Abstract: A vehicle device may execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. The vehicle device may further pass the input to a backend server for further analysis and the backend server can detect safety events based on the input. The vehicle device may analyze the output of the vehicle device and the output of the backend server to determine whether the output of the vehicle device is correct. If the output of the vehicle device is incorrect, the vehicle device can adjust how the vehicle device identifies safety events.

Abstract: Methods and systems for offload of data from a wireless sensing device to a gateway device. A certificate that is generated by the management server in response to a determination that the gateway device is associated with a wireless sensing device is received during an initial connection with the management server. In response to confirming, based on the certificate, that the gateway device is authorized to connect to the wireless sensing device, the certificate is transmitted to the wireless sensing device; and data is received from the wireless sensing device in response to confirming that the wireless sensing device is authorized to connect with the gateway device based on the certificate.

Abstract: Techniques for managing access control policies are described herein. According to one embodiment, access control policies (ACPs) and access control rules (ACRs) are downloaded from a management server to a network access device (NAD) over the Internet, where the network access device is one of a plurality of network access devices managed by the management server over the Internet. In response to a request from a network client device for entering a network, a device type of the network client device is detected and an ACP identifier is determined based on the device type using the ACRs An ACP is selected from the ACPs based on the ACP identifier and enforced against the network client device. At least the selected ACP is reported to the management server to distribute the selected ACP to other network access devices.

Abstract: A vehicle device may execute one or more neural networks (and/or other artificial intelligence), based on input from one or more of the cameras and/or other sensors, to intelligently detect safety events in real-time. The one or more neural networks may be an ensemble neural network that includes neural networks for detecting a head and hand of a user, neural networks for detecting hand actions of the user, neural networks for detecting the head pose of the user, neural networks for predicting an occurrence of an event, and neural networks for predicting a start time and end time of the event. Further, the neural networks can be segmented into a modular neural network based on metadata. The segmentation of the neural network can define a thin layer of the modular neural network to enable independent tuning of the thin layer of the modular neural network.

Abstract: A vehicle device may execute one or more neural networks (and/or other artificial intelligence), based on input from one or more of the cameras and/or other sensors, to intelligently detect safety events in real-time. The one or more neural networks may be an ensemble neural network that includes neural networks for detecting a head and hand of a user, neural networks for detecting hand actions of the user, neural networks for detecting the head pose of the user, neural networks for predicting an occurrence of an event, and neural networks for predicting a start time and end time of the event. Further, the neural networks can be segmented into a modular neural network based on metadata. The segmentation of the neural network can define a thin layer of the modular neural network to enable independent tuning of the thin layer of the modular neural network.

Abstract: A vehicle device may execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. The vehicle device may further pass the input to a backend server for further analysis and the backend server can detect safety events based on the input. The vehicle device may analyze the output of the vehicle device and the output of the backend server to determine whether the output of the vehicle device is correct. If the output of the vehicle device is incorrect, the vehicle device can adjust how the vehicle device identifies safety events.

Abstract: Machine vision devices may be configured to automatically connect to a remote management server (e.g., a “cloud”-based management server), and may offload and/or communicate images and analyses to the remote management server via wired or wireless communications. The machine vision devices may further communicate with the management server, user computing devices, and/or human machine interface devices, e.g., to provide remote access to the machine vision device, provide real-time information from the machine vision device, receive configurations/updates, provide interactive graphical user interfaces, and/or the like.

Abstract: Controller devices may be configured to automatically connect to a remote management server (e.g., a “cloud”-based management server), and may offload received data and analyses to the remote management server via wired or wireless communications. The controller devices may further communicate with the management server, user computing devices, and/or human machine interface devices, e.g., to provide remote access to the controller device, provide real-time information from the controller device, receive configurations/updates, provide interactive graphical user interfaces, and/or the like.

Abstract: A vehicle device may execute one or more neural networks (and/or other artificial intelligence), based on input from one or more of the cameras and/or other sensors, to intelligently detect safety events in real-time. The one or more neural networks may be an ensemble neural network that includes neural networks for detecting a head and hand of a user, neural networks for detecting hand actions of the user, neural networks for detecting the head pose of the user, neural networks for predicting an occurrence of an event, and neural networks for predicting a start time and end time of the event. Further, the neural networks can be segmented into a modular neural network based on metadata. The segmentation of the neural network can define a thin layer of the modular neural network to enable independent tuning of the thin layer of the modular neural network.

Abstract: A vehicle device may execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. The vehicle device may further pass the input to a backend server for further analysis and the backend server can detect safety events based on the input. The vehicle device may analyze the output of the vehicle device and the output of the backend server to determine whether the output of the vehicle device is correct. If the output of the vehicle device is incorrect, the vehicle device can adjust how the vehicle device identifies safety events.

