scholarly journals A Novel Method for Polar Form of Any Degree of Multivariate Polynomials with Applications in IoT

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 903 ◽  
Author(s):  
Sedat Akleylek ◽  
Meryem Soysaldı ◽  
Djallel Boubiche ◽  
Homero Toral-Cruz
Keyword(s):  
Polynomial Systems ◽  
Multivariate Polynomials ◽  
Polar Form ◽  
Identification Schemes ◽  
Property Identification ◽  
Cubic Polynomial ◽  
Novel Method ◽  
Iot Devices ◽  
Polar Forms ◽  
Rfid Applications

Identification schemes based on multivariate polynomials have been receiving attraction in different areas due to the quantum secure property. Identification is one of the most important elements for the IoT to achieve communication between objects, gather and share information with each other. Thus, identification schemes which are post-quantum secure are significant for Internet-of-Things (IoT) devices. Various polar forms of multivariate quadratic and cubic polynomial systems have been proposed for these identification schemes. There is a need to define polar form for multivariate dth degree polynomials, where d ≥ 4 . In this paper, we propose a solution to this need by defining constructions for multivariate polynomials of degree d ≥ 4 . We give a generic framework to construct the identification scheme for IoT and RFID applications. In addition, we compare identification schemes and curve-based cryptoGPS which is currently used in RFID applications.

DYNAMICAL RECURRENT NEURO-FUZZY IDENTIFICATION SCHEMES EMPLOYING SWITCHING PARAMETER HOPPING

2012 ◽  
Vol 22 (02) ◽  
pp. 1250004 ◽  
Author(s):  
DIMITRIOS THEODORIDIS ◽  
YIANNIS BOUTALIS ◽  
MANOLIS CHRISTODOULOU
Keyword(s):  
Neural Networks ◽  
Fuzzy Model ◽  
Weighted Average ◽  
Identification Problem ◽  
High Order ◽  
Identification Schemes ◽  
Neuro Fuzzy ◽  
Switching Parameter ◽  
Input Signals ◽  
Novel Method

In this paper we analyze the identification problem which consists of choosing an appropriate identification model and adjusting its parameters according to some adaptive law, such that the response of the model to an input signal (or a class of input signals), approximates the response of the real system for the same input. For identification models we use fuzzy-recurrent high order neural networks. High order networks are expansions of the first-order Hopfield and Cohen-Grossberg models that allow higher order interactions between neurons. The underlying fuzzy model is of Mamdani type assuming a standard defuzzification procedure such as the weighted average. Learning laws are proposed which ensure that the identification error converges to zero exponentially fast or to a residual set when a modeling error is applied. There are two core ideas in the proposed method: (1) Several high order neural networks are specialized to work around fuzzy centers, separating in this way the system into neuro-fuzzy subsystems, and (2) the use of a novel method called switching parameter hopping against the commonly used projection in order to restrict the weights and avoid drifting to infinity.

Asymptotic behavior of eigen energies of non-Hermitian cubic polynomial systems

2007 ◽  
Vol 85 (12) ◽  
pp. 1473-1480 ◽  
Author(s):  
A Nanayakkara
Keyword(s):  
Asymptotic Behavior ◽  
Polynomial System ◽  
Expansion Method ◽  
Polynomial Systems ◽  
Level Spacing ◽  
Spacing Distribution ◽  
Cubic Polynomial ◽  
Asymptotic Energy ◽  
04.20 Jb ◽  
Energy Expansion

The asymptotic behavior of the eigenvalues of a non-Hermitian cubic polynomial system H = (P2/2) + µx3 + ax2 + bx, where µ, a, and b are constant parameters that can be either real or complex, is studied by extending the asymptotic energy expansion method, which has been developed for even degree polynomial systems. Both the complex and the real eigenvalues of the above system are obtained using the asymptotic energy expansion. Quantum eigen energies obtained by the above method are found to be in excellent agreement with the exact eigenvalues. Using the asymptotic energy expansion, analytic expressions for both level spacing distribution and the density of states are derived for the above cubic system. When µ = i, a is real, and b is pure imaginary, it was found that asymptotic energy level spacing increases with the coupling strength a for positive a while it decreases for negative a. PACS Nos.: 03.65.Ge, 04.20.Jb, 03.65.Sq, 02.30.Mv, 05.45

