M
M. Sivaram
Researcher at University of Kurdistan
Publications - 59
Citations - 887
M. Sivaram is an academic researcher from University of Kurdistan. The author has contributed to research in topics: Computer science & Wireless sensor network. The author has an hindex of 12, co-authored 55 publications receiving 424 citations. Previous affiliations of M. Sivaram include Ton Duc Thang University.
Papers
More filters
Journal ArticleDOI
Distributed Feedback Laser (DFB) for Signal Power Amplitude Level Improvement in Long Spectral Band
Debabrata Samanta,M. Sivaram,Ahmed Nabih Zaki Rashed,C. S. Boopathi,Iraj Sadegh Amiri,P. P. Yupapin +5 more
TL;DR: In this paper, a distributed feedback laser for signal power amplitude level improvement in the long spectral band of 1550-nm wavelength within supporting pumped wavelength of 1480-nm was presented, where the bias and modulation peak currents were varied to test the signal power level, peak signal amplitude variations after the fiber-optic channel and light detectors.
Journal ArticleDOI
Different modulation schemes for direct and external modulators based on various laser sources
Hazem M. El-Hageen,P. Kuppusamy,Aadel M. Alatwi,M. Sivaram,Z. Ahamed Yasar,Ahmed Nabih Zaki Rashed +5 more
TL;DR: In this article, different types of laser source modulation techniques have been used in various applications depending on the objective, as optical systems extract the laws and the best solutions from experiments and simulations, using simulation software with different modulation types so the output signals can be compared.
Journal ArticleDOI
Malicious node detection using heterogeneous cluster based secure routing protocol (HCBS) in wireless adhoc sensor networks
TL;DR: The proposed heterogeneous cluster based secure routing scheme provides trust based secure network for detection of attacks such as wormhole and black hole caused by malicious nodes presence in wireless Adhoc network.
Journal ArticleDOI
A novel method of motor imagery classification using eeg signal.
TL;DR: The current research proposes a Hybrid-KELM (Kernel Extreme Learning Machine) method based on PCA (Principal Component Analysis) and FLD (Fisher's Linear Discriminant) for MI BCI classification of EEG data.
Journal ArticleDOI
An efficient hybrid methodology for detection of cancer-causing gene using CSC for micro array data
TL;DR: A modified bio-inspired algorithm namely cuckoo search with crossover (CSC) for choosing genes from technology of micro array that are able to classify numerous cancer sub-types with extraordinary accuracy is demonstrated.