The present disclosure relates to methods and apparatus for graphics processing. The apparatus may identify at least one mesh associated with at least one frame. The apparatus may also divide the at least one mesh into a plurality of groups of primitives, each of the plurality of groups of primitives including at least one primitive and a plurality of vertices. The apparatus may also compress the plurality of groups of primitives into a plurality of groups of compressed primitives, the plurality of groups of compressed primitives being associated with random access. Additionally, the apparatus may decompress the plurality of groups of compressed primitives, at least one first group of the plurality of groups of compressed primitives being decompressed in parallel with at least one second group of the plurality of groups of compressed primitives.
Techniques and systems are provided for image processing. For instance, a process can include obtaining, from one or more image sensors, a first image and a second image; determining local motion between the first image and the second image for features of the first image and the second image; generating motion vectors based on the local motion; and identifying an object based on the motion vectors.
Systems and techniques are provided for performing point map registration. For example, a process can include obtaining an image comprising a plurality of 3D points representing a scene and determining, based on the image, a first point map representing the scene. The process includes determining a first plurality of point groupings based on proximity of pairs of points of the first plurality of 3D points. The process includes obtaining a second point map comprising a second plurality of point groupings representing the scene. The process includes determining a correspondence between individual point groupings of the first plurality of point groupings and individual point groupings of the second plurality of point groupings. The process includes determining, based on the correspondence between individual point groupings of the first plurality and individual point groupings of the second plurality, an alignment transformation for aligning the first and the second plurality of 3D points.
Embodiments include methods, and processing devices for implementing the methods. Various embodiments may include calculating a batch softmax normalization factor using a plurality of logit values from a plurality of logits of a layer of a neural network, normalizing the plurality of logit values using the batch softmax normalization factor, and mapping each of the normalized plurality of logit values to one of a plurality of manifolds in a coordinate space. In some embodiments, each of the plurality of manifolds represents a number of labels to which a logit can be classified. In some embodiments, at least one of the plurality of manifolds represents a number of labels other than one label.
Methods, systems, and apparatuses to determine a first topology of a reference skeleton model and a second topology of a sensed skeleton model. The first topology and the second topology each identifying and characterizing one or more nodes. In some examples, an apparatus may perform operations that adjust a positioning of one or more data points of the second dataset based at least on the one or more nodes of the first topology and the one or more nodes of the second topology.
Systems, methods, and devices for vehicle driving assistance systems that support image processing are provided. In a first aspect, a computing device (402) may receive a first point cloud (404) and a second point cloud (406). The first and second point clouds may be captured from at least two different positions. The computing device (402) may determine, based on the first and second point clouds, a transformation matrix (412) and may determine an combined point cloud (414) based on the transformation matrix (412) and the first and second point clouds. Other aspects and features are also claimed and described.
G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads
7.
POINT CLOUD ALIGNMENT AND COMBINATION FOR VEHICLE APPLICATIONS
This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In one embodiment, a computing device may receive a plurality of point clouds containing position information for a scene and may determine corresponding trajectories for the plurality of point clouds. The computing device may determine, based on the corresponding trajectories, a baseline trajectory for the scene and may determine a plurality of projected coordinate sets based on the plurality of point clouds and the baseline trajectory. The computing device may determine common objects that are present within at least two of the plurality of projected coordinate sets and may determine a transformation matrix based on the common objects. A combined point cloud for the scene may be determined by applying the transformation matrix to at least a subset of the plurality of point clouds. Other aspects and features are also claimed and described.
According to the invention a method is provided for accessing a resource of a control unit comprising: executing a virtualization system on at least one processor of the control unit, the virtualization system including an interpreter (28) for bytecode and/or a script, the virtualization system assigning processor time and memory space to at least one guest system; executing a first guest system (24) running on the virtualization system; emitting, by the first guest system (24), an access request (38) to access a resource (5, 7, 32) of the control unit to the virtualization system (22); determining, by the virtualization system (22), that the access request is not allowable for the first guest system (24); loading by the interpreter (28) bytecode instructions and/or script instructions based on the request of the first guest system (24); and executing, by the interpreter (28), the loaded bytecode or script instructions to access the resource (5, 7, 32).
G06F 9/455 - EmulationInterpretationSoftware simulation, e.g. virtualisation or emulation of application or operating system execution engines
G06F 21/53 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by executing in a restricted environment, e.g. sandbox or secure virtual machine
9.
APPARATUS AND METHODS FOR A ROBUST OUTLIER REJECTION
Methods, systems, and apparatus to determine a threshold for an iterative process configured to generate sets of model parameters for another process that determines pose estimations. For example, an apparatus may determine a first set of model parameters of a process based on a subset of the match data and determine a first threshold based on an uncertainty parameter. Additionally, the apparatus may apply the process to the match data in accordance with the first set of model parameters. Further, the apparatus may determine a number of inlier data elements based on the application of the process to the match data and the determined first threshold. In some examples, each inlier data element may characterize a particular three-dimensional data point and corresponding two-dimensional data point that is within the determined first threshold.
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
Methods, systems, and apparatuses are provided to classify features of a geographical area based on map information. For example, a computing device receives map data characterizing roadways of a geographical area. Further, the computing device determines, based on the map data, a plurality of nodes and a plurality of segments connecting the plurality of nodes. The computing device filters the plurality of nodes based on a first feature to determine a portion of the plurality of nodes, and filters the plurality of segments based on a second feature to determine a portion of the plurality of segments. Further, the computing device clusters the portion of the plurality of segments and the portion of the plurality of nodes, and generates final clusters based on the clustered portions of the plurality of segments and the plurality of nodes. The computing device then generates classification data classifying the final clusters.
Systems and techniques for environment mapping are described. In some examples, a system receives image data and depth data captured using at least one sensor. The image data and the depth data both include respective representations of an environment. The system processes the image data using semantic segmentation to identify segments of the environment that represent different types of objects in the environment in the image data. The system combines the depth data with the semantic segmentation to generate a voxel-based three-dimensional map of the environment.
Certain aspects of the present disclosure provide techniques for pose estimation for three-dimensional object reconstruction. In one example, a method, includes receiving image data, wherein the image data comprises a plurality of images taken from varying poses; identifying one or more pairs of spatially related images within the plurality of images; generating a synchronization graph indicative of at least one similarity metric between the plurality of images, based at least in part on the identified one of more pairs of spatially related images; and estimating a pose of an object depicted in the plurality of images based on the synchronization graph.
A pose tracking method and system, a mobile device, an electronic device and a storage medium. The pose tracking method includes: acquiring an image of a mobile device whereon is disposed with a light emitting unit for emitting signal light; based on the image, extracting, as a reference feature, a light spot feature corresponding to the light emitting unit on the image, and extracting a two-dimensional feature point corresponding to a three-dimensional feature point of the mobile device in the image; obtaining an initialization pose of the mobile device based on the two-dimensional feature point; and based on the initialization pose and the at least two reference features, optimizing the initialization pose for fine-tuning the initialization pose, to make the light spot feature corresponding to the initialization pose coincide with the reference feature. According to the embodiments of the present invention, the precision of pose tracking is improved, and the number of light emitting units required is reduced, such that a structure is simplified, and power consumption is reduced.
G06F 3/0346 - Pointing devices displaced or positioned by the userAccessories therefor with detection of the device orientation or free movement in a 3D space, e.g. 3D mice, 6-DOF [six degrees of freedom] pointers using gyroscopes, accelerometers or tilt-sensors
14.
