Presently breast cancer detection is a very important role for worldwide women to save the life. Doctors and radio logistic can miss the abnormality due to inexperience in the field of cancer detection. The pre-processing is the most important step in the mammogram analysis due to poor captured mammogram image quality. Pre-processing is very important to correct and adjust the mammogram image for further study and processing. There are Different types of filtering techniques are available for pre-processing. This filters used to improve image quality, remove the noise, preserves the edges within an image, enhance and smoothen the image. In this paper, we have performed various filters namely, average filter, adaptive median filter, average or mean filter, and wiener filter
Abstract-Currently almost of the public having an own vehicle, theft is happening on parking and sometimes driving insecurity places. The safe of vehicles is ext remely essential for public vehicles. Vehicle tracking and locking system installed in the vehicle, to track the p lace and locking engine motor. The place of the vehicle identified using Global Positioning system (GPS) and Global system mobile communicat ion (GSM). These systems constantly watch a moving Vehicle and report the status on demand. When the theft identified, the responsible person send SMS to the microcontroller, then microcontroller issue the control signals to stop the engine motor. Authorized person need to send the password to controller to restart the vehicle and open the door. This is more secured, reliable and low cost.
In this paper, the speech signal is enhanced from the noisy speech signal using the proposed Least Mean Square (LMS) adaptive noise reduction algorithm. In this, the speech signal is enhanced by varying the step size as the function of the input signal. Objective and subjective measures are made under various noises for the proposed and existing algorithms. From the experimental results, it is seen that the proposed LMS adaptive noise reduction algorithm reduces Mean square Error (MSE) as compared to the earlier method under various noise conditions with different input SNR levels. In addition, the proposed spectral subtraction method improves the Peak Signal to Noise Ratio (PSNR) as compared to that of various existing LMS adaptive noise reduction algorithms. From these experimental results, it is observed that the proposed LMS adaptive noise reduction algorithm reduces the speech distortion and residual noise as compared to existing methods.
Cancer is one of the most leading causes of deaths among the women in the world. Among the cancer diseases, breast cancer is especially a concern in women. Mammography is one of the methods to find tumor in the breast, which is helpful for the doctor or radiologists to detect the cancer. Doctor or radiologists can miss the abnormality due to inexperience's in the field of cancer detection. Segmentation is very valuable for doctor and radiologists to analysis the data in the mammogram. Accuracy rate of breast cancer in mammogram depends on the image segmentation. This paper is a survey of recent clustering techniques for detection of breast cancer. These fuzzy clustering algorithms have been widely studied and applied in a variety of application areas. In order to improve the efficiency of the searching process clustering techniques recommended. In this paper, we have presented a survey of clustering techniques.
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