Detecting whether a target device that includes multiple electronic components is genuine or suspected counterfeit by: performing a test sequence of energizing and de-energizing the target device and collecting electromagnetic interference (EMI) signals emitted by the target device; generating a target EMI fingerprint from the EMI signals collected; retrieving a plurality of reference EMI fingerprints from a database library, each of which corresponds to a different configuration of electronic components of a genuine device of the same make and model as the target device; iteratively comparing the target EMI fingerprint to the retrieved reference EMI fingerprints and generating a similarity metric between each compared set; and indicating that the target device (i) is genuine where the similarity metric for any individual reference EMI fingerprint satisfies a threshold test, and is a suspect counterfeit device where no similarity metric for any individual reference EMI fingerprint satisfies the test.

Tracking and alerting Traffic management system using IoT for smart city to determine a social distance or norms violation between a plurality of communicative pairs, each of the image have plurality of communicative pairs including two communicating entities participating in a corresponding one or more of the communicative acts, the system comprising: CCTV for captured User's data i.e User movements, facial data, Smartphone data in case of accident detection; wireless trans-receiver for event propagation and sending the data to database; Sensor for getting the data of smart phones based on GPS system specially in case of accidental case; processor having CNN technology for analyzing and reverting data to control room based and configured to determine the pairwise social distancing based on particular behavior like movement and stopping or falling; hardware for storing data captured based on classification and analyzed parameters; machine learning for integration of data received from processor or sensors for visualization and processing final data to the citizens or to governments for monitoring and sending data to alarming sensor for non instructive alert if violations of social distancing norms.

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

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

Detecting whether a target utility device that includes multiple electronic components is genuine or suspected counterfeit by: performing a test sequence of energizing and de-energizing the target device and collecting electromagnetic interference (EMI) signals emitted by the target device; generating a target EMI fingerprint from the EMI signals collected; retrieving a plurality of reference EMI fingerprints from a database library, each of which corresponds to a different configuration of electronic components of a genuine device of the same make and model as the target device; iteratively comparing the target EMI fingerprint to the retrieved reference EMI fingerprints and generating a similarity metric between each compared set; and indicating that the target device (i) is genuine where the similarity metric for any individual reference EMI fingerprint satisfies a threshold test, and is a suspect counterfeit device where no similarity metric for any individual reference EMI fingerprint satisfies the test.

In general, the techniques of this disclosure describe a computing device that is configured to verify an identity of a user based on authentication factors received from multiple authentication devices. The computing device, which may be configured to operate as a server device, may receive an authentication factor from at least three authentication devices in a group of three or more authentication devices via a guard device. The computing device may determine a probability that the respective user of each respective authentication device is a particular trusted user based on the received authentication factors. If the probability exceeds a threshold authentication probability, the computing device may send an authentication confirmation to a client device.

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.

A system and method for processing human related data to make personalized and context aware decisions with distributed machine learning at an edge and a cloud is disclosed. A nearest edge computing device receives first, second and third sensed signals from first, second and third sensory devices, determines when the first, second and third sensed signals exceed corresponding thresholds, correlates pairs of the sensed signals to generate multiple correlation patterns, determines a lag time between the first sensed signal exceeding the first threshold and the second sensed signal exceeding the second threshold, provides each of the multiple correlation patterns and the lag time as inputs to multiple long short term memory (LSTM) neural networks, controls the multiple LSTM neural networks to provide outputs, and maps the patient to a stage of a medical condition based at least in part on the multiple correlation patterns and the lag time.

Detecting a counterfeit status of a target utility device by: selecting a set of frequencies that best reflect load dynamics or other information content of a reference utility device while undergoing a power test sequence; obtaining target electromagnetic interference (EMI) signals emitted by the target utility device while undergoing the same power test sequence; creating a sequence of target kiviat plots from the amplitude of the target EMI signals at each of the set of frequencies at observations over the power test sequence to form a target kiviat tube EMI fingerprint; comparing the target kiviat tube EMI fingerprint to a reference kiviat tube EMI fingerprint for the reference utility device undergoing the power test sequence to determine whether the target utility device and the reference utility device are of the same type; and generating a signal to indicate a counterfeit status based at least in part on the results of the comparison.

A method of reading a coupon channel that displays a test section pattern after being exposed to a target substance, the method uses a device having a computer readable memory, digital camera, logic assembly and user interface; providing a pixel target intensity profile; placing the coupon in the device and exposing the coupon channel to a test fluid mixture; automatically using the digital camera to take a digital image of the coupon channel test section after the exposure. The improvement in the method includes finding the contiguous set of pixels from the test section of the coupon channel that best matches the intensity profile of the target pattern representation and determining if this best match set of pixels exceeds a similarity threshold and in response to a best match set of pixels passing the similarity threshold, automatically providing a human perceptible indication that the target substance has been detected.