APPARATUS AND METHODS FOR TRACKING FEATURES WITHIN IMAGES
Methods, systems, and apparatuses are provided to track features across multiple images for use in various systems. For example, a computing device receives at least a first image and a second image captured by a camera, and detects a feature within each of the first image and the second image. The feature is located at a first feature position within the first image and at a second feature position within the second image. The computing device also receives a first sensor pose of the sensor used to capture the first image and a second sensor pose of the sensor used to capture the second image. The computing device determines a portion of third image based on the first sensor pose, the second sensor pose, the first feature position, and the second feature position. The computing device then generates feature detection data characterizing whether the feature is detected.
A method for classifying a human-object interaction includes identifying a human-object interaction in the input. Context features of the input are identified. Each identified context feature is compared with the identified human-object interaction. An importance of the identified context feature is determined for the identified human-object interaction. The context feature is fused with the identified human-object interaction when the importance is greater than a threshold.
G06V 20/52 - Surveillance or monitoring of activities, e.g. for recognising suspicious objects
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/778 - Active pattern-learning, e.g. online learning of image or video features
G06V 10/80 - Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
16.
POSE TRACKING METHOD AND SYSTEM, AND APPARATUS AND STORAGE MEDIUM
A pose tracking method and system, and an apparatus and a storage medium. The method includes: acquiring a current frame image of a mobile device whereon is disposed with a plurality of positioning lamps; extracting, based on the current frame image, light spot features corresponding to the positioning lamps; acquiring inertial measurement data; determining whether a pose has been initialized; if so, acquiring, as a reference pose, a pose corresponding to a previous frame image, performing tracking and matching based on the light spot features, the reference pose and the inertial measurement data, and obtaining, as a light spot serial number, a serial number of the positioning lamp corresponding to a light spot on the mobile device; otherwise, performing an initialization search based on the light spot features to obtain, as the light spot serial number, the serial number of the positioning lamp corresponding to the light spot on the mobile device; and obtaining a current pose of the mobile device at the current frame image time by fusing the light spot serial number and the inertial measurement data in a tightly coupled manner. According to the embodiments of the present invention, the robustness of pose tracking is improved, and user experience is optimized.
A control method and system, a tracking method and system, a device, and a storage medium are provided. The control method includes: acquiring, using a detector (200), an image of a movable device (100) at a current frame time, the movable device (100) being equipped with a plurality of light output units (110) for outputting signal light (S1); predicting, based on the image, a motion state of the movable device (100) at a next frame time (S2); and obtaining, based on the motion state of the movable device (100) at the next frame time, configuration information of a light output state of each of the light output units (110) at the next frame time, to control the light output unit (110) (S3). The present invention is conductive to adaptively adjusting the light output states of all the light output units (110) based on a real-time state of the movable device (100), which facilitates the light output units (110) to achieve accurate and low-power light output states to ensure the use accuracy of the movable device(100) and reduce the power consumption of the movable device (100).
Methods, systems, and apparatuses are provided to cluster and match image feature descriptors for use in various systems. For example, a computing device receives a location from a remote device. The computing device applies a first clustering process to a plurality of descriptors associated with the location to determine a number of descriptor clusters. The computing device also applies a second clustering process to the number of descriptor clusters to determine a descriptor cluster center for each of the number of descriptor clusters. Further, the computing device generates descriptor cluster data characterizing a similarity between the plurality of descriptors and the descriptor cluster centers. The computing device then transmits the descriptor cluster data to the remote device. The remote device may match descriptors to the descriptor cluster centers based on the descriptor cluster data.
G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
G06V 10/762 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
A method for generating a causal graph includes receiving a data set including observation data and intervention data corresponding to multiple variables. A probability distribution is determined for each variable based on the observation data. A likelihood of including each edge in the graph is computed based on the probability distribution and the intervention data. Each edge is a causal connection between variables of the multiple variables. The graph is generated based on the likelihood of including each edge. The graph may be updated by iteratively repeating the determination of the probability distribution and the computing of the likelihood of including each edge.
Certain aspects of the present disclosure provide techniques and apparatuses for inferencing against a multidimensional point cloud using a machine learning model. An example method generally includes generating a score for each respective point in a multidimensional point cloud using a scoring neural network. Points in the multidimensional point cloud are ranked based on the generated score for each respective point in the multidimensional point cloud. The top points are selected from the ranked multidimensional point cloud, and one or more actions are taken based on the selected top k points.
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
G06V 10/42 - Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 20/70 - Labelling scene content, e.g. deriving syntactic or semantic representations
21.
PROCESSING IMAGES USING TEMPORALLY-PROPAGATED CLUSTER MAPS
Systems and techniques are provided for processing image data. For example, a process can include processing a source image to generate a first features for the source image and a target image to generate a second features for the target image. The process can include generating a first cluster map for the source image based on prototypes and the first features for the source image, and generating a second cluster map for the target image based on the prototypes and the second features for the target image. The process can include determining a propagated cluster map for the source image based on the first cluster map and a correspondence between regions of the source image and regions of the target image. The process can include determining a loss based on a comparison of the propagated cluster map for the source image and the second cluster map for the target image.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 20/40 - ScenesScene-specific elements in video content
22.
SELF-SUPERVISED POINT CLOUD ORDERING USING MACHINE LEARNING MODELS
A method for recognizing long-range activities in videos includes segmenting an input video stream to generate multiple frame sets. For each of the frame sets, a frame with a highest likelihood of including one or more actions of a set of predefined actions is identified regardless of its order in the frame set. A global representation of the input stream is generated based on pooled representations of the identified frames. A long-range activity in the video stream is classified based on the global representation.
An occupancy map processing method includes: obtaining an occupancy map of a region comprising a plurality of cells, corresponding to sub-regions, each including an occupancy indication indicative of occupier type of the sub-region, and the plurality of cells comprising delimiter cells and non-delimiter cells; and providing, from the apparatus, occupancy information comprising first occupancy information corresponding to the delimiter cells and either second occupancy information corresponding to fewer than all of the non-delimiter cells or no second occupancy information.
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads
A processor-implemented method for implementing graph cuts for explainability using an artificial neural network (ANN) includes receiving, via the ANN, an input. The input is represented as a graph. The graph includes nodes connected by edges. The ANN determines a graph cut between a source node and a sink node associated with the input by solving a quadratic process with equality constraints. The ANN processes a subset of the input based on the graph cut to generate a prediction.
An occupancy map processing method includes: obtaining an occupancy map of a region comprising a plurality of cells, corresponding to sub-regions, each including an occupancy indication indicative of occupier type of the sub-region, and the plurality of cells comprising delimiter cells and non-delimiter cells; and providing, from the apparatus, occupancy information comprising first occupancy information corresponding to the delimiter cells and either second occupancy information corresponding to fewer than all of the non-delimiter cells or no second occupancy information.
A method for managing model updates by a first zone server, associated with a first zone model of a plurality of zone models, includes receiving a global model from a global server associated with the global model. The method also includes transmitting the global model to user equipment (UEs) in a first group of UEs associated with the first zone model. The method further includes receiving, from one or more UEs in the first group, model updates associated with the global model based on transmitting the global model. The method further includes transmitting, to the global server, an average of the model updates received from the one or more UEs. The method also includes updating the global model to generate the first zone model based on the model updates. The method further includes transmitting the first zone model to one or more UEs in the first group.
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
H04W 8/18 - Processing of user or subscriber data, e.g. subscribed services, user preferences or user profilesTransfer of user or subscriber data
A processor-implemented method includes receiving, by a user equipment (UE), a zone determination function based on registering for a federated learning process for training a first federated learning model. The method also includes determining, by the UE, a zone membership in accordance with UE parameters and the zone determination function. The method further includes selecting the first federated learning model, by the UE, based on the zone membership. The method includes training the first federated learning model by the UE.
