School of Physical and Mathematical Sciences (SPMS)

This dataverse is a collection of the School of Physical and Mathematical Sciences’ research data.

Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

4,271 to 4,280 of 8,093 Results

phase [f = 1 THz, p = 75 um, lz = 200 um].txt Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications Plain Text - 71.7 KB - MD5: 2d3b3db4d545915dd2c2a75cbf1a87f0 Required data for Figure 8.
phaseMN.txt Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications Plain Text - 9.8 KB - MD5: da6964b94fd024dd97002166b2d1aabd Required data for Figure 9 and 10.
Power Spectrum.txt Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications Plain Text - 26.8 KB - MD5: 33a7a349124a83922ecbfb353d3c7b20 Required data for Figure 9.
Si [dir = +1, f = 1 THz, p = 75 um, lz = 200 um].mat Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications MATLAB Data - 321.9 KB - MD5: 65f897d8e37babc2f03e30047887ed8a Required data for Figure 8.
Si - phase [dir = +1, f = 1 THz, p = 75 um, lz = 200 um].txt Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications Plain Text - 64.1 KB - MD5: 3c6169c85e79f4019eb4940f04738588 Required data for Figure 8.
theta = 15, phi = -135.csv Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications Comma Separated Values - 1003.8 KB - MD5: 90a49dfc446e79cc32578e8350016143 Required data for Figure 9.
theta = 20, phi = -10.tab Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications Tabular Data - 1000.8 KB - 340 Variables, 339 Observations - UNF:6:EK30gJefUqwbv/jo/IQ9pA== Required data for Figure 9.
theta = 25, phi = 50.tab Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications Tabular Data - 1000.3 KB - 340 Variables, 339 Observations - UNF:6:S8colQsykhGUDAgWGPlQxw== Required data for Figure 9.
theta = 25, phi = 95.csv Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications Comma Separated Values - 1003.8 KB - MD5: a22c3ebb53f165681a2ce4e468c746f2 Required data for Figure 9.
theta = 30, phi = 100.tab Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications Tabular Data - 1000.6 KB - 340 Variables, 339 Observations - UNF:6:zAHyTQahbK0ZjrT0z9FVaA== Required data for Figure 9.

phase [f = 1 THz, p = 75 um, lz = 200 um].txt

Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications

Plain Text - 71.7 KB -

Required data for Figure 8.

phaseMN.txt

Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications

Plain Text - 9.8 KB -

Required data for Figure 9 and 10.

Power Spectrum.txt

Sep 15, 2022 - Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications

Plain Text - 26.8 KB -

Required data for Figure 9.