Abstract: The calibration system of the farming machine receives images from the camera array. The images comprise visual information representing a view of a portion of an area surrounding the farming machine. To calibrate the camera array, the system determines a relative pose between pairs of cameras by extracting relative position and orientation characteristics from visual information in images captured by the camera pairs. The calibration system determines a calibration error in part by propagating the relative poses between camera pairs. The calibration system may perform automated self-calibration by adjusting one or more of the cameras in the camera array, or may transmit remedial instructions to an operator to adjust the one or more cameras in the camera array.

Abstract: The method includes: receiving a communication that relates to a request for a price quote for at least one equity derivative product; extracting from the communication, attributes of the requested price quote for the at least one equity derivative product; generating based on the extracted attributes, a template request that has a predetermined format for each of the at least one equity derivative product; displaying, via a graphical user interface (GUI), each template request for review by a user, wherein the GUI includes an input mechanism for at least one of accepting and modifying each template request; transmitting each reviewed template request to a pricing system; receiving, from the pricing system, a quote for each of the at least one equity derivative product; and displaying, by the GUI, the price quote.

Abstract: As a farming machine travels through a field of plants, the farming machine operates in a normal operational state to perform one or more farming operations. The farming machine detects an operational failure of a component of the farming machine using measurements obtained from one or more sensors coupled to and monitoring the farming machine. The operational failure of the component impacts performance of a first farming operation of the farming operations. The farming machine configures the farming machine to operate in a remedial operational state. In the remedial operational state, the farming machine diagnoses the operational failure of the component using the obtained measurements. In the remedial operational state, the farming machine selects a solution operation to address the operational failure of the component based on the diagnosis. The farming machine performs the determined solution operation.

Abstract: A farming machine identifies and treats a plant as the farming machine travels through a field. The farming machine includes an array of tiled image sensors for capturing images of the field. A control system identifies an active region in the captured images and generates a tiled image that includes the active region. The control system applies image processing functions to identify the plant in the tiled image and actuates a treatment mechanism to treat the identified plant. The control system causes the array of image sensors to capture the image, identifies the plant, and actuates the treatment mechanism in real time as the farming machine travels through the field.

Abstract: A farming machine identifies and treats a plant as the farming machine travels through a field. The farming machine includes an array of tiled image sensors for capturing images of the field. A control system identifies an active region in the captured images and generates a tiled image that includes the active region. The control system applies image processing functions to identify the plant in the tiled image and actuates a treatment mechanism to treat the identified plant. The control system causes the array of image sensors to capture the image, identifies the plant, and actuates the treatment mechanism in real time as the farming machine travels through the field.

Abstract: The calibration system of the farming machine receives images from the camera array. The images comprise visual information representing a view of a portion of an area surrounding the farming machine. To calibrate the camera array, the system determines a relative pose between pairs of cameras by extracting relative position and orientation characteristics from visual information in images captured by the camera pairs. The calibration system determines a calibration error in part by propagating the relative poses between camera pairs. The calibration system may perform automated self-calibration by adjusting one or more of the cameras in the camera array, or may transmit remedial instructions to an operator to adjust the one or more cameras in the camera array.

Abstract: The calibration system of the farming machine receives images from each camera of the camera array. The images comprise visual information representing a view of a portion of an area surrounding the farming machine. To calibrate a pair of cameras including a first camera and second camera, the calibration system determines a relative pose between the pair of cameras by extracting relative position and orientation characteristics from visual information in both an image received from the first camera and an image received from the second camera. The calibration system identifies a calibration error for the pair of cameras based on a comparison of the relative pose with an expected pose between the first pair of cameras. The calibration system transmits a notification to an operator of the farming machine that describes the calibration error and instructions for remedying the calibration error.

Abstract: Disclosed is a system for encoding and/or rendering animations without temporal or spatial restrictions. The system may encode an animation as a point cloud with first data points having a first time value and different positional and non-positional values, and second data points having a second time value and different positional and non-positional values. Rendering the animation may include generating and presenting a first image for the first time value of the animation based on the positional and non-positional values of the first data points, and generating and presenting a second image for the second time value of the animation by changing a visualization at a first position in the first image based on the positional values of a data point from the second data points corresponding to the first position and the data point non-positional values differing from the visualization.

Abstract: As a farming machine travels through a field of plants, the farming machine operates in a normal operational state to perform one or more farming operations. The farming machine detects an operational failure of a component of the farming machine using measurements obtained from one or more sensors coupled to and monitoring the farming machine. The operational failure of the component impacts performance of a first farming operation of the farming operations. The farming machine configures the farming machine to operate in a remedial operational state. In the remedial operational state, the farming machine diagnoses the operational failure of the component using the obtained measurements. In the remedial operational state, the farming machine selects a solution operation to address the operational failure of the component based on the diagnosis. The farming machine performs the determined solution operation.

