Abstract: A metric monitor may obtain data that measures network traffic of a network, through which a host may be accessed by clients, over one or more time windows. The metric monitor may determine one or more metrics based on the data obtained from the network over the time windows, which may represent performance of the network to the host. The metric monitor may provide the metrics to a workload management system to determine placement of a particular workload at the host.

Abstract: In computing networks, path availabilities are estimated according to the present disclosure. The path availabilities may be calculated based on connectivity and performance measurements provided by multiple data sources, including passive, active, and/or route monitoring data sources. The measurements may be classified using network topology and processed to determine availability indicators corresponding to the measurements. The availability indicators may be aggregated to determine an overall path availability score for a path associated with the indicators.

Abstract: Packet transmission rate and packet drop rate for discrete network devices in a network are used to estimate end-to-end traffic demand and loss in the network. Data regarding the packet transmission rate and drop rate are passively collected for each network device and transmitted to a network monitoring unit. The network monitoring unit compiles the data and generates a series of simultaneous equations that represent traffic demand and loss between the discrete network devices along the paths connecting respective source-destination pairs. By determining an optimal solution to the simultaneous equations, an estimate of end-to-end traffic loss and corresponding traffic demand, which takes into account packet loss at each network device, can be generated for each source-destination pair. The optimal solution can be formed as a traffic matrix, which aggregates source-to-destination traffic demands, and a loss matrix, which aggregates source-to-destination traffic losses.

Abstract: In computing networks, end-to-end loss distributions are estimated according to the present disclosure. The loss distributions may be calculated based on loss values reported by nodes in the network along an end-to-end communication path, which may be identified by a route tracing process. The loss values for each node in the path for a given time window may be reduced by a rounding process, and the reduced values for adjacent nodes in the path may be iteratively multiplied with one another in a pairwise manner to generate an end-to-end loss value vector, where the resulting product from each iteration may be reduced by the rounding process. A graphical or other representation of the values in the end-to-end loss value vector indicating a distribution of losses experienced along the path may be output via a user interface.

Abstract: The numbers of packets transmitted and packets lost for network devices in a computer network are used to estimate a probability of end-to-end packet loss for traffic between a source-destination pair in the network. Metrics of packet transmission and loss at each network device interface are passively collected and transmitted to a network monitoring unit, which uses the metrics to calculate probability of successful packet transmission at each hop. For a particular end-to-end path between the source-destination pair, the network monitoring unit combines the probabilities of successful packet transmission for the hops along the path to yield a probability of successful end-to-end packet transmission. Probability of packet loss along the end-to-end path is determined based on the probability of successful end-to-end packet transmission.

Abstract: Methods and apparatus for real time machine vision and point-cloud data analysis are provided, for remote sensing and vehicle control. Point cloud data can be analyzed via scalable, centralized, cloud computing systems for extraction of asset information and generation of semantic maps. Machine learning components can optimize data analysis mechanisms to improve asset and feature extraction from sensor data. Optimized data analysis mechanisms can be downloaded to vehicles for use in on-board systems analyzing vehicle sensor data. Semantic map data can be used locally in vehicles, along with onboard sensors, to derive precise vehicle localization and provide input to vehicle to control systems.

Abstract: Systems and methods for providing vehicle cognition through localization and semantic mapping are provided. Localization may involve in vehicle calculation of voxel signatures, such as by hashing weighted voxel data (S900, S910) obtained from a machine vision system (110), and comparison of calculated signatures to cached data within a signature localization table (630) containing previously known voxel signatures and associated geospatial positions. Signature localization tables (630) may be developed by swarms of agents (1000) calculating signatures while traversing an environment and reporting calculated signatures and associated geospatial positions to a central server (1240). Once vehicles are localized, they may engage in semantic mapping. A swarm of vehicles (1400, 1402) may characterize assets encountered while traversing a local environment. Asset characterizations may be compared to known assets within the locally cached semantic map.

Abstract: Systems and methods for providing vehicle cognition through localization and semantic mapping are provided. Localization may involve in vehicle calculation of voxel signatures, such as by hashing weighted voxel data (S900, S910) obtained from a machine vision system (110), and comparison of calculated signatures to cached data within a signature localization table (630) containing previously known voxel signatures and associated geospatial positions. Signature localization tables (630) may be developed by swarms of agents (1000) calculating signatures while traversing an environment and reporting calculated signatures and associated geospatial positions to a central server (1240). Once vehicles are localized, they may engage in semantic mapping. A swarm of vehicles (1400, 1402) may characterize assets encountered while traversing a local environment. Asset characterizations may be compared to known assets within the locally cached semantic map.

Abstract: Systems and methods allow for devices, such as sensory systems and mixed reality content consuming systems installed within a vehicle, to discover each other, share multidimensional realities, inherit context for mixed reality content, and/or coordinate whilst providing a mixed reality user experience. A discovery service can receive information from multiple devices and correlate digital and physical characteristics thereof to enable devices to discover each other. Characteristics of a device that may be used for pairing may include its orientation physically, the observable reality of a device, the state-machine of the device, the intentions of the device whilst operating autonomously or with external assistance, the location of a device, the temporal/spatial information describing the device's movement over time in a multi-dimensional reality or any unique identifiers contained within or generated by the device.

Abstract: Systems and methods for providing vehicle cognition through localization and semantic mapping are provided. Localization may involve in vehicle calculation of voxel signatures, such as by hashing weighted voxel data (S900, 5910) obtained from a machine vision system (110), and comparison of calculated signatures to cached data within a signature localization table (630) containing previously known voxel signatures and associated geospatial positions. Signature localization tables (630) may be developed by swarms of agents (1000) calculating signatures while traversing an environment and reporting calculated signatures and associated geospatial positions to a central server (1240). Once vehicles are localized, they may engage in semantic mapping. A swarm of vehicles (1400, 1402) may characterize assets encountered while traversing a local environment. Asset characterizations may be compared to known assets within the locally cached semantic map.

