Computers and Electrical Engineering

ISSN: 0045-7906
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  • Simulation experiments results (search efficiency, region coverage rate, environment uncertainty reduction and the probability of collision of the proposed method, search methods based on the random search, greedy search, particle swarm optimization algorithm, ant colony algorithm, Q learning, conventional IWD algorithm, and the IWD algorithm with co-evolutionary strategy).
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  • the data consists of two folders, namely Training and Test sets. each of these folders is made up of images from cameras which have been used in the analysis of the system in this article. the data is applied as training and test, and afterwards the test set is trained while the training set is tested. this is aimed at getting an average recognition rate for the system.
    Data Types:
    • Dataset
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  • In this data-set, 39 different classes of plant leaf and background images are available. The data-set containing 61,486 images. We used six different augmentation techniques for increasing the data-set size. The techniques are image flipping, Gamma correction, noise injection, PCA color augmentation, rotation, and Scaling. The classes are, 1.Apple_scab 2.Apple_black_rot 3.Apple_cedar_apple_rust 4.Apple_healthy 5.Background_without_leaves 6.Blueberry_healthy 7.Cherry_powdery_mildew 8.Cherry_healthy 9.Corn_gray_leaf_spot 10.Corn_common_rust 11.Corn_northern_leaf_blight 12.Corn_healthy 13.Grape_black_rot 14.Grape_black_measles 15.Grape_leaf_blight 16.Grape_healthy 17.Orange_haunglongbing 18.Peach_bacterial_spot 19.Peach_healthy 20.Pepper_bacterial_spot 21.Pepper_healthy 22.Potato_early_blight 23.Potato_healthy 24.Potato_late_blight 25.Raspberry_healthy 26.Soybean_healthy 27.Squash_powdery_mildew 28.Strawberry_healthy 29.Strawberry_leaf_scorch 30.Tomato_bacterial_spot 31.Tomato_early_blight 32.Tomato_healthy 33.Tomato_late_blight 34.Tomato_leaf_mold 35.Tomato_septoria_leaf_spot 36.Tomato_spider_mites_two-spotted_spider_mite 37.Tomato_target_spot 38.Tomato_mosaic_virus 39.Tomato_yellow_leaf_curl_virus
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  • Cyclostationary feature detection is one of the most common methods used in spectrum sensing. The parallel algorithm presented here can be used in multi-core processors to speedup the execution time. The implementation uses OpenMP and the Fastest Fourier Transform in the West (a efficient Fourier transform method).
    Data Types:
    • Dataset
    • File Set