Abstract: Systems, methods, and computer-readable media are disclosed for autonomous tennis assistant systems having autonomous ball machines such as an autonomous and interactive tennis ball ejection robot. Example methods include determining, by a device, the position of a first player on a tennis court, determining a first target location for a first tennis ball to be ejected toward the first player, generating a trajectory for the first tennis ball to reach the first target location, and causing ejection of the first tennis ball from the tennis ball ejection robot along the generated trajectory toward the first target location. A base station may be used to provide image data for use by the tennis ball ejection robot from one or more additional vantage points.

Abstract: An air blower has an inlet muffler and an outlet muffler to reduce the noise of the air blower during operation. Each of the mufflers may have two sections. One section is configured to attenuate high frequency noise and the other section is configured to attenuate lower frequency noise. A tubular internal wall in each of the mufflers defines the through passage for each of the mufflers. A noise absorbent material may be fitted about the tubular wall within each muffler. A plurality of holes are formed along the tubular wall of each through passage to expose the through passage to the noise absorbent material. A heater plenum may be interposed between the blower plenum and the outlet muffler to heat the air from the blower plenum. A filter at the input muffler filters the air sucked into the air blower.

Abstract: An air blower has an inlet muffler and an outlet muffler to reduce the noise of the air blower during operation. Each of the mufflers may have two sections. One section is configured to attenuate high frequency noise and the other section is configured to attenuate lower frequency noise. A tubular internal wall in each of the mufflers defines the through passage for each of the mufflers. A noise absorbent material may be fitted about the tubular wall within each muffler. A plurality of holes are formed along the tubular wall of each through passage to expose the through passage to the noise absorbent material. A heater plenum may be interposed between the blower plenum and the outlet muffler to heat the air from the blower plenum. A filter at the input muffler filters the air sucked into the air blower.

Abstract: A dilation system comprising a plurality of springs axially disposed in an elastic tube.

Abstract: Systems, methods, and computer-readable media are disclosed for autonomous tennis assistant systems having autonomous ball machines such as an autonomous and interactive tennis ball ejection robot. Example methods include determining, by a device, the position of a first player on a tennis court, determining a first target location for a first tennis ball to be ejected toward the first player, generating a trajectory for the first tennis ball to reach the first target location, and causing ejection of the first tennis ball from the tennis ball ejection robot along the generated trajectory toward the first target location. A base station may be used to provide image data for use by the tennis ball ejection robot from one or more additional vantage points.

Abstract: Systems, methods, and computer-readable media are disclosed for autonomous tennis assistant systems. Example methods include determining, by a device, an outer boundary line of a tennis court, generating a digital representation of the tennis court using the outer boundary line, where the digital representation includes at least a portion of an out-of-bounds area adjacent to the outer boundary line, determining a first location of a first tennis ball, and causing a tennis ball retrieval robot to move to the first location to retrieve the first tennis ball, where the tennis ball retrieval robot is wirelessly connected to the device.

Abstract: Systems, methods, and computer-readable media are disclosed for autonomous tennis assistant systems. Example methods include determining, by a device, an outer boundary line of a tennis court, generating a digital representation of the tennis court using the outer boundary line, where the digital representation includes at least a portion of an out-of-bounds area adjacent to the outer boundary line, determining a first location of a first tennis ball, and causing a tennis ball retrieval robot to move to the first location to retrieve the first tennis ball, where the tennis ball retrieval robot is wirelessly connected to the device.

Abstract: Described are computing systems and methods configured to detect a small, but meaningful, anomaly within one or more metrics associated with a platform. The system displays visuals of the metrics so that a user monitoring the platform can effectively notice a problem associated with the anomaly and take appropriate action to remediate the problem. An operational visual includes a radar-based visual with a heatmap arranging metrics, and a node representing a state of the metrics. Moreover, the system uses an ensemble of unsupervised machine learning algorithms for multi-dimensional clustering of hundreds of thousands of monitored metrics. Via the visuals and the implementation of the machine learning algorithms, the described techniques provide an improved way of representing and simulating many metrics being monitored for a platform. Moreover, the techniques are configured to expose actionable and useful information associated with the platform in a manner that can be effectively interpreted.