DESERT: A Continuous SPARQL Query Engine for On-Demand Query Answering

2018 ◽  
Vol 12 (03) ◽  
pp. 373-397 ◽  
Author(s):  
Farah Karim ◽  
Ioanna Lytra ◽  
Christian Mader ◽  
Sören Auer ◽  
Maria-Esther Vidal
Keyword(s):  
Query Processing ◽  
Window Size ◽  
Sparql Query ◽  
Knowledge Graph ◽  
Stream Data ◽  
Industrial Manufacturing ◽  
Query Engine ◽  
Novel Method ◽  
Continuous Query Processing ◽  
Iot Devices

The Internet of Things (IoT) has been rapidly adopted in many domains ranging from household appliances e.g. ventilation, lighting, and heating, to industrial manufacturing and transport networks. Despite the, enormous benefits of optimization, monitoring, and maintenance rendered by IoT devices, an ample amount of data is generated continuously. Semantically describing IoT generated data using ontologies enables a precise interpretation of this data. However, ontology-based descriptions tremendously increase the size of IoT data and in presence of repeated sensor measurements, a large amount of the data are duplicates that do not contribute to new insights during query processing or IoT data analytics. In order to ensure that only required ontology-based descriptions are generated, we devise a knowledge-driven approach named DESERT that is able to on-[Formula: see text]emand factoriz[Formula: see text] and [Formula: see text]emantically [Formula: see text]nrich st[Formula: see text]eam da[Formula: see text]a. DESERT resorts to a knowledge graph to describe IoT stream data; it utilizes only the data that is required to answer an input continuous SPARQL query and applies a novel method of data factorization to reduce duplicated measurements in the knowledge graph. The performance of DESERT is empirically studied on a collection of continuous SPARQL queries from SRBench, a benchmark of IoT stream data and continuous SPARQL queries. Furthermore, data streams with various combinations of uniform and varying data stream speeds and streaming window size dimensions are considered in the study. Experimental results suggest that DESERT is capable of speeding up continuous query processing while creates knowledge graphs that include no replications.

Polynomial Solution of Predictive Optimal Control Problems For Systems in State-Equation Form

2003 ◽  
Vol 125 (3) ◽  
pp. 439-447 ◽  
Author(s):  
M. J. Grimble
Keyword(s):  
Optimal Control ◽  
Optimal Control Problems ◽  
Systems Approach ◽  
Polynomial Solution ◽  
Polynomial Systems ◽  
State Equation ◽  
Current Control ◽  
Control Problems ◽  
Linear Quadratic ◽  
Novel Method

The solution of linear quadratic predictive optimal control problems for systems represented in state-equation form, but using a polynomial systems approach, is considered. A multistep cost-function is used that includes future set-point information. A novel method is introduced for computing the vector of future controls and for solving a simpler optimization problem for the current control.

Estimation of the domain of attraction for non-polynomial systems: A novel method

2011 ◽  
Vol 44 (1) ◽  
pp. 10976-10981 ◽  
Author(s):  
Ahmed Saleme ◽  
Bernd Tibken ◽  
Sascha A. Warthenpfuhl ◽  
Christian Selbach
Keyword(s):  
Domain Of Attraction ◽  
Polynomial Systems ◽  
Novel Method

scholarly journals Novel Method for DNA-Based Elliptic Curve Cryptography for IoT Devices

ETRI Journal ◽  
2018 ◽  
Vol 40 (3) ◽  
pp. 396-409 ◽  
Author(s):  
Harsh Durga Tiwari ◽  
Jae Hyung Kim
Keyword(s):  
Elliptic Curve ◽  
Elliptic Curve Cryptography ◽  
Novel Method ◽  
Iot Devices
Sign in / Sign up

Export Citation Format

Share Document