15 accepted papers
Entangled quantum states serve as important resources in quantum communication, quantum computing, and quantum sensing. Creating entangled states between remote nodes is referred to as remote entanglement establishment (REE). REE typically consists of three types of quantum operations: entanglement…
Determining the distances from the hull of the own ship to obstacles or land, i.e. water clearance, is a fundamental task in navigation. This is particularly relevant during maneuvering in the harbor or navigating in confined waters. We introduce the concepts of area water clearance and line water c…
Reliable monitoring of vessel motions is crucial for safe and efficient operation of marine vessels. Pitching and rolling motions are commonly monitored using high-grade inertial measurement units (IMUs). However, such sensors become unreliable in presence of long-lasting accelerations. In this work…
All practical sensing operations must work with quantized data. Along with measurements, each sensor is assumed to have some "label" value that is relevant to its stochastic measurement parameterization and must be communicated to the decision center. We are interested in cases that require very low…
Quickest detection problems are fairly common in surveillance applications, as framing surveillance alerts as a change in an observation sequence’s statistics is often apt. In this work, we consider the scenario where an appropriate statistical description of our observations is not available, neith…
Consider a test at a particular point in space for the existence of a point target using intensity measurements from passive sensors distributed uniformly around the test location. The distance from the test location of a particular sensor is relevant to the decision making, and is considered "label…
Adaptation and learning over multi-agent networks is a topic of great relevance with important implications. Elaborating on previous works on single-task networks engaged in decision problems, here we consider the multi-task version in the challenging scenario where the state of nature may change ar…
Unlabeled detection is an emerging paradigm for modern decentralized decision systems faced with big-data applications, and for all those applications in which data must be fused without exploiting their identity, due to the lack of provenance labels, or to uncontrolled data shuffling. Our focus her…
Suppose that the energy made available to a sensor network at the beginning of a time slot is proportional to the success of the network inferential task during the previous slot. And further, assume that such energy is to be apportioned to charge the individual sensors, such that the more energy on…
Consider a binary hypothesis where data are independent and identically distributed under the null hypothesis, and known only to be independent under the alternative. The statistician observes an n- vector X <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlin…
In modern heterogeneous sensor networks huge volumes of information rapidly flow across the system, and it is often too difficult or costly to associate data to the sensors that produced them. Then, the set of observations appears to be unlabeled: What comes from whom? We study the classical problem…
Parametric estimation for the generative social sensing model proposed in [19,20] is addressed. First, we provide a detailed analysis of the estimation performance bounds, in terms of the Fisher information matrix, with emphasis on the fundamental scaling laws as the number of network agents and/or…
The analysis of wave and diffusion fields is a task central to a myriad of applications. Wave fields are encountered in acoustic, radar, and geophysics to name a few. Diffusion fields are found, for example, in physics, chemistry, and biology.
In practical target tracking problems, the target detection performance of the sensors may be unknown and may change rapidly with time. In this work we develop a target tracking procedure able to adapt and react to time-varying changes of the detection capability for a network of sensors. The propos…
In [1], an important step toward the characterization of distributed detection over adaptive networks has been made by establishing the fundamental scaling law of the error probabilities. However, empirical evidence reported in [1] revealed that a refined asymptotic analysis is necessary in order to…