A method of inter-frequency or inter-RAT (Radio Access Technology) measurement for a user equipment in a wireless communication system is provided. The method comprises receiving configuration information of measurement gap from a network of the wireless communication system, obtaining a list of frequencies or RATs to be measured, and allocating at least two of the frequencies or RATs in the measurement gap, to perform cell search or measurement on the at least two frequencies or RATs within the measurement gap.

A method of inter-frequency or inter-RAT (Radio Access Technology) measurement for a user equipment in a wireless communication system is provided. The method comprises receiving configuration information of measurement gap from a network of the wireless communication system, obtaining a list of frequencies or RATs to be measured, and allocating at least two of the frequencies or RATs in the measurement gap, to perform cell search or measurement on the at least two frequencies or RATs within the measurement gap.

Systems, methods, and computer-readable media for call classification and for training a model for call classification, an example method comprising: receiving DTMF information from a plurality of calls; determining, for each of the calls, a feature vector including statistics based on DTMF information such as DTMF residual signal comprising channel noise and additive noise; training a model for classification; comparing a new call feature vector to the model; predicting a device type and geographic location based on the comparison of the new call feature vector to the model; classifying the call as spoofed or genuine; and authenticating a call or altering an IVR call flow.

Systems, methods, and computer-readable media for call classification and for training a model for call classification, an example method comprising: receiving DTMF information from a plurality of calls; determining, for each of the calls, a feature vector including statistics based on DTMF information such as DTMF residual signal comprising channel noise and additive noise; training a model for classification; comparing a new call feature vector to the model; predicting a device type and geographic location based on the comparison of the new call feature vector to the model; classifying the call as spoofed or genuine; and authenticating a call or altering an IVR call flow.

Systems, methods, and computer-readable media for call classification and for training a model for call classification, an example method comprising: receiving DTMF information from a plurality of calls; determining, for each of the calls, a feature vector including statistics based on DTMF information such as DTMF residual signal comprising channel noise and additive noise; training a model for classification; comparing a new call feature vector to the model; predicting a device type and geographic location based on the comparison of the new call feature vector to the model; classifying the call as spoofed or genuine; and authenticating a call or altering an IVR call flow.

Systems, methods, and computer-readable media for call classification and for training a model for call classification, an example method comprising: receiving DTMF information from a plurality of calls; determining, for each of the calls, a feature vector including statistics based on DTMF information such as DTMF residual signal comprising channel noise and additive noise; training a model for classification; comparing a new call feature vector to the model; predicting a device type and geographic location based on the comparison of the new call feature vector to the model; classifying the call as spoofed or genuine; and authenticating a call or altering an IVR call flow.

Systems, methods, and computer-readable media for call classification and for training a model for call classification, an example method comprising: receiving DTMF information from a plurality of calls; determining, for each of the calls, a feature vector including statistics based on DTMF information such as DTMF residual signal comprising channel noise and additive noise; training a model for classification; comparing a new call feature vector to the model; predicting a device type and geographic location based on the comparison of the new call feature vector to the model; classifying the call as spoofed or genuine; and authenticating a call or altering an IVR call flow.

Systems, methods, and computer-readable media for call classification and for training a model for call classification, an example method comprising: receiving DTMF information from a plurality of calls; determining, for each of the calls, a feature vector including statistics based on DTMF information such as DTMF residual signal comprising channel noise and additive noise; training a model for classification; comparing a new call feature vector to the model; predicting a device type and geographic location based on the comparison of the new call feature vector to the model; classifying the call as spoofed or genuine; and authenticating a call or altering an IVR call flow.

The present invention is directed to device and method for monitoring power input to a networked device The device includes a housing; an optional power converter configured for AC to DC, AC to AC or DC to DC conversion; a controller enclosed in the housing and configured to send one or more notifications and receive one or more instructions from a remote server; a relay operably coupled to the controller; a first circuit electrically connecting the power converter to power mains; a second circuit electrically connecting the power converter to the relay; and a third circuit electrically connecting the relay to the networked device, wherein the controller is configured to determine alternating current status on the first circuit and a direct current status on the second circuit or the third circuit.

The present invention is directed to device and method for monitoring power input to a networked device The device includes a housing; an optional power converter configured for AC to DC, AC to AC or DC to DC conversion; a controller enclosed in the housing and configured to send one or more notifications and receive one or more instructions from a remote server; a relay operably coupled to the controller; a first circuit electrically connecting the power converter to power mains; a second circuit electrically connecting the power converter to the relay; and a third circuit electrically connecting the relay to the networked device, wherein the controller is configured to determine alternating current status on the first circuit and a direct current status on the second circuit or the third circuit.