Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of Super-Resolution Methods for Direction Finding of Broadband Signals by an Antenna Array

The possibilities of direction finding of broadband signals by an antenna array in the presence of interferences are considered on the basis of methods of super-resolution of Capone and MUSIC. Based on the known relationships describing the direction-finding patterns formed by the antenna array in the direction-finding of narrow-band signals, expressions were obtained in the article for optimal detection of broadband signals. To ensure the possibility of technical implementation of methods in excess of the resolution for the direction-finding of broadband signals, it is proposed in the article for the finite number of frequencies of the broadband signal spectrum to optimally form the direction-finding relief. For the remaining frequencies of the signal spectrum, approximate using various functions. The results of the simulation showed that the more frequencies of the signal spectrum in which the direction-finding relief is formed optimally and the higher the degree of the approximating function, the higher the accuracy of the direction-finding spectrum of the broadband signal. However, this also leads to an increase in hardware and time costs. The main regularities providing formation of the direction finding relief when receiving and processing of broadband signals are derived. Numerical simulation of the obtained regularities is carried out.
High Performance LQR Control of Modular Multilevel Converters with Simple Control Structure and Implementation

In this paper, a novel feedback controller concept for grid-connected Modular Multilevel Converters (MMC) is proposed. The controller is based on the Linear Quadratic Regulator (LQR) method and shows outstanding control performance since it is a multi-input multi-output (MIMO) controller. The proposed controller achieves efficient energy balancing control with high bandwidth, such that the required margin for the module capacitance value for dynamic control is reduced and an excellent transient behavior can be accomplished. Alternatively, compact MMC designs can be realized with a reduced capacitance value by sacrificing some transient performance. The implementation requires a relatively low computational effort, and the simple control structure enables a straightforward tuning and time-delay compensation.
Melanoma Detection Using an Objective System Based on Multiple Connected Neural Networks

Melanoma is a common form of skin cancer that dangerously affects many people around the world. Detection of melanoma with the naked eye by dermatologists may be subject to errors. Therefore, the implementation of image processing devices equipped with artificial intelligence can act as a support for the dermatologist in examination and decision making. However, due to the various characteristics of this type of lesions and the presence of noises and artifacts in the images, it is difficult to distinguish melanomas from benign lesions. In this article, we propose a new type of intelligent system which is based on several neural networks connected on two levels of classification. The first level contains five classifiers (subjective classifiers): the perceptron coupled with color local binary patterns, the perceptron coupled with color histograms of oriented gradients, the generative adversarial network (for segmentation) coupled with ABCD rule, the ResNet, and the AlexNet. They are chosen experimentally and consider the following features of melanomas: texture, shape, color, size, and convolutional pixel connections. At the second level (objective level), one classifier (perceptron-type) decides whether the lesion is a melanoma, based on learning-adjusted weight and the decisions at the first level. The second level is based on back-propagation perceptron that provides the final decision (melanoma or non-melanoma). The subjective and objective levels undergo two separate training phases. This approach allows an easier transition of the system from one database to another. This study shows that the use of the objective classifier brings an accuracy of 97.5% and an $F1$ score of 97.47%. These results are better than those of the individual classifier and those of the previous literature mentioned in References.
Improved Multi-Camera Coverage Control of Unmanned Multirotors

Unmanned aerial vehicles (UAVs) are becoming one of the most versatile robots working with various sensors such as camera, Lidar and GPS. This trend comes from the numerous studies in the network of UAVs and the control domain. Currently, coverage control, the control of UAVs to cover a given region, is an important problem in the study of multi-UAV networks. In this paper, an existing coverage control algorithm [17] has been significantly improved by maximising the sensing field of view (SFOV) over the area of interest, making it much more efficient. The camera used in each UAV allows pan-tilt-zoom (PTZ) in the bottom hemisphere of the UAV. We propose two network coverage algorithms for multiple UAVs to make this improvement: one is the Voronoi based coverage control, and the other one is the constrained optimization coverage algorithm. Finally, the proposed schemes are tested in simulation and a real outdoor environment.
Pitch-invariant Speech Features Extraction for Voice Activity Detection

A new method for extracting the characteristic features of a speech signal that can be used in machine learning models to solve speech processing problems is proposed. The method is based on pitch-invariant convolutions on frequency axis of amplitude spectrum. On the example of voice activity detection, it is shown that the use of the proposed features makes it possible to get rid of excessive information content in the input data due to pitch invariance, which can significantly simplify neural network models, as well as reduce the amount of data needed for training. The proposed voice activity detection model is robust to a wide range of noises. The comparison with the publicly available voice activity detection model from the WebRTC showed higher F1 scores (0.94 vs 0.87).
Age-related difference in use-dependent plasticity after divergent thinking session matches posterior-anterior shift in aging (PASA) model

