Miracle-dz/UniFAR-FTUs huggingface dataset:
Blockchain-based open infrastructure for URL filtering in an Internet browser

this research is dedicated to the development of a prototype of open infrastructure for users’ internet traffic filtering on a browser level. We described the advantages of a distributed approach in comparison with current centralized solutions. Besides, we suggested a solution to define the optimum size for a URL storage block in Ethereum network. This solution may be used for the development of infrastructure of DApps applications on Ethereum network in future. The efficiency of the suggested approach is supported by several experiments.
A Physical Unclonable Function Using a Configurable Tristate Hybrid Scheme With Non-Volatile Memory

The physical unclonable function (PUF) is a promising low-cost hardware security primitive. Recent advances in nanotechnology have provided new opportunities for nanoscale PUF circuits. The resistive random access memory (RRAM) is extensively used in nanoscale circuits due to its low cost, non-volatility and easy integration with CMOS. This paper proposes a novel tristate hybrid PUF (TH-PUF) design based on a one-transistor-one-RRAM (1T1R) cell; this cell can be configured into two weak PUFs and a strong PUF using few control signals. To assess the proposed PUF design, a compact RRAM model at UMC 65 nm technology is employed. Simulation results show that the proposed TH-PUF achieves good uniqueness, reliability as well as a higher gate usability compared with an entire CMOS PUFs. The number of challenge response pairs (CRPs) of the proposed TH-PUF is larger than other RRAM-based PUFs. Moreover, the TH-PUF is more resistant to a modeling machine learning attack than traditional PUF designs.
Video Augmentation Method for the Facilitation of Skill Learning in Karate

This paper describes an image processing method that can facilitate skill learning in karate using recorded karate competition videos. The proposed method compensates for a partially filmed karate court in the input video image by generating a complete court image via a network-based generator. To evaluate our method, a player-focused video was augmented with complete competition field information. The augmented video would be useful for observing both players’ actions as well as the player positioning within the entire competition court. In particular, a complete court image generator was developed based on a generative adversarial network (GAN) framework to perform this video augmentation.
A Response Surface Method Approach to Modular Stator, Segmented Rotor Switched Reluctance Motor Design

The switched reluctance motor (SRM) lends itself well to structural modification to improve its torque performance and to enhance its fault tolerance. These alterations include rotor and stator segmentation, with the latter showing promise in increasing the fault tolerance through motor modularization. A statistical, design of experiments approach is used in this study to design a segmented stator, segmented rotor 12-8 SRM (SSSR-SRM) approximately the size of a NEMA 56 frame. The modularity of this SRM is improved by having the coils along the rim instead of the usual pole tooth and having these independently wound of each other. By using response surface methodology (RSM), the optimal sizing that produces the maximum average output torque for the SSSR-SRM of the given size and dimensional constraints is found. The response surface generated can also be used to predict the output torque of SSSR-SRM with different physical dimensions.
A Crowd-enabled Task Execution Approach in UAV Networks Towards Fog Computing

In this paper, the problem of constrained computational resources in the UAV networks to serve users in a limited coverage area for diverse applications is studied. In particular, a flexible crowd-enabled task execution solution with multiple participating UAVs over a broadband wireless local area network (WLAN) is proposed in order to achieve mobility advantages, high throughput, and low latency. Then, an incentive mechanism is developed so as to encourage UAVs to trade their unused computing resources for the cooperative task execution, while benefiting, jointly, both the task owner and the participating UAVs. We analyze the interaction for determining the offered price against the availability of the participants and the computational resource units for the task execution using a two-stage Stackelberg game. Furthermore, we derive the unique Stackelberg equilibria of the game with a simplified solution approach. Finally, we provide some insightful results to evaluate the efficacy of the proposed solution.
Preparation of cellulose sulfate from α‐cellulose isolated from Lantana camara by the direct esterification method

