Approximate decoherence free subspaces for distributed sensing

We consider the sensing of scalar valued fields with specific spatial dependence using a network of sensors, e.g. multiple atoms located at different positions within a trap. We show how to harness the spatial correlations to sense only a specific signal, and be insensitive to others at different positions or with unequal spatial dependence by constructing a decoherence-free subspace for noise sources at fixed, known positions. This can be extended to noise sources lying on certain surfaces, where we encounter a connection to mirror charges and equipotential surfaces in classical electrostatics. For general situations, we introduce the notion of an approximate decoherence-free subspace, where noise for all sources within some volume is significantly suppressed, at the cost of reducing the signal strength in a controlled way. We show that one can use this approach to maintain Heisenberg-scaling over long times and for a large number of sensors, despite the presence of multiple noise sources in large volumes. We introduce an efficient formalism to construct internal states and sensor configurations, and apply it to several examples to demonstrate the usefulness and wide applicability of our approach.

Chaos in the Real World: Recent Applications to Communications, Computing, Distributed Sensing, Robotic Motion, Bio-Impedance Modelling and Encryption Systems

Most of the papers published so far in literature have focused on the theoretical phenomena underlying the formation of chaos, rather than on the investigation of potential applications of chaos to the real world. This paper aims to bridge the gap between chaos theory and chaos applications by presenting a survey of very recent applications of chaos. In particular, the manuscript covers the last three years by describing different applications of chaos as reported in the literature published during the years 2018 to 2020, including the matter related to the symmetry properties of chaotic systems. The topics covered herein include applications of chaos to communications, to distributed sensing, to robotic motion, to bio-impedance modelling, to hardware implementation of encryption systems, to computing and to random number generation.

Microwave reflectometric systems and monitoring apparatus for diffused-sensing applications

<p class="Abstract">Most sensing networks rely on punctual/local sensors; they thus lack the ability to spatially resolve the quantity to be monitored (e.g. a temperature or humidity profile) without relying on the deployment of numerous inline sensors. Currently, most quasi-distributed or distributed sensing technologies rely on the use of optical fibre systems. However, these are generally expensive, which limits their large-scale adoption. Recently, elongated sensing elements have been successfully used with time-domain reflectometry (TDR) to implement diffused monitoring solutions. The advantage of TDR is that it is a relatively low-cost technology, with adequate measurement accuracy and the potential to be customised to suit the specific needs of different application contexts in the 4.0 era. Based on these considerations, this paper addresses the design, implementation and experimental validation of a novel generation of elongated sensing element networks, which can be permanently installed in the systems that need to be monitored and used for obtaining the diffused profile of the quantity to be monitored. Three applications are considered as case studies: monitoring the irrigation process in agriculture, leak detection in underground pipes and the monitoring of building structures.</p>