Abstract: Systems and methods are disclosed for performing fault detection and prediction for power electronics and switching devices for power electronics, such as power inverters. Systems and methods disclosed herein can include determining, by a partial least squares model that evaluates values for one or more switching parameters for a switching device, the one or more switching parameters selected from a first set of switching parameters, a predicted value for the on-state current Ids of the switching device. The predicted value for the on-state current Ids can be based on the values of the one or more switching parameters for the switching device. Systems and methods disclosed herein can determine a residual comprising the difference between the predicted value for the another switching parameter of the switching device and an actual value of the predicted value for the another switching parameter, and generate a test statistic based on the residual.

Abstract: Systems, methods, computer-readable media, techniques, and methodologies are disclosed for performing fault detection and prediction for power electronics and switching devices for power electronics. Systems and methods can determine, using a machine learning model, a prediction for a value of a first switching parameter of a switching device, the prediction based on the present value of a second switching parameter of a switching device and a prior value of the first switching parameter. Systems and methods disclosed herein can further determine a residual comprising the difference between the prediction and an actual value of the switching parameter, generate a test statistic based on the residual, and compare the test statistic to a first threshold value. Systems and methods disclosed herein can determine the presence of a fault in the switching device based on a comparison of the test statistic to a threshold value.

Abstract: The present disclosure is related to utilizing contactless brainwave detectors to gather a user's unique electroencephalogram (EEG) data. EEG data, as well as an additional biometric, such as gait, can be used to determine whether users have access to enter a locked system, such as a door. An access list can be created and pre-registered with the EEG data and any additional biometric data of users who have access to enter the locked system. As the user nears the locked system, they can generate the EEG data to unlock the locked system by thinking of a pre-registered password stored in the access list.

Abstract: A device that uses brainwave activity to allow a user to perform hands-free tasks is disclosed. For example, the device can include a transparent display, an electroencephalogram (EEG) sensor, and a processing circuitry coupled to the transparent display and the EEG sensor. The EEG sensor can be configured to sense an EEG signal corresponding to brain activity associated with a user of the device. The processing circuitry can be configured to display a visual stimulus on the transparent display and generate a control signal associated with a task of the user based on the EEG signal. Therefore, the user can use his brainwave activity to perform hands-free tasks.

Abstract: A device that uses brainwave activity to allow a user to perform hands-free tasks is disclosed. For example, the device can include a transparent display, an electroencephalogram (EEG) sensor, and a processing circuitry coupled to the transparent display and the EEG sensor. The EEG sensor can be configured to sense an EEG signal corresponding to brain activity associated with a user of the device. The processing circuitry can be configured to display a visual stimulus on the transparent display and generate a control signal associated with a task of the user based on the EEG signal. Therefore, the user can use his brainwave activity to perform hands-free tasks.

Abstract: The disclosure is related configuring a vehicle according to a user profile, where an individual user profile can be selected from a registry containing one or more user profiles using electroencephalogram (EEG). An additional biometric, such as gait, can also be used. The user profiles can include a variety of the user preferences, such as the preferred climate, seat settings, mode of payment, route to take, and more. A mobile device can also be used to add a layer of security and privacy, where the mobile device has sole access to the user's user profile.

Abstract: The present disclosure is related to utilizing contactless brainwave detectors to gather a user's unique electroencephalogram (EEG) data. EEG data, as well as an additional biometric, such as gait, can be used to determine whether users have access to enter a locked system, such as a door. An access list can be created and pre-registered with the EEG data and any additional biometric data of users who have access to enter the locked system. As the user nears the locked system, they can generate the EEG data to unlock the locked system by thinking of a pre-registered password stored in the access list.

Abstract: An emotion-aware smart refrigerator is provided which may include various sensors and presentation mechanisms to identify a user's interest in item(s) contained within the emotion-aware smart refrigerator via an evoked physiological response, such as a visual evoked potential (VEP). The user's level of interest may also be gauged by the emotion-aware smart refrigerator. With consideration given to the user's VEP as well as the user's level of interest, and other considerations such as the user's diet, desired health goals, dietary restrictions, etc., the emotion-aware smart refrigerator may present suggested items and item amounts to the user.