Cellulose, a (1→4)-linked β-D-glucopyranosyl polysaccharide, is the main plant cell wall constituent and is water insoluble in its native form. Sodium cellulose sulfate possesses excellent water solubility at degree of substitution (DS) levels as low as 0.25. Cellulose sulfate was prepared from α-cellulose isolated from Lantana camara by sulfation in a heterogeneous medium. The variables studied were reaction time, reaction temperature, normality of sulfuric acid, and quantity of sulfuric acid. A water-soluble cellulose sulfate ester with a DS of 0.392 was obtained at a reaction time of 60 min, a temperature 0°C, a normality of 34.2N aqueous sulfuric acid, and a quantity of aqueous sulfuric acid of 40 mL/g of α-cellulose. The optimized product exhibited cold-water solubility and a clear solution in an aqueous medium, which suggested interesting rheological properties. The optimized product was further evaluated with IR spectroscopy, scanning electron microscopy, thermogravimetric analysis/differential thermal analysis, and wide-angle X-ray diffraction. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008
Simultaneous Nonlinear Self-Interference Cancellation and Signal of Interest Recovery Using Dual Input Deep Neural Network in New Radio Access Networks

An efficient method for simultaneous nonlinear self-interference (SI) cancellation and signal-of-interest (SOI) recovery is proposed and experimentally verified in a mmWave over fiber testbed, based on a specially designed dual-input deep neural network (DI-DNN). The mmWave band has been adopted in the U.S. to provide 5G wireless access services due to its wide-bandwidth, light-of-sight propagation, and inherent compatibility with small cell architecture. To further meet the uprising bandwidth-demanding services, the in-band full-duplex (IBFD) scheme is introduced to magnify the mmWave channel capacity for the 5G and beyond wireless communications. Fortunately, the mmWave band is more realistic to implement IBFD compared with the conventional sub-6GHz band, as it operates as a highly directional beam and shorter transmission distance resulting in a higher SOI to SI ratio. In our proposed solution, a DI-DNN is, for the first time, proposed and implemented to simultaneously realize the SI cancellation and the SOI recovery. Moreover, we firstly mitigate the nonlinearity arising from the transmitters of both the SI and SOI, as well as the nonlinear crosstalk from the SI to the SOI after detection by a nonlinear receiver. The demonstration of the DI-DNN joint with the experimental implementation in a mmWave over fiber system, promise spectral efficient, high-capacity signal transport in the 5G and beyond wireless communications.
Constraint Governed Association Rule Mining for Identification of Strong SNPs to Classify Autism Data

Autism is a heterogeneous neuro developmental disorder found among all age groups. Nowadays more patients are detected with autism but very less awareness is prevailing in the society related to it. This paved a way for many researchers to carry out serious study on autism and its characteristics. Studying behavior and characteristics of Autistic patients is very important for diagnosing the level of autism. Classifying the association of different characteristic in autistic patients at gene level using machine learning techniques can give an important insight to the doctors and the care takers of the patients. Research is being carried out to identify the genes responsible for autism. The changes in gene sequence may lead to different characteristics in different people. Thus genotypic research is found to reveal well defined insight about various characteristics in autistic patients and their associations with genes. Single Nucleotide Polymorphism (SNP) being high in features indicate human genome variability and is associated with identification of traits for many human diseases including autism. The main aim of the proposed work is to identify SNP sequences which are responsible for carrying the autistic traits. This paper explore the application of Constraint Governed Association Rule Mining (CGARM) technique on SNP data for dimensionality reduction and thereby selecting the strong predominant SNP features which are relevant enough to accomplish classification with high accuracy. The research work incorporates the application of CGARM and is carried out in two stages. In the first stage CGARM was used to choose significant SNP features resulting in dimensionality reduction. In the second stage classification was carried out by subjecting the selected features to Artificial Neural Network (ANN) algorithm. The main advantage of the proposed work is its ability to reduce the dimensions without compromising the quality i.e. using CGARM strong SNPs were selected by applying various constraints like Syntactical constraints, Semantical constraints and Dimensionality Constraints resulting in higher accuracy. The CGARM technique is applied on Autism data collected from National Center for Biotechnology Information (NCBI) repository. The data is divided into a set of 118 features, out of 118 features CGARM contributed in identifying 22 predominant SNPs. Further by applying forward selection method top 17 features were selected and were given as input to ANN. The 10 fold cross validation resulted in 76.9% accuracy which was found to be 50% more than that of original features. The proposed work contributed in reducing the dimension by 85% and provided 76.9% accuracy with the help of only 15% features.
Microwave-Assisted Synthesis of Substituted 4-Oxo-4H-1-benzopyran-3-carboxaldehydes