This chapter describes componentwise Least Squares Support Vector Machines (LS-SVMs) for the estimation of additive models consisting of a sum of nonlinear components. The primal-dual derivations characterizing LS-SVMs for the estimation of the additive model result in a single set of linear equations with size growing in the number of data-points. The derivation is elaborated for the classific...

The aim of this paper is to enhance the quality of Orthogonal Frequency Division Multiplexing OFDM estimation in dedicated vehicular communication transmission V2X networks. Wireless Access in Vehicular Environment WAVE as also known IEEE 802.11p represents the standard for these networks. Developing a reliable inter-vehicular V2X communication has to focus on optimizing its real performances. ...

Coastal sea level variation as an indicator of climate change is extremely important due to its large socioeconomic and environmental impacts. The ground-based global navigation satellite system (GNSS)-reflectometry (GNSS-R) becoming a reliable alternative for surface altimetry. We investigate the impact antenna polarization orientation on GNSS-R altimetric performance at different carrier freq...

This paper proposes the least-squares support vector machine (LS-SVM) as an intelligent method applied on absorption spectra for the simultaneous determination of paracetamol (PCT), caffeine (CAF) and ibuprofen (IB) in Novafen. The signal to noise ratio (S/N) increased. Also, In the LS - SVM model, Kernel parameter (σ2) and capacity factor (C) were optimized. Excellent prediction was shown usin...

The aim of this article is to simulate bit error rate (BER) of the IEEE 802.11p standard with different numbers of transmit and receive antennas. Assuming a simple flat Rayleigh fading channel, we have investigated three different antenna settings, namely, single input single output (SISO), multiple input single output (MISO), and multiple input multiple output (MIMO). When multiple antennas ar...

We consider a block fading frequency selective multi-input multioutput (MIMO) channel in additive white Gaussian noise (AWGN) The channel input is a training vector superimposed on a linearly precoded vector of Gaussian symbols. This form of precoding is referred to as aj%e pncoding. We derive the channel CramerRao bound (CRB) and we show that tr(CRB) can be lowered if we design the precoder an...

In this paper, a modified estimation algorithm has been developed refers to covariance shaping least square estimation based on the quantum mechanical concepts and constraints. The algorithm has been applied to the speech signal and the performance is estimated using probability theories. The same models can be applied with additive white Gaussian Noise which estimates the bias in the parameter...

A prerequisite for reliable mobile positioning in WCDMA systems is the estimation with high accuracy of the LineOf-Sight (LOS) propagation delay between 3 or more Base Stations (BSs) and the mobile station (MS). LOS component may be followed by Non Line-Of-Sight components arriving within less than one chip interval. Correlation based methods are limited in accuracy by the width of the main lob...

All the available algorithms for blind estimation namely constant modulus algorithm (CMA), Decision-Directed Algorithm (DDA/DFE) suffer from the problem of convergence to local minima. Also, if the channel drifts considerably, any DDA looses track of the channel. So, their usage is limited in varying channel conditions. The primary limitation in such cases is the requirement of certain overhead...

A novel efficient time domain threshold based sparse channel estimation technique is proposed for orthogonal frequency division multiplexing (OFDM) systems. The proposed method aims to realize effective channel estimation without prior knowledge of channel statistics and noise standard deviation within a comparatively wide range of sparsity. Firstly, classical least squares (LS) method is used ...