Miracle-dz/UniFAR-FTUs huggingface dataset:
Alternative transparent conducting electrode for flexible optoelectronics

Transparent conducting electrode for the organic optoelectronic devices are gathering a tremendous amount of interest. Most of the conducting electrodes like Indium Tin Oxide (ITO) are fabricated at high temperature. In recent years, flexible transparent electrodes fabricated with silver nanowires (AgNWs) extensively studied. This is a promising alternative to ITO film in an optoelectronic application. ITO may present good conductivity and transparency for the optoelectronic device but, due to its mechanical non-robust nature, it is hard to fabricate, process and use on flexible substrates like poly (ethylene terephthalate) (PET) and epoxy. These solution process methods are also important due to ease of process and large area coating without losing its electronic property. In this work alternative conducting electrodes are prepared by solution process and effect of the silver nanowire on sheet resistance has been studied. Optical and mechanical properties correlated to electronic properties are also evaluated.
MV-GCN: Multi-View Graph Convolutional Networks for Link Prediction

Link prediction is a demanding task in real-world scenarios, such as recommender systems, which targets to predict the unobservable links between different objects by learning network-structured data. In this paper, we propose a novel multi-view graph convolutional neural network (MV-GCN) model to solve this problem based on Matrix Completion method by simultaneously exploiting the interactive relationship and the content information of different objects. Unlike existing approaches directly concatenate the interactive and content information as a single view, the proposed MV-GCN improves the accuracy of the predictions by restricting the consistencies on the graph embedding from multiple views. Experimental results on six primary benchmark datasets, including two homogeneous datasets and four heterogeneous datasets, both show that MV-GCN outperforms the recent state-of-the-art methods.
An Optimization Scheme based on Transmitters Selection for MIMO VLC Systems

Multi-input and multi-output (MIMO) visible light communication (VLC) is one of the promising solutions for future network environment. Considering in most cases, the transmitters and the receivers cannot meet the requirements of the ideal layout. This paper presents an in-depth study on the channel matrix of the MIMO visible light communication system. The singular value decomposition (SVD) is used to select the transmitters which have good performance for transmission, and the corresponding power is allocated according to the principle of equal bit error rate (BER) of the receivers. Simulation results show that this method can effectively improve the channel capacity of MIMO VLC system, and can achieve good BER performance for each receiver.
Improved Piecewise Constant Approximation Method for Compressing Data Streams

Stream data, obtained from the various sensors and devices with high speed, used daily, are indispensable parts in our day-to-day life. There is an immense necessity to store these data comprising of large volume. Data compression is one of the effective ways of storing data efficiently. In this paper, different data compression techniques such as PCA, APCA have been improved and thus we propose a new hybrid model. In the proposed model, the window size is fixed but data can be compressed efficiently than PCA. Besides, the delay time is minimized compared to the other existing techniques since the model is developed by considering both the delay time and several data points to be compressed. Our experiment shows that the proposed method performs twice as better than PCA in terms of compression and minimizes RMSE by 10%-25% compared to APCA method.
A Booting Fingerprint of Device for Network Access Control

The rapid growth of the Internet of Things (IoT) is enhancing customers' experience with an ever-increasing mass of intelligent devices. However, the security doesn't get enough attention compared with novel concepts and features by manufactures which even not provide service of updates and patches. These issues could have been mitigated by the mechanism of the network access control. But traditional authentication mechanisms based on computational complexity of cryptographic protocols cannot be implemented on IoT devices with limited physical and computational resources. Therefore, we turn our attention to the device fingerprint which is like a biometric fingerprint that be able to provide identity authentication. The fingerprint proposed in this paper generated by network traffic features divided into the packet-feature and the IAT-feature. These features are extracted from the traffic when the device is booted, because at this time the device's network behavior is stable compared to other times. Experiments illustrate that the average identification accuracy of our device fingerprint reaches 92.4%
Multi-label Classification Method Based on the Labeling-Importance Degree

The task of multi-label classification is to predict multiple possible labels for the unseen instance. Currently the prevalence of multi-label data in daily life has stirred up a spree of research activities. But most of those established multi-label classification methods mainly focus on such issues as class imbalance, label relations and dimension compression. These methods usually have an untenable hypothesis that each instance is of equal importance for all labels, based on which an identical set of instances is used to establish the learning model for all labels. In this paper we introduce the concept of labeling-importance and work out each instance’s labeling-important degrees that are in line with the polynomial distribution. Afterwards, a relatively important set of instances is selected for each label before establishing its classification model. The superior performance of this proposed method is validated by comprehensive experiments.
Performance Analysis Spectrum Sensing using Eigenvalue-Moment-Ratio for Internet of Things Devices

