Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Multi-Class MI Based BCI Scheme Using Statistical Fusion Techniques of Classifiers

An efficient implementation of a multi-class motor imagery (MI) brain computer interface (BCI) classification scheme is presented in this work. The proposed method uses the common spatial pattern (CSP) and filter bank CSP (FBCSP) algorithms, with both one versus all (OVA) and one versus one (OVO) approach for multi-class extension. Mutual information (MInf) based feature selection algorithm has been used to obtain the features to train different linear discriminant analysis (LDA) classifiers. To improve the performance, the outputs of these classifiers are combined using two statistical methods: the mode of the OVA and OVO classifiers, and the more sophisticated Dempster-Shafer (DS) theory. The method has been evaluated on the 4-class MI dataset (BCI competition IV 2a), and the results showed that it has outperformed the winner of the competition (maximum kappa value of 0.593 vs 0.569). The proposed method proved the benefits of combining classifiers with appropriate techniques.
Effect of NO2 on O3 Using Complex Network Topology Analysis

In order to study the variation law of the influence degree of air quality factors under time series, this article selects the hourly concentration values of NO2 and O3 from November 2017 to October 2018 in Lanzhou city as the research object, and based on the coarse granulation method, NO2 and O3. The correlation value of hourly concentration value is transformed into the influence degree symbol, and the sequence of effect degree variation under time series is obtained. The network model is constructed by using complex network theory to analyze the time series of NO2 and O3 influence degree series, and the differences and changes in network topology characteristics in overall and different season effect degree symbols of Lanzhou are analyzed. The results show the following. First, from the network analysis, the number of negative correlations of NO2 to O3 is far greater than that of positive correlation and basic noncorrelation effect degree, indicating that NO2 and O3 are negatively correlated. From the factor of season, the law of negative correlation is summer > autumn > spring > winter, indicating that light and temperature are the main factors for the conversion of NO2 to O3. Second, through the analysis of Lanzhou’s overall and seasonal related network topological features, such as node degree distribution, node intensity distribution, and network betweenness, the change sequence consisting of the influence degree symbols h, H, and m plays an important role in each network. The sequence of changes consisting of M, H, and S has the lowest effect in each network, indicating that in the day of the time series, the conversion of NO2 to O3 in Lanzhou is mainly dominated by different degrees of negative correlation. Third, through the analysis of network cluster degree, it is found that most of the changes in the degree of change have a transformation and a high degree of transformation, indicating that the effect of NO2 on O3 in the original sequence is less stable. Fourth, through the network average shortest path and diameter analysis, it is found that the conversion between the changes in the degree of influence requires at least one day and at most three days. That is to say, the influence of NO2 on O3 can be maintained for up to three days. From the network, the complexity of the influence of NO2 on O3 in the original sequence of four seasons was founded.
CMOS Transceiver Pixels for Terahertz Imaging

Pixels integrating a transmitter and a coherent receiver in an area of half-wavelength square are proposed for terahertz active imaging. 300- and 427-GHz transceiver pixels are demonstrated using a 65-nm CMOS process. The 300-GHz pixel employs a push-push stacked cross-coupled VCO, which doubles as a transmitter and an LO, and a pair of diode-connected NMOS transistors for mixing. The pixel occupies an area of 450×580µm2, and provides −21.3-dBm total radiated power and −79.5-dBm sensitivity for a 1-kHz noise bandwidth at 260GHz. The 426-GHz pixel using a star-type triple-push Colpitts oscillator works as a transmitter and a self-oscillating mixer. The pixel occupies an area of 380×470µm2. The radiated power is −17.9 dBm and the sensitivity is −89.6 dBm for a 1-kHz noise bandwidth. Link-budget analyses suggest that camera-like reflection-mode imaging of targets 5m away should be possible using the pixel and a 4.6cm×4.6cm reflector.
Characterizing Leveraged Stack Overflow Posts

