Abstract: Methods and systems for the super resolution of high dynamic range (HDR) video are described. Given a sequence of video frames, a current frame and two or more neighboring frames are processed by a neural-network (NN) feature extraction module, followed by a NN upscaling module, and a NN reconstruction module. In parallel, the current frame is upscaled using traditional up-sampling to generate an intermediate up-sampled frame. The output of the reconstruction module is added to the intermediate up-sampled frame to generate an output frame. Additional traditional up-sampling may be performed on the output frame to match the desired up-scaling factor, beyond the up-scaling factor for which the neural network was trained.

Abstract: Methods and systems for the super resolution of high dynamic range (HDR) video are described. Given a sequence of video frames, a current frame and two or more neighboring frames are processed by a neural-network (NN) feature extraction module, followed by a NN upscaling module, and a NN reconstruction module. In parallel, the current frame is upscaled using traditional up-sampling to generate an intermediate up-sampled frame. The output of the reconstruction module is added to the intermediate up-sampled frame to generate an output frame. Additional traditional up-sampling may be performed on the output frame to match the desired up-scaling factor, beyond the up-scaling factor for which the neural network was trained.

Abstract: Methods and systems for multi-step display mapping and metadata reconstruction for high-dynamic range (HDR) images are described. In an encoder, given an HDR input image with input HDR metadata in a first dynamic range, an intermediate, base layer image in a second dynamic range is constructed based on the input image. In a decoder, using base-layer metadata, the input HDR metadata, and dynamic range characteristics of a target display, a processor generates reconstructed metadata which when used in combination with the base layer image allow a display mapping process to map the base layer image to the target display as if it was mapping directly the HDR image to the target display.

Abstract: A system for detecting autonomic dysreflexia (AD) may measure skin nerve activity (skNA), galvanic skin response (GSR), heart rate, and skin temperature of a subject. The system may extract, a plurality of features from the measurements. The features may include medianNN, average iskNA, number of bursts, RMSSD, pNN5, or a combination thereof. The system may classify, based on a machine learning model, the plurality of features to identify the onset of AD in the subject. The system may output, in response to the onset of AD, a message indicative of the onset of AD.

Abstract: A multifunctional wrist orthotic comprising an electromyography (EMG) sensor having at least two electrodes for attachment to a wrist of a user, an intertial measurement sensor (IMU), a microcontroller unit (e.g., a Arduino® Mini) connected to the IMU, a power supply unit. The microcontroller unit is configured to perform two-tiered gesture recognition, with the first tier comprising a fine gesture sensed by the EMG sensor and the second tier comprising a gross gesture sensed by the IMU sensor.

Abstract: System, device and methods for brainwave-based technologies. The system for measuring and processing brainwave data of a user comprises one or more electrodes for measuring the brainwave data of the user, and a processing unit coupled to the electrodes and configured to process the brainwave data for determining a current mental state of the user and to generate, based on the current mental state of the user, a control signal for instructing activation of a means for manipulating the current mental state of the user.