Internet of things (IoT) is one famous technology now. IoT fit objects will be interconnected through wired and wireless communication technologies. It makes demand high. One of the solutions using cognitive radio (CR) for better utilization, CR has spectrum sensing to find unoccupied channel and occupied channel. This study using Eigen Moment Ratio (EMR) for better utilization than Energy Detection (ED). The evaluation shows EMR better than ED, because EMR using a new formula eigenvalue to detect. EMR can distinguish signal + noise from background noise only. EMR also can detect unoccupied channels in multi antennas.
Curriculum for Hands-on Artificial Intelligence Cybersecurity

Interest and awareness of Artificial Intelligence (AI) grows at such a rate that academia and higher education struggle keeping up with the accelerating demand of industry. It is forecast that 75% of enterprise applications will use AI, Machine Learning or Deep Learning technology by 2021, yet university programs commonly place the burden on students to obtain their data science educations through elective coursework spanning multiple disparate departments. Data science requires specific mathematics preparation especially for cybersecurity students whose programs have reduced the requirements for advance mathematics to a bare minimum. To facilitate curricula preparation, and hands-on usage of AI tools, a notebook (“A Trellis For Novice AI Practitioners”) was prepared in the R programming language as a first step in introducing computer science and cybersecurity students to the concepts and capabilities of AI. A focus on an intrusion detection data set to mitigate nine common cyber vulnerabilities is used in this analysis. Trellis bridges the theoretical and practical chasm for students by building an ANN network intrusion predictive model from scratch. It serves as a template but also encourages heavy contextual modification, and may be relied upon in the beginning stages of a cybersecurity practitioner's data science activities on a wide variety of data sets in all areas of the discipline.
Development of Educational Software for Electrical Engineering Subjects using MATLAB

In this paper, an educational software was developed using MATLAB and its Graphical User Interface (GUI). This interactive and user-friendly software focuses on three subjects which are electromagnetic, communication engineering and power engineering. The user uses MATLAB GUI in designing the educational software and compiled together with M-file program to develop a standalone program. Standalone program was created to ease the students to use this educational software without installing MATLAB software in their personal computer. This software may help user to get a better understanding on electrical engineering subjects such as electromagnetic, communication engineering and power engineering.
Development of High Voltage Multilevel Step-wave Voltage Source

With the development of energy Internet, the proportion of modular multilevel converter in the system further increases. High-voltage multilevel stepped waves become a typical waveform stressed on the related insulation. In order to study the insulation characteristics of equipment under multilevel stepped wave stress, it is necessary to develop a high-voltage multilevel step-wave voltage source. The regnant single-phase MMC topology in the existing topology is analyzed, while the topology output voltage waveform is biased due to the polarity effect of the load discharge, which does not meet the stability requirements of the voltage source. A new topology structure using positive and negative half-bridge submodules is proposed, which requires fewer switching devices, and is easy to expand to multi-level high voltage, with stable output waveform and flexible frequency and amplitude. The working principle of this topology and the nearest level modulation strategy are introduced. The control system is realized by STM32F407ZGT6. A multistage staircase voltage source prototype is built to verify the feasibility of the proposed topology.
Battery Charging Station for Multiple Electric Vehicles with Power Conditioning

In this paper, dual active bridge (DAB) converter based off-board charging station is proposed for multiple electric vehicle battery charging. The new feature of improvement in power quality at utility is also added which reduces the cost of grid utility and also, it makes the revenue cheaper for the owners of the charging station. Additionally, this will lead to improvement in climatic conditions caused by pollution caused due to more fuel consumption. This charger lessens the amount of harmonics and also provides reactive power compensation to the non linear loads at the residential installations during the absence of vehicle for charging. The control algorithm consists of constant current and constant voltage charging block along with adaptive transverse filter for the extraction of fundamental component of the load current with the help of LMS algorithm. The Simulink model of DAB based charging station is developed for charging four units of vehicles. To investigate the performance of system, four units of EVs are connected at charging ports in which two vehicle batteries are charged in CC mode and remaining two vehicle batteries are charged in CV mode and provide power to each vehicle of 340W, 56.5V/6A, 360W 60V/6A, 30W 50V/0.6A and 125W 125V/1A respectively through charging ports. Further, a non-linear load is connected to utility grid as local load.
Instrumentation and Control System for Test Bench

