Samsung Electronics Co., Ltd.

Samsung Sdi Co., Inc.

Samsung Display Co., Ltd.

Sungkyunkwan University

Cheil Industries Inc.

Toshiba Samsung Storage Technology Korea Corporation

Samsung Eleotronicc Co., Ltd

Hyun-jung Kim

Jun-ho Koh

In-hak Na

Byeong-hoon Kwak

Chang-han Kim

Kang-jin Yoon

Young-hwan Park

Yong-Chan Lee

Sung-chan Kim

Han-su Cho

Dae-Kwang Jung

Jae-woo Ko

Suk-jin Kim

Kyoung-hoon Kim

Dae-Hyun Kim

Jae-Ho Jung

Mi-Young Chae

In-kuk Yun

Yang-Wook Kim

Nam-Heon Lee

Dong-kwan Suh

Je-in Yu

Jae-Young Lee

Chang-Hyun Lee

Kiseok Lee

Jae-young Lee

Dong-Min Kang

Jeong-Won Lee

Yoon-woo Lee

Ho-Kuk Jung

Ji-Yeon Han

Soo-Young Jeong

Chang-Ju Shin

Hong-rak Son

Keshava Prasad Nagaraja

Jong-Woo Won

Hyong-suk Kim

Kyung-geun Lee

Jae-Hyun Park

Sung-Jin Kim

Yong-Seok Kim

Kwang-pyo Choi

Seung-jun Lee

Wook Hwan Kim

Shi-yun Cho

Sung-ho Lee

Dong-Hoon Jang

Hee-chul Han

Bo-seok Moon

Hyun-Mog Park

Kang-min Lee

Sang-Il Lee

Sang-wook Lee

Kyung-Wan Park

Young-chul Cho

Dal-Ho Huh

Taik-heon Rhee

Jae-Cheol Bae

Kyung-hoon Kim

Joo-Ho Kim

Ki-hyun Kim

Hee-bum Ahn

Yong-sup Park

In-Soo Kim

Kyu-sub Kwak

Ju-Seung Lee

Dong-wook Kwon

Parichay Kapoor

Dong-soo Lee

Jun-Seok Kim

Young-eun Lee

Ji-hun Oh

Ho-chul Shin

Albert Fayrushin

Youn-Hwan Kim

Jae-keun Na

Bong-jin Lee

Doo-woong Lee

Keshava Prasad

Young-Gil Park

Vladyslav Zakharchenko

Myong-jo Choi

Woo-Seok Jeong

Ju-Yeon Jung

Hyo-young Lee

Ik-joon Kim

Kwang-min Byeon

Jung-Un Kim

Sun-min Park

Jae-hyuck Shin

Seong-Hwan Oh

Chun-Keun Jang

Mee-ree Kim

Dong-Hyun Gong

Jae-Kwon Lee

Do-Soon Jung

Geum-Tae Kim

Hyun Gyu Lee

Kwang-hoon Son

Jae-Woo Ko

Provided are a method and apparatus for processing a convolution operation in a neural network. The apparatus may include a memory, and a processor configured to read, from the memory, one of divided blocks of input data stored in a memory; generate an output block by performing the convolution operation on the one of the divided blocks with a kernel; generate a feature map by using the output block, and write the feature map to the memory.

Device and method for processing convolution operation using kernel

An electronic apparatus is provided. The electronic apparatus includes a first memory configured to store a first artificial intelligence (AI) model including a plurality of first elements and a processor configured to include a second memory. The second memory is configured to store a second AI model including a plurality of second elements. The processor is configured to acquire output data from input data based on the second AI model. The first AI model is trained through an AI algorithm. Each of the plurality of second elements includes at least one higher bit of a plurality of bits included in a respective one of the plurality of first elements.

Electronic apparatus and control method thereof

Disclosed is an electronic device. The An electronic device including a storage, and a processor configured to perform convolution processing on target data and kernel data based on stride information that indicates an interval at which the kernel data is applied to the target data stored in the storage, in which the processor is further configured to divide the target data into a plurality of pieces of sub-data based on first stride information, perform the convolution processing on the plurality of pieces of sub-data and a plurality of pieces of sub-kernel data respectively corresponding to the plurality of pieces of sub-data based on second stride information that is different from the first stride information, and combine a plurality of processing results, the plurality of pieces of sub-kernel data are obtained by dividing the kernel data based on the first stride information, and the second stride information indicates that the interval at which the kernel data is applied to the target data is 1.

Electronic device and control method thereof

Disclosed is an electronic device and method for controlling same. The electronic device comprises: a memory; and a processor which checks an operation instruction for filtering input data of a neural network for each filter of a main pattern selected from a plurality of filters generated according to learning by the neural network, and stores the checked operation instruction in the memory.

