In general, the techniques of this disclosure describe a computing device in a secure domain that is configured to receive, via a guard device, an authentication factor from at least one authentication device of a plurality of authentication devices each in a non-secure domain. The respective authentication factor comprises a respective identifier of a respective user of the respective authentication device. The computing device may then determine whether the respective user of each respective authentication device is a particular trusted user based on the received authentication factors. Responsive to determining that the respective user of each respective authentication device is the particular trusted user, the computing device may enable access to one or more applications on the computing device. Once access is enabled, the computing device may continue to enable access so long as the authentication devices send additional authentication factors that confirm the identity of the user.

Embodiments for continuous time alignment of a collection of independent sensors monitoring a common entity by one or more processors. One or more activity events associated with a monitored entity may be identified in the time series sensor data collected from a plurality of sensors. The one or more activity events may be dynamically characterized in the time series sensor data using a machine learning operation. The time series data streams from each of the plurality of sensors may be time-aligned by aligning the one or more activity events.

In general, the techniques of this disclosure describe a computing device in a secure domain that is configured to receive, via a guard device, an authentication factor from a biometric authentication device in a non-secure domain. The biometric authentication device is in a non-secure domain, and the authentication factor comprises an identifier of a prospective user of the biometric authentication device. The computing device may then determine, based on the received authentication factor, whether the prospective user is a trusted user of the computing device based on the authentication factor. Responsive to determining that the prospective user of the biometric authentication device is the trusted user, the computing device may enable access to one or more applications on the computing device.

Similarities between events that include a plurality of dimensions are computed, the similarities computed based on binary comparisons between the events and based on user-specified weights for the dimensions. Multidimensional scaling (MDS) values are calculated based on the computed similarities between the events. A graphical visualization is generated of a temporal plot of the events, the temporal plot comprising a first axis corresponding to time, and a second axis corresponding to the MDS values, and the temporal plot representing overlapping time slices each containing pixels representing a respective subset of the events.

Disclosed herein are methods and systems for detection and discrimination of optical signals from a densely packed substrate. These have broad applications for biomolecule detection near or below the diffraction limit of optical systems, including in improving the efficiency and accuracy of polynucleotide sequencing applications.

An information processing terminal capable of communicating with a sensor terminal including a first biological sensor which measures a first type of biological information includes: a reception unit receiving a measurement result of the first biological sensor from the sensor terminal; a second biological sensor measuring the first type of biological information; and a control unit authenticating the sensor terminal and establishing connection with the sensor terminal when the measurement result of the first biological sensor satisfies a predetermined condition on a measurement result of the second biological sensor.

A method and apparatus are provided for characterizing a product sample for example in comparison to a reference sample using a sensor such as a gas chromatograph or a MOS sensor. This characterization may comprise an indication of whether or not the product sample conforms to a quality criterion. The comparison of the sensor output measurements for the product sample is compared to maximum and minimum value curves, which may be derived from measurements of the reference sample, whereby adjacent samples outside the envelope defined by these maximum and minimum values are grouped together. A dissimilarity index may be determined for the anomalous values as a whole, or on a per group basis. The groups may be classified depending on the shape they describe, in particular the presence, or not, of peaks, and correspondingly the shape of the corresponding part of the envelope. These determinations may then be used as the basis of the conformity indication, and also the basis for attempting to identify the cause of any anomalies, in particular the identification of foreign components.

Embodiments for continuous time alignment of a collection of independent sensors monitoring a common entity by one or more processors. One or more activity events associated with a monitored entity may be identified in the time series sensor data collected from a plurality of sensors. The one or more activity events may be dynamically characterized in the time series sensor data using a machine learning operation. The time series data streams from each of the plurality of sensors may be time-aligned by aligning the one or more activity events.

A method and system for determining a patient's risk of ventricular tachycardia are disclosed. The method includes receiving ECG signals from a patient and filtering the collected ECG signals to generate filtered ECG signals. The method further includes identifying a heart vector from the filtered ECG signals, and measuring a velocity of the heart vector movement. A change in curvature of the identified heart vector movement is quantified and a risk of ventricular tachycardia is determined based at least on the measured velocity and the quantified change in curvature of the identified heart vector movement.

A road surface is classified by providing a set of data points that is attributable to a same road surface object. Each data point specifies a first variable and a second variable. For each data point, the first variable characterizes a horizontal motion exhibited by a vehicle when driving over the road surface object and the second variable characterizes a vertical motion exhibited by said vehicle when driving over the road surface object. The set of data points are classified using an artificial neural network with regard to a relevance of the road surface object for a driver warning function or an automated driving function.