An Improved Signal Segmentation Method using Genetic Algorithm
نویسندگان
چکیده
منابع مشابه
An Improved Signal Segmentation Method using Genetic Algorithm
In many signal processing application, the signal of interest is often divided into epochs. In these applications, the segmented signal is preferred to have no change on the statistical characteristics of the epochs. Modified Varri is among the segmentation methods with an acceptable accuracy. There are three parameters affecting on the accuracy of this method. These parameters are set experime...
متن کاملAn Improved Automatic EEG Signal Segmentation Method based on Generalized Likelihood Ratio
It is often needed to label electroencephalogram (EEG) signals by segments of similar characteristics that are particularly meaningful to clinicians and for assessment by neurophysiologists. Within each segment, the signals are considered statistically stationary, usually with similar characteristics such as amplitude and/or frequency. In order to detect the segments boundaries of a signal, we ...
متن کاملAn Improved Image Segmentation Algorithm Based on MET Method
Image segmentation is a basic component of many computer vision systems and pattern recognition. Thresholding is a simple but effective method to separate objects from the background. A commonly used method, Kittler and Illingworth's minimum error thresholding (MET), improves the image segmentation effect obviously. It’s simpler and easier to implement. However, it fails in the presence of skew...
متن کاملAn Improved Algorithm Based on Wellner’s Threshold Segmentation Method
According to the Wellner algorithm whose speed is slow and the image threshold is not smooth for computing a large amount of defects, this paper presents two kinds of improved scheme, the first scheme is onedimensional smoothing algorithm (ODSA), the second is based on the first one and its name is integral image algorithm(IIA). The former one mainly considering the spatial relationship between...
متن کاملAn Improved Genetic Algorithm
In this paper, an improved genetic algorithm for multi-object optimization is proposed. Simulated annealing is used to local search in genetic algorithms. Furthermore, fuzzy reasoning is adopted to modify crossover probability and mutation probability according to characteristics of population in genetic algorithms instead of fixed parameters. And so, it can be convergence to global optimum qui...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2011
ISSN: 0975-8887
DOI: 10.5120/3586-4967