Electronic device and method for controlling same

Provided are a wearable display device and a light guide element thereof, the display device including: a display element configured to project a first light forming a virtual image; and a light guide element configured to guide the first light from the display element and a second light input from outside of the wearable display device to a predetermined position. The light guide element includes: a first optical surface facing the display element; a second optical surface and a third optical surface configured to reflect the first light input through the first optical surface; and a fourth optical surface configured to reflect the reflected first light to the predetermined position. The first to third optical surfaces are flat surfaces, and the fourth optical surface is a rotationally asymmetric reflective surface.

Wearable display apparatus having a light guide element that guides light from a display element and light from an outside

A processor for performing deep learning is provided herein. The processor includes a processing element unit including a plurality of processing elements arranged in a matrix form including a first row of processing elements and a second row of processing elements. The processing elements are fed with filter data by a first data input unit which is connected to the first row processing elements. A second data input unit feeds target data to the processing elements. A shifter composed of registers feeds instructions to the processing elements. A controller in the processor controls the processing elements, the first data input unit and second data input unit to process the filter data and target data, thus providing sum of products (convolution) functionality.

Processor and control methods thereof for performing deep learning

A hub apparatus and a method are provided for selecting a device. The method includes receiving a service request; determining a sensor based on the received service request; receiving state information from a device including the determined sensor; and selecting the device based on the received state information.

Hub apparatus and method for providing service thereof

Example methods and devices are provided of pairing glasses with an electronic device is provided. The glasses store identification information and device information regarding a plurality of electronic devices, the device information being usable to pair the glasses with the plurality of electronic devices. The glasses obtain an image of at least one electronic device via a camera of the glasses. The glasses extract identification information of the at least one included in the image. The glasses select the electronic device based on the extracted identification information and the stored identification information. The glasses pair with the electronic device based on the device information of the electronic device.

Mobile device and method of pairing the same with electronic device

Disclosed is a technology for a sensor network, machine to machine (M2M) communication, machine type communication (MTC), and the Internet of Things (IoT). The disclosed technology can be used for intelligent services (smart home, smart building, smart city, smart car or connected car, health care, digital education, retail, security and safety-related services, and the like) based on the technology. A terminal according to one embodiment of the present disclosure generates previous moving line information based on a movement path of the terminal, transmits the generated previous moving line information to a server, and receives, from the server, new moving line information generated based on the transmitted previous moving line information.

Method and device for generating novel moving line information based on previous moving line information

Provided is an image processing method including: generating an omnidirectional spherical image; determining information regarding a director's view that indicates some regions of the omnidirectional spherical image; generating a projection image by projecting the omnidirectional spherical image to a development view of a polyhedron; dividing, from the projection image, a director's view projection image corresponding to the director's view, based on the information regarding the director's view; reshaping the director's view projection image into a director's view rectangular image by moving a location of at least one of pixels of the director's view projection image; and generating a bitstream which the director's view rectangular image is encoded into.

Image processing method and apparatus

A super-resolution processing method of a moving image is provided. The super-resolution processing method of a moving image includes sequentially inputting a plurality of input frames included in the video to any one of a recurrent neural network (RNN) for super-resolution processing and a convolutional neural network (CNN) for super-resolution processing, sequentially inputting a frame sequentially output from the any one of the RNN and the CNN to an additional one of the RNN and the CNN, and upscaling a resolution of the output frame by carrying out deconvolution with respect to a frame sequentially output from the additional one of the RNN and the CNN.

Super-resolution processing method for moving image and image processing apparatus therefor

Methods and devices are provided of pairing glasses with an electronic device is provided. The glasses store identification information and device information regarding a plurality of electronic devices, the device information being usable to pair the glasses with the plurality of electronic devices. The glasses obtain an image of at least one electronic device via a camera of the glasses. The glasses extract identification information of the at least one included in the image. The glasses select the electronic device based on the extracted identification information and the stored identification information. The glasses pair with the electronic device based on the device information of the electronic device.

The present disclosure relates to a sensor network, Machine Type Communication (MTC), Machine-to-Machine (M2M) communication, and technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the above technologies, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. The present disclosure provides a method for operating an electronic device. The method comprises transmitting a signal including a list of one or more objects, receiving a signal including beacon recognition information of at least one object included in the list of one or more objects, storing the beacon recognition information, and making a request for information on the at least one object to a server when receiving at least one beacon signal that matches with the stored beacon recognition information.

Method for providing service in wireless network and electronic device thereof

A user authentication method for an electronic device is provided. The user authentication method includes detecting a content execution event, confirming an authentication level of content related to the detected content execution event, acquiring an appearance image through a camera unit, extracting valid regions related to an iris of a user from the acquired appearance image, and performing an authentication on the user by comparing iris authentication reference information related to a confirmed authentication level with the extracted valid regions.

User authentication method and electronic device supporting the same

Growing community of inventors

Suwon-si, South Korea

Hyun-jung Kim