Stack Overflow is the most popular question and answer website on computer programming with more than 2.5M users, 16M questions, and a new answer posted, on average, every five seconds. This wide availability of data led researchers to develop techniques to mine Stack Overflow posts. The aim is to find and recommend posts with information useful to developers. However, and not surprisingly, not every Stack Overflow post is useful from a developer's perspective. We empirically investigate what the characteristics of "useful" Stack Overflow posts are. The underlying assumption of our study is that posts that were used (referenced in the source code) in the past by developers are likely to be useful. We refer to these posts as leveraged posts. We study the characteristics of leveraged posts as opposed to the non-leveraged ones, focusing on community aspects (e.g., the reputation of the user who authored the post), the quality of the included code snippets (e.g., complexity), and the quality of the post's textual content (e.g., readability). Then, we use these features to build a prediction model to automatically identify posts that are likely to be leveraged by developers. Results of the study indicate that post meta-data (e.g., the number of comments received by the answer) is particularly useful to predict whether it has been leveraged or not, whereas code readability appears to be less useful. A classifier can classify leveraged posts with a precision of 65% and recall of 49% and non-leveraged ones with a precision of 95% and recall of 97%. This opens the road towards an automatic identification of "high-quality content" in Stack Overflow.
The Method of Transfer of Research Project Results of Institution of Higher Education

It was found the problem of the lack of implementation of a large part of the products obtained of research projects, causes the presence of unjustified cost for conduct scientific activity. The establishment of a technology transfer at the institution of higher education will allow the realize attractive to particular customer, the product features of the research project and use them for other research projects. Proved the feasibility of creating in institutions of higher education departments of technology transfer will allow the introduction of the innovative decisions in the economy. Developed a method of determining the competitive products features of research projects based on the use of functional cost analysis with the aim of technology transfer for their implementation, the patenting and commercialization. When this balance of the five existing product features of the research projects turned out to be two functions which are suitable for further technologies transfer. A mechanism of encouraging participants in research projects at the expense of, derived from technology transfer allows to increase the efficiency of research work of institution of higher education, namely Odessa National Maritime University.
Study of Induced EHD Flow by Microplasma Vortex Generator

For flow control, plasma actuators have the advantages of no moving parts. An experimental study was carried out to generate vortexes using a microplasma actuator. Also, a 3-D numerical simulation code was developed to calculate the flow generated by the microplasma actuator. The numerical simulation used the Suzen–Huang model coupled with Navier–Stokes equations. Our microplasma actuator has a thin dielectric layer with a thickness of $25~\mu \text{m}$ between the grounded and high-voltage energized electrodes, which enables to drive our device at less than 1 kV. The high-voltage and grounded electrodes have both holes. In the series of experiments, an ac voltage with an amplitude 1 kV and a frequency of 20 kHz was applied to the electrode. The induced flow was visualized using an Nd: YVO4 532-nm laser, and the flow velocity was measured using the particle tracking velocimetry (PTV) method. Incense smoke was utilized as a tracer particle. The electrohydrodynamic (EHD) flow was induced around the holes of high-voltage electrode, thus vortexes appeared above these holes. In order to study the basic phenomena of the flow, one and four holes were isolated from the electrode; thus, the phenomena could be observed in a simplified version of the electrode. The 3-D numerical simulation code showed similar results both in values and flow configuration compared with the experimental results.
Design of a Three-cavity Dual-mode Bandpass SIW Filter

A three-cavity dual-mode bandpass substrate integrated waveguide (SIW) filter is proposed in this paper. Two dual-mode isosceles triangle cavities with one port on the waist, the other port on the base and a dual-mode isosceles triangle cavity with two ports on the waist of the triangle cavities upper surface are used as basic elements. By cascading three basic elements and adding metallic via-holes between coupling windows, a compact and more wider bandpass three-cavity dual-mode SIW filter is realized. Additionally, S parameter and Z parameter fit in with theoretical analysis. Finally, a 10.11% fractional bandwidth filter is achieved.
AyurLeaf: A Deep Learning Approach for Classification of Medicinal Plants

