Abstract: A compression system trains a machine-learned compression model that includes components for an encoder and decoder. In one embodiment, the compression model is trained to receive parameter information on how a target frame should be encoded with respect to one or more encoding parameters, and encodes the target frame according to the respective values of the encoding parameters for the target frame. In particular, the encoder of the compression model includes at least an encoding system configured to encode a target frame and generate compressed code that can be transmitted by, for example, a sender system to a receiver system. The decoder of the compression model includes a decoding system trained in conjunction with the encoding system. The decoding system is configured to receive the compressed code for the target frame and reconstruct the target frame for the receiver system.

Abstract: A cloud service system manages a filter repository including filters for encoding and decoding media content (e.g. text, image, audio, video, etc.). The cloud service system may receive a request from a client device to provide a filter for installation on a node such as an endpoint device (e.g. pipeline node). The request includes information such as a type of bitstream to be processed by the requested filter. The request may further include other information such as hardware configuration and functionality attribute. The cloud service system may access the filter repository that stores the plurality of filters including encoder filters and decoder filters and may select a filter that is configured to process the type of bitstream identified in the request and provide the selected filter to the client device.

Abstract: A cloud service system manages a filter repository including filters for encoding and decoding media content (e.g. text, image, audio, video, etc.). The cloud service system may receive a request from a client device to provide a filter for installation on a node such as an endpoint device (e.g. pipeline node). The request includes information such as a type of bitstream to be processed by the requested filter. The request may further include other information such as hardware configuration and functionality attribute. The cloud service system may access the filter repository that stores the plurality of filters including encoder filters and decoder filters and may select a filter that is configured to process the type of bitstream identified in the request and provide the selected filter to the client device.

Abstract: A compression system trains a machine-learned compression model that includes components for an encoder and decoder. In one embodiment, the compression model is trained to receive parameter information on how a target frame should be encoded with respect to one or more encoding parameters, and encodes the target frame according to the respective values of the encoding parameters for the target frame. In particular, the encoder of the compression model includes at least an encoding system configured to encode a target frame and generate compressed code that can be transmitted by, for example, a sender system to a receiver system. The decoder of the compression model includes a decoding system trained in conjunction with the encoding system. The decoding system is configured to receive the compressed code for the target frame and reconstruct the target frame for the receiver system.

Abstract: A compression system trains a compression model for an encoder and decoder. In one embodiment, the compression model includes a machine-learned in-loop flow predictor that generates a flow prediction from previously reconstructed frames. The machine-learned flow predictor is coupled to receive a set of previously reconstructed frames and output a flow prediction for a target frame that is an estimation of the flow for the target frame. In particular, since the flow prediction can be generated by the decoder using the set of previously reconstructed frames, the encoder may transmit a flow delta that indicates a difference between the flow prediction and the actual flow for the target frame, instead of transmitting the flow itself. In this manner, the encoder can transmit a significantly smaller number of bits to the receiver, improving computational efficiency.

Abstract: A cloud service system manages a filter repository including filters for encoding and decoding media content (e.g. text, image, audio, video, etc.). The cloud service system may receive a request from a client device to provide a filter for installation on a node such as an endpoint device (e.g. pipeline node). The request includes information such as a type of bitstream to be processed by the requested filter. The request may further include other information such as hardware configuration and functionality attribute. The cloud service system may access the filter repository that stores the plurality of filters including encoder filters and decoder filters and may select a filter that is configured to process the type of bitstream identified in the request and provide the selected filter to the client device.

Abstract: A compression system trains a machine-learned compression model that includes components for an encoder and decoder. In one embodiment, the compression model is trained to receive parameter information on how a target frame should be encoded with respect to one or more encoding parameters, and encodes the target frame according to the respective values of the encoding parameters for the target frame. In particular, the encoder of the compression model includes at least an encoding system configured to encode a target frame and generate compressed code that can be transmitted by, for example, a sender system to a receiver system. The decoder of the compression model includes a decoding system trained in conjunction with the encoding system. The decoding system is configured to receive the compressed code for the target frame and reconstruct the target frame for the receiver system.

Abstract: A compression system trains a compression model for an encoder and decoder. In one embodiment, the compression model includes a machine-learned in-loop flow predictor that generates a flow prediction from previously reconstructed frames. The machine-learned flow predictor is coupled to receive a set of previously reconstructed frames and output a flow prediction for a target frame that is an estimation of the flow for the target frame. In particular, since the flow prediction can be generated by the decoder using the set of previously reconstructed frames, the encoder may transmit a flow delta that indicates a difference between the flow prediction and the actual flow for the target frame, instead of transmitting the flow itself. In this manner, the encoder can transmit a significantly smaller number of bits to the receiver, improving computational efficiency.

