This paper presents a new algorithm for detecting and characterizing potholes and bumps directly from noisy signals acquired using an Accelerometer. A wavelet transformation based filter was used to decompose the signals into multiple scales. These coefficients were correlated across adjacent scales and filtered using a spatial filter. Road anomalies were then detected based on a fixed threshold system, while characterization was achieved using unique features extracted from the filtered wavelet coefficients. Our analyses show that the proposed algorithm detects and characterizes road anomalies with high levels of accuracy, precision and low false alarm rates.
As man continues to encroach on the natural habitat of the arboreal (birds and other wildlife), problems like aircraft-bird strikes continues to aggravate as air traffic volume increases. This study focused on the Aminu Kano International Airport in Nigeria due to importance of its geographical location and the air traffic flow. The analysis result reveals that 12 bird species were involved in 44 strikes at the airport between the year 2001-2010 and mostly during the rainy seasons. The airport during the same period recorded 55,205 aircraft movement that carried 3,551,587 passengers. Distribution of these strikes by phase indicates that 32% occurred on approach, 25% on landing roll, 20% on takeoff, 4.5% on descend, 4.5% during climb while 14% at other phases. Using the International Bird Strike Committee strike rate (per 10,000 flights), the computed strike rate for this airport is 7.97 per 10,000 flights. It is therefore recommended that "Bird Strike Avoidance Radar" equipment that will alert the control tower which will in turn alert the pilot of the presence of birds along his way be installed in major airports in the country.
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