A user equipment (UE) receives a SIB1 message from a base station. The SIB1 message lists first and second public land mobile network identifiers (PLMN IDs), the first PLMN ID having a corresponding tracking area code (TAC) and the second PLMN ID not having a TAC. The UE reports the first PLMN ID and TAC but not the second PLMN ID while performing PLMN selection. In another aspect, the UE unsuccessfully attempts to select or reselect to a shared cell of the base station with the second PLMN ID. In response to the failed attempt, the UE bars the shared cell as a candidate for cell selection/reselection, due to a missing TAC. When the UE attempts to select or reselect to the shared cell with the first PLMN ID, which may or may not have a TAC, the UE reevaluates the barring due to selection of the first PLMN ID.
A microphone including a casing having a front wall, a back wall, and a side wall joining the front wall to the back wall, a transducer mounted to the front wall, the transducer including a substrate and a transducing element, the transducing element having a transducer acoustic compliance dependent on the transducing element dimensions, a back cavity cooperatively defined between the back wall, the side wall, and the transducer, the back cavity having a back cavity acoustic compliance. The transducing element is dimensioned such that the transducing element length matches a predetermined resonant frequency and the transducing element width, thickness, and elasticity produces a transducer acoustic compliance within a given range of the back cavity acoustic compliance.
B81B 7/02 - Microstructural systems containing distinct electrical or optical devices of particular relevance for their function, e.g. microelectro-mechanical systems [MEMS]
A computer-implemented method for contrastive object representation from temporal data using an artificial neural network (ANN) includes receiving, by the ANN, a video. The video comprises a temporal sequence of frames including images of one or more objects. The ANN generates object representations corresponding to the one or more objects based on temporal data of multiple frames of the temporal sequence of frames. The object representations are communicated to a receiver.
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 20/40 - ScenesScene-specific elements in video content
H04N 19/136 - Incoming video signal characteristics or properties
H04N 19/436 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation using parallelised computational arrangements
Systems, devices, methods, and implementations related to contact detection are described herein. In one aspect, a system is provided. The system includes a first piezoelectric microelectromechanical systems (MEMS) transducer coupled to configured to generate a first analog signal when the first analog signal is transduced from vibrations propagating through the object. The system includes a second piezoelectric MEMS transducer having configured to generate a second analog signal transduced from acoustic vibrations at a location of the object, and classification circuitry coupled to the output of first piezoelectric MEMS transducer and the output of the second piezoelectric MEMS transducer, where the classification circuitry is configured to process data from the first analog signal and data from the second analog signal, and to categorize combinations of the first analog signal and the second analog signal received during one or more time frames.
G01P 15/09 - Measuring accelerationMeasuring decelerationMeasuring shock, i.e. sudden change of acceleration by making use of inertia forces with conversion into electric or magnetic values by piezoelectric pick-up
H10N 39/00 - Integrated devices, or assemblies of multiple devices, comprising at least one piezoelectric, electrostrictive or magnetostrictive element covered by groups
H10N 30/30 - Piezoelectric or electrostrictive devices with mechanical input and electrical output, e.g. functioning as generators or sensors
Aspects include piezoelectric acoustic transducers and systems for acoustic transduction. In some aspects, an acoustic transducer is structured with a silicon substrate having a top surface and a bottom surface, where the top surface has a first portion and an edge along the first portion associated with an acoustic aperture. The transducer has a first silicon oxide layer disposed over the first portion of the top surface of the silicon substrate, a polysilicon layer disposed over the first silicon oxide layer, and a second silicon oxide layer disposed over the polysilicon layer. A cantilevered beam comprising a fixed end, a deflection end, a top surface, and a bottom surface, has a first portion of the bottom surface at the fixed end disposed over the second silicon oxide layer, where a second portion of the bottom surface at the deflection end is formed over the acoustic aperture. In some aspects. transducer elements are reconfigurable between parallel and serial configurations depending on a system operating mode.
G01H 11/08 - Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by detecting changes in electric or magnetic properties by electric means using piezoelectric devices
B06B 1/06 - Processes or apparatus for generating mechanical vibrations of infrasonic, sonic or ultrasonic frequency making use of electrical energy operating with piezoelectric effect or with electrostriction
34.
CAUSAL REPRESENTATION LEARNING FOR INSTANTANEOUS TEMPORAL EFFECTS
A processor-implemented method for causal representation learning of temporal effects includes receiving, via an artificial neural network (ANN), temporal sequence data for high-dimensional observations. The ANN generates a latent representation based on latent variables for the temporal sequence data. The latent variables of the temporal sequence data are assigned to causal variables. The ANN determines a representation of causal factors for each dimension of the temporal sequence data based on the assignment.
Certain aspects of the present disclosure provide techniques for pose estimation for three-dimensional object reconstruction. In one example, a method, includes receiving image data, wherein the image data comprises a plurality of images taken from varying poses; identifying one or more pairs of spatially related images within the plurality of images; generating a synchronization graph indicative of at least one similarity metric between the plurality of images, based at least in part on the identified one of more pairs of spatially related images; and estimating a pose of an object depicted in the plurality of images based on the synchronization graph.
Aspects of transducers with feedback transduction are described. One aspect is a transducer system comprising an operational amplifier having an inverting input, a non-inverting input, and an output. The transducer system also includes a piezoelectric microelectromechanical system (MEMS) transducer having a first node and a second node, wherein the first node is coupled to the inverting input of the operational amplifier, and wherein the piezoelectric MEMS transducer is configured to generate an electrical signal across the first node and the second node in response to a signal incident upon the piezoelectric MEMS transducer. The transducer system also includes an attenuator having an input and an output, wherein the input of the attenuator is coupled to the output of the operational amplifier, and wherein the output of the attenuator is coupled to the second node of the piezoelectric MEMS transducer.
Aspects of acoustic transducers are described. One aspect is a microelectromechanical (MEMS) transducer comprising a substrate and multiple cantilevered beams. A first cantilevered beam comprises a first protrusion and a first piezoelectric structure, where the first piezoelectric structure comprises a first deflection end and a first fixed end, where the first fixed end is coupled to the substrate, and where the first deflection end is cantilevered away from the substrate. The first cantilevered beam is separated from a second cantilevered beam by a gap. The first protrusion is disposed at the first deflection end and increases a thickness of the first cantilevered beam along the gap at the first deflection end. A second protrusion of the second beam is disposed at a second deflection end and increases a thickness of the second cantilevered beam along the gap at the second deflection end.
A method for mobility and zone management in zone-based federated learning includes receiving, at a zone management device of multiple zone management devices, a global model from a first network device associated with the global model. Each of the multiple zone management devices is associated with a corresponding zone model of multiple zone models. The zone management device transmits the global model to mobile devices in a first zone associated with the first zone model based on a zone membership. The zone management device receives weights associated with the global model from each mobile device in the first zone. The zone management device updates the first zone model based on the received weights and the zone membership. The zone management device transmits the updated first zone model to each mobile device in the first zone.
A method for mobility and zone management in zone-based federated learning includes receiving, at a zone management device of multiple zone management devices, a global model from a first network device associated with the global model. Each of the multiple zone management devices is associated with a corresponding zone model of multiple zone models. The zone management device transmits the global model to mobile devices in a first zone associated with the first zone model based on a zone membership. The zone management device receives weights associated with the global model from each mobile device in the first zone. The zone management device updates the first zone model based on the received weights and the zone membership. The zone management device transmits the updated first zone model to each mobile device in the first zone.
