公开(公告)号：US10862630B2

公开(公告)日：2020-12-08

申请号：US14847320

申请日：2015-09-08

Applicant: Samsung Electronics Co., Ltd.

Inventor： Mostafa El-Khamy ,  Arvind Yedla ,  SangHyuck Ha ,  Hyunsang Cho ,  Inyup Kang

IPC: H04L1/18 ,  H04L5/00

Abstract: Apparatuses (including user equipment (UE) and modem chips for UEs), systems, and methods for UE downlink Hybrid Automatic Repeat reQuest (HARQ) buffer memory management are described. In one method, the entire UE DL HARQ buffer memory space is pre-partitioned according to the number and capacities of the UE's active carrier components. In another method, the UE DL HARQ buffer is split between on-chip and off-chip memory so that each partition and sub-partition is allocated between the on-chip and off-chip memories in accordance with an optimum ratio.

公开(公告)号：US11784760B2

公开(公告)日：2023-10-10

申请号：US17108555

申请日：2020-12-01

Applicant: Samsung Electronics Co., Ltd.

Inventor： Mostafa El-Khamy ,  Arvind Yedla ,  Sang-Hyuck Ha ,  Hyunsang Cho ,  Inyup Kang

IPC: H04L5/00 ,  H04L1/1829 ,  H04L1/1822

CPC classification number: H04L1/1835 ,  H04L1/1822 ,  H04L5/001 ,  H04L5/0055

Abstract: Apparatuses (including user equipment (UE) and modern chips for UEs), systems, and methods for UE downlink Hybrid Automatic Repeat reQuest (HARQ) buffer memory management are described. In one method, the entire UE DL HARQ buffer memory space is pre-partitioned according to the number and capacities of the UE's active carrier components. In another method, the UE DL HARQ buffer is split between on-chip and off-chip memory so that each partition and sub-partition is allocated between the on-chip and off-chip memories in accordance with an optimum ratio.

公开(公告)号：US11645869B2

公开(公告)日：2023-05-09

申请号：US16808357

申请日：2020-03-03

Applicant: Samsung Electronics Co., Ltd.

Inventor： Mostafa El-Khamy ,  Arvind Yedla ,  Marcel Nassar ,  Jungwon Lee

IPC: G06V40/16 ,  G06T7/70

CPC classification number: G06V40/168 ,  G06T7/70 ,  G06V40/161 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30201

Abstract: A system to recognize objects in an image includes an object detection network outputs a first hierarchical-calculated feature for a detected object. A face alignment regression network determines a regression loss for alignment parameters based on the first hierarchical-calculated feature. A detection box regression network determines a regression loss for detected boxes based on the first hierarchical-calculated feature. The object detection network further includes a weighted loss generator to generate a weighted loss for the first hierarchical-calculated feature, the regression loss for the alignment parameters and the regression loss of the detected boxes. A backpropagator backpropagates the generated weighted loss. A grouping network forms, based on the first hierarchical-calculated feature, the regression loss for the alignment parameters and the bounding box regression loss, at least one of a box grouping, an alignment parameter grouping, and a non-maximum suppression of the alignment parameters and the detected boxes.

公开(公告)号：US10032067B2

公开(公告)日：2018-07-24

申请号：US15224487

申请日：2016-07-29

Applicant: Samsung Electronics Co., Ltd.

Inventor： Mostafa El-Khamy ,  Arvind Yedla ,  Marcel Nassar ,  Jungwon Lee

IPC: G06K9/00 ,  G06T7/00

Abstract: A system to recognize objects in an image includes an object detection network outputs a first hierarchical-calculated feature for a detected object. A face alignment regression network determines a regression loss for alignment parameters based on the first hierarchical-calculated feature. A detection box regression network determines a regression loss for detected boxes based on the first hierarchical-calculated feature. The object detection network further includes a weighted loss generator to generate a weighted loss for the first hierarchical-calculated feature, the regression loss for the alignment parameters and the regression loss of the detected boxes. A backpropagator backpropagates the generated weighted loss. A grouping network forms, based on the first hierarchical-calculated feature, the regression loss for the alignment parameters and the bounding box regression loss, at least one of a box grouping, an alignment parameter grouping, and a non-maximum suppression of the alignment parameters and the detected boxes.

公开(公告)号：US10380741B2

公开(公告)日：2019-08-13

申请号：US15478947

申请日：2017-04-04

Applicant: Samsung Electronics Co., Ltd.

Inventor： Arvind Yedla ,  Marcel Nassar ,  Mostafa El-Khamy ,  Jungwon Lee

IPC: G06T7/11 ,  G06T7/194 ,  G06N3/04 ,  G06K9/00 ,  G06K9/32 ,  G06K9/46 ,  G06K9/62

Abstract: Apparatuses and methods of manufacturing same, systems, and methods for object detection using a region-based deep learning model are described. In one aspect, a method is provided, in which a region proposal network (RPN) is used to identify regions of interest (RoI) in an image by assigning a confidence levels, the assigned confidence levels of the RoIs are used to boost the background score assigned by the downstream classifier to each RoI, and the background scores are used in a softmax function to calculate the final class probabilities for each object class.