Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Assessment of Software Defined Networks Management Protocol in Real Environments

Software Defined Networks (SDN) determine a new paradigm for the development of a computer network. Up to now, SDN has gained attention from both the academic and industry communities. So far, it has been focused on the OpenFlow standard, a network protocol that made this approach possible. However, SDNs extend beyond OpenFlow, opening new perspectives in terms of abstractions, environment controls, and network applications. Thus, this work proposes an SDN-based mechanism for network management in the context of the Internet of Things (IoT) using the OpenDayLight platform with a customized OpenFlow. For IoT, data collection and analysis is the point where the SDN provides the flexibility to include multiple scenarios. It is also discussed how OpenFlow and other proposals available in the literature contribute to the flexibilization of IoT networks. Experimental results showed the benefits that can be achieved with this architecture, including, energy savings, lower costs, and better traffic control. However, it is concluded that the OpenFlow protocol is not feasible for an IoT scenario due to the obtained high error message percentage while performing the management of an SDN network.
Counter Based Low Power, Low Latency Wallace Tree Multiplier Using GDI Technique for On-chip Digital Filter Applications

This paper represents a new design of a low power, low latency Wallace tree multiplier. Wallace Tree algorithm is one of the most commonly used operations in modern days DSP applications as it can provide a fast and area efficient strategy for higher operand multiplication. For higher bits of multiplications the addition operation of partial products includes greater delay and complexity. In this present communication a number of techniques are applied in the partial products addition circuitry to optimize the area delay and speed of the Wallace multiplier. Proposed Design is synthesized for $4\mathrm{x}4$ bit multiplication using standard CAD tool design compiler in 250nm process technology. Simulation results show that the proposed multiplier design has the best power and delay results as compared to other available multipliers.
A Vision-Based Human Action Recognition System for Companion Robots and Human Interaction

This paper presents a vision-based human action recognition system to support human interaction for companion robots. The system is divided into three parts: motion map construction, feature extraction, and human action classification. First, the Kinect 2.0 captures depth images and color images simultaneously using its depth sensor and RGB camera. Second, the information in the depth images and the color images are respectively used to three depth motion maps and a color motion map. These are then combined into one image to calculate the corresponding histogram of oriented gradient (HOG) features. Finally, a support vector machine (SVM) recognizes these HOG features as human actions. The proposed system can recognize eight kinds of human actions, wave left hand, wave right hand, holding left hand, holding right hand, hugging, bowing, walking, and punching. Three databases were used to test the proposed system: Database1 includes videos of adult actions, Database2 includes videos of child actions, and Database3 includes human action videos taken with a moving camera. The recognition accuracy rates of the three tests were 88.7%, 74.37%, and 51.25%, respectively. The experimental results show that the proposed system is efficient and robust.
A Space Vector Modulated Quasi-Z-Source Based Four-Level VSI for PV Application

A Four-level Inverter system is presented in this paper, which employs a Space Vector Modulated Quasi-Z-Source to obtain high-boost inversion for PV applications. Unlike the conventional Multilevel Inverters, such as Neutral-Point Clamped (NPC) and the Cascaded H-Bridge (CHB) configurations, the proposed inverter uses only 19 power semiconductor switching devices and three Quasi-Impedance networks. It is also shown that by employing level-shifted carrier modulation, one can achieve higher shoot through duty ratio, enhancing the boosting capability of the proposed inverter. The performance of the proposed power circuit is assessed with simulation studies, which reveal its applicability for PV applications.
Detection of Microcalcifications in Digitized Mammograms using Discrete Wavelet Transform and Hybridized Algorithm

Breast Cancer, is a type of cancer that originates in the breast tissue. For the earlier detection of breast cancer, mammography is considered as the best modality by finding malignant (cancerous) lesions, masses and microcalcifications (MC's) in the breast tissue. MC's are the calcium deposits in the breast tissues with no regular patterns, shape and size. They are often located across non-homogeneous backgrounds; their intensity could be similar to that of noise. So, it is difficult to detect in the naked eyes even by the experienced radiologists. To assist them, a fully automated Computer Aided Diagnosis (CAD) system has been proposed. In the proposed work, mammogram images are enhanced using Discrete Wavelet Transform (DWT) for denoising and localization. The spatial location range High-High (HH) from DWT is considered and represented as data vector. It is, then, segmented using Possibilistic Fuzzy C-Means clustering (PFCM) algorithm. PFCM clustering algorithm segments the image into normal tissues and MC's suspected regions by finding atypicality values of each pixel of data vector. Finally, features of the segments are extracted using window-based feature extraction method and given as input to the Multi-Layer Perceptron (MLP) classifier to classify the tissue as normal tissue or malignant tissue. As per the experiments and results of the proposed system, the accuracy is calculated as 96.15%.
The Impact of Number of Partitions on Transient Stability of Intentional Controlled Islanding

