Abstract: Described is a method for extraction of a region of interest (ROI) from a composite synthetic aperture radar (SAR) phase history data. The method comprising receiving, with a system comprising a processor, the composite SAR phase history data of a plurality of backscattered return signals produced by a SAR system illuminating a scene with a SAR beam. The method also comprises obtaining a location of a first ROI within the scene and extracting from the composite SAR phase history data a first component SAR phase history data corresponding to the ROI at the location of the ROI.

Abstract: A control method for stabilizing excimer laser pulse energy includes: obtaining a measured energy value of the n-th pulse in a pulse sequence; calculating the difference between the measured energy value and the preset energy value; taking the z-th pulse of the sequence as a demarcation point, when n is a positive integer less than z, the discharge voltage value of the next pulse is calculated according to a first mathematical model, and when n is an integer greater than z?1, the discharge voltage value of the next pulse is calculated according to a second mathematical model; and the next pulse is generated according to the discharge voltage value. A control system based on the control method includes: a high-voltage discharge module, a laser cavity, a laser parameter measurement module, and an energy stability controller.

Abstract: A backplane, a backlight source, an illumination device and a displaying device. The backplane comprises a substrate; a first metal trace layer disposed on one surface of the substrate; an insulating layer disposed on a side, away from the substrate, of the first metal trace layer; a second metal trace layer disposed on a side, away from the substrate, of the insulating layer, an overlapping area existing between an orthographic projection of the second metal trace layer on the substrate and an orthographic projection of the first metal trace layer on the substrate; and a barrier layer disposed between the first metal trace layer and the second metal trace layer, an orthographic projection of the barrier layer on the substrate covering the overlapping area, and the barrier layer being used for preventing metals in the first metal trace layer and the second metal trace layer from growing towards each other.

Abstract: Disclosed is a synthetic aperture radar (SAR) system for target recognition with complex range profile. The SAR system comprising a memory, a recurrent neural network (RNN), a multi-layer linear network in signal communication the RNN, and a machine-readable medium on the memory. The machine-readable medium is configured to store instructions that, when executed by the RNN, cause the SAR system to perform various operations. The various operation comprise: receiving raw SAR data associated with observed views of a scene, wherein the raw SAR data comprises information captured via the SAR system; radio frequency (RF) preprocessing the received raw SAR data to produce a processed raw SAR data; converting the processed raw SAR data to a complex SAR range profile data; processing the complex SAR range profile data with the RNN having RNN states; and mapping the RNN states to a target class with the multi-layer linear network.

Abstract: A gene chip includes a chip carrier, a plurality of DNA nanoballs assembled on the chip carrier, and a polymer film formed on the chip carrier and wrapping the DNA nanoballs. The polymer film includes at least one of a film of a positively charged polymer, a film of a positively charged polymer which is modified, a film of a zwitterionic polymer, and a composite polymer film. The composite polymer film is formed by a layer-by-layer self-assembly process of a positively charged polymer and a negatively charged polymer. The gene chip has good sequencing quality and different functions can be achieved by coating with different polymers, such as the chip surface rapidly drying out and surface non-specific adsorption. A method of preparing a gene chip is further disclosed.

Abstract: The present invention provides a novel dielectric filter, which comprises a dielectric body, N+4 through holes, and an end surface metal layer, wherein the end surface metal layer comprises a first metal block, a second metal block, N middle metal blocks, a penultimate metal block, a last metal block, a first metal sideline, a second metal line segment, a third metal line segment, a fourth metal line segment, and a fifth metal line segment; the first metal block, the second metal block, the N middle metal blocks, the penultimate metal block and the last metal block are respectively formed on peripheral sides of a first resonance hole, a second resonance hole, N middle resonance holes, a penultimate resonance hole and a last resonance hole.

Abstract: Examples for detection of defects in an additively manufactured object are provided. In one aspect, a method is provided. The method comprises receiving in-situ spectral data measured from the additively manufactured object during an additive manufacturing process, constructing a graph data structure using the in-situ spectral data, and outputting a predicted defect region using the graph data structure and a trained graph-learning neural network.

