Split Distributed Computing in Wireless Sensor Networks

We designed a novel method intended to improve the performance of distributed computing in wireless sensor networks. Our proposed method is designed to rapidly increase the speed of distributed computing and decrease the number of the messages required for a network to achieve the desired result. In our analysis, we chose Average consensus algorithm. In this case, the desired result is that every node achieves the average value calculated from all the initial values in the reduced number of iterations. Our method is based on the idea that a fragmentation of a network into small geographical structures which execute distributed calculations in parallel significantly affects the performance

Connectivity-Based Self-Localization in WSNs

Efficient localization methods are among the major challenges in wireless sensor networks today. In this paper, we present our so-called connectivity based approach i.e, based on local connectivity information, to tackle this problem. At first the method fragments the network into larger groups labeled as packs. Based on the mutual connectivity relations with their surrounding packs, we identify border nodes as well as the central node. As this first approach requires some a-priori knowledge on the network topology, we also present a novel segment-based fragmentation method to estimate the central pack of the network as well as detecting so-called corner packs without any a-priori knowledge. Based on these detected points, the network is fragmented into a set of even larger elements, so-called segments built on top of the packs, supporting even more localization information as they all reach the central node

The Distributed Convergence Classifier Using the Finite Difference

The paper presents a novel distributed classifier of the convergence, which allows to detect the convergence/the divergence of a distributed converging algorithm. Since this classifier is supposed to be primarily applied in wireless sensor networks, its proposal makes provision for the character of these networks. The classifier is based on the mechanism of comparison of the forward finite differences from two consequent iterations. The convergence/the divergence is classifiable only in terms of the changes of the inner states of a particular node and therefore, no message redundancy is required for its proper functionality

A fűrészlábú szöcske (Saga pedo) előkerülése kisalföldi homokpusztagyepben

Abstract: Predatory Bush Cricket is one of the largest and most characteristic Orthopteran species of Europe. The main habitats of the species are natural mosaics of steppe grasslands, low scrubs and open rocky grasslands occurring between 200 and 400 m a.s.l. Occurrences of Saga pedo on flatlands are very rare. This paper reports about finding specimens of the species in open sandy grassland of Little Hungarian Plain (~100 m a.s.l.). This is the second known population in Hungary and third population in the Carpathian Basin of Saga pedo which have been found in flatland sandy grasslands

A New Combined European Permanent Network Station Coordinates Solution

The EUREF (International Association of Geodesy (IAG) Reference Frame Sub-Commission for Europe) network of continuously operating GPS stations (EPN) was primarily established for reference frame maintenance, and also plays an important role for geodynamical research in Europe. The main goal of this paper is to obtain an independent homogeneous time-series of the EPN station coordinates, which is also available in SINEX format. A new combined solution of the EPN station coordinates was computed. The combination was performed independently for every week, in three steps: 1. the stated constraints on the coordinates were removed from the individual solutions of the Analysis Centers; 2. the de-constrained solutions were aligned to ITRF2000; 3. the resulting solutions were combined using the Helmert block-ing technique. All the data from GPS week 900 to week 1302 (April 1997 - December 2004) were used. We investigated in detail the behavior of the transformation parameters aligning the new combined solution to ITRF2000. In general, the time-series of the transformation parameters show a good stability in time although small systematic effects can be seen, most likely caused by station instabilities. A comparison of the new combined solution to the official EUREF weekly combined solution is also presented

Expression of invasion-related extracellular matrix molecules in human glioblastoma versus intracerebral lung adenocarcinoma metastasis

Tumor cell invasion into the surrounding brain tissue is mainly responsible for the failure of radical surgical resection, with tumor recurrence in the form of microdisseminated disease. Extracellular matrix (ECM)-related molecules and their receptors predominantly participate in the invasion process, including cell adhesion to the surrounding microenvironment and cell migration. The extent of infiltration of the healthy brain by malignant tumors strongly depends on the tumor cell type. Malignant gliomas show much more intensive peritumoral invasion than do metastatic tumors. In this study, the mRNA expression of 30 invasion-related molecules (twenty-one ECM components, two related receptors, and seven ECM-related enzymes) was investigated by quantitative reverse transcriptase-polymerase chain reaction. Fresh frozen human tissue samples from glioblastoma (GBM), intracerebral lung adenocarcinoma metastasis, and normal brain were evaluated. Significant differences were established for 24 of the 30 molecules. To confirm our results at the protein level, immunohistochemical analysis of seven molecules was performed (agrin, neurocan, syndecan, versican, matrix metalloproteinase 2 [MMP-2], MMP-9, and hyaluronan). Determining the differences in the levels of invasion-related molecules for tumors of different origins can help to identify the exact molecular mechanisms that facilitate peritumoral infiltration by glioblastoma cells. These results should allow the selection of target molecules for potential chemotherapeutic agents directed against highly invasive malignant gliomas

Regional integration of long-term national dense GNSS network solutions

The EUREF Permanent Network Densification is a collaborative effort of 26 European GNSS analysis centers providing series of daily or weekly station position estimates of dense national and regional GNSS networks, in order to combine them into one homogenized set of station positions and velocities. During the combination, the station meta-data, including station names, DOMES numbers, and position offset definitions were carefully homogenized, position outliers were efficiently eliminated, and the results were cross-checked for any remaining inconsistencies. The results cover the period from March 1999 to January 2017 (GPS week 1000-1933) and include 31 networks with positions and velocities for 3192 stations, well covering Europe. The positions and velocities are expressed in ITRF2014 and ETRF2014 reference frames based on the Minimum Constraint approach using a selected set of ITRF2014 reference stations. The position alignment with the ITRF2014 is at the level of 1.5, 1.2, and 3.2\ua0mm RMS for the East, North, Up components, respectively, while the velocity RMS values are 0.17, 0.14, and 0.38\ua0mm/year for the East, North, and Up components, respectively. The high quality of the combined solution is also reflected by the 1.1, 1.1, and 3.5\ua0mm weighted RMS values for the East, North, and Up components, respectively

Regional integration of long-term national dense GNSS network solutions

The EUREF Permanent Network Densification is a collaborative effort of 26 European GNSS analysis centers providing series of daily or weekly station position estimates of dense national and regional GNSS networks, in order to combine them into one homogenized set of station positions and velocities. During the combination, the station meta-data, including station names, DOMES numbers, and position offset definitions were carefully homogenized, position outliers were efficiently eliminated, and the results were cross-checked for any remaining inconsistencies. The results cover the period from March 1999 to January 2017 (GPS week 1000-1933) and include 31 networks with positions and velocities for 3192 stations, well covering Europe. The positions and velocities are expressed in ITRF2014 and ETRF2014 reference frames based on the Minimum Constraint approach using a selected set of ITRF2014 reference stations. The position alignment with the ITRF2014 is at the level of 1.5, 1.2, and 3.2 mm RMS for the East, North, Up components, respectively, while the velocity RMS values are 0.17, 0.14, and 0.38 mm/year for the East, North, and Up components, respectively. The high quality of the combined solution is also reflected by the 1.1, 1.1, and 3.5 mm weighted RMS values for the East, North, and Up components, respectively