A hardware-in-the-loop (HiL) test system is for testing sensor fusion of an advance driver assistance system (ADAS). The ADAS includes a plurality of sensors and an electronic control unit (ECU) processing outputs of the sensors. The HiL test system includes a three-dimensional (3D) scenario simulator for generating drive scenarios including objects in a surrounding environment of a simulated vehicle, and a sensor target emulator for generating emulated sensors inputs to the plurality of sensors corresponding to the drive scenarios generated by the 3D scenario simulator. The sensor target emulator is configured to synchronize the emulated sensor inputs by interpolating a current location of the objects from parameters extracted from the drive scenarios.

A driver condition estimating device includes circuitry configured to measure movement of the head of a driver from output of a driver camera and detect a sign of abnormality of the driver from the movement of the head. On condition that lateral acceleration acting on the head of the driver is a predetermined value or less, the circuitry is configured to calculate a periodic feature amount from time series data showing the movement of the head of the driver, calculate a time series variation pattern from the obtained periodic feature amount, and compare of the obtained time series variation pattern with a predetermined threshold.

A system comprises a computer that includes a processor and a memory. The memory stores instructions executable by the processor to estimate a first joint probability distribution of first data with respect to second data based on first and second marginal probability distributions of the first and second data, wherein the first data is from a first sensor based on a first sensor coordinate system, and the second data is from a second sensor based on a second sensor coordinate system, to estimate a second joint probability distribution of the first data with respect to the second data based on a projection of the first data onto a plane defined in the second sensor coordinate system, to estimate a rigid transformation between the first sensor coordinate system and the second sensor coordinate system by minimizing a distance between the first and second joint probability distributions, wherein the second joint probability distribution is a function of a set of extrinsic calibration parameters, and based on the set of the extrinsic calibration parameters, to detect an object in the first and second data.

A computer implemented method for object detection comprises the following steps carried out by computer hardware components: acquiring a plurality of lidar data sets from a lidar sensor; acquiring a plurality of radar data sets from a radar sensor; acquiring at least one image from a camera; determining concatenated data based on casting the plurality of lidar data sets and the plurality of radar data sets to the at least one image; and detecting an object based on the concatenated data.

Embodiments of the present disclosure disclose a method and apparatus for training a cross-modal face recognition model, a device and a storage medium. The method may include: acquiring a first modal face recognition model having a predetermined recognition precision; acquiring a first modality image of a face and a second modality image of the face; acquiring a feature value of the first modality image of the face and a feature value of the second modality image of the face; and constructing a loss function based on a difference between the feature value of the first modality image of the face and the feature value of the second modality image of the face, and tuning a parameter of the first modal face recognition model based on the loss function until the loss function converges, to obtain a trained cross-modal face recognition model.

Systems, methods, apparatuses, and computer program products for recognizing human emotion in images or video. A method for recognizing perceived human emotion may include receiving a raw input. The raw input may be processed to generate input data corresponding to at least one context. Features from the raw input data may be extracted to obtain a plurality of feature vectors and inputs. The plurality of feature vectors and the inputs may be transmitted to a respective neural network. At least some of the plurality of feature vectors may be fused to obtain a feature encoding. Additional feature encodings may be computed from the plurality of feature vectors via the respective neural network. A multi-label emotion classification of a primary agent may be performed in the raw input based on the feature encoding and the additional feature encodings.

The invention relates to a method for microscopic evaluation (120) of a sample (2), in particular at least one uncolored object or cell sample (2), in an optical detection system (1), where the following steps are performed: providing at least two different detection information (110) about the sample (2), in particular by the detection system (1),
performing an evaluation (120) of the detection information (110), in particular by an analysis means (60), on the basis of machine-learned transfer information (200), in order to determine result information (140) about the sample (2),
the transfer information (200) being trained for a different detection parameterization of the detection information (110), in which the detection information (110) differs from one another in terms of at least one illumination parameter of the detection system (1), in particular in terms of polarization and/or color coding.

A computer implemented method for determining a command of an occupant of a vehicle comprises the following steps carried out by computer hardware components: determining object information indicating information about at least one object outside the vehicle; determining occupant gesture information indicating information related to the occupant; and selecting a task to be carried out based on the object information and the occupant gesture information.

A radiographic image processing device includes a radiographic image acquisition unit that acquires a plurality of radiographic images of a specific subject taken using radiations having energies different from each other, a structure recognition unit that recognizes structures, which is included in the subject, using the radiographic images, an attenuation coefficient calculation unit that calculates attenuation coefficients μ of the radiation for the structures, which are recognized by the structure recognition unit, using recognition results of the structure recognition unit and the plurality of radiographic images, and an image processing unit that performs image processing on the radiographic images using the attenuation coefficients.

The invention discloses a failure diagnosis method for a power transformer winding based on a GSMallat-NIN-CNN network. The failure diagnosis method includes: measuring a vibration condition of the transformer winding by using a multi-channel sensor to obtain multi-source vibration data of the transformer; converting the multi-source vibration data obtained through measurement into gray-scale images through GST gray-scale conversion; decomposing, by using a Mallat algorithm, each gray-scale image layer by layer into a high-frequency component sub-image and a low-frequency component sub-image, and fusing the sub-images; reconstructing fused gray-scale images, and coding vibration gray-scale images according to respective failure states of the transformer winding; establishing a failure diagnosis model for the transformer based on the GSMallat-NIN-CNN network; and randomly initializing network parameters to divide a training set and a test set, and training and tuning the network by using the training set; and testing the trained network by using the test set.