Contents of Volume 32 (2022)


  • [1] Likhonina R., Uglickich E. (CZ)
    Hand detection application based on QRD RLS lattice algorithm and its implementation on Xilinx Zynq Ultrascale+ , pp. 73-92

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    Abstract: The present paper describes hand detection application implemented on Xilinx Zynq Ultrascale+ device, comprising multi-core processor ARM Cortex A53 and FPGA programmable logic. It uses ultrasound data and is based on adaptive QRD RLS lattice algorithm extended with hypothesis testing. The algorithm chooses between two use-cases: (1) “there is a hand in front of the device” vs (2) “there is no hand in front of the device”. For these purposes a new structure of the identification models was designed. The model presenting use-case (1) is a regression model, which has the order sufficient to cover all incoming data. The model responsible for use-case (2) is a regression model, which has a smaller order than the model (1) and a certain time delay, covering the maximal distance where the hand can possibly appear. The offered concept was successfully verified using real ultrasound data in MATLAB optimized for parallel processing and implemented in parallel on four cores of ARM Cortex A53 processor. It was proved that computational time of the algorithm is sufficient for applications requiring real-time processing.

  • [2] Ji K.K., Li Z.Z., Chen J., Liu K.L., Wang G.Y. (China)
    Freeway accident duration prediction based on social network information, pp. 93-112

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    Abstract: Accident duration prediction is the basis of freeway emergency management, and timely and accurate accident duration prediction can provide a reliable basis for road traffic diversion and rescue agencies. This study proposes a method for predicting the duration of freeway accidents based on social network information by collecting Weibo data of freeway accidents in Sichuan province and using the advantage that human language can convey multi-dimensional information. Firstly, text features are extracted through a TF-IDF model to represent the accident text data quantitatively; secondly, the variability between text data is exploited to construct an ordered text clustering model to obtain clustering intervals containing temporal attributes, thus converting the ordered regression problem into an ordered classification problem; finally, two nonparametric machine learning methods, namely support vector machine (SVM) and k-nearest neighbour method (KNN), to construct an accident duration prediction model. The results show that when the ordered text clustering model divides the text dataset into four classes, both the SVM model and the KNN model show better prediction results, and their average absolute error values are less than 22 %, which is much better than the prediction results of the regression prediction model under the same method.

  • [3] Qing D., Li J., Deng Q., Liu S. (China)
    Mining and quantifying the optimal DBH range of loblolly pine with improved particle algorithm, pp. 113-130

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    Abstract: In order to fully understand the objective law of height and DBH growth of loblolly pine trees and exploring the best DBH (Diameter at Breast Height) Range for loblolly pine tree height growth, 13 340 loblolly pines with initial DBH between 1 inch and 7 inch were selected from Alabama as research objects, and statistics on its growth from 2000 to 2015. Because particle swarm optimization (PSO) is suitable for solving non-linear problems, the optimal DBH of loblolly pine is transformed into the optimization problem of PSO, which quantifies the optimal DBH range of loblolly pine at different scales by mapping strategy. The experimental results show that the range of the breast diameter suitable for the high growth of the pine tree is concentrated between 3.7 inch and 7.3 inch. The height of the pine tree begins to enter a period of rapid growth from a breast diameter of 3.9 inch (±0.2 inch ). The tree height growth rate reached a maximum at a breast diameter of 6.4 inch (±0.6 inch ), and the tree height entered a slow growth period after the breast diameter of 11.92 inch (±0.3 inch). In general, when the breast diameter exceeds 15.26 inch (±0.3 inch), the height of the pine tree stops growing.


