Abstract: A swappable battery and a swappable battery system for a near-eye device are described. In one aspect, the swappable battery may be detachably integrated into an outer surface of a near-eye device such that the swappable battery may be easily removable and replaceable by the user. In another aspect, the lid of the swappable battery may form an outer surface of the near-eye device when the swappable battery is attached. In some examples, the swappable battery may be detachably attached within a temple arm of a pair of smartglasses such that the outer surface of the swappable battery and the outer surface of the temple arm are substantially contiguous and flush.

Abstract: According to examples, systems and methods for implementing a conductive trace via laser direct structuring (LDS) and a light-emitting diode (LED) via surface-mount technology (SMT) on a carrier structure of a display device are described. A method may include printing a conductive trace onto a carrier, mounted a light-emitting diode (LED) onto the carrier, and attaching a flexible printed circuit (FPC) to the carrier, wherein the light-emitting diode (LED) is communicatively coupled to the flexible printed circuit (FPC) via the conductive trace.

Abstract: An FMCW LiDAR system comprises an optical source to transmit an optical beam towards a target. The LiDAR system comprises a first layer, folding optics and a second layer. The first layer comprises a silicon photonics chip coupled to an electrical power source to transmit electrical power to optical components resident on a second layer, and a plurality of different interfaces to couple the silicon photonics chip to the optical components. The folding optics is to receive the optical beam from the first layer and transmit the optical beam to the second layer. The second layer is disposed directly over the first layer. The second layer comprises the optical components including an LO to general an LO signal, and a receiver to mix a target return signal and the LO signal to extract at least one of range or velocity information related to the target.

Abstract: A depth sensing dilator system for dilating a penetration in a tissue plane includes an elongate flexible body, having a proximal end and a distal end. The body has a tapered dilator segment, and at least a first electrode on a distal end of the body. The system includes a processor and a user interface output device. The processor is configured to send a first signal to the output device when a change in impedance at the first electrode indicates that the first electrode has reached a predetermined relationship with the tissue plane.

Abstract: A system and method receive a first beam pattern from an optical source that includes optical beams transmitted towards a target. The system and method measure a first vertical angle between at least two of the optical beams along a first axis relative to the FMCW LIDAR system. The system and method calculate a second beam pattern based on the first vertical angle and a pivot point. The second beam pattern produces a second vertical angle between the two optical beams. The system and method adjust one or more components from a first position that forms the first beam pattern to a second position that forms the second beam pattern for transmission towards the target. The system and method receive one or more return optical beams from the target, based on the second beam pattern, to produce a plurality of points to form the point cloud.

Abstract: A system including a multi-sided scanner including a plurality of sides, and optical source to transmit, at a first angle, an optical beam towards a first side of the plurality of sides to produce a first adjusted beam transmitted within the multi-sided scanner towards a second side of the plurality of sides to produce a second adjusted beam. A trajectory of the second adjusted beam traverses a third side of the plurality of sides to exit the multi-sided scanner to produce a first FOV portion. The optical source is to transmit, at the second angle, the optical beam towards the first side to produce a third adjusted beam transmitted within the multi-sided scanner towards the second side to produce a fourth adjusted beam. A trajectory of the fourth adjusted beam traverses the third side of the plurality of sides to exit the multi-sided scanner to produce a second FOV portion.

Abstract: A method for facilitating dynamic-transparency chart overlays via a graphical user interface is provided. The method includes displaying, via a client device, the graphical user interface, the graphical user interface including a graphical element to receive an input; receiving, via the graphical user interface, a request relating to an overlay request that corresponds to a visualization of a chart; retrieving data corresponding to the chart; converting the data into a structured data set based on a parameter; generating an overlay chart based on the structured data set, the overlay chart corresponding to a superimposition of the chart; and displaying, via the client device, the overlay chart on the graphical user interface in response to the request.

Abstract: A machine learning system for maintaining distributed computer control systems for a train may include a data acquisition hub communicatively connected to a plurality of sensors configured to acquire real-time configuration data from one or more of the computer control systems. The machine learning system may also include an analytics server communicatively connected to the data acquisition hub. The analytics server may include a virtual system modeling engine configured to model an actual train control system comprising the distributed computer control systems, a virtual system model database configured to store one or more virtual system models of the distributed computer control systems, wherein each of the one or more virtual system models includes preset configuration settings for the distributed computer control systems, and a machine learning engine configured to monitor the real-time configuration data and the preset configuration settings.

Abstract: Various methods, apparatuses/systems, and media for generating a two-dimensional selectable user experience (UX) element are disclosed. A client device having a graphical user interface (GUI) for receiving user input is configured to run interface applications. The client device is coupled with one or more server devices via a communication network. The one or more server devices provides user interface (UI) metadata. A processor causes a receiver to receive the UI metadata from the one or more server devices; generates, at the client device, a two-dimensional (2D) selectable user experience (UX) element based on the received UI metadata; receives a single action user input to select a single desired field, which conveys two desired actions, from the 2D selectable UX element; and executes the two desired actions based on the selected single desired field from the 2D selectable UX element.

Abstract: Systems and methods for adjusting operation of a train are disclosed. A method may include: receiving one or more infrared images of a brake line of the train; detecting one or more leaks of the brake line based on the one or more infrared images; and adjusting a brake command based on the detected one or more leaks.