Abstract: Described are computing systems and methods configured to detect a small, but meaningful, anomaly within one or more metrics associated with a platform. The system displays visuals of the metrics so that a user monitoring the platform can effectively notice a problem associated with the anomaly and take appropriate action to remediate the problem. An operational visual includes a radar-based visual with a heatmap arranging metrics, and a node representing a state of the metrics. Moreover, the system uses an ensemble of unsupervised machine learning algorithms for multi-dimensional clustering of hundreds of thousands of monitored metrics. Via the visuals and the implementation of the machine learning algorithms, the described techniques provide an improved way of representing and simulating many metrics being monitored for a platform. Moreover, the techniques are configured to expose actionable and useful information associated with the platform in a manner that can be effectively interpreted.

Abstract: Systems, methods, and computer-readable media are disclosed for autonomous tennis assistant systems. Example methods include determining, by a device, an outer boundary line of a tennis court, generating a digital representation of the tennis court using the outer boundary line, where the digital representation includes at least a portion of an out-of-bounds area adjacent to the outer boundary line, determining a first location of a first tennis ball, and causing a tennis ball retrieval robot to move to the first location to retrieve the first tennis ball, where the tennis ball retrieval robot is wirelessly connected to the device.

Abstract: Systems, methods, and computer-readable media are disclosed for autonomous tennis assistant systems that may automatically determine performance metrics and generate tennis statistics. Example methods include determining, by a device, various performance metrics for users via images captured during gameplay. For example, the device may determine whether a tennis ball landed within, outside, or on a boundary line, whether a point was scored, a ball speed of a tennis ball, topspin of a tennis ball, a type of stroke used to hit a tennis ball, whether a tennis match was won, hitting accuracy of a player, distance moved by a player, and/or other metrics. A base station may be used to provide image data for use by the tennis ball ejection robot from one or more additional vantage points.

Abstract: Systems, methods, and computer-readable media are disclosed for autonomous tennis ball retrieval robots. Example robots include a housing comprising a ball inlet, a first wheel coupled to the housing, a ball collection portion coupled to the housing and configured to receive a plurality of balls, a first arm and a second arm removably coupled to the housing, where the first arm and the second arm together guide balls towards the ball inlet, a ball direction device configured to direct balls from the ball inlet to the ball collection portion, and a first sensor coupled to the first arm, where the autonomous tennis ball retrieval robot is configured to detect obstacles using the first sensor. The autonomous tennis ball retrieval robot may be configured to retrieve balls disposed in an ambient environment, and may be configured to be transported in a rolling mode using the first wheel.

Abstract: Systems, methods, and computer-readable media are disclosed for autonomous tennis assistant systems having autonomous ball machines such as an autonomous and interactive tennis ball ejection robot. Example methods include determining, by a device, the position of a first player on a tennis court, determining a first target location for a first tennis ball to be ejected toward the first player, generating a trajectory for the first tennis ball to reach the first target location, and causing ejection of the first tennis ball from the tennis ball ejection robot along the generated trajectory toward the first target location. A base station may be used to provide image data for use by the tennis ball ejection robot from one or more additional vantage points.

Abstract: Systems, methods, and computer-readable media are disclosed for autonomous tennis assistant systems having autonomous ball machines such as an autonomous and interactive tennis ball ejection robot. Example methods include determining, by a device, the position of a first player on a tennis court, determining a first target location for a first tennis ball to be ejected toward the first player, generating a trajectory for the first tennis ball to reach the first target location, and causing ejection of the first tennis ball from the tennis ball ejection robot along the generated trajectory toward the first target location. A base station may be used to provide image data for use by the tennis ball ejection robot from one or more additional vantage points.

