Abstract: Container identification data from a container inspection unit that analyzes a container containing a liquid is combined with liquid level detection raw data from a liquid level detection unit that analyzes the container containing the liquid and a liquid level detection result is generated. The liquid level detection result is cross-checked with additional data from the container inspection unit. The result can be used to plan a route for the container in the laboratory automation system.

Abstract: Container identification data from a container inspection unit that analyzes a container containing a liquid is combined with liquid level detection raw data from a liquid level detection unit that analyzes the container containing the liquid and a liquid level detection result is generated. The liquid level detection result is cross-checked with additional data from the container inspection unit. The result can be used to plan a route for the container in the laboratory automation system.

Abstract: Disclosed herein is a programming tool stored on a computer-readable medium and adapted for implementation by a computer for designing an artificial neural network. The programming tool includes a network configuration module to provide a first display interface to support configuration of the artificial neural network, and a pattern data module to provide a second display interface to support establishment and modification of first and second pattern data sets for training and testing the artificial neural network, respectively.

Abstract: Disclosed herein is a programming tool stored on a computer-readable medium and adapted for implementation by a computer for designing an artificial neural network. The programming tool includes a network configuration module to provide a first display interface to support configuration of the artificial neural network, and a pattern data module to provide a second display interface to support establishment and modification of first and second pattern data sets for training and testing the artificial neural network, respectively.

Abstract: Disclosed herein is a method of configuring a human-computer interface system having an eye gaze device that generates eye gaze data to control a display pointer. The method includes selecting a user profile from a user profile list to access an artificial neural network to address eye jitter effects arising from controlling the display pointer with the eye gaze data, training the artificial neural network to address the eye jitter effects using the eye gaze data generated during a training procedure, and storing customization data indicative of the trained artificial neural network in connection with the selected user profile.