Analysis of Coverage Region for MIMO Relay Network with Multiple Cooperative DF-Relays

We study and analyze coverage region in MIMO communication systems for a multiple-relay network with decode-and-forward (DF) strategy at the relays. Assuming that there is a line-of-sight (LOS) propagation environment for source-relay channels and channel state information is available at receivers (CSIR), we consider the objective of maximizing coverage region for a given transmission rate and show numerically the significant effect of propagation environment on capacity bounds, optimal relay location and coverage region. Also, we study the situation in which two adjacent relays cooperate in transmission signals to the destination and show analytically that the coverage region is extended compared to noncooperative scenario.Comment: Accepted for publication in International Symposium on Wireless Communication Systems (ISWCS) 201

Direct classification of human G-banded chromosome images using support vector machines

Automatic classification of chromosome images used in karyotyping has been of interest for many years. Regardless of the efforts put into this field, due to the complexity of the matter, still the functional accuracy of current automated systems is much lower than a human operator. Since the interdiction of SVM and its proven efficacy in pattern recognition both in theory and application, we decided to test it's efficacy on G-banded chromosomal images. The results were significantly more favorable. The recognition rate in chromosomal subgroups averaged at 95.9%. Furthermore, alongside this study an unmatched database of chromosomal images with about 42000 items was created which can be used as a reference database for further research in this field.</p

Digital-Signal-Type Identification Using an Efficient Identifier

Automatic digital-signal-type identification plays an important role for various applications. This paper presents a highly efficient identifier (technique) that identifies a variety of digital signal types. In this technique, a selected number of the higher-order moments and the higher-order cumulants up to eighth are utilized as the effective features. A hierarchical support-vector-machine- (SVMs) based structure is proposed for multiclass classification. A genetic algorithm is proposed in order to improve the performance of the identifier. Genetic algorithm selects the suitable parameters of SVMs that are used in the structure of the classifier. Simulation results show that the proposed identifier has high performance for identification of the considered digital signal types even at very low SNRs.</p