Abstract: Examples include detecting objects and determining a set of features for the objects. Examples include receiving a first image input, generating a number of feature maps from the first image input using a number of convolution filters, generating a first number of fully connected layers directly based on the number of feature maps, and detecting a number of objects in the first image and determining a set of features for each object from the first number of fully connected layers.

Abstract: Examples herein disclose capturing a wifi signal from a computing device corresponding to a computing accessory and harvesting energy from the captured wifi signal. The examples power the computing accessory based on the harvested energy.

Abstract: An example system comprising: a processing resource; and a memory resource storing machine readable instructions executable to cause the processing resource to: receive a Bluetooth Low Energy (BLE) signal transmitted from a user device; generate, from the BLE signal, a BLE moving pattern of the user device, wherein the BLE moving pattern is generated at a different entity than an entity that transmits the BLE signal; track an object carrying the user device via visual information of the object such that a visual moving pattern of the object is generated from the tracking; determine the visual moving pattern matches the BLE moving pattern; and assign, responsive to the determination, an identity obtained from the user device to the object being tracked via the visual information.

Abstract: A method of adjusting a communication link of a client device of a wireless local area network is described. The method includes monitoring two or more communication links between two or more client devices and a network device in a wireless local area network. The two or more client devices including a first client device and a second client device. The method also includes detecting a degradation in a first communication link between the first client device and the network device. The communication link degradation is not detected in a second communication link between the second client device and the network device. The method further includes determining the degradation in the first communication link is caused at least in part by interference from cellular network communications.

Abstract: An antenna apparatus includes a substrate formed of a dielectric material; a pattern unit disposed on one surface of the substrate and radiating energy of electromagnetic wave; and a patch unit disposed on the substrate to be spaced apart from one side of the pattern unit with a first distance and configured to limit radiation of an energy radiated from the one side of the pattern unit.

Abstract: In some examples, a system for an aggregate interface including a mmWave interface and a WLAN interface consistent with the disclosure includes a sending device to assign a plurality of frames to the WLAN interface and the mmWave interface and send the plurality of frames in a sequence via an aggregate interface, where the aggregate interface includes the WLAN interface and the mmWave interface. Additionally, the system includes a receiving device communicatively coupled to the sending device to determine the plurality of frames is received in a different sequence than the sequence the plurality of frames is sent by the sending device and place the plurality of frames in the sequence the plurality of frames is sent by the sending device.

Abstract: A method is provided for real-time processing of IoT data. For example, a first physical processor of an edge computing device may receive a set of data from a first IoT device communicably coupled to the edge device. The first physical processor may split the set of data into a set of individual data packets. A second physical processor of the edge device process the set of individual data packets by: concurrently applying, by a plurality of instances of the second physical processor of the edge computing device, a learning model to each of a corresponding plurality of data packets from the set of individual data packets; and annotating, by a subset of the plurality of instances of the second physical processor, a corresponding subset of the plurality of data packets with a corresponding output from the concurrent application of the learning model.

Abstract: Examples provided herein describe a method for determining car positions. For example, a physical processor of an edge computing device may receive position data for a legacy car and information about a make and model of the legacy car. The first edge device may also receive, from a sensor-rich car, a set of sensor data about a set of observed cars in the vicinity of the sensor-rich car, a set of position data for the set of observed cars, and a set of visual data of the set of observed cars, wherein the set of observed cars includes the legacy car and the sensor-rich car. The edge device may then determine an updated position for the legacy car based on the set of position data for the set of observed cars, the set of visual data, and the set of sensor data and provide the updated position of the legacy car.

Abstract: Examples disclosed herein involve determining a location of a Bluetooth device relative to a detecting device based on measured received signal strength indication (RSSI). In examples herein, a regression analysis of RSSIs of a Bluetooth signal and locations of a detecting device are used to determine the location of the target Bluetooth device.

Abstract: In some examples, a non-transitory machine-readable storage medium having stored thereon machine-readable instructions to cause a processing resource to determine a intra coded bitrate of a captured video, determine a stutter ratio of the captured video, and estimate a video quality of experience based on the intra coded bitrate and the stutter ratio of the captured video.

