The described aspects and implementations enable fast and accurate verification of radar detection of objects in autonomous vehicle (AV) applications using combined processing of radar data and camera images. In one implementation, disclosed is a method and a system to perform the method that includes obtaining a radar data characterizing intensity of radar reflections from an environment of the AV, identifying, based on the radar data, a candidate object, obtaining a camera image depicting a region where the candidate object is located, and processing the radar data and the camera image using one or more machine-learning models to obtain a classification measure representing a likelihood that the candidate object is a real object.

The present invention provides navigation assistance that is for small ships and that enables clear depiction of a radar image. An administrative server in a ship navigation assistance system, the server being equipped with: a communication means that performs, via a network, data transmission/reception with a user terminal which is connected to a device for detecting a target; and a vertex information management means that, of a plurality of target vertices extracted on the basis of detection result information of the target, identifies vertices moving together as a group as belonging to the same target, wherein, on the basis of the vertices that have been identified as belonging to the same target, the communication means transmits an instruction to the user terminal to display a rough outline of said target.

Provided are a representative point extraction circuitry that extracts a representative reflection point of a target a line segment setting circuitry that sets a plurality of detection line segments of the same length that extend from the representative reflection point and are arranged within the irradiation field at intervals about the representative reflection point as a center, a reflection point counter that counts a coordinate point indicating a reflection level higher than a second threshold value as a reflection point by using the second threshold value, and a reflection point determinator that determines a reflection point counted in the detection line segments to a top second place in which the number of reflection points is large, as a reflection point from the target.

The invention relates to a method for detecting at least one road user on a traffic route by means of a radar sensor and an optical detector, wherein with said method radar radiation is emitted by at least one radar transmitter of the radar sensor and reflected by the at least one road user, the reflected radar radiation is detected by means of at least one radar receiver of the radar sensor, the detected radar radiation is evaluated in such a way that at least one distance and one radial velocity of the at least one road user relative to the radar sensor is determined, an optical image of the at least one road user is detected by means of the optical detector, and the optical image is evaluation,
wherein at least one parameter of the at least one road user is determined both from the detected radar radiation and the optical image.

A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing hybrid machine learning. More specifically, the system and method produce predictions of MCI conversions to dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is trained using transfer learning. A platform may then receive a request from a clinician for a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.

A method is provided for determining a road condition by using a radar system having transmitter and receiving units for transmitting and receiving radar waves having two different polarizations and providing transmit and receive signals indicating an intensity of the transmitted and received radar waves. Co-polarized backscattering coefficients and at least one cross-polarized backscattering coefficient are determined based on the transmit and receive signals. If the cross-polarized backscattering coefficient is greater than or equal to a threshold, the road condition is determined based on a ratio of the co-polarized backscattering coefficients and based on a difference of one of the co-polarized backscattering coefficients and the cross-polarized backscattering coefficient. If the cross-polarized backscattering coefficient is smaller than the threshold, the road condition is determined based on the ratio and a difference of the co-polarized backscattering coefficients.

An apparatus includes an extendable wand, and a sensor head coupled to the wand. The sensor head includes a continuous wave metal detector (CWMD) and a radar. When the wand is collapsed, the wand and the sensor head collapse to fill a volume that is smaller than a volume filled by the sensor head and the wand when the wand is extended. Frequency-domain data from a sensor configured to sense a region is accessed, the frequency-domain data is transformed to generate a time-domain representation of the region, a first model is determined based on the accessed frequency-domain data, a second model is determined based on the generated time-domain representation, the second model being associated with a particular region within the sensed region, and a background model that represents a background of the region is determined based on the first model and the second model.

An autonomous guidance system that operates a vehicle in an autonomous mode includes a camera module, a radar module, and a controller. The camera module outputs an image signal indicative of an image of an object in an area about a vehicle. The radar module outputs a reflection signal indicative of a reflected signal reflected by the object. The controller determines an object-location of the object on a map of the area based on a vehicle-location of the vehicle on the map, the image signal, and the reflection signal. The controller classifies the object as small when a magnitude of the reflection signal associated with the object is less than a signal-threshold.

A trailer-detection system includes a radar-sensor and a controller. The radar-sensor is used to determine a range, an azimuth-angle, and an elevation-angle of a radar-signal reflected by a trailer towed by a host-vehicle. The controller is in communication with the radar-sensor. The controller is configured to determine a size of the trailer towed by the host-vehicle based on the range, the azimuth-angle, and the elevation-angle of the radar-signal.

A sensor includes a transmit antenna, a receive antenna, circuitry, and a memory. The transmit antenna includes N transmit antenna elements each transmitting a transmit signal. The receive antenna includes M receive antenna elements each receiving N receive signals including reflection signals reflected by an organism. The circuitry extracts a second matrix corresponding to a predetermined frequency range from an N×M first matrix representing propagation characteristics between each transmit antenna element and each receive antenna element calculated from the receive signals. The circuitry estimates the position of the organism by using the second matrix, and calculates a radar cross-section value with respect to the organism, based on the estimated position and the positions of the transmit antenna and the receive antenna. The circuitry then estimates the posture of the organism by using the calculated radar cross-section value and information indicating associations between radar cross-section values and postures of the organism.