Abstract: In this paper, a target signal detection method using
multiple signal classification (MUSIC) algorithm is proposed. The
MUSIC algorithm is a subspace-based direction of arrival (DOA)
estimation method. The algorithm detects the DOAs of multiple
sources using the inverse of the eigenvalue-weighted eigen spectra. To
apply the algorithm to target signal detection for GSC-based
beamforming, we utilize its spectral response for the target DOA in
noisy conditions. For evaluation of the algorithm, the performance of
the proposed target signal detection method is compared with that of
the normalized cross-correlation (NCC), the fixed beamforming, and
the power ratio method. Experimental results show that the proposed
algorithm significantly outperforms the conventional ones in receiver
operating characteristics(ROC) curves.
Abstract: Silver nano-particles have been used for antibacterial
purpose and it is also believed to have removal of odorous compounds,
oxidation capacity as a metal catalyst. In this study, silver
nano-particles in nano sizes (5-30 nm) were prepared on the surface of
NaHCO3, the supporting material, using a sputtering method that
provided high silver content and minimized conglomerating problems
observed in the common AgNO3 photo-deposition method. The silver
nano-particles were dispersed by dissolving Ag-NaHCO3 into water,
and the dispersed silver nano-particles in the aqueous phase were
applied to remove inorganic odor compounds, H2S, in a scrubbing
reactor. Hydrogen sulfide in the gas phase was rapidly removed by the
silver nano-particles, and the concentration of sulfate (SO4
2-) ion
increased with time due to the oxidation reaction by silver as a
catalyst. Consequently, the experimental results demonstrated that the
silver nano-particles in the aqueous solution can be successfully
applied to remove odorous compounds without adding additional
energy sources and producing any harmful byproducts