This paper studies genetic algorithms as a means of estimating the number of changepoints and their locations in a climatic time series. Such methods bypass classic subsegmentation algorithms, which sometimes yield suboptimal conclusions. Minimum description length techniques are introduced. These techniques require optimizing an objective function over all possible changepoint numbers and location times. The general objective functions allow for correlated data, reference station aspects, and/or nonnormal marginal distributions, all common features of climate time series. As an exhaustive evaluation of all changepoint configurations is not possible, the optimization is accomplished via a genetic algorithm that randomly walks through a subset of good models in an intelligent manner. The methods are applied in the analysis of 173 yr of annual precipitation measurements from New Bedford, Massachusetts, and the North Atlantic basin tropical cyclone record.

This paper develops trend estimation techniques for monthly maximum and minimum temperature time series observed in the 48 conterminous United States over the last century. While most scientists concur that this region has warmed on aggregate, there is no a priori reason to believe that temporal trends in extremes and averages will exhibit the same patterns. Indeed, under minor regularity conditions, the sample partial sum and maximum of stationary time series are asymptotically independent (statistically). Previous authors have suggested that minimum temperatures are warming faster than maximum temperatures in the United States; such an aspect can be investigated via the methods discussed in this study. Here, statistical models with extreme value and changepoint features are used to estimate trends and their standard errors. A spatial smoothing is then done to extract general structure. The results show that monthly maximum temperatures are not often greatly changing-perhaps surprisingly, there are many stations that show some cooling. In contrast, the minimum temperatures show significant warming. Overall, the southeastern United States shows the least warming (even some cooling), and the western United States, northern Midwest, and New England have experienced the most warming.

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The adaptive driving beam headlamp system is helpful to solve the traffic safety problems caused by the abuse of high beams at night. The fundamental problem for the adaptive driving beam system is to detect and track the vehicles at specific night environment for light shape control. This paper proposes a new and efficient algorithm for detecting and tracking based on video collected by a low-cost camera. The related automobile regulations are analyzed for classifying the application scenarios of the adaptive driving beam system and summarizing the performance requirements of the algorithm. A standard CMOS camera mounted behind windshield captures the test video from different scenarios. Some preprocessing approaches are designed to optimize the captured video so that the algorithm can work independently on specific camera. The color and morphological characteristics of the rear lights are utilized to extract the rear lamps of the vehicle. The symmetry of rear lights is checked by a correlation coefficient method to pair the rear lamps and determine the vehicle ahead preliminary. Then, the Hungarian algorithm and Kalman filter are performed to track the multiple occurrences in two consecutive frames and correct the detection results. Finally, an estimation method is given for calculating the vehicle position in real world. The experiments are designed according to referred regulations and the test video is obtained from a low-cost camera mounted on test vehicles driving in specific scenarios. The experimental results demonstrate that the algorithm can get high detection rates and adaptability to the working condition of adaptive driving beam system. Moreover, the proposed algorithm has low time cost and can be applied in embedded devices of the vehicle. INDEX TERMS Adaptive Driving Beam, image processing, rear lights recognition, video processing, system testing.