Abstract: In some embodiments, systems and methods for remote trauma assessment are provided, a system comprising, a robot arm; an ultrasound probe and a depth sensor coupled to the robot arm; and a processor programmed to: cause the depth sensor to acquire depth data indicative of a three dimensional shape of at least a portion of a patient; generate a 3D model of the patient; automatically identify scan positions using the 3D model; cause the robot arm to move the ultrasound probe to a first identified scan position; receive movement information indicative of input provided via a remotely operated haptic device; cause the robot arm to move the ultrasound probe from the first scan position to a second position based on the movement information; cause the ultrasound probe to acquire ultrasound data at the second position; and transmit an ultrasound image based on the ultrasound data to the remote computing device.

Abstract: Methods described herein relate to using vision-based mapping to provide augmented reality of objects in the environment of the vehicle in response to objects being obscured. Methods may include: receiving sensor data from a vehicle traveling along a road segment; identifying a location of the vehicle on the road segment; retrieving map data from a map database of an environment of the road segment at the location of the vehicle on the road segment; identifying an object in the sensor data representing an obscured object in an environment of the vehicle; and providing for display of at least one object from the map data in an augmented reality of the environment, where the at least one object appears overlaid on the obscured object to a driver of the vehicle. The object in the sensor data may include a road sign having information relating to driving along the road segment.

Abstract: An apparatus, method and computer program product are provided for predicting feature space decay using variational auto-encoder networks. Methods may include: receiving a first image of a road segment including a feature disposed along the road segment; applying a loss function to the feature of the first image; generating a revised image, where the revised image includes a weathered iteration of the feature; generating a predicted image using interpolation between the image and the revised image of a partially weathered iteration of the feature; receiving a user image, where the user image is received from a vehicle traveling along the road segment; correlating a feature in the user image to the partially weathered iteration of the feature in the predicted image; and establishing that the feature in the user image is the feature disposed along the road segment.

Abstract: An approach is provided for fully-automated learning to match heterogeneous feature spaces for mapping. The approach involves determining a first feature space comprising first features and a second feature space comprising second features, and classified by a feature detector into a first attribution category and a second attribution category, respectively. The approach further involves calculating a first similarity score for the first feature space based on a first distance metric applied to the first features, and a second similarity score for the second feature space based on a second distance metric applied to the second features. The approach also involves determining a transformation space comprising a first weight to be applied to the first similarity score and a second weight to be applied to the second similarity score based on matching the first attribution category and the second attribution category.

Abstract: Described herein are methods of generating learning data to facilitate de-biasing the labeled location of an object of interest within an image. Methods may include: receiving sensor data, where the sensor data is a first image; determining reference corner locations of an object in the first image using image processing; generating observed corner locations of the object in the first image from the determined reference corner locations; generating a bias transformation based, at least in part, on a difference between the reference corner locations and the observed corner locations of the object in the first image; receiving sensor data from another image sensor of a second image; receiving observed corner locations of an object in the second image from a user; and applying the bias transformation to the observed corner locations of the object in the second image to generate de-biased corners for the object in the second image.

Abstract: A method, apparatus, and computer program product are provided to determine sensor orientation. In the context of a method, the orientation of a sensor is predicted based on a predetermined dataset. The method also includes receiving information regarding a location of the sensor and information from which a gravity direction of the sensor is derivable. The method further includes determining a relative orientation of the sensor in relation to a frame of reference and the gravity direction of the sensor. The method still further includes comparing the orientation of the sensor that was predicted and the relative orientation of the sensor to determine a prediction accuracy. The method finally includes updating the predetermined dataset based on the prediction accuracy of the sensor orientation.

Abstract: Methods described herein relate to creating a visual map of an environment free of scene clutter. Methods may include: receiving sensor data from at least one image sensor, where the sensor data is representative of a plurality of images, each image representative of a scene at a scene location; processing each image using semantic scene segmentation to identify segments of the image of the scene; classifying the segments of each of the images of the scene into one of static elements or dynamic elements; generating a decluttered image of the scene, where the decluttered image includes only elements classified as static elements; providing for storage of the decluttered image of the scene in a database; and identifying a location of a device as the scene location in response to sensor data from the device corresponding to the decluttered image of the scene.

Abstract: A task definition is received. The task definition indicates at least a location from which one or more software image can be obtained and information usable to determine an amount of resources to allocate to one or more software containers for the one or more software image. A set of virtual machine instances in which to launch the one or more software containers is determined, the one or more software image is obtained from the location included in the task definition and is launched as the one or more of software containers within the set of virtual machine instances.

Abstract: Systems and methods for utilizing microphone array information for acoustic modeling are disclosed. Audio data may be received from a device having a microphone array configuration. Microphone configuration data may also be received that indicates the configuration of the microphone array. The microphone configuration data may be utilized as an input vector to an acoustic model, along with the audio data, to generate phoneme data. Additionally, the microphone configuration data may be utilized to train and/or generate acoustic models, select an acoustic model to perform speech recognition with, and/or to improve trigger sound detection.

Abstract: An approach is provided for generating a reverse sequence or real-time streamed images for triangulation. The approach includes receiving a real-time stream of images captured by a sensor of a vehicle during a drive; extracting a sequence of two or more images from the real-time stream; reversing the sequence of the two or more images; and providing the reversed sequence of the two or more images for feature triangulation.

