Enhancing the quality of communication of cellular networks using big data applications

Both software-defined networking and big data have gained approval and preferences from both industry and academia. These two important realms have conventionally been addressed independently in wireless cellular networks. The discussion taken into consideration in this study was to analyze the wireless cellular technologies with the contrast of efficient and enhanced spectral densities at a reduced cost. To accomplish the goal of this study, Welch's method has been used as the core subject. With the aid of previous research and classical techniques, this study has identified that the spectral densities can be enhanced at reduced costs with the help of the power spectral estimation methods. The Welch method gives the result on power spectrum estimation. By reducing the effect of noise, the Welch method is used to calculate the power spectral density of a signal. When data length is increased, Welch's method is considered the best as a conclusion to this paper because excellent results are yielded by it in the area of power spectral density estimation.

Power spectral densities of electroencephalographic theta and alpha frequency waves during information processing of language comprehension

A cross sectional study was conducted to assess electroencephalograph (EEG) Power Spectral Densities (PSD) of alpha and theta frequency bands for an integrative functional role of working memory (WM) in the architectonics of a synthesized and coordinated communication system as exemplified by the observable phenomenon of the evolved structured Language of Human Mind by using Visuo-Spatial Delayed Match to Sample (DMTS) task. The analysis exhibited significant Event Related Synchronization (ERS) along theta wave-form at temporal region along with Lateral Asymmetry Index (LAI) of Alpha Event Related Desynchronization (ERD) at parietal region suggestive of the phenomenal singularity of ERS of theta along temporal regions that seems to be intricately interwoven onto the spectacle of LAI of alpha ERD, presumably evolving a synthesized enveloped working memory, along the virtual phase-space of Human Mind and eventually translating into the comprehensible means of communication of Humans, i.e., Language.

Mutual spectral densities calculation of the moments of resistance on the peat milling unit working bodies

When performing technological operations in the peat industry, various units with milling-type working bodies are used. They differ in design, layout, number and type of cutting elements, operating modes, and may have one or more working bodies. During operation, random forces and moments act on the cutters, which have a dramatically variable nature, which is associated with the periodic interaction of the knives with the peat deposit, its structural heterogeneity, variations in the milling depth, physical and mechanical properties of peat, the rotational speed of the cutter and the movement speed of the machine. In this case, significant dynamic loads arise in the structural elements, which leads to a decrease in their reliability, deterioration of the energy characteristics of the engine operation and technical and economic indicators of use. In the dynamic analysis of drive elements, when using machines with several working bodies, it is necessary to know both spectral and mutual spectral load densities. For their calculation, expressions were obtained that take into account the physical and mechanical properties of peat, the operating modes of the unit and their probabilistic characteristics, as well as the design features of the working body. The expressions are obtained for the case when there are several working bodies with the same diameters and the number of knives in the cutting plane. In this case, the number of planes, width, type of cutting element and type of cutting (locked, semi-locked, etc.) may differ. As an example of using the developed approaches, the calculation of spectral and mutual spectral densities of moments on cutters and loads in the drive elements of the machine for surface-layer milling MTF-14 is presented.

Variant-Coherence Gaussian Sources

The celebrated Gaussian Schell model source with its shift-invariant degree of coherence may be the basis for devising sources with space-variant properties in the spirit of structured coherence. Starting from superpositions of Gaussian Schell model sources, we present two classes of genuine cross-spectral densities whose degree of coherence varies across the source area. The first class is based on the use of the Laplace transform while the second deals with cross-spectral densities that are shape-invariant upon paraxial propagation. For the latter, we present a set of shape-invariant cross-spectral densities for which the modal expansion can be explicitly found. We finally solve the problem of ascertain whether an assigned cross-spectral density is shape-invariant by checking if it satisfies a simple differential constraint.

Minimax prediction of sequences with periodically stationary increments

The problem of optimal estimation of linear functionals constructed from unobserved values of a stochastic sequence with periodically stationary increments based on its observations at points $ k<0$ is considered. For sequences with known spectral densities, we obtain formulas for calculating values of the mean square errors and the spectral characteristics of the optimal estimates of the functionals. Formulas that determine the least favourable spectral densities and minimax (robust) spectral characteristics of the optimal linear estimates of functionals are derived in the case where spectral densities of the sequence are not exactly known while some sets of admissible spectral densities are given.

Robust Filtering of Sequences with Periodically Stationary Multiplicative Seasonal Increments

We consider stochastic sequences with periodically stationary generalized multiple increments of fractional order which combines cyclostationary, multi-seasonal, integrated and fractionally integrated patterns. We solve the filtering problem for linear functionals constructed from unobserved values of a stochastic sequence of this type based on observations of the sequence with a periodically stationary noise sequence. For sequences with known matrices of spectral densities, we obtain formulas for calculating values of the mean square errors and the spectral characteristics of the optimal filtering of the functionals. Formulas that determine the least favourable spectral densities and the minimax (robust) spectral characteristics of the optimal linear filtering of the functionals are proposed in the case where spectral densities of the sequence are not exactly known while some sets of admissible spectral densities are given.