Abstract: A computing system including a processor configured to train a synthetic aperture radar (SAR) classifier neural network. The SAR classifier neural network is trained at least in part by, at a SAR encoder, receiving training SAR range profiles that are tagged with respective first training labels, and, at an image encoder, receiving training two-dimensional images that are tagged with respective second training labels. Training the SAR classifier neural network further includes, at a shared encoder, computing shared latent representations based on the SAR encoder outputs and the image encoder outputs, and, at a classifier, computing respective classification labels based on the shared latent representations. Training the SAR classifier neural network further includes computing a value of a loss function based on the plurality of first training labels, the plurality of second training labels, and the plurality of classification labels and performing backpropagation based on the value of the loss function.

Abstract: A radar anti-spoofing system for an autonomous vehicle includes a plurality of radar sensors that generate a plurality of input detection points representing radio frequency (RF) signals reflected from objects and a controller in electronic communication with the plurality of radar sensors. The controller executes instructions to determine time-matched clusters that represent objects located in an environment surrounding the autonomous vehicle based on the input detection points from the plurality of radar sensors. The controller determines an adjusted signal to noise (SNR) measure for a specific time-matched cluster by dividing an SNR of the specific time-matched cluster by a range measurement of the specific time-matched cluster. The controller determines a velocity-ratio measure of the time-matched cluster by dividing a motion-based velocity by a Doppler-frequency velocity, and identifies the time-matched cluster as either a ghost object or a real object.

Abstract: A computing system including a processor configured to train a synthetic aperture radar (SAR) classifier neural network. The SAR classifier neural network is trained at least in part by, at a SAR encoder, receiving training SAR range profiles that are tagged with respective first training labels, and, at an image encoder, receiving training two-dimensional images that are tagged with respective second training labels. Training the SAR classifier neural network further includes, at a shared encoder, computing shared latent representations based on the SAR encoder outputs and the image encoder outputs, and, at a classifier, computing respective classification labels based on the shared latent representations. Training the SAR classifier neural network further includes computing a value of a loss function based on the plurality of first training labels, the plurality of second training labels, and the plurality of classification labels and performing backpropagation based on the value of the loss function.

Abstract: The utility model provides a building block type combined storage device, at least including any two of a large storage basket group, a small storage basket group and a lid. The large storage basket group includes a first large storage basket without a first handle and/or b second large storage basket with a first handle, wherein the small storage basket group includes 2*n first small storage baskets without a second handle and/or 2*m second small storage baskets with a second handle, and a, b, n, and m are all natural numbers greater than or equal to 1. Through the design of a first clamping groove mortise and tenon joint structure and a second clamping groove mortise and tenon joint structure, the stability of connection between the large and small storage basket groups is enhanced. The utility model has the advantages of flexible stacking and combination, reasonable and firm structure, high utilization rate and ability of self-arrangement and storage.

Abstract: A radar anti-spoofing system for an autonomous vehicle includes a plurality of radar sensors that generate a plurality of input detection points representing radio frequency (RF) signals reflected from objects and a controller in electronic communication with the plurality of radar sensors. The one or more controllers execute instructions to determine a signal to noise ratio (SNR) distance ratio for the input detection points generated by the plurality of radar sensors, where a value of the SNR distance ratio is indicative of an object being a ghost vehicle. The one or more controllers also determine an effective particle number indicating a degree of particle degradation for the importance sampling for each variable that is part of the state variable. In response to determining the effective particle number is equal to or less than a predetermined threshold, the one or more controllers estimate a ghost position for the ghost vehicle.

Abstract: A method, system, and computer program product for predicting abnormal operation of at least one component of a machine is provided. Real time monitoring data from an operating machine is received and monitoring features that are informative of likely abnormal operation are extracted and/or calculated. The monitoring features are applied to a prediction matrix that outputs probabilities of abnormal operation within one or more prediction time horizons. If the output probabilities exceed a threshold probability, then an alert can be output. Maintenance can be automatically scheduled in response to the alert.

Abstract: A radar anti-spoofing system for an autonomous vehicle includes a plurality of radar sensors that generate a plurality of input detection points representing radio frequency (RF) signals reflected from objects and a controller in electronic communication with the plurality of radar sensors. The controller executes instructions to determine time-matched clusters that represent objects located in an environment surrounding the autonomous vehicle based on the input detection points from the plurality of radar sensors. The controller determines an adjusted signal to noise (SNR) measure for a specific time-matched cluster by dividing an SNR of the specific time-matched cluster by a range measurement of the specific time-matched cluster. The controller determines a velocity-ratio measure of the time-matched cluster by dividing a motion-based velocity by a Doppler-frequency velocity, and identifies the time-matched cluster as either a ghost object or a real object.

