Abstract: A tracking system for a food commodity supply chain includes a tracking device and a computing device. The tracking device is mounted to a conveyance structure that is configured to receive a unit load of a food commodity. The tracking device includes a sensor to track an environmental condition of an environment of the tracking device while the tracking device is traveling along the food commodity supply chain. The computing device is configured to receive an environmental value of the environmental condition sensed by the sensor, process the environmental value to determine whether the environmental condition is within a predetermined environmental range, and transmit an alert when the environmental condition falls outside the predetermined environmental range. The alert includes a suggested interventive action based on the environmental condition that falls outside the predetermined environmental range.

Abstract: A satellite is provided, including an onboard computing device. The onboard computing device may include a processor configured to receive training data while the satellite is in orbit. The processor may be further configured to perform training at a machine learning model based at least in part on the training data. The processor may be further configured to generate model update data that specifies a modification made to the machine learning model during the training. The processor may be further configured to transmit the model update data from the satellite to an additional computing device.

Abstract: The techniques disclosed herein enable systems to enable multi-market optimization of renewable energies using data-driven models. To achieve this, a model retrieves a current state from a resource generation system and associated resource markets. The model can then compute a policy based on the state as well physical and technical constraints. The policy defines various actions that direct operation of the resource generation system such as resource production and dispatch to markets. Applying the policy to the resource generation results in a modified state which the model extracts along with a measure of optimality which quantifies the success of the policy. Based on these metrics, the model can generate an updated iteration of the policy defining a different set of actions. In this way, the model can gradually develop an optimal policy for controlling the resource generation system.

Abstract: A computing system including an edge computing device. The edge computing device may include an edge device processor configured to receive edge device contextual data including computing resource availability data. Based at least in part on the edge device contextual data, the edge device processor may select a processing stage machine learning model of a plurality of processing stage machine learning models and construct a runtime processing pipeline of one or more runtime processing stages including the processing stage machine learning model. The edge device processor may receive a runtime input, and, at the runtime processing pipeline, generate a runtime output based at least in part on the runtime input. The edge device processor may generate runtime pipeline metadata that indicates the one or more runtime processing stages included in the runtime processing pipeline. The edge device processor may output the runtime output and the runtime pipeline metadata.

Abstract: A traceability system for a bulk commodity supply chain is provided. The system includes a tracking device, a location determination subsystem, and at least one computing device having at least one processor. The location determination subsystem is configured to determine positional information of the tracking device while placed in a bulk commodity traveling along the bulk commodity supply chain. The processor receives the positional information from the location subsystem, extracts positional values from the positional information, and processes the positional values to identify motion primitives. A modeling tool is applied to the identified motion primitives to produce a positional path of the tracking device, which is output, for example, via a user interface. The positional path represents travel of the bulk commodity along the supply chain.

Abstract: A computing device is provided, including a processor configured to receive imaging relevance data for a geographic area. The processor may be further configured to generate, based at least in part on the imaging relevance data, image mask instructions specifying a region of interest included in the geographic area. The processor may be further configured to transmit the image mask instructions to a satellite. The processor may be further configured to receive, from the satellite, filtered satellite image data of the region of interest. One or more deprioritized regions of the geographic area outside the region of interest may be excluded from the filtered satellite image data.

Abstract: The disclosure described herein configures a client device for communication using dynamic spectrum access within a frequency spectrum, such as television white space (TVWS), using a determined location of the client device based on location information, such as from a global positioning system. A dynamic spectrum access database of channels is accessed based on the location information. Available channels are determined for the client device from the channels based on the location information. A list of the available channels for use by the client device are transmitted to the client device, thereby allowing narrowband communication over the channels.

Abstract: A wireless networking system is provided. The wireless networking system includes a base station device including processing circuitry configured to detect a transmission rate from a portion of a preamble of an incoming packet transmission signal and adapt a radio configuration to receive a remainder of the incoming packet transmission signal at the transmission rate.

Abstract: A device and method for managing communication frequencies, including generating a geographical grid with a plurality of cells and allocating communication frequencies to the cells. Each of the communication frequencies is used for communication between a satellite and a ground device or ground IoT modem. A communication frequency allocated to one cell is different from the communication frequencies allocated to each of the immediately surrounding cells. A set of communication frequencies is selected for communication between the satellite and ground devices based on a ground track of the satellite. The selected set is transmitted to the satellite to communicate with the ground station located in a cell with the communication frequency allocated to that cell.

Abstract: The disclosure herein describes using satellites and ground sinks and/or stations for routing IoT device data packets from IoT devices. A target ground sink in range of the satellite is identified and an expected reception (ER) score for the target ground sink is calculated based on ER parameter data and location data of the satellite. A data packet in a first level of a multi-level data structure of the satellite is sent to the target ground sink and, based on an ER threshold exceeding the ER score, the packet is moved to a second level of the multi-level data structure, whereby the data packet is queued to be sent to another ground sink. The disclosure further includes using cell towers as ground sinks and/or using them for backhauling with other ground sinks. The flexibility of the disclosure enables large ground sink networks to be established, reducing latency of packet routing.

