The processes of intelligent data processing in computer vision systems have been researched. The problem of structural image recognition is relevant. This is a promising way to assess the degree of similarity of objects. This approach provides the simplicity of construction and the high reliability of decision making. The main problem of an effective description of characteristic features is the distortion of fragments of analyzed objects. The reasons for changing the input data can be the actions of geometric transformations, the influence of background or interference. The elements of false objects with similar characteristics are formed. The problem of ensuring high-quality recognition requires the implementation of effective means of image processing. Methods of statistical modeling, granulation of data and fuzzy sets, detection and comparison of keypoints on the image, classification and clustering of data, and simulation modelling are used in this research. The implementation of the proposed approaches provides the formation of a concise description of features or a vector representation of unique keypoints. The verification of theoretical foundations and evaluation of the effectiveness of the proposed data processing methods for real image bases is performed using the OpenCV library. The applied significance of the work is substantiated according to the criterion of data processing time without reducing the characteristics of reliability and interference immunity. The developed methods allow to increase the structural recognition of images by several times. Perspectives of research may involve identifying the optimal number of keypoints of the base set.
Autism spectrum disorder (ASD) is a neurological-related disorder. Patients with ASD have poor social interaction and lack of communication that lead to restricted activities. Thus, early diagnosis with a reliable system is crucial as the symptoms may affect the patient’s entire lifetime. Machine learning approaches are an effective and efficient method for the prediction of ASD disease. The study mainly aims to achieve the accuracy of ASD classification using a variety of machine learning approaches. The dataset comprises 16 selected attributes that are inclusive of 703 patients and non-patients. The experiments are performed within the simulation environment and analyzed using the Waikato environment for knowledge analysis (WEKA) platform. Linear support vector machine (SVM), k-nearest neighbours (k-NN), J48, Bagging, Stacking, AdaBoost, and naïve bayes are the methods used to compute the prediction of ASD status on the subject using 3, 5, and 10-folds cross validation. The analysis is then computed to evaluate the accuracy, sensitivity, and specificity of the proposed methods. The comparative result between the machine learning approaches has shown that linear SVM, J48, Bagging, Stacking, and naïve bayes produce the highest accuracy at 100% with the lowest error rate.
A frequency division multiplexing technique, nonorthogonal frequency division multiplexing (n-OFDM), is proposed in [1]-[2] to enhance the efficiency of bandwidth utilization. This paper reveals that the smaller the frequency separation, the larger sum capacity can be achieved compared with the conventional OFDM technique. However, n-OFDM system introduces inter-carrier interference (ICI) at the transmitter because the orthogonality between the subcarriers no longer holds. Moreover, since the channel covariance matrix of n-OFDM has high condition number when the overlapping factor, 1 − α, is large, conventional linear detectors suffers from severe noise enhancement. To solve this problem, this paper proposes the use of soft cancellation-minimum mean-squared error (SC-MMSE) turbo equalization. Binary constellation constrained mutual information (CCMI) is calculated by utilizing the area property for the EXtrinsic Information Transfer (EXIT) chart of the SC-MMSE equalizer. Results of the EXIT chart analysis and bit-error-rate (BER) simulations in additive white Gaussian noise (AWGN) channel are presented.
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