Abstract: A live flaw detection system for a multi-bundled conductor splicing sleeve and an application method thereof are disclosed. The system includes an upper apparatus and a lower apparatus, where the upper apparatus includes an unmanned aerial vehicle and an industrial X-ray machine, and a laser sensor, and the lower apparatus includes a press plate frame apparatus, vertical screw slide table modules, a horizontal screw slide table module, a projection imager, and a linear retractable apparatus. The unmanned aerial vehicle functions as a power apparatus that controls the system to be online or offline, the industrial X-ray machine is configured to perform ray flaw detection on each splicing sleeve, the laser sensor is configured to guide the unmanned aerial vehicle to land the lower apparatus on splicing sleeves accurately, and the press plate frame apparatus is configured to fixedly clamp the splicing sleeves.

Abstract: Technologies for measuring and controlling live video latency are disclosed. Embodiments capture a live video scene, ingest the live video scene into a live video stream, and encode the live video stream with data that can be used to compute latency measurements. Embodiments communicate the live video stream to a content distribution network. The live video stream is distributed, directly or indirectly by the content distribution network, to one or more user systems. The one or more user systems present the live video stream to one or more users. Embodiments determine a latency of the live video stream based on, for example, a measurement that is obtained during the capturing of the live video stream and another measurement that is obtained during or in response to the presenting of the live video stream to the one or more users.

Abstract: Machine learning based method and system for video profile creation are described. The technical problem of permitting users to create professional quality video profile without requiring the expertise in using specialized video creation tools is addressed by a video profile creation system powered by machine learning methodologies. In one embodiment, the video profile creation system provides a guided video recording process, and also provides a video processing pipeline, where a recorded video is transformed into a professional-looking video profile.

Abstract: Machine learning based method and system for video profile creation are described. The technical problem of permitting users to create professional quality video profile without requiring the expertise in using specialized video creation tools is addressed by a video profile creation system powered by machine learning methodologies. In one embodiment, the video profile creation system provides a guided video recording process, and also provides a video processing pipeline, where a recorded video is transformed into a professional-looking video profile.

Abstract: The disclosed embodiments provide a system for coordinating distributed task execution. During operation, the system retrieves a first batch of tasks from multiple message queues in a distributed messaging system. Next, the system assigns the first batch of tasks to multiple task executors in a thread pool based on availabilities of the multiple task executors. The system also tracks statuses associated with processing the first batch of tasks based on communications from the multiple task executors. The system further periodically commits, based on the tracked statuses, offsets of completed tasks in the multiple message queues to the distributed messaging system.

Abstract: Computer-implemented techniques facilitate end-to-end testing of a live streaming service and a content management service providing a live streaming platform integrated with live streaming service. The techniques can be used to test live stream ingestion as well as live stream delivery. Further, the techniques encompass a live player that can be used to test delivery and consumption of adaptive bitrate live streams that are delivered from endpoint servers of the live streaming service.

Abstract: The disclosed embodiments provide a system for coordinating distributed task execution. During operation, the system retrieves a first batch of tasks from multiple message queues in a distributed messaging system. Next, the system assigns the first batch of tasks to multiple task executors in a thread pool based on availabilities of the multiple task executors. The system also tracks statuses associated with processing the first batch of tasks based on communications from the multiple task executors. The system further periodically commits, based on the tracked statuses, offsets of completed tasks in the multiple message queues to the distributed messaging system.

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: Systems and techniques for a large scale online lossless animated GIF processor are described herein. In an example, a lossless animated GIF processor is adapted to receive an animated GIF image and decode a first and second frame of the animated GIF image, wherein the decoding identifies a disposal method for each frame. The lossless animated GIF processor may determine an optimized disposal method for the second frame based on transparency pixels in the second frame and an overlap estimation between the second frame and the first frame. The lossless animated GIF processor may encode the second frame with the optimized disposal method. The lossless animated GIF processor may be further adapted to identify pixels in an area of interest, designate pixels outside the area of interest as transparent, and encode the area of interest and the pixels designated as transparent for the second frame.

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 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: Systems and techniques for a large scale online lossless animated GIF processor are described herein. In an example, a lossless animated GIF processor is adapted to receive an animated GIF image and decode a first and second frame of the animated GIF image, wherein the decoding identifies a disposal method for each frame. The lossless animated GIF processor may determine an optimized disposal method for the second frame based on transparency pixels in the second frame and an overlap estimation between the second frame and the first frame. The lossless animated GIF processor may encode the second frame with the optimized disposal method. The lossless animated GIF processor may be further adapted to identify pixels in an area of interest, designate pixels outside the area of interest as transparent, and encode the area of interest and the pixels designated as transparent for the second frame.

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: 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: 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: 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: 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.