Identification of pavement distress such as potholes and humps not only helps drivers to avoid accidents, but also helps authorities to maintain roads. This project discusses previous pothole detection methods that have been developed and proposes a cost-effective solution to identify the potholes and humps on rods and provide timely alerts to drivers to avoid accidents or vehicle damages. Lasers are used to identify the potholes and humps. The proposed system captures the geographical location coordinates of the potholes and humps. It can actively learn the knowledge about the suspension system of the host vehicle without any human intervention vibration model to infer the presence of pothole while the vehicle is hitting the pothole. As a proof of concept Raspberry Pi based prototype of proposed system is designed and going to be developed for small application.

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Digital Image Processing (DIP) is a rapidly evolving field with blooming applications inScience and Engineering. The accuracy of human face recognition system is mostly affected by varying lighting conditions. To overcome the illumination invariant problems, decomposition method is used. At various scales and frequencies the facial features are extracted by multiresolution property of Discrete Wavelet Transform (DWTs). The wavelet sub bands are used to represent well-lit face images. Fusion of match scores depends on low and high frequency which is based on the human face representation to improve the accuracy in varying lighting conditions. For obtaining better performance in human face recognition under different illumination conditions, here this paper contributes via adaptive face recognition by decomposing the images using wavelet transform for image quality.