Ayurvedic medicines have a vital role in preserving physical and mental health of human beings. Identification and classification of medicinal plants are essential for better treatment. Lack of experts in this field makes proper identification and classification of medicinal plants a tedious task. Hence, a fully automated system for medicinal plant classification is highly desirable. This work proposes AyurLeaf, a Deep Learning based Convolutional Neural Network (CNN) model, to classify medicinal plants using leaf features such as shape, size, color, texture etc. This research work also proposes a standard dataset for medicinal plants, commonly seen in various regions of Kerala, the state on southwestern coast of India. The proposed dataset contains leaf samples from 40 medicinal plants. A deep neural network inspired from Alexnet is utilised for the efficient feature extraction from the dataset. Finally, the classification is performed using Softmax and SVM classifiers. Our model, upon 5-cross validation, achieved a classification accuracy of 96.76% on AyurLeaf dataset. AyurLeaf helps us to preserve the traditional medicinal knowledge carried by our ancestors and provides an easy way to identify and classify medicinal plants.
A 60GHz Compact Wideband Differential Hybrid Based on Folded Transformer

This paper presents a 60 GHz wideband differential lumped-element hybrid based on folded transformer. With a 65nm CMOS process, the hybrid utilizes a coplanar folded transformer to achieve an enhanced coupling factor, resulting in competitive results with low loss, differential wideband operation, and compact size at 60 GHz band. Simulations demonstrate a maximum differential quadrature outputs phase error within ± 2° from 42 to 85 GHz, maximum magnitude mismatch within ± 1 dB from 48 to 76 GHz, and a worst-case image rejection ratio of 30 dB from 52 to 73 GHz, a passive loss of 2.28 dB at 60 GHz. This hybrid occupies a core area of 0.015 mm2.
Estimation of residual life-time of pumping units of electric power stations

With long-term operation of pumping and other equipment, there is often a need to extend the life and assess the residual life. Modern life-time assessment methods are based on three-dimensional modelling and the finite element method. Within the framework of these methods, geometric models are constructed that take into account the thinning of the walls of the pumps as a result of operation. By constructing geometric models, finite element models are constructed. These models are made taking into account the conditions of loading of the structure, tightening of bolted connections, technological and temperature loads. The constructed models allow to assess the strength of the pump casing and the main bolted connections during normal operation and gyro testing. The paper assesses the cyclic strength of these structural elements. The results of the work confirmed a sufficient residual life-time for safety pump operation in the next 15 years.
Beamspace Precoding and Beam Selection for Wideband Millimeter-Wave MIMO Relying on Lens Antenna Arrays

Millimeter-wave (mmWave) multiple-input multiple-out (MIMO) systems relying on lens antenna arrays are capable of achieving a high antenna-gain at a considerably reduced number of radio frequency (RF) chains via beam selection. However, the traditional beam selection network suffers from significant performance loss in wideband systems due to the effect of beam squint. In this paper, we propose a phase shifter-aided beam selection network, which enables a single RF chain to support multiple focused-energy beams, for mitigating the beam squint in wideband mmWave MIMO systems. Based on this architecture, we additionally design an efficient transmit precoder (TPC) for maximizing the achievable sum-rate, which is composed of beam selection and beamspace precoding. Specifically, we decouple the design problems of beamspace precoding and beam selection by exploiting the fact that the beam selection matrix has a limited number of candidates. For the beamspace precoding design, we propose a successive interference cancellation (SIC)-based method, which decomposes the associated optimization problem into a series of subproblems and solves them successively. For the beam selection design, we propose an energy-max beam selection method for avoiding the high complexity of exhaustive search, and derive the number of required beams for striking an attractive trade-off between the hardware cost and system performance. Our simulation results show that the proposed beamspace precoding and beam selection methods achieve both a higher sum-rate and a higher energy efficiency than its conventional counterparts.
Dental Biometric Identification System using AR Model

