Expiratory breath contains various VOCs(Volatile Organic Compounds) produced from the human. When a certain disease exists, the
exhalation has specific VOCs which may be generated from diseases. Many researchers have been actively working to find different
types of biomarkers which are characteristic for particular diseases. Research regarding the identification of specific diseases from
exhalation is still in progress. The aim of this research is to implement early detection of lung disease such as lung cancer and
COPD(Chronic Obstructive Pulmonary Disease), which was nominated on the 6th of domestic death rate in 2010, based on multi-sensor
array system. The system has been used to acquire sampled expiratory gases data and PCA(Principle Component Analysis)
technique was applied to analyze signals from multi-sensor array. Throughout the experimental trials, a clearly distinguishable
difference between lung disease patients and healthy controls was found from the measurement and analysis of their respective
expiratory gases.
Distortions caused by the DC-biased laser input can be modeled as DC biased Gaussian noise and removing DC bias is important in
the demodulation process of the electrical signal in most optical communications. In this paper, a new performance criterion and a
related algorithm for unsupervised equalization are proposed for communication systems in the environment of channel distortions
and DC biased Gaussian noise. The proposed criterion utilizes the Euclidean distance between the Dirac-delta function located at zero
on the error axis and a probability density function of biased constant modulus errors, where constant modulus error is defined by the
difference between the system out and a constant modulus calculated from the transmitted symbol points. From the results obtained
from the simulation under channel models with fading and DC bias noise abruptly added to background Gaussian noise, the proposed
algorithm converges rapidly even after the interruption of DC bias proving that the proposed criterion can be effectively applied to
optical communication systems corrupted by channel distortions and DC bias noise.
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