Abstract: In various examples, to support training a deep neural network (DNN) to predict a dense representation of a 3D surface structure of interest, a training dataset is generated using a parametric mathematical modeling. A variety of synthetic 3D road surfaces may be generated by modeling a 3D road surface using varied parameters to simulate changes in road direction and lateral surface slope. In an example embodiment, a synthetic 3D road surface may be created by modeling a longitudinal 3D curve and expanding the longitudinal 3D curve to a 3D surface, and the resulting synthetic 3D surface may be sampled to form a synthetic ground truth projection image (e.g., a 2D height map). To generate corresponding input training data, a known pattern that represents which pixels may remain unobserved during 3D structure estimation may be generated and applied to a ground truth projection image to simulate a corresponding sparse projection image.

Abstract: A transient electromagnetic device with variable shape and turns includes squares, a transmitting coil carrying frame, a transmitting coil, a turns-variable device, a current generator and a working power supply. The transmitting coil carrying frame is arranged inside the transmitting coil; the transmitting coil carrying frame is configured as a carrier of the transmitting coil, and configured for adjusting a side length of the transmitting coil and a shape of the transmitting coil; the square is configured for clamping and connecting the transmitting coil and the transmitting coil carrying frame to fix the transmitting coil to the transmitting coil carrying frame. The current generator is configured for generating transient current; the turns-variable device is configured for changing the turns of the transmitting coil; and the transmitting coil is configured for transmitting the transient current to a target area to be measured.

Abstract: The present disclosure is concerned with a method of forming a seal with a polyethylene based film structure. The polyethylene based film structure has at least one layer formed with an oriented polyethylene having a predetermined melting temperature (Tm). A conductive heat sealing device provides heat to form the seal, where a first sealing bar of the conductive heat sealing device operates at a first operating temperature of at least 10 degrees Celsius (° C.) below the Tm of the oriented polyethylene in the polyethylene based film structure and a second sealing bar of the conductive heat sealing device operates at a second operating temperature of at least 15° C. higher than the operating temperature of the first sealing bar. The seal formed with the polyethylene based film structure retains at least 99 percent of its original surface area prior to forming the seal.

Abstract: The present invention discloses a brain machine interface decoding method based on spiking neural network, comprising: (1) constructing a liquid state machine model based on a spiking neural network, the liquid state machine model consists of an input layer, an middle layer and an output layer, wherein, a connection weight from the input layer to the middle layer is Whh, a loop connection weight inside the middle layer is Whh, a readout weight from the middle layer to the output layer is Wyh; (2) Inputting a neuron spike train signal, and training each weight with the following strategy: (2-1) Using STDP without supervision to train the connection weight Whh from the input layer to the middle layer; (2-2) Setting the loop connection weight Whh inside the middle layer by means of distance model and random connection, and obtaining a middle layer liquid information R(t); (2-3) Using ridge regression with supervision to train the readout weight Wyh from the middle layer to the output layer, and establishing a ma

Abstract: A touch control method is provided. The touch control method is applied in a touch device including a plurality of touch electrodes, the touch control method includes: step S1, sending a scanning signal to the plurality of touch electrodes, the scanning signal being a multi-frequency scanning signal; step S2, acquiring touch data according to the multi-frequency scanning signal; and step S3, calculating a current touch position according to the touch data.

Abstract: Disclosed herein are system, method, and computer program product embodiments for automated autonomous vehicle pose validation. An embodiment operates by generating a range image from a point cloud solution comprising a pose estimate for an autonomous vehicle. The embodiment queries the range image for predicted ranges and predicted class labels corresponding to lidar beams projected into the range image. The embodiment generates a vector of features from the range image. The embodiment compares a plurality of values to the vector of features using a binary classifier.

Abstract: A method and a platform for detecting an immunogenicity of a tumor neoantigen are provided. Specifically, the detection method includes the following steps: culturing human peripheral blood monocytes ex vivo for 13 days, adding an antigenic peptide fragment of human influenza virus and stimulating and activating cytokines, antigenic peptides, and immunoadjuvants during the 13 days, and finally conducting enzyme-linked immunospot (ELISPOT) chromogenic reaction and instrument-based scanning, counting, and analysis to detect the immunogenicity of tumor neoantigen. An application of the detection method and platform in biomedicine is provided. Compared with the prior art, the detection method and platform have advantages and characteristics of a short detection period, high convenience, low consumption of experimental cells, and low detection cost. Therefore, the detection method and platform can be used for ex vivo high-throughput assay for the immunogenicity of the tumor neoantigen.

