Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store sensor data captured by one or more sensors associated with a first device. Further, the processor comprises circuitry to: access the sensor data captured by the one or more sensors associated with the first device; determine that an incident occurred within a vicinity of the first device; identify a first collection of sensor data associated with the incident, wherein the first collection of sensor data is identified from the sensor data captured by the one or more sensors; preserve, on the memory, the first collection of sensor data associated with the incident; and notify one or more second devices of the incident, wherein the one or more second devices are located within the vicinity of the first device.

Abstract: User inputs received through a graphical user interface of a programming tool are used to define a set of capability abstractions for a particular application and further define, based on the one or more user inputs, relationships between the set of capability abstractions. The particular application is to utilize a machine-to-machine network, and the set of capability abstractions include: a sensor capability abstraction, an actuator capability abstraction, a computation logic capability abstraction, an input user interface (UI) capability abstraction, and an output UI capability abstraction for the particular application. The relationships include a relationship between the input UI capability abstraction and the computation logic capability abstraction, where the input UI capability is to provide an input to the computation logic capability abstraction.

Abstract: In one embodiment, an apparatus comprises processing circuitry to: receive, via a communication interface, a compressed video stream captured by a camera, wherein the compressed video stream comprises: a first compressed frame; and a second compressed frame, wherein the second compressed frame is compressed based at least in part on the first compressed frame, and wherein the second compressed frame comprises a plurality of motion vectors; decompress the first compressed frame into a first decompressed frame; perform pixel-domain object detection to detect an object at a first position in the first decompressed frame; and perform compressed-domain object detection to detect the object at a second position in the second compressed frame, wherein the object is detected at the second position in the second compressed frame based on: the first position of the object in the first decompressed frame; and the plurality of motion vectors from the second compressed frame.

Abstract: Techniques related to applying computer vision to decompressed video are discussed. Such techniques may include generating a region of interest in an individual video frame by translating spatial indicators of a first detected computer vision result from a reference video frame to the individual video frame and applying a greater threshold within the region of interest than outside of the region of interest for computer vision evaluation in the individual frame.

Abstract: Techniques related to applying computer vision to decompressed video are discussed. Such techniques may include generating a region of interest in an individual video frame by translating spatial indicators of a first detected computer vision result from a reference video frame to the individual video frame and applying a greater threshold within the region of interest than outside of the region of interest for computer vision evaluation in the individual frame.

Abstract: Disclosed herein is a novel apparatus and the uses thereof in the prophylaxis and/or treatment of neuropsychiatric disorders. The present apparatus comprises a detecting means, a stimulation means, a virtual reality means and a processor. According to some embodiments of the present disclosure, the present apparatus produces an additive or synergistic effect on the treatment of neuropsychiatric disorders.

Abstract: User inputs received through a graphical user interface of a programming tool are used to define a set of capability abstractions for a particular application and further define, based on the one or more user inputs, relationships between the set of capability abstractions. The particular application is to utilize a machine-to-machine network, and the set of capability abstractions include: a sensor capability abstraction, an actuator capability abstraction, a computation logic capability abstraction, an input user interface (UI) capability abstraction, and an output UI capability abstraction for the particular application. The relationships include a relationship between the input UI capability abstraction and the computation logic capability abstraction, where the input UI capability is to provide an input to the computation logic capability abstraction.

Abstract: Methods and systems to translate input labels of arcs of a network, corresponding to a sequence of states of the network, to a list of output grammar elements of the arcs, corresponding to a sequence of grammar elements. The network may include a plurality of speech recognition models combined with a weighted finite state machine transducer (WFST). Traversal may include active arc traversal, and may include active arc propagation. Arcs may be processed in parallel, including arcs originating from multiple source states and directed to a common destination state. Self-loops associated with states may be modeled within outgoing arcs of the states, which may reduce synchronization operations. Tasks may be ordered with respect to cache-data locality to associate tasks with processing threads based at least in part on whether another task associated with a corresponding data object was previously assigned to the thread.

Abstract: In one embodiment, an apparatus comprises a storage device and a processor. The storage device may store a plurality of compressed images comprising one or more compressed master images and one or more compressed slave images.

