Visual crowd counting estimates the density of the crowd using deep learning models such as convolution neural networks (CNNs). The performance of the model heavily relies on the quality of the training data that constitutes ...

Deep learning models require an enormous amount of data for training. However, recently there is a shift in machine learning from model-centric to data-centric approaches. In datacentric approaches, the focus is to refine ...

The rapid outbreak of COVID-19 pandemic invoked scientists and researchers to prepare the world for future disasters. During the pandemic, global authorities on healthcare urged the importance of disinfection of objects ...

Density estimation is one of the most widely used method for crowd counting in which a deep learning model learns from head annotated crowd images to estimate crowd density in unseen images. Typically, the learning performance ...

In Vehicular Ad-hoc Networks (VANETs), any vehicle uses a location service to get an up-To-date data of all vehicles' positions in its vicinity. Many location services rely on infrastructures to perform location update and ...

Providing efficient routing service over tactical multi-hop ad-hoc networks is a crucial building block in wireless communication networks especially during a disaster relief. To date, there is still a lack of routing ...

Wi-Fi Direct is a new protocol that standardizes the direct communication between Wi-Fi enabled mobile devices without requiring an access point. The specification proposes enhanced peer-to-peer functions to be supported ...

Cellular operators are increasingly turning toward renewable energy (RE) as an alternative to using traditional electricity in order to reduce operational expenditure and carbon footprint. Due to the randomness in both RE ...

Frequent traffic congestion and gridlocks are causing global economies staggering cost in terms of fuel consumption, time wastage, and public health. To rectify this problem, many advocates combining Information and ...