In this paper we present a classifier called bi-density twin support vector machines (BDTWSVMs) for data classification. In the training stage, BDTWSVMs first compute the relative density degrees for all training points using the intra-class graph whose weights are determined by a local scaling heuristic strategy, then optimize a pair of nonparallel hyperplanes through two smaller sized support vector machine (SVM)-typed problems. In the prediction stage, BDTWSVMs assign to the class label depending
on the kernel density degree-based distances from each test point to the two hyperplanes. BDTWSVMs not only inherit good properties from twin support vector machines (TWSVMs) but also give good description for data points. The experimental results on toy as well as publicly available datasets
indicate that BDTWSVMs compare favorably with classical SVMs and TWSVMs in terms of generalization
Multiple-Input Multiple-Output (MIMO) systems have recently been the
subject of intensive consideration in modem wireless communications as they
offer the potential of providing high capacity, thus unleashing a wide range of
applications in the wireless domain. The main feature of MIMO systems is the
use of space-time processing and Space-Time Codes (STCs). Among a variety
of STCs, orthogonal Space-Time Block Codes (STBCs) have a much simpler
decoding method, compared to other STCs
Free Space Optical Communication (FSOC) is an effective alternative technology to
meet the Next Generation Network (NGN) demands as well as highly secured (mili-
tary) communications. FSOC includes various advantages like last mile access, easy
installation, free of Electro Magnetic Interference (EMI)/Electro Magnetic Compatibil-
ity (EMC) and license free access etc. In FSOC, the optical beam propagation in the
turbulentatmosphereisseverelyaffectedbyvariousfactorssuspendedinthechannel,
geographicallocationoftheinstallationsite,terraintypeandmeteorologicalchanges.
Therefore a rigorous experimental study over a longer period becomes significant to
analyze the quality and reliability of the FSOC channel and the maximum data rate
that the system can operate since data transmission is completely season dependent.
Driven by the desire to boost the quality of service of wireless systems closer to that afforded
by wireline systems, space-time processing for multiple-input multiple-output (MIMO)
wireless communications research has drawn remarkable interest in recent years. Excit-
ing theoretical advances, complemented by rapid transition of research results to industry
products and services, have created a vibrant and growing area that is already established
by all counts. This offers a good opportunity to reflect on key developments in the area
during the past decade and also outline emerging trends.
In this thesis several asp ects of space-time pro cessing and equalization for wire-
less communications are treated. We discuss several di?erent metho ds of improv-
ing estimates of space-time channels, such as temp oral parametrization, spatial
parametrization, reduced rank channel estimation, b o otstrap channel estimation,
and joint estimation of an FIR channel and an AR noise mo del. In wireless commu-
nication the signal is often sub ject to intersymb ol interference as well as interfer-
ence from other users.
