Humans rely increasingly on sensors to address grand challenges and to improve quality of
life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are …

Compressive Sensing (CS) is a new sensing modality, which compresses the signal being
acquired at the time of sensing. Signals can have sparse or compressible representation …

Two-way image communication in a wireless channel needs to be viable with channel
properties such as transfer speed, energy-effective, time usage, and security because image …

Purpose The manufacturing of intelligent and secure visual data transmission over the
wireless sensor network is key requirement nowadays to many applications. The two-way …

There is a significant need to build efficient non-von Neumann computing systems for highly
data-centric artificial intelligence related applications. Brain-inspired computing is one such …

Nowadays, a large amount of information has to be transmitted or processed. This implies
high-power processing, large memory density, and increased energy consumption. In …

Sparse polynomial chaos expansions (PCE) are a popular surrogate modelling method that
takes advantage of the properties of PCE, the sparsity-of-effects principle, and powerful …

Emergence of Internet-of-Things brings a whole new class of applications and higher
efficiency for existing services. Application-specific requirements, as well as connectivity and …

We propose a novel method for compressed sensing recovery using untrained deep
generative models. Our method is based on the recently proposed Deep Image Prior (DIP) …

In the Internet of Things (IoT) domain, many applications are running machine learning
algorithms to assimilate the data collected in the swarm of devices. Sending all data to the …