Dental biometry has a leading role in forensic human identification. Identifying a person in mass disasters and major catastrophes, which have frequently happened due to airplanes crashes, tsunamis, fire accidents, etc is a challenging problem if conventional biometrics, e.g., face, fingerprint, iris, etc. is not available. Dental characteristics of persons are naturally unique and can be used to identify individuals based on their dental radiographs. In this paper, we present a new and workable method for identifying humans, that extracts the dental mandibular information from panoramic dental radiographs which is used as a biometric identifier. The system segments the mandible from dental panoramic X-ray images to obtain the outer mandibular contour coordinates. Time series is then obtained from the extracted contour coordinates which gives the structural information of the mandible. AR model is then fitted to this time series and the AR coefficients thus obtained form the feature vector representing each mandible. These feature vectors are later used for matching and identification using the Euclidian distance classification criteria. In order to assess the proposed system, we have carried out experiments at three different orders of AR model using a database of 120 ante-mortem and 90 post mortem panoramic dental images, obtained from 30 individuals. The experimental results show that the proposed system is effective in identifying individuals and exhibits better results at order 3 of AR model with a Recognition rate (RR) up to 75.52%, low Equal error rate (EER) of 23% and a rank-1 identification rate of 76.66%.
Development and Operations - the Modern Paradigm of the Work of IT Project Teams

It is formed the concept of DevOps as an innovative paradigm for the formation and operation of teams for the IT project implementation. Such an approach facilitates the rapid and reliable creation, testing and implementation of advanced information and technological products for industrial exploitation and it is based on building the productive cooperation between all members of the project team. The benefits of this technology are the implementation of the concept of shared responsibility, transparency and faster feedback as well as it is a reliable basis for the operation of the DevOps highperformance team. The peculiarities of the intellectual system development of higher education institution management at Lviv Polytechnic National University have been analyzed, based on the application of common scientific and methodological principles and several requirements.
Electric load demand forecasting with RNN cell generated by DARTS

Neural Architecture Search (NAS) had produced highly competitive results in generating neural architectures for many deep learning applications, some of them achieving state of the art performances. Even though there are many Recurrent Neural Network (RNN) variations like Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), etc., available for electric load demand forecasting, finding an optimal internal structure of RNN is still of much interest. This work uses one of the NAS algorithms - Differentiable Architecture Search (DARTS) to generate a new RNN cell optimum for electric load demand forecasting. The generated RNN cell is used to construct models of different complexity - from single cell model to multi-layer models obtained by stacking these RNN cells appropriately. These models are compared with other popular RNN models, and the results establish the advantage of customizing the internal RNN structure over the general RNN variants.
Automatically Curated Data Sets

o validate hypotheses and tools that depend on the semantics of software, it is necessary to assemble, prepare and maintain (i.e. curate) large, high-quality corpora of executable software systems exhibiting certain desired behavior and/or properties. Today this is a highly tedious and laborious activity requiring significant human time and effort. In this paper we therefore present a prototype platform that supports the notion of “live data sets” where almost all aspects of the data set curation process are automated. Instead of curating data sets by hand, or writing dedicated tools to select and check software samples on a case-by-case basis, a live data set allows users to simply describe their requirements as abstract scripts written in a declarative domain specific language. After explaining the approach and the key ideas behind its implementation, in this paper we present two examples of executable corpora generated automatically from a live data set populated from Maven Central. The first illustrates a “semantics agnostic” use case where the actual behavior of the software is unimportant, while the second illustrates a “semantics specific” use case where software implementing a specific functional abstraction is selected.
Robust Incremental Nonlinear Channel Estimation based on Spline Adaptive Filtering