Intentional Controlled Islanding (ICI) is the last protective measure that must be adopted in power systems to prevent forming unstable islands with load-generation imbalance power. If conventional protective schemes fail to save the power system against wide-area blackout, some cutsets are selected to keep the system operating while feeding essential loads. This paper investigates the effect of the number of partitions on the dynamic performance of controlled islanding and proposes a unified algorithm to identify the coherent generators, optimal cutsets, and timing of islanding in online applications. The algorithm provides an islanding solution with minimum power flow disruption while each island contains coherent groups of generators. The concept of Centre of Inertia (COI) referred angles of generators has been used to determine the critical timing of islanding. Different scenarios in NE 39-bus and IEEE 118-bus test systems were simulated in PowerFactory that led to instability in the power system, and then controlled islanding schemes with a different number of partitions were applied to create stable islands. To better demonstrate the shortcomings of the existing clustering techniques, the effect of the number of required islands on transient stability of controlled islanding and the necessity of devising an automatic method to recognise the number of islands is discussed.
Research on Chinese Named Entity Recognition Based on Neural Network

Named entity recognition is a fundamental and important task in the field of natural language processing. The effect of existing solutions in practical applications do not meet need of people. Firstly, this paper studies the scheme of recurrent neural networks with conditional random fields. Further, a series of experiments were performed on the input word vector dimension, the number of neural nodes within the neural network, and the variant structure of neural networks. Finally, a method of combining neural network with attention mechanism is proposed. An attention layer is built over the output of the recurrent neural network and the F1 score reaches 91.28%.
A Monolithic I2V2-Controlled Dual-Phase LED Matrix Driver for Automotive Adaptive Driving Beam (ADB) Headlighting

Conventional LED matrix drivers for automotive Adaptive Driving Beam (ADB) headlighting drive a serial-connected LED matrix, hence resulting in two power-conversion stages, low power-efficiency, and large current ripples. This paper presents a monolithic parallel-connected LED matrix driver. It exhibits a high power-efficiency (94.8%) because of only one power-conversion stage, and achieves substantially small LED current-ripple factor (5%). Attributed to the proposed $I^{2} V^{2}$ controller, good current balance between dual phases, accurate LED current regulation and fast transient response are achieved.
ADALINE Based MPPT With Indirect Control Mode for Photovoltaic Systems

This paper presents a new adaptive neural control technique for maximum power point tracking (MPPT) in standalone photovoltaic (PV) system. The proposed strategy exploits the online-trained adaptive linear neuron (ADALINE). The proposal is based on the PV panel power characteristic to control its output voltage for reaching the maximum power point. For more reliability, the ADALINE based MPPT strategy is implemented with indirect control mode. Therefore, a proportional-integral controller is designed in order to generate a desired duty cycle. Efficiency and dynamic performances of the proposed MPPT methods are tested according to the European Standard EN 50530 Test. Experimental tests are conducted to prove the proposals efficiency. Comparisons with the conventional perturb & observe (P&O) algorithm is performed. Obtained results lead to conclude that the ADALINE based MPPT methods yields better performances than the conventional P&O algorithm, especially, in term of convergence speed and efficiency at low and fast irradiance profile.
Development and Validation of Information Literacy Assessment Tool for Primary Students

This study utilized a three-step approach for developing and validating an information literacy assessment tool for primary students. Firstly, 52 multiple-choice items were devised as the item pool based on the existing information literacy evaluation indicators system. After gathering feedbacks from the experts and teachers, 49 items were retained to form the pilot test items of the information literacy assessment tool. Then, pilot test was conducted among 323 students of 4th and 5th grades from four primary schools in a district of central China region. The analyses of item difficulty, item discrimination, Rasch model were employed to validate the information literacy assessment tool. Finally, 37 items were retained as reliable and valid to measure primary students' information literacy.
An Adaptive Descriptor for Functional Correspondence Between Non-Rigid 3D Shapes

In this paper, we propose an adaptive descriptor for functional correspondence between non-rigid 3D shapes. This descriptor is computed based on a norm between points in Global Point Signature (GPS) domain. We calculate the initial descriptors by utilizing initial correspondences estimating in GPS domain. Moreover, we present an iteration method for extending the initial descriptor based on candidate correspondences. Our method is implemented and benchmarked with others; the results shows that our paradigm improves overall matching canability,
The influence of fatigue on psychophysiological indicators during 24 hours testing of pilots

