Abstract: A scanning system includes a plurality of light detection and ranging (LiDAR) sensors, each LiDAR sensor of the plurality of LiDAR sensors being configured to generate a point cloud based on interactions of emitted light with a surrounding environment, and a processor configured to split a full 360° rotation of each LiDAR sensor into a plurality of slices including a first slice and a second slice, to generate a coherent fused point cloud by fusing together portions of point clouds of the plurality of LiDAR sensors corresponding to the first slice, and fusing together portions of point clouds of the plurality of LiDAR sensors corresponding to the second slice, to determine an action for the scanning system to take in response to the generation of the coherent fused point cloud, and to cause the scanning system to implement the action.

Abstract: An object trajectory prediction system of a vehicle includes: one or more sensors to generate sensory data corresponding to one or more objects within a range of the vehicle; one or more processors; and memory connected to the one or more processors and having instructions that, when executed by the one or more processors, cause the one or more processors to: identify a first person of interest (POI) from the sensory data; estimate a 3-dimensional (3D) pose of the first POI from the sensory data; calculate a trajectory of the first POI according to at least the 3D pose of the first POI; and determine a navigational path of the vehicle according to the trajectory of the first POI.

Abstract: A scanning system includes a plurality of light detection and ranging (LiDAR) sensors, each LiDAR sensor of the plurality of LiDAR sensors being configured to generate a point cloud based on interactions of emitted light with a surrounding environment, and a processor configured to split a full 360° rotation of each LiDAR sensor into a plurality of slices including a first slice and a second slice, to generate a coherent fused point cloud by fusing together portions of point clouds of the plurality of LiDAR sensors corresponding to the first slice, and fusing together portions of point clouds of the plurality of LiDAR sensors corresponding to the second slice, to determine an action for the scanning system to take in response to the generation of the coherent fused point cloud, and to cause the scanning system to implement the action.

Abstract: An object trajectory prediction system of a vehicle includes: one or more sensors to generate sensory data corresponding to one or more objects within a range of the vehicle; one or more processors; and memory connected to the one or more processors and having instructions that, when executed by the one or more processors, cause the one or more processors to: identify a first person of interest (POI) from the sensory data; estimate a 3-dimensional (3D) pose of the first POI from the sensory data; calculate a trajectory of the first POI according to at least the 3D pose of the first POI; and determine a navigational path of the vehicle according to the trajectory of the first POI.

Abstract: Apparatuses, methods, and systems for automated cell classification, embryo ranking, and/or embryo categorization are provided. An apparatus includes a classification module configured to apply classifiers to images of one or more cells to determine, for each image, a classification probability associated with each classifier. Each classifier is associated with a distinct first number of cells, and is configured to determine the classification probability for each image based on cell features including one or more machine learned cell features. The classification probability indicates an estimated likelihood that the distinct first number of cells is shown in each image. The classification module is further configured to classify each image as showing a second number of cells based on the distinct first number of cells and the classification probabilities associated therewith. The classification module is implemented in at least one of a memory or a processing device.

Abstract: Apparatuses, methods, and systems for automated, non-invasive evaluation of cell activity are provided. In one embodiment, an apparatus includes a hypothesis selection module configured to select a hypothesis from a plurality of hypotheses characterizing one or more cells shown in an image. Each of the plurality of hypotheses includes an inferred characteristic of the one or more cells based on geometric features of the one or more cells shown in the image. The hypothesis selection module is implemented in at least one of a memory or a processing device.

Abstract: Apparatuses, methods, and systems for the automated imaging and evaluation of human embryos, oocytes, or pluripotent cells are described. An apparatus and method for automated dish detection and well occupancy determination are described. In addition, a multi-well culture dish and an illumination assembly for bimodal imaging are described. These inventions find use at least in identifying or in facilitating identification of embryos and oocytes in vitro that are most useful in treating infertility in humans.

Abstract: Apparatuses, methods, and systems for the automated imaging and evaluation of human embryos, oocytes, or pluripotent cells are described. An apparatus and method for automated dish detection and well occupancy determination are described. In addition, a multi-well culture dish and an illumination assembly for bimodal imaging are described. These inventions find use at least in identifying or in facilitating identification of embryos and oocytes in vitro that are most useful in treating infertility in humans.

Abstract: Apparatuses, methods, and systems for automated cell classification, embryo ranking, and/or embryo categorization are provided. An apparatus includes a classification module configured to apply classifiers to images of one or more cells to determine, for each image, a classification probability associated with each classifier. Each classifier is associated with a distinct first number of cells, and is configured to determine the classification probability for each image based on cell features including one or more machine learned cell features. The classification probability indicates an estimated likelihood that the distinct first number of cells is shown in each image. The classification module is further configured to classify each image as showing a second number of cells based on the distinct first number of cells and the classification probabilities associated therewith. The classification module is implemented in at least one of a memory or a processing device.