Abstract: An approach is provided for aligning one or more drive segments based on a consensus set of user defined inputs. The approach involves, for example, retrieving manual drive alignment data collected from a plurality of human users, wherein the manual drive alignment data indicates one or more regions of at least two drive data segments selected by the human users to align the at least two drive segments. The approach also involves processing the manual drive alignment data to determine a set of common drive alignment locations of the one or more regions. The approach further involves processing a plurality of subsequent drive segments to automatically align the plurality of subsequent drive segments based on the set of common drive alignment locations.

Abstract: Methods described herein relate to culling training data for machine learning in a neural network. Methods may include: receiving sensor data from at least one image sensor, where the sensor data is representative of at least one image; processing the at least one image using a neural network; identifying a number of neurons of the neural network that are activated for each of the at least one image; identifying an image as an informative training image in response to the number of neurons activated for the respective image satisfying a predetermined value; and updating the neural network with the informative training image in response to at least one object of the informative training image being manually labeled. The at least one image may be a plurality of images, where the predetermined value may be established based on a distribution of a number of neurons fired for the plurality of images.

Abstract: A method, apparatus and computer program product are provided for establishing the reliability of crowd sourced data based on the source of the data and the region in which the data is gathered. Methods may include: receiving map data for a network of roads in a geographic area; receiving location accuracy data from different regions within the geographic area, where the location accuracy data for each region may be indicative of an accuracy with which location can be established in the respective region; receiving a plurality of probe data points; determining, for each probe data point, the location accuracy data associated with the location information associated with the respective probe apparatus; determining, for each probe apparatus, a reliability of the one or more sensors; and updating a map database based on at least one probe data point.

Abstract: An approach is provided for localizing a vehicle pose on a map. The approach involves, receiving an input specifying the vehicle pose with respect to a road lane of the map. The approach also involves searching over a set of candidate lateral offsets to select a lateral offset that minimizes a lateral error between the vehicle position with the lateral offset applied and a lateral location of the road lane, wherein the lateral location and the travel direction of the lane are determined from the map. The approach further involves searching over a set of candidate vehicle headings at the selected lateral offset to select a vehicle heading that minimizes a heading error. The approach further involves determining a local optimum of the vehicle pose based on the selected lateral offset and vehicle heading, wherein the vehicle pose is localized to the map based on the local optimum.

Abstract: An approach is provided for feature triangulation from images. The approach includes retrieving the plurality of images, wherein the plurality of images is captured by a sensor of a vehicle during a drive; determining a vehicle trajectory of the vehicle during the drive; selecting a first image and a second image from the plurality of images, wherein the first image and second image are arranged in reverse time order based on respective image capture times determined using the vehicle trajectory; after detecting the feature in the first image, processing the second image to detect the feature and to associate the feature as detected in the second image with the feature previously detected in the first image; and processing the detected feature in the first image and the second image to triangulate the location of the feature.

Abstract: An approach is provided for providing a lower-power perception architecture. The approach involves, for example, determining that a device is equipped with a first perception system and a second perception system. The second perception system operates in a lower-power consumption mode than the first perception system to process image data for image recognition. The approach also involves determining a battery level of the device. The approach further involves switching from the first perception system to the second perception system based on determining that the battery level is below a threshold battery level.

Abstract: A memory stores a first cache and a second cache. A processor copies a first portion of data from a first table stored in a database into a second table, performs statistical analysis on the first portion, and stores the results into the second table. The processor further determines that a second portion of data from the first table will be overwritten, copies the second portion into a third table, performs statistical analysis on the second portion and stores the results into the third table. The processor further determines that a probability that a user will access a third portion of the first table is greater than a threshold and copies the third portion into the first cache. The processor further determines a fourth portion of the first table that the user accesses at a frequency greater than a set frequency and copies the fourth portion into the second cache.

Abstract: A memory stores a first and second cache. A processor stores a first query in a transaction log. The processor selects information from the transaction log, uses it to determine that a user will likely access a first portion of the database at a future date, and copies that portion into the first cache prior to the date. The processor further copies a second, frequently accessed portion of the database into the second cache. The processor additionally determines that a portion of data is in both caches and deletes it from the second cache. The processor receives a second query from the user, determines that it is directed at data in the first cache and accesses that cache. The processor also determines that the second query is not directed at data in the first cache but is directed at data in the second cache, and accesses that cache.

Abstract: An imaging system that includes a camera mounted on an aerial platform, for example a balloon, allows a user to increase the longevity of the camera's battery by remote control. A user may capture imagery at a time scale of interest and desired power consumption by adjusting parameters for image capture by the camera. A user may adjust a time to capture an image, a time to capture a video, or a number of cycles per time period to capture one or more images as the aerial platform moves in a region of interest to change power consumption for imaging. The system also provides imaging alignment to account for unwanted movement of the aerial platform when moved in the region of interest. Additionally, a mounting device is provided that is simple and inexpensive, and that allows a camera to remain positioned in a desired position relative to the ground.

Abstract: Distributed storage systems, devices, and associated methods of data replication are disclosed herein. In one embodiment, a server in a distributed storage system is configured to write, with an RDMA enabled NIC, a block of data from a memory of the server to a memory at another server via an RDMA network. Upon completion of writing the block of data to the another server, the server can also send metadata representing a memory location and a data size of the written block of data in the memory of the another server via the RDMA network. The sent metadata is to be written into a memory location containing data representing a memory descriptor that is a part of a data structure representing a pre-posted work request configured to write a copy of the block of data from the another server to an additional server via the RDMA network.