Abstract: Fastener-free deck clips, methods of use, and associated installation tools. The clips use a gripping element to secure the clip to a joist and do not require the use of a separate screw or other fastener, simplifying the installation process. The clip has a central body with lateral wings extending to an endpoint, where the gripping element is located. The gripping element may include puncturing elements to adhere better to the joist. Likewise, the main body may have tabs that can be depressed to further adhere to the joist. The clips may be installed by use of a handheld tool.

Abstract: A mobile edge computing system, a communication network and a method of using a mobile edge computing device. The mobile edge computing system includes a communication module that establishes signal communication over numerous wireless communication protocols at least one of which uses a low power wide area network protocol. A logic device is made up of a distributed set of processing units including both a central processing unit and one or more processors configured for performing machine learning operations the latter of which include one or more of a graphical processor unit and a tensor processing unit. When the system receives event data that has been acquired by one or more of the communication module and a sensor, the system executes a trained machine learning model and conveys, using the low power wide area network protocol, an output that has been produced by the trained machine learning model.

Abstract: Filter assemblies are described. In particular, a filter securement assembly for receiving and retaining a filter media in an exhaust hood includes a main panel defining a first end and an opposed second end, a first retainment surface adjacent the first end of the main panel, a first retainment lip adjacent the first retainment surface, a second retainment surface adjacent the second end of the main panel and a second retainment lip adjacent the second retainment surface.

Abstract: A system for tracking the performance of airborne objects and a method of assessing at least one performance metric of the airborne object. For example, the system is used to improve performance of falcons in completing one or more tasks. The system includes an electronic device that monitors key physiological parameters, such as heart rate, body temperature, and activity levels, to provide real-time feedback to the falconer. The device also includes an accelerometer and gyroscope to track the bird's movements and provide information about its orientation and direction of flight. The system utilizes machine learning algorithms to analyze the collected data and provide recommendations for optimizing the bird's performance, such as adjusting its diet or training regimen. By utilizing this technology, falconers can gain a deeper understanding of their birds' performance and make informed decisions to improve their success in completing tasks, such as hunting and aerial pursuit.

Abstract: A deck clip system, in which a deck clip or series of deck clips are provide for rapid deployment of deck boarding on joists. The deck clips have a base element and engaging elements. The base element can be affixed to a joist and the engaging elements extend from the base vertically to engage planks or deck boarding. The planks may have recesses configured to receive the engaging elements. The clips may be arranged or disposed into a strip for even faster deployment.

Abstract: A mobile edge computing system, a communication network and a method of using a mobile edge computing device. The mobile edge computing system includes a communication module that establishes signal communication over numerous wireless communication protocols at least one of which uses a low power wide area network protocol. A logic device is made up of a distributed set of processing units including both a central processing unit and one or more processors configured for performing machine learning operations the latter of which include one or more of a graphical processor unit and a tensor processing unit. When the system receives event data that has been acquired by one or more of the communication module and a sensor, the system executes a trained machine learning model and conveys, using the low power wide area network protocol, an output that has been produced by the trained machine learning model.

Abstract: A wearable electronic device, a system and methods of monitoring with a wearable electronic device. The device includes a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources, as well as a wireless communication sub-module to selectively transmit an LPWAN signal to provide location information based on the acquired data. The device may also include one or more sensors to collect one or more of environmental data, activity data and physiological data. The device may transmit some or all of its acquired data to a larger system, including a cloud-based server to, in addition to providing location-based data, be used as a part of a predictive health care protocol to correlate changes in acquired data to salient indicators of the health of a wearer of the device. In one form, the predictive health care protocol uses a machine learning model.

Abstract: A wearable electronic device, a system and methods of monitoring with a wearable electronic device. The device includes a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources, as well as a wireless communication sub-module to selectively transmit a cellular-based LPWAN signal to provide location information based on the acquired data. The device may also include sensors to collect one or more of environmental, activity and physiological data. The device may transmit some or all of its acquired data to the system to provide a predictive model to correlate changes in the acquired data to corresponding health, safety or related changes to a wearer of the device. In one form, the predictive health care protocol uses a machine learning model at least some of which may be performed on the device.

Abstract: A wearable electronic device, a system and methods of monitoring with a wearable electronic device. The device includes a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources, as well as a wireless communication sub-module to selectively transmit an LPWAN signal to provide location information based on the acquired data. The device may also include one or more sensors to collect one or more of environmental data, activity data and physiological data. The device may transmit some or all of its acquired data to a larger system, including a cloud-based server to, in addition to providing location-based data, be used as a part of a predictive health care protocol to correlate changes in acquired data to salient indicators of the health of a wearer of the device. In one form, the predictive health care protocol uses a machine learning model.

Abstract: A wearable electronic device, a system and methods of monitoring with a wearable electronic device. The device includes a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources, as well as a wireless communication sub-module to selectively transmit a cellular-based LPWAN signal to provide location information based on the acquired data. The device may also include sensors to collect one or more of environmental, activity and physiological data. The device may transmit some or all of its acquired data to the system to provide a predictive model to correlate changes in the acquired data to corresponding health, safety or related changes to a wearer of the device. In one form, the predictive health care protocol uses a machine learning model at least some of which may be performed on the device.

Abstract: A personal area network that includes a wearable electronic device, a system and methods of using the personal area network that includes a wearable electronic device. The wearable electronic device can act as an aggregator of the data that is being acquired by the one or more sensors and from other devices that are within wireless signal range of the personal area network in order to send some or all of the data over a wireless low power wide area network to remote locations within a larger network for subsequent processing, user notification, analysis of location-determination, contact tracing or the like. Data may flow in a bidirectional manner between the wearable electronic device and at least some of the other devices within the personal area network. In one form, the aggregated data may be used to control access to a hazard-prone environment in order to reduce the likelihood of exposure of a service technician to unsafe conditions within such environment.

