Abstract: The disclosure provides a method for assembling and interconnecting FBAR filter and an electronic device. The method includes constructing an equivalent circuit model of an assembled FBAR filter according to a circuit model of a filter chip and the grounding circuit of the FBAR filter; modeling, simulating and calculating the grounding circuit to extract parasitic parameters corresponding to the grounding pad and a grounding bond-wire of the grounding circuit, respectively; feedbacking the parasitic parameters back into the equivalent circuit model, and using the circuit simulation software to obtain an S parameter of the filter; adjusting the parasitic parameters of the grounding circuit to optimize an S parameter performance of the FBAR filter: obtaining an optimal assembly configuration of the FBAR filter to guide the assembly. The parasitic parameters include a parasitic inductance of the grounding bond-wire and a parasitic capacitance and parasitic inductance of the grounding pad.

Abstract: A controller for scheduling mmWave communication is provided. The controller is programmed to identify a plurality of states, each of the plurality of states indicating status of mmWave communication links among a plurality of nodes, calculate an updated value for each of the plurality of states iteratively based on a value iteration algorithm and a previous value for each of the plurality of states until the updated value for each of the plurality of states converges, and select one of the plurality of states based on the converged values for the plurality of states.

Abstract: Provided are a data processing method for a multimedia conference, a data processing apparatus for a multimedia conference, and an electronic device. The method includes: receiving a multimedia data stream sent by a conference participant user of the multimedia conference; generating, according to a received recording instruction for the multimedia conference, storage data of the multimedia conference based on the multimedia data stream sent by the conference participant user; and storing the storage data in cloud.

Abstract: Embodiments of the present disclosure disclose an information display method and apparatus, and an electronic device. The method in a specific embodiment comprises: receiving a multimedia information stream, wherein the information stream comprises a multimedia data stream, and interactive information sent by a user according to multimedia information content; determining data types corresponding to the interactive information, the data types comprising an emoji data type and a chat data type; and in response to determining that the interactive information is to be displayed on a display interface displaying the multimedia data stream, displaying the interactive information on the display interface in an interactive information display region corresponding to the data types, the interactive information display region comprising a chat information display region and an emoji information display region.

Abstract: A vehicle includes a processor configured to transmit a mmWave beacon signal during a probe phase of a first period; receive one or more mmWave beacon signals from one or more vehicles; generate a mmWave communication intention message for another period that is after the first period based on the received one or more mmWave beacon signals; and broadcast, during the first period, a packet including a mmWave transmission schedule for the another period generated based on the mmWave communication intention message.

Abstract: Two vehicles—an ego vehicle and an other vehicle—can share sensor data in a streamlined manner. One or more sensors can be configured to acquire first environment data of an external environment of the ego vehicle. A data summary based on second environment data of an external environment of the other vehicle can be received. Whether there is a common region of sensor coverage between the ego vehicle and the other vehicle can be determined. In response to there being a common region, the first environment data that is located within the common region can be identified and the resolution level of the identified first environment data can be reduced. The first environment data that has the reduced resolution level and a remainder of the first environment data excluding the identified first environment data can be transmitted to the other vehicle.

Abstract: A method and apparatus for caching a data block are provided. The method includes: obtaining, from a terminal, an access request for requesting access to a first data block; determining that the first data block is missed in a cache space of a storage system; detect whether a second data block satisfies a lazy condition, the second data block being a candidate elimination block in the cache space and the lazy condition being a condition for determining whether to delay replacing the second data block from the cache space according to a re-access probability; determining that the second data block satisfies the lazy condition; and accessing the first data block from a storage space of the storage system and skipping replacing the second data block from the cache space.

Abstract: The disclosure includes embodiments for a sensor system for multiple perspective sensor data sets. In some embodiments, a method executed by an ego vehicle includes receiving, from a set of remote vehicles, a set of sensor data describing a set of preliminary heatmaps for a roadway environment. The method includes reconciling discrepancies in the set of heatmaps and a preliminary heatmap of the ego vehicle to form a combined heatmap that describes the objects in the roadway environment as collectively observed by the onboard sensors of the set of remote vehicles and the ego vehicle. The method includes providing the combined heatmap to one or more of the remote vehicles included in the set of remote vehicles. The method includes modifying an operation of the ego vehicle based on the combined heatmap. For example, an operation of an autonomous driving system of the ego vehicle is modified.