Linear modelling of the communication channel is an oversimplified assumption, as there may be nonlinear distortions in the transmitted symbol. Spline adaptive filtering based on least mean square criteria (SAF-LMS) is used by researchers to estimate the nonlinear system in additive white Gaussian noise (AWGN). However, the failure of SAF-LMS in an impulsive noise environment prompts us to research further on robust cost functions. Hence a robust spline adaptive algorithm is proposed to estimate the nonlinear channel. Robustness comes with a toll on the convergence rate. To make the algorithm energy-efficient, distributed technique with an incremental way of co-operation, where multiple nodes are connected in a ring topology, is incorporated to estimate the channel. The simulation results show the robustness of the algorithm against impulsive noise.
Deep Fade Event's Probability Performance Analysis of Non-Orthogonal Multiple Access Scheme Realization in Power Domain

In this paper, the author investigates the deep fade event probability performance of non-orthogonal multiple access (NOMA). For the analysis, the author considers the downlink of a mobile wireless network scenario wherein the users assumes random locations. The author develops an insightful analysis which comprises of analytical results for deep fade event probability. Specifically, the author derives a tractable closed-form upper bound on deep fade probability in a more practical and insightful scenario in which a user is probabilistically active. To numerically evaluate the performance of insightful analytical results, the author plots the deep fade probability for various simulation parameters. From the numerical plots, the author observes that the user's deep fade event probability improves as the number of users increases. Finally, the author suggests a few adaptive techniques to improve deep fade probability performance.
Lung Nodule Detection from low dose CT scan using Optimization on Intel Xeon and Core processors with Intel Distribution of OpenVINO Toolkit

With the advancement of AI in the field of medical imaging, medical diagnosis is getting faster and viable for medical practitioners especially for cancer diagnosis. Earlier Deep Learning solutions had to be deployed on High Performance Computing devices like GPU for achieving real time performance. But with Optimization on Intel Core and Xeon processors with Intel Distribution of OpenVINO Toolkit (Open Visual Inference and Neural Network Optimization), it is possible to deploy Deep Learning models with accelerated performance, than running Tensorflow / Caffe models on CPU machines. In this paper we describe the proposed work wherein we ported our DetectNet Deep Learning Model with NVIDIA specific custom layer for lung nodule detection trained on LIDC dataset, using Intel Distribution of OpenVINO, and deployed the same in Intel Core/Xeon processors with accelerated performance.
Design of a Broadband SIW based Shunt Slot Array Antenna for X-band Applications

In this paper, a new approach of substrate integrated waveguide (SIW) based array antenna with wide bandwidth is presented. Each element of this proposed antenna array consists of a shunt slot and a post placed oppositely offset from center line in a SIW. Shunt slots radiate electromagnetic energy in upper hemisphere. Post in each antenna array element is used to achieve enhanced impedance matching. So by introducing inductive post with shunt slot results wider impedance bandwidth of the antenna array. The proposed array antenna has impedance bandwidth of almost 1.2 GHz and cross-polarization level is 44 dB below. For the whole frequency band the array radiates almost along the broadside direction. The antenna array has been designed and analyzed using two commercially available electromagnetic simulators. Both the simulators give similar kind of results.
Multisurface Proximal SVM Based Decision Trees For Heart Disease Classification

This paper presents a Multisurface proximal SVM (MPSVM) based decision tree system, combined with ensemble methods of Random Forest and Gradient Boosting, for heart disease classification. Decision trees are very popular for classification tasks and normally use a set of axis-parallel decision boundaries to classify the data. The MPSVM based trees, used here, can learn decision boundaries of any orientation, and are, hence, more flexible in learning the data. The dataset used in this study is the Cleveland heart disease dataset, which is a 5-class classification problem, and has data of 303 patients, each with 13 diagnostic features. Our method is able to achieve state of the art results on the 5-class classification problem with a testing accuracy of 73.33% and on the 2-class classification problem with a testing accuracy of 91.53%.