Abstract: A computer implemented method includes providing a user interface to a user that includes an original image and an option to generate a high-resolution portion of the original image. The method includes receiving a selection of the option to generate the high-resolution portion of the original image and dimensions of a portion of the original image. The method includes providing the portion of the original image as input to a machine-learning model. The method includes generating, with the machine-learning model, the high-resolution image. The method includes updating the user interface to include the high-resolution portion of the original image.

Abstract: At least one processor may be configured to receive a first image frame of a sequence of image frames from an image capture device and select a first portion of a first image frame. The at least one processor may also be configured to obtain alignment information and determine a first portion and a second portion of a second image frame based on the alignment information. Further, the at least one processor may be configured to determine a bounding region within the second image frame and fetch image data corresponding to the bounding region of the second image frame from memory. In some examples, the first image frame may comprise a base image and the second image frame may comprise an alternative image frame. Further, the first image frame may comprise any one of the image frames of the sequence of image frames.

Abstract: Described examples relate to an apparatus comprising a first sensor configured to scan an area of interest during a first time period and a second sensor configured to capture a plurality of images of a field of view. The apparatus may include at least one controller configured to receive the plurality of images captured by the second sensor, compare the timestamp information associated with at least one image of the plurality of images to at least one time period of the first time period, and select a base image from the plurality of images based on the comparison.

Abstract: The present disclosure describes systems and techniques for creating a super-resolution image (122) of a scene captured by a user device (102). Natural handheld motion (110) introduces, across multiple frames (204, 206, 208) of an image of a scene, sub-pixel offsets that enable the use of super-resolution computations (210) to form color planes (212, 214, 216), which are accumulated (218) and combined (220) to create a super-resolution image (122) of the scene.

Abstract: Removal of the support structure from the workpiece is in an easily automated way. A method for removing support structure includes: suspending abrasive into a process liquid; cutting a support structure along a cutting surface from an additively manufactured workpiece having a support surface, the support surface including the support structure for fixing the workpiece to a build platform; installing the workpiece with the support structure remaining on the support surface in the process liquid with suspended abrasive; immersing a nozzle for ejecting a cavitation jet of the process liquid into the process liquid; and ejecting the cavitation jet onto the workpiece from the nozzle immersed in the process liquid such that an angle between an ejection axis of the cavitation jet ejected from the nozzle and the support surface is between 0 to 20 degrees to remove the remaining support structure adhered to the workpiece.

Abstract: Described examples relate to an apparatus comprising a memory for storing a sequence of image frames and at least one processor. The at least one processor may be configured to receive a first image frame of a sequence of image frames from an image capture device and select a first portion of a first image frame. The at least one processor may also be configured to obtain alignment information and determine a first portion and a second portion of a second image frame based on the alignment information. Further, the at least one processor may be configured to determine a bounding region within the second image frame and fetch image data corresponding to the bounding region of the second image frame from memory. In some examples, the first image frame may comprise a base image and the second image frame may comprise an alternative image frame. Further, the first image frame may comprise any one of the image frames of the sequence of image frames.

Abstract: Described examples relate to an apparatus comprising a first sensor configured to scan an area of interest during a first time period and a second sensor configured to capture a plurality of images of a field of view. The apparatus may include at least one controller configured to receive the plurality of images captured by the second sensor, compare the timestamp information associated with at least one image of the plurality of images to at least one time period of the first time period, and select a base image from the plurality of images based on the comparison.

Abstract: The present invention relates to a lubricating composition comprising: water, especially purified water, for example ultrapure water, demineralized water, osmotic water, deionized water; glycerol; nanosheets of h-MoS2, wherein the active content of h-MoS2 nanosheets is comprised between 0.01 and 0.07 wt %, preferably between 0.02 to 0.06 wt %, based on the total weight of the lubricating composition, wherein, the nanosheets of h-MoS2 are obtained in-situ in the lubricating composition by liquid-phase exfoliation of bulk MoS2 particles water.

Abstract: The present invention relates to a lubricating composition comprising: water; surfactant; and h-MoS2 nanosheets; wherein the h-MoS2 nanosheets are obtained in-situ in the lubricating composition by liquid-phase exfoliation of bulk MoS2 nanoparticles.

Abstract: An energy monitoring system for discharging energy in an energy transfer device of a vehicle, including an isolation monitoring unit, a capacitor unit, a voltage terminal unit and a control unit. The voltage terminal unit is connectable to an energy storage system and transfers energy from the energy storage system to a vehicle subsystem. The isolation monitoring unit includes a first isolation resistor element and switch element. The resistor element is connectable to the voltage terminal unit via the switch element. The capacitor unit is connected to the voltage terminal unit and filters electromagnetic interference by storing energy during energy transfer to the subsystem. The control unit closes the first isolation switch element in case of a disconnection of the energy transfer to form a discharge circuit connecting the isolation monitoring unit and the capacitor unit. The discharge circuit discharges energy stored in the capacitor unit.

