International Business Machines Corporation

Benjamin Scott Mashford

Stefan Harrer

Julia S Baldauf

Subhrajit Roy

Filiz Isabell Kiral-Kornek

Christine Schieber

Julian De Hoog

Alexe Bojovschi

Mahtab Mirmomeni

George Yiapanis

Khalid Abdulla

Jianbin Tang

Fatemeh Jalali

Susmita Saha

Mahsa Salehi

In one example, a method includes providing a pulp composed of a combination of particulate materials including particles of a target material. The pulp is mixed with a collector composed of anisotropic particles having at least two separate spatial domains that have different physiochemical properties, and the mixture of pulp and collector is fed into an aqueous solution containing air bubbles.

Froth flotation with anisotropic particle collectors

One or both of epilepsy seizure detection and prediction at least by performing the following: running multiple input signals from sensors for epilepsy seizure detection through multiple classification models, and applying weights to outputs of each of the classification models to create a final classification output. The weights are adjusted to tune relative output contribution from each classifier model in order that accuracy of the final classification output is improved, while power consumption of all the classification models is reduced. One or both of epilepsy seizure detection and prediction are performed with the adjusted weights. Another method uses streams from sensors for epilepsy seizure detection to train and create the classification models, with fixed weights once trained. Information defining the classification models with fixed weights is communicated to wearable computer platforms for epilepsy seizure detection and prediction. The streams may be from multiple people and applied to an individual person.

Epilepsy seizure detection and prediction using techniques such as deep learning methods

A recommendation method, system, and computer program product include monitoring a patient using a plurality of sensors, receiving patient information including a comfort level corresponding to a sensor of the plurality of sensors, determining a relevance of each sensor of the plurality of sensors to at least one health conditions of the patient, and determining at least one sensor of the plurality of sensors to disconnect based on the comfort level and the relevance of each sensor.

Dynamic recommendations of sensors for patient monitoring

A health-monitoring method, system, and computer program product include operating at least one sensor of a health-monitoring device having a plurality of sensors, detecting a health condition event that requires operation of an additional sensor of the plurality of sensors to monitor the health condition event, activating the additional sensor of the health-monitoring device, and deactivating the additional sensor when the health condition event that requires the second sensor is no longer detected by the detecting.

Energy-efficient multisensory system for mobile health-monitoring

A computer-implemented method includes receiving an input, from a user, in the form of a destination to be navigated to; calculating a route to the destination based on the input; recognizing at least one object on a route taken by the user; processing data from the received input, the calculated route, and the recognized at least one object; and providing an output to the user based on the recognized at least one object, the output being based on natural language processing.

User-friendly navigation system

A method for neuromodulation includes monitoring brain activity of a patient using one or more electrodes attached to the patient, and using a first machine learning model to predict whether a patient will have a seizure based on the monitored brain activity of the patient. The method also includes, responsive to the first machine learning model predicting that the patient will have a seizure, using a second machine learning model to determine a neuromodulation signal pattern for preventing the predicted seizure. The method further includes using a neurostimulator to apply the determined neuromodulation signal pattern to the patient. The method also includes, after applying the determined neuromodulation signal pattern to the patient, detecting whether the patient had the predicted seizure based on the monitored brain activity of the patient. The method further includes adjusting at least the second machine learning model based on whether the patient had the predicted seizure.

Seizure detection, prediction and prevention using neurostimulation technology and deep neural network

A method comprises generating power at a solar photovoltaic cell; receiving, at a controller, one or more input parameters pertaining to a power output of the solar photovoltaic cell; introducing liquid to the solar photovoltaic cell in response to the power output of the solar photovoltaic cell to increase the efficiency of the solar photovoltaic cell; recovering heat from the liquid introduced to the solar photovoltaic cell; and selecting an appropriate distribution of the power output of the solar photovoltaic cell to one or more power-drawing components in a way that maximizes a chosen objective for the whole of system.

Optimal distributed energy resource management system

In an approach, a processor receives classified data, wherein the classified data has been output by a second processor. A processor adjusts a count based on the classified data. A processor determines whether the count is greater than a pre-set threshold, wherein the pre-set threshold is set by a switching module of the processor. Responsive to determining that the count is greater than the pre-set threshold, the processor triggers an alarm of a pre-set alarm length, wherein the pre-set alarm length is set by the switching module of the processor.

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Malvern East, Australia

Benjamin Scott Mashford