Supply chains provide a network describing how resources move from a source to delivery for consumption by the end user. Supply chains also involve the components involved in production and transportation of the commodities being moved in the supply chain. There is a need for modeling links in a supply chain to estimate supply and demand and to ensure...

Federated learning (FL) is envisaged to be a promising solution for collaboratively training a machine learning model while keeping the training data decentralized and private. For example, in FL, different participants might have different samples of data from different classes. Some participants might have no samples from some of the subclasses...

Present day autonomous systems are cyber-physical systems (CPS) that involve multi-dimensional interaction between the cyber machine and the operational environment. One of the key aspects of these autonomous systems is that interaction patterns change over the lifetime of usage. Changes in CPS interaction patterns can be unintentional or intentional...

5G wireless systems are designed to meet the increasing demand for high data rates but face challenges like signal obstruction, poor penetration, high propagation loss, and inadequate scattering. Active relays have emerged as a solution to these issues by re-routing signals to avoid obstructions, ensuring uninterrupted 5G service. However, in dense...

AI systems are increasingly being used in critical applications where safety and reliability are paramount such as health care, autonomous driving, and financial services. Numerous AI systems operate as “black boxes,” meaning a system that processes inputs and produces outputs without revealing how it reaches its conclusions. This makes...

Quantum computing has the potential to significantly impact massive signal processing operations. Research is being done in exploring ways to harness its potential for signal processing applications. The use of quantum computing for signal processing holds great promise for improving the speed and accuracy in several signal processing applications...

Quantum computing offers unprecedented computational power, enabling the solution of complex problems beyond the reach of classical computers. In signal processing, autocorrelation is used to analyze signals and extract valuable information, but traditional methods involve high computational complexity due to numerous multiplications and additions....

Many modern science and engineering applications, such as machine learning, hyperparameter optimization of neural networks, robotics, cyber-physical systems, etc., call for modeling techniques to model black-box functions. Gaussian Process (GP) modeling is a popular Bayesian non-parametric framework heavily employed to model expensive black-box functions...

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...

Networks like those of healthcare infrastructure have been a primary target of cyberattacks for over a decade. From just a single cyberattack, a healthcare facility would expect to see millions of dollars in losses from legal fines, business interruption, and malpractice lawsuits. As more medical devices become interconnected, more cyber vulnerabilities...