Real-time monitoring and control of distribution networks was traditionally deemed unnecessary because it had radial configuration, unidirectional power flows, and predictable load patterns. However, the fast growth of behind-the-meter generation, particularly solar photovoltaic, electric vehicles, and storage, is transitioning the distribution system...

The architecture information of a Deep Neural Network (DNN) model is considered a valuable, sensitive piece of property for a company. Knowledge of a DNN’s exact architecture allows any adversary to build a substitute model and use this substitute model to launch devastating adversarial attacks. Side-channel based DNN architecture stealing can...

Machine learning models and neural networks in particular can be used in the field of image processing. Machine learning models are trained to better improve their output quality. During training, domain generalization is the problem of making accurate predictions on previously unseen domains, especially when these domains are very different from...

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...

The rapid advancement of the internet and data-collecting devices has sparked increasing demand for computing solutions that are both energy-efficient and high-performance. Coarse-grained reconfigurable arrays (CGRAs) have gained traction as this low-power alternative, offering accelerators capable of supporting the compute-intensive process of collecting,...

Deep neural networks (DNNs) have shown extraordinary performance in recent years for various applications, including image classification, object detection, speech recognition, etc. Accuracy-driven DNN architectures tend to increase model sizes and computations in a very fast pace, demanding a massive amount of hardware resources. Frequent communication...

Material fracture failure is a critical issue for many engineering structures and components. Accurate fracture prediction is necessary to ensure the safety of these structures and components. The finite element method (FEM) is a widely used approach for material mechanical modelling; however, FEM is known to have difficulties in solving problems...

Nowadays, one practical limitation of deep neural networks (DNNs) is their high degree of specialization to a single task. This motivates researchers to develop algorithms that can adapt the DNN model to multiple tasks sequentially, while still performing well on past tasks. This process of gradually adapting the DNN model to learn from different...

Photovoltaic (PV) energy systems have played a major part in meeting renewable energy requirements. However, power production from PV systems faces impediments such as partial shading due to environmental and man-made obstructions. Shading causes voltage and current mismatch losses that can significantly reduce the power supplied to the grid. Reconfiguring...