Abstract: Techniques related to applying computer vision to decompressed video are discussed. Such techniques may include generating a region of interest in an individual video frame by translating spatial indicators of a first detected computer vision result from a reference video frame to the individual video frame and applying a greater threshold within the region of interest than outside of the region of interest for computer vision evaluation in the individual frame.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store sensor data captured by one or more sensors associated with a first device. Further, the processor comprises circuitry to: access the sensor data captured by the one or more sensors associated with the first device; determine that an incident occurred within a vicinity of the first device; identify a first collection of sensor data associated with the incident, wherein the first collection of sensor data is identified from the sensor data captured by the one or more sensors; preserve, on the memory, the first collection of sensor data associated with the incident; and notify one or more second devices of the incident, wherein the one or more second devices are located within the vicinity of the first device.

Abstract: In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of devices. The processor is to: receive compressed data from a first device, wherein the compressed data is associated with visual data captured by sensor(s); perform a current stage of processing on the compressed data using a current CNN, wherein the current stage of processing corresponds to one of a plurality of processing stages associated with the visual data, and wherein the current CNN corresponds to one of a plurality of CNNs associated with the plurality of processing stages; obtain an output associated with the current stage of processing; determine, based on the output, whether processing associated with the visual data is complete; if the processing is complete, output a result associated with the visual data; if the processing is incomplete, transmit the compressed data to a second device.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

Abstract: In one embodiment, an apparatus comprises a processor to: identify a workload comprising a plurality of tasks; generate a workload graph based on the workload, wherein the workload graph comprises information associated with the plurality of tasks; identify a device connectivity graph, wherein the device connectivity graph comprises device connectivity information associated with a plurality of processing devices; identify a privacy policy associated with the workload; identify privacy level information associated with the plurality of processing devices; identify a privacy constraint based on the privacy policy and the privacy level information; and determine a workload schedule, wherein the workload schedule comprises a mapping of the workload onto the plurality of processing devices, and wherein the workload schedule is determined based on the privacy constraint, the workload graph, and the device connectivity graph.

Abstract: Multiple devices are detected in an environment and a user input is received to define a relationship between two or more devices in the plurality of devices. A system can determine that a first of the two or more devices includes a sensor resource and a second of the two or more devices includes an actuator resource. Data is identified describing outputs of the first device corresponding to the sensor resource and inputs of the second device corresponding to the actuator resource. A model is generated modeling interoperation of the sensor resource and actuator resource based at least in part on the data.

Abstract: According to a first aspect, efficient data transfer operations can be achieved by: decoding by a processor device, a single instruction specifying a transfer operation for a plurality of data elements between a first storage location and a second storage location; issuing the single instruction for execution by an execution unit in the processor; detecting an occurrence of an exception during execution of the single instruction; and in response to the exception, delivering pending traps or interrupts to an exception handler prior to delivering the exception.

Abstract: Data is received describing a local model of a first device generated by the first device based on sensor readings at the first device and a global model is updated that is hosted remote from the first device based on the local model and modeling devices in a plurality of different asset taxonomies. A particular operating state affecting one or more of a set of devices deployed in a particular machine-to-machine network is detected and the particular machine-to-machine network is automatically reconfigured based on the global model.

Abstract: A plurality of devices are detected within range of a particular device, and capabilities of each of the plurality of devices are determined, as well as a respective taxonomy to be associated with each device based on the device's capabilities. A set of asset abstractions are identified, referenced by a particular software application configured to manage a particular machine-to-machine network. Each asset abstraction can correspond to a respective one of the set of taxonomies, and the particular machine-to-machine network can be built from an instance of each one of the set of asset abstractions. A subset of the plurality of devices can be selected for deployment in the particular machine-to-machine network based on the associated taxonomies, the subset of devices representing an instance of each one of the set of asset abstractions. The subset of devices are then automatically deployed to implement the instance of the particular machine-to-machine network.

Abstract: Various systems and methods for lossless data compression are described herein. A process for lossless data compression includes hashing an input byte stream to produce a hash key; identifying a set of dictionary entries in a hash table using the hash key, the hash key associated with a word from a compact dictionary; identifying a set of candidate words from the compact dictionary based on the identified set of dictionary entries, the compact dictionary being a subset of a standard dictionary; determining a best match of the set of candidate words with the input byte stream; and encoding the best match of the set of candidate words as a compressed output of the input byte stream, the encoding including an operation to determine an index into the standard dictionary of the best match and using the index in the encoding operation.

Abstract: An adaptive video end-to-end network is described that uses local abstraction. One example includes an image sensor to generate a sequence of images, a processor coupled to the image sensor to analyze the sequence of images to detect an event, to select images related to the event and to generate metadata regarding the event, and a communications interface coupled to the processor to send the metadata information through a network connection to a central node.