A method of collaboratively training a neural network model, includes receiving a local update from a subset of the multiple users. The local update is related to one or more subsets of a dataset of the neural network model. A local component of the neural network model identifies a subset of the one or more subsets to which a data point belongs. A global update is computed for the neural network model based on the local updates from the subset of the users. The global updates for each portion of the network are aggregated to train the neural network model.
Aspects presented herein relate to methods and devices for graphics processing including an apparatus, e.g., a GPU. The apparatus may divide at least one scene into a plurality of me shlets, each of the me shlets including a plurality of primitives, and each of the primitives including plurality of vertices. The apparatus may also calculate a pair of texture coordinates for each of the plurality of vertices. Further, the apparatus may select a size of each of the plurality of meshlets in the at least one scene based on the pair of the texture coordinates and based on a perspective projection of each of the plurality of meshlets. The apparatus may also calculate layout information in a meshlet atlas for each of the meshlets in the at least one scene. Moreover, the apparatus may shade each of a plurality of pixels in the meshlet atlas based on the calculated layout information.
A method for object tracking includes receiving a target image of an object of interest. Latent space features of the target image is modified at a forward pass for a neural network by dropping at least one channel of the latent space features, dropping a channel corresponding to a slice of the latent space features, or dropping one or more features of the latent space features. At the forward pass, a location of the object of interest in a search image is predicted based on the modified latent space features. The location of the object of interest is identified by aggregating predicted locations from the forward pass.
Aspects are provided for multiband multiplexers. One example is a multiband multiplexer with a first filter element configured to have a first passband that spans a first predefined frequency range of a first communication band and a second predefined frequency range of a second communication band, wherein the first predefined frequency range overlaps a portion of the second predefined frequency range, a second filter element configured to have a second passband distinct from the first passband, a third filter element configured to have a third passband distinct from the first and second passbands, and a fourth filter element configured to have a fourth passband distinct from the first, second, and third passbands.
H04B 1/00 - Details of transmission systems, not covered by a single one of groups Details of transmission systems not characterised by the medium used for transmission
Aspects described herein provide a method of processing data, including: receiving a set of global parameters for a plurality of machine learning models; processing data stored locally on an processing device with the plurality of machine learning models according to the set of global parameters to generate a machine learning model output; receiving, at the processing device, user feedback regarding machine learning model output for the plurality of machine learning models; performing an optimization of the plurality of machine learning models based on the machine learning output and the user feedback to generate locally updated machine learning model parameters; sending the locally updated machine learning model parameters to a remote processing device; and receiving a set of globally updated machine learning model parameters for the plurality of machine learning models.
A method for managing model updates by a first network device includes receiving, at the first network device associated with a first zone model of multiple zone models, a global model from a second network device associated with the global model. The method also includes transmitting, from the first network device, the global model to user equipment (UEs) in a first group of UEs associated with the first zone model, a different group of UEs associated with each of the plurality of zone models. The method further includes receiving, at the first network device, weights associated with the global model from each UE in the first group. The method still further includes updating, at the first network device, the first zone model based on the received weights. The method also includes transmitting, from the first network device, the updated first zone model to each UE in the first group.
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
H04W 8/18 - Processing of user or subscriber data, e.g. subscribed services, user preferences or user profilesTransfer of user or subscriber data
A method for human-object interaction detection includes receiving an image. A set of features are extracted from multiple positions of the image. One or more human-object pairs may be predicted based on the extracted set of features. A human-object interaction may be determined based on a set of candidate interactions and the predicted human-object pairs.
G06V 40/10 - Human or animal bodies, e.g. vehicle occupants or pedestriansBody parts, e.g. hands
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
G06F 18/241 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
G06F 18/213 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods
A method for managing model updates by a first network device includes receiving, at the first network device associated with a first zone model of multiple zone models, a global model from a second network device associated with the global model. The method also includes transmitting, from the first network device, the global model to user equipment (UEs) in a first group of UEs associated with the first zone model, a different group of UEs associated with each of the plurality of zone models. The method further includes receiving, at the first network device, weights associated with the global model from each UE in the first group. The method still further includes updating, at the first network device, the first zone model based on the received weights. The method also includes transmitting, from the first network device, the updated first zone model to each UE in the first group.
A method for human-object interaction detection includes receiving an image. A set of features are extracted from multiple positions of the image. One or more human-object pairs may be predicted based on the extracted set of features. A human-object interaction may be determined based on a set of candidate interactions and the predicted human-object pairs.
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 40/10 - Human or animal bodies, e.g. vehicle occupants or pedestriansBody parts, e.g. hands
G06V 10/80 - Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
Disclosed are apparatuses and methods for fabricating the apparatuses. In one aspect, an apparatus includes a high-power die mounted on a backside of a package substrate. A heat transfer layer is disposed on the backside of the high-power die. A plurality of heat sink interconnects is coupled to the heat transfer layer, where each of the plurality of heat sink interconnects is directly coupled to the heat transfer layer in a vertical orientation.
Disclosed are apparatuses and methods for fabricating the apparatuses. In one aspect, an apparatus includes a high-power die mounted on a backside of a package substrate. A heat transfer layer is disposed on the backside of the high-power die. A plurality of heat sink interconnects is coupled to the heat transfer layer. The plurality of heat sink interconnects is located adjacent the high-power die in a horizontal direction.
H01L 23/367 - Cooling facilitated by shape of device
H01L 21/56 - Encapsulations, e.g. encapsulating layers, coatings
H01L 23/31 - Encapsulation, e.g. encapsulating layers, coatings characterised by the arrangement
H01L 23/373 - Cooling facilitated by selection of materials for the device
H01L 23/522 - Arrangements for conducting electric current within the device in operation from one component to another including external interconnections consisting of a multilayer structure of conductive and insulating layers inseparably formed on the semiconductor body
H03F 3/213 - Power amplifiers, e.g. Class B amplifiers, Class C amplifiers with semiconductor devices only in integrated circuits
Disclosed are apparatuses and methods for fabricating the apparatuses. In one aspect, an apparatus includes a high-power die mounted on a backside of a package substrate. A heat transfer layer is disposed on the backside of the high-power die. A plurality of heat sink interconnects is coupled to the heat transfer layer. The plurality of heat sink interconnects is located adjacent the high-power die in a horizontal direction.
A method comprising for generating an equivariant neural network includes receiving a set of irreducible representations for an origin-preserving group. A network that is equivariant to the origin-preserving group is dynamically generated based on the set of irreducible representation.
A method comprising for generating an equivariant neural network includes receiving a set of irreducible representations for an origin-preserving group. A network that is equivariant to the origin-preserving group is dynamically generated based on the set of irreducible representation.
Systems and techniques are provided for determining one or more poses of one or more objects. For example, a process can include determining, using a machine learning system, a plurality of keypoints from an image. The plurality of keypoints are associated with at least one object in the image. The process can include determining a plurality of features from the machine learning system based on the plurality of keypoints. The process can include classifying the plurality of features into a plurality of joint types. The process can include determining pose parameters for the at least one object based on the plurality of joint types.
G06V 40/20 - Movements or behaviour, e.g. gesture recognition
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
55.
PACKAGE COMPRISING METAL LAYER CONFIGURED FOR ELECTROMAGNETIC INTERFERENCE SHIELD AND HEAT DISSIPATION
A package that includes a substrate (202), an integrated device (204, 206, 208) coupled to the substrate, an encapsulation layer (209) located over the substrate, at least one encapsulation layer interconnect (211, 212) located in the encapsulation layer (209), and a metal layer (210) located over the encapsulation layer. The substrate (202) includes at least one dielectric layer (220) and a plurality of interconnects (221). The encapsulation layer interconnect (211, 212) is coupled to the substrate (202). The metal layer (210) is configured as an electromagnetic interference (EMF) shield for the package. The metal layer is located over a backside of the integrated device (204, 206, 208).