Abstract: A computing device has an energy storage device system with multiple energy storage devices. Various different criteria are used to determine which one or more of the multiple energy storage devices to charge or discharge at any given time to provide power to the computing device. The criteria can include characteristics of the energy storage devices as well as hardware and/or physical characteristics of the computing device, characteristics of the energy storage devices and/or the computing device that change while the computing device operates, and predicted behavior or usage of the computing device. These criteria are evaluated during operation of the computing device, and the appropriate energy storage device(s) from which to draw power or to charge at any given time based on these criteria are determined.

Abstract: A computing device (such as a computer gaming console) uses only a single radio to concurrently communicate with a wireless network access point and wireless client devices such as game controllers or peripherals. To establish and maintain both a high-throughput link with the access point, and a low-latency link with the client device(s), the single Wi-Fi radio of the computing device is configured to periodically switch between a channel used for the high-throughput link and a different channel that is used for the low-latency link—thus implementing a combination of frequency division multiplexing (FDM) and time division multiplexing (TDM). The console may use aspects of the Wi-Fi protocol standard to ensure that periodically switching its single radio between the two channels is accomplished while maintaining reliable communication on both channels.

Abstract: Synthetic molecular tags are placed on an item at various points in a supply chain to create a molecular record of movement through the supply chain. Associations between each unique synthetic molecular tag and individual locations in the supply chain are stored in an electronic record which may be maintained in the cloud. The synthetic molecular tags are collected from the item and sequenced to determine movement of the item through the supply chain by reference to the electronic record. The synthetic molecular tags can be used for identifying recalled items based on locations in the supply chain associated with a recall. The synthetic molecular tags may be polynucleotides such as deoxyribose nucleic acid (DNA). The item may be any type of item including food.

Abstract: A computing system including a processor configured to receive training data including, for each of a plurality of training timesteps, training forecast states associated with respective training-phase agents included in a training supply chain graph. The processor may train a reinforcement learning simulation of the training supply chain graph using the training data via policy gradient reinforcement learning. At each training timestep, the training forecast states may be shared between simulations of the training-phase agents during training. The processor may receive runtime forecast states associated with respective runtime agents included in a runtime supply chain graph. For a runtime agent, at the trained reinforcement learning simulation, the processor may generate a respective runtime action output associated with a corresponding runtime forecast state of the runtime agent based at least in part on the runtime forecast states. The processor may output the runtime action output.

Abstract: The disclosure described herein configures a client device for communication using dynamic spectrum access within a frequency spectrum, such as television white space (TVWS), using a determined location of the client device based on location information, such as from a global positioning system. A dynamic spectrum access database of channels is accessed based on the location information. Available channels are determined for the client device from the channels based on the location information. A list of the available channels for use by the client device are transmitted to the client device, thereby allowing narrowband communication over the channels.

Abstract: Examples disclosed herein are related to using a machine learning model to generate image data. One example provides a system, comprising one or more processors, and storage comprising instructions executable by the one or more processors to obtain image data comprising an image with unoccluded features, apply a mask to the unoccluded features in the image to form partial observation training data comprising a masked region that obscures at least a portion of the unoccluded features, and train a machine learning model comprising a generator and a discriminator at least in part by generating image data for the masked region and comparing the image data generated for the masked region to the image with unoccluded features.

Abstract: A computing system configured to execute a predictive program is provided. The predictive program, in a run-time phase, receives a current value for a remotely sourced forecast as run-time input into an artificial intelligence model. The artificial intelligence model has been trained on training data including a time series of locally sourced measurements for a parameter and a time series of remotely sourced forecast data for the parameter. The predictive program outputs a predicted forecast offset between the current value of a remotely sourced forecast and a future locally sourced measurement for the parameter. The predictive program outputs from the artificial intelligence model a predicted forecast offset based on the run-time input.

Abstract: A deployment of sensors transmit radio frequency (RF) signals into an area of interest. The radar maps are generated from the reflected signals, including a static radar map and a dynamic radar map. Multipath and radar sidelobes are removed from the radar maps using a neural network to produce a density map. The neural network can be trained in two phases: a training phase that uses training data from a training site and a transfer learning phase that uses training data from the area of interest.

Abstract: The disclosure herein describes transmitting data from a satellite using a primary ground station and a set of secondary ground stations. An orbit of the satellite is determined over a schedule period and a subset of secondary ground stations is identified based on the determined orbit of the satellite, wherein secondary ground stations are configured to receive from the satellite and not transmit to the satellite. A transmission schedule associated with the satellite is then generated. For each secondary ground station of the subset, a time interval during which the satellite is within communication range is determined, an expected transmission rate is estimated, and the time interval and the expected transmission rate are included in the transmission schedule. The transmission schedule is provided to the satellite via the primary ground station, whereby the satellite is configured to transmit data to the subset of ground stations based on the transmission schedule.

Abstract: A computer system that includes a plurality of compute clusters that are located at different geographical locations. Each compute cluster is powered by a local energy source at a geographical location of that compute cluster. Each local energy source has a pattern of energy supply that is variable over time based on an environmental factor. The computer system further includes a server system that executes a global scheduler that distributes virtual machines that perform compute tasks for server-executed software programs to the plurality of compute clusters of the distributed compute platform. To distribute virtual machines for a target server-executed software program, the global scheduler is configured to select a subset of compute clusters that have different complementary patterns of energy supply such that the subset of compute clusters aggregately provide a target compute resource availability for virtual machines for the target server-executed software program.