Abstract: A vehicle-mounted controller includes: a GNSS module, an MPU and an MCU. The GNSS module transmits a pulse signal to the MPU via the rigid line and transmits recommended positioning information to the MPU via the serial port line at the moment when a change occurs in high and low levels of the pulse signal; the MPU determines first absolute time according to first reception time when the pulse signal is received, second reception time when the recommended positioning information is received, and the recommended positioning information, and broadcasts the first absolute time to Ethernet network for absolute time synchronization; the MCU determines second absolute time according to time when the pulse signal is received, time when the first absolute time is received, and the recommended positioning information, and broadcasts the second absolute time to a CAN bus for absolute time synchronization.

Abstract: Decision feedback equalization (DFE) is used to help reduce inter-symbol interference (ISI) from a data signal received via a band-limited (or otherwise non-ideal) channel. A first PAM-4 DFE architecture has low latency from the output of the samplers to the application of the first DFE tap feedback to the input signal. This is accomplished by not decoding the sampler outputs in order to generate the feedback signal for the first DFE tap. Rather, weighted versions of the raw sampler outputs are applied directly to the input signal without further analog or digital processing. Additional PAM-4 DFE architectures use the current symbol in addition to previous symbol(s) to determine the DFE feedback signal. Another architecture transmits PAM-4 signaling using non-uniform pre-emphasis. The non-uniform pre-emphasis allows a speculative DFE receiver to resolve the transmitted PAM-4 signals with fewer comparators/samplers.

Abstract: Some embodiments include apparatus having multiple samplers in a decision feedback equalizer (DFE). The multiple samplers include at least two samplers and are configured to be activated in a first mode of the DFE to receive first input information from a summing circuit. At least one of the samplers is configured to be deactivated in a second mode of the DFE. At least one of the samplers is configured to be activated in the second mode of the DFE to receive second input information from the summing circuit.

Abstract: A vehicle-mounted controller includes: a GNSS module, an MPU and an MCU. The GNSS module transmits a pulse signal to the MPU via the rigid line and transmits recommended positioning information to the MPU via the serial port line at the moment when a change occurs in high and low levels of the pulse signal; the MPU determines first absolute time according to first reception time when the pulse signal is received, second reception time when the recommended positioning information is received, and the recommended positioning information, and broadcasts the first absolute time to Ethernet network for absolute time synchronization; the MCU determines second absolute time according to time when the pulse signal is received, time when the first absolute time is received, and the recommended positioning information, and broadcasts the second absolute time to a CAN bus for absolute time synchronization.

Abstract: The invention discloses an internal functional device for simulative snowfall, comprising a lampshade installing a lamp cover and an air blower on the top and bottom thereof, and a transparent sheet framework at upper part thereof with a foam particle dispersion net and an air guide elbow at the right-side upper end thereof, wherein the side part of the air blower is connected to an air guide slot with a material collecting slot having upper and lower hose clamps at left-side upper and lower ends as well as air guide tubes at right-side upper end in the upper end thereof. The device effectively pushes foam particles therein to move, making them convey onto the upper part of the foam particle dispersion net through the air guide tubes and further freely fall into the material collecting slot slowly in a circulating mode.

Abstract: A ball picking robot and a ball unloading method using the same. The ball picking robot includes a chassis, an L-shaped hanging lug, a ball storage frame, an electric push rod disposed on the chassis, a baffle disposed on the chassis, a bracket disposed on the chassis and a drive motor for driving the electric push rod to move. A movable end of the electric push rod is connected with one end of the L-shaped hanging lug, the other end of the L-shaped hanging lug is connected with a side of the ball storage frame, and a camera is disposed on the bracket.

Abstract: Decision feedback equalization (DFE) is used to help reduce inter-symbol interference (ISI) from a data signal received via a band-limited (or otherwise non-ideal) channel. A first PAM-4 DFE architecture has low latency from the output of the samplers to the application of the first DFE tap feedback to the input signal. This is accomplished by not decoding the sampler outputs in order to generate the feedback signal for the first DFE tap. Rather, weighted versions of the raw sampler outputs are applied directly to the input signal without further analog or digital processing. Additional PAM-4 DFE architectures use the current symbol in addition to previous symbol(s) to determine the DFE feedback signal. Another architecture transmits PAM-4 signaling using non-uniform pre-emphasis. The non-uniform pre-emphasis allows a speculative DFE receiver to resolve the transmitted PAM-4 signals with fewer comparators/samplers.

Abstract: A multi-dimensional task facial beauty prediction method and system, and a storage medium are disclosed. The method includes the steps of: at a training phase, using first facial images to optimize a shared feature extraction network for extracting shared features and to train a plurality of sub-task networks for performing facial beauty classification tasks; at a testing phase, extracting shared features of second facial images; inputting the shared features to the trained plurality of sub-task networks; and obtaining a first beauty prediction result based on first output results of the plurality of sub-task networks.

