Amorphization kinectics under swift heavy ion irradiation: a cumulative overlapping-track approach

A simple illustrative physical model is presented to describe the kinetics of damage and amorphization by swiftheavyions (SHI) in LiNbO3. The model considers that every ion impact generates initially a defective region (halo) and a full amorphous core whose relative size depends on the electronic stopping power. Below a given stopping power threshold only a halo is generated. For increasing fluences the amorphized area grows monotonically via overlapping of a fixed number N of halos. In spite of its simplicity the model, which provides analytical solutions, describes many relevant features of the kinetic behaviour. In particular, it predicts approximate Avrami curves with parameters depending on stopping power in qualitative accordance with experiment that turn into Poisson laws well above the threshold value

Distributed static linear Gaussian models using consensus

Algorithms for distributed agreement are a powerful means for formulating distributed versions of existing centralized algorithms. We present a toolkit for this task and show how it can be used systematically to design fully distributed algorithms for static linear Gaussian models, including principal component analysis, factor analysis, and probabilistic principal component analysis. These algorithms do not rely on a fusion center, require only low-volume local (1-hop neighborhood) communications, and are thus efficient, scalable, and robust. We show how they are also guaranteed to asymptotically converge to the same solution as the corresponding existing centralized algorithms. Finally, we illustrate the functioning of our algorithms on two examples, and examine the inherent cost-performance tradeoff.

Failure Detectors in Homonymous Distributed Systems (with an Application to Consensus).

This paper is on homonymous distributed systems where processes are prone to crash failures and have no initial knowledge of the system membership (?homonymous? means that several processes may have the same identi?er). New classes of failure detectors suited to these systems are ?rst de?ned. Among them, the classes H? and H? are introduced that are the homonymous counterparts of the classes ? and ?, respectively. (Recall that the pair h?,?i de?nes the weakest failure detector to solve consensus.) Then, the paper shows how H? and H? can be implemented in homonymous systems without membership knowledge (under different synchrony requirements). Finally, two algorithms are presented that use these failure detectors to solve consensus in homonymous asynchronous systems where there is no initial knowledge ofthe membership. One algorithm solves consensus with hH?, H?i, while the other uses only H?, but needs a majority of correct processes. Observe that the systems with unique identi?ers and anonymous systems are extreme cases of homonymous systems from which follows that all these results also apply to these systems. Interestingly, the new failure detector class H? can be implemented with partial synchrony, while the analogous class A? de?ned for anonymous systems can not be implemented (even in synchronous systems). Hence, the paper provides us with the ?rst proof showing that consensus can be solved in anonymous systems with only partial synchrony (and a majority of correct processes).

Distributed target tracking based on belief propagation consensus

Distributed target tracking in wireless sensor networks (WSN) is an important problem, in which agreement on the target state can be achieved using conventional consensus methods, which take long to converge. We propose distributed particle filtering based on belief propagation (DPF-BP) consensus, a fast method for target tracking. According to our simulations, DPF-BP provides better performance than DPF based on standard belief consensus (DPF-SBC) in terms of disagreement in the network. However, in terms of root-mean square error, it can outperform DPF-SBC only for a specific number of consensus iterations.

Implementing the weakest failure detector for solving consensus

The concept of unreliable failure detector was introduced by Chandra and Toueg as a mechanism that provides information about process failures. This mechanism has been used to solve several agreement problems, such as the consensus problem. In this paper, algorithms that implement failure detectors in partially synchronous systems are presented. First two simple algorithms of the weakest class to solve the consensus problem, namely the Eventually Strong class (⋄S), are presented. While the first algorithm is wait-free, the second algorithm is f-resilient, where f is a known upper bound on the number of faulty processes. Both algorithms guarantee that, eventually, all the correct processes agree permanently on a common correct process, i.e. they also implement a failure detector of the class Omega (Ω). They are also shown to be optimal in terms of the number of communication links used forever. Additionally, a wait-free algorithm that implements a failure detector of the Eventually Perfect class (⋄P) is presented. This algorithm is shown to be optimal in terms of the number of bidirectional links used forever.

Providing a consensus definition for the term "Smart Product"

The term "Smart Product" has become commonly used in recent years. This is because there has been an increasing interest in these kinds of products as part of the consumer goods industry, impacting everyday life and industry. Nevertheless, the term "Smart Product" is used with different meanings in different contexts and application domains. The use of the term "Smart Product" with different meanings and underlying semantics can create important misunderstandings and dissent. The aim of this paper is to analyze the different definitions of Smart Product available in the literature, and to explore and analyze their commonalities and differences, in order to provide a consensus definition that satisfies, and can therefore be used by, all parties. To embrace the identified definitions, the concept of "Smart Thing" is introduced. The methodology used was a systematic literature review. The definition is expressed as an ontology.

Interaction of spatially overlapping standing electromagnetic solitons in plasmas

Numerical investigations on mutual interactions between two spatially overlapping standing electromagnetic solitons in a cold unmagnetized plasma are reported. It is found that an initial state comprising of two overlapping standing solitons evolves into different end states, depending on the amplitudes of the two solitons and the phase difference between them. For small amplitude solitons with zero phase difference, we observe the formation of an oscillating bound state whose period depends on their initial separation. These results suggest the existence of a bound state made of two solitons in the relativistic cold plasma fluid model.

