Samsung Electronics Co., Ltd.

Korea Advanced Institute of Science and Technology

Hyundai Motor Company

Kia Motors Corporation

Korea University Industrial Academic Collaboration Foundation

Jun Mo Kim

Chang Soon Park

Sung Jin Kim

Hee Chul Jung

Chang Kyu Choi

Jaejoon Han

Do Yeon Kim

Byungin Yoo

Jang Hyeon Lee

Si Haeng Lee

Jun Ho Yim

Ki Il Kim

Young Hyun Kim

Min Woo Park

Byung Chang Kang

Young-Chai Ko

Jung Hoon Suh

Ki-hong Park

Duck Dong Hwang

Yang Shin Kim

Pramod Viswanath

Ha Yong Woo

Beom Young Kim

Hae Chang Jung

A method for detecting an anchor-free object based on deep learning may include receiving a target image for object detection, inputting the image to a backbond network and performing learning to generate a heatmap, post-processing the heatmap, and outputting the post-processed result on a screen.

Method and apparatus for detecting anchor-free object based on deep learning

An apparatus for recognizing an object using an image includes a depth map generator that generates a depth map using a feature map of the image based on a dilated convolutional neural network (DCNN) and an object recognition device that recognizes the object using the depth map generated by the depth map generator and the image.

Apparatus and method for recognizing object using image

At least some example embodiments disclose a device and a method for generating a synthetic image and a different-angled image and eliminating noise. The method may include receiving input images, extracting feature values corresponding to the input images using an image learning model, the image learning model permitting an input and an output to be identical and generating a synthetic image based on the feature values corresponding to the input images using the image learning model.

Device and method to generate image using image learning model

The video-motion segmentation method using a randomized voting is provided which includes receiving the video, extracting a plurality of feature points from the video, and grouping the plurality of feature points by applying a randomized voting method using a score histogram on each of the at least some feature points of the plurality of feature points.

Method of motion segmentation in video using randomized voting and system having the same

A communication system for supporting a primary user and a secondary user is provided. A base station of a communication system, includes a scheduler to group at least one primary user and at least one secondary user corresponding to each of the at least one primary user according to characteristics of services desired by users, a superposition coding unit to perform superposition coding for primary data associated with the at least one primary user and secondary data associated with the at least one secondary user to generate a transmission data stream, and a beamformer to perform beamforming for the transmission data stream.

Communication system for supporting primary user and secondary user

A Multiple-Input Multiple-Output (MIMO) communication system and method. The MIMO communication system includes: an antenna number determination unit to determine a number of active antennas among a plurality of transmitting antennas based on a power of a transmission signal wherein the number of active antennas is at least one and the at least one active antenna transmits the transmission signal; and a beam generator to generate the transmission signal corresponding to the number of active antennas by using channel information wherein the channel information is fed back from each of a plurality of user terminals.

Multiple-input multiple-output communication method and multiple-input multiple-output communication system of enabling the method

Provided is a receiver for feeding back channel information, which includes a channel vector measuring unit to measure channel vectors corresponding to a plurality of receiving antennas, respectively, that receive a plurality of data streams, a candidate codebook vector selecting unit to select at least two candidate codebook vectors from codebook vectors included in a codebook, by considering a quantization error based on the channel vectors with respect to each of the data streams, and a selection codebook vector determining unit to determine selection codebook vectors corresponding to the plurality of data streams respectively from the at least two candidate codebook vectors, based on a signal-to-interference and noise ratio (SINR) of each of the data streams that is calculated according to each of the candidate codebook vectors.

Method of feeding back channel information and receiver for feeding back channel information

A system for allocating a wireless resource for a Space Division Multiple Access (SDMA) communication is provided. The system for allocating the wireless resource for the SDMA communication includes: a channel state recognition unit to recognize a channel state of wireless channels generated among adjacent nodes including a source node, a relay node, and a destination node; and a wireless resource allocation unit to control at least one of an amount of channel state information fed back from the destination node to the source node, and a relay level of a relay signal, the relay signal being generated by relaying a source signal transmitted from the source node, according to the channel state.

Method of allocating wireless resource for space division multiple access communication and wireless resource allocation system of enabling the method

Growing community of inventors

Daejeon, South Korea

Jun Mo Kim