Image processing applications are currently spreading rapidly in industrial agriculture. The process of sorting agricultural fruits according to their color comes first among many studies conducted in industrial agriculture. Therefore, it is necessary to conduct a study by developing an agricultural crop separator with a low economic cost, however automatically works to increase the effectiveness and efficiency in sorting agricultural crops. In this study, colored pepper fruits were sorted using a Pixy2 camera on the basis of algorithm image analysis, and by using a TCS3200 color sensor on the basis of analyzing the outer surface of the pepper fruits, thus This separation process is done by specifying the pepper according to the color of its outer surface, afterward selecting the fruit is achieved, then the crop is sorted by color. An electromechanical system was developed for this process with three different belt conveyor speeds (0.8, 2 and 3 m /s). The image processing algorithms and external surface color analysis that were developed within the scope of the study were tested on this system in real practical time. Moreover, choosing the appropriate speed for the conveyor belt, depending on the time sufficient to process the images or analyze the colors of the outer surface of the pepper fruits. The highest successav erage of 93.33% was recorded along with the lowest error average of 6.66%, at the first speed using the Pixy2 camera, whereas the sorting process using the TCS3200 color sensor recorded the highest success average of 83.33% along with the lowest error average of 16.66%, at the first speed. It is evident from the above-mentioned values, that the method of sorting the pepper with the Pixy2 camera is more successful than the second method of using the TCS3200 color sensor, nevertheless, the second method can also be used in the process of sorting the pepper fruits.
This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it
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In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database
Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to
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This research studying the phenomenon of Doppler (frequency Doppler) as a method through which the direction and speed of the blood cells flows in blood vessels wear measured. This Doppler frequency is relied upon in medicine for measuring the speed of blood flow, because the blood flow is an important concept from the concepts of medicine. It represents the function and efficient of the heart and blood vessels in the body so any defect in this function will appear as a change in the speed of blood flow from the normal value assumed. As this speed changes alot in cases of disease and morbidity of the heart, so in order to identify the effect of changing the Doppler frequency on the speed of blood flow and the relationship of
... Show MoreThe crystalline zeolite, namely faujasite type Y with SiO2/Al2O3 mole ratio of 5 was used as raw material for preparation of isomerization catalysts. A 0.5 wt % Pt/HY-zeolite catalyst was prepared by impregnation of the decationized HY-zeolite with chloroplatinic acid. The dectionized HY-zeolite was treated with HCl, HNO3 and HI promoters using different normalities and with different concentrations of Sn, Ni and Ti promoters by impregnation method to obtain acidic and metallic promoters' catalysts, respectively. A 0.5 wt% of Pt was added to above catalysts using impregnation method. Isomerization of n-hexane was carried out at different prepared catalysts. The isomerization temperature varied from 250–325° C over weight hourly space
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