Abstract: A method for supporting autonomous driving of an autonomous vehicle includes detecting, by an in-vehicle internet-of-things (IoT) platform of the autonomous vehicle, a vulnerable road user (VRU) having a mobile device in a vicinity of the autonomous vehicle. A mobility application runs on the mobile device of the VRU and sends VRU-specific data to the in-vehicle IoT platform of the autonomous vehicle. The VRU is detected based on the VRU-specific data and/or in-vehicle sensor data of the autonomous vehicle. The method further includes determining, by the in-vehicle IoT platform, a movement intention prediction based on the VRU-specific data. The movement intention prediction is computed by use of a machine learning model. The VRU-specific data of the mobile device are provided as input data for the machine learning model. In addition, the method includes performing an autonomous driving decision for the autonomous vehicle based on the movement intention prediction.

Abstract: The disclosure includes embodiments for modifying a vehicle-to-everything (V2X) radio of an ego vehicle that is a connected vehicle. In some embodiments, a method includes analyzing, by a machine learning module executed by a processor, a local dynamic map generated by the ego vehicle to determine schedule data describing a schedule for the ego vehicle to transmit a millimeter wave (mmWave) message to a remote vehicle. The method includes transmitting a V2X message including the schedule data for receipt by the remote vehicle so that the remote vehicle has access to the schedule. The method includes modifying an operation of the V2X radio of the ego vehicle based on the schedule so that the V2X radio transmits the mmWave message to the remote vehicle in compliance with the schedule. The method includes transmitting the mmWave message to the remote vehicle in compliance with the schedule.

Abstract: A vehicle includes one or more sensors configured to obtain raw data related to a scene, one or more processors, and machine readable instructions stored in one or more memory modules. The one machine readable instructions, when executed by the one or more processors, cause the vehicle to: process the raw data with a first neural network stored in the one or more memory modules to obtain a first prediction about the scene, transmit the raw data to a computing device external to the vehicle, receive a second prediction about the scene from the computing device in response to transmitting the raw data to the computing device, and determine an updated prediction about the scene based on a combination of the first prediction and the second prediction.

Abstract: The disclosure includes embodiments for modifying a vehicle-to-everything (V2X) radio of an ego vehicle that is a connected vehicle. In some embodiments, a method includes analyzing, by a machine learning module executed by a processor, a local dynamic map generated by the ego vehicle to determine schedule data describing a schedule for the ego vehicle to transmit a millimeter wave (mmWave) message to a remote vehicle. The method includes transmitting a V2X message including the schedule data for receipt by the remote vehicle so that the remote vehicle has access to the schedule. The method includes modifying an operation of the V2X radio of the ego vehicle based on the schedule so that the V2X radio transmits the mmWave message to the remote vehicle in compliance with the schedule. The method includes transmitting the mmWave message to the remote vehicle in compliance with the schedule.

Abstract: The disclosure includes embodiments for modifying a vehicle-to-everything (V2X) radio of an ego vehicle that is a connected vehicle. In some embodiments, a method includes analyzing, by a machine learning module executed by a processor, a local dynamic map generated by the ego vehicle to determine schedule data describing a schedule for the ego vehicle to transmit a millimeter wave (mmWave) message to a remote vehicle. The method includes transmitting a V2X message including the schedule data for receipt by the remote vehicle so that the remote vehicle has access to the schedule. The method includes modifying an operation of the V2X radio of the ego vehicle based on the schedule so that the V2X radio transmits the mmWave message to the remote vehicle in compliance with the schedule. The method includes transmitting the mmWave message to the remote vehicle in compliance with the schedule.

