Abstract: Methods, systems, and apparatuses for managing a wireless network are described herein. A wireless network may include a gateway device, an access point, and a group of client devices. An analytics engine may determine a signal characteristic data for a client device, which may indicate network telemetry data for each of the client devices or associated locations during a time period. The analytics engine may utilize a trained classifier to determine that a signal characteristic indicated by the signal characteristic data for a client device satisfies a threshold.

Abstract: Systems, apparatuses, and methods are described for presence detection of a wireless device in a sensing region. Communication parameters determined by a computing device over one or more periods may be used to generate criteria for determining the occurrence of enter events or leave events. The criteria may comprise, for values of one or more communication parameters, rates of change associated with movement into or out of the sensing region. The criteria may be compared against subsequent data associated with the communication parameters to determine if an enter event or a leave event has occurred.

Abstract: Methods, systems, and apparatuses for predicting an end of a command in a voice recognition input are described herein. The system may receive data comprising a voice input. The system may receive a signal comprising a voice input. The system may detect, in the voice input, data that is associated with a first portion of a command. The system may predict, based on the first portion and while the voice input is being received, a second portion of the command. The prediction may be generated by a machine learning algorithm that is trained based at least in part on historical data comprising user input data. The system may cause execution of the command, based on the first portion and the predicted second portion, prior to an end of the voice input.

Abstract: Methods, systems, and apparatuses for improved speech recognition and transcription of user utterances are described herein. User utterances may be processed by a speech recognition computing device as well as an acoustic model. The acoustic model may be trained using historical user utterance data and machine learning techniques. The acoustic model may be used to determine whether a transcription determined by the speech recognition computing device should be overridden with an updated transcription.

Abstract: A device may be configured to determine whether an audio file is a first type of audio file that is capable of being processed to recognize the voice query based on a characteristic of the audio file itself or a second type of audio file that may require speech recognition processing in order to recognize the voice query associated with the audio file. In determining whether the audio file is a first type of audio file or a second type of audio file, a query filter associated with the device may be configured to access one or more guidance queries. Using the one or more guidance queries, the device may classify the audio file as a first type of audio file or a second type of audio file based on receiving only a portion of the audio file, thereby improving the speed at which the audio file can be processed.

Abstract: Methods, systems, and apparatuses for improved speech recognition and transcription of user utterances are described herein. User utterances may be processed by a speech recognition computing device as well as an acoustic model. The acoustic model may be trained using historical user utterance data and machine learning techniques. The acoustic model may be used to determine whether a transcription determined by the speech recognition computing device should be overridden with an updated transcription.

Abstract: Methods and systems are described for recognizing, based on a voice input, a user and/or a voice command. An algorithm is described herein that processes data associated with a voice input. The data may indicate characteristics of the voice such as a gender, an age, or accent associated with the voice and other metadata. For example, the system may process the data and determine the gender of a voice. The determined characteristics may be used as an input into a voice recognition engine to improve the accuracy of identifying the user who spoke the voice input and identifying a voice command associated with the voice input. For example, the determined gender may be used as a parameter to improve the accuracy of an identified user (e.g., the speaker) or command. The algorithm may adjust, based on gender, parameters such as confidence thresholds used to match voices and voice commands.

Abstract: At least one device can be classified based on user activity. At least one telemetry variable with respect to time may be received for the at least one device. The at least one telemetry variable with respect to time may be used to determine an activity model for the at least one device. Based on the activity model, a probability of the at least one device belonging to a profile or to a type of user may be determined. Based on the type of user of the at least one device, a setting associated with the at least one device may be changed.

Abstract: The present disclosure provides a method for trapping molecules with optical fiber tweezers based on phase transition and crystallization and a method for detecting a Raman spectrum of a persistent organic pollutant, belonging to the technical field of surface-enhanced Raman spectroscopy. Based on quite different solubilities of a substance to be detected in different solvents, dissolved phase small molecules to be detected are transformed into large size crystalline phase molecules through the physical process of phase transition and crystallization. Further, effective trapping of molecules to be detected that are not prone to bonding to noble metal nanoparticles in the vicinity of the noble metal nanoparticles can be achieved by combining the physical process of phase transition and crystallization with the physical trapping technique using optical fiber tweezers, so that the sensitivity of surface-enhanced Raman scattering (SERS) spectrum detection is significantly improved.

Abstract: Methods, systems, and/or apparatuses are described for detecting relevant motion of objects of interest (e.g., persons and vehicles) in surveillance videos. As described herein input data based on a plurality of captured images and/or video is received. The input data may then be pre-processed and used as an input into a convolution network that may, in some instances, have elements that perform both spatial-wise max pooling and temporal-wise max pooling. The convolution network may be used to generate a plurality of prediction results of relevant motion of the objects of interest.