Overlapping Range Images using Genetics Algorithms

El artículo aborda el problema del encaje de diversas imágenes de una misma escena capturadas por escáner 3d para generar un único modelo tridimensional. Para ello se utilizaron algoritmos genéticos. ABSTRACT: This work introduces a solution based on genetic algorithms to find the overlapping area between two point cloud captures obtained from a three-dimensional scanner. Considering three translation coordinates and three rotation angles, the genetic algorithm evaluates the matching points in the overlapping area between the two captures given that transformation. Genetic simulated annealing is used to improve the accuracy of the results obtained by the genetic algorithm.

Searching for a consensus similarity function for generalized trapezoidal fuzzy numbers

There is controversy regarding the use of the similarity functions proposed in the literature to compare generalized trapezoidal fuzzy numbers since conflicting similarity values are sometimes output for the same pair of fuzzy numbers. In this paper we propose a similarity function aimed at establishing a consensus. It accounts for the different approaches of all the similarity functions. It also has better properties and can easily incorporate new parameters for future improvements. The analysis is carried out on the basis of a large and representative set of pairs of trapezoidal fuzzy numbers.

Linear color correction for multiple illumination changes and non-overlapping cameras

Many image processing methods, such as techniques for people re-identification, assume photometric constancy between different images. This study addresses the correction of photometric variations based upon changes in background areas to correct foreground areas. The authors assume a multiple light source model where all light sources can have different colours and will change over time. In training mode, the authors learn per-location relations between foreground and background colour intensities. In correction mode, the authors apply a double linear correction model based on learned relations. This double linear correction includes a dynamic local illumination correction mapping as well as an inter-camera mapping. The authors evaluate their illumination correction by computing the similarity between two images based on the earth mover's distance. The authors compare the results to a representative auto-exposure algorithm found in the recent literature plus a colour correction one based on the inverse-intensity chromaticity. Especially in complex scenarios the authors’ method outperforms these state-of-the-art algorithms.

Eventual election of multiple leaders for solving consensus in anonymous systems

In classical distributed systems, each process has a unique identity. Today, new distributed systems have emerged where a unique identity is not always possible to be assigned to each process. For example, in many sensor networks a unique identity is not possible to be included in each device due to its small storage capacity, reduced computational power, or the huge number of devices to be identified. In these cases, we have to work with anonymous distributed systems where processes cannot be identified. Consensus cannot be solved in classical and anonymous asynchronous distributed systems where processes can crash. To bypass this impossibility result, failure detectors are added to these systems. It is known that ? is the weakest failure detector class for solving consensus in classical asynchronous systems when amajority of processes never crashes. Although A? was introduced as an anonymous version of ?, to find the weakest failure detector in anonymous systems to solve consensus when amajority of processes never crashes is nowadays an open question. Furthermore, A? has the important drawback that it is not implementable. Very recently, A? has been introduced as a counterpart of ? for anonymous systems. In this paper, we show that the A? failure detector class is strictly weaker than A? (i.e., A? provides less information about process crashes than A?). We also present in this paper the first implementation of A? (hence, we also show that A? is implementable), and, finally, we include the first implementation of consensus in anonymous asynchronous systems augmented with A? and where a majority of processes does not crash.

Bayesian network modeling of the consensus between experts: an application to neuron classification

Neuronal morphology is hugely variable across brain regions and species, and their classification strategies are a matter of intense debate in neuroscience. GABAergic cortical interneurons have been a challenge because it is difficult to find a set of morphological properties which clearly define neuronal types. A group of 48 neuroscience experts around the world were asked to classify a set of 320 cortical GABAergic interneurons according to the main features of their three-dimensional morphological reconstructions. A methodology for building a model which captures the opinions of all the experts was proposed. First, one Bayesian network was learned for each expert, and we proposed an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts was induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts was built. A thorough analysis of the consensus model identified different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types was defined by performing inference in the Bayesian multinet. These findings were used to validate the model and to gain some insights into neuron morphology.

3D Maize Plant Reconstruction Based on Georeferenced Overlapping LiDAR Point Clouds

3D crop reconstruction with a high temporal resolution and by the use of non-destructive measuring technologies can support the automation of plant phenotyping processes. Thereby, the availability of such 3D data can give valuable information about the plant development and the interaction of the plant genotype with the environment. This article presents a new methodology for georeferenced 3D reconstruction of maize plant structure. For this purpose a total station, an IMU, and several 2D LiDARs with different orientations were mounted on an autonomous vehicle. By the multistep methodology presented, based on the application of the ICP algorithm for point cloud fusion, it was possible to perform the georeferenced point clouds overlapping. The overlapping point cloud algorithm showed that the aerial points (corresponding mainly to plant parts) were reduced to 1.5%–9% of the total registered data. The remaining were redundant or ground points. Through the inclusion of different LiDAR point of views of the scene, a more realistic representation of the surrounding is obtained by the incorporation of new useful information but also of noise. The use of georeferenced 3D maize plant reconstruction at different growth stages, combined with the total station accuracy could be highly useful when performing precision agriculture at the crop plant level.