Abstract: Disclosed are systems and methods for scintillation-based neural network for RADAR target classification. In some aspects, a method includes generating a point cloud from radio frequency (RF) scene responses received from a radio detection and ranging (RADAR) sensor for a scanned scene; populating a rolling buffer with frame data from the point cloud, the frame data including radar cross section (RCS) values, RCS scintillation measurements, and velocity values for objects in the scanned scene; inputting the RCS scintillation measurements and velocity values for an object of the objects to a convolutional neural network (CNN); and receiving a classification of the object from the CNN, wherein the CNN is to utilize a probability density function (PDF) estimate of the RCS scintillation measurements and the velocity values to determine fits with one or more reference PDFs based on a Neyman-Pearson evaluation, and wherein the fits are assessed to classify the object.

Abstract: A storage box includes a compartment having a base and opposed sidewalls extending from the base. Each sidewall of the opposed sidewalls includes two first retaining surfaces extend away from the base and along the sidewall where the first retaining surfaces are a first distance away from each other. Each sidewall includes two parallel second retaining surfaces extending away from the first retaining surfaces and along the sidewall. The second retaining surfaces are a second distance away from each other and the second distance is greater than the first distance. The storage box includes a rod, and support surfaces associated with each sidewall of the first pair of opposed sidewalls are configured to support the rod.

Abstract: Curvelet-based low level fusion of camera and RADAR sensor information is disclosed and includes processing RADAR data corresponding to a scene to generate a RADAR point cloud and processing camera image data corresponding to the scene using a curvelet transform to identify a target of interest in the scene and generate for the target of interest a target type, (x,y) coordinate values, and a curvelet magnitude per decomposition level. If discrepancies exist between (x,y) coordinate values of the RADAR point cloud and the target type, (x,y) coordinate values, and curvelet magnitude per composition level of the target of interest, a portion of the RADAR data processing is repeated to regenerate the RADAR point cloud; otherwise, the RADAR point cloud to a perception stack of a vehicle.

Abstract: Disclosed are systems and methods for detecting weather conditions at a LiDAR sensor level. In some aspects, a method includes calculating a reference probability mass function (PMF) of at least one field of a point cloud generated from reference scene responses received from a light detection and ranging (LiDAR) sensor; calculating a current PMF for the at least one field of the point cloud generated from a current scene response received from the LiDAR sensor; determining a statistical difference between the reference PMF and the current PMF using a Kullbeck-Leibler (KL) divergence calculation; and responsive to the statistical difference satisfying a threshold for the at least one field, flagging an environmental change in the current scene response.

Abstract: For some embodiments of the present disclosure, systems and methods for using computer vision to guide processing of receive responses of RADAR sensors of a vehicle are described. A computer implemented method comprises receiving, with one or more receivers of RADAR sensors, environment receive RF responses, receiving region of interest (ROI) information including segments of a tentative set of ROIs with free space area boundaries from an image processor of an image processing chain for the computer vision, dynamically determining a number of polyphase filters based on a number of ROIs having dynamic scenes that are provided by the image processing chain, and adjusting parameters of the polyphase filters of a RADAR processing chain based on the ROI information.

Abstract: Providing, hosting, and playing a bingo game associated with real or virtual events occurring independently, which may be randomly occurring or skill-based, real or virtual events occurring in real-time, such as those occurring in sporting events or one or more video games being played by one or more players, which may be human players playing or a computer player using artificial intelligence.

Abstract: A shield enclosure includes a housing with a peripheral wall that defines a cavity, and a cover removably coupleable to the housing to at least partially seal the cavity. The cavity is sized to receive a printed circuit board therein. The housing shields the printed circuit board from electromagnetic interference and noise during noise figure testing of a radiofrequency component on the printed circuit board.

Abstract: The present technology determines noise in an image of a representation of an environment around an autonomous vehicle (AV). A sensor system receives a first image that represents an environment around the AV including at least one object that may be obscured by environmental conditions. The first image is analyzed to determine if pixels in the first image represent noise from the environmental conditions in comparison to other pixels in the first image that represents a higher degree of image detail. A second image can be generated using the pixels in the first image that represent the higher degree of image detail to result in an output that is less affected by noise from environmental conditions and may result in better performance by downstream systems relying of date from the sensor system.

Abstract: A storage box includes a compartment having a base and opposed sidewalls extending from the base. Each sidewall of the opposed sidewalls includes two first retaining surfaces extend away from the base and along the sidewall where the first retaining surfaces are a first distance away from each other. Each sidewall includes two parallel second retaining surfaces extending away from the first retaining surfaces and along the sidewall. The second retaining surfaces are a second distance away from each other and the second distance is greater than the first distance. The storage box includes a rod, and support surfaces associated with each sidewall of the first pair of opposed sidewalls are configured to support the rod.

Abstract: High-resolution point cloud formation based on the use of a point spread function kernel is disclosed. The proposed disclosure provides the means to generate high angular resolution point clouds by performing a matching process of the point spread function (PSF) to a series of adaptive hypotheses to accurately identify the target signatures using the maximum amount of available information. The proposed method comprises a two-stage processing chain. In the first step, the spatial and the Doppler spectrum of the target responses is calculated and decomposed in terms of the sensor point spread function (PSF). In the second step, the target PSF representation is then processed using a support vector machine to determine the presence of closely located targets that would not be possible to detect using typical automotive RADAR signal processing chains.

Abstract: Adaptive differential quantization for ranging sensors and sensor data encoding is enclosed. The proposed disclosure comprises a differential quantization algorithm that extracts the raw signal data gradients to maximize the amount of effective information (using information theory approaches) transferred to a sensor processing data unit. This approach results in an efficient use of the data links between the sensor suite and a central sensor processing data engine, which can allow the use of cost-effective physical links. The proposed disclosure takes advantage of the gradual environment changes observed in typical vehicular platform trajectories.

