Abstract: An interactive entity relationship diagram discovers explicitly defined relationships, and also dynamically discovers and represents non-explicit relationships. This entails calculating metadata and references by parsing the queries. An entity relationship diagram thereby provides novel visibility on the queries being related as the basis for a user interface which integrates several configuration utilities.

Abstract: A tile saw includes a saw with a cutting blade, a frame supporting the saw, a table supporting a workpiece and being slidable relative to the frame, and a rear fence secured to the table. The rear fence has an engagement surface and is adjustable between an operating position and a bypassed position. The bypassed position includes the engagement surface of the fence being no higher than even with the planar surface of the table to allow a workpiece to extend beyond a rear edge of the table. In the operating position, the engagement surface of the rear fence projects axially from the planar surface of the table to support the workpiece as the workpiece is pushed into the cutting blade during operation. The fence can be adjusted between the operating and bypassed positions by using any one of a cam lever clamp, pin and guideway, or rotatable latch.

Abstract: A method and system are disclosed for monitoring passengers in within a cabin of a vehicle and determining whether the passengers are engaging in abnormal behavior. The method and system uses a novel vector to robustly and numerically represent the activity of the passengers in a respective frame, which is referred to herein as an “activity vector.” Additionally, a Gaussian Mixture Model is utilized by the method and system to distinguish between normal and abnormal passenger behavior. Cluster components of the Gaussian Mixture Model are advantageously learned using an unsupervised approach in which training data is not labeled or annotated to indicate normal and abnormal passenger behavior. In this way, the Gaussian Mixture Model can be trained at a very low cost.

Abstract: A method and system are disclosed for monitoring passengers in within a cabin of a vehicle and determining whether the passengers are engaging in abnormal behavior. The method and system uses a novel vector to robustly and numerically represent the activity of the passengers in a respective frame, which is referred to herein as an “activity vector.” Additionally, a Gaussian Mixture Model is utilized by the method and system to distinguish between normal and abnormal passenger behavior. Cluster components of the Gaussian Mixture Model are advantageously learned using an unsupervised approach in which training data is not labeled or annotated to indicate normal and abnormal passenger behavior. In this way, the Gaussian Mixture Model can be trained at a very low cost.

Abstract: A method and system are disclosed for monitoring passengers in within a cabin of a vehicle and determining whether the passengers are engaging in abnormal behavior. The method and system uses a novel vector to robustly and numerically represent the activity of the passengers in a respective frame, which is referred to herein as an “activity vector.” Additionally, a Gaussian Mixture Model is utilized by the method and system to distinguish between normal and abnormal passenger behavior. Cluster components of the Gaussian Mixture Model are advantageously learned using an unsupervised approach in which training data is not labeled or annotated to indicate normal and abnormal passenger behavior. In this way, the Gaussian Mixture Model can be trained at a very low cost.

Abstract: A method and system are disclosed for monitoring passengers in within a cabin of a vehicle and determining whether the passengers are engaging in abnormal behavior. The method and system uses a novel vector to robustly and numerically represent the activity of the passengers in a respective frame, which is referred to herein as an “activity vector.” Additionally, a Gaussian Mixture Model is utilized by the method and system to distinguish between normal and abnormal passenger behavior. Cluster components of the Gaussian Mixture Model are advantageously learned using an unsupervised approach in which training data is not labeled or annotated to indicate normal and abnormal passenger behavior. In this way, the Gaussian Mixture Model can be trained at a very low cost.

Abstract: A tile saw includes a saw with a cutting blade, a frame supporting the saw, a table supporting a workpiece and being slidable relative to the frame, and a rear fence secured to the table. The rear fence has an engagement surface and is adjustable between an operating position and a bypassed position. The bypassed position includes the engagement surface of the fence being no higher than even with the planar surface of the table to allow a workpiece to extend beyond a rear edge of the table. In the operating position, the engagement surface of the rear fence projects axially from the planar surface of the table to support the workpiece as the workpiece is pushed into the cutting blade during operation. The fence can be adjusted between the operating and bypassed positions by using any one of a cam lever clamp, pin and guideway, or rotatable latch.