A package that includes a substrate, an integrated device coupled to the substrate, an encapsulation layer located over the substrate, at least one encapsulation layer interconnect located in the encapsulation layer, and a metal layer located over the encapsulation layer. The substrate includes at least one dielectric layer and a plurality of interconnects. The encapsulation layer interconnect is coupled to the substrate. The metal layer is configured as an electromagnetic interference (EMI) shield for the package. The metal layer is located over a backside of the integrated device.
H01L 23/552 - Protection against radiation, e.g. light
H01L 21/48 - Manufacture or treatment of parts, e.g. containers, prior to assembly of the devices, using processes not provided for in a single one of the groups or
H01L 21/56 - Encapsulations, e.g. encapsulating layers, coatings
H01L 21/768 - Applying interconnections to be used for carrying current between separate components within a device
H01L 23/367 - Cooling facilitated by shape of device
H01L 23/373 - Cooling facilitated by selection of materials for the device
H01L 23/48 - Arrangements for conducting electric current to or from the solid state body in operation, e.g. leads or terminal arrangements
H01L 23/49 - Arrangements for conducting electric current to or from the solid state body in operation, e.g. leads or terminal arrangements consisting of soldered or bonded constructions wire-like
Certain aspects of the present disclosure provide techniques for training a first model based on a first labeled video dataset; generating a plurality of action-words based on output generated by the first model processing motion data in videos of an unlabeled video dataset; defining labels for the videos in the unlabeled video dataset based on the generated action-words; and training a second model based on the labels for the videos in the unlabeled video dataset.
A computer-implemented method for tracking with visual object constraints includes receiving a lingual constraint and a video. A word embedding is generated based on the lingual constraint. A set of features is extracted for one or more frames of the video. The word embedding is cross-correlated to the set of features for the one or more frames of the video. A prediction indicating whether the lingual constraint is in the one or more frames of the video is generated based on the cross-correlation.
G06N 3/04 - Architecture, e.g. interconnection topology
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/94 - Hardware or software architectures specially adapted for image or video understanding
A computer-implemented method for tracking with visual object constraints includes receiving a lingual constraint and a video. A word embedding is generated based on the lingual constraint. A set of features is extracted for one or more frames of the video. The word embedding is cross-correlated to the set of features for the one or more frames of the video. A prediction indicating whether the lingual constraint is in the one or more frames of the video is generated based on the cross-correlation.
A method is presented. The method includes receiving a first sequence of frames depicting a dynamic element. The method also includes decomposing each spatial position from multiple spatial positions in the first sequence of frames to a frequency domain. The method further includes determining a distribution of spectral power density over a range of frequencies of the multiple spatial positions. The method still further includes generating a first set of feature maps based on the determined distribution of spectral power density over the range of frequencies. The method still further includes estimating a first physical property of the dynamic element.
The present disclosure relates to methods and apparatus for graphics processing at a server and/or a client device. In some aspects, the apparatus may convert application data for at least one frame, the application data corresponding to one or more image functions or one or more data channels. The apparatus may also encode the application data for the at least one frame, the application data being associated with a data stream, the application data being encoded via a video encoding process. The apparatus may also transmit the encoded application data for the at least one frame. Additionally, the apparatus may receive application data for at least one frame, the application data being associated with a data stream. The apparatus may also decode the application data for the at least one frame; and convert the application data for the at least one frame.
H04N 19/59 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving spatial sub-sampling or interpolation, e.g. alteration of picture size or resolution
H04N 19/597 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding specially adapted for multi-view video sequence encoding
The present disclosure relates to methods and apparatus for graphics processing. The apparatus may identify at least one mesh associated with at least one frame. The apparatus may also divide the at least one mesh into a plurality of groups of primitives, each of the plurality of groups of primitives including at least one primitive and a plurality of vertices. The apparatus may also compress the plurality of groups of primitives into a plurality of groups of compressed primitives, the plurality of groups of compressed primitives being associated with random access. Additionally, the apparatus may decompress the plurality of groups of compressed primitives, at least one first group of the plurality of groups of compressed primitives being decompressed in parallel with at least one second group of the plurality of groups of compressed primitives.
The present disclosure relates to methods and apparatus for graphics processing. The apparatus may configure a plurality of billboards associated with a viewpoint of a first frame of a plurality of frames, the plurality of billboards being configured in one or more layers at least partially around the viewpoint, the configuration of the plurality of billboards being based on one or more volumetric elements between at least one of the plurality of billboards and the viewpoint. The apparatus may also render an image associated with each of the one or more volumetric elements between at least one billboard of the plurality of billboards and the viewpoint, the rendered image including a set of pixels. The apparatus may also store data in the at least one billboard based on the rendered image associated with each of the one or more volumetric elements, the data corresponding to the set of pixels.
A method of collaboratively training a neural network model, includes receiving a local update from a subset of the multiple users. The local update is related to one or more subsets of a dataset of the neural network model. A local component of the neural network model identifies a subset of the one or more subsets to which a data point belongs. A global update is computed for the neural network model based on the local updates from the subset of the users. The global updates for each portion of the network are aggregated to train the neural network model.
A method for object tracking includes receiving a target image of an object of interest. Latent space features of the target image is modified at a forward pass for a neural network by dropping at least one channel of the latent space features, dropping a channel corresponding to a slice of the latent space features, or dropping one or more features of the latent space features. At the forward pass, a location of the object of interest in a search image is predicted based on the modified latent space features. The location of the object of interest is identified by aggregating predicted locations from the forward pass.
Certain aspects of the present disclosure provide a method for performing machine learning, comprising: determining a plurality of vertices in a neighborhood associated with a mesh including a target vertex; determining a linear transformation configured to parallel transport signals along all edges in the mesh to the target vertex; applying the linear transformation to the plurality of vertices in the neighborhood to form a combined signal at the target vertex; determining a set of basis filters; linearly combining the basis filters using a set of learned parameters to form a gauge equivariant convolution filter, wherein the gauge equivariant convolution filter is constrained to maintain gauge equivariance; applying the gauge equivariant convolution filter to the combined signal to form an intermediate output; and applying a nonlinearity to the intermediate output to form a convolution output.
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
Certain aspects of the present disclosure provide a method for performing machine learning, comprising: determining a plurality of vertices in a neighborhood associated with a mesh including a target vertex; determining a linear transformation configured to parallel transport signals along all edges in the mesh to the target vertex; applying the linear transformation to the plurality of vertices in the neighborhood to form a combined signal at the target vertex; determining a set of basis filters; linearly combining the basis filters using a set of learned parameters to form a gauge equivariant convolution filter, wherein the gauge equivariant convolution filter is constrained to maintain gauge equivariance; applying the gauge equivariant convolution filter to the combined signal to form an intermediate output; and applying a nonlinearity to the intermediate output to form a convolution output.
Aspects described herein provide a method of processing data, including: receiving a set of global parameters for a plurality of machine learning models; processing data stored locally on an processing device with the plurality of machine learning models according to the set of global parameters to generate a machine learning model output; receiving, at the processing device, user feedback regarding machine learning model output for the plurality of machine learning models; performing an optimization of the plurality of machine learning models based on the machine learning output and the user feedback to generate locally updated machine learning model parameters; sending the locally updated machine learning model parameters to a remote processing device; and receiving a set of globally updated machine learning model parameters for the plurality of machine learning models.
A method for classifying a human-object interaction includes identifying a human-object interaction in the input. Context features of the input are identified. Each identified context feature is compared with the identified human-object interaction. An importance of the identified context feature is determined for the identified human-object interaction. The context feature is fused with the identified human-object interaction when the importance is greater than a threshold.