Repetitive cognitive activity has the potential to improve cognitive functioning through neuroplasticity. Despite evidence for task-specific traces after task performance in young adults, age differences of experience-related neuroplasticity remains understudied. Common patterns of age-related changes in brain activity across a variety of cognitive functions suggest the hemispheric asymmetry reduction in older adults (HAROLD) and the posterior-to-anterior shift in aging (PASA). We can expect that those models appear as age specificity of experience-related neuroplasticity. The aim of the current study was to investigate the age-related specificity in use-dependent changes between pre-training and post-training baseline alpha EEG rhythm measures. 31 younger (Mean age = 21.3) and 30 older adults (Mean age=64.2) underwent a divergent thinking training session with concomitant 52-channel EEG registration. Upper alpha power was calculated via Fourier transform; current source density estimates and statistical nonparametric mapping were calculated via LORETA. Alpha power increased from baseline to post-session interval in the both age groups, indicating use-dependent plasticity. The anterior-posterior gradient (posterior>anterior) of alpha power increases from baseline to post-session interval in the left hemisphere was more pronounced in younger adults, than in older. Use-dependent plasticity has the same pattern of age differences as PASA postulates, that is, decrease in posterior coupled with increase in anterior areas. The results emphasize that PASA model reflects a global age-associated shift in brain function.
Analysis of the impact of manufacturing dissymmetry on current distribution for magnetically coupled interleaved inverters

The impact of realistic cases of manufacturing dissymmetry on the distribution of the leg currents is presented for magnetically coupled interleaved inverters. The study of the parametric variations of the passive elements constituting the equivalent electrical circuit is carried out with an open loop control strategy. This parametric variation is made in two stages. The first deals with simple cases of dissymmetry that only affect one ICT. The second is based on the worst-case statistics, formed by multiple cases of dissymmetry on several ICTs. This study allows the understanding of the evolution of the currents in the cases of dissymmetry to ensure their control and their balancing. Then, these dissymmetries are simulated with a closed loop control strategy in order to test their impacts on the duty cycles.
The IEEE 754-2019 Compatibility of the Binary Coded Chiliad (BCC) Encoding

The IEEE 754-2019 standard for decimal floating-point arithmetic includes the densely packed decimal (DPD) encoding for secondary and primary storage of three binary coded decimal (BCD) digits. Equi-efficient packing can be achieved via binary coded chiliad (BCC) representation of 3 BCD digits (i.e., radix-1000). The main advantage is that arithmetic operations can directly manipulate BCC operands, while DPD operands need to undergo DPD-to-BCD and reverse conversions afore and ahead of each arithmetic operation. Hence, following previous BCC works, we are interested to design the arithmetic unit that receives BCC operands and yield BCC results. In this paper, we show the IEEE 754-2019 compatibility of the BCC Encoding.
Bidirectional Isolated Ripple Cancel Triple Active Bridge DC-DC Converter

To achieve a highly reliable DC distribution microgrid network, a bidirectional isolated ripple cancel triple active bridge (TAB) converter is proposed in this paper. In a conventional full-bridge TAB converter, the DC-link capacitors suffer from high ripple current, which significantly reduces their lifetime. To solve this issue, the proposed converter can reduce the ripple current to nearly zero by adding the clamping capacitors as an internal ripple-cancellation circuit topology. In addition, the proposed converter inherits the advantages of the conventional full-bridge TAB converter such as a wide range of soft switching and bidirectional power conversion. This makes it easier to replace the conventional full-bridge TAB converters. Finally, a 1 kW prototype was built to demonstrate its feasibility. In the experiment, the ripple current reduced to nearly zero (0.16 A) under 400 V/400 V/400 V and rated power 1 kW operating conditions. The proposed converter could be used to make highly reliable and efficient DC distribution microgrid as a power router.
Multiple Regression Analysis of Basal Metabolic Rate Using Dataset of 50 Adults at Federal Medical Center, Otuoke Outreach

This data analysis aimed at investigating Basal Metabolic Rate (BMR) of patients around Otuoke region, in Ogbia Local Government Area, and the data were collated at Federal Medical Centre, Otuoke Outreach. The data collated involving 50 patients, of which, 25 are males and 25 female volunteers of different ages. The variables involved in this analysis include age, gender and basal metabolic index, using SPSS version 25. Descriptive analysis was carried out to summarize the data in terms of mean and standard deviation of the gender and age. Biserial correlation was carried out on gender, age and BMR, and Cohen standard was done to investigate the strength of the relationship between the variables. The results of the analysis showed a negative correlation between gender and BMR with a correlation coefficient of -0.70, indicating a large effect size. In addition, it is seen that the linear regression model is significant, F(2,47) = 25.09, p<0.001, and Rsq = 0.52, indicating 52% variance in BMR. The result goes further to reveal that a unit increase in age doesn’t cause an effect on BMR. However, the female Review Article Ligeiaziba et al.; AJPAS, 8(4): 61-66, 2020; Article no.AJPAS. 60278 62 category can significantly predict BMR, B = -267.10, t(47) = -7.06, p<0.001. Based on this sample, this suggests that moving from the Male to Female category of Gender will decrease the mean value of BMR by 267.10 units on average.
Comparison of deep neural networks for reference evapotranspiration prediction using minimal meteorological data

