Robert Boschgmbh

Vmware, Inc.

Apple Inc.

Mao Ye

Liu Ren

Sudipto Mukhopadhyay

Benjamin Davini

Chitrank Seshadri

Benzun Pious Wisely Babu

Arun Mahajan

Atanu Panda

Shimon Elkayam

Kisun Lee

Swapneel Dattaram Ambre

Zhixin Yan

Amin Jourabloo

Manisha P Pandya

Hao Zhou

Yongxuan Hu

Yen-Lin Chen

Chongli Cai

Bilal Alsallakh

Naveen Chandra Gorijala

Bharatkumar K Patel

Vijayanish Varadharajan

Chao Du

Bin Chen

Xiaoming Liu

Madhavi V Tagare

Soledad Calderon Arroyo

Benedict Foo

Rong Guo

Chao Do

A power management circuit for a computer system is disclosed. The power management circuit includes a power storage circuit, a first power converter circuit, and a second power converter circuit. The first power converter circuit sources a current to a regulated power supply node via a first inductor under average load conditions using a voltage level of an input power supply node. During high-load transient conditions, the second power converter circuit provides additional current to the regulated power supply node via a second inductor using a voltage generated by the power storage circuit.

Distributed power management with separate peak and average power paths

Examples described herein include systems and methods for efficiently and effectively applying upgrade bundles to an SDDC. The upgrade bundles can update various software components of the SDDC. A version-compliance configuration matrix provides version-compliance information across various software components to ensure that all components function properly after upgrading. Each upgrade bundle can include metadata that provides information sufficient to utilize the configuration matrix. A super bundle can include multiple upgrade bundles, as well as instructions for applying the multiple upgrade bundles in a particular order to avoid compatibility issues. The super bundle can be used to upgrade multiple software components of an SDDC without disrupting the functionality of the SDDC.

Super bundles for software-defined datacenter upgrades

In a computer-implemented method for allocating a host of a pre-configured hyper-converged computing device to a workload domain, a pre-configured hyper-converged computing device including a plurality of hosts is managed, wherein the plurality of hosts is allocable to workload domains. A pool of unallocated hosts of the plurality of hosts is managed within the pre-configured hyper-converged computing device, wherein hosts of the pool of unallocated hosts have associated hypervisor versions. An allocation request to allocate at least one host of the pool of unallocated hosts to a workload domain is received, the allocation request including a requested hypervisor version of at least one host upon allocation. The at least one host is updated to the requested hypervisor version of the allocation request while the at least one host is in the pool of unallocated hosts.

Allocating a host of a pre-configured hyper-converged computing device to a workload domain

A helmet includes a transceiver configured to receive vehicle data from one or more sensors located on a vehicle. The helmet also includes an inertial movement unit (IMU) configured to collect helmet motion data of a rider of the vehicle and a processor in communication with the transceiver and IMU, and programmed to receive, via the transceiver, vehicle data from the one or more sensors located on the vehicle and determine a rider attention state utilizing the vehicle data from the one or more sensors located on the vehicle and the helmet motion data from the IMU.

Apparatus and system related to an intelligent helmet

A method for operating a vehicle including a vehicle sensing system includes generating a baseline image model of an cabin of the vehicle based on image data of the cabin of the vehicle generated by an imaging device of the vehicle sensing system, the baseline image model generated before a passenger event, and generating an event image model of the cabin of the vehicle based on image data of the cabin of the vehicle generated by the imaging device, the event image model generated after the passenger event. The method further includes identifying image deviations by comparing the event image model to the baseline image model with a controller of the vehicle sensing system, the image deviations corresponding to differences in the cabin of the vehicle from before the passenger event to after the passenger event, and operating the vehicle based on the identified image deviations.

Method and system for vision-based vehicle interior environment sensing guided by vehicle prior information

A system and method for generating a tracking state for a device includes synchronizing measurement data from exteroceptive sensors and an inertial measurement unit (IMU). A processing unit is programmed to offset one of the measurement signals by a time offset that minimizes a total error between a change in rotation of the device predicted by the exteroceptive sensor data over a time interval defined by an exteroceptive sensor sampling rate and a change in rotation of the device predicted by the IMU sensor data over the time interval.

System and method for enhancing non-inertial tracking system with inertial constraints

A visual analytics method and system is disclosed for visualizing an operation of an image classification model having at least one convolutional neural network layer. The image classification model classifies sample images into one of a predefined set of possible classes. The visual analytics method determines a unified ordering of the predefined set of possible classes based on a similarity hierarchy such that classes that are similar to one another are clustered together in the unified ordering. The visual analytics method displays various graphical depictions, including a class hierarchy viewer, a confusion matrix, and a response map. In each case, the elements of the graphical depictions are arranged in accordance with the unified ordering. Using the method, a user a better able to understand the training process of the model, diagnose the separation power of the different feature detectors of the model, and improve the architecture of the model.

