment is one of the critical limits to enhancing the performance of high-speed communication systems. Traditional digital signal processing (DSP)-based nonlinear channel equalization schemes are influenced by limited bandwidth, high power consumption, and high processing latency. Optoelectronic reservoir computing (RC) is considered a promising optical signal processing (OSP) technique with merits such as large bandwidth, high power efficiency, and low training complexity. In this paper, optoelectronic RC was employed to solve the nonlinear channel equalization problem. A parallel optoelectronic RC scheme with a dual-polarization Mach–Zehnder modulator (DPol-MZM) is proposed and demonstrated numerically. The nonlinear channel equalization performance was greatly enhanced compared with the traditional optoelectronic RC and the Volterra-based nonlinear DSP schemes. In addition, the system efficiency was improved with a single DPol-MZM.

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RX Equalization for a High-Speed Channel based on Bayesian Active Learning using Dropout

2020 IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS) ◽

10.1109/epeps48591.2020.9231471 ◽

2020 ◽

Author(s):

Xianbo Yang ◽

Junyan Tang ◽

Hakki M. Torun ◽

Wiren D. Becker ◽

Jose A. Hejase ◽

...

Keyword(s):

Active Learning ◽

High Speed

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Active Learning and Neural Network Potentials Accelerate Molecular Screening of Ether-based Solvate Ionic Liquids

10.26434/chemrxiv.12317465 ◽

2020 ◽

Author(s):

Wujie Wang ◽

Tzuhsiung Yang ◽

William Harris ◽

Rafael Gomez-Bombarelli

Keyword(s):

Neural Network ◽

Ionic Liquids ◽

Active Learning ◽

High Speed ◽

Density Functional ◽

Interatomic Potential ◽

Functional Theory ◽

Composition Space ◽

Speed And Accuracy ◽

Density Functional Theory Data

Solvate Ionic Liquids (SIL) have promising applications as electrolyte materials. Despite the broad design space of oligoether ligands, most reported SILs are based on simple tri- and tetraglyme. Here, we describe a computational search for complex ethers that can better stabilize SILs. Through active learning, a neural network interatomic potential is trained from density functional theory data. The learned potential fulfills two key requirements: transferability across composition space, and high speed and accuracy to find low-energy ligand-ion poses across configurational space. Candidate ether ligands for Li+, Mg+2 and Na+ SILs with higher binding affinity and electrochemical stability than the reference compounds are identified. Lastly, their properties are related to the geometry of the coordination sphere.

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