Abstract: A system is configured to detect a small, but meaningful, anomaly within one or more metrics associated with a platform. The system displays visuals of the metrics so that a user monitoring the platform can effectively notice a problem associated with the anomaly and take appropriate action to remediate the problem. A first visual includes a radar-based visual that renders an object representing data for a set of metrics being monitored. A second visual includes a tree map visual that includes sections where each section is associated with an attribute used to compose the set of metrics. Via the display of the visuals, the techniques provide an improved way of representing a large number of metrics (e.g., hundreds, thousands, etc.) being monitored for a platform. Moreover, the techniques are configured to expose useful information associated with the platform in a manner that can be effectively interpreted by a user.

Abstract: A machine learning (“ML”) pipeline that includes unsupervised learning, supervised learning, and Bayesian learning is utilized to train a ML classifier that can classify machine metrics as being indicative of an anomaly. A boosting process can be utilized during the unsupervised learning portion of the ML pipeline that scores clusters of training data for completeness, and further splits clusters of training data based upon the completeness scores in order to optimize the clustering of the training data. Supervised learning is then performed on the cluster-labeled training data. Bayesian learning can also be utilized to assign incident probability inferences to the clusters of training data. Once the ML classifier has been trained, the ML classifier can be utilized in a production environment to classify multi-dimensional machine metrics generated by computing devices in the production environment as being indicative of an anomaly.

Abstract: Systems, methods, and computer-readable media are disclosed for autonomous tennis assistant systems. Example methods include determining, by a device, an outer boundary line of a tennis court, generating a digital representation of the tennis court using the outer boundary line, where the digital representation includes at least a portion of an out-of-bounds area adjacent to the outer boundary line, determining a first location of a first tennis ball, and causing a tennis ball retrieval robot to move to the first location to retrieve the first tennis ball, where the tennis ball retrieval robot is wirelessly connected to the device.

Abstract: A machine learning (“ML”) pipeline that includes unsupervised learning, supervised learning, and Bayesian learning is utilized to train a ML classifier that can classify machine metrics as being indicative of an anomaly. A boosting process can be utilized during the unsupervised learning portion of the ML pipeline that scores clusters of training data for completeness, and further splits clusters of training data based upon the completeness scores in order to optimize the clustering of the training data. Supervised learning is then performed on the cluster-labeled training data. Bayesian learning can also be utilized to assign incident probability inferences to the clusters of training data. Once the ML classifier has been trained, the ML classifier can be utilized in a production environment to classify multi-dimensional machine metrics generated by computing devices in the production environment as being indicative of an anomaly.

Abstract: Described are computing systems and methods configured to detect a small, but meaningful, anomaly within one or more metrics associated with a platform. The system displays visuals of the metrics so that a user monitoring the platform can effectively notice a problem associated with the anomaly and take appropriate action to remediate the problem. An operational visual includes a radar-based visual with a heatmap arranging metrics, and a node representing a state of the metrics. Moreover, the system uses an ensemble of unsupervised machine learning algorithms for multi-dimensional clustering of hundreds of thousands of monitored metrics. Via the visuals and the implementation of the machine learning algorithms, the described techniques provide an improved way of representing and simulating many metrics being monitored for a platform. Moreover, the techniques are configured to expose actionable and useful information associated with the platform in a manner that can be effectively interpreted.

Abstract: Described are computing systems and methods configured to detect a small, but meaningful, anomaly within one or more metrics associated with a platform. The system displays visuals of the metrics so that a user monitoring the platform can effectively notice a problem associated with the anomaly and take appropriate action to remediate the problem. An operational visual includes a radar-based visual with a heatmap arranging metrics, and a node representing a state of the metrics. Moreover, the system uses an ensemble of unsupervised machine learning algorithms for multi-dimensional clustering of hundreds of thousands of monitored metrics. Via the visuals and the implementation of the machine learning algorithms, the described techniques provide an improved way of representing and simulating many metrics being monitored for a platform. Moreover, the techniques are configured to expose actionable and useful information associated with the platform in a manner that can be effectively interpreted.