This Special Issue of Nanomaterials is focused on the continuing implementation of nanomaterials and nanostructures in the development of more sensitive and more specific sensing devices. As a result, these new devices employ smaller sensing elements and provide more "real time" capability. Often, the inclusion of nanomaterials leads to sensing elements for targets that were previously inaccess...

Magnetic resonance imaging provides superior imaging capability because of unmatched soft tissue contrast and inherent three-dimensional visualization. Force sensing in robot-assisted systems is crucial for providing tactile feedback and measuring tissue interaction forces in needle-based percutaneous procedures in MRI. To address the issues imposed by electromagnetic compatibility in the high-...

The approach of human-based sensing is based on the assumption that sensor systems for individual use have optimal performance if coherent with the human perception system. The benefits of complementing the human sensing with sensor systems that strengthen the information and make the human able to perform more adequate and optimized decisions based on the sensor information, will improve the h...

BACKGROUND Despite the widespread use of sensors in engineering systems like robots and automation systems, the common paradigm is to have fixed sensor morphology tailored to fulfill a specific application. On the other hand, robotic systems are expected to operate in ever more uncertain environments. In order to cope with the challenge, it is worthy of note that biological systems show the imp...

Digital networks and the capability of addressable sensing devices ooer new challenges for system theory. Rather than reading sensors continuously or periodically , as has traditionally been done, addressable sensing devices allow readings to be taken aperiodically and only as needed. In this paper, we introduce the notion of a sensing policy to determine which sensors should be read and when. ...

Different probabilistic models for classification and prediction problems are anlyzed in this article studying their behaviour and capability in data classification. To show the capability of Bayesian Networks to deal with classification problems four types of Bayesian Networks are introduced, a General Bayesian Network, the Naive Bayes, a Bayesian Network Augmented Naive Bayes and the Tree Aug...