The main problem of the article is the lack of automated structural synthesis of information systems based on the semantic relations of subject area. As presented in the article, in technology creation software system expert developing the domain ontology with help user-friendly the program interfaces. This program is a mechanism for automatically formalizing domain description and automatically synthesizing the class diagram in the Unified Modeling Language. In the article discusses the method for automatic design based on automated synthesis of Unified Modeling Language models of the application program to control the test stand. The method of its use in solving the problems of designing an instrumentation and control system for test bench of an unmanned aerial vehicle is described. Expert inputs a domain ontology, then system automatically formalizes domain description and synthesizes the class diagram in the Unified Modeling Language.
An Empirical Study of Cervical Cancer Diagnosis using Ensemble Methods

Cervical Cancer, being one of the most pressing issues now-a-days, needs to be addressed properly. With a view to achieving an accurate diagnosis method for Cervical Cancer by screening the risk factors, different machine learning approaches have been taken over time. But by analyzing the performances of most of state-of-the-art approaches, it was inferred that there is still room for improvement by developing a more accurate model. Hence, in this paper an approach using ensemble methods with SVM as the base classifier has been taken. The ensemble method with Bagging technique achieved an accuracy of 98.12% with very high precision, recall and f-measure value.
Simulation of Ferrofluid Based Soft Actuator Using Magnetic Field Optimization Approach

In recent times, soft robot has become an unconventional field of research in robotics. In comparison to classic robotics, soft robot shows an excellent adaptability in nature, surveillance with more pliability and of course a simpler structure. In this paper, a new approach for ferrofluid based soft actuator which shows various vertical motions when varying magnetic field is applied, has been introduced for the first time. Finite element method is used here to model different motions of the ferrofluid tube together with field, force and stress analysis. Eventually this simulation approach gave us values of the force exerted on the tube, the magnetic stream lines, direction of the flux, force direction and also the displacement of the tube when introduced to varying magnetic field.
Broken Relationship of Mobile User Intentions and Permission Control of Shared System Resources

Android, which accounts for 76.08% of the market1, is the most popular and widely used mobile system today. Users install various applications from different sources and for different functionalities to get all sorts of services. Since smartphones carry a large amount of private user information, their security has been widely concerned in recent years. Android uses permissions to restrict the behaviors of applications and protect system resources and user privacy, such as location, password, and contact list carried on mobile devices. The research community has previously explored a variety of methods to enhance the permission system with the help of the system context and application program. However, such methods cannot address the permission violation issues raised by resources sharing, e.g., screen sharing and clipboard sharing, which are designed for more convenient human-computer interactions. In this work, we perform several proof-of-concept attacks on resources sharing to illustrate the deficiencies in the permission control system. We believe that the cause of these issues is that the permission control system does not fully understand the user's intentions. Then, we propose a non-intrusive user-action based permission control system, which can achieve more precise permission control by mining the user's intentions when operating the device.
Implementation and Harmonic Analysis on Single Carrier Modulation Technique for Modular Multilevel Converter

The paper mainly reports on the study carried out to implement an appropriate modulation technique for the modular multilevel converter with less harmonic distortion and reduced carrier signals. The proposed technique was implemented by modulating the reference signal within the range of single carrier signal and providing gating pulse to each sub module by comparing these signals. The modulation technique presented in this work focused on elimination of total harmonics distortion (THD) in the output voltage of MMC and also reduction of carrier signals used to switch each of the sub module power switches in MMC. The efficiency of the proposed SC-PWM technique based on THD was ascertained by comparing with the existing PWM techniques. Single carrier signal is used for implementing any number of levels in MMC without any deviation in the output voltage levels. The system was simulated using MatLab-Simulink and validated experimentally using the digital signal processor.
Effects of Advanced Cooling of Physical Solvents in the Rectisol Process

Physical absorption method is used predominantly prior to the electricity generation in different energy systems. In these cases the carbon dioxide is separated from the synthesis gas obtained as a result of coal or biomass/waste gasification. For Rectisol process, the carbon dioxide capture performance using physical solvents (e.g. methanol, methylpyrrolidone, etc.) increases with the pressure (usual: 20-50 bar) and decreases with the temperature (usual: -10 . -60 °C) inside the absorption unit. In some cases, in order to increase the solubility of the acid gases (CO2, H2S) and reduce the physical solvent flow, it is necessary that the physical absorption process takes place at temperatures below the ambient temperature. In this case, it’s required to cool the synthesis gas before entering the absorption column, then reheating it before injection into the gas turbine (or other energy systems). The additional processes of the heating and cooling steps of the synthesis gas as well as the cooling of the physical solvent before the absorption column leads to energy penalties (additional electricity/thermal energy consumption) and consequently to the significant reduction of the physical absorption technology performance. In this paper an analysis of the technical effects of different refrigeration fluids on the Rectisol process is developed. The energy consumption in the Rectisol process reduces drastically the performances of the energy system.
Refining the Measurement of Topic Similarities Through Bibliographic Coupling and LDA

