ACCESS POINT TRANSMISSION OPTIMIZATIONS USING MACHINE LEARNING BASED TRAFFIC CLASSIFICATION

Techniques are described herein for optimizing access point transmissions. According to these techniques, configuration, state, and tuning of an access point transmission scheduler are externalized to a network core (e.g., a wireless LAN controller (WLC) or Dynamic Network Access Control (DNAC)) and a machine-learning system is used to classify wireless clients based on traffic analysis. The described techniques permit the use of fine-tuned configurations for each access point station and these configurations can be shared across all access points in the case of roaming

Experimental Analysis and Numerical Modelling of the Mechanical Behavior of a Sisal-Fiber-Reinforced Geopolymer

The present paper is devoted to the proposal of appropriate numerical modelling able to provide a suitable description of the mechanical behavior of a composite geopolymer. Reference is made to a natural sisal-fiber-reinforced geopolymer. The study is based on the results of appropriate experimental investigations for compressive, flexural and splitting loadings, taking into account different weight percentages of fibers to evidence their role in the mechanical behavior. The main objective of the paper is to calibrate the microplane constitutive model, available in ANSYS software version 18.1, where the numerical analyses are performed. Therefore, the present study is structured in two different steps. Firstly, the mechanical behavior of geopolymers reinforced with sisal fibers is experimentally investigated, and subsequently, the gathered test data are interpreted and utilized to calibrate the relevant constitutive model to be used in the numerical stage. The obtained results are compared with experimental data, yielding good correlations. The paper's results supply the parameters required to obtain an affordable numerical model of the reinforced geopolymer for different percentages of fibers to be adopted for material design with assigned mechanical properties

PROACTIVE MANAGEMENT OF CUSTOMER NETWORKS FOR STABILITY AND RELIABILITY LEVERAGING APPDY INTEGRATION INTO NETWORK SERVICE PRODUCTS

Techniques are described for leveraging the integration of the AppDynamics (AppDy) Software Development Kit (SDK) with network service products that allows for run-time retrieval of key process data on an AppDy platform. The run-time retrieval of key process data will be managed by a network service provider\u27s customer support teams, which can proactively detect customer network degradations before they have an impact on end user experience

SECURE WIRELESS CLIENT ONBOARDING AND SEGMENTATION

Techniques are described herein for preventing Media Access Control (MAC) address spoofing attacks based on the two-step onboarding process for open Service Set Identifiers (SSIDs) due to Virtual Local Area Network (VLAN) override after the Internet Protocol (IP) address is learned. These techniques leverage Opportunistic Wireless Encryption (OWE) and an access token to provide a secure channel between the wireless network and the client

MOVING/ROTATING ANTENNAS FOR WIRELESS ACCESS POINTS

Conventionally, the position and orientation of antennas for wireless access points depends on the position in which a wireless access point is oriented during installation and typically does not change unless the positioning of the wireless access point is changed. Thus, wireless antenna position/orientation typically remains static once an access point is installed. Techniques proposed herein introduce a sensor fusion approach for controlling the direction/orientation of wireless access point antennas in order to improve wireless communications for wireless networks

A Parsimonious Granger Causality Formulation for Capturing Arbitrarily Long Multivariate Associations.

High-frequency neuroelectric signals like electroencephalography (EEG) or magnetoencephalography (MEG) provide a unique opportunity to infer causal relationships between local activity of brain areas. While causal inference is commonly performed through classical Granger causality (GC) based on multivariate autoregressive models, this method may encounter important limitations (e.g., data paucity) in the case of high dimensional data from densely connected systems like the brain. Additionally, physiological signals often present long-range dependencies which commonly require high autoregressive model orders/number of parameters. We present a generalization of autoregressive models for GC estimation based on Wiener-Volterra decompositions with Laguerre polynomials as basis functions. In this basis, the introduction of only one additional global parameter allows to capture arbitrary long dependencies without increasing model order, hence retaining model simplicity, linearity and ease of parameters estimation. We validate our method in synthetic data generated from families of complex, densely connected networks and demonstrate superior performance as compared to classical GC. Additionally, we apply our framework to studying the directed human brain connectome through MEG data from 89 subjects drawn from the Human Connectome Project (HCP) database, showing that it is able to reproduce current knowledge as well as to uncover previously unknown directed influences between cortical and limbic brain regions.MR