Abstract: A service verification system for a rear loading refuse vehicle includes a motion sensor on a tailgate of the refuse vehicle. The motion sensor is configured to sense refuse being loaded into a tailgate hopper and to provide a time-stamped triggering signal corresponding to a refuse loading event. An onboard processor is configured to receive the triggering signal and evaluate other sensor data to correlate the refuse loading event with an entity.

Abstract: A service verification system for a rear loading refuse vehicle includes a motion sensor on a tailgate of the refuse vehicle. The motion sensor is configured to sense refuse being loaded into a tailgate hopper and to provide a time-stamped triggering signal corresponding to a refuse loading event. An onboard processor is configured to receive the triggering signal and evaluate other sensor data to correlate the refuse loading event with an entity.

Abstract: A computer-implemented method for analyzing refuse includes operations of receiving sensor data indicating an operational state of a vehicle body component of a refuse collection vehicle (RCV); analyzing the sensor data to detect a presence of a triggering condition based at least partly on a particular operational state of the vehicle body component, as indicated by the sensor data; in response to detecting the triggering condition, accessing image data indicating a physical state of refuse collected by the RCV; providing the image data as input to at least one contaminant detection model trained, using at least one machine learning (ML) algorithm, to output a classification of the image data, the classification indicating a degree of contamination of the refuse; and storing, in a machine-readable medium, the classification of the image data.

Abstract: Techniques are described for correlating entity identification information with refuse containers being serviced by a refuse collection vehicle (RCV). Location data can be collected by location sensor(s) on the RCV at a time when a triggering condition is present, such as a time when a lift arm is operating to empty a refuse container into the hopper of the RCV. The location data can be provided as input to an algorithm that estimates a container location through a vector offset to account for the distance and direction of the RCV lift arm relative to the location sensor in the RCV. The container location can be correlated with parcel data to determine the parcel that the container was on or near to when it was serviced, and the customer or other entity associated with the parcel can be correlated to the particular container based on the analysis.

Abstract: Systems and methods for operating a refuse collection vehicle include detecting, using at least one radar sensor coupled to the refuse collection vehicle, a hazard positioned within a detection distance of the at least one radar sensor; transmitting, from the at least one radar sensor to a computing device, a signal indicating the hazard detected by the at least one radar sensor; and in response to receiving the signal, controlling the refuse collection vehicle to prevent damage to the refuse collection vehicle resulting from contact between the hazard and the refuse collection vehicle.

Abstract: Systems and methods for optimizing data flow in an integrated circuit includes creating a task graph based on transforming an optimized network graph for a neural network application, wherein creating the task graph includes: enumerating a plurality of distinct tasks based on a decomposition of each of a plurality of network operations of the optimized network graph; and allocating a data buffer to each of pairs of dependent tasks of the plurality of distinct tasks based on the decomposition of each of the plurality of network operations of the optimized network graph; encoding a token-informed task scheduler based on a composition of the task graph, wherein the encoding the token-informed task scheduler includes: programming the token-informed task scheduler to cause an execution of the plurality of distinct tasks based on identifying a state of a respective data buffer between each of the pairs of dependent tasks.

Abstract: Techniques are described for correlating entity identification information with refuse containers being serviced by a refuse collection vehicle (RCV). Location data can be collected by location sensor(s) on the RCV at a time when a triggering condition is present, such as a time when a lift arm is operating to empty a refuse container into the hopper of the RCV. The location data can be provided as input to an algorithm that estimates a container location through a vector offset to account for the distance and direction of the RCV lift arm relative to the location sensor in the RCV. The container location can be correlated with parcel data to determine the parcel that the container was on or near to when it was serviced, and the customer or other entity associated with the parcel can be correlated to the particular container based on the analysis.

Abstract: Methods and apparatuses including apparatuses that can be used in a unicompartmental knee replacement procedure, a bicompartmental knee replacement procedure comprised of two unicompartmental knee replacements, a knee replacement procedure that utilizes a single femoral component and two unicompartmental tibial components, and other types of knee replacement procedures are disclosed. According to one example, an apparatus for guiding a tibial bone cut during knee replacement surgery is disclosed. The apparatus can comprise a mounting portion and a cutting portion. The mounting portion can be configured to couple with an alignment mechanism. The cutting portion can be connected to the mounting portion and can define a capture for a proximal cut. The cutting portion can have an aperture disposed adjacent a first end of the capture.

Abstract: Systems and methods for optimizing data flow in an integrated circuit includes creating a task graph based on transforming an optimized network graph for a neural network application, wherein creating the task graph includes: enumerating a plurality of distinct tasks based on a decomposition of each of a plurality of network operations of the optimized network graph; and allocating a data buffer to each of pairs of dependent tasks of the plurality of distinct tasks based on the decomposition of each of the plurality of network operations of the optimized network graph; encoding a token-informed task scheduler based on a composition of the task graph, wherein the encoding the token-informed task scheduler includes: programming the token-informed task scheduler to cause an execution of the plurality of distinct tasks based on identifying a state of a respective data buffer between each of the pairs of dependent tasks.

Abstract: Techniques are described for correlating entity identification information with refuse containers being serviced by a refuse collection vehicle (RCV). Location data can be collected by location sensor(s) on the RCV at a time when a triggering condition is present, such as a time when a lift arm is operating to empty a refuse container into the hopper of the RCV. The location data can be provided as input to an algorithm that estimates a container location through a vector offset to account for the distance and direction of the RCV lift arm relative to the location sensor in the RCV. The container location can be correlated with parcel data to determine the parcel that the container was on or near to when it was serviced, and the customer or other entity associated with the parcel can be correlated to the particular container based on the analysis.