Abstract: Disclosed is a system for encoding and/or rendering animations without temporal or spatial restrictions. The system may encode an animation as a point cloud with first data points having a first time value and different positional and non-positional values, and second data points having a second time value and different positional and non-positional values. Rendering the animation may include generating and presenting a first image for the first time value of the animation based on the positional and non-positional values of the first data points, and generating and presenting a second image for the second time value of the animation by changing a visualization at a first position in the first image based on the positional values of a data point from the second data points corresponding to the first position and the data point non-positional values differing from the visualization.

Abstract: A farming machine identifies and treats a plant as the farming machine travels through a field. The farming machine includes a pair of image sensors for capturing images of a plant. The image sensors are different, and their output images are used to generate a depth map to improve the plant identification process. A control system identifies a plant using the depth map. The control system captures images, identifies a plant, and actuates a treatment mechanism in real time.

Abstract: Disclosed is a system for encoding and/or rendering animations without temporal or spatial restrictions. The system may encode an animation as a point cloud with first data points having a first time value and different positional and non-positional values, and second data points having a second time value and different positional and non-positional values. Rendering the animation may include generating and presenting a first image for the first time value of the animation based on the positional and non-positional values of the first data points, and generating and presenting a second image for the second time value of the animation by changing a visualization at a first position in the first image based on the positional values of a data point from the second data points corresponding to the first position and the data point non-positional values differing from the visualization.

Abstract: Disclosed is a system for encoding and/or rendering animations without temporal or spatial restrictions. The system may encode an animation as a point cloud with first data points having a first time value and different positional and non-positional values, and second data points having a second time value and different positional and non-positional values. Rendering the animation may include generating and presenting a first image for the first time value of the animation based on the positional and non-positional values of the first data points, and generating and presenting a second image for the second time value of the animation by changing a visualization at a first position in the first image based on the positional values of a data point from the second data points corresponding to the first position and the data point non-positional values differing from the visualization.

Abstract: The calibration system of the farming machine receives images from each camera of the camera array. The images comprise visual information representing a view of a portion of an area surrounding the farming machine. To calibrate a pair of cameras including a first camera and second camera, the calibration system determines a relative pose between the pair of cameras by extracting relative position and orientation characteristics from visual information in both an image received from the first camera and an image received from the second camera. The calibration system identifies a calibration error for the pair of cameras based on a comparison of the relative pose with an expected pose between the first pair of cameras. The calibration system transmits a notification to an operator of the farming machine that describes the calibration error and instructions for remedying the calibration error.

Abstract: As a farming machine travels through a field of plants, the farming machine operates in a normal operational state to perform one or more farming operations. The farming machine detects an operational failure of a component of the farming machine using measurements obtained from one or more sensors coupled to and monitoring the farming machine. The operational failure of the component impacts performance of a first farming operation of the farming operations. The farming machine configures the farming machine to operate in a remedial operational state. In the remedial operational state, the farming machine diagnoses the operational failure of the component using the obtained measurements. In the remedial operational state, the farming machine selects a solution operation to address the operational failure of the component based on the diagnosis. The farming machine performs the determined solution operation.

Abstract: As a farming machine travels through a field of plants, the farming machine operates in a normal operational state to perform one or more farming operations. The farming machine detects an operational failure of a component of the farming machine using measurements obtained from one or more sensors coupled to and monitoring the farming machine. The operational failure of the component impacts performance of a first farming operation of the farming operations. The farming machine configures the farming machine to operate in a remedial operational state. In the remedial operational state, the farming machine diagnoses the operational failure of the component using the obtained measurements. In the remedial operational state, the farming machine selects a solution operation to address the operational failure of the component based on the diagnosis. The farming machine performs the determined solution operation.

Abstract: As a farming machine travels through a field of plants, the farming machine operates in a normal operational state to perform one or more farming operations. The farming machine detects an operational failure of a component of the farming machine using measurements obtained from one or more sensors coupled to and monitoring the farming machine. The operational failure of the component impacts performance of a first farming operation of the farming operations. The farming machine configures the farming machine to operate in a remedial operational state. In the remedial operational state, the farming machine diagnoses the operational failure of the component using the obtained measurements. In the remedial operational state, the farming machine selects a solution operation to address the operational failure of the component based on the diagnosis. The farming machine performs the determined solution operation.

Abstract: An expandable porous framework, the framework containing a dry cementitious powder fill that when exposed to an aqueous media, will expand against the constraint of the framework and set to form a solid, hard and coherent material, the formwork being porous to liquids but substantially impermeable to the powder fill.

Abstract: A farming machine identifies and treats a plant as the farming machine travels through a field. The farming machine includes an array of tiled image sensors for capturing images of the field. A control system identifies an active region in the captured images and generates a tiled image that includes the active region. The control system applies image processing functions to identify the plant in the tiled image and actuates a treatment mechanism to treat the identified plant. The control system causes the array of image sensors to capture the image, identifies the plant, and actuates the treatment mechanism in real time as the farming machine travels through the field.

Abstract: A farming machine identifies and treats a plant as the farming machine travels through a field. The farming machine includes a pair of image sensors for capturing images of a plant. The image sensors are different, and their output images are used to generate a depth map to improve the plant identification process. A control system identifies a plant using the depth map. The control system captures images, identifies a plant, and actuates a treatment mechanism in real time.