Abstract: Detecting anomalous behavior of a device, including: generating, using information describing historical activity associated with a user device, a trained model for detecting normal activity for the user device; gathering information describing current activity associated with the user device; and determining, by using the information describing current activity associated with the user device as input to the trained model, whether the user device has deviated from normal activity.

Abstract: A tire pressure monitoring system monitors the pressure in at least one tire supporting a vehicle. The system includes at least one sensor mounted on the tire for measuring a pressure and a temperature of the tire. Transmission means transmit the measured pressure data and temperature data to a processor, and a tire pressure model is executed on the processor. The tire pressure model includes a driving event extractor to extract cold pressure data from the measured pressure data, and a temperature compensator to generate a compensated cold tire pressure from the cold pressure data. A noise filter filters sensor noise and generates a filtered cold tire pressure from the compensated cold tire pressure. A detection module receives the filtered cold tire pressure and determines an air pressure leak rate of the tire, and a leak notification corresponding to the air pressure leak rate is generated.

Abstract: A tire wear state estimation system includes a first sensor unit that is mounted on a tire and includes a footprint centerline length measurement sensor. A second sensor unit is mounted on the tire and includes a shoulder length measurement sensor. At least one of the first sensor unit and the second sensor unit includes a pressure sensor, a temperature sensor, and electronic memory capacity for storing tire identification information. A processor is in electronic communication with the sensor units and receives the measured centerline length, the measured pressure, the measured temperature, the identification information, and the measured shoulder length. The identification information is correlated to tire construction data. An analysis module stored on the processor receives the measured values, the identification information, and the tire construction data as inputs. The analysis module includes a prediction model that generates an estimated wear state for the tire from the inputs.

Abstract: An auto-location system locates a position of a tire. A tire sensor unit is mounted on the tire and measures a length of a footprint of the tire, and electronic memory capacity stores identification information for the tire sensor unit. A vehicle sensor unit is mounted on the vehicle and measures a lateral acceleration and a longitudinal acceleration. A processor is in electronic communication with the tire sensor unit and the vehicle sensor unit, and receives the measured footprint length, the identification information, the lateral acceleration, and the longitudinal acceleration. A virtual footprint length estimator employs the lateral acceleration and the longitudinal acceleration to estimate a virtual footprint length of the tire. A correlation module receives the virtual footprint length and the measured footprint length to generate correlation values. A decision arbitrator applies a set of decision rules to the correlation values to generate a wheel position indication.

Abstract: Elastic privileges in a secure access service edge, including: identifying, based on one or more access policies, an application accessible to a user; determining, for the user, an access pattern of the application; and restricting, without modifying the one or more access policies, access to the application by the user based on the access pattern.

Abstract: Systems, methods and media for adaptive real time modeling and scoring are provided. In one example, a system for automatically generating predictive scoring models comprises a trigger component to determine, based on a threshold or trigger, such as a detection of new significant relationships, whether a predictive scoring model is ready for a refresh or regeneration. An automated modeling sufficiency checker receives and transforms user-selectable system input data. The user-selectable system input data may comprise at least one of email, display or social media traffic. An adaptive modeling engine operably connected to the trigger component and modeling sufficiency checker is configured to monitor and identify a change in the input data and, based on an identified change in the input data, automatically refresh or regenerate the scoring model for calculating new lead scores. A refreshed or regenerated predictive scoring model is output.

Abstract: A logical graph is generated using at least a portion of log data received from a set of agents executing on one or more nodes in one or more data centers. The logical graph is augmented using data obtained from one or more agents executing in containerized environments, including by representing communications between pods within the logical graph. The augmented logical graph is used to detect an anomaly.

Abstract: Detecting anomalous behavior using a browser extension, including: gathering first information describing activity associated with a user and generated by a browser extension on a user device; gathering second information describing activity associated with the user and generated by an application executed on the user device; and determining, based on the first information and the second information, whether the user has deviated from normal activity.

Abstract: An apparatus for wireless communications includes a processor, and a memory coupled with the processor and storing instructions operable, when executed by the processor, to cause the apparatus to output a pre-configured uplink resource (PUR) configuration information comprising a least significant bit (LSB) associated with a hyper system frame number (H-SFN). Execution of the instructions further cause the apparatus to obtain, from a user equipment (UE) at a PUR start time, a first message on a PUR associated with the PUR configuration information, the PUR start time associated with the H-SFN corresponding to the LSB.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may start a timer after a repetition of a communication, where calculation of a length of the timer includes an offset that corresponds to a propagation delay between the UE and a base station via a non-terrestrial network (NTN) entity. The UE may start a discontinuous reception (DRX) retransmission timer after expiration of the timer. Numerous other aspects are described.