This study presents the issue of fatigue and its quantification by the means of psychophysiological indicators. For the purpose of the study, 24 hours experiment was proposed consisting of eight about an hour long simulated IFR flights. Eight students of the Department of Air Transport, Faculty of Transportation Sciences, Czech Technical University in Prague participated in the study, all with comparable theoretical and practical skills. During individual flights, ECG curve was recorded as a heart activity signal. From the ECG signal, individual RR intervals were extracted and subsequently an analysis in three areas was performed: time and frequency analysis and analysis by the means of Pointcaré plot. The acquired data were statistically evaluated and compared. Based on Friedman ANOVA and subsequent post-hoc analysis, statistically significant differences between first and sixth measurement were identified, which corresponded to the circadian low. It is also possible to claim that Pointcaré plot appears as a suitable means for fatigue quantification.
Affine and Magnitude Transforms Based Data Augmentation Comparison for SAR Automatic Target Recognition

In this paper, we investigate and compare multiple data augmentation techniques in image classification targeting SAR images. Previous research showed that simple data augmentation techniques are still proven to be effective such as Affine transformations, cropping, flipping and color inverting training images. We use the SDMS MSTAR Public dataset, apply data augmentation techniques once at a time and record the results for the same convoluted neural network. Traditional transformations are powerful, however there is still no clear way to choose among infinitely many ways to augment the training data. A comparative study of simple Affine and color based augmentation techniques for SAR images is proposed. Finally we discuss the reasons why some strategies are more successful than others for the case of radar images.
Two–Stage Segmentation of SAR Images Distorted by Additive Noise with Uncorrelated Samples

In this paper the algorithm of SAR images segmentation is investigated, which is developed on the basis of two-stage approach the essence of which is in conducting of one-dimensional processing independently along rows and columns followed by combining the obtained results and forming of segmented image. Analysis of the algorithm is performed using simulation while processing of test synthesized image.
Mobility of Polarons in Semiconductors

The polaron mobility of a semiconductor is studied on the basis of kinetic equation for polaron subsystem interacting with equilibrium phonon subsystem in the presence of small external electric field. The mobility is discussed with a connection to polaron subsystem velocity relaxation. Relaxation processes in the system are studied on the basis of spectral theory of the collision integral operator in the presence of the electric field. Contribution of the electric field to the velocity relaxation time equation is found in the simplex approximation comparing this equation with the Newton second law. The basic equations of the theory are solved by the method of truncated expansion in the Sonine polynomial series. The velocity relaxation coefficient and mobility of polaron are calculated in one-polynomial approximation. It is established that the nonequilibrium polaron distribution function differs from the Maxwell distribution with polaron velocity and temperature. Relation of the developed theory with the Bogolyubov method of the reduced description of nonequilibrium systems is discussed. For semiconductors of the groups AIIIBV velocity relaxation coefficient and mobility are found numerically.
Remote Hardware-In-the-Loop Measurement System for Electrolyser Characterization

The installation of facilities replicating the realworld condition is often required for carrying out meaningful tests on new devices and for collecting data with the aim to create realistic device model. However, these facilities require huge investments, as well as areas where they can be properly installed. In this paper, we present a test infrastructure exploiting the concept of Remote Power Hardware-In-the-Loop (RPHIL), applied for characterizing the performances of a 8kW Proton Exchange Membrane (PEM) electrolyser installed at the Hanze University of Applied Sciences in Groningen (The Netherlands). The electrolyser is subjected to different test conditions imposed both locally and remotely. The results show that this measurement procedure is effective and can open new perspectives in the way to share and exploit the existing research infrastructure in Europe.
The Quantum Cost, Garbage Outputs and Constant Input Optimized Implementation of 2:4 Decoder Using Peres Gate

This paper presents the quantum cost (QC), garbage output (GO) and constant input (CI) optimized 2:4 decoder using reversible logic gate. The proposed reversible circuit is design using existing reversible logic gate, such as reversible NOT gate, Feynman gate and Peres gate. The proposed decoder can be design using one Peres gate, one NOT gate and four Feynman gate. The QC, GO and CI of the proposed 2:4 decoder is 9, 0 and 2. Which is better w.r. t previously reported work. This is the first reversible 2:4 decoder circuit, which has ‘0’ garbage output. The improvement % of QC, GO and CI are 0 – 74.28 %, 100 % and 33.33 – 75 %.
Students' Academic Performance Prediction using Data Mining

