Abstract: A method for controlling data transmission and reception and an application system thereof are provided. In the method, the peer-to-peer communication system is initialized to set each communication unit in the peer-to-peer communication system to be in a data receiving state. A communication host controls, according to a communication status of the peer-to-peer communication system, a controlled electrical connection path between the communication host and a communication interface to the communication transmission path, to work in a mode allowing data transmission from the communication slave to a communication host side of the controlled electrical connection path, or a mode allowing data transmission from the communication host side of the controlled electrical connection path to the communication slave.

Abstract: Novel communication techniques for Bluetooth LE devices. In some cases, a central device transmits broadcast messages, which can be received by one or more peripheral devices, which then transmit responses in turn. In an aspect, these techniques can condense multiple advertising/scan request/scan response packets into a single transmission window and/or multiple, contiguous receive windows for the central device and single transmit/single receive windows for each peripheral device.

Abstract: The present disclosure discloses a method and a system for detecting an abnormal traffic behavior. The method of the present disclosure includes: retaining an abnormal static target vehicle in a traffic surveillance video in a background through background modeling; performing abnormal target detection, and obtaining a cropped picture of an abnormal target vehicle and a cropped video clip through cropping; performing anomaly start time estimation, inputting the cropped picture and the cropped video clip to a network model combining twin cross-correlation with pseudo three-dimensional (P3D)-Attention, labeling a classification label on the cropped video clip, and determining a video frame when abnormal behavior occurs; and determining whether a to-be-matched vehicle is an abnormal target vehicle, and determining a start time and an end time of abnormal traffic behavior with reference to the video frame that is obtained when the abnormal behavior occurs.

Abstract: Techniques for performing automatic PHY updates in Bluetooth devices. In some cases, a device might detect the reception of a packet sent with a particular PHY and automatically update the device's physical layer to employ that PHY for receiving and/or transmitting subsequent packets.

Abstract: Provided is a vehicle 4D millimeter-wave radar inertial odometry method and a computer-readable medium.

Abstract: An energy conversion system, and a method and a device for controlling the energy conversion system at an overtemperature are provided. The energy conversion system includes at least one power conversion circuit each including at least two power modules that are cascaded. Respective operating temperatures of all the power modules are measured in real time, to determine the overheated power module that is operating at an overtemperature and the underheated power module that is operating at a normal temperature. Once an overheated power module is detected, a decreased power and a power difference for the overheated power module are acquired, and an increased power for the underheated power module is determined based on the power difference. The operating power of the overheated power module is decreased at the module level with the total operating power of the system remains constant or approximately constant.

Abstract: The present invention relates to an improved KNN-based 6LoWPAN network intrusion detection method. The present invention selects quantifiable security features which can reflect a self-security state of network elements of a 6LoWPAN network for training, and establishes a 6LoWPAN network feature space. The present invention assigns the weights to the features and transfers zero points, to alleviate the bias caused by large and small impact factors and simplify calculation; realizes construction and update of a state data table of network elements by extracting the feature data of network elements in real time, thus forming a normal contour updated according to the real-time state of the network in the feature space of the 6LoWPAN network based on the clustering effect of a KNN algorithm; and the present invention improves the KNN algorithm and redefines a basis for judging the invasion, to meet the requirements for 6LoWPAN network intrusion detection.

Abstract: A system and method for accelerated anomaly detection and replacement of an anomaly-experiencing machine learning-based ensemble includes identifying a machine learning-based digital threat scoring ensemble having an anomalous drift behavior in digital threat score inferences computed by the machine learning-based digital threat scoring ensemble for a target period; executing a tiered anomaly evaluation for the machine learning-based digital threat scoring ensemble that includes identifying at least one errant machine learning-based model of the machine learning-based digital threat scoring ensemble contributing to the anomalous drift behavior, and identifying at least one errant feature variable of the at least one machine learning-based model contributing to the anomalous drift behavior; generating a successor machine learning-based digital threat scoring ensemble to the machine learning-based digital threat scoring ensemble based on the tiered anomaly evaluation; and replacing the machine learning-based di

Abstract: A system and method for accelerated anomaly detection and replacement of an anomaly-experiencing machine learning-based ensemble includes identifying a machine learning-based digital threat scoring ensemble having an anomalous drift behavior in digital threat score inferences computed by the machine learning-based digital threat scoring ensemble for a target period; executing a tiered anomaly evaluation for the machine learning-based digital threat scoring ensemble that includes identifying at least one errant machine learning-based model of the machine learning-based digital threat scoring ensemble contributing to the anomalous drift behavior, and identifying at least one errant feature variable of the at least one machine learning-based model contributing to the anomalous drift behavior; generating a successor machine learning-based digital threat scoring ensemble to the machine learning-based digital threat scoring ensemble based on the tiered anomaly evaluation; and replacing the machine learning-based di

Abstract: A driver fatigue detection method based on combining a pseudo-three-dimensional (P3D) convolutional neural network (CNN) and an attention mechanism includes: 1) extracting a frame sequence from a video of a driver and processing the frame sequence; 2) performing spatiotemporal feature learning through a P3D convolution module; 3) constructing a P3D-Attention module, and applying attention on channels and a feature map through the attention mechanism; and 4) replacing a 3D global average pooling layer with a 2D global average pooling layer to obtain more expressive features, and performing a classification through a Softmax classification layer. By analyzing the yawning behavior, blinking and head characteristic movements, the yawning behavior is well distinguished from the talking behavior, and it is possible to effectively distinguish between the three states of alert state, low vigilant state and drowsy state, thus improving the predictive performance of fatigue driving behaviors.

