Abstract:
A method and an apparatus are provided. The method includes receiving a video with a first plurality of frames having a first resolution; generating a plurality of warped frames from the first plurality of frames based on a first type of motion compensation; generating a second plurality of frames having a second resolution, wherein the second resolution is of higher resolution than the first resolution, wherein each of the second plurality of frames having the second resolution is derived from a subset of the plurality of warped frames using a convolutional network; and generating a third plurality of frames having the second resolution based on a second type of motion compensation, wherein each of the third plurality of frames having the second resolution is derived from a fusing a subset of the second plurality of frames.
Abstract:
An apparatus and a method. The apparatus includes a plurality of polarization processors, including n inputs and n outputs, where n is an integer; and at least one permutation processor, including n inputs and n outputs, wherein each of the at least one permutation processor is connected between two of the plurality of polarization processors, and connects the n outputs of a first of the two of the plurality of polarizations processors to the n inputs of a second of the two of the plurality of polarization processors between which each of the at least one permutation processor is connected in a permutation pattern that maximally polarizes the n outputs of the second of the two of the plurality of polarization processors.
Abstract:
A system to recognize objects in an image includes an object detection network outputs a first hierarchical-calculated feature for a detected object. A face alignment regression network determines a regression loss for alignment parameters based on the first hierarchical-calculated feature. A detection box regression network determines a regression loss for detected boxes based on the first hierarchical-calculated feature. The object detection network further includes a weighted loss generator to generate a weighted loss for the first hierarchical-calculated feature, the regression loss for the alignment parameters and the regression loss of the detected boxes. A backpropagator backpropagates the generated weighted loss. A grouping network forms, based on the first hierarchical-calculated feature, the regression loss for the alignment parameters and the bounding box regression loss, at least one of a box grouping, an alignment parameter grouping, and a non-maximum suppression of the alignment parameters and the detected boxes.
Abstract:
A method and apparatus are provided. The method includes configuring a plurality of long short term memory (LSTM) networks, wherein each of the plurality of LSTM networks is at a different network layer, configuring a plurality of memory cells in a spatial domain of the plurality of LSTM networks, configuring the plurality of memory cells in a temporal domain of the plurality of LSTM networks, controlling an output of each of the plurality of LSTM networks based on highway connections to outputs from at least one previous layer and at least one previous time of the plurality of LSTM networks, and controlling the plurality of memory cells based on highway connections to memory cells from the at least one previous time.
Abstract:
A system, method and device for object identification is provided. The method of identifying objects includes, but is not limited to, calculating feature vectors of the object, calculating feature vectors of the object's context and surroundings, combining feature vectors of the object, calculating likelihood metrics of combined feature vectors, calculating verification likelihood metrics against contact list entries, calculating a joint verification likelihood metric using the verification likelihood metrics, and identifying the object based on the joint verification likelihood metric.
Abstract:
A computing system includes: an interface configured to communicate a coordination profile for coordinating a second transmitter device with a first transmitter device; and a unit, coupled to the interface, configured to generate a first encoded message using a message polarization mechanism based on the coordination profile for coordinating the first encoded message with a second encoded message concurrently transmitting through the second transmitter device.A further embodiment of the computing system includes: an interface configured to communicate a receiver signal for representing a first encoded message and a second encoded message coordinated for concurrent transmission; a unit, coupled to the interface, configured to: determine a communication rate associated with the receiver signal, and decode the receiver signal based on a message polarization mechanism and the communication rate for identifying the first encoded message based on a coordination profile corresponding to the communication rate.
Abstract:
A computing system includes: a communication unit configured to: determine a relaxed coding profile including a polar-processing range for processing content data over a bit channel; process the content data based on a total polarization level being within the polar-processing range, the polar-processing range for controlling a polar processing mechanism or a portion therein corresponding to the bit channel for the content data; and an inter-device interface, coupled to the communication unit, configured to communicate the content data.
Abstract:
A computing system includes: an inter-device interface configured to communicate content; and a communication unit, coupled to the inter-device interface, configured to process the content based on a polar communication mechanism utilizing multiple processing dimensions for communicating the content, including: generating a node result with a first orthogonal mechanism, and processing the node result from the first orthogonal mechanism with a second orthogonal mechanism.
Abstract:
A method, apparatus, and non-transitory computer-readable recording medium for generating an algebraic Spatially-Coupled Low-Density Parity-Check (SC LDPC) code are provided. The method includes selecting an LDPC block code over a finite field GF(q) with a girth of at least 6; constructing a parity-check matrix H from the selected LDPC block code; replicating H a user-definable number of times to form a two-dimensional array Hrep; constructing a masking matrix W with a user-definable spatially-coupled pattern; and masking a sub-matrix of Hrep using W to obtain a spatially-coupled parity-check matrix HSC, wherein a null space of HSC is the algebraic SC LDPC code.
Abstract:
A computing system includes: an interface configured to communicate a coordination profile for coordinating a second transmitter device with a first transmitter device; and a unit, coupled to the interface, configured to generate a first encoded message using a message polarization mechanism based on the coordination profile for coordinating the first encoded message with a second encoded message concurrently transmitting through the second transmitter device.A further embodiment of the computing system includes: an interface configured to communicate a receiver signal for representing a first encoded message and a second encoded message coordinated for concurrent transmission; a unit, coupled to the interface, configured to: determine a communication rate associated with the receiver signal, and decode the receiver signal based on a message polarization mechanism and the communication rate for identifying the first encoded message based on a coordination profile corresponding to the communication rate.