Abstract: A personal area network that includes a wearable electronic device, a system and methods of using the personal area network that includes a wearable electronic device. The wearable electronic device can act as an aggregator of the data that is being acquired by the one or more sensors and from other devices that are within wireless signal range of the personal area network in order to send some or all of the data over a wireless low power wide area network to remote locations within a larger network for subsequent processing, user notification, analysis of location-determination, contact tracing or the like. Data may flow in a bidirectional manner between the wearable electronic device and at least some of the other devices within the personal area network. In one form, the aggregated data may be used to provide information related to one or more operational parameters of an industrial asset and, if necessary, take control-based action in order to adjust one or more such operational parameters.

Abstract: A personal area network that includes a wearable electronic device, a system and methods of using the personal area network that includes a wearable electronic device. The wearable electronic device can act as an aggregator of the data that is being acquired by the one or more sensors and from other devices that are within wireless signal range of the personal area network in order to send some or all of the data over a wireless low power wide area network to remote locations within a larger network for subsequent processing, user notification, analysis of location-determination, contact tracing or the like. Data may flow in a bidirectional manner between the wearable electronic device and at least some of the other devices within the personal area network.

Abstract: A wristband locking mechanism for a wearable electronic device, a wristband, a wearable electronic device and a method of securing an article to a person. The wristband locking mechanism includes a clasp assembly with independently-operable spring-biased actuators such that the wristband locking mechanism cannot be unlocked using one hand. The use of sensors, processors, communication equipment and associated components within the wearable electronic device allows a caregiver to monitor one or more of location, environmental, physiological and activity data of a wearer of the device, while the requirement for two-handed operation for unlocking of the wristband locking mechanism provides a deterrent against intentional or unintentional removal of the attached article or wearable electronic device by the wearer.

Abstract: A wearable electronic device, a system and methods of monitoring with a wearable electronic device. The device includes a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources, as well as a wireless communication sub-module to selectively transmit an LPWAN signal to provide location information based on the acquired data. The device may also include one or more sensors to collect one or more of environmental data, activity data and physiological data. The device may transmit some or all of its acquired data to a larger system, including a cloud-based server to, in addition to providing location-based data, be used as a part of a predictive health care protocol to correlate changes in acquired data to salient indicators of the health of a wearer of the device. In one form, the predictive health care protocol uses a machine learning model.

Abstract: A wearable electronic device, a system and methods of monitoring with a wearable electronic device. The device includes a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources, as well as a wireless communication sub-module to selectively transmit an LPWAN signal to provide location information based on the acquired data. The device may also include one or more sensors to collect one or more of environmental data, activity data and physiological data. The device may transmit some or all of its acquired data to a larger system, including a cloud-based server to, in addition to providing location-based data, be used as a part of a predictive health care protocol to correlate changes in acquired data to salient indicators of the health of a wearer of the device. In one form, the predictive health care protocol uses a machine learning model.

Abstract: A wearable electronic device, a system and methods of monitoring with a wearable electronic device. The device includes a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources, as well as a wireless communication sub-module to selectively transmit a cellular-based LPWAN signal to provide location information based on the acquired data. The device may also include sensors to collect one or more of environmental, activity and physiological data. The device may transmit some or all of its acquired data to the system to provide a predictive model to correlate changes in the acquired data to corresponding health, safety or related changes to a wearer of the device. In one form, the predictive health care protocol uses a machine learning model at least some of which may be performed on the device.

Abstract: A wearable electronic device, a system and methods of monitoring with a wearable electronic device. The device includes a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources, as well as a wireless communication sub-module to selectively transmit an LPWAN signal to provide location information based on the acquired data. The device may also include one or more sensors to collect one or more of environmental data, activity data and physiological data. The device may transmit some or all of its acquired data to a larger system, including a cloud-based server to, in addition to providing location-based data, be used as a part of a predictive health care protocol to correlate changes in acquired data to salient indicators of the health of a wearer of the device. In one form, the predictive health care protocol uses a machine learning model.

Abstract: A base station (103) assigns a set of mobile stations (101) to a group wherein the group will share a set of radio resources (770). A shared control channel information element (501) is sent to the group of mobile stations (101) and provides a bitmap having fields for group ordering (511), resource allocations (530), continuation resources (540) for HARQ, and an ordering pattern (513). If a mobile station requires retransmission it will access the resources indicated by the continuation resources field (54) in order to receive data. The HARQ blocks may be assigned to a mobile station based upon an index (601) which may correspond to the mobile station vocoder rate. Further, HARQ subgroups may be defined to associate subgroups of mobile stations with specific HARQ transmission opportunities on the super-frame and allocated by a rotating bitmap.

Abstract: A base station (103) assigns a set of mobile stations (101) to a group wherein the group will share a set of radio resources (770). A shared control channel information element (501) is sent to the group of mobile stations (101) and provides a bitmap having fields for group ordering (511), resource allocations (530), continuation resources (540) for HARQ, and an ordering pattern (513). If a mobile station requires retransmission it will access the resources indicated by the continuation resources field (54) in order to receive data. The HARQ blocks may be assigned to a mobile station based upon an index (601) which may correspond to the mobile station vocoder rate. Further, HARQ subgroups may be defined to associate subgroups of mobile stations with specific HARQ transmission opportunities on the super-frame and allocated by a rotating bitmap.