Abstract: A system and method for accelerated anomaly detection and replacement of an anomaly-experiencing machine learning-based ensemble includes identifying a machine learning-based digital threat scoring ensemble having an anomalous drift behavior in digital threat score inferences computed by the machine learning-based digital threat scoring ensemble for a target period; executing a tiered anomaly evaluation for the machine learning-based digital threat scoring ensemble that includes identifying at least one errant machine learning-based model of the machine learning-based digital threat scoring ensemble contributing to the anomalous drift behavior, and identifying at least one errant feature variable of the at least one machine learning-based model contributing to the anomalous drift behavior; generating a successor machine learning-based digital threat scoring ensemble to the machine learning-based digital threat scoring ensemble based on the tiered anomaly evaluation; and replacing the machine learning-based di

Abstract: A transmitter for inductive charging of a device comprising a receiver is provided. The transmitter includes a set of coils, including a first coil having a first turn and a second turn. The first turn and the second turn are adjacent. The first turn has the first sense and the second turn has the second sense, opposed to the first sense; whereby, in use, current flows through the first turn and the second turn in opposed senses.

Abstract: A driver fatigue detection method based on combining a pseudo-three-dimensional (P3D) convolutional neural network (CNN) and an attention mechanism includes: 1) extracting a frame sequence from a video of a driver and processing the frame sequence; 2) performing spatiotemporal feature learning through a P3D convolution module; 3) constructing a P3D-Attention module, and applying attention on channels and a feature map through the attention mechanism; and 4) replacing a 3D global average pooling layer with a 2D global average pooling layer to obtain more expressive features, and performing a classification through a Softmax classification layer. By analyzing the yawning behavior, blinking and head characteristic movements, the yawning behavior is well distinguished from the talking behavior, and it is possible to effectively distinguish between the three states of alert state, low vigilant state and drowsy state, thus improving the predictive performance of fatigue driving behaviors.

Abstract: A system and method for accelerated anomaly detection and replacement of an anomaly-experiencing machine learning-based ensemble includes identifying a machine learning-based digital threat scoring ensemble having an anomalous drift behavior in digital threat score inferences computed by the machine learning-based digital threat scoring ensemble for a target period; executing a tiered anomaly evaluation for the machine learning-based digital threat scoring ensemble that includes identifying at least one errant machine learning-based model of the machine learning-based digital threat scoring ensemble contributing to the anomalous drift behavior, and identifying at least one errant feature variable of the at least one machine learning-based model contributing to the anomalous drift behavior; generating a successor machine learning-based digital threat scoring ensemble to the machine learning-based digital threat scoring ensemble based on the tiered anomaly evaluation; and replacing the machine learning-based di

Abstract: A method for controlling data transmission and reception and an application system thereof are provided. In the method, the peer-to-peer communication system is initialized to set each communication unit in the peer-to-peer communication system to be in a data receiving state. A communication host controls, according to a communication status of the peer-to-peer communication system, a controlled electrical connection path between the communication host and a communication interface to the communication transmission path, to work in a mode allowing data transmission from the communication slave to a communication host side of the controlled electrical connection path, or a mode allowing data transmission from the communication host side of the controlled electrical connection path to the communication slave.

Abstract: A two-stage converter, a method for starting the two-stage converter, an LLC converter, and an application system are provided. A controller of the LLC converter of the two-stage converter first controls a main circuit of the LLC converter to perform hiccup charging on a direct current bus of a later-stage converter at a preset interval, so that the direct current bus voltage of the later-stage converter gradually increases, until an auxiliary power supply of the later-stage converter starts to operate, to supply power to the controller of the later-stage converter. After the controller of the later-stage converter reports the detected direct current bus voltage, the controller of the LLC converter determines whether the output voltage of the LLC converter increases to a hiccup starting voltage. If so, the controller of the LLC converter controls the main circuit to operate in a hiccup voltage stabilization phase.

Abstract: An apparatus includes multiple radio-frequency (RF) circuits, a number of Bluetooth (BT) cores and a processor. The BT cores are coupled to the plurality of RF circuits. The processor is coupled to the BT cores to control operations of the RF circuits and the BT cores. The RF circuits, the BT cores and the processor are implemented as a system on a chip (SoC). Controlling the operations of the RF circuits and the BT cores can be performed by controlling register spaces associated with the RF circuits and the BT cores.

Abstract: The present invention relates to an improved KNN-based 6LoWPAN network intrusion detection method. The present invention selects quantifiable security features which can reflect a self-security state of network elements of a 6LoWPAN network for training, and establishes a 6LoWPAN network feature space. The present invention assigns the weights to the features and transfers zero points, to alleviate the bias caused by large and small impact factors and simplify calculation; realizes construction and update of a state data table of network elements by extracting the feature data of network elements in real time, thus forming a normal contour updated according to the real-time state of the network in the feature space of the 6LoWPAN network based on the clustering effect of a KNN algorithm; and the present invention improves the KNN algorithm and redefines a basis for judging the invasion, to meet the requirements for 6LoWPAN network intrusion detection.