Abstract: In one example, a semiconductor device can comprise a substrate, a device stack, first and second internal interconnects, and an encapsulant. The substrate can comprise a first and second substrate sides opposite each other, a substrate outer sidewall between the first substrate side and the second substrate side, and a substrate inner sidewall defining a cavity between the first substrate side and the second substrate side. The device stack can be in the cavity and can comprise a first electronic device, and a second electronic device stacked on the first electronic device. The first internal interconnect can be coupled to the substrate and the device stack. The encapsulant can cover the substrate inner sidewall and the device stack and can fill the cavity. Other examples and related methods are disclosed herein.

Abstract: There is provided a method for providing driver's driving information of a user terminal device, including accessing a driving information providing server storing data generated in a driving recording device for a vehicle, receiving driving record data including event record data corresponding to a driving-related event of a driver from the driving information providing server, and displaying a driving-related event occurrence location on a map using the received driving record data. The driving-related event may include at least two or more of a lane departure event, a forward collision possibility event, a rear side collision possibility event, a sudden deceleration event, a sudden acceleration event, a sudden stop event, a sudden start event, and a speeding event.

Abstract: A display apparatus comprises a substrate including a display area and a non-display area adjacent to the display area, a plurality of pixels disposed in the display area, a gate driver disposed on both sides of the display area in the non-display area, the gate driver comprising a plurality of stages including a first stage and a second stage, and a plurality of gate lines extending from the gate driver to the display area, wherein the plurality of gate lines include a first gate line including a linear portion and coupled to the first stage, and a second gate line including a linear portion and a curved portion and coupled to the second stage, and wherein a size of the second stage may be larger than a size of the first stage.

Abstract: Disclosed is a method for encoding/decoding a still image or moving image based on an intra-prediction mode and an apparatus supporting the same. Particularly, a method for processing an image based on an intra prediction mode may include determining an intra prediction mode set used for an intra prediction in a current prediction processing unit among multiple intra prediction mode sets having different resolutions with each other, wherein the resolution represents a degree of distribution of the intra prediction mode in the intra prediction mode set; deriving an intra prediction mode applied to the current prediction processing unit in the determined intra prediction mode set using index information received from an encoder; and generating a prediction sample of the current prediction processing unit based on the derived intra prediction mode, and each of the intra prediction mode sets includes one or more intra prediction modes.

Abstract: Disclosed is a method for encoding/decoding a still image or moving image based on an intra-prediction mode and an apparatus supporting the same. Particularly, a method for processing an image based on an intra prediction mode may include determining an intra prediction mode set used for an intra prediction in a current prediction processing unit among multiple intra prediction mode sets having different resolutions with each other, wherein the resolution represents a degree of distribution of the intra prediction mode in the intra prediction mode set; deriving an intra prediction mode applied to the current prediction processing unit in the determined intra prediction mode set using index information received from an encoder; and generating a prediction sample of the current prediction processing unit based on the derived intra prediction mode, and each of the intra prediction mode sets includes one or more intra prediction modes.

Abstract: The present disclosure provides a vehicle controller area network (CAN) communication attack detection method and apparatus. The apparatus includes: a learning module, a profiling module, and a detection module. The learning module identifies a CAN identifier (ID) upon receiving a predefined CAN packet and generates learning statistical information expressed as bits with respect to a data field corresponding to the identified CAN ID. The profiling module maintains the learning statistical information received and a precollected attack type, and the detection module extracts a CAN ID and a bit stream of a data field from a real-time input CAN packet. In addition, the detection module configures a neural network by acquiring the learning statistical information from the profiling module, inputs the extracted bit stream to the configured neural network, and determines whether attack is detected, based on an output value of the neural network.

Abstract: The present disclosure provides a vehicle controller area network (CAN) communication attack detection method and apparatus. The apparatus includes: a learning module, a profiling module, and a detection module. The learning module identifies a CAN identifier (ID) upon receiving a predefined CAN packet and generates learning statistical information expressed as bits with respect to a data field corresponding to the identified CAN ID. The profiling module maintains the learning statistical information received and a precollected attack type, and the detection module extracts a CAN ID and a bit stream of a data field from a real-time input CAN packet. In addition, the detection module configures a neural network by acquiring the learning statistical information from the profiling module, inputs the extracted bit stream to the configured neural network, and determines whether attack is detected, based on an output value of the neural network.