Abstract: The present disclosure generally relates select data (e.g., sensor data and/or depth values generated using sensor data). In some examples, techniques for determining whether to use sensor fusion are provided. In some examples, techniques for determining whether to use a voting mechanism are provided. In some examples, techniques for responding to different types of decisions are provided.

Abstract: The present disclosure generally relates select data (e.g., sensor data and/or depth values generated using sensor data). In some examples, techniques for determining whether to use sensor fusion are provided. In some examples, techniques for determining whether to use a voting mechanism are provided. In some examples, techniques for responding to different types of decisions are provided.

Abstract: In an embodiment, an automation system for a vehicle may employ a variety of diversities to enhance reliability, accuracy, and stability in automating operation of the vehicle. For example, in an embodiment, an automation system for a vehicle may include multiple sensor pods with overlapping fields of view. Each sensor pod may include multiple different sensors in an embodiment, providing diverse views of the environment surrounding the vehicle. A set of sensor pods with overlapping fields of view may also transmit their object data at different points in time, providing diversity in time. Redundancy in other areas, such as the network switches which connect the sensor pods to an automation controller, may also aid in provided fail operational functionality. In an embodiment, the sensor pods may include local processing to process the data captured by the sensors into object identification.

Abstract: Embodiments relate to a neural processor circuit with scalable architecture for instantiating one or more neural networks. The neural processor circuit includes a data buffer coupled to a memory external to the neural processor circuit, and a plurality of neural engine circuits. To execute tasks that instantiate the neural networks, each neural engine circuit generates output data using input data and kernel coefficients. A neural processor circuit may include multiple neural engine circuits that are selectively activated or deactivated according to configuration data of the tasks. Furthermore, an electronic device may include multiple neural processor circuits that are selectively activated or deactivated to execute the tasks.

Abstract: Fault tolerance for an automation controller for a machine is provided. A first portion of phases of the automation controller may be processed with fail operational protection, in which a failure of one of the computers used for the first portion still permits full operational functionality in the machine. The remaining portion of the phases are processed with fail degraded protection, in which a failure of a computer used for the remaining portion permits continued operation but with one or more constraints, as compared to the fail operational portions.

Abstract: Embodiments relate to a neural processor circuit with scalable architecture for instantiating one or more neural networks. The neural processor circuit includes a data buffer coupled to a memory external to the neural processor circuit, and a plurality of neural engine circuits. To execute tasks that instantiate the neural networks, each neural engine circuit generates output data using input data and kernel coefficients. A neural processor circuit may include multiple neural engine circuits that are selectively activated or deactivated according to configuration data of the tasks. Furthermore, an electronic device may include multiple neural processor circuits that are selectively activated or deactivated to execute the tasks.

Abstract: In an embodiment, an automation controller periodically generates stop trajectories and controls actuators to follow the stop trajectories. As long as new stop trajectories continue to be generated, the automation controller may follow a destination trajectory that is formed from the first portion of each stop trajectory. If stop trajectories are not generated for a period of time (e.g., due to failure in one or more computers generating the stop trajectories), the automation controller may continue to follow the most recent stop trajectory and bring the mobile machine to a stop.

Abstract: In an embodiment, an automation system for a vehicle may employ a variety of diversities to enhance reliability, accuracy, and stability in automating operation of the vehicle. For example, in an embodiment, an automation system for a vehicle may include multiple sensor pods with overlapping fields of view. Each sensor pod may include multiple different sensors in an embodiment, providing diverse views of the environment surrounding the vehicle. A set of sensor pods with overlapping fields of view may also transmit their object data at different points in time, providing diversity in time. Redundancy in other areas, such as the network switches which connect the sensor pods to an automation controller, may also aid in provided fail operational functionality. In an embodiment, the sensor pods may include local processing to process the data captured by the sensors into object identification.

Abstract: Data generated by one or more data producers may be transmitted via multiple communication paths according to a path transmission scheme that divides transmission of different portions of the data amongst different communication paths. Upon a failure of a communication path, transmission of data may continue for those portions of data that are not assigned to the failed communication path. In some embodiments, modifications to the path transmission scheme may be made to change the division of data amongst remaining communication paths in the event of failure.