Visual analytics system for convolutional neural network based classifiers

In a computer-implemented method for updating schema of a database, execution of an application in a computing environment is initiated, where the application persists data in a database, and where the schema of the database is updatable responsive to an update of the application. Responsive to determining that the schema is updated, a first instance of the schema executing in the database is received, a second instance of the schema maintained in the application is received, the first instance of the schema is compared to the second instance of the schema, a difference between the first instance of the schema and the second instance of the schema is determined, and the first instance of the schema is updated to incorporate the difference between the first instance of the schema and the second instance of the schema.

Updating schema of a database

In a computer-implemented method for provisioning a host of a workload domain of a pre-configured hyper-converged computing device, a pre-configured hyper-converged computing device comprising a plurality of hosts is managed, where the plurality of hosts is allocable to workload domains, and where allocated hosts of a particular workload domain are of a same operating system version. A pool of unallocated hosts is maintained within the pre-configured hyper-converged computing device, where the unallocated hosts of the pool have operating system versions within a range of supported operating system versions.

Provisioning a host of a workload domain of a pre-configured hyper-converged computing device

In a computer-implemented method for maintaining unallocated hosts of a pre-configured hyper-converged computing device at a baseline operating system version, a plurality of hosts of a pre-configured hyper-converged computing device is managed, where the plurality of hosts are allocable to workload domains, where the plurality of hosts each have an operating system version within a range of supported operating system versions, where unallocated hosts of the plurality of hosts are maintained within a pool of unallocated hosts, and where the unallocated hosts of the pool of unallocated hosts have a baseline operating system version of the range of supported operating system versions. A new unallocated host is received at the pre-configured hyper-converged computing device for inclusion to the pool of unallocated hosts. An operating system version of the new unallocated host is determined. Provided the new unallocated host has an operating system version other than the baseline operating system version, the new unallocated host is updated to the baseline operating system version. The new unallocated host is released to the pool of unallocated hosts for allocation to a workload domain.

Maintaining unallocated hosts of a pre-configured hyper-converged computing device at a baseline operating system version

In a computer-implemented method for managing hosts of a pre-configured hyper-converged computing device, a pre-configured hyper-converged computing device comprising a plurality of hosts is managed, where the plurality of hosts is allocable to workload domains, where unallocated hosts of the plurality of hosts is maintained within a pool of unallocated hosts, and where the plurality of hosts each have an operating system version. An unallocated host of the pool of unallocated hosts is determined as having an operating system version that is outside of a range of supported operating system versions. The operating system version of the unallocated host is updated to an operating system version within the range of supported operating system versions.

Managing hosts of a pre-configured hyper-converged computing device

A computing system includes a processing system with at least one processing unit. The processing system is configured to receive a depth map with a first boundary of an object. The processing system is configured to receive a color image that corresponds to the depth map. The color image includes a second boundary of the object. The processing system is configured to extract depth edge points of the first boundary from the depth map. The processing system is configured to identify target depth edge points on the depth map. The target depth edge points correspond to color edge points of the second boundary of the object in the color image. In addition, the processing system is configured to snap the depth edge points to the target depth edge points such that the depth map is enhanced with an object boundary for the object.

Systems and methods for dynamic occlusion handling

A method for motion estimation in an augmented reality (AR) system includes receiving inertial sensor data and image data during movement of the AR system generating a probability map based on the inertial sensor data and the image data, the probability map corresponding to one frame in the image data and including probability values indicating that each pixel in the one frame is in an inertial coordinate frame or a local coordinate frame with a convolutional neural network encoder/decoder, identifying visual observations of at least one landmark in the local coordinate frame based on the image data and the probability map, and generating an estimate of secondary motion in the local coordinate frame based on a first prior state in a hidden Markov model (HMM) corresponding to the local coordinate frame and the visual observations of the at least one landmark in the local coordinate frame.

Methods and systems for exploiting per-pixel motion conflicts to extract primary and secondary motions in augmented reality systems

A novel map representation called Probabilistic Surfel Map (PSM) for dense visual SLAM is disclosed. The PSM maintains a globally consistent map with both photometric and geometric uncertainties encoded in order to address inconsistency and sensor noise. A key aspect of the visual SLAM method disclosed herein is the proper modeling and updating of the photometric and geometric uncertainties encoded in the PSM. Strategies for applying the PSM for improving both the front-end pose estimation and the back-end optimization are disclosed. Moreover, the PSM enables generation of a high quality dense point cloud with high accuracy.

Dense visual SLAM with probabilistic surfel map

A computing system includes a processing system with at least one processing unit. The processing system is configured to execute a face alignment method upon receiving image data with a facial image. The processing system is configured to apply a neural network to the facial image. The neural network is configured to provide a final estimate of parameter data for the facial image based on the image data and an initial estimate of the parameter data. The neural network includes at least one visualization layer, which is configured to generate a feature map based on a current estimate of the parameter data. The parameter data includes head pose data and face shape data.

Growing community of inventors

San Jose, CA, United States of America

Mao Ye