Abstract: In various examples, to support training a deep neural network (DNN) to predict a dense representation of a 3D surface structure of interest, a training dataset is generated using a parametric mathematical modeling. A variety of synthetic 3D road surfaces may be generated by modeling a 3D road surface using varied parameters to simulate changes in road direction and lateral surface slope. In an example embodiment, a synthetic 3D road surface may be created by modeling a longitudinal 3D curve and expanding the longitudinal 3D curve to a 3D surface, and the resulting synthetic 3D surface may be sampled to form a synthetic ground truth projection image (e.g., a 2D height map). To generate corresponding input training data, a known pattern that represents which pixels may remain unobserved during 3D structure estimation may be generated and applied to a ground truth projection image to simulate a corresponding sparse projection image.

Abstract: A computing platform (10), a method, and an apparatus (20) for spiking neural network (SNN) learning and simulation are provided. The computing platform (10) includes a neuron dynamics simulation module (11), a neuron conversion module (12), an SNN construction and weight learning module (13), and a neural network level parameter and weight access module (14). The neuron dynamics simulation module (11) simulates changing features of neurons. The neuron conversion module (12) performs operations on a calculation graph. The SNN construction and weight learning module (13) updates and iterates connection weights. The neural network level parameter and weight access module (14) stores overall network detail parameters.

Abstract: Disclosed herein are system, method, and computer program product embodiments for automated autonomous vehicle pose validation. An embodiment operates by generating a range image from a point cloud solution comprising a pose estimate for an autonomous vehicle. The embodiment queries the range image for predicted ranges and predicted class labels corresponding to lidar beams projected into the range image. The embodiment generates a vector of features from the range image. The embodiment compares a plurality of values to the vector of features using a binary classifier. The embodiment validates the autonomous vehicle pose based on the comparison of the plurality of values to the vector of features using the binary classifier.

Abstract: The present invention discloses a neuromorphic computer supporting billions of neurons, comprising hierarchical extended architecture and algorithmic process control within the architecture; the architecture comprises multiple neuromorphic computing chips with hierarchical organization management for implementing computing tasks, each containing computing neurons and synaptic resources and forming a neural network, spike events between computing neurons within the architecture are transmitted through a hierarchical transmission mode; the algorithmic process control comprises controlling parallel processing of computing tasks within the architecture, controlling management of synchronization time within the architecture, and controlling reconstruction of neural networks within the architecture to achieve fault tolerance and robust management of computing neurons and synaptic resources. The neuromorphic computer can support spiking neural network inference calculations with a neuron scale of billions.

Abstract: A target tracking method and a target tracking system of a spiking neural network based on an event camera are provided. The method includes: acquiring a data stream of asynchronous events in a high dynamic scene of a target by an event camera as input data; dividing the data stream of the asynchronous events into synchronous event frames with millisecond time resolution; training a twin network based on a spiking neural network by a gradient substitution algorithm with a target image as a template image and a complete image as a searched image; and tracking the target by a trained twin network with interpolating a result of feature mapping to up-sample and obtaining the position of the target in an original image. The twin network includes a feature extractor and a cross-correlation calculator.

Abstract: In various examples, systems and methods for machine learning based hazard detection for autonomous machine applications using stereo disparity are presented. Disparity between a stereo pair of images is used to generate a path disparity model. Using the path disparity model, a machine learning model can recognize when a pixel in the first image corresponds to a pixel in the second image even though the pixel in the two images does not have identical characteristics. Similarities in extracted feature vectors can be computed and represented by a vector similarity metric that is input to a machine learning classifier, along with feature information extracted from the stereo image pair, to differentiate hazard pixels from non-hazard pixels. In some embodiments, a V-space disparity map, where a first axis corresponds to disparity values and the second axis corresponds to pixel rows, may be used to simplify estimation of the path disparity model.

Abstract: In various examples, system and methods for stereo disparity based hazard detection for autonomous machine applications are presented. Example embodiments may assist an ego-machine in detecting hazards within its path of travel. The systems and methods may use disparity between a stereo pair of images to generate a baseline path disparity model and further identify hazards from detected disparities that deviate from that path disparity model. A disparity map for the image pair is constructed in which each pixel represents a disparity for a corresponding element of the image captured. Blockwise division may be optionally used to subdivide the disparity map into a plurality of smaller disparity maps, each corresponding to a block of pixels of the disparity map. A V-space disparity map, where a first axis corresponds to disparity values and the second axis corresponds to pixel rows, may be used to simplify estimation of the path disparity model.

Abstract: In various examples, a hazard detection system plots hazard indicators from multiple detection sensors to grid cells of an occupancy grid corresponding to a driving environment. For example, as the ego-machine travels along a roadway, one or more sensors of the ego-machine may capture sensor data representing the driving environment. A system of the ego-machine may then analyze the sensor data to determine the existence and/or location of the one or more hazards within an occupancy grid—and thus within the environment. When a hazard is detected using a respective sensor, the system may plot an indicator of the hazard to one or more grid cells that correspond to the detected location of the hazard. Based, at least in part, on a fused or combined confidence of the hazard indicators for each grid cell, the system may predict whether the corresponding grid cell is occupied by a hazard.

Abstract: The present invention discloses a brain machine interface decoding method based on spiking neural network, comprising: (1) constructing a liquid state machine model based on a spiking neural network, the liquid state machine model consists of an input layer, an middle layer and an output layer, wherein, a connection weight from the input layer to the middle layer is Whh, a loop connection weight inside the middle layer is Whh, a readout weight from the middle layer to the output layer is Wyh; (2) Inputting a neuron spike train signal, and training each weight with the following strategy: (2-1) Using STDP without supervision to train the connection weight Whh from the input layer to the middle layer; (2-2) Setting the loop connection weight Whh inside the middle layer by means of distance model and random connection, and obtaining a middle layer liquid information R(t); (2-3) Using ridge regression with supervision to train the readout weight Wyh from the middle layer to the output layer, and establishing a ma

Abstract: The present invention discloses an adaptive brain-computer interface decoding method based on multi-model dynamic ensemble, where a traditional state-space model is improved, and a set of measurement functions instead of one fixed measurement function are used to dynamically characterize a relationship between observation variables and state variables; and, by using a pool of linear and nonlinear decoders, and in a decoding process of a brain-computer interface system, decoders are automatically switched according to the data, so as to realize adaptive brain signal decoding. Through the above multi-model ensemble strategy, linear and nonlinear decoder capabilities can be integrated, the accuracy and stability of the brain-computer interface system can be improved, and decoding unstability caused by the non-stationary neural signal of the brain-computer interface system can be solved to a certain extent.

Abstract: The present invention discloses a prediction method of brain region pulse neural signals, comprising the following steps: 1) synchronously acquiring pulse signals of neural groups in multiple brain regions; 2) calibrating the pulse signals of the neural groups; 3) pre-processing the pulse signals of the neural groups; 4) constructing a non-discrete neural pulse sequence kernel function; 5) performing dimensionality reduction on a reproducing Kernel Hilbert Space; 6) solving for an artificial neural pathway model in the reproducing Kernel Hilbert Space; 7) evaluating the artificial neural pathway model; and 8) visualizing the artificial neural pathway model. The method uses the non-discrete neural pulse sequence kernel function input on the basis of a time sequence neural pulse, has higher output signal prediction accuracy, higher computing efficiency, and higher stability performances, and is used for guiding the rehabilitation of a cognitive nerve function.

Abstract: Disclosed herein are system, method, and computer program product embodiments for automated autonomous vehicle pose validation. An embodiment operates by generating a range image from a point cloud solution comprising a pose estimate for an autonomous vehicle. The embodiment queries the range image for predicted ranges and predicted class labels corresponding to lidar beams projected into the range image. The embodiment generates a vector of features from the range image. The embodiment compares a plurality of values to the vector of features using a binary classifier.

Abstract: The present disclosure is related to a microwave source. The microwave source may include a cathode heater and a thermionic emitter. The cathode heater may include a first component, and a second component enclosing at least a portion of the first component. The thermionic emitter may be configured to release electrons when the thermionic emitter is heated by the cathode heater. At least a portion of the second component of the cathode heater may be in contact with the thermionic emitter.