Abstract: Systems and methods are provided for detecting the presence of a hidden camera on a network. When video is encoded and transmitted over/across a network, the data packet carrying the video tend to exhibit certain characteristics or features specific to video traffic from a hidden camera. A machine learning model for detecting the presence of a hidden camera can be trained based on these characteristics and features. Once trained, the machine learning model can be operationalized on an access point that can analyze real-time network traffic to determine whether a hidden camera(s) is operating on the network.

Abstract: Implementations of the present disclosure relate to communication simulation between an access point and an electronic device. A method comprises obtaining a MAC protocol data unit (MPDU) for simulating a communication between an access point and an electronic device, and configuring a receiver address and a transmitter address of the MPDU. The method further comprises updating a header of the MPDU based on the configured receiver address and the configured transmitter address, and transmitting the MPDU according to the updated header. With these implementations, communications between the AP and the electronic device can be simulated by the AP.

Abstract: In implementations of the present disclosure, there is provided an approach for scheduling uplink traffic of a station. A method comprises determining a type of uplink traffic from a station (STA), wherein the type of uplink traffic is associated with a traffic size and an urgency. The method further comprises determining uplink scheduling information for the STA based on the traffic size and the urgency, wherein the uplink scheduling information indicates at least one of: a UL-OFDMA scheduling manner or a UL-MU-MIMO scheduling manner. The method further comprises sending the uplink scheduling information to the STA, and receiving uplink traffic from the STA using the uplink scheduling information. Implementations of the present disclosure can help AP to schedule the STA(s) more intelligent, effective and accurate, and thus the user experience can be enhanced and the transmission performance can be improved.

Abstract: A display panel and a display device, related to the field of display technology. The display panel includes a first sub-pixel, a first pixel circuit, a second sub-pixel and a second pixel circuit. The first sub-pixel and the second sub-pixel have the same luminous color. When the display panel is configured to display a same frame of image, the first sub-pixel is driven by the first pixel circuit to emit light, or the second sub-pixel is driven by the second pixel circuit to emit light. In this case, when multiple frames of images are continuously displayed by the display panel, the first sub-pixel and the second sub-pixel can emit light alternately, such that the service life of the display panel can be prolonged, and the performance and competitiveness of the product can be improved.

Abstract: A display panel and a display device, related to the field of display technology. The display panel includes a first sub-pixel, a first pixel circuit, a second sub-pixel and a second pixel circuit. The first sub-pixel and the second sub-pixel have the same luminous color. When the display panel is configured to display a same frame of image, the first sub-pixel is driven by the first pixel circuit to emit light, or the second sub-pixel is driven by the second pixel circuit to emit light. In this case, when multiple frames of images are continuously displayed by the display panel, the first sub-pixel and the second sub-pixel can emit light alternately, such that the service life of the display panel can be prolonged, and the performance and competitiveness of the product can be improved.

Abstract: Examples of techniques for optimizing Neighbour Report (NR) are disclosed. In an example, a first virtual access point (VAP) operating in a 2.4 Giga Hertz (GHz) or 5 GHz band may determine a first Service Set Identifier (SSID) of the first VAP operating in a 2.4 GHz or 5 GHz band. The first VAP may identify a set of co-located VAPs operating in a 6 GHz band. The first VAP may filter the set of co-located VAPs to obtain a neighbour list comprising a second VAP, from the set, having a second SSID identical to the first SSID. Using the neighbour list, the first VAP may generate a Neighbour Report.

Abstract: Examples described herein relate to a method of handling block acknowledgements. A transmitting device transmits a first packet to a receiving device in response to determining that a count of unacknowledged frames is smaller than a threshold value. In particular, the transmitting device configures the first packet such that the receiving device does not need to send a block acknowledgement (BA) immediately after receiving the first packet. Further, before transmitting any additional packet to the receiving device, the transmitting device may again ascertain whether the count of the unacknowledged frames is smaller than the threshold value. In response to determining that the count of the unacknowledged frames is greater than or equal to the threshold value, the transmitting device transmits a second packet requesting an immediate BA. The transmitting device may receive a BA for the unacknowledged frames in response to receipt of the second packet by the receiving device.

Abstract: Examples of techniques for optimizing Neighbour Report (NR) are disclosed. In an example, a first virtual access point (VAP) operating in a 2.4 Giga Hertz (GHz) or 5 GHz band may determine a first Service Set Identifier (SSID) of the first VAP operating in a 2.4 GHz or 5 GHz band. The first VAP may identify a set of co-located VAPs operating in a 6 GHz band. The first VAP may filter the set of co-located VAPs to obtain a neighbour list comprising a second VAP, from the set, having a second SSID identical to the first SSID. Using the neighbour list, the first VAP may generate a Neighbour Report.

Abstract: Systems and methods are provided for detecting the presence of a hidden camera on a network. When video is encoded and transmitted over/across a network, the data packet carrying the video tend to exhibit certain characteristics or features specific to video traffic from a hidden camera. A machine learning model for detecting the presence of a hidden camera can be trained based on these characteristics and features. Once trained, the machine learning model can be operationalized on an access point that can analyze real-time network traffic to determine whether a hidden camera(s) is operating on the network.