Hyo Jin Kim

6728026, Apr 27, 2004

Aug 29, 2001

09/942457

Chang-Hee Lee - Taejon, KR
Byoung Yoon Kim - Mountain View CA
Seok Hyun Yun - Santa Clara CA
Hyo Sang Kim - Santa Clara CA
Jin-Serk Baik - Kyungki-do, KR
Kun-Youl Park - Pusan, KR
Wayne V. Sorin - Mountain View CA

Novera Optics, Inc. - Palo Alto CA

H01S 300

35933711, 3593375

An optical amplifier comprises a wavelength tunable filter, one or more optical gain stages, and a controller for controlling a spectral profile of the wavelength tunable filter in response to a measured spectral characteristic of the amplifier. The controller may also control gain of the gain stage(s). The controller determines the filter spectral profile necessary to obtain a desired amplifier spectral characteristic. The spectral characteristic may, for example, be a power spectral output of the amplifier or a gain profile of the amplifier. The amplifier may incorporate a dispersion compensator. The controller may control a spectral profile of the wavelength tunable filter and gain of the dispersion compensator. A tunable fiber light source is also described.

20220197512, Jun 23, 2022

Mar 14, 2022

17/694032

- Palo Alto CA, US
Hyo Jun Kim - San Jose CA, US
Prasenjit Sarkar - Los Gatos CA, US
Maohua Lu - Fremont CA, US
Ajaykrishna Raghavan - Santa Clara CA, US
Pin Zhou - San Jose CA, US

G06F 3/06
G06F 11/14
G06F 16/00

Embodiments disclosed herein provide systems, methods, and computer readable media for sub-cluster recovery in a data storage environment having a plurality of storage nodes. In a particular embodiment, the method provides scanning data items in the plurality of nodes. While scanning, the method further provides indexing the data items into an index of a plurality of partition groups. Each partition group includes data items owned by a particular one of the plurality of storage nodes. The method then provides storing the index.

20210286521, Sep 16, 2021

Jun 2, 2021

17/336535

- Palo Alto CA, US
Hyo Jun Kim - San Jose CA, US
Prasenjit Sarkar - Los Gatos CA, US
Maohua Lu - Fremont CA, US
Ajaykrishna Raghavan - Santa Clara CA, US
Pin Zhou - San Jose CA, US

G06F 3/06
G06F 16/00
G06F 11/14

The method disclosed is for instantiating a second cluster based on a first cluster. For at least one node of a second plurality of nodes, generating per node data based on mappings between a plurality of partition groups and a first plurality of nodes, the first plurality of nodes corresponding to the first cluster. The method further discloses identifying data items included in the plurality of partition groups based on the mappings between the plurality of partition groups and the first plurality of nodes. The method further discloses each partition group corresponding to a node of the first plurality of nodes and comprising a subset of data items stored in the node. The method further discloses loading the data items included in the plurality of partition groups onto the second plurality of nodes, the second plurality of nodes corresponding to the second cluster.

20210286522, Sep 16, 2021

Jun 2, 2021

17/336628

- Palo Alto CA, US
Hyo Jun Kim - San Jose CA, US
Prasenjit Sarkar - Los Gatos CA, US
Maohua Lu - Fremont CA, US
Ajaykrishna Raghavan - Santa Clara CA, US
Pin Zhou - San Jose CA, US

G06F 3/06
G06F 16/00
G06F 11/14

The method disclosed includes scanning data items stored in the first plurality of nodes of a first cluster. While scanning, creating a partition group index indexing the data items into a plurality of partition groups. Each partition group corresponds to a node of the first plurality of nodes and comprises a subset of data items stored in the node. Storing the index. Instantiating a second cluster, comprising generating per node data, for each node of a second plurality of nodes, based on mappings between the partition groups and the first plurality of nodes. Identifying the data items included in the partition groups according to the partition group index and loading the data items included in the partition groups onto the second plurality of nodes.

20210278971, Sep 9, 2021

Oct 22, 2020

17/077245

- Palo Alto CA, US
Hyo Jun Kim - San Jose CA, US
Prasenjit Sarkar - Los Gatos CA, US
Maohua Lu - Fremont CA, US
Ajaykrishna Raghavan - Santa Clara CA, US
Pin Zhou - San Jose CA, US

G06F 3/06
G06F 11/14
G06F 16/00

Embodiments disclosed herein provide systems, methods, and computer readable media for sub-cluster recovery in a data storage environment having a plurality of storage nodes. In a particular embodiment, the method provides scanning data items in the plurality of nodes. While scanning, the method further provides indexing the data items into an index of a plurality of partition groups. Each partition group includes data items owned by a particular one of the plurality of storage nodes. The method then provides storing the index.

20190063932, Feb 28, 2019

Aug 10, 2018

16/100462

- Princeton NJ, US
Manmohan Chandraker - Santa Clara CA, US
Hyo Jin Kim - Chapel Hill NC, US

G01C 21/32
G05D 1/00
G06T 7/73
G06N 3/08
G06N 5/04
G06F 17/30

A computer-implemented method, system, and computer program product are provided for a guidance control system utilizing pose estimation in an autonomous vehicle. The method includes receiving, by a pose estimation system, a plurality of images from one or more cameras. The method also includes predicting, by the pose estimation system, a pose from the score map and a combined feature map, the combined feature map correlated from a pair of the plurality of images. The method additionally includes moving, by a propulsion system, the autonomous vehicle responsive to the pose.

20190064851, Feb 28, 2019

Aug 10, 2018

16/100479

- Princeton NJ, US
Manmohan Chandraker - Santa Clara CA, US
Hyo Jin Kim - Chapel Hill NC, US

G05D 1/08
G06T 7/73
G05D 1/00
G08G 5/04
B64C 39/02

A computer-implemented method, system, and computer program product are provided for a stabilization system utilizing pose estimation in an aerial drone. The method includes receiving, by a pose estimation system, a plurality of images from one or more cameras. The method also includes predicting, by the pose estimation system, a pose from the score map and a combined feature map, the combined feature map correlated from a pair of the plurality of images. The method additionally includes moving, by a propulsion system, the aerial drone responsive to the pose.

20190066326, Feb 28, 2019

Aug 10, 2018

16/100445

- Princeton NJ, US
Manmohan Chandraker - Santa Clara CA, US
Hyo Jin Kim - Chapel Hill NC, US

G06T 7/73
G06T 7/00
G06N 5/04
G06N 3/08

A computer-implemented method, system, and computer program product are provided for pose estimation. The method includes receiving, by a processor, a plurality of images from one or more cameras. The method also includes generating, by the processor with a feature extraction convolutional neural network (CNN), a feature map for each of the plurality of images. The method additionally includes estimating, by the processor with a feature weighting network, a score map from a pair of the feature maps. The method further includes predicting, by the processor with a pose estimation CNN, a pose from the score map and a combined feature map. The method also includes controlling an operation of a processor-based machine to change a state of the processor-based machine, responsive to the pose.