Abstract: A semiconductor structure includes a substrate and a first capacitor. The substrate includes an active region. The first capacitor is over the substrate and free from overlapping the active region from a top view perspective.

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.

Abstract: A system and method for automated anomaly detection in automated disposal decisions of an automated decisioning workflow includes collecting a time-series of automated disposal decision data for a current period from an automated decisioning workflow, wherein the automated decisioning workflow computes one of a plurality of distinct disposal decisions for each distinct input comprising subject online event data and a machine learning-based threat score computed for the subject online event data; selecting an anomaly detection algorithm from a plurality of distinct anomaly detection algorithms based on a type of online abuse or online fraud that the automated decisioning workflow is configured to evaluate; evaluating, using the selected anomaly detection algorithm, the time-series of automated decision data for the current period; computing whether anomalies exist in the time-series of automated disposal decision data for the current period based on the evaluation; and generating an anomaly alert based on the

Abstract: A system and method for automated anomaly detection in automated disposal decisions of an automated decisioning workflow includes collecting a time-series of automated disposal decision data for a current period from an automated decisioning workflow, wherein the automated decisioning workflow computes one of a plurality of distinct disposal decisions for each distinct input comprising subject online event data and a machine learning-based threat score computed for the subject online event data; selecting an anomaly detection algorithm from a plurality of distinct anomaly detection algorithms based on a type of online abuse or online fraud that the automated decisioning workflow is configured to evaluate; evaluating, using the selected anomaly detection algorithm, the time-series of automated decision data for the current period; computing whether anomalies exist in the time-series of automated disposal decision data for the current period based on the evaluation; and generating an anomaly alert based on the

Abstract: A system and method for automated anomaly detection in automated disposal decisions of an automated decisioning workflow includes collecting a time-series of automated disposal decision data for a current period from an automated decisioning workflow, wherein the automated decisioning workflow computes one of a plurality of distinct disposal decisions for each distinct input comprising subject online event data and a machine learning-based threat score computed for the subject online event data; selecting an anomaly detection algorithm from a plurality of distinct anomaly detection algorithms based on a type of online abuse or online fraud that the automated decisioning workflow is configured to evaluate; evaluating, using the selected anomaly detection algorithm, the time-series of automated decision data for the current period; computing whether anomalies exist in the time-series of automated disposal decision data for the current period based on the evaluation; and generating an anomaly alert based on the

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 integration navigation device and method in which wireless signal strength data collecting and processing module obtains first position information of a target to be detected; a MEMS sensor data collecting and processing module obtains, according to state change information of the target to be detected, second position information of the target to be detected; a data integration module integrates the first position information and the second position information and feeds back the integrated result to the wireless signal strength data collecting and processing module and the MEMS sensor data collecting and processing module to perform data processing of the next moment.

Abstract: In some aspects, the disclosure is directed to methods and systems for an enhanced data rate, low energy wireless communication. Devices may communicate over predetermined wireless bands such as the 2.4 GHz and 5 GHz ISM bands, using ?/4-differential quadrature phase shift keying, with physical layer rates of 1, 2, 4, 8 Mbps or higher. Symbol timing may be at any frequency, and may be 256 ns in some implementations. In some implementations, to extend device battery life, devices may use a low latency reconnect mechanism allowing frequent hibernation or shutdown of transmitters and/or carriers.

Abstract: A positioning method using height-constraint-based extended Kalman filter, suitable for a GNSS navigation and positioning system, comprises: obtaining an estimated state value of a current epoch by using an extended Kalman filter algorithm and according to an estimated state value of a previous epoch; constraining a positioning height of the current epoch by establishing a height constraint condition, so as to obtain an optimum estimated value of the current epoch and a corresponding mean square error, wherein the optimum estimated value satisfies the height constraint condition; further correcting the estimated state value by using a pseudorange obtained from the mean square error and a measured Doppler shift residual to obtain a final estimated state value of the current epoch, thus more accurately obtaining positioning information of a target to be positioned in the current epoch and enhancing the accuracy of GNSS navigation and positioning.