Abstract: Embodiments relate to a neural processor circuit with scalable architecture for instantiating one or more neural networks. The neural processor circuit includes a data buffer coupled to a memory external to the neural processor circuit, and a plurality of neural engine circuits. To execute tasks that instantiate the neural networks, each neural engine circuit generates output data using input data and kernel coefficients. A neural processor circuit may include multiple neural engine circuits that are selectively activated or deactivated according to configuration data of the tasks. Furthermore, an electronic device may include multiple neural processor circuits that are selectively activated or deactivated to execute the tasks.

Abstract: Data generated by one or more data producers may be transmitted via multiple communication paths according to a path transmission scheme that divides transmission of different portions of the data amongst different communication paths. Upon a failure of a communication path, transmission of data may continue for those portions of data that are not assigned to the failed communication path. In some embodiments, modifications to the path transmission scheme may be made to change the division of data amongst remaining communication paths in the event of failure.

Abstract: Data generated by one or more data producers may be transmitted via multiple communication paths according to a path transmission scheme that divides transmission of different portions of the data amongst different communication paths. Upon a failure of a communication path, transmission of data may continue for those portions of data that are not assigned to the failed communication path. In some embodiments, modifications to the path transmission scheme may be made to change the division of data amongst remaining communication paths in the event of failure.

Abstract: Embodiments of the invention provide a DPD system where the transmit reference signal is transformed, including sub-sampling, frequency translation, and the like, to match the feedback signal, which goes thru a similar transformation process, to obtain an error signal. The same transformation is applied to a system model, which may be Jacobian, Hessian, Gradient, or the like, in an adaptation algorithm to minimize error.

Abstract: Embodiments of the invention provide a DPD system where the transmit reference signal is transformed, including sub-sampling, frequency translation, and the like, to match the feedback signal, which goes thru a similar transformation process, to obtain an error signal. The same transformation is applied to a system model, which may be Jacobian, Hessian, Gradient, or the like, in an adaptation algorithm to minimize error.

Abstract: Embodiments of the invention provide a DPD system where the transmit reference signal is transformed, including sub-sampling, frequency translation, and the like, to match the feedback signal, which goes thru a similar transformation process, to obtain an error signal. The same transformation is applied to a system model, which may be Jacobian, Hessian, Gradient, or the like, in an adaptation algorithm to minimize error.

Abstract: Performing digital predistortion (DPD) for widely spaced narrowband signals, such as the signal used in multi-carrier GSM, can be very difficult. Here, a system is provided the performs DPD for widely spaced narrowband signals. In particular, this system uses a polynomial curve for values of a cross-correlation function (above a predetermined threshold) to determine a delay estimate, which allows for a more robust and accurate system.

Abstract: Compressive sensing is an emerging field that attempts to prevent the losses associated with data compression and improve efficiency overall, and compressive sensing looks to perform the compression before or during capture, before energy is wasted. Here, several analog-to-digital converter (ADC) architectures are provided to perform compressive sensing. Each of these new architectures selects resolutions for each sample substantially at random and adjusts the sampling rate as a function of these selected resolutions.

Abstract: An apparatus and system are provided for crest factor reduction (CFR). Preferably, a peak from the wideband signal is detected. A gain from the magnitude of the peak and a threshold can then be calculated. Based on this information, each carrier's contribution to the peak can be approximated, and a cancellation pulse coefficient for each carrier from its contribution to the peak can be calculated. A base cancellation pulse can be calculated from the cancellation pulse coefficients for each carrier, and a cancellation pulse can be calculated from the base cancellation pulse and the gain, which can then be applied to the wideband signal.

Abstract: Compressive sensing is an emerging field that attempts to prevent the losses associated with data compression and improve efficiency overall, and compressive sensing looks to perform the compression before or during capture, before energy is wasted. Here, several analog-to-digital converter (ADC) architectures are provided to perform compressive sensing. Each of these new architectures selects resolutions for each sample substantially at random and adjusts the sampling rate as a function of these selected resolutions.

Abstract: Conventional transceivers do provide some compensation for in-phase/quadrature (I/Q) imbalance. However, these techniques do not separately compensate for I/Q imbalance for the transmitter and receiver sides of the transceiver. Here, a transceiver is provided that allows for compensation of I/Q imbalance in the transmitter and receiver irrespective of the other to allow for a more accurate transceiver.