Abstract: Some embodiments provide a novel method for deploying containerized applications. The method of some embodiments deploys a data collecting agent on a machine that operates on a host computer and executes a set of one or more workload applications. From this agent, the method receives data regarding consumption of a set of resources allocated to the machine by the set of workload applications. The method assesses excess capacity of the set of resources for use to execute a set of one or more containers, and then deploys the set of one or more containers on the machine to execute one or more containerized applications. In some embodiments, the set of workload applications are legacy workloads deployed on the machine before the installation of the data collecting agent. By deploying one or more containers on the machine, the method of some embodiments maximizes the usages of the machine, which was previously deployed to execute legacy non-containerized workloads.

Abstract: Some embodiments provide a novel method for deploying containerized applications. The method of some embodiments deploys a data collecting agent on a machine that operates on a host computer and executes a set of one or more workload applications. From this agent, the method receives data regarding consumption of a set of resources allocated to the machine by the set of workload applications. The method assesses excess capacity of the set of resources for use to execute a set of one or more containers, and then deploys the set of one or more containers on the machine to execute one or more containerized applications. In some embodiments, the set of workload applications are legacy workloads deployed on the machine before the installation of the data collecting agent. By deploying one or more containers on the machine, the method of some embodiments maximizes the usages of the machine, which was previously deployed to execute legacy non-containerized workloads.

Abstract: Systems and methods for automated accident analysis such as automatic multi-device accident detection and verification, automated multi-device information capture, and automated geospatial and geo-relational digital scene recreation.

Abstract: Systems and/or devices for efficient and intuitive methods for implementing artificial neural networks specifically designed for parallel AI processing are provided herein. In various implementations, the disclosed systems, devices, and methods complement or replace conventional systems, devices, and methods for parallel neural processing that (a) greatly reduce neural processing time necessary to process more complex problem sets; (b) implement neuroplasticity necessary for self-learning; and (c) introduce the concept and application of implicit memory, in addition to explicit memory, necessary to imbue an element of intuition. With these properties, implementations of the disclosed invention make it possible to emulate human consciousness or awareness.

Abstract: A system includes Referential Global Positioning System (RGPS) base stations, servers and mobile devices. Each RGPS station includes: (i) global navigation satellite system (GNSS) receivers and antennas for receiving GNSS geolocation data, Cellular Location Technology (CLT) geolocation data, Wi-Fi Positioning System (WPS) geolocation data; (ii) a data processor for positioning error correction, and signal processing; and (iii) a transmitter for transmitting the error corrections and processed GPS data to the servers. The servers (i) receive, aggregate, and store, the GNSS, CLT, and Wi-Fi error corrections and processed GPS data, and (ii) transmit the location-specific GPS data to any mobile device in proximity. Each mobile device calculates a calculate a highly accurate location of the mobile device by obtaining and combining the location-specific GPS data from a RGPS server, and device-specific geolocation data from a GNSS sensor, a CLT sensor and a WPS sensor, on the mobile device.

Abstract: Systems and methods for automated accident analysis such as automatic multi-device accident detection and verification, automated multi-device information capture, and automated geospatial and geo-relational digital scene recreation.

Abstract: A system includes a microcontroller, one or more sensors affixed to an object, and memory storing one or more programs including instructions for receiving and storing first sensor data from the one or more sensors in response to motion of the object, determining whether the first sensor data meets a first threshold, in accordance with a determination that the first sensor data meets the first threshold: receiving and storing second sensor data from the one or more sensors in response to subsequent motion of the object for as long as the second sensor data meets a second threshold, performing pattern recognition on the second sensor data, and identifying a first position of the object based on the pattern recognition of the second sensor data.

Abstract: Systems and methods for automated accident analysis such as automatic multi-device accident detection and verification, automated multi-device information capture, and automated geospatial and geo-relational digital scene recreation.

Abstract: Some embodiments provide a novel method for deploying containerized applications. The method of some embodiments deploys a data collecting agent on a machine that operates on a host computer and executes a set of one or more workload applications. From this agent, the method receives data regarding consumption of a set of resources allocated to the machine by the set of workload applications. The method assesses excess capacity of the set of resources for use to execute a set of one or more containers, and then deploys the set of one or more containers on the machine to execute one or more containerized applications. In some embodiments, the set of workload applications are legacy workloads deployed on the machine before the installation of the data collecting agent. By deploying one or more containers on the machine, the method of some embodiments maximizes the usages of the machine, which was previously deployed to execute legacy non-containerized workloads.

Abstract: Some embodiments provide a novel method for deploying containerized applications. The method of some embodiments deploys a data collecting agent on a machine that operates on a host computer and executes a set of one or more workload applications. From this agent, the method receives data regarding consumption of a set of resources allocated to the machine by the set of workload applications. The method assesses excess capacity of the set of resources for use to execute a set of one or more containers, and then deploys the set of one or more containers on the machine to execute one or more containerized applications. In some embodiments, the set of workload applications are legacy workloads deployed on the machine before the installation of the data collecting agent. By deploying one or more containers on the machine, the method of some embodiments maximizes the usages of the machine, which was previously deployed to execute legacy non-containerized workloads.