Educational Data Mining has been an emerging topic nowadays due to the growth of educational data. This field makes it possible to develop methods in order to find out hidden patterns from educational data. The methods extracted from Educational Data Mining discipline are then used to understand students including their learning behavior as well as to predict their academic performance. This study proposes a model for predicting the academic performance of Computer Science students using Data Mining technique. The data were collected using questionnaires that contain the students' demographics, previous GPA, and family background information. Two Data Mining models (Decision Tree and Random Forest) are applied to the students' data to create the best student's academic performance prediction model. The result of this study shows that Decision Tree is the best model compared to Random Forest by receiving the highest accuracy value of 66.9%. This study shows that there are some relevant feature that influenced student's academic performance.
A Robust Algorithm for the Detection of Cloning Forgery

Today's era is of the digital images, what we think, imagined is created in the form of digital images. The forgery detection is technique which can detect the cloning from the images. The availability of different images, editing tools have made it easier for a common user to mislead the images and create digital fake images from it. The forgery detection has various techniques like active and passive techniques, moreover the properties like analysis, classification etc, are used to identify the forgery. The work presented in this paper is based on cloning forgery detection. The cloning forgery technique is one of the type of forgery detection techniques in which some area of the image is copied and moved to some another area of the same image which is not easy for the naked eyes to detect easily. The PCA (Principle Component Analysis) technique is used to find the mismatched pixels from the image. In the presented work, the GLCM (Grey Level Co-occurrence Matrix) technique is applied with the PCA for the forgery detection. The proposed work is implemented in MATLAB and results are analyzed in terms of PSNR, Recall, Precision and accuracy. In the presented work, the results are better due to less fault rate or able to recover two different attacks (Gaussian noise and motion blur). It is analyzed that the proposed algorithm performs well as compared to the existing algorithm. In existing method the results of their parameters like accuracy were less accurate then proposed method. Moreover, the proposed method achieves more accuracy precession and recall.
Aging Assessment of Distribution Transformers Based on Thermal Imaging

A novel method for aging assessment of distribution transformers is presented in this paper. Based on this method lifetime of transformers can be achieved without using traditional methods such as oil test, investigation the paper insulation, DGA(Dissolved Gas Analysis), DP (Degree of Polymerization) and etc. In proposed method only analysis of thermal imaging and some environmental parameters are used for transformers remaining lifetime determination. For this purpose, 193 transformers are selected in a real network and taken some thermal images from each side. Furthermore, humidity, ambient temperature and transformer load are considered. Moreover, to test the proposed methods, oil of 49 transformers were sampled, and their lifetime was estimated through conducting oil tests. A new algorithm based on Confidence Interval (CI) is proposed for achieving aim. The most important data are hot spot (HS) and cold spot (CS) temperatures of transformers body which are obtained from thermal images. Results show that the proposed procedure has high accuracy for determining of aging transformers.
A High Performance Consensus Algorithm for Consortium Blockchain

Blockchain is an emerging decentralized infrastructure and distributed computing paradigm. Blockchain has been widely used in many industries including finance, energy, and cloud computing. Consortium blockchain is known as the permissioned blockchain, in which each user needs to be authorized. Distributed consensus is the core issue of consortium blockchain technology. In order to solve the disadvantages of using Proof of Work (POW) algorithm in consortium blockchain, such as computing resources waste, long block confirmation delay and low throughput, we designed and implemented a high performance blockchain consensus algorithm based on message passing technology called HPBC. HPBC algorithm can work well in asynchronous networks with Byzantine nodes. Results from theoretical analysis and performance evaluation show that HPBC algorithm provides safe and reliable distributed consensus services in consortium blockchain with low overhead. Finally, we summarized HPBC algorithm and briefly explore its further research directions.
SWIPT-Based Three-Step Multiplicative Amplify-and-Forward Two-Way Relay Networks with Non-Linear Energy Conversion Model

Extending the battery life of energy-constrained sensor nodes is one of the most critical challenges in wireless sensor networks (WSN). In this paper, we evaluate the performance of a two-way multiplicative amplify-and-forward (AF) relay based energy harvesting enabled network with a non-linear energy conversion model. The proposed scheme considers an energy constrained network where two transceiver nodes transmit and receive their data via an intermediate multiplicative AF relay node. A Power Splitting (PS) based relaying protocol is used for energy harvesting (EH) and information processing at relay node. In order to perform a more practical and realistic analysis of the proposed scheme, a non-linear energy conversion model is used for EH at the relay node. Furthermore, for more detailed performance analysis of two different transmission modes namely; Delay non-tolerant transmission mode and Delay tolerant transmission mode are considered. The numerical results provide in-depth insights into practical aspects of a non-linear energy conversion model based SWIPT multiplicative relay networks.