Abstract: Apparatuses, methods, and systems for automated cell classification, embryo ranking, and/or embryo categorization are provided. An apparatus includes a classification module configured to apply classifiers to images of one or more cells to determine, for each image, a classification probability associated with each classifier. Each classifier is associated with a distinct first number of cells, and is configured to determine the classification probability for each image based on cell features including one or more machine learned cell features. The classification probability indicates an estimated likelihood that the distinct first number of cells is shown in each image. The classification module is further configured to classify each image as showing a second number of cells based on the distinct first number of cells and the classification probabilities associated therewith. The classification module is implemented in at least one of a memory or a processing device.

Abstract: A touch device includes a plurality of sensors, a storage module and a processing module. The sensors are placed on a substrate for detecting acoustic waves and generating electrical signals. The storage module stores a plurality of templates of delay measurement for a plurality of locations on the substrate. The processing module is coupled to the sensors and to the storage module for calculating a received delay measurement and comparing the received delay measurement to the plurality of templates of delay measurement to estimate touch location. A detection method for the touch device is also provided.

Abstract: A method and apparatus for reducing or eliminating tracking errors associated with accelerometer-based input devices. The method and apparatus calculates a velocity of the input device; determines if the calculated velocity indicates a motion tracking error; and ignores the calculated velocity if the motion tracking error is indicated.

Abstract: Apparatuses, methods, and systems for automated, non-invasive evaluation of cell activity are provided. In one embodiment, an apparatus includes a hypothesis selection module configured to select a hypothesis from a plurality of hypotheses characterizing one or more cells shown in an image. Each of the plurality of hypotheses includes an inferred characteristic of the one or more cells based on geometric features of the one or more cells shown in the image. The hypothesis selection module is implemented in at least one of a memory or a processing device.

Abstract: Apparatuses, methods, and systems for automated cell classification, embryo ranking, and/or embryo categorization are provided. An apparatus includes a classification module configured to apply classifiers to images of one or more cells to determine, for each image, a classification probability associated with each classifier. Each classifier is associated with a distinct first number of cells, and is configured to determine the classification probability for each image based on cell features including one or more machine learned cell features. The classification probability indicates an estimated likelihood that the distinct first number of cells is shown in each image. The classification module is further configured to classify each image as showing a second number of cells based on the distinct first number of cells and the classification probabilities associated therewith. The classification module is implemented in at least one of a memory or a processing device.

Abstract: Methods and apparatus for incremental prediction of input device motion. In one embodiment, the input device comprises one or more sensors adapted to output motional data of the input device as measured at a certain period. A prediction of input device motion is generated based upon the last prediction and a weighted error in estimate determined by the sensory output. According to one embodiment, the weight is calculated as a Kalman gain. In one embodiment, once the prediction has been generated, it is provided to a display update algorithm adapted to orient a navigational object upon an associated display screen.

Abstract: A method and apparatus for tracking coarse and fine motions associated with an electronic input device is disclosed. The electronic input device can have both an inertial sensor and a touch sensor. The method includes receiving inputs from an inertial sensor and a touch sensor, and dynamically adjusting a velocity estimate of the electronic input device based on weighting or predetermined thresholds of the inputs to maximize a tracking range of the input device.

Abstract: Apparatuses, methods, and systems for the automated imaging and evaluation of human embryos, oocytes, or pluripotent cells are described. An apparatus and method for automated dish detection and well occupancy determination are described. In addition, a multi-well culture dish and an illumination assembly for bimodal imaging are described. These inventions find use at least in identifying or in facilitating identification of embryos and oocytes in vitro that are most useful in treating infertility in humans.

Abstract: An electronic motion-based input device and method for correcting errors in acceleration due to a deviation from a horizontal plane of motion by correcting for gravitational acceleration components due to the deviation in the plane of motion.

Abstract: A touch device includes a plurality of sensors, a storage module and a processing module. The sensors are placed on a substrate for detecting acoustic waves and generating electrical signals. The storage module stores a plurality of templates of delay measurement for a plurality of locations on the substrate. The processing module is coupled to the sensors and to the storage module for calculating a received delay measurement and comparing the received delay measurement to the plurality of templates of delay measurement to estimate touch location. A detection method for the touch device is also provided.

Abstract: A method and apparatus for tracking coarse and fine motions associated with an electronic input device is disclosed. The electronic input device can have both an inertial sensor and a touch sensor. The method includes receiving inputs from an inertial sensor and a touch sensor, and dynamically adjusting a velocity estimate of the electronic input device based on weighting or predetermined thresholds of the inputs to maximize a tracking range of the input device.