Evapotranspiration, the process of water loss through surface evaporation and plant transpiration, is hugely influenced by numerous meteorological variables in different climatic zones. A precise and accurate prediction of reference-evapotranspiration (ET0), a crucial factor in the crop evapotranspiration estimation, facilitates efficient management of agricultural water supplies. This paper proposes an ET0 prediction method, employing minimal meteorological data, as well as exploring the potential of deep learning to learn the time series data pattern. In this study, four variants of a deep neural network model were developed using different feature combinations of two datasets of California Irrigation Management Information System (CIMIS) weather stations in California, USA for ET0 modeling and evaluated their predictive performance. The results showed that among the four deep neural network model variants (DnnV1, DnnV2, DnnV3, and DnnV6), the two input deep neural network, DnnV2 (RMSE =0.36 Millimeter/day and 0.52 Millimeter/day, R2=0.94 and 0.94) showed a comparable performance to the six input neural network, DnnV6 (RMSE =0.3 Millimeter/day and 0.43 Millimeter/day, R2=0.96 and 0.96). However, by considering the minimalism factor in the selection of input variables, we recommend DnnV2 for ET0 modeling.
Simulation of Semiconductor Converter for Electromagnetic Separator

The electromagnetic separator for removing magnetic particles from liquid materials is considered in the paper. The model of semiconductor converter for electromagnetic separator is developed in MATLAB Simulink. The converter load parameters (active resistance and inductance of magnetizing coils) are defined. Inductance of the coils is calculated in the Elcut application. The main parameters of the power source and its elements are defined via the rectifier simulation. Using the obtained results the experimental laboratory prototype of the electromagnetic separator is developed and assembled. To evaluate the efficiency of the developed separator, its magnetic system was calculated by the finite element method. The maximum magnetic induction in the concentrator, according to calculations, occurs above the pole piece with a magnetizing coil, the minimum is between the two cores of the electromagnet.
Shedding and restoration algorithms for an EV charging station to maximize available power

Due to its green and environmentally friendly characteristics, the number of electric vehicles continues to increase, which leads to a greater demand for power, therefore, how to meet driver needs while maintaining electric vehicle charging station power balance is the major problem needed to be solved. This paper presents shedding and restoration optimization algorithms for an electric vehicle charging station based on a DC microgrid to maximize the utilization of available power to meet driver needs, meanwhile, taking into consideration the intermittency of the photovoltaic source, the capacity limitation of the storage, and the power limitation of the public grid. The simulation results show that compared with rule-based algorithm, the proposed EV shedding and restoration optimization algorithms respect the user's choice while reducing total charging time, increasing the full rate, and maximizing the available power utilization, which shows the feasibility and effectiveness of EV shedding and restoration optimization algorithms.
A Method of Motor Imagery EEG Recognition Based on CNN-ELM

It is the key of brain-computer interface technology to extract electroencephalogram (EEG) data features effectively and classify them accurately. In view of the characteristics of non-stationarity and obvious time-frequency characteristics of motor imagery EEG signals, this paper proposes a method for recognition of motor imagery EEG signals based on S-transform time-frequency image combined with convolutional neural network (CNN) and extreme learning machine (ELM). In the BCI competition dataset, firstly, the S-transform time-frequency image of C3 and C4 electrode signals is obtained, and then the characteristic frequency bands are extracted from the time-frequency image for combination. Finally, the combined image is used as the input of neural network to realize the recognition of left-right hand motor imagery EEG signals. Experimental results show that this method is superior to the ordinary convolutional neural network.
Research on Data Augmentation for Object Detection Based on X- ray Security Inspection Picture

The deep learning model has a large number of parameters that need to be adjusted during the training process, so training a model that can perform well in object detection requires a large amount of valid data. And the accuracy and generalization ability of the model are guaranteed by both quantity and diversity of data. However, it’s a huge expense to get pictures and then annotate them, and we can hardly ever get all types of data that exist in reality. Apparently, data augmentation is an effective way to solve this problem. In our paper, the data set we use is composed of x-ray pictures come from subway security inspection. Based on the characteristics of x- ray pictures, we conducted experiments on a variety of augmentation methods, aimed at finding suitable data augmentation methods for this data set. After the experiment, it is concluded that the augmentation method that conforms to the actual form of the data set is more effective. Compared with the monotonous data augmentation, the hybrid way can be more effective, which can improve the accuracy and generalization ability of the model better. Meanwhile the complexity of backbone can also affect data augmentation, more complex backbone can be more sensitive to data augmentation.
Towards a Deep Attention-Based Sequential Recommender System