Abstract: Two vehicles—an ego vehicle and an other vehicle—can share sensor data in a streamlined manner. One or more sensors can be configured to acquire first environment data of an external environment of the ego vehicle. A data summary based on second environment data of an external environment of the other vehicle can be received. Whether there is a common region of sensor coverage between the ego vehicle and the other vehicle can be determined. In response to there being a common region, the first environment data that is located within the common region can be identified and the resolution level of the identified first environment data can be reduced. The first environment data that has the reduced resolution level and a remainder of the first environment data excluding the identified first environment data can be transmitted to the other vehicle.

Abstract: The disclosure includes embodiments for a location data correction service for connected vehicles. A method includes receiving, by an operation center via a serverless ad-hoc vehicular network, a first wireless message that includes legacy location data that describes a geographic location of a legacy vehicle. The method includes causing a rich sensor set included in the operation center to record sensor data describing the geographic locations of objects in a roadway environment. The method includes determining correction data that describes a variance between the geographic location of the legacy vehicle as described by the sensor data and the legacy location data. The method includes transmitting a second wireless message to the legacy vehicle, wherein the second wireless message includes the correction data so that the legacy vehicle receives a benefit by correcting the legacy location data to minimize the variance.

Abstract: A method of the invention manages data stream processing tasks of an application topology in a stream processing system, the stream processing system having one or more data producers, one or more compute nodes, and one or more result consumers, includes: assigning geographic scope granularities to the data stream processing tasks of the application topology; receiving, by a task manager from a result consumer of the one or more result consumers, a subscription in order to trigger data processing for computing a result, the subscription including geographic scope related information concerning the triggered data processing]; generating, by the task manager based on the geographic scope related information of the subscription, an execution plan including task instances for performing the triggered data processing; and generating, by the task manager from the execution plan, a deployment plan for deploying the task instances of the execution plan on the compute nodes.

Abstract: Systems and methods for controlling data usage in a distributed environment among multiple entity domains. A method include steps of: receiving, in a local entity domain, a data consuming application comprising or identifying at least a first analytics task, wherein the first analytics task processes data inputs to produce first output data; determining availability of the data inputs; interpreting data usage policies, or data control policies, to determine atomic actions to be executed, wherein at least one of the data usage policies indicates that one of the data inputs for the first analytics task must remain in a remote entity domain; and executing the atomic actions, wherein the atomic actions include dispatching the first analytics task to the remote entity domain for remote execution of the first analytics task and receiving the first output data of the first analytics task from the remote entity domain.

Abstract: A method for using knowledge infusion for robust and transferable machine learning models includes receiving a plurality of adaptive and programmable knowledge functions comprising a plurality of strong functions and a plurality of weak functions. A knowledge model is generated based on the plurality of strong functions and the plurality of weak functions. A machine learning model is trained based on the generated knowledge model.

Abstract: Provided is a real-time aliasing rendering method for a 3D VR video and a virtual three-dimensional scene, including: capturing 3D camera video signals in real time and process the same to generate texture data; creating a virtual three-dimensional scene according to the proportion of a real scene; generating virtual camera rendering parameters according to a physical position of the 3D camera and a shooting angle relationship; aliasing the texture data onto a virtual three-dimensional object in a virtual scene, and adjusting the position of the virtual three-dimensional object according to a physical positional relationship between the virtual three-dimensional scene and the real scene, so as to form a complete virtual reality combined three-dimensional scene; rendering the virtual reality combined three-dimensional scene by using the virtual camera rendering parameters to obtain a simulated rendering picture.

Abstract: A method is for matching a set of first classes assigned to a first data set with a set of second classes assigned to a second data set. The method includes constructing, via a set of pre-processing functions, a plurality of alignment profiles such that at least one alignment profile is assigned to each of the first classes and each of the second classes. The method includes generating a comparison matrix for each group of the alignment profiles, such that each group includes at least one of the first classes and at least one of the second classes. The method includes training a first machine learning model, through supervised training, based on the generated comparison matrices and based on probabilistic labels generated by a second machine learning model.