Abstract: A method of determining locations of devices in a peer-to-peer network includes, at a first device, performing an intrinsic location measurement using a plurality of inertial measurement units of the first device and broadcasting to other devices first location information of the first device based on the intrinsic location measurement. The method further includes, at a second device, receiving the first location measurement information of the first device from the first device, receiving second location measurement information of the first device from a third device, and comparing the first and second location information of the first device. Based on whether the first and second location information match, the method includes, at the second device, adjusting a trust factor for the first device and broadcasting the adjusted trust factor and consensus location information of the first device.

Abstract: Systems and/or devices for efficient and intuitive methods for implementing artificial neural networks specifically designed for parallel AI processing are provided herein. In various implementations, the disclosed systems, devices, and methods complement or replace conventional systems, devices, and methods for parallel neural processing that (a) greatly reduce neural processing time necessary to process more complex problem sets; (b) implement neuroplasticity necessary for self-learning; and (c) introduce the concept and application of implicit memory, in addition to explicit memory, necessary to imbue an element of intuition. With these properties, implementations of the disclosed invention make it possible to emulate human consciousness or awareness.

Abstract: A method is provided for calculating a position and/or orientation of a first object relative to a second object. The method includes receiving a first object initial absolute position. The method includes sensing, using a first IMUs, motion of the first object and generating sensed motion data of the first object. The method includes generating, using the controller, a motion signal representative of the motion of the first object. The method includes calculating, using the controller, a first object current absolute position using the motion signal and the first object initial absolute position. The method includes receiving, from the second object, a second object current absolute position calculated using a second IMUs associated with the second object. The method includes calculating a relative position and/or orientation of the first object relative to the second object using the first object current absolute position and the second object current absolute position.

Abstract: A system includes a microcontroller, one or more sensors affixed to an object, and memory storing one or more programs including instructions for receiving and storing first sensor data from the one or more sensors in response to motion of the object, determining whether the first sensor data meets a first threshold, in accordance with a determination that the first sensor data meets the first threshold: receiving and storing second sensor data from the one or more sensors in response to subsequent motion of the object for as long as the second sensor data meets a second threshold, performing pattern recognition on the second sensor data, and identifying a first position of the object based on the pattern recognition of the second sensor data.

Abstract: Systems and/or devices for efficient and intuitive methods for implementing artificial neural networks specifically designed for parallel AI processing are provided herein. In various implementations, the disclosed systems, devices, and methods complement or replace conventional systems, devices, and methods for parallel neural processing that (a) greatly reduce neural processing time necessary to process more complex problem sets; (b) implement neuroplasticity necessary for self-learning; and (c) introduce the concept and application of implicit memory, in addition to explicit memory, necessary to imbue an element of intuition. With these properties, implementations of the disclosed invention make it possible to emulate human consciousness or awareness.

Abstract: Systems and/or devices for implementing a tracking device for tracking a position/location and orientation of an object are provided herein. The device comprises one or more sides that define a predetermined shape, and a plurality of inertial measurement units (IMU) mounted to the one or more sides of the predetermined shape. Each IMU is configured to detect movement of the object and generate inertial output data representing a position and/or orientation of the object. Each IMU includes a first sub-sensor and a second sub-sensor. Each IMU is positioned at a predetermined distance and orientation relative to a center point of the tracking device.

Abstract: A system includes a microcontroller, one or more sensors affixed to an object, and memory storing one or more programs including instructions for receiving and storing first sensor data from the one or more sensors in response to motion of the object, determining whether the first sensor data meets a first threshold, in accordance with a determination that the first sensor data meets the first threshold: receiving and storing second sensor data from the one or more sensors in response to subsequent motion of the object for as long as the second sensor data meets a second threshold, performing pattern recognition on the second sensor data, and identifying a first position of the object based on the pattern recognition of the second sensor data.

Abstract: Systems and/or devices for implementing a tracking device for tracking a position/location and orientation of an object are provided herein. The device comprises one or more sides that define a predetermined shape, and a plurality of inertial measurement units (IMU) mounted to the one or more sides of the predetermined shape. Each IMU is configured to detect movement of the object and generate inertial output data representing a position and/or orientation of the object. Each IMU includes a first sub-sensor and a second sub-sensor. Each IMU is positioned at a predetermined distance and orientation relative to a center point of the tracking device.

Abstract: A system includes a microcontroller, one or more sensors affixed to an object, and memory storing one or more programs including instructions for receiving and storing first sensor data from the one or more sensors in response to motion of the object, determining whether the first sensor data meets a first threshold, in accordance with a determination that the first sensor data meets the first threshold: receiving and storing second sensor data from the one or more sensors in response to subsequent motion of the object for as long as the second sensor data meets a second threshold, performing pattern recognition on the second sensor data, and identifying a first position of the object based on the pattern recognition of the second sensor data.

Abstract: A system includes a microcontroller, one or more sensors affixed to an object, and memory storing one or more programs including instructions for receiving and storing first sensor data from the one or more sensors in response to motion of the object, determining whether the first sensor data meets a first threshold, in accordance with a determination that the first sensor data meets the first threshold: receiving and storing second sensor data from the one or more sensors in response to subsequent motion of the object for as long as the second sensor data meets a second threshold, performing pattern recognition on the second sensor data, and identifying a first position of the object based on the pattern recognition of the second sensor data.