Abstract: A radio detection and ranging (RADAR) system calibration and compensation approach is described. receive aa reference signal is received from a signal generation architecture. A pre-selected number of cycles of the reference signal are stored. A spectrum calculation is performed on the stored reference signal to generate a spectrum representation. Component analysis operations are performed on the spectrum representation to generate a set of coefficients. Non-linear classification is performed on the set of coefficients to determine collection cycle parameters. Noise parameters are estimated utilizing the coefficients and the collection cycle parameters. The noise parameters are phase noise parameter estimations and/or system characteristic colored noise parameter estimations. Point clouds can be generated using the noise estimation parameters.

Abstract: Methods and apparatus disclosed within provide a solution to problems associated with the use of either high frequency or low frequency radar signals. Methods of the present disclosure may transmit high frequency radar signals, transmit low frequency radar signals, receive reflected radar signals, and process the received radar signals using parameters respectively suited for processing high frequency and low frequency radar signals. Evaluations may be performed that allow an apparatus to adapt for limitations associated with the processing of high frequency radar data, the processing of low frequency radar data, or both. Different correlation functions may be performed that allow the apparatus to identify objects and to identify object velocities using different sets of program code instructions. These different evaluations and correlations may result in the generation of a set of “point-cloud” information that may be used by other processes of a sensing apparatus.

Abstract: Methods and apparatus disclosed within provide a solution to problems associated with objects located in a blind spot of a radar apparatus of a first vehicle. An identifier included a set of received radar signals may be extracted from the received radar signals to identify a radar device of a second vehicle. Information may then be received that identifies a location of the second vehicle. This received information may also include operational characteristics of the received radar signals. Evaluations may then be performed on the received radar signals using the received information such that a location of an object located in the blind spot of the radar apparatus may be identified. Data associated with the object may then be stored in a set of radar detection data that that also includes locations of other objects that were detected by direct observations made by the radar apparatus of the first vehicle.

Abstract: Processing of a fractalet radio detection and ranging (RADAR) signal is described. A reference fractalet waveform is received. The fractalet waveform includes self-similar waveforms having lower frequency bands and frequency bands. A reflected fractalet waveform received via one or more antennae is decoded. A waveform profile of chirplet transforms of signals in the lower frequency bands within the reflected fractalet waveform are compared to the reference fractalet waveform. Time spans corresponding to the subset of lower frequency bands are determined. Signals from the higher frequency bands are extracted from the reflected fractalet waveform. Chirplet transforms for the extracted signals from the higher frequency bands are determined for the determined time spans. Spatial frequency components along azimuth direction and elevation directions are calculated for targets based on the chirplet transforms for the extracted signals from the higher frequency bands.

Abstract: A radio detection and ranging (RADAR) sensor system signal generator is disclosed. The signal generator includes a lower frequency oscillator circuit to generate a first reference signal having a first frequency, a higher frequency oscillator circuit to generate a second reference signal having a second frequency that is higher than the first frequency, a set of frequency multipliers and signal mixers coupled to receive the first reference signal and the second reference signal, the set of frequency multipliers and signal mixers to generate a plurality of baseline signals, voltage-to-frequency converters to generate radar frequency waveforms at a plurality of specified frequencies according to chirp parameters for a plurality of frequency bands, and modulating radio frequency mixers to mix the plurality of baseline signals with the radar frequency waveforms at the plurality of specified frequencies to generate a multifrequency wavelet RADAR waveform.

Abstract: Described is a passenger seat having a seat bottom, a seat back, and a privacy divider. The seat back may have a stationary portion and a moveable portion, wherein the moveable portion is substantially upright in stowed position and is substantially horizontal over the seat bottom in a deployed position. The privacy divider may be coupled to at least an upper surface of the moveable portion in the deployed position.

Abstract: A method for providing a notification of applications that relate to a business process is provided. The method includes collecting identification data that relates to each of a plurality of applications connected to the at least one processor; storing the identification data; receiving a user request for information that includes a list of first applications that relate to the business process; analyzing the identification data to determine whether each respective one of the plurality of applications is to be included in the list; and generating the list based on a result of the analyzing and notifying the user of the list. The method also includes collecting network data that relates to each of the plurality of applications; storing the network data; and analyzing the network data to determine whether each respective one of the plurality of applications is to be added to the list.

Abstract: Architectures and techniques for near field beamforming are disclosed. RADAR waveform data is received from a radio frequency front end. Range and movement information for one or more objects is generated from the received RADAR waveform data. A spatial frequency representation of the received RADAR waveform data is calculated. The spatial frequency representation of the received RADAR waveform data is migrated to a spatial space representation using a mapping function and interpolation. Signal processing operations are performed on the spatial space representation of the received RADAR waveform data. The spatial space representation of the received RADAR waveform data is converted to a Cartesian space representation. Information corresponding to the one or more objects in the Cartesian space representation is generated.

Abstract: Use of camera information for radio detection and ranging (RADAR) beamforming is disclosed. Camera information for a scene having a target object is received. Digital RADAR waveforms corresponding to a frame of reference including the at target object are received. Coordinates for the scene in the camera frame of reference are translated to the RADAR frame of reference. A radar cross section estimation is determined for the object based on the transformed coordinates. A kernel is selected based on the radar cross section estimation. RADAR signal processing is performed on the digital RADAR waveforms utilizing the selected kernel. A point cloud is populated based on results from the RADAR signal processing.