A method for recognizing long-range activities in videos includes segmenting an input video stream to generate multiple frame sets. For each of the frame sets, a frame with a highest likelihood of including one or more actions of a set of predefined actions is identified regardless of its order in the frame set. A global representation of the input stream is generated based on pooled representations of the identified frames. A long-range activity in the video stream is classified based on the global representation.
A method including running a virtualization layer on a processor, the virtualization layer being adapted to assign processor time and memory to first and second guest operating systems running on the virtualization layer, wherein the first guest operating system is a real time operating system, obtaining, by the second guest system, information to be displayed on a display, preparing, by the second guest system, a display frame to be sent to the display, reading, by the first guest system, a portion of the display frame, or retrieving, by the first guest system, information about a read portion of the display frame, and determining, by the first guest system, whether the information sent to the second guest system is correctly generated in the display frame.
Certain aspects provide a method for determining a solution to a combinatorial optimization problem, including: determining a plurality of subgraphs, wherein each subgraph of the plurality of subgraphs corresponds to a combinatorial variable of the plurality of combinatorial variables; determining a combinatorial graph based on the plurality of subgraphs; determining evaluation data comprising a set of vertices in the combinatorial graph and evaluations on the set of vertices; fitting a Gaussian process to the evaluation data; determining an acquisition function for vertices in the combinatorial graph using a predictive mean and a predictive variance from the fitted Gaussian process; optimizing the acquisition function on the combinatorial graph to determine a next vertex to evaluate; evaluating the next vertex; updating the evaluation data with a tuple of the next vertex and its evaluation; and determining a solution to the problem, wherein the solution comprises a vertex of the combinatorial graph.
Techniques are provided for one or more three-dimensional models representing one or more objects. For example, an input image including one or more objects can be obtained. From the input image, a location field can be generated for each object of the one or more objects. A location field descriptor can be determined for each object of the one or more objects, and a location field descriptor for an object of the one or more objects can be compared to a plurality of location field descriptors for a plurality of three-dimensional models. A three-dimensional model can be selected from the plurality of three-dimensional models for each object of the one or more objects. A three-dimensional model can be selected for the object based on comparing a location field descriptor for the object to the plurality of location field descriptors for the plurality of three-dimensional models.
G06T 17/20 - Wire-frame description, e.g. polygonalisation or tessellation
G06K 9/62 - Methods or arrangements for recognition using electronic means
G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
G06T 19/20 - Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
Techniques are provided for one or more three-dimensional models representing one or more objects. For example, an input image including one or more objects can be obtained. From the input image, a location field can be generated for each object of the one or more objects. A location field descriptor can be determined for each object of the one or more objects, and a location field descriptor for an object of the one or more objects can be compared to a plurality of location field descriptors for a plurality of three-dimensional models. A three-dimensional model can be selected from the plurality of three-dimensional models for each object of the one or more objects. A three-dimensional model can be selected for the object based on comparing a location field descriptor for the object to the plurality of location field descriptors for the plurality of three-dimensional models.
A control unit including at least one processor and at least one memory connected to the at least one processor, a virtualization system, the virtualization system including a scheduler for scheduling a plurality of virtual machines to assign processing time to each of the virtual machines according to a predetermined fixed sequence of virtual machine switches forming a cycle period, which is repeated, the cycle period being the minimum time period after which the scheduling is repeated, wherein the virtualization system and the plurality of virtual machines are real-time systems, the virtual machines having respectively at least one real-time attribute, wherein at least one the real time attribute of a first virtual machine are different to the corresponding real-time attribute(s) of a second virtual machine, wherein predetermined fixed sequence of virtual machine switches is calculated based on the at least one real time attribute.
G06F 9/48 - Program initiatingProgram switching, e.g. by interrupt
G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
76.
Transistor noise tolerant, non-volatile (NV) resistance element-based static random access memory (SRAM) physically unclonable function (PUF) circuits, and related systems and methods
YONSEI UNIVERSITY, UNIVERSITY—INDUSTRY Foundation (Republic of Korea)
Inventor
Jung, Seong-Ook
Song, Byungkyu
Lim, Sehee
Kang, Seung Hyuk
Kim, Sungryul
Abstract
Transistor noise tolerant, non-volatile (NV) resistance element-based static random access memory (SRAM) physically unclonable function (PUF) circuits and related systems and methods. In exemplary aspects, a transistor and its complementary transistor, such as a pull-up transistor and complement pull-down transistor or pull-down transistor and complement pull-up transistor, of the PUF circuit are replaced with passive NV resistance elements coupled to the respective output node and complement output node to enhance imbalance between cross-coupled transistors of the PUF circuit for improved PUF output reproducibility. The added passive NV resistance elements replacing pull-up or pull-down transistors in the PUF circuit reduces or eliminates transistor noise that would otherwise occur if the replaced transistors were present in the PUF circuit as a result of changes in temperature, voltage variations, and aging effect. The bit error rate of the PUF circuit is reduced by the reduction in transistor noise thereby improving PUF output reproducibility.
G11C 11/00 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor
G11C 14/00 - Digital stores characterised by arrangements of cells having volatile and non-volatile storage properties for back-up when the power is down
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
G11C 11/412 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using electric elements using semiconductor devices using transistors forming cells with positive feedback, i.e. cells not needing refreshing or charge regeneration, e.g. bistable multivibrator or Schmitt trigger using field-effect transistors only
G11C 11/16 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using magnetic elements using elements in which the storage effect is based on magnetic spin effect
A method for processing an image is presented. The method locates a subject and an object of a subject-object interaction in the image. The method determines relative weights of the subject, the object, and a context region for classification. The method further classifies the subject-object interaction based on a classification of a weighted representation of the subject, a weighted representation of the object, and a weighted representation of the context region.
A method for classifying subject activities in videos includes learning latent (previously generated) concepts that are analogous to nodes of a graph to be generated for an activity in a video. The method also includes receiving video segments of the video. A similarity between the video segments and the previously generated concepts is measured to obtain segment representations as a weighted set of latent concepts. The method further includes determining a relationship between the segment representations and their transitioning pattern over time to determine a reduced set of nodes and/or edges for the graph. The graph of the activity in the video represented by the video segments is generated based on the reduced set of nodes and/or edges. The nodes of the graph are represented by the latent concepts. Subject activities in the video are classified based on the graph.
A method for processing an image is presented. The method locates a subject and an object of a subject-object interaction in the image. The method determines relative weights of the subject, the object, and a context region for classification. The method further classifies the subject-object interaction based on a classification of a weighted representation of the subject, a weighted representation of the object, and a weighted representation of the context region.
A method is presented. The method includes receiving a first sequence of frames. The method also includes decomposing each spatial position from multiple spatial positions in the first sequence of frames to a frequency domain. The method further includes determining a distribution of spectral power density over a range of frequencies of the multiple spatial positions. The method still further includes generating a first set of feature maps based on the determined distribution of spectral power density over the range of frequencies.
The present disclosure relates to methods and devices for operation of a GPU. The device can determine a first subset of primitives associated with a set of objects within an image. The first subset of primitives can be based on a first viewpoint with respect to the set of objects. The device can also determine, for a second viewpoint with respect to the set of objects, a second subset of primitives excluding the first subset of primitives. In some aspects, the second subset of primitives can have a difference in depth with respect to the first subset of primitives that is less than a threshold depth. Additionally, the device can mark the first subset of primitives and the second subset of primitives as visible. Further, the device can generate graphical content based on the marked first subset of primitives and the marked second subset of primitives.