Abstract: Embodiments described herein provide a processing apparatus comprising compute circuitry to generate neural network data for a convolutional neural network (CNN) and write the neural network data to a memory buffer. The compute circuitry additionally includes a direct memory access (DMA) controller including a hardware codec having encode circuitry and a decode circuitry. The DMA controller reads the neural network data from the memory buffer, encode the neural network data via the encode circuit, writes encoded neural network data to a memory device coupled with the processing apparatus, writes metadata for the encoded neural network data to the memory device coupled with the processing apparatus, and decodes encoded neural network data via the decode circuit in response to a request from the compute circuitry.

Abstract: Communication, authentication, and/or validation may be required in various types of device interactions. For example, for some types of device functions, user or device authentication may be required to confirm that the function can and should be performed. Authentication information may be communicated and authentication and/or validation may be performed using LoRaWAN signaling. LoRaWAN signaling provides low power and long-range communication capabilities to efficiently and effectively perform authentication processes. In some arrangements, LoRaWAN signals may be generated by both a first device (e.g., a user device) and a second device (e.g., a function-performing device) and subsequently compared to determine whether they correspond. This correspondence may provide the requisite authentication or validation of the first device and/or an associated user.

Abstract: Transactions between entities may be validated using LoRaWAN communications to more quickly process those transactions. In the example of check depositing, a first entity associated with a check payee may transmit a check clearance request to a second entity associated with a check payor using LoRaWAN communications. The LoRaWAN signaling and communications may be performed between devices within LoRaWAN range. For example, a check depositing device may receive a check deposit where the payor is associated with a different financial institution or entity. Accordingly, the check depositing device may communicate with another device associated with the payor institution or entity using LoRaWAN signals to confirm check clearance.

Abstract: Device information may be obtained using LoRaWAN communications. A device information request may be generated and transmitted by a user device and include request parameters such as a device type for which information is desired, a type of information requested, a geographic range for the request, and the like and/or combinations thereof. Device information requests may also be propagated or repeated by intermediary devices to achieve the desired request range. Accordingly, a recipient device of the original request may propagate or repeat, using LoRaWAN, a request signal if it determines that the request range has not been met. If the request range has been met, the recipient device might not repeat the request.

Abstract: Transactions may be performed using a mobile device such as a user-wearable device using LoRaWAN protocol to minimize power consumption while maximizing communication range. Transaction information may be stored in the user-wearable device and transmitted to a transaction processing device using LoRaWAN communications. Transaction information may include user-specific information, payment information, merchant information, product information, user preference information and the like. In some instances, the only communication interface (or wireless communication interface) provided in the mobile device is a LoRaWAN communication interface.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may determine a position (e.g., a global navigation satellite system (GNSS)) position of the UE based on a relative position of the UE to another UE. For example, a first UE may determine (e.g., calculate, receive) a relative position of the first UE to a second UE that is selected from a set of UEs including the first UE and the second UE to perform a position acquisition procedure to determine a position of the second UE. The first UE may determine (e.g., calculate, receive) its position based on the relative position of the first UE to the second UE and the position of the second UE and may reset a timer that indicates for the first UE to perform a position acquisition procedure upon expiration of the timer.

Abstract: Generating user-specific polygraphs for network activity, including: gathering information describing network activity associated with a user and generating, based on the information, a user-specific polygraph that includes one or more destinations associated with the network activity.

Abstract: Detecting deviations from typical user behavior, including: identifying a geographic location of a device that is associated with a user; determining device activity associated with the user; and detecting, based on a profile associated with the user, that the device activity associated with the user deviates from normal activity for the user.

Abstract: A method, comprising: measuring a rotational speed of a wheel of a vehicle, determining a longitudinal acceleration of the vehicle relative to a plurality of ambient terrestrial objects using a measurement system onboard the vehicle, determining a longitudinal speed of the vehicle relative to the plurality of ambient terrestrial objects using the measurement system, judging a slippage state of the vehicle using the longitudinal acceleration, and if a result of the judging indicates the vehicle is in a low-slippage state, estimating a coefficient of traction using the rotational speed and the longitudinal speed, wherein the measurement system is structured to radiate electromagnetic energy and to receive reflections of the electromagnetic energy reflected from the plurality of ambient terrestrial objects.

Abstract: Detecting anomalous behavior of a device, including: generating, using information describing historical activity associated with a user device, a trained model for detecting normal activity for the user device; gathering information describing current activity associated with the user device; and determining, by using the information describing current activity associated with the user device as input to the trained model, whether the user device has deviated from normal activity.