With the availability of a large amount of user-generated online data, discovering users’ sequential behaviour has become an integral part of a Sequential Recommender System (SRS). Combining the recent observed items (i.e., short-term preferences) with prior interacted items (i.e., long-term preferences) has gained increasing attention in recent years. However, the existing methods mostly assume that all the adjacent items in a sequence are highly dependent, which may not be practical in real-world scenarios due to the uncertainty of customers’ shopping behaviours. A user-item interaction sequence may contain some irrelevant items which may in turn lead to false dependencies between items. Moreover, current studies usually assign a static representation to each item when modeling a user’s long-term preferences. Therefore, they cannot differentiate the contributions of the items. Specifically, these two types of users’ preferences have been separately modeled and then linearly combined, which may fail to model complicated user-item interactions. In order to overcome the above mentioned problems, we propose a novel Deep Attention-based Sequential (DAS) model. DAS consists of three different blocks, <inline-formula> <tex-math notation="LaTeX">$(i)$ </tex-math></inline-formula> <italic>an embedding block:</italic> which embeds users and items into low-dimensional spaces; <inline-formula> <tex-math notation="LaTeX">$(ii)$ </tex-math></inline-formula> <italic>an attention block:</italic> which aims to discriminatively learn dependencies among items in both users’ long-term and short-term item sets; and <inline-formula> <tex-math notation="LaTeX">$(iii)$ </tex-math></inline-formula> <italic>a fully-connected block</italic>: which first learns a mixture of users’ preferences representation through a nonlinear way and then combines it with users’ embeddings to have a personalized recommendation. Extensive experiments demonstrate the superiority of our proposed model compared to the state-of-the-art approaches in SRSs.
Flight Controller Optimization of Unmanned Aerial Vehicles using a Particle Swarm Algorithm

In this paper, a simultaneous calibration algorithm of the parameters of the attitude and altitude control for an unmanned aerial vehicle (UAV) is proposed. The algorithm is based on the multi-objective particle swarm optimization (MOPSO) technique. This algorithm is implemented by using the free PX4 software for the Pixhawk2 controller. The behavior of the UAV is simulated given its physical characteristics by means of a non-linear model and a search of the controller parameters. This latter is based on a proportional (P) position controller in cascade with a proportional-integral-derivative (PID) speed controller of its height and each one of its Euler angles. To perform this search, the PID gains Kp1, Kp2, Ki and Kd of each of the degrees of freedom are used to define vectors considered particle positions by the MOPSO algorithm, which moves them through a search space to find sets of optimum values according to Pareto, or the Pareto Front. The search is carried out based exclusively on Pareto dominance concepts, comparing parameters of step responses (overshoot, rise time, root-mean-square error) of each of the degrees of freedom. In order to show the efficiency of the proposal, simulation results are provided by using the calibration methodology obtaining good results.
A PNN based Malign Attack Detection and Classification Model

For manipulation of communication, a Command and Control (C2) server is used by various attackers. Further, a domain generation algorithm is employed by threat actors for performing an attack as it enables the communication of malware with C2 by generation of various network locations. The existing method of malware control for instant blacklisting is not much efficient in handling the DGA threats. In this paper, considered to be as optimization enhancement and an evaluation study among normal classifier with the 3 level-discrete traveler transformation PCA. Gray Co-matrix characteristics in malware determine, proposing the novel method with 3level-discrete wavelet transformation-PCA. Gray Co-matrix characteristics in malware determine. Even if the determined malware is malign or not is categorize by using ANN (In ANN, the probabilistic neural network [PNN] is used). The malign type (either it belongs to Spoofing or Spamming or Denial of service) is also categorized along with the URL.
Dynamics Characterization of a U-Shaped Micro-Resonator Portal Frame

We present a study of the natural (resonance) frequencies of a tunable in-plane Microelectromechanical portal frame (U-shape) under DC electrostatic loads. The structure is designed and fabricated to excite the in-plane anti-symmetric (sway, first) and the symmetric (second) modes. Experimental results show high tunability of the resonance frequencies of both modes due to the electrostatic forces. Finite element simulations show good agreement with the experimental measurements. Further simulations are presented for the tunability of the natural frequencies of other higher-order modes using various electrode configurations. Frequency-response curves are presented for pure AC actuation of the first two modes showing strong nonlinear softening behavior due to the geometric nonlinearities of the portal frame. [2020-0269]