公开(公告)号：US20240422997A1

公开(公告)日：2024-12-19

申请号：US18700819

申请日：2022-10-11

Applicant: KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Ahmed M. ELTAWIL ,  Mohammed E. FOUDA ,  Boon S. OOI ,  Khaled Nabil SALAMA ,  Mani Teja VIJJAPU

IPC: H10K10/10 ,  G06N3/049 ,  H10K10/84 ,  H10K19/00 ,  H10K30/82 ,  H10K39/30 ,  H10K85/50 ,  H10K102/10

Abstract: A capacitive photoresistor array having frequency-independent capacitance includes first and second electrodes and a composite material including a perovskite and a terpolymer. The composite material is sandwiched between the first electrode and the second electrode, and a capacitance of the array changes proportionally with a light intensity for visible light and is independent of light frequency due to a combination of the perovskite and the terpolymer.

公开(公告)号：US20240364388A1

公开(公告)日：2024-10-31

申请号：US18563439

申请日：2022-05-23

Applicant: KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Abdulkadir ÇELIK ,  Ahmed M. ELTAWIL

IPC: H04B7/04 ,  H04W28/02 ,  H04W28/10

CPC classification number: H04B7/04013 ,  H04W28/0273 ,  H04W28/10

Abstract: A data center system having plural components including core switches and racks Ri, each rack Ri having plural servers; a global controller configured to control a traffic flow to each rack of the plural racks Ri; plural reconfigurable intelligent surface, RIS, modules, a RIS module being configured to receive a first electromagnetic signal from a first component and emit a second electromagnetic signal toward a second component, the second electromagnetic signal carrying a same information as the first electromagnetic signal; and a local controller configured to adjust an emitting direction of the second electromagnetic signal by changing a current flow through the RIS module.

公开(公告)号：US20240016383A1

公开(公告)日：2024-01-18

申请号：US18028583

申请日：2021-09-15

Applicant: KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Abdulkadir ÇELIK ,  Ahmed M. ELTAWIL

IPC: A61B5/00 ,  G08C17/02 ,  G16H20/30 ,  G16H40/67 ,  H02J50/40 ,  H02J50/27 ,  H02J50/00

CPC classification number: A61B5/0024 ,  A61B5/0006 ,  G08C17/02 ,  G16H20/30 ,  G16H40/67 ,  H02J50/402 ,  H02J50/27 ,  H02J50/001 ,  A61B2560/0214

Abstract: A physiological data acquisition system includes an array electrode sensor (220) having plural electrodes and configured to acquire physiological data; a single electrode sensor having a single electrode and configured to acquire additional physiological data; and a hub that is configured to receive the physiological data from the array electrode sensor and the additional physiological data from the single electrode sensor only along body communication channels. At least one of the array electrode sensor and the single electrode sensor is configured to send an energy request signal to the hub, along the body communication channels. The hub, in response to the received energy request signal, emits radio frequency signals, which are used by the at least one of the array electrode sensor and the single electrode sensor to harvest energy. Alternatively, the hub may be configured to send a wake up signal to at least one of the array electrode sensor and the single electrode sensor before emitting the radio frequency signals.

公开(公告)号：US20220245038A1

公开(公告)日：2022-08-04

申请号：US17581327

申请日：2022-01-21

Applicant: UNIST Academy-Industry Research Corporation ,  THE REGENTS OF THE UNIVERSITY OF CALIFORNIA ,  KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Jong Eun LEE ,  Su Gil LEE ,  Gi Ju JUNG ,  Mohammed FOUDA ,  Fadi KURDAHI ,  Ahmed M. ELTAWIL

IPC: G06F11/14 ,  G06F11/07

Abstract: A stuck-at fault mitigation method for resistive random access memory (ReRAM)-based deep learning accelerators, includes: confirming a distorted output value (Y0) due to a stuck-at fault (SAF) by using a correction data set in a pre-trained deep learning network, by means of ReRAM-based deep learning accelerator hardware; updating an average (μ) and a standard deviation (σ) of a batch normalization (BN) layer by using the distorted output value (Y0), by means of the ReRAM-based deep learning accelerator hardware; folding the batch normalization (BN) layer in which the average (μ) and the standard deviation (σ) are updated into a convolution layer or a fully-connected layer, by means of the ReRAM-based deep learning accelerator hardware; and deriving a normal output value (Y1) by using the deep learning network in which the batch normalization (BN) layer is folded, by means of the ReRAM-based deep learning accelerator hardware.