Background Image fusion is an image processing technique that has grown in prevalence in recent years. Image fusion can be used for many different applications including medical imaging, aerospace, remote sensing, military surveillance, and manufacturing flaw identification. Quantum image fusion is a more recent development that uses quantum computing...

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...

Quantum computing (QC) is a multidisciplinary field comprising aspects of computer science, physics, and mathematics that utilizes quantum mechanics to solve complex problems faster than on classical computers (e.g., QC promises to process data with estimated speeds exceeding 100 million times relative to classical computers). Signal processing is...

Unlike traditional semi-supervised learning, positive unlabeled learning requires only some labeled data from the positive class. All other data, both positive and negative, is unlabeled. The goal is the same, i.e., to construct a classification model that correctly labels unlabeled images and create a model to label future images. This problem is...

­Background With the outbreak of COVID-19 caused by the coronavirus SARS-CoV-2, the severity of infection and associated fatality rates motivate the exploration for rapid and reliable screening approaches. Although reverse transcriptase-polymerase chain reaction (RT-PCR) testing is commonly adopted, coughing sounds have been found to reveal useful...

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...

The prevalence of relational data in several real-world applications, e.g., social network analysis, recommendation systems, and neurological modeling has led to crucial advances in machine learning techniques for graph-structured data. This encompasses a wide-range of formulations to mine and gather insights from complex network datasets — node...

­Wireless sensor network (WSN) is a distributed network consisting of multi-functional sensors, which can communicate with neighboring sensors over wireless channels. Estimating statistics of sensor measurements in WSNs is necessary for detecting anomalous sensors, supporting nodes with insufficient resources, determining network area estimation,...