Samsung

About: Samsung is a company organization based out in Seoul, South Korea. It is known for research contribution in the topics: Layer (electronics) & Signal. The organization has 134067 authors who have published 163691 publications receiving 2057505 citations. The organization is also known as: Samsung Group & Samsung chaebol.

Method and apparatus for providing wireless charging power to a wireless power receiver

Abstract: A method and apparatus for transmitting charging power to a wireless power receiver. The method includes detecting the wireless power receiver by applying different detection powers with different power levels; applying a driving power to drive the detected wireless power receiver; receiving a request signal for communication from the detected wireless power receiver using the driving power; determining whether or not to subscribe the detected wireless power receiver to a wireless power network; transmitting, to the detected wireless power receiver, a response signal to the request signal for communication, the response signal indicating whether or not the detected wireless power receiver is subscribed to the wireless power network; and transmitting charging power to the detected wireless power receiver, when the detected wireless power receiver is subscribed to the wireless power network.

Cell reselection method for receiving packet data in a mobile communication system supporting MBMS

Abstract: A method for selecting a cell by a user equipment (UE) to receive a Multimedia Broadcast/Multicast Service (MBMS) service in a mobile communication system which supports the MBMS service with different frequency allocations (FAs) in the same area. In the method, a radio network controller (RNC) transmits information on an MBMS cell to the UE, and the MBMS cell information includes an MBMS offset for guaranteeing priority for cell reselection to the MBMS cell. The UE performs cell reselection using the MBMS cell information and receives the MBMS service from the reselected cell.

Television system with wireless power transmission function, television set, and set-top box

Abstract: A television (TV) system with a wireless power transmission function is provided. The TV system includes a TV set, a set-top box (STB) and a shielding unit. The STB includes a source resonating unit and the TV set includes a target resonating unit to receive a resonance power from the source resonating unit. The shielding unit may be configured to focus a magnetic field to the target resonating unit, where the magnetic is field radiated by the source resonating unit.

Method and apparatus for amplifying nucleic acids

Abstract: A method and apparatus for amplifying nucleic acids. The method includes introducing into a reaction vessel via different inlet channels a reactant aqueous solution containing reactants for nucleic acid amplification and a fluid that is phase-separated from the reactant aqueous solution and does not participate in amplification reaction, creating a plurality of reactant aqueous solution droplets surrounded by the fluid by contacting the reactant aqueous solution with the fluid in the reaction vessel, and amplifying the nucleic acids in the reactant aqueous solution droplets. The apparatus includes a substrate, a reaction vessel formed inside of the substrate, at least one first inlet channel formed inside the substrate, connected to an end of the reaction vessel, and allowing introduction of a reactant aqueous solution containing reactants for nucleic acid amplification into the reaction vessel, a second inlet channel formed inside the substrate, connected to the end of the reaction vessel, and allowing introduction of a fluid that is phase-separated from the reactant aqueous solution and does not participate in amplification reaction into the reaction vessel, and a heating unit installed on the substrate in such a way to thermally contact with the substrate and heating the substrate.

Continual Learning with Deep Generative Replay

Abstract: Attempts to train a comprehensive artificial intelligence capable of solving multiple tasks have been impeded by a chronic problem called catastrophic forgetting. Although simply replaying all previous data alleviates the problem, it requires large memory and even worse, often infeasible in real world applications where the access to past data is limited. Inspired by the generative nature of hippocampus as a short-term memory system in primate brain, we propose the Deep Generative Replay, a novel framework with a cooperative dual model architecture consisting of a deep generative model ("generator") and a task solving model ("solver"). With only these two models, training data for previous tasks can easily be sampled and interleaved with those for a new task. We test our methods in several sequential learning settings involving image classification tasks.