Abstract: A rail vehicle has a central buffer coupling with a coupling head, a rod-shaped coupling shaft and a spring mechanism. The coupling head connects to a coupling head of another rail vehicle and is connected to the coupling shaft. The shaft is inserted into the spring mechanism and is mounted and fastened therein such that, during operation of the rail vehicle, impact forces or tensile forces acting on the coupling head are resiliently absorbed by the spring mechanism and transferred to a wagon body. A support is coupled to the coupling shaft and is connected to the wagon body. A collision element is arranged between the support and the wagon body. The collision element has a deformation region configured such that kinetic energy, transferred to the collision element via the coupling shaft and via the coupled support due to a collision, irreversibly deforms the deformation region in a predetermined manner.

Abstract: The present disclosure describes systems and techniques directed to optical image stabilization movement to create a super-resolution image of a scene. The systems and techniques include a user device (102) introducing (502), through an optical image stabilization system (114), movement to one or more components of a camera system (112) of the user device (102). The user device (102) then captures (504) respective and multiple frames (306) of an image of a scene, where the respective and multiple frames (306) of the image of the scene have respective, sub-pixel offsets of the image of the scene across the multiple frames (306) as a result of the introduced movement to the one or more components of the camera system (112). The user device (102) performs (506), based on the respective, sub-pixel offsets of the image of the scene across the respective, multiple frames (306), super-resolution computations and creates (508) the super-resolution image of the scene based on the super-resolution computations.

Abstract: An example embodiment may involve capturing a sequence of images, wherein there are 4 or more images in the sequence of images, and wherein each of the sequence of images has an exposure length of 4-100 seconds; applying a sliding window over the sequence of images as downsampled, wherein at least 4 images are encompassed within the sliding window’, and wherein for each position of the sliding window the applying involves: (i) aligning a set of images within the sliding window, and (ii) merging the set of images as aligned into a video frame; combining video frames generated by way of the sliding window into a video file; and storing, by the mobile device, the video file in memory of the mobile device.

Abstract: Generating a location-specific three-dimensional model in response to a location query can provide users with a better understanding of a location through providing better interactivity, better perspective, and better understanding of dimensionality. Generation of the models can be enabled by leveraging a three-dimensional asset database and segmentation methods. The location-specific models can provide further utility by further including situation specific simulated effects, such as simulated weather or traffic.

Abstract: Described examples relate to an apparatus comprising a memory for storing a sequence of image frames and at least one processor. The at least one processor may be configured to receive a first image frame of a sequence of image frames from an image capture device and select a first portion of a first image frame. The at least one processor may also be configured to obtain alignment information and determine a first portion and a second portion of a second image frame based on the alignment information. Further, the at least one processor may be configured to determine a bounding region within the second image frame and fetch image data corresponding to the bounding region of the second image frame from memory. In some examples, the first image frame may comprise a base image and the second image frame may comprise an alternative image frame. Further, the first image frame may comprise any one of the image frames of the sequence of image frames.

Abstract: Described examples relate to an apparatus comprising one or more image sensors coupled to a vehicle and at least one processor. The at least one processor may be configured to capture, in a burst sequence using the one or more image sensors, multiple frames of an image of a scene, the multiple frames having respective, relative offsets of the image across the multiple frames and perform super-resolution computations using the captured, multiple frames of the image of the scene. The at least one processor may also be configured to accumulate, based on the super-resolution computations, color planes and combine, using the one or more processors, the accumulated color planes to create a super-resolution image of the scene.

Abstract: Generating a location-specific three-dimensional model in response to a location query can provide users with a better understanding of a location through providing better interactivity, better perspective, and better understanding of dimensionality. Generation of the models can be enabled by leveraging a three-dimensional asset database and segmentation methods. The location-specific models can provide further utility by further including situation specific simulated effects, such as simulated weather or traffic.

Abstract: Described examples relate to an apparatus comprising one or more image sensors coupled to a vehicle and at least one processor. The at least one processor may be configured to capture, in a burst sequence using the one or more image sensors, multiple frames of an image of a scene, the multiple frames having respective, relative offsets of the image across the multiple frames and perform super-resolution computations using the captured, multiple frames of the image of the scene. The at least one processor may also be configured to accumulate, based on the super-resolution computations, color planes and combine, using the one or more processors, the accumulated color planes to create a super-resolution image of the scene.

Abstract: Described examples relate to an apparatus comprising a memory for storing a sequence of image frames and at least one processor. The at least one processor may be configured to receive a first image frame of a sequence of image frames from an image capture device and select a first portion of a first image frame. The at least one processor may also be configured to obtain alignment information and determine a first portion and a second portion of a second image frame based on the alignment information. Further, the at least one processor may be configured to determine a bounding region within the second image frame and fetch image data corresponding to the bounding region of the second image frame from memory. In some examples, the first image frame may comprise a base image and the second image frame may comprise an alternative image frame. Further, the first image frame may comprise any one of the image frames of the sequence of image frames.

Abstract: Described examples relate to an apparatus comprising a first sensor configured to scan an area of interest during a first time period and a second sensor configured to capture a plurality of images of a field of view. The apparatus may include at least one controller configured to receive the plurality of images captured by the second sensor, compare the timestamp information associated with at least one image of the plurality of images to at least one time period of the first time period, and select a base image from the plurality of images based on the comparison.