Abstract: An example video analytics device can include a memory, a processor executing instructions stored in the memory, an active region detector to identify a plurality of active regions of a plurality of video streams, wherein the plurality of active regions are sections in the plurality of video streams that change from a first frame to a second frame, and a bin packer communicatively coupled with the processor to combine the plurality of active regions to produce a multi-batch of an individual slice, wherein the multi-batch of the individual slice is a batch including the plurality of active regions that is processed at once.

Abstract: An example method of operating a network may include determining whether a flow is to be added to the network based on: a flow type of the flow, a link condition of the flow, and for each possible combination of flow type and link condition out of multiple flow types and multiple link conditions, the number of flows currently carried on the network that correspond to the respective combination.

Abstract: Examples relate to determining finger movements. In one example, a computing device may: receive input from at least one of: a first proximity sensor coupled to the frame at a first position and facing a first direction; or a second proximity sensor coupled to the frame at a second position and facing a second direction; determine, based on the input, that a finger action occurred, the finger action being one of: a first movement of a first finger, the first movement being detected by the first proximity sensor; a second movement of a second finger, the second movement being detected by the second proximity sensor; generate, based on the finger action, output that includes data defining an event that corresponds to the finger action; and provide the output to another computing device.

Abstract: An example system comprising: a processing resource; and a memory resource storing machine readable instructions executable to cause the processing resource to: receive a Bluetooth Low Energy (BLE) signal transmitted from a user device; generate, from the BLE signal, a BLE moving pattern of the user device, wherein the BLE moving pattern is generated at a different entity than an entity that transmits the BLE signal; track an object carrying the user device via visual information of the object such that a visual moving pattern of the object is generated from the tracking; determine the visual moving pattern matches the BLE moving pattern; and assign, responsive to the determination, an identity obtained from the user device to the object being tracked via the visual information.

Abstract: A method for power management of a mobile device. The method includes evaluating content of a plurality of applications received at a mobile device operated by a user and determining latency information for each of the plurality of applications. The method further includes dynamically determining a priority of the plurality of applications based on the latency information for each application, and dynamically adjusting the mobile device between at least two wireless power modes based on the priority of the plurality of applications.

Abstract: Examples disclosed herein relate to locating and tracking a wireless beacon from a wireless device. A data collection module in the wireless device collects and processes sensory data from the wireless beacon. A location estimation module classifies an environment surrounding the wireless beacon based on the sensory data, determines a movement of the wireless device and a movement of the wireless beacon, and estimates a location of the wireless beacon based on the sensory data, the classified environment, the determined movement of the wireless device and the determined movement of the wireless beacon. A calibration module refines the location of the wireless beacon based on neighboring wireless beacons.

Abstract: An example method of operating a network may include determining whether a flow is to be added to the network based on: a flow type of the flow, a link condition of the flow, and for each possible combination of flow type and link condition out of multiple flow types and multiple link conditions, the number of flows currently carried on the network that correspond to the respective combination.

Abstract: A signal transmitted from a mobile device is received at an antenna array of a device. Motion information of the mobile device is received from a sensor of the mobile device. A change in the AoA of the signal is computed when the mobile device moves from a first position to a second position. The location of the mobile device relative to the antenna array is determined based on the change in AoA of the signal and the motion information of the mobile device.

Abstract: Examples include classifying high frequency radio signals. Some examples include receiving a fast Fourier transform (FFT) of a high frequency radio signal, determining a first signal strength at a first guard frequency bin, determining a second signal strength at a second guard frequency bin, and determining a third signal strength at a direct current carrier frequency bin. Examples also include classifying the high frequency radio signal based on the first signal strength, the second signal strength, and the third signal strength.

Abstract: Example implementations relate to mobile virtual private network (mVPN) configuration. For example, a system for mVPN configuration may include a configuration selector to intercept an internet protocol (IP) packet in a mobile virtual private network (mVPN) and select a mVPN configuration for the IP packet using a lookup table. The system may further include a configuration adapter to adapt the IP packet according to the selected mVPN configuration.