Abstract: A radar anti-spoofing system for an autonomous vehicle includes a plurality of radar sensors that generate a plurality of input detection points representing radio frequency (RF) signals reflected from objects and a controller in electronic communication with the plurality of radar sensors. The one or more controllers execute instructions to determine a signal to noise ratio (SNR) distance ratio for the input detection points generated by the plurality of radar sensors, where a value of the SNR distance ratio is indicative of an object being a ghost vehicle. The one or more controllers also determine an effective particle number indicating a degree of particle degradation for the importance sampling for each variable that is part of the state variable. In response to determining the effective particle number is equal to or less than a predetermined threshold, the one or more controllers estimate a ghost position for the ghost vehicle.

Abstract: This invention relates to benzodiazepine derivatives, compositions comprising therapeutically effective amounts of those derivatives and methods of using those derivatives or compositions in treating cognitive impairment associated with CNS disorders. It also relates to the use of an ?5-containing GABAA receptor agonist (e.g., an ?5-containing GABAA receptor positive allosteric modulator) in treating cognitive impairment associated with CNS disorders in a subject in need or at risk thereof, including age-related cognitive impairment, Mild Cognitive Impairment (MCI), amnestic MCI, Age-Associated Memory Impairment, Age Related Cognitive Decline, dementia, Alzheimer's Disease (AD), prodromal AD, PTSD, schizophrenia, bipolar disorder, ALS, cancer-therapy-related cognitive impairment, mental retardation, Parkinson's disease, autism spectrum disorders, fragile X disorder, Rett syndrome, compulsive behavior, and substance addiction. It also relates to the use of an ?5-containing GABAA receptor agonist (e.g.

Abstract: A computing system including a processor configured to train a synthetic aperture radar (SAR) classifier neural network. The SAR classifier neural network is trained at least in part by, at a SAR encoder, receiving training SAR range profiles that are tagged with respective first training labels, and, at an image encoder, receiving training two-dimensional images that are tagged with respective second training labels. Training the SAR classifier neural network further includes, at a shared encoder, computing shared latent representations based on the SAR encoder outputs and the image encoder outputs, and, at a classifier, computing respective classification labels based on the shared latent representations. Training the SAR classifier neural network further includes computing a value of a loss function based on the plurality of first training labels, the plurality of second training labels, and the plurality of classification labels and performing backpropagation based on the value of the loss function.

Abstract: This invention relates to benzodiazepine derivatives, compositions comprising therapeutically effective amounts of those derivatives and methods of using those derivatives or compositions in treating cognitive impairment associated with CNS disorders. It also relates to the use of an ?5-containing GABAA receptor agonist (e.g, an ?5-containing GABAA receptor positive allosteric modulator) in treating cognitive impairment associated with CNS disorders in a subject in need or at risk thereof, including age-related cognitive impairment, Mild Cognitive Impairment (MCI), amnestic MCI, Age-Associated Memory Impairment, Age Related Cognitive Decline, dementia, Alzheimer's Disease (AD), prodromal AD, PTSD, schizophrenia, bipolar disorder, ALS, cancer-therapy-related cognitive impairment, mental retardation, Parkinson's disease, autism spectrum disorders, fragile X disorder, Rett syndrome, compulsive behavior, and substance addiction. It also relates to the use of an ?5-containing GABAA receptor agonist (e.g.

Abstract: This invention relates to benzodiazepine derivatives, compositions comprising therapeutically effective amounts of those derivatives and methods of using those derivatives or compositions in treating cognitive impairment associated with CNS disorders. It also relates to the use of an ?5-containing GABAA receptor agonist (e.g., an ?5-containing GABAA receptor positive allosteric modulator) in treating cognitive impairment associated with CNS disorders in a subject in need or at risk thereof, including age-related cognitive impairment, Mild Cognitive Impairment (MCI), amnestic MCI, Age-Associated Memory Impairment, Age Related Cognitive Decline, dementia, Alzheimer's Disease (AD), prodromal AD, PTSD, schizophrenia, bipolar disorder, ALS, cancer-therapy-related cognitive impairment, mental retardation, Parkinson's disease, autism spectrum disorders, fragile X disorder, Rett syndrome, compulsive behavior, and substance addiction. It also relates to the use of an ?5-containing GABAA receptor agonist (e.g.