Deep learning-based generative models are an active area of research with numerous advancements in recent years. Most widely, generative models are based on convolutional neural network (CNN) architectures. In signal and image processing tasks, such as superresolution, 3D modeling, and more, implicit neural representations (INRs) can represent an image...

Advances in deep learning have resulted in state-of-the-art performance for a wide variety of computer vision tasks. The large quantity of training data and high computation resources have made convolutional neural networks (CNNs) a common backbone model for many of these tasks, including image classification, object detection, segmentation, unsupervised...

Background A critical performance requirement for embedded computer vision is energy-efficiency in order to preserve battery life for mobile and autonomous platforms. In particular, the image sensor and readout can take up a significant amount of energy in a computer vision pipeline, particularly if the sensor is capturing and processing video data...

Background Despite the upward trends in the solar array industry, adoption of solar technology by main power grids has been challenged by environmental fluctuations. In particular, the shading of arrays caused by cloud cover leads to inconsistent and thus unreliable energy generation. However, effective sensing and characterization of cloud movements...

Compressed Sensing (CS) technologies are becoming increasingly important in the signal processing field. Sensors in CS do not output the signal of interest, but instead, output a vector of measurement composed of mixtures of the signal’s components. This compressively sensed data can be reconstructed, and the results may be used for purposes such...