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Our work in this paper presents a prediction of quality of experience based on full reference parametric (SSIM, VQM) and application metrics (resolution, bit rate, frame rate) in SDN networks. First, we used DCR (Degradation Category Rating) as subjective method to build the training model and validation, this method is based on not only the quality of received video but also the original video but...
The need for more efficient transmission of video stream over the network has become more exigent due to the use of video in several domains(video conference, VOD, etc). The video quality is much affected because of delay, packet loss, etc in the network which degrades the user's perceptions. In this paper, we introduce some of objective metrics (network parameters and encoding video parameters) of...
Radio frequency identification (RFID) technology is paramount for the automation and optimization of supply-chain activities. RFID tags can drastically improve the efficiency of inventory management, especially in the pharmacy supply chain. Thus, the drugs tracking can be enhanced in the pharmaceutical manufacturing plants all across the world paving the way for the wide deployment of the Internet...
Supply-chain and inventory management are the main areas in the internet of things (IoT) requiring low cost and secure batch mode privacy and authentication of radio frequency identification (RFID) tags. Some additional security and quality of service requirements are also necessary for a secure RFID based supply chain application. In this paper, our contribution is twofold: first, we propose a new...
A non-uniform weighted two-components distribution is proposed in the present study for highly right skewed data modeling. We consider a G-GPD model that links a Gaussian distribution to a Generalized Pareto Distribution (GPD) at a junction point, with different weights for each component. It improves a G-GPD model with uniform weights that had been introduced in a preliminary study (see [1]). An...
In this paper, we present a novel method for R-peak detection in the ElectroCardioGram (ECG) signal in a noisy environment. We interpret the R-peak occurrence as an irregularity in the signal. Thereby, we transform the problem of R-peaks detection into irregularity instants estimation. To point out these irregularities, we use an algebraic approach based on differential algebra and operational calculus...
In this paper, a new time delay estimation technique is applied for the R-R interval analysis. The key idea is about the R-R interval estimation of the ECG signal that is regarded as a time delay estimation in which we cast the change-point detection problem. This work aims to present an ad-hoc time delay technique that stems from operational calculus, differential algebra and non commutative algebra...
This paper uses the Extreme Value Theory (EVT) for threshold selection in a previously proposed algebraic spike detection method. The algebraic method characterizes the occurrence of a spike by an irregularity in the neural signal and devises a nonlinear (Volterra) filter which enhances the presence of such irregularities. These appear as (positive) high amplitude pulses in the output signal. The...
In this paper, a new robust method for R wave detection in ECG signal is proposed by using algebraic derivative estimation based technique. In fact, this new and efficient method relies on differential algebra, non-commutative algebra together with operational calculus. This technique allows noisy signal to be filtered via iterated time integrals and R wave slope to be emphasized. The ECG signal is...
In this paper, the problem of nonlinear equalization is addressed. We use an algebraic approach which allows us to define the existence conditions of a left inverse, for a nonlinear system and therefore the equalization conditions. These existence conditions need the computation of the rank of some jacobian matrices. This approach is applied to a Volterra filter, which represents a nonlinear system...
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