Abstract: A camera system may automatically identify high frequency motion regions in a field of view of the camera system. Analysis of motion events detected at different regions of a scene in the field of view, may be performed. Similar events frequently occurring at a certain region may be recorded/accumulated and compared to a threshold. Regions with high frequency activity may be determined and a user may be notified. If the user is not interested in events within the high activity regions, the regions may be ignored and notifications to the user may cease. The camera system may determine specific actions/events occurring in a region, such as a person approaching or leaving a home.

Abstract: The present disclosure provides a method for trapping molecules with optical fiber tweezers based on phase transition and crystallization and a method for detecting a Raman spectrum of a persistent organic pollutant, belonging to the technical field of surface-enhanced Raman spectroscopy. Based on quite different solubilities of a substance to be detected in different solvents, dissolved phase small molecules to be detected are transformed into large size crystalline phase molecules through the physical process of phase transition and crystallization. Further, effective trapping of molecules to be detected that are not prone to bonding to noble metal nanoparticles in the vicinity of the noble metal nanoparticles can be achieved by combining the physical process of phase transition and crystallization with the physical trapping technique using optical fiber tweezers, so that the sensitivity of surface-enhanced Raman scattering (SERS) spectrum detection is significantly improved.

Abstract: Systems, apparatuses, and methods are described for presence detection of a wireless device in a sensing region. Communication parameters determined by a computing device over one or more periods may be used to generate criteria for determining the occurrence of enter events or leave events. The criteria may comprise, for values of one or more communication parameters, rates of change associated with movement into or out of the sensing region. The criteria may be compared against subsequent data associated with the communication parameters to determine if an enter event or a leave event has occurred.

Abstract: Methods and systems are disclosed for estimating locations of devices operating on a network (such as a wireless network) in a premises. Signal strength measurements of signals sent by the devices may be used to estimate locations of the devices within the premises as well as to estimate anchor points where signals are received from the devices. The devices may be shown on a map of the premises. The anchor points may be shown on a map of the premises. A method may be implemented iteratively to estimate the device locations as well as the locations of the anchor points. Estimated locations of devices and/or anchor points may be used to estimate a location of a device misplaced or lost in the premises.

Abstract: Monitoring a premises may comprise receiving signals from a plurality of devices. One or more spatially static devices of the plurality of devices may be determined based on strengths of the received signals. A user activity pattern may be determined based on changes in strengths of signals received from the spatially static devices. Abnormal user activity may be determined based on the user activity pattern and a strength of at least one signal received from at least one of the spatially static devices.

Abstract: Methods, systems, and/or apparatuses are described for detecting relevant motion of objects of interest (e.g., persons and vehicles) in surveillance videos. As described herein input data based on a plurality of captured images and/or video is received. The input data may then be pre-processed and used as an input into a convolution network that may, in some instances, have elements that perform both spatial-wise max pooling and temporal-wise max pooling. Based on The convolution network may be used to generate a plurality of prediction results of relevant motion of the objects of interest.

Abstract: Methods, systems, and apparatuses for improved speech recognition and transcription of user utterances are described herein. User utterances may be processed by a speech recognition computing device as well as an acoustic model. The acoustic model may be trained using historical user utterance data and machine learning techniques. The acoustic model may be used to determine whether a transcription determined by the speech recognition computing device should be overridden with an updated transcription.

Abstract: Methods and systems are disclosed for determining a probability that a prospective wakeup or activation word is not an actual wakeup or activation word for a user device but instead is a word that has characteristics of, or otherwise sounds similar to, a wakeup or activation word and is received as a result of output of a content asset as opposed to being spoken by a user of the user device. Audio data associated with output of a content asset may be received and evaluated to determine if a prospective wakeup word in the audio data is an actual wakeup word or is, instead, not a wakeup word.

Abstract: Systems, apparatuses, and methods are described for presence detection of wireless device in a sensing region. Communication parameters determined by a computing device over one or more periods may be used to generate criteria for determining occurrence of enter events or of leave events. The criteria may comprise, for values of one or more communication parameters, rates of change associated with movement into or out of a sensing region. The criteria may be compared against subsequent data, associated with the communication parameters, to determine if an enter event or a leave event has occurred.

Abstract: A camera system may automatically identify high frequency motion regions in a field of view of the camera system. Analysis of motion events detected at different regions of a scene in the field of view, may be performed. Similar events frequently occurring at a certain region may be recorded/accumulated and compared to a threshold. Regions with high frequency activity may be determined and a user may be notified. If the user is not interested in events within the high activity regions, the regions may be ignored and notifications to the user may cease. The camera system may determine specific actions/events occurring in a region, such as a person approaching or leaving a home.