Abstract: A vehicle device may execute one or more neural networks (and/or other artificial intelligence), based on input from one or more of the cameras and/or other sensors, to intelligently detect safety events in real-time. The one or more neural networks may be an ensemble neural network that includes neural networks for detecting a head and hand of a user, neural networks for detecting hand actions of the user, neural networks for detecting the head pose of the user, neural networks for predicting an occurrence of an event, and neural networks for predicting a start time and end time of the event. Further, the neural networks can be segmented into a modular neural network based on metadata. The segmentation of the neural network can define a thin layer of the modular neural network to enable independent tuning of the thin layer of the modular neural network.

Abstract: A method and a gateway system for enabling adaptive power management. The gateway system is powered by a rechargeable battery that is coupled with a solar power source. A location reading indicating a location of the gateway system is transmitted at a first time. A solar profile is received from a management server. The solar profile indicates a measure of power expected to be generated at the location during an interval of time that occurs after the first time by the solar power source. The gateway system determines based on a current battery level of the rechargeable battery and the solar profile an optimal power usage plan for the gateway system and operates according to the optimal power usage plan during the interval of time.

Abstract: Controller devices may be configured to automatically connect to a remote management server (e.g., a “cloud”-based management server), and may offload received data and analyses to the remote management server via wired or wireless communications. The controller devices may further communicate with the management server, user computing devices, and/or human machine interface devices, e.g., to provide remote access to the controller device, provide real-time information from the controller device, receive configurations/updates, provide interactive graphical user interfaces, and/or the like.

Abstract: Techniques for managing access control policies are described herein. According to one embodiment, access control policies (ACPs) and access control rules (ACRs) are downloaded from a management server to a network access device (NAD) over the Internet, where the network access device is one of a plurality of network access devices managed by the management server over the Internet. In response to a request from a network client device for entering a network, a device type of the network client device is detected and an ACP identifier is determined based on the device type using the ACRs An ACP is selected from the ACPs based on the ACP identifier and enforced against the network client device. At least the selected ACP is reported to the management server to distribute the selected ACP to other network access devices.

Abstract: Machine vision devices may be configured to automatically connect to a remote management server (e.g., a “cloud”-based management server), and may offload and/or communicate images and analyses to the remote management server via wired or wireless communications. The machine vision devices may further communicate with the management server, user computing devices, and/or human machine interface devices, e.g., to provide remote access to the machine vision device, provide real-time information from the machine vision device, receive configurations/updates, provide interactive graphical user interfaces, and/or the like.

Abstract: A method and a gateway system for enabling adaptive power management. The gateway system is powered by a rechargeable battery that is coupled with a solar power source. A location reading indicating a location of the gateway system is transmitted at a first time. A solar profile is received from a management server. The solar profile indicates a measure of power expected to be generated at the location during an interval of time that occurs after the first time by the solar power source. The gateway system determines based on a current battery level of the rechargeable battery and the solar profile an optimal power usage plan for the gateway system and operates according to the optimal power usage plan during the interval of time.

Abstract: Techniques for managing access control policies are described herein. According to one embodiment, access control policies (ACPs) and access control rules (ACRs) are downloaded from a management server to a network access device (NAD) over the Internet, where the network access device is one of a plurality of network access devices managed by the management server over the Internet. In response to a request from a network client device for entering a network, a device type of the network client device is detected and an ACP identifier is determined based on the device type using the ACRs An ACP is selected from the ACPs based on the ACP identifier and enforced against the network client device. At least the selected ACP is reported to the management server to distribute the selected ACP to other network access devices.

Abstract: A set of certificates are received at a gateway device from a management server, where each one of the certificates was generated by the management server upon determination that the gateway device is associated with a respective wireless sensing device (WSD). The gateway device receives from a first WSD an advertisement message indicating it is available for connecting to a gateway device. In response to confirming based on a first certificate of the set of certificates associated with the first WSD, that it is authorized to connect to the WSD, the gateway device transmits to the first WSD the first certificate and an identifier of the gateway device for enabling authentication of the gateway device at the WSD. The gateway device receives data from the first WSD, upon confirmation at the WSD that it is authorized to connect with the gateway device.

Abstract: A method and a gateway system for enabling adaptive power management. The gateway system is powered by a rechargeable battery that is coupled with a solar power source. A location reading indicating a location of the gateway system is transmitted at a first time. A solar profile is received from a management server. The solar profile indicates a measure of power expected to be generated at the location during an interval of time that occurs after the first time by the solar power source. The gateway system determines based on a current battery level of the rechargeable battery and the solar profile an optimal power usage plan for the gateway system and operates according to the optimal power usage plan during the interval of time.

Abstract: Techniques for managing access control policies are described herein. According to one embodiment, access control policies (ACPs) and access control rules (ACRs) are downloaded from a management server to a network access device (NAD) over the Internet, where the network access device is one of a plurality of network access devices managed by the management server over the Internet. In response to a request from a network client device for entering a network, a device type of the network client device is detected and an ACP identifier is determined based on the device type using the ACRs An ACP is selected from the ACPs based on the ACP identifier and enforced against the network client device. At least the selected ACP is reported to the management server to distribute the selected ACP to other network access devices.