Abstract: A method for facilitating dynamic-transparency chart overlays via a graphical user interface is provided. The method includes displaying, via a client device, the graphical user interface, the graphical user interface including a graphical element to receive an input; receiving, via the graphical user interface, a request relating to an overlay request that corresponds to a visualization of a chart; retrieving data corresponding to the chart; converting the data into a structured data set based on a parameter; generating an overlay chart based on the structured data set, the overlay chart corresponding to a superimposition of the chart; and displaying, via the client device, the overlay chart on the graphical user interface in response to the request.

Abstract: A train control system may include a data acquisition hub connected to a database and sensors associated with one or more locomotives, systems, or components of a train and configured to acquire data in association with inputs derived from contextual data relating to a plurality of trains being operated by experienced train engineers under a variety of different conditions and in different geographical areas for use as training data. A machine learning engine of the train control system may receive the training data from the data acquisition hub, encode a modified learning function as a statistical model of desirable train handling behavior, evaluate train handling behavior by comparing to the statistical model, and update a certification of one or more of a train engineer, a semi-autonomous control system, or a fully autonomous control system.

Abstract: Various methods, apparatuses/systems, and media for generating a two-dimensional selectable user experience (UX) element are disclosed. A client device having a graphical user interface (GUI) for receiving user input is configured to run interface applications. The client device is coupled with one or more server devices via a communication network. The one or more server devices provides user interface (UI) metadata. A processor causes a receiver to receive the UI metadata from the one or more server devices; generates, at the client device, a two-dimensional (2D) selectable user experience (UX) element based on the received UI metadata; receives a single action user input to select a single desired field, which conveys two desired actions, from the 2D selectable UX element; and executes the two desired actions based on the selected single desired field from the 2D selectable UX element.

Abstract: A system may include a data acquisition hub connected to databases and sensors associated with locomotives, systems, or components of a train and configured to acquire real-time and historical configuration, structural, and operational data in association with inputs derived from real time and historical contextual data relating to a plurality of trains. The system may include a virtual system modeling engine configured to receive results of a non-destructive evaluation of a train component, simulate in-train forces, determine a predicted time of failure for the train component based on an evaluation of stresses that have already been applied to the component and expected future stresses, and implement repair, replacement, or operational protocols for the train component before or at a repair facility that will be reached by the train ahead of a predetermined minimum threshold time period before the predicted time of failure.

Abstract: A train control system uses artificial intelligence for maintaining synchronization between centralized and distributed train control models. A machine learning engine receives training data from a data acquisition hub, a first set of output control commands from a centralized virtual system modeling engine, and a second set of output control commands from a distributed virtual system modeling engine. The machine learning engine compares the first set of output control commands and the second set of output control commands, and trains a learning system using the training data to enable the machine learning engine to safely mitigate any difference between the first and second sets of output control commands using a learning function including at least one learning parameter.

Abstract: Various methods, apparatuses/systems, and media for generating a two-dimensional selectable user experience (UX) element are disclosed. A client device having a graphical user interface (GUI) for receiving user input is configured to run interface applications. The client device is coupled with one or more server devices via a communication network. The one or more server devices provides user interface (UI) metadata. A processor causes a receiver to receive the UI metadata from the one or more server devices; generates, at the client device, a two-dimensional (2D) selectable user experience (UX) element based on the received UI metadata; receives a single action user input to select a single desired field, which conveys two desired actions, from the 2D selectable UX element; and executes the two desired actions based on the selected single desired field from the 2D selectable UX element.

Abstract: A train control system uses machine learning for implementing handovers between centralized and distributed train control models. A machine learning engine receives training data from a data acquisition hub, receives a centralized train control model from a centralized virtual system modeling engine, and receives an edge-based train control model from an edge-based virtual system modeling engine. The machine learning engine trains a learning system using the training data to enable the machine learning engine to predict when a locomotive of the train will enter a geo-fence where communication between the edge-based computer processing system and the centralized computer processing system will be inhibited.

Abstract: Various methods, apparatuses/systems, and media for generating a two-dimensional selectable user experience (UX) element are disclosed. A client device having a graphical user interface (GUI) for receiving user input is configured to run interface applications. The client device is coupled with one or more server devices via a communication network. The one or more server devices provides user interface (UI) metadata. A processor causes a receiver to receive the UI metadata from the one or more server devices; generates, at the client device, a two-dimensional (2D) selectable user experience (UX) element based on the received UI metadata; receives a single action user input to select a single desired field, which conveys two desired actions, from the 2D selectable UX element; and executes the two desired actions based on the selected single desired field from the 2D selectable UX element.

Abstract: Systems and methods for adjusting operation of a train are disclosed. A method may include: receiving one or more infrared images of a brake line of the train; detecting one or more leaks of the brake line based on the one or more infrared images; and adjusting a brake command based on the detected one or more leaks.

Abstract: A train control system may include a data acquisition hub connected to a database and sensors associated with one or more locomotives, systems, or components of a train and configured to acquire data in association with inputs derived from contextual data relating to a plurality of trains being operated by experienced train engineers under a variety of different conditions and in different geographical areas for use as training data. A machine learning engine of the train control system may receive the training data from the data acquisition hub, encode a modified learning function as a statistical model of desirable train handling behavior, evaluate train handling behavior by comparing to the statistical model, and update a certification of one or more of a train engineer, a semi-autonomous control system, or a fully autonomous control system.