A particle measurement system and method of operation thereof are described. The system and method render a characteristic for a set of particles measured while passing through a measurement volume. The system includes a source that generates a particle-laden field containing the set of particles. The system further includes a sensor that generates a raw particle data corresponding to the set particles passing through the measurement volume of the particle measurement system, where the raw particle data comprises a set of raw particle records and each of one of the raw particle records includes a particle data content. A preconditioning stage carries out a preconditioning operation on the particle data content of the set of raw particle records to render a conditioned input data. A machine learning stage processes the conditioned input data to render an output characteristic parameter value for the set of particles.

The present disclosure relates to an image display apparatus and an operating method thereof. The image display device according to an embodiment of the present disclosure includes a display; a network interface unit that performs communication through a network; and a controller, wherein the controller generates data for a screen output through the display, when a preset user input is received while a first content is output through the display, obtains data, which corresponds to data of the screen, that is related to an object included in the screen, from a first server through the network interface unit, determines at least one first object related to a position corresponding to the user input, among object included in the screen, based on the data that is related to the object, and outputs a user interface (UI) for the at least one first object through the display. Various other embodiments are possible.

The present disclosure relates to an image display apparatus and an operating method thereof. The image display device according to an embodiment of the present disclosure includes a display; a network interface unit that performs communication through a network; and a controller, wherein the controller generates data for a screen output through the display, when a preset user input is received while a first content is output through the display, obtains data, which corresponds to data of the screen, that is related to an object included in the screen, from a first server through the network interface unit, determines at least one first object related to a position corresponding to the user input, among object included in the screen, based on the data that is related to the object, and outputs a user interface (UI) for the at least one first object through the display. Various other embodiments are possible.

Methods, systems, and apparatus, including medium-encoded computer program products, for a secure edge platform that uses image classification machine learning models. An edge platform can include at least one camera and can identify image classification models that generate classification output data from image data generated by the cameras. The edge platform can receive image data generated by the camera, and provide the image data to the models. In response to providing the image data classification models, the edge platform can receive classification output data. In response to receiving the classification output data from the image classification models, the edge platform can generate augmentation data that is associated with the image data, then transmit detection data to a central server platform. The detection data can include (i) the classification output data and (ii) the augmentation data associated with the image data. Data can be made recordable, reportable, searchable, and alarmable.

One application of deep learning methods and labelled data is for industrial production or work applications. For such applications implemented with machine learning applications, massive amounts of data are required to train, validate, and/or tune models for better fitting the requirements. However, obtaining such data has typically be costly and difficult. Embodiments provide adaptable processes that provide data labelling methods for work settings. Embodiments take advantage of the work or production processes to label and collect data, which save time and money and improves accuracy. Embodiments prevent or reduce the need for worker training costs and human mistake-triggered data labelling problems. Embodiments also improve data labelling quality and speed-up of the development cycle.

A sectioning system includes a chuck assembly configured to receive a tissue block, a cutting assembly configured to remove a tissue section from the tissue block, at least one sensor configured to sense data regarding dynamics of one or more components of at least one of the chuck assembly or the cutting assembly, and a control system. The control system is configured to receive data from the at least one sensor, determine whether the data from the at least one sensor shows normal behavior of the one or more components of at least one of the chuck assembly or the cutting assembly, and output a signal if it is determined the data from the at least one sensor does not show normal behavior of the one or more components.

An imaging apparatus (100) comprises a signal processor (130) generating image data according to an imaging result of an imaging device (110), and a data transmission status notifying part (180) controlling, when the image data has been output to the outside, a data transmission status displaying part (181) to notify that the image data has been output to the outside.

A trajectory for an autonomous vehicle (AV) can be generated using curvature segments. A decision planner component can receive a reference trajectory for the AV to follow in an environment. A number of subdivisions (frames) of the reference trajectory may be associated with a curvature value and a tangent vector. Starting with an initial position of the AV, a candidate trajectory can be determined by continuously intersecting a segment with an origin at the initial position of the AV and a reference line associated with a particular frame. The reference line can be substantially perpendicular to the tangent vector of the particular frame. A location of the intersection between the segment and the reference line can be based on a curvature value of the segment. Optimizing a candidate trajectory can include varying curvature values associated with various segments and determining costs of the various candidate trajectories.

A method for assigning a number of lanes and a direction of travel on a path includes receiving location data including a plurality of location points, projecting the plurality of location points on to an aggregation axis perpendicular to a centerline of the vehicle path, grouping the plurality of location points as projected onto the aggregation axis into one or more clusters, and determining the number of vehicle lanes of the vehicle path based on a count of the one or more clusters.

In various examples, a deep neural network (DNN) is trained—using image data alone—to accurately predict distances to objects, obstacles, and/or a detected free-space boundary. The DNN may be trained with ground truth data that is generated using sensor data representative of motion of an ego-vehicle and/or sensor data from any number of depth predicting sensors—such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. The DNN may be trained using two or more loss functions each corresponding to a particular portion of the environment that depth is predicted for, such that—in deployment—more accurate depth estimates for objects, obstacles, and/or the detected free-space boundary are computed by the DNN. In some embodiments, a sampling algorithm may be used to sample depth values corresponding to an input resolution of the DNN from a predicted depth map of the DNN at an output resolution of the DNN.