The vision of Self-Organizing Networks (SON) has been drawing considerable attention as a major axis for the development of future networks. As an essential functionality in SON, cell outage detection is developed to autonomously detect macrocells or femtocells that are inoperative and unable to provide service. However, due to the two-tier macro femto network architecture and the small coverage nature of femtocells, it is challenging to enable outage detection functionality in femtocell networks. Self-healing functionality in femtocell aims to resolve the loss of coverage or capacity induced by cell outage to the extent possible in the femtocell networks. Existing systems uses local cooperation architecture which seeks solutions with the need for local collaboration among femtocells. Specifically, an outage is detected based on the measurements of surrounding femtocells. Based on these local measurements, a proper set of neighbor femto APs tune their parameters to compensate for the outage. The outage occurs due improper arrangements of femtocell network. Proper placing of the femtocell access points reduces the outage problems. The signal strength, threshold and various parameters are calculated for different configurations and for different modulation technique using a simulation mechanism. The analysis of the same is done .from this analysis proper configuration of the femtocell network is obtained.

Computer-aided detection (CADe) of pulmonary nodules is critical to assist radiologist in early detection of lung cancer from computed tomography (CT) scans. So in proposed system we use CADe system based on hierarchical vector quantization (VQ) scheme. On comparing with commonly-used simple thresholding approach, the high-level VQ yields accurate segmentation of lungs from chest volume and in identifying initial nodule candidates (INCs) within lungs, low-level VQ proves to be effective for INC detection and segmentation, as well as computationally efficient compared to existing approaches. This proposed system also reduces false positive detection. False positive reduction is conducted via the rule based filtering operation in combination with feature-based support vector machine classifier. This proposed system shows out performance and demonstrate its potential for early detection of pulmonary nodules via CT imaging.