A method is described. The method includes mapping features extracted from an unannotated red-green-blue (RGB) image of the object to a depth domain. The method further includes determining a three-dimensional (3D) pose of the object by providing the features mapped from the unannotated RGB image of the object to the depth domain to a trained pose estimator network.
A method for labeling a spherical target includes receiving an input including a representation of an object. The method also includes estimating unconstrained coordinates corresponding to the object. The method further includes estimating coordinates on a sphere by applying a spherical exponential activation function to the unconstrained coordinates. The method also associates the input with a set of values corresponding to a spherical target based on the estimated coordinates on the sphere.
A microphone including a casing having a front wall, a back wall, and a side wall joining the front wall to the back wall, a transducer mounted to the front wall, the transducer including a substrate and a transducing element, the transducing element having a transducer acoustic compliance dependent on the transducing element dimensions, a back cavity cooperatively defined between the back wall, the side wall, and the transducer, the back cavity having a back cavity acoustic compliance. The transducing element is dimensioned such that the transducing element length matches a predetermined resonant frequency and the transducing element width, thickness, and elasticity produces a transducer acoustic compliance within a given range of the back cavity acoustic compliance.
B81B 7/02 - Microstructural systems containing distinct electrical or optical devices of particular relevance for their function, e.g. microelectro-mechanical systems [MEMS]
B81B 3/00 - Devices comprising flexible or deformable elements, e.g. comprising elastic tongues or membranes
85.
System and method for scheduling a plurality of guest systems and/or threads
A method for scheduling guest systems and/or threads in a virtualization system that assigns processor time and memory space to guest systems and including a virtualization system scheduler, the method including running a first guest system that includes at least one first thread and at least one second thread running in the first guest system, and a guest system scheduler that assigns processing time to the at least one second thread, assigning a plurality of time reservations to the first guest system, wherein the plurality of time reservations include a first time reservation associated to one first thread and a second time reservation associated to the guest system scheduler of the first guest system, assigning processor time to the first guest system according to the second time reservation, and assigning processor time to the at least one first thread of the first guest system according to the first time reservation.
In an example aspect, an apparatus includes a balanced power amplifier, which performs amplification in the presence of a variable antenna impedance. The balanced power amplifier includes a quadrature output power combiner coupled to a first power amplifying path and a second power amplifying path, detection circuitry, and control circuitry. The detection circuitry includes at least one power detector coupled to an isolated port of the quadrature output power combiner and a resistor coupled between the isolated port and a ground. The at least one power detector is configured to measure power at the isolated port, which is based on a resistance of the resistor. The control circuitry is configured to adjust operating conditions of a first power amplifier of the first power amplifying path and the second power amplifier of the second power amplifying path based on the power that is measured at the isolated port.
H01Q 1/00 - Details of, or arrangements associated with, antennas
H03F 1/56 - Modifications of input or output impedances, not otherwise provided for
H03F 3/24 - Power amplifiers, e.g. Class B amplifiers, Class C amplifiers of transmitter output stages
H03F 3/19 - High-frequency amplifiers, e.g. radio frequency amplifiers with semiconductor devices only
H01Q 23/00 - Antennas with active circuits or circuit elements integrated within them or attached to them
H01P 5/18 - Conjugate devices, i.e. devices having at least one port decoupled from one other port consisting of two coupled guides, e.g. directional couplers
A method, an apparatus, and a computer-readable medium for wireless communication are provided. In one aspect, an example method may include determining to control a bit rate of a content encoder. The method may include generating a first number of shaded texture atlases for use in rendering a second number of frames by a second device based on the determination to control the bit rate of the content encoder. Each respective shaded texture atlas may include a respective plurality of shaded primitives. The method may include encoding, by the content encoder of the first device, a first shaded texture atlas of the first number of shaded texture atlases. The method may include transmitting, by the first device, the encoded first shaded texture atlas to the second device.
A method, an apparatus, and a computer-readable medium for wireless communication are provided. In one aspect, an example method may include determining to control a bit rate of a content encoder. The method may include generating a first number of shaded texture atlases for use in rendering a second number of frames by a second device based on the determination to control the bit rate of the content encoder. Each respective shaded texture atlas may include a respective plurality of shaded primitives. The method may include encoding, by the content encoder of the first device, a first shaded texture atlas of the first number of shaded texture atlases. The method may include transmitting, by the first device, the encoded first shaded texture atlas to the second device.
H04N 19/172 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
An apparatus may be configured to obtain, for a Siamese neural network having a recurrent neural network (RNN), an initial representation associated with a target object at a first time step and a set of candidate regions at a current time step. The apparatus may determine an updated representation associated with the target object based on the initial representation at the first time step and observed information associated with the target object at a set of previous time steps, and the observed information associated with the target object may be represented by a hidden state of the RNN. The apparatus may output the updated representation associated with the target object for matching with the set of candidate regions at the current time step by the Siamese neural network. The apparatus may determine the updated representation further based on a hidden state at a previous time step.
A system including a processor with a plurality of cores having the same instruction set architecture, at least one memory connected to the processor, a plurality of virtualization systems adapted to run respectively on one core, the plurality of virtualization systems including a first virtualization system adapted to run on a first core and a second virtualization system adapted to run on a second core, wherein the first virtualization system has a first characteristic with a first parameter and the second virtualization system has a second characteristic with a second parameter, wherein the parameter of the first characteristic and the parameter of the second characteristic are incompatible when implemented in a single virtualization system; and communication module for enabling the plurality of virtualization systems to communicate with each other.
G06F 9/455 - EmulationInterpretationSoftware simulation, e.g. virtualisation or emulation of application or operating system execution engines
91.
SENSING VOLTAGE BASED ON A SUPPLY VOLTAGE APPLIED TO MAGNETO-RESISTIVE RANDOM ACCESS MEMORY (MRAM) BIT CELLS IN AN MRAM FOR TRACKING WRITE OPERATIONS TO THE MRAM BIT CELLS
YONSEI UNIVERSITY, UNIVERSITY INDUSTRY FOUNDATION (Republic of Korea)
Inventor
Kang, Seung Hyuk
Kim, Sungryul
Jung, Seong-Ook
Choi, Sara
Ahn, Hong Keun
Abstract
Sensing voltage based on supply voltage applied to an MRAM bit cell in a write operation can be used to detect completion of magnetic tunnel junction (MTJ) switching in an MRAM bit cell to terminate the write operation to reduce power and write times. In exemplary aspects disclosed herein, reference and write operation voltages sensed from the MRAM bit cell in response to the write operation are compared to each other to detect completion of MTJ switching of voltage based on the supply voltage applied to the MRAM bit cell regardless of whether the write operation is logic '0' or logic T write operation. This provides a higher sensing margin, because the change in MTJ resistance after MTJ switching completion is larger at the supply voltage rail.
G11C 11/16 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using magnetic elements using elements in which the storage effect is based on magnetic spin effect
G11C 13/00 - Digital stores characterised by the use of storage elements not covered by groups , , or
Yonsei University, University-Industry Foundation (Republic of Korea)
Inventor
Jung, Seong-Ook
Song, Byungkyu
Kim, Sungryul
Kim, Jung Pill
Kang, Seung Hyuk
Abstract
Offset-cancellation sensing circuit (OCSC)-based Non-volatile (NV) memory circuits are disclosed. An OCSC-based NV memory circuit includes a latch circuit configured to latch a memory state from an input signal. The OCSC-based NV memory circuit also includes a sensing circuit that includes NV memory devices configured to store the latched memory state in the latch circuit for restoring the memory state in the latch circuit when recovering from a reduced power level in an idle mode. To avoid the need to increase transistor size in the sensing circuit to mitigate restoration degradation, the sensing circuit is also configured to cancel an offset voltage of a differential amplifier in the sensing circuit. In other exemplary aspects, the NV memory devices are included in the sensing circuit and coupled to the differential transistors as NMOS transistors in the differential amplifier, eliminating contribution of offset voltage from other differential PMOS transistors not included.