Abstract: A computer-implemented method for analyzing refuse includes operations of receiving sensor data indicating an operational state of a vehicle body component of a refuse collection vehicle (RCV); analyzing the sensor data to detect a presence of a triggering condition based at least partly on a particular operational state of the vehicle body component, as indicated by the sensor data; in response to detecting the triggering condition, accessing image data indicating a physical state of refuse collected by the RCV; providing the image data as input to at least one contaminant detection model trained, using at least one machine learning (ML) algorithm, to output a classification of the image data, the classification indicating a degree of contamination of the refuse; and storing, in a machine-readable medium, the classification of the image data.

Abstract: Techniques are described for correlating entity identification information with refuse containers being serviced by a refuse collection vehicle (RCV). Location data can be collected by location sensor(s) on the RCV at a time when a triggering condition is present, such as a time when a lift arm is operating to empty a refuse container into the hopper of the RCV. The location data can be provided as input to an algorithm that estimates a container location through a vector offset to account for the distance and direction of the RCV lift arm relative to the location sensor in the RCV. The container location can be correlated with parcel data to determine the parcel that the container was on or near to when it was serviced, and the customer or other entity associated with the parcel can be correlated to the particular container based on the analysis.

Abstract: The present disclosure provides knee prostheses that replicate at least a portion of the function of an individual patient's anterior cruciate ligament (ACL). An exemplary knee prosthesis includes a femoral component configured to be implanted on the distal end of the patient's femur and a tibial component configured to be implanted on the proximal end of the patient's tibia. In extension, the femoral component and the tibial component may cooperate to limit anterior movement of the tibial component relative to the femoral component. In flexion, the femoral component may be free to rotate relative to the tibial component.

Abstract: An interchangeable electro-mechanical lock core for use with a lock device having a locked state and an unlocked state is disclosed. The interchangeable electromechanical lock core may include a moveable plug having a first position relative to a lock core body which corresponds to the lock device being in the locked state and a second position relative to a lock core body which corresponds to the lock device being in the unlocked state.

Abstract: Techniques are described for correlating entity identification information with refuse containers being serviced by a refuse collection vehicle (RCV). Location data can be collected by location sensor(s) on the RCV at a time when a triggering condition is present, such as a time when a lift arm is operating to empty a refuse container into the hopper of the RCV. The location data can be provided as input to an algorithm that estimates a container location through a vector offset to account for the distance and direction of the RCV lift arm relative to the location sensor in the RCV. The container location can be correlated with parcel data to determine the parcel that the container was on or near to when it was serviced, and the customer or other entity associated with the parcel can be correlated to the particular container based on the analysis.

Abstract: A joint alignment method comprises using imaging data of at least a portion of a leg to create a leg model, wherein the leg model includes a femur having medial and lateral condyles and a tibia having tibial plateaus that are configured to engage the medial and lateral condyles at a knee joint, displaying an image of the leg model for manipulation by a user, locating a pivot point within one of the medial or lateral condyles, and rotating, in the displayed image, the tibia with respect to the femur, about the pivot point, to obtain a desired knee joint articulation in a specified plane.

Abstract: Systems and methods for optimizing data flow in an integrated circuit includes creating a task graph based on transforming an optimized network graph for a neural network application, wherein creating the task graph includes: enumerating a plurality of distinct tasks based on a decomposition of each of a plurality of network operations of the optimized network graph; and allocating a data buffer to each of pairs of dependent tasks of the plurality of distinct tasks based on the decomposition of each of the plurality of network operations of the optimized network graph; encoding a token-informed task scheduler based on a composition of the task graph, wherein the encoding the token-informed task scheduler includes: programming the token-informed task scheduler to cause an execution of the plurality of distinct tasks based on identifying a state of a respective data buffer between each of the pairs of dependent tasks.

Abstract: A computer-implemented method for analyzing refuse includes operations of receiving sensor data indicating an operational state of a vehicle body component of a refuse collection vehicle (RCV); analyzing the sensor data to detect a presence of a triggering condition based at least partly on a particular operational state of the vehicle body component, as indicated by the sensor data; in response to detecting the triggering condition, accessing image data indicating a physical state of refuse collected by the RCV; providing the image data as input to at least one contaminant detection model trained, using at least one machine learning (ML) algorithm, to output a classification of the image data, the classification indicating a degree of contamination of the refuse; and storing, in a machine-readable medium, the classification of the image data.

Abstract: Systems and methods for optimizing data flow in an integrated circuit includes creating a task graph based on transforming an optimized network graph for a neural network application, wherein creating the task graph includes: enumerating a plurality of distinct tasks based on a decomposition of each of a plurality of network operations of the optimized network graph; and allocating a data buffer to each of pairs of dependent tasks of the plurality of distinct tasks based on the decomposition of each of the plurality of network operations of the optimized network graph; encoding a token-informed task scheduler based on a composition of the task graph, wherein the encoding the token-informed task scheduler includes: programming the token-informed task scheduler to cause an execution of the plurality of distinct tasks based on identifying a state of a respective data buffer between each of the pairs of dependent tasks.

Abstract: Techniques are described for correlating entity identification information with refuse containers being serviced by a refuse collection vehicle (RCV). Location data can be collected by location sensor(s) on the RCV at a time when a triggering condition is present, such as a time when a lift arm is operating to empty a refuse container into the hopper of the RCV. The location data can be provided as input to an algorithm that estimates a container location through a vector offset to account for the distance and direction of the RCV lift arm relative to the location sensor in the RCV. The container location can be correlated with parcel data to determine the parcel that the container was on or near to when it was serviced, and the customer or other entity associated with the parcel can be correlated to the particular container based on the analysis.