Abstract: Various embodiments provide for a front-pivot, pan-tilt-zoom camera designed to fit into an opening formed in a chain-link fence to enable video recording and photography of activities through the narrow opening in the fence. In some embodiments, the camera can be mounted to the chain-link fence and allow remote operation of the motorized pan and tilt control as well as zoom, focus, exposure and other common camera features. The pan and tilt mechanism pivots the imaging device in the camera around an axis of rotation in front of the imaging device which allows the field of view of the imaging device to be changed while remaining inserted into the fence opening.

Abstract: Various embodiments provide for a camera clustering and tracking system that can facilitate communications between cameras at an event to coordinate filming and coverage of an event. The camera can identify, locate, and cooperate with other similar cameras in order to share video analytics of the event and coordinate viewing angles to optimize event coverage. The camera can self-select the image they capture by controlling pan, tilt, zoom, focus, iris, frame-rate and/or rotate based on the shared video analytics and can communicate with other cameras through a wireless, wired, infra-red, or audio-modulated communication channel.

Abstract: Various embodiments provide for a telescoping monopod or a tripod with a telescoping pole that allows an imaging device to be mounted thereon and be remotely controlled via a mobile device. A mounting assembly on the monopod can include motors that facilitate pan and tilt functionality, and a controller that operates the motors and the imaging device. The controller can control the motors and imaging device based on instruction received wirelessly from a mobile device. A base enclosure in the monopod can include one or more batteries that power the imaging device and mounting assembly and also include memory for digital storage of the images recorded by the imaging device.

Abstract: Various embodiments provide for a front-pivot, pan-tilt-zoom camera designed to fit into an opening formed in a chain-link fence to enable video recording and photography of activities through the narrow opening in the fence. In some embodiments, the camera can be mounted to the chain-link fence and allow remote operation of the motorized pan and tilt control as well as zoom, focus, exposure and other common camera features. The pan and tilt mechanism pivots the imaging device in the camera around an axis of rotation in front of the imaging device which allows the field of view of the imaging device to be changed while remaining inserted into the fence opening.

Abstract: Various embodiments provide for a telescoping monopod or a tripod with a telescoping pole that allows an imaging device to be mounted thereon and be remotely controlled via a mobile device. A mounting assembly on the monopod can include motors that facilitate pan and tilt functionality, and a controller that operates the motors and the imaging device. The controller can control the motors and imaging device based on instruction received wirelessly from a mobile device. A base enclosure in the monopod can include one or more batteries that power the imaging device and mounting assembly and also include memory for digital storage of the images recorded by the imaging device.

Abstract: A heterogeneous device including multiple types of resources is provided to implement multiple logic functions. Logic functions are provided with multiple configuration options. In one example, an optimal set of configuration options along with a target device are selected using cost and resource availability information associated with multiple heterogeneous programmable chips and the configuration options provided with the logic blocks.

Abstract: Various techniques permit more thorough development of digital systems and devices by designers while protecting the proprietary interests of the owners of the intellectual property incorporated in such systems and devices. More specifically, systems, apparatus, methods and computer program products control use of hardware and software and IP implemented as user logic in a programmed device. The programmed device includes programmed logic that has designed logic and control logic. The designed logic includes user logic and protected logic such as IP cores, the use of which is to be controlled. The control logic includes a signal generator external to the designed logic. This signal generator contains clock means and a timeout circuit. A controller is connected to the programmed device by a tether. The controller periodically sends a keep alive signal via the tether to the signal generator.

Abstract: Various techniques permit more thorough development of digital systems and devices by designers while protecting the proprietary interests of the owners of the intellectual property incorporated in such systems and devices. More specifically, systems, apparatus, methods and computer program products control use of hardware and software and IP implemented as user logic in a programmed device. The programmed device includes programmed logic that has designed logic and control logic. The designed logic includes user logic and protected logic such as IP cores, the use of which is to be controlled. The control logic includes a signal generator external to the designed logic. This signal generator contains clock means and a timeout circuit. A controller is connected to the programmed device by a tether. The controller periodically sends a keep alive signal via the tether to the signal generator.