Abstract: Systems and methods for providing vehicle cognition through localization and semantic mapping are provided. Localization may involve in vehicle calculation of voxel signatures, such as by hashing weighted voxel data (S900, S910) obtained from a machine vision system (110), and comparison of calculated signatures to cached data within a signature localization table (630) containing previously known voxel signatures and associated geospatial positions. Signature localization tables (630) may be developed by swarms of agents (1000) calculating signatures while traversing an environment and reporting calculated signatures and associated geospatial positions to a central server (1240). Once vehicles are localized, they may engage in semantic mapping. A swarm of vehicles (1400, 1402) may characterize assets encountered while traversing a local environment. Asset characterizations may be compared to known assets within the locally cached semantic map.

Abstract: Systems and methods for providing vehicle cognition through localization and semantic mapping are provided. Localization may involve in vehicle calculation of voxel signatures, such as by hashing weighted voxel data (S900, S910) obtained from a machine vision system (110), and comparison of calculated signatures to cached data within a signature localization table (630) containing previously known voxel signatures and associated geospatial positions. Signature localization tables (630) may be developed by swarms of agents (1000) calculating signatures while traversing an environment and reporting calculated signatures and associated geospatial positions to a central server (1240). Once vehicles are localized, they may engage in semantic mapping. A swarm of vehicles (1400, 1402) may characterize assets encountered while traversing a local environment. Asset characterizations may be compared to known assets within the locally cached semantic map.

Abstract: A system, method, and apparatus are disclosed for a machine vision system that incorporates hardware and/or software, remote databases, and algorithms to map assets, evaluate railroad track conditions, and accurately determine the position of a moving vehicle on a railroad track. One benefit of the invention is the possibility of real-time processing of sensor data for guiding operation of the moving vehicle.

Abstract: A system, method, and apparatus are disclosed for a machine vision system that incorporates hardware and/or software, remote databases, and algorithms to map assets, evaluate railroad track conditions, and accurately determine the position of a moving vehicle on a railroad track. One benefit of the invention is the possibility of real-time processing of sensor data for guiding operation of the moving vehicle.

Abstract: Systems and methods allow for devices, such as sensory systems and mixed reality content consuming systems installed within a vehicle, to discover each other, share multidimensional realities, inherit context for mixed reality content, and/or coordinate whilst providing a mixed reality user experience. A discovery service can receive information from multiple devices and correlate digital and physical characteristics thereof to enable devices to discover each other. Characteristics of a device that may be used for pairing may include its orientation physically, the observable reality of a device, the state-machine of the device, the intentions of the device whilst operating autonomously or with external assistance, the location of a device, the temporal/spatial information describing the device's movement over time in a multi-dimensional reality or any unique identifiers contained within or generated by the device.

Abstract: Methods and apparatus for real time machine vision and point-cloud data analysis are provided, for remote sensing and vehicle control. Point cloud data can be analyzed via scalable, centralized, cloud computing systems for extraction of asset information and generation of semantic maps. Machine learning components can optimize data analysis mechanisms to improve asset and feature extraction from sensor data. Optimized data analysis mechanisms can be downloaded to vehicles for use in on-board systems analyzing vehicle sensor data. Semantic map data can be used locally in vehicles, along with onboard sensors, to derive precise vehicle localization and provide input to vehicle to control systems.

Abstract: Methods and apparatus for real time machine vision and point-cloud data analysis are provided, for remote sensing and vehicle control. Point cloud data can be analyzed via scalable, centralized, cloud computing systems for extraction of asset information and generation of semantic maps. Machine learning components can optimize data analysis mechanisms to improve asset and feature extraction from sensor data. Optimized data analysis mechanisms can be downloaded to vehicles for use in on-board systems analyzing vehicle sensor data. Semantic map data can be used locally in vehicles, along with onboard sensors, to derive precise vehicle localization and provide input to vehicle to control systems.

Abstract: Systems and methods for providing vehicle cognition through localization and semantic mapping are provided. Localization may involve in vehicle calculation of voxel signatures, such as by hashing weighted voxel data (S900, S910) obtained from a machine vision system (110), and comparison of calculated signatures to cached data within a signature localization table (630) containing previously known voxel signatures and associated geospatial positions. Signature localization tables (630) may be developed by swarms of agents (1000) calculating signatures while traversing an environment and reporting calculated signatures and associated geospatial positions to a central server (1240). Once vehicles are localized, they may engage in semantic mapping. A swarm of vehicles (1400, 1402) may characterize assets encountered while traversing a local environment. Asset characterizations may be compared to known assets within the locally cached semantic map.

Abstract: The magnetic field in the vicinity of a conductor can be sensed by wire loops to measure the current in the nearby conductor. A sensor in a printed circuit board can detect and measure currents flowing through the PCB itself, yielding a thin, inexpensive, scalable solution for energy monitoring.

Abstract: Methods and apparatus for real time machine vision and point-cloud data analysis are provided, for remote sensing and vehicle control. Point cloud data can be analyzed via scalable, centralized, cloud computing systems for extraction of asset information and generation of semantic maps. Machine learning components can optimize data analysis mechanisms to improve asset and feature extraction from sensor data. Optimized data analysis mechanisms can be downloaded to vehicles for use in on-board systems analyzing vehicle sensor data. Semantic map data can be used locally in vehicles, along with onboard sensors, to derive precise vehicle localization and provide input to vehicle to control systems.