Abstract: According to one aspect, an actuator of a robotic drawer is disclosed. The actuator may include a plurality of articulation components that form an articulated rack. The actuator may include a plurality of pins each coupled to an exterior of one of the plurality of articulation components. The actuator may include a transformer box coupled to a pinion and configured to move along an exterior of the articulated rack as the pinion moves along the surface. The transformer box may include a first guide configured to apply a first force to an arm of each of the pins as the transformer box moves in a first direction. The first force may cause the pin to rotate in a first rotational direction until the arm is fitted into a cam of the articulated rack such that adjacent articulation components are held in a fixed position, causing the articulated rack to extend.

Abstract: Systems, apparatuses and methods may provide for technology that identifies a sequence of events associated with a computer architecture, categorizes, with a natural language processing system, the sequence of events into a sequence of words, identifying an anomaly based on the sequence of words and triggering an automatic remediation process in response to an identification of the anomaly.

Abstract: Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.

Abstract: In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of cameras. The processor is to obtain metadata associated with an initial state of an object, wherein the object is captured by a first camera in a first video stream at a first point in time, and wherein the metadata is obtained based on the first video stream. The processor is further to predict, based on the metadata, a future state of the object at a second point in time, and identify a second camera for capturing the object at the second point in time. The processor is further to configure the second camera to capture the object in a second video stream at the second point in time, wherein the second camera is configured to capture the object based on the future state of the object.

Abstract: Systems, apparatuses and methods may provide for origination camera technology that extracts data from multimodal sensor signals associated with a scene and generates a five-dimensional (5D) representation of the scene, wherein the 5D representation includes a three-dimensional (3D) visual representation, a one-dimensional (1D) temporal representation, and a 1D branch representation. The technology may also store the 5D representation as a set of abstract descriptors.

Abstract: A robotic assistant includes a wheeled base, a storage unit including drawers, a foldable arm connected to a top of the storage unit and including an end of arm tooling (EOAT) connected to a distal end of the foldable arm, an elevation mechanism positioned on the wheeled base and used to move the storage unit up and down, and a control system that receives command instructions. In response to the command instructions, the control system is configured to move the wheeled base, open or close the one or more drawers, actuate movement of the foldable arm and the EOAT to pick up and place external objects from/to a determined location, and control the storage unit to move up/down.

Abstract: Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.

Abstract: In one embodiment, a compute device implements a node of a computing infrastructure used to execute a computer vision (CV) pipeline. The compute device receives a video stream to be processed through the CV pipeline and performs a first portion of the CV pipeline. The compute device then receives a peer node availability indicating peer nodes in the computing infrastructure that are available to perform a second portion of the CV pipeline. Based on the peer node availability, the compute device partitions the second portion of the CV pipeline into one or more partial CV pipelines and offloads the partial CV pipeline(s) to a subset of the peer nodes.

Abstract: In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of cameras. The processor is to obtain metadata associated with an initial state of an object, wherein the object is captured by a first camera in a first video stream at a first point in time, and wherein the metadata is obtained based on the first video stream. The processor is further to predict, based on the metadata, a future state of the object at a second point in time, and identify a second camera for capturing the object at the second point in time. The processor is further to configure the second camera to capture the object in a second video stream at the second point in time, wherein the second camera is configured to capture the object based on the future state of the object.

Abstract: Disclosed herein are systems and methods for vehicle-occupancy-based and user-preference-based smart routing, and autonomous volumetric-occupancy measurement. In an embodiment, a system is configured to receive from a user device associated with a user, a routing-options request for routing options between two locations, and to responsively identify one or more routing options between the two locations based at least in part on occupancy data for a vehicle that would be utilized for at least a portion of at least one of the identified routing options. The occupancy data is based on an output of an automated occupancy-measurement system onboard the vehicle. The system is also configured to provide the one or more identified routing options to the user device. In some embodiments, the occupancy data is obtained using volumetric-occupancy measurement. Some embodiments relate to volumetric-occupancy measurement conducted by autonomous mesh nodes.

Abstract: System and techniques for hands-free deployment of geographically linked applications are described herein. A radio transceiver of a vehicle may detect a beacon. Here, the beacon includes an indication that an application linked to a geographical area covered by the beacon is available. The vehicle may establish a secure communications link with an application server corresponding to the beacon and receive, through the secure communications link, an application. Then, the vehicle may execute the application in a protected execution environment without intervention by a user of the vehicle.

Abstract: Systems, apparatuses and methods may provide for technology that identifies a sequence of events associated with a computer architecture, categorizes, with a natural language processing system, the sequence of events into a sequence of words, identifying an anomaly based on the sequence of words and triggering an automatic remediation process in response to an identification of the anomaly.

Abstract: Systems, apparatuses and methods may provide for origination camera technology that generates a cell representation of a local space associated with an origination camera in a multicast domain, predicts that an object in the local space will exit the local space and enter one or more adjacent spaces associated with additional cameras in the multicast domain, and sends the cell representation and a trajectory of the object to the additional cameras before the object exits the local space. Additionally, transition camera technology may generate a leader election message based on a multicasted trajectory of an object and a predicted trajectory of the object, send the leader election message from a transition camera to one or more additional cameras in a multicast domain, and track the object in the local space in response to a leader notification message.

Abstract: Systems, apparatuses and methods may provide for origination camera technology that extracts data from multimodal sensor signals associated with a scene and generates a five-dimensional (5D) representation of the scene, wherein the 5D representation includes a three-dimensional (3D) visual representation, a one-dimensional (1D) temporal representation, and a 1D branch representation. The technology may also store the 5D representation as a set of abstract descriptors.

Abstract: Systems, apparatuses and methods may provide for technology that detects an unidentified individual at a first location along a trajectory in a scene based on a video feed of the scene, wherein the video feed is to be associated with a stationary camera, and selects a non-stationary camera from a plurality of non-stationary cameras based on the trajectory and one or more settings of the selected non-stationary camera. The technology may also automatically instruct the selected non-stationary camera to adjust at least one of the one or more settings, capture a face of the individual at a second location along the trajectory, and identify the unidentified individual based on the captured face of the unidentified individual.

Abstract: Apparatus, systems, methods, and articles of manufacture for automated quality assurance and software improvement are disclosed. An example apparatus includes a data processor to process data corresponding to events occurring with respect to a software application in i) a development and/or a testing environment and ii) a production environment. The example apparatus includes a model tool to: generate a first model of expected software usage based on the data corresponding to events occurring in the development and/or the testing environment; and generate a second model of actual software usage based on the data corresponding to events occurring in the production environment. The example apparatus includes a model comparator to compare the first model to the second model. The example apparatus includes a correction generator to generate an actionable recommendation to adjust the development and/or the testing environment to reduce a difference between the first model and the second model.

Abstract: In embodiments, an apparatus for selectively delivering software updates to nodes in a network includes a receiver to receive a software update and a list of nodes of the network scheduled to receive the software update. In embodiments, the apparatus further includes a device management agent (DMA) to: identify a set of traversals to leaf nodes of the list of nodes necessary to traverse all nodes on the list, and distribute the software updates to the nodes on the list using the set of traversals.

Abstract: In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of cameras. The processor is to obtain metadata associated with an initial state of an object, wherein the object is captured by a first camera in a first video stream at a first point in time, and wherein the metadata is obtained based on the first video stream. The processor is further to predict, based on the metadata, a future state of the object at a second point in time, and identify a second camera for capturing the object at the second point in time. The processor is further to configure the second camera to capture the object in a second video stream at the second point in time, wherein the second camera is configured to capture the object based on the future state of the object.

Abstract: Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.