Generally, two topics with vastly different terminology probably indicate different implied concepts. However, these topics themselves might share common references (bibliographic coupling), which suggest the underlying joint concept. Therefore, searching for these joint concepts in different topics would be of scientific interest. Previous studies have measured the similarity between topics based on comparison of the topics’ word probability distributions. In contrast, this paper presents an approach for measuring the similarity between topics based on the bibliographic coupling. Besides, the similarity is independent of the topic’s word probability distributions generated by a Latent Dirichlet Allocation (LDA) model. The proposed approach was evaluated using its counterpart (intra-topic similarity), baseline topic similarity matrices, and cosine measure. The method was exampled on brain cancer patents. A cross-topic similarity network of eight topics showcases 28 cross-topic pairs to profile which topics were associated with particular topics. Interestingly, some of the 28 combinations may be of scientific interest. For instance, the findings of the top five cross-topic pairs suggest that “growth of cancer cells” and “imbalances in the hormones” have common knowledge sources with the highest similarity value. These two entirely different concepts may suggest some common causative factors within the field. We believe that finding such an association between unrelated innovative inventions across various industries may help public and private research units in planning research direction and serve as a reference for future research.
Analysis of Daubechies Wavelet and Neural Network for Audio Classification

Biodiversity that exists in nature shows the overall variation between living things both from the smallest levels, namely genes, species and ecosystem. One animal with a fairly high level of variation, namely birds chirping. Chirping has an identifier for each type both of the color of the feather, body shape, shape of the beak, food, how to find food and the most obvious is the difference in the chirping of birds. The problem faced is the number of species of birds chirping that are almost similar to each other so the introduction of birds with sound becomes quite difficult. This makes the introduction of birds with sound requires a special technique. The techniques used are transform wavelets and neural networks. At the end of the study, obtained Wavelet Package Decomposition extraction with training data used as many as 500 data. There is 1 preprocessing done by resampling (downsampling). The most optimal number of neurons to be used in hidden layers is 12 neurons with 500 epochs, the highest accuracy is 80.99% with momentum 0.2, learning rate 0.2 and wavelet daubechies2 while the lowest accuracy is 70.40% with momentum 0.8, learning rate 0.8 and wavelet daubechies10.
Human Activity Recognition Using Smartphone Sensors: A Dense Neural Network Approach

Smartphones are now more and more advanced and the recent smartphone generation incorporates many varied and strong sensors like linear or tri-axial, linear or tri-axial gyroscopes, GPS, etc. The accessibility of this sensor in the mass communication systems provides fascinating possibilities in information mining and machine learning; in addition, the human activity recognition a.k. a. HAR generates a fresh study arena. Wireless sensor-based HAR has an important effect on outpatient surveillance illnesses, physical activity, elderly care, sports and physical therapy. In order to detect ten human actions, we want to walk, walking upstairs, walking downstairs, sitting, standing, lying, using toilet, jogging, writing and typing, positioning the smartphone on hand. We gathered information from both the male and female subject while performing the listed tasks from labeled tri-axial accelerometer and tri-axial gyroscope. In order to achieve necessary sensor information, we developed an Android application with a frequency of 1 Hz. Then we used a model known as DNN for the Dense Neural Network to predict the operations of information from gathered sensors and gyroscope. In addition to DNN for operating studies we have also practiced Support Vector Machines, Decision Tree, K-Nearest Neighbor, Random Forest, Naïve Bayes and Logistical Regression. Our key objective was to examine a comparative analysis with statistical and profound classifiers on male and female subject. We also confirmed that information gathered at reduced frequencies can also perform well. We obtained maximum level of precision of 94.38% and 93.35% for male and female data sets only with DNN model respectively.
DAKAA: Double Authentication and Key Agreement Algorithm for securing Vehicular Ad hoc Networks

Vehicular ad hoc network plays a vital role in communication paradigm that aims to provide innovative services. Vehicle-to-Vehicle communication is a realistic wireless transmission between the vehicles, which needs security services for safety communication. Priority-based authentication and fuzzy-based techniques are used to ensure the authentication process in existing works. Even though, some attack can break the authentication process such as brute force attack and private keys can also be hacked. Hence, the proposed scheme uses double authentication and key agreement algorithm (DAKAA) to prevent the unauthorized users or vehicles entering into the system to provide largest and competent private key using RSA cryptosystem in order to avert brute force attacks. The performance of DAKAA was compared with the existing schemes concerning verification delay, transmission overhead and service response time. DAKAA achieves better results due to prior detection of malicious vehicles for effective safety communication.