Samsung Electronics Co., Ltd.

Seongwon Han

Woojin Park

Daehyun Ban

Choonghoon Lee

Seowoo Jang

Donguk Kim

Hosun Lee

Jaewoo Seo

Jungil Cho

Unkyu Park

Sangwon Gil

Jeongyoon Heo

According to an embodiment, an electronic device comprises: a communication circuit, at least one processor, and memory storing instructions. The electronic device may convert a first image into a latent vector using an encoder. The electronic device may obtain a first hash value by applying a hashing method to the latent vector for the first image. The electronic device may compare the first hash value for the first image with a second hash value for a second image; and determine whether the first image is plagiarized based on a result of the comparison.

Electronic device and method for preventing non-fungible token plagiarism in electronic device

Disclosed is an electronic device including a learning model having been learned according to an artificial intelligence algorithm. An electronic device according to the present disclosure may comprise: an input unit; and a processor which, when a two-dimensional image including at least one object is received through the input unit, acquires first depth information relating to at least one object by applying the two-dimensional image to a first learning model, acquires second depth information relating to the at least one object by applying the first depth information and actually measured depth data of the at least one object to a second learning model, and acquires three-dimensional information relating to the two-dimensional image on the basis of the second depth information, wherein the first depth information is implemented to include depth data according to a type of the at least one object.

Electronic device and method for controlling same

According to various embodiments, an electronic device includes a wireless communication module, and at least one processor, wherein the at least one processor is configured to: acquire data rate information of at least one external electronic device through the wireless communication module; identify a first external electronic device having a data rate pattern corresponding to a first condition among the at least one external electronic device based on the data rate information; identify channel state information (CSI) of a channel through which the first external electronic device performs communication, through the wireless communication module; identify whether the data rate pattern of the first external electronic device matches a CSI pattern of the channel; and identify whether a space where the electronic device is located is captured by the first external electronic device based on the identification result.

Electronic device for identifying external electronic device and method of operating same

An electronic apparatus determining whether an external device spoofs a location thereof includes a communicator, a memory, and a processor configured to receive a first beacon signal and a second beacon signal from a first external device and a second external device, respectively, through the communicator, obtain information on a first distance between the electronic apparatus and the first external device, and information on a second distance between the electronic apparatus and the second external device, based on the first beacon signal and the second beacon signal, respectively, receive information on a third distance between the first external device and the second external device from at least one of the first external device and the second external device through the communicator, and identify whether the first external device spoofs an actual location of the first external device relative to the electronic apparatus, based on the first distance, the second distance, and the third distance.

Electronic apparatus and method of controlling thereof

An autonomous driving device is provided. The autonomous driving device includes a driver comprising circuitry configured to drive the autonomous driving device, a sensor, a storage configured to store characteristic information for each type of obstacle, and a processor configured to control the driver based on a result of sensing of the sensor. The processor is configured to, based on an external object sensed by the sensor being recognized as an obstacle with a possibility of collision with the autonomous driving device, identify a type of the obstacle based on the characteristic information, and to control the driver to change at least one of a velocity and direction of the autonomous driving device based on the identified type of obstacle.

Autonomous driving device and driving method thereof

Disclosed are an autonomous driving apparatus for performing autonomous driving of a vehicle and a controlling method thereof. An autonomous driving apparatus according to an example aspect of the present disclosure includes a sensor configured to acquire sensing information to determine a driving state of the vehicle; a storage configured to store a plurality of autonomous driving models; and at least one processor configured to perform autonomous driving of the vehicle using one of the plurality of autonomous driving models stored in the storage based on sensing information sensed by the sensor. Accordingly, the autonomous driving apparatus is capable of performing autonomous driving of a vehicle by rapidly changing a driving mode to an autonomous driving mode suitable for an event occurring during autonomous driving of the vehicle.

Autonomous driving apparatus and method for autonomous driving of a vehicle

Disclosed is a vehicle. The vehicle comprises: storage in which a plurality of reference images and position information corresponding to respective plurality of reference images are stored; and a processor which acquires through a camera an image of the surrounding environment in which the vehicle is traveling, acquires a reference image corresponding to the current position of the vehicle among the plurality of reference images if the visibility of the surrounding environment image satisfies a predetermined condition, acquires an image in which the surrounding environment image and the reference image are merged, and controls the operation of the vehicle on the basis of the merged image.

Vehicle and method for controlling same

A method for controlling an autonomous driving apparatus of a vehicle includes receiving a destination for setting a route, obtaining driving history of a user and traffic information, determining a driving route to the destination based on information on the destination, information on the driving history, and the traffic information provided to a model trained through an artificial intelligence algorithm as input data, and performing autonomous driving along the determined driving route.

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Suwon-si, South Korea

Seongwon Han