Abstract: Disclosed are a method and device for predicting face beauty based on migration and weak supervision and a storage medium. The method includes: preprocessing an inputted face image; training a source domain network by using the preprocessed image, and migrating a parameter of the source domain network to a target domain network; inputting a noise image marked with a noise label and a truth-value image marked with a truth-value label into the target domain network to obtain an image feature; and inputting the image feature into a classification network to obtain a final face beauty prediction result.

Abstract: A beauty prediction method and device based on multitasking and weak supervision, and a storage medium are disclosed. The method includes the steps of pre-processing inputted facial images; allocating the pre-processed images to multiple tasks; extracting shared image features; and obtaining a plurality of classification results via a plurality of classification networks each including a residual network, a standard neural network and a classifier.

Abstract: Disclosed are a facial beauty prediction method and device based on multi-task migration. The method includes: performing similarity measurement based on a graph structure on a plurality of tasks to obtain an optimal combination of the plurality of tasks; constructing a facial beauty prediction model including a feature sharing layer based on the optimal combination; migrating feature parameters of an existing large-scale facial image network to the feature sharing layer of the facial beauty prediction model; inputting facial images for training to pre-train the facial beauty prediction model; and inputting a facial image to be tested to the trained facial beauty prediction model to obtain facial recognition results.

Abstract: Some embodiments include an encoder to convert a thermometer code into a binary code output information or a Gray code output information. The encoder supports blind input swapping, such that it provides correct output information without prior knowledge of the input swapping. Some embodiments also include a truth table that has additional rows to describe output information when input information at inputs of the encoder is swapped. The encoder includes symmetrical logic functions with respect to information at its inputs as building blocks.

Abstract: Decision feedback equalization (DFE) is used to help reduce inter-symbol interference (ISI) from a data signal received via a band-limited (or otherwise non-ideal) channel. A first PAM-4 DFE architecture has low latency from the output of the samplers to the application of the first DFE tap feedback to the input signal. This is accomplished by not decoding the sampler outputs in order to generate the feedback signal for the first DFE tap. Rather, weighted versions of the raw sampler outputs are applied directly to the input signal without further analog or digital processing. Additional PAM-4 DFE architectures use the current symbol in addition to previous symbol(s) to determine the DFE feedback signal. Another architecture transmits PAM-4 signaling using non-uniform pre-emphasis. The non-uniform pre-emphasis allows a speculative DFE receiver to resolve the transmitted PAM-4 signals with fewer comparators/samplers.

Abstract: A method for measuring an antenna downtilt angle based on a multi-scale deep semantic segmentation network is disclosed, including: collecting base station antenna data by using an unmanned aerial vehicle, and labeling an acquired antenna image with a labeling tool to make a data set; calling the data set for training and debugging a model; recognizing and detecting a target antenna, performing semantic segmentation on an output image, finally obtaining a target image finally segmented, and calculating a downtilt angle of the target image. The method is highly applicable, cost-effective, and safe.

Abstract: Some embodiments include apparatus having multiple samplers in a decision feedback equalizer (DFE). The multiple samplers include at least two samplers and are configured to be activated in a first mode of the DFE to receive first input information from a summing circuit. At least one of the samplers is configured to be deactivated in a second mode of the DFE. At least one of the samplers is configured to be activated in the second mode of the DFE to receive second input information from the summing circuit.

Abstract: Some embodiments include apparatus having multiple samplers in a decision feedback equalizer (DFE). The multiple samplers include at least two samplers and are configured to be activated in a first mode of the DFE to receive first input information from a summing circuit. At least one of the samplers is configured to be deactivated in a second mode of the DFE. At least one of the samplers is configured to be activated in the second mode of the DFE to receive second input information from the summing circuit.

Abstract: Disclosed are a method and device for predicting face beauty based on migration and weak supervision and a storage medium. The method includes: preprocessing an inputted face image; training a source domain network by using the preprocessed image, and migrating a parameter of the source domain network to a target domain network; inputting a noise image marked with a noise label and a truth-value image marked with a truth-value label into the target domain network to obtain an image feature; and inputting the image feature into a classification network to obtain a final face beauty prediction result.

Abstract: A beauty prediction method and device based on multitasking and weak supervision, and a storage medium are disclosed. The method includes the steps of pre-processing inputted facial images; allocating the pre-processed images to multiple tasks; extracting shared image features; and obtaining a plurality of classification results via a plurality of classification networks each including a residual network, a standard neural network and a classifier.

Abstract: A multi-dimensional task facial beauty prediction method and system, and a storage medium are disclosed. The method includes the steps of: at a training phase, using first facial images to optimize a shared feature extraction network for extracting shared features and to train a plurality of sub-task networks for performing facial beauty classification tasks; at a testing phase, extracting shared features of second facial images; inputting the shared features to the trained plurality of sub-task networks; and obtaining a first beauty prediction result based on first output results of the plurality of sub-task networks.