  • [1] Abeska Y.Y., Cavas L. (Turkey)
    Artificial neural network modelling of green synthesis of silver nanoparticles by honey , pp. 1-14

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    Abstract: Nanomaterials draw attention because of their unique physical, chemical and biological properties in areas such as catalysis, electronic, optics, medicine, solar energy conversion and water treatment. Green synthesis of silver nanoparticles has many superiorities compared to physical and chemical methods such as lowcost, nontoxicity, eco-sensitive. In this paper, experimental conditions related togreen synthesis of silver nanoparticles by honey were modelled using artificial neural network (ANN). While agitation time, agitation rate, pH, temperature, honey concentration, AgNO3 concentration were selected as input parameters, production of silver nanoparticles was used as an output parameter. According to the results, optimum hidden neuron number was found as 40 with Levenberg–Marquardt back-propagation algorithm. In this conditions, the percentages of training, validationand testing were 75, 20 and 5, respectively. After creating neural network separated input data set was applied and then experimental and ANN predicted data were compared. In conclusion, ANN can be an alternative modelling and robust approach that could help researchers in this field to estimate production of silver nanoparticles.

  • [2] Jozová Š., Uglickich E., Nagy I., Likhonina R. (CZ)
    Modeling of discrete questionnaire data with dimension reduction, pp. 15-41

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    Abstract: The paper deals with the task of modeling discrete questionnaire data with a reduced dimension of the model. The discrete model dimension is reduced using the construction of local models based on independent binomial mixtures estimated with the help of recursive Bayesian algorithms in the combination with the naive Bayes technique. The main contribution of the paper is a three-phase algorithm of the discrete model dimension reduction, which allows to model high-dimensional questionnaire data with high number of explanatory variables and their possible realizations. The proposed general solution is applied to the traffic accident questionnaire analysis, where it takes the form of the classification of the accident circumstances and prediction of the traffic accident severity using the currently measured discrete data. Results of testing the obtained model on real data and comparison with theoretical counterparts are demonstrated.

  • [3] Daqrouq K., Alkhateeb A., Ahmad W., Khalaf E., Awad M., Noeth E., Alharbey R.A., Rushdi A.M. (Pakistan, Saudi Arabia) ,
    A universal ECG signal classification system using the wavelet transform, pp. 43-54

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    Abstract: The electrocardiograph (ECG) is one of the most successful medical diagnostic tools. The ECG can show, roughly speaking, all types of heart disordersthat appear as ECG signal arrhythmias or problems with the rate or rhythm of thehuman heartbeat. In this paper, a universal ECG signal arrhythmia classificationsystem is proposed. The proposed system is based on using the wavelet transformin two of its known forms, namely, the discrete wavelet transform (DWT) andthe wavelet packet transform (WPT), or a combination thereof. The purpose ofthe research reported herein is to find out a universal classification system; in thesense of providing a capability for simultaneous classification of all types of known heart arrhythmias. Three algorithms based on the wavelet transform are tested for different wavelet levels, wavelet functions, training and testing ratios, and elapsed times. We rank these algorithms according to the elapsed times needed for their processing over the whole loop of the eight different arrhythmia classes. This ranking nominates the WPT-based algorithm to be the most superior method among the competing methods. A different ranking according to successful recognition rates assigns priority instead to the method combining the WPT and the DWT.

  • [4] Qiao F.J., Li B., Gao, M.Q., Li J.J. (China)
    ECG signal classification based on adaptive multi-channel weighted neural network, pp. 55-72

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    Abstract: The intelligent diagnosis of cardiovascular diseases is a topic of great interest. Many electrocardiogram (ECG) recognition technologies have emerged, but most of them have low recognition accuracy and poor clinical application. To improve the accuracy of ECG classification, this paper proposes a multi-channel neural network framework. Concretely, a multi-channel feature extractor is constructed by using four types of filters, which are weighted according to their importance, as measured by kurtosis. A bidirectional long short-term memory (BLSTM) network structure based on attention mechanism is constructed, and the extracted features are taken as the input of the network, and the algorithm is optimized by attention mechanism. An experiment conducted on the MIT-BIH arrhythmia database shows that the proposed algorithm obtains excellent results, with 99.20 % specificity, 99.87 % sensitivity, and 99.89 % accuracy. Therefore, the algorithm is practical and effective in the clinical diagnosis of cardiovascular diseases.