Hierarchical Clustering of Seismic Activity Local Territories Globe

In article, the interrelation between energy parameters of Globe moving in a system Sun-Earth-Moon has been established. It includes features of a seasonal energy condition of an internal core of Earth and the key energy parameters of tectonic activity of seismically dangerous local territories of the planet. These parameters have been systematized by means of a clustering method hierarchical. The mechanism of energy influence of core Earth fluctuations on variations of axial rotation speed of the Globe and level of seismic activity has been grounded. The phenomenon of a periodic oscillation of planet condition seismic and effect of asymmetric distribution of the emergency situations (ES) of tectonic origin on the Earth's surface has been established. For the first time, ranging of the seismically fissile local territories of the Globe in the parameters determining the level of seismic activity and ranges of magnitudes was carried out. Based on these results the effect of division of the seismically fissile local territories into three main clusters that characterized by rather high, average and low degrees of seismic activity was established. Join of the ranged seismically fissile local territories of the Globe permit to establish zones with various degree of seismic activity along the section of various geophysical plates.The results received in article are a basis for further carrying out complex assessment of interrelations between key parameters of moving Globe in a system Sun-Earth-Moon and key parameters of tectonic danger of the seismically fissile local territories of Earth. It is base for further increase in effectiveness of monitoring of origin tectonic emergency by development of neural network prognostic models

Feasibility study ASCS remote sensing/compliance determination system

A short-term technical study was performed by the MSC Earth Observations Division to determine the feasibility of the proposed Agricultural Stabilization and Conservation Service Automatic Remote Sensing/Compliance Determination System. For the study, the term automatic was interpreted as applying to an automated remote-sensing system that includes data acquisition, processing, and management

Data exploration systems for databases

Data exploration systems apply machine learning techniques, multivariate statistical methods, information theory, and database theory to databases to identify significant relationships among the data and summarize information. The result of applying data exploration systems should be a better understanding of the structure of the data and a perspective of the data enabling an analyst to form hypotheses for interpreting the data. This paper argues that data exploration systems need a minimum amount of domain knowledge to guide both the statistical strategy and the interpretation of the resulting patterns discovered by these systems

Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe