Abstract: The invention provides an advanced, modular, intraoperative neurophysiological monitoring (IONM) system, referred to as a “NeuroNet-VII” System, which is the first IONM system designed with a USB hub architecture comprising serially-connected functional modules which provide multi-modality simultaneous data acquisition which support all data types useful in operating rooms, diagnostic laboratories, intensive care units, and epilepsy monitoring units. The unique module architecture makes the IONM system highly modular compared to current systems which typically place components in a limited number of centralized enclosures.

Abstract: The invention provides an advanced, modular, intraoperative neurophysiological monitoring (IONM) system, referred to as a “NeuroNet-VII” System, which is the first IONM system designed with a USB hub architecture comprising serially-connected functional “pods which provides multi-modality simultaneous data acquisition which supports all data types useful in operating rooms, diagnostic laboratories, intensive care units, and epilepsy monitoring units. The unique pod architecture makes the IONM system highly modular compared to current systems which typically place components in a limited number of centralized enclosures.

Abstract: The present invention relates to an electronic monitoring device for autonomously tracking, detecting and reporting the installation/re-installation and detachment/uninstallation of a wear element (GET) and a GET clamping element on an earthmoving machine. The device is installed in a cylindrical cavity in the GET. The electronic monitoring device and GET have an identifier code for maintaining continuous wireless communication despite the highly metallic surrounding environment, even reporting during transit.

Abstract: The invention provides an advanced, modular, intraoperative neurophysiological monitoring (IONM) system, referred to as a “NeuroNet-VII” System, which is the first IONM system designed with a USB hub architecture comprising serially-connected functional “pods which provides multi-modality simultaneous data acquisition which supports all data types useful in operating rooms, diagnostic laboratories, intensive care units, and epilepsy monitoring units. The unique pod architecture makes the IONM system highly modular compared to current systems which typically place components in a limited number of centralized enclosures.

Abstract: Techniques to generate experiment trials using artificial intelligence are disclosed. A training set for an experiment generator is continuously built up by using assessed experiment trials. The experiment generator is optimized using one of a plurality of optimization algorithms, depending on which mode experiment generator is to run in for an experiment. The mode is dependent on the experiment mode of the experiment. The experiment generator generates a batch of one or more experiment trials for the experiment. Any of the generated experiment trials may be tried or experimented by a user and may be updated with assessment data as an assessed experiment trial.

Abstract: Techniques to generate experiment trials using artificial intelligence are disclosed. A training set for an experiment generator is continuously built up by using assessed experiment trials. The experiment generator is optimized using one of a plurality of optimization algorithms, depending on which mode experiment generator is to run in for an experiment. The mode is dependent on the experiment mode of the experiment. The experiment generator generates a batch of one or more experiment trials for the experiment. Any of the generated experiment trials may be tried or experimented by a user and may be updated with assessment data as an assessed experiment trial.

Abstract: Techniques to mimic a target food item using artificial intelligence are disclosed. A formula generator is trained using ingredients and using recipes and, given a target food item, determines a formula that matches the given target food item. A flavor generator is trained using recipes and their associated flavor information and, given a formula, the flavor generator determines a flavor profile for the given formula. The flavor profile may be used to assist the formula generator in generating a subsequent formula. A recipe generator is trained using recipes and, given a formula, determines a cooking process for the given formula. A food item may be cooked according to a recipe, and feedback, including a flavor profile, may be provided for the cooked food item. The recipe and its feedback may be added to a training set for the flavor generator.

Abstract: Techniques to generate experiment trials using artificial intelligence are disclosed. A training set for an experiment generator is continuously built up by using assessed experiment trials. The experiment generator is optimized using one of a plurality of optimization algorithms, depending on which mode experiment generator is to run in for an experiment. The mode is dependent on the experiment mode of the experiment. The experiment generator generates a batch of one or more experiment trials for the experiment. Any of the generated experiment trials may be tried or experimented by a user and may be updated with assessment data as an assessed experiment trial.

Abstract: Techniques to mimic a target food item using artificial intelligence are disclosed. A formula generator is trained using ingredients and using recipes and, given a target food item, determines a formula that matches the given target food item. A flavor generator is trained using recipes and their associated flavor information and, given a formula, the flavor generator determines a flavor profile for the given formula. The flavor profile may be used to assist the formula generator in generating a subsequent formula. A recipe generator is trained using recipes and, given a formula, determines a cooking process for the given formula. A food item may be cooked according to a recipe, and feedback, including a flavor profile, may be provided for the cooked food item. The recipe and its feedback may be added to a training set for the flavor generator.