Abstract: Uncontrolled growth of abnormal cells in the lung in the form of tumor can be either benign (non-cancerous) or malignant (cancerous). Patients with Lung Cancer (LC) have an average of five years life span expectancy provided diagnosis, detection and prediction, which reduces many treatment options to risk of invasive surgery increasing survival rate. Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI) for earlier detection of cancer are common. Gaussian filter along with median filter used for smoothing and noise removal, Histogram Equalization (HE) for image enhancement gives the best results without inviting further opinions. Lung cavities are extracted and the background portion other than two lung cavities is completely removed with right and left lungs segmented separately. Region properties measurements area, perimeter, diameter, centroid and eccentricity measured for the tumor segmented image, while texture is characterized by Gray-Level Co-occurrence Matrix (GLCM) functions, feature extraction provides Region of Interest (ROI) given as input to classifier. Two levels of classifications, K-Nearest Neighbor (KNN) is used for determining patient condition as normal or abnormal, while Artificial Neural Networks (ANN) is used for identifying the cancer stage is employed. Discrete Wavelet Transform (DWT) algorithm is used for the main feature extraction leading to best efficiency. The developed technology finds encouraging results for real time information and on line detection for future research.
Abstract: A new concept of response system is proposed for
filling the gap that exists in reducing vulnerability during immediate
response to natural disasters. Real Time Early Response Systems
(RTERSs) incorporate real time information as feedback data for
closing control loop and for generating real time situation assessment.
A review of the state of the art on works that fit the concept of
RTERS is presented, and it is found that they are mainly focused on
manmade disasters. At the same time, in response phase of natural
disaster management many works are involved in creating early
warning systems, but just few efforts have been put on deciding what
to do once an alarm is activated. In this context a RTERS arises as a
useful tool for supporting people in their decision making process
during natural disasters after an event is detected, and also as an
innovative context for applying well-known automation technologies
and automatic control concepts and tools.
Abstract: In this paper, we focused primarily on Istanbul data
that is gathered by using intelligent transportation systems (ITS), and
considered the developments in traffic information delivery and
future applications that are being planned for implementation. Since
traffic congestion is increasing and travel times are becoming less
consistent and less predictable, traffic information delivery has
become a critical issue. Considering the fuel consumption and wasted
time in traffic, advanced traffic information systems are becoming
increasingly valuable which enables travelers to plan their trips more
accurately and easily.