A system for use in locating and testing a cable having a first end connected to a first connector and a second end connected to a second connector where the second connector is one of a plurality of connectors within a patch panel. The system includes a first device having a first cable certification and test circuitry which is couplable to the first connector and a second device having a second cable certification and test circuitry which is couplable to the second connector. One of the first device and the second device further includes circuitry for generating a tone signal that is to be applied to the cable and the other one of the first device and the second device further includes a probe for receiving the tone signal and for generating an output indicative of a strength of the received tone signal.

An aspect of the present disclosure includes methods, systems, and computer-readable media for receiving a beam failure detection reference signal configuration, detecting an absence of a configured and quasi-co-located beam failure detection reference signal in a monitored control resource set (CORESET), and utilizing reference signals indicated in the CORESET transmission configuration indicator state for beam failure detection.

An aspect of the present disclosure includes methods, systems, and computer-readable media for receiving a beam failure detection reference signal configuration, detecting an absence of a configured and quasi-co-located beam failure detection reference signal in a monitored control resource set (CORESET), and utilizing reference signals indicated in the CORESET transmission configuration indicator state for beam failure detection.

To improve accuracy of an evaluation in an acoustic quality evaluation test performed by comparing an evaluation target sound and a reference sound. A data correction apparatus 3 compares, in a call performed between a near-end terminal 1 and a far-end terminal 2, an evaluation target sound in which a voice output from the near-end terminal 1 is recorded and a reference sound in which a voice spoken by a call partner using the far-end terminal 2 to correct test data used in a listening test for evaluating acoustic quality of the call. A correction target determination unit 31 determines, as a correction target section, a voiced section that does not include the voice of the call partner detected from an acoustic signal representing the reference sound. A correction execution unit 32 updates the correction target section of the acoustic signal representing the reference sound with a non-voice signal predetermined.

To improve accuracy of an evaluation in an acoustic quality evaluation test performed by comparing an evaluation target sound and a reference sound. A data correction apparatus 3 compares, in a call performed between a near-end terminal 1 and a far-end terminal 2, an evaluation target sound in which a voice output from the near-end terminal 1 is recorded and a reference sound in which a voice spoken by a call partner using the far-end terminal 2 to correct test data used in a listening test for evaluating acoustic quality of the call. A correction target determination unit 31 determines, as a correction target section, a voiced section that does not include the voice of the call partner detected from an acoustic signal representing the reference sound. A correction execution unit 32 updates the correction target section of the acoustic signal representing the reference sound with a non-voice signal predetermined.

The method includes inputting first groups of frequency samples to be transmitted over, respectively received from, first and second subsets of subscriber lines at respective first tones and following a first group selection order, alternated with mirrored second groups of frequency samples to be transmitted over, respectively received from, the second subset of subscriber lines at respective mirrored second tones of the first tones, and following a mirrored second group selection order of the first group selection order, to a vectoring processor for joint mitigation of first crosstalk present over the first and second subsets of subscriber lines at the respective first tones and second crosstalk present over the second subset of subscriber lines at the respective second tones. The first tones are common to the first and second transmit spectra. Mirroring is with respect to a folding frequency derived from a reference sampling frequency applicable to the first subset of subscriber lines.

A system for use in locating and testing a cable having a first end connected to a first connector and a second end connected to a second connector where the second connector is one of a plurality of connectors within a patch panel. The system includes a first device having a first cable certification and test circuitry which is couplable to the first connector and a second device having a second cable certification and test circuitry which is couplable to the second connector. One of the first device and the second device further includes circuitry for generating a tone signal that is to be applied to the cable and the other one of the first device and the second device further includes a probe for receiving the tone signal and for generating an output indicative of a strength of the received tone signal.

A method and an apparatus for classifying a telephone dialing test audio based on AI is provided. Data of a telephone dialing test audio is acquired first, then the data of the telephone dialing test audio is processed via a preset classifier so as to obtain similarities among the data of the telephone dialing test audio and telephone types in the preset classifier, in which the preset classifier is a deep-learning model determined by historical data of telephone dialing test audios and their corresponding telephone types; finally, the telephone type corresponding to the telephone dialing test audio is determined according to the similarities. With the method and apparatus, the telephone dialing test audio is classified via machine learning to determine whether a user is a normal user, thus human costs is saved and an efficiency of dialing test is increased.

A method and an apparatus for classifying a telephone dialing test audio based on AI is provided. Data of a telephone dialing test audio is acquired first, then the data of the telephone dialing test audio is processed via a preset classifier so as to obtain similarities among the data of the telephone dialing test audio and telephone types in the preset classifier, in which the preset classifier is a deep-learning model determined by historical data of telephone dialing test audios and their corresponding telephone types; finally, the telephone type corresponding to the telephone dialing test audio is determined according to the similarities. With the method and apparatus, the telephone dialing test audio is classified via machine learning to determine whether a user is a normal user, thus human costs is saved and an efficiency of dialing test is increased.

Solutions for testing emergency call handling capability include: receiving, into a user equipment (UE), a dialed number (e.g., 922); mapping, by the UE, the dialed number to a test service uniform resource name (URN); and transmitting the test service URN to a wireless network. Further solutions include: receiving, by the wireless network, from the UE, the test service URN; differentiating, by the wireless network, the test service URN from an SOS URN; and based on at least receiving the test service URN, establishing an emergency test voice call between the UE and a public safety answering point (PSAP) emulator. In some examples, the test service URN comprises urn:service and test (e.g., urn:service:test.sos or urn:service:sos.test), and is flagged by a bit field of an emergency service category value. In some examples, the wireless network transmits to the UE, an indication that the dialed number maps to the test service URN.