G11C 11/16 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using magnetic elements using elements in which the storage effect is based on magnetic spin effect
93.
POSE ESTIMATION AND MODEL RETRIEVAL FOR OBJECTS IN IMAGES
Techniques are provided for selecting a three-dimensional model. An input image including an object can be obtained, and a pose of the object in the input image can be determined. One or more candidate three-dimensional models representing one or more objects in the determined pose can be obtained. From the one or more candidate three-dimensional models, a candidate three-dimensional model can be determined to represent the object in the input image.
A method of pixel-wise localization of an actor and an action in a sequence of frames includes receiving a natural language query describing the action and the actor. The method also includes receiving the sequence of frames. The method further includes localizing the action and the actor in the sequence of frames based on the natural language query.
Techniques are provided for selecting a three-dimensional model. An input image including an object can be obtained, and a pose of the object in the input image can be determined. One or more candidate three-dimensional models representing one or more objects in the determined pose can be obtained. From the one or more candidate three-dimensional models, a candidate three-dimensional model can be determined to represent the object in the input image.
G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
G06T 19/00 - Manipulating 3D models or images for computer graphics
G06K 9/62 - Methods or arrangements for recognition using electronic means
G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
G06T 19/20 - Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
96.
Offset-canceling (OC) write operation sensing circuits for sensing switching in a magneto-resistive random access memory (MRAM) bit cell in an MRAM for a write operation
Yonsei University, University-Industry Foundation (Republic of Korea)
Inventor
Jung, Seong-Ook
Choi, Sara
Ahn, Hong Keun
Kang, Seung Hyuk
Kim, Sungryul
Abstract
Aspects disclosed in the detailed description include offset-canceling (OC) write operation sensing circuits for sensing switching in a magneto-resistive random access memory (MRAM) bit cell in an MRAM for a write operation. The OC write operation sensing circuit is configured to sense when MTJ switching occurs in MRAM bit cell. In an example, the OC write operation sensing circuit includes a voltage sensing circuit and a sense amplifier. The voltage sensing circuit employs a capacitive-coupling effect so that the output voltage drops in response to MTJ switching for both logic ‘0’ and logic ‘1’ write operations. The sense amplifier has a single input and a single output node with an output voltage indicating when MTJ switching has occurred in the MRAM bit cell. A single input transistor and pull-up transistor are provided in the sense amplifier in one example to provide an offset-canceling effect.
G11C 11/16 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using magnetic elements using elements in which the storage effect is based on magnetic spin effect
Sensing voltage based on a supply voltage applied to magneto-resistive random access memory (MRAM) bit cells in an MRAM for tracking write operations to the MRAM bit cells
Yonsei University, University Industry Foundation (Republic of Korea)
Inventor
Jung, Seong-Ook
Choi, Sara
Ahn, Hong Keun
Kang, Seung Hyuk
Kim, Sungryul
Abstract
Sensing voltage based on a supplied to magneto-resistive random access memory (MRAM) bit cells in an MRAM for tracking write operations. Sensing voltage based on supply voltage applied to an MRAM bit cell in a write operation can be used to detect completion of magnetic tunnel junction (MTJ) switching in an MRAM bit cell to terminate the write operation to reduce power and write times. In exemplary aspects provided herein, reference and write operation voltages sensed from the MRAM bit cell in response to the write operation are compared to each other to detect completion of MTJ switching of voltage based on the supply voltage applied to the MRAM bit cell regardless of whether the write operation is logic ‘0’ or logic ‘1’ write operation. This provides a higher sensing margin, because the change in MTJ resistance after MTJ switching completion is larger at the supply voltage rail.
G11C 11/00 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor
G11C 11/16 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using magnetic elements using elements in which the storage effect is based on magnetic spin effect
H01L 27/22 - Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate using similar magnetic field effects
H01L 43/02 - Devices using galvano-magnetic or similar magnetic effects; Processes or apparatus specially adapted for the manufacture or treatment thereof or of parts thereof - Details
The present disclosure describes methods, apparatuses, and non-transitory computer-readable mediums for estimating a three-dimensional (“3D”) pose of an object from a two-dimensional (“2D”) input image which contains the object. Particularly, certain aspects of the disclosure are concerned with 3D pose estimation of a symmetric or nearly-symmetric object. An image or a patch of an image includes the object. A classifier is used to determine whether a rotation angle of the object in the image or the patch of the image is within a first predetermined range. In response to a determination that the rotation angle is within the first predetermined range, a mirror image of the object is determined. Two-dimensional (2D) projections of a three-dimensional (3D) bounding box of the object are determined by applying a trained regressor to the mirror image of the object in the image or the patch of the image. The 3D pose of the object is estimated based on the 2D projections.
The present invention relates to a method for updating a control unit (1) for an automotive vehicle, the control unit comprising a runtime system (22, 122) with a virtualization layer (32, 132) adapted to run on the processor (3), the virtualization layer being adapted to assign processor time and memory space to a plurality of guest systems (24, 26, 28, 30, 124, 126, 128, 130, 158), the method comprising: downloading (1018), by a first update client (38, 40, 138, 140) of a first untrusted guest system (26, 28) running on the virtualization layer (32, 132), one or more first update files or data segments from a first remote server (44, 46, 48, 144, 146, 148); storing, by the first update client (38, 40, 138, 140), the one or more first update files or data segments in an untrusted memory (5) accessible to the first untrusted guest system; running an update operating system (58, 158) adapted to update one or more files or data segments of the control unit; retrieving, by the updating operating system (58, 158), the one or more first update files or data segments from the untrusted memory (5); and updating (1108, 1110, 1112, 1114) the one or more files or data segments of the control unit (1).
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06F 21/64 - Protecting data integrity, e.g. using checksums, certificates or signatures
G06F 9/455 - EmulationInterpretationSoftware simulation, e.g. virtualisation or emulation of application or operating system execution engines
H04L 29/06 - Communication control; Communication processing characterised by a protocol
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
100.
MOS TRANSISTOR OFFSET-CANCELLING DIFFERENTIAL CURRENT-LATCHED SENSE AMPLIFIER
INDUSTRY-ACADEMIC COOPERATION FOUNDATION, YONSEI UNIVERSITY (Republic of Korea)
Inventor
Na, Taehui
Song, Byung Kyu
Jung, Seong-Ook
Kim, Jung Pill
Kang, Seung, Hyuk
Abstract
Metal-oxide semiconductor (MOS) transistor offset-cancelling (OC), zero-sensing (ZS) dead zone, current-latched sense amplifiers (SAs) (CLSAs) (OCZS-SAs) for sensing differential voltages are provided. An OCZS-SA is configured to amplify received differential data and reference input voltages with a smaller sense amplifier offset voltage to provide larger sense margin between different storage states of memory bitcell(s). The OCZS-SA is configured to cancel out offset voltages of input and complement input transistors, and keep the input and complement input transistors in their activated state during sensing phases so that sensing is not performed in their "dead zones" when their gate-to-source voltage (Vgs) is below their respective threshold voltages. In other aspects, sense amplifier capacitors are configured to directly store the data and reference input voltages at gates of the input and complement input transistors during voltage capture phases to avoid additional layout area that would otherwise be consumed with additional sensing capacitor circuits.
G11C 11/16 - Digital stores characterised by the use of particular electric or magnetic storage elementsStorage elements therefor using magnetic elements using elements in which the storage effect is based on magnetic spin effect
patents
