Acoustic monitoring of birds in the vicinity of wind turbines is becoming an important public policy issue. Acoustic monitoring involves preprocessing, feature extraction and classification. A novel Spectrogram-based Image Frequency Statistics (SIFS) feature extraction algorithm has been developed. Features extracted from proposed algorithms were then combined with various classification algorithms such as k-NN, Multilayer Perceptron (MLP) and Hidden Markov Models (HMM) and Evolutionary Neural Network (ENN). SIFS and MMS algorithms, combined with ENN, provided the most accurate results. Proposed algorithms were tested with real data collected during spring migration around Lake Erie in Ohio.
Economic clusters have been delineated using Local Moran's I and GetisOrd G * i because they distinguish relationships across areal unit boundaries within a specified neighborhood. A problem using spatial statistics with U.S. county data are the great variations in county sizes. We examined the relationship between the values for Local Moran's I and G * i , in groups of counties of differing size. The impact of county size on both spatial statistics using a contiguity spatial weights matrix and an inverse centroid distance matrix are assessed. In small counties, the choice in spatial weight matrices is immaterial, especially when using Local Moran's I . For large counties the differences between the spatial weights methodologies is more apparent, due to edge effects being more prevalent. Selection of an optimal combination of spatial weight methodology and clustering statistic should depend on the study's purpose, the distribution of county sizes, and the industry being studied.
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