1,724,440 research outputs found
Thompson, John Alan, NX56124
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/421267Surname: THOMPSON. Given Name(s) or Initials: JOHN ALAN. Military Service Number or Last Known Location: NX56124. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 38356.245982
Item: [2016.0049.53528] "Thompson, John Alan, NX56124
Thompson, John Berverland, WX3390
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/421348Surname: THOMPSON. Given Name(s) or Initials: JOHN BERVERLAND. Military Service Number or Last Known Location: WX3390. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 37051.246063
Item: [2016.0049.53609] "Thompson, John Berverland, WX3390
Thompson, John H.
No full textMilitary Information: Officers Training School for Heavy Artillery.This project was assisted by a grant from the New Jersey Historical Commission, a division of the Department of State
John Lee Thompson, John Calvin and the Daughters of Sarah
No full textSoulié Marguerite. John Lee Thompson, John Calvin and the Daughters of Sarah. In: Bulletin de l'Association d'étude sur l'humanisme, la réforme et la renaissance, n°35, 1992. pp. 83-85
Considerations for Predicting Thermal Contact Resistance in ANSYS
No full textThe author would like to thank Prof. Alexander H. Slocum for his advice, support and many conversations about this work; Karta Khalsa from Zygo, Inc. for his collaboration in developing the surface data translation tool used in this work; ANSYS, Inc. for donating the software used in this work; and Berk Yesin and ABB, Ltd. for their support of this work
Hyde-Thompson, John, [No Service Number]
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/394282Surname: HYDE-THOMPSON. Given Name(s) or Initials: JOHN. Military Service Number or Last Known Location: [No Registration Number]. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 998.217361
Item: [2016.0049.26575] "Hyde-Thompson, John, [No Service Number]
Ado-ing with Shakespearean Metaphor — Much or Little?
No full textThompson John O. Ado-ing with Shakespearean Metaphor — Much or Little?. In: Revue belge de philologie et d'histoire, tome 68, fasc. 3, 1990. Langues et littératures modernes - Moderne taal- en letterkunde. pp. 672-679
Channelformer Neural Network Software
No full textThis code was prepared for the IEEE Transactions on Wireless Communications Paper "Channelformer: Attention based Neural Solution for Wireless Channel Estimation and Effective Online Training" (https://hdl.handle.net/20.500.11820/244a98cb-c237-497c-bbf2-2d8f3ad0068b). The paper abstract is as follows:
In this paper, we propose an encoder-decoder neural architecture (called Channelformer) to achieve
improved channel estimation for orthogonal frequency-division multiplexing (OFDM) waveforms in
downlink scenarios. The self-attention mechanism is employed to achieve input precoding for the input
features before processing them in the decoder. In particular, we implement multi-head attention in the
encoder and a residual convolutional neural architecture as the decoder, respectively. We also employ a
customized weight-level pruning to slim the trained neural network with a fine-tuning process, which
reduces the computational complexity significantly to realize a low complexity and low latency solution.
This enables reductions of up to 70% in the parameters, while maintaining an almost identical perfor-
mance compared with the complete Channelformer. We also propose an effective online training method
based on the fifth generation (5G) new radio (NR) configuration for the modern communication systems,
which only needs the available information at the receiver for online training. Using industrial standard
channel models, the simulations of attention-based solutions show superior estimation performance
compared with other candidate neural network methods for channel estimation.
The software was prepared in MATLAB 2021B and a Readme file is provided with the code to give a short description of how it works
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