Validating method classifying digitally modulated signals

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The INC chalk talk series meets bi-weekly as a forum for interactive exchange on all aspects of neural computation.The purpose of these meetings is to foster the collaborative interactions between INC members and with colleagues across campus, and to stimulate new ideas and research initiatives.Each meeting features one of the core or affiliated INC faculty labs/groups, with informal presentation of late-breaking research and new research directions.The meetings are open to the community, and we encourage broad participation across campus.In order to increase the efficiency of generating low vs high resolution training data, we used manually acquired image pairs to generate a model for downsampling large amounts of high resolution data.Testing was performed by comparing the resolution of the upsampled output from the DNN with its corresponding “ground truth” high-resolution image.Instead of time stepping all simulation variables uniformly in space using time-centering techniques, EMAPS builds a self-adaptive trajectory for each variable (“state”) based on a predicted change to that variable and a posteriori self-driven corrections caused by dynamic interactions with other dependent variables (states). Karimabadi, A time-accurate explicit multi-scale technique for gas dynamics, J. In the process of obtaining a digital image, there is a loss of spatial resolution caused by diffraction due to wave phenomena, by optical distortion due to imperfections in the optical path, motion blur due to limited shutter speed, noise that occurs within the sensor or during transmission, etc.As a result, the numerical model progresses in simulation time via asynchronous computation of discrete events (local updates) generated by the underlying physics in a “game of life” fashion. Hence, an increase in spatial resolution is not guaranteed simply by reducing the pixel size in the design of the image sensor.

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EMAPS (Event-driven Multiscale Asynchronous Parallel Simulation) offers a different approach to time integration, derived from Discrete-Event Simulation. For example, HR medical images are critical in making a correct diagnosis; HR satellite imagery offer unprecedented detail in object detection and registration, and HR computer vision boosts the performance of pattern recognition.The staggering statistics related to opioid use highlight the importance of developing, testing, and validating fast-acting nonpharmacological approaches to treat pain. He was recently awarded the National Institutes of Health’s Mitchel Max Award in Research Excellence.Mindfulness meditation is a technique that has been found to significantly reduce pain in experimental and clinical settings. Zeidan, UCSD Assistant Professor of Anesthesiology, will delineate findings from recent work in his and other laboratories demonstrating that mindfulness meditation significantly attenuates pain through multiple, unique psychological, physiological and neural mechanisms that are distinct from placebo analgesia. His work is currently funded by the National Institutes of Health and the Mind and Life Institute.In multi-scale systems the number of active states at any moment may constitute only a small fraction of the total computational space, which results in a significant performance boost. Superresolution (SR) image enhancement is obtained from the processing of observed multiple low-resolution (LR) images.In the presence of strong nonlinearities and numerical noise (e.g., in particle-based models) event-driven integration has been shown to preserve numerical accuracy and stability better than formally higher order explicit schemes. Karimabadi, Self-adaptive time integration of flux-conservative equations with sources, J. Model-based SR techniques extract additional information from the sequence of LR images given knowledge on the underlying physics of the imaging process.

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