Likelihood Consensus and Its Application to Distributed Particle Filtering

We consider distributed state estimation in a wireless sensor network without a fusion center. Each sensor performs a global estimation task---based on the past and current measurements of all sensors---using only local processing and local communications with its neighbors. In this estimation task, the joint (all-sensors) likelihood function (JLF) plays a central role as it epitomizes the measurements of all sensors. We propose a distributed method for computing, at each sensor, an approximation of the JLF by means of consensus algorithms. This "likelihood consensus" method is applicable if the local likelihood functions of the various sensors (viewed as conditional probability density functions of the local measurements) belong to the exponential family of distributions. We then use the likelihood consensus method to implement a distributed particle filter and a distributed Gaussian particle filter. Each sensor runs a local particle filter, or a local Gaussian particle filter, that computes a global state estimate. The weight update in each local (Gaussian) particle filter employs the JLF, which is obtained through the likelihood consensus scheme. For the distributed Gaussian particle filter, the number of particles can be significantly reduced by means of an additional consensus scheme. Simulation results are presented to assess the performance of the proposed distributed particle filters for a multiple target tracking problem

The evidence for histamine H3 receptor-mediated endothelium-dependent relaxation in isolated rat aorta

The presence of histamine H3 receptors was evaluated on the rat aorta endothelium. In the presence of pyrilamine (1 nM, 7 nM, 10 nM) or thioperamide (1 nM, 10 nM, 30 nM) the concentration–response curve for histamine-induced (0.1 nM − 0.01 mM) endothelium-dependent rat aorta relaxation was shifted to the right without significant change of the Emax indicating competitive antagonism by pyrilamine (pA2 = 9.33 ± 0.34, slope = 1.09 ± 0.36) or thioperamide (pA2 =9.31 ± 0.16, slope=0.94 ± 0.10). Cimetidine (1 μM) did not influence histamine-induced endothelium-dependent rat aorta relaxation. In the presence of thioperamide (1 nM, 10 nM, 30 nM) the concentration–response curve for (R)α-MeHA-induced (0.1 nM − 0.01 mM) endothelium-dependent relaxation was shifted to the right without significant change of Emax indicated competitive antagonism by thioperamide (pA2 = 9.21 ± 0.4, slope = 1.03 ± 0.35). Pyrilamine (100 nM) or cimetidine (1 μM) did not influence (R)α-MeHA-induced endothelium-dependent rat aorta relaxation. These results suggest the presence of a heterogenous population of histamine receptors, H1 and H3, on rat aorta endothelium

On Sequential Estimation of Linear Models From Data with Correlated Noise

Publication in the conference proceedings of EUSIPCO, Lisbon, Portugal, 201

Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach

Nowadays there is a growing research interest on the possibility of enriching small flying robots with autonomous sensing and online navigation capabilities. This will enable a large number of applications spanning from remote surveillance to logistics, smarter cities and emergency aid in hazardous environments. In this context, an emerging problem is to track unauthorized small unmanned aerial vehicles (UAVs) hiding behind buildings or concealing in large UAV networks. In contrast with current solutions mainly based on static and on-ground radars, this paper proposes the idea of a dynamic radar network of UAVs for real-time and high-accuracy tracking of malicious targets. To this end, we describe a solution for real-time navigation of UAVs to track a dynamic target using heterogeneously sensed information. Such information is shared by the UAVs with their neighbors via multi-hops, allowing tracking the target by a local Bayesian estimator running at each agent. Since not all the paths are equal in terms of information gathering point-of-view, the UAVs plan their own trajectory by minimizing the posterior covariance matrix of the target state under UAV kinematic and anti-collision constraints. Our results show how a dynamic network of radars attains better localization results compared to a fixed configuration and how the on-board sensor technology impacts the accuracy in tracking a target with different radar cross sections, especially in non line-of-sight (NLOS) situations

Endothelium-dependent relaxation of rat aorta to a histamine H3 agonist is reduced by inhibitors of nitric oxide synthase, guanylate cyclase and Na+,K+-ATPase

The possible involvement of different effector systems (nitric oxide synthase, guanylate cyclase, β-adrenergic and muscarinic cholinergic receptors, cyclooxygenase and lipoxygenase, and Na+,K+-ATPase) was evaluated in a histamine H3 receptor agonist-induced ((R)α-methylhistamine, (R)α-MeHA) endothelium-dependent rat aorta relaxation assay. (R)α-MeHA (0.1 nM – 0.01 mM) relaxed endothelium-dependent rat aorta, with a pD2 value of 8.22 ± 0.06, compared with a pD2 value of 7.98 ± 0.02 caused by histamine (50% and 70% relaxation, respectively). The effect of (R)α-MeHA (0.1 nM – 0.01 mM) was competitively antagonized by thioperamide (1, 10 and 30 nM) (pA2 = 9.21 ± 0.40; slope = 1.03 ± 0.35) but it was unaffected by pyrilamine (100 nM), cimetidine (1 μM), atropine (10 μM), propranolol (1 μM), indomethacin (10 μM) or nordthydroguaiaretic acid (0.1 mM). Inhibitors of nitric oxide synthase, L-NG-monomethylarginine (L-NMMA, 10 μM) and NG-nitro-L-arginine methylester (L-NOARG, 10 μM) inhibited the relaxation effect of (R)α-MeHA, by approximately 52% and 70%, respectively). This inhibitory effect of L-NMMA was partially reversed by L-arginine (10 μM). Methylene blue (10 μM) and ouabain (10 μM) inhibited relaxation (R)α-MeHA-induced by approximately 50% and 90%, respectively. The products of cyclooxygenase and lipoxygenase are not involved in (R)α-MeHA-induced endothelium-dependent rat aorta relaxation nor are the muscarinic cholinergic and β-adrenergic receptors. The results also suggest the involvement of NO synthase, guanylate cyclase and Na+,K+-ATPase in (R)α-MeHA-induced endothelium-dependent rat aorta relaxation

Preparing Students for the Advanced Manufacturing Environment Through Robotics, Mechatronics, and Automation Training

Automation is one of the key areas for modern manufacturing systems. It requires coordination of different machines to support manufacturing operations in a company. Recent studies show that there is a gap in the STEM workforce preparation in regards to highly automated production environments. Industrial robots have become an essential part of these semi-automated and automated manufacturing systems. Their control and programming requires adequate education and training in robotics theory and applications. Various engineering technology departments offer different courses related to the application of robotics. These courses are a great way to inspire students to learn about science, math, engineering, and technology while providing them with workforce skills. However, some challenges are present in the delivery of such courses. One of these challenges includes the enrollment of students who come from different engineering departments and backgrounds. Such a multidisciplinary group of students can pose a challenge for the instructor to successfully develop the courses and match the content to different learning styles and math levels. To overcome that challenge, and to spark students\u27 interest, the certified education robot training can greatly support the teaching of basic and advanced topics in robotics, kinematics, dynamics, control, modeling, design, CAD/CAM, vision, manufacturing systems, simulation, automation, and mechatronics. This paper will explain how effective this course can be in unifying different engineering disciplines when using problem solving related to various important manufacturing automaton problems. These courses are focused on educational innovations related to the development of student competency in the use of equipment and tools common to the discipline, and associated curriculum development at three public institutions, in three different departments of mechanical engineering technology. Through these courses students make connections between the theory and real industrial applications. This aspect is especially important for tactile or kinesthetic learners who learn through experiencing and doing things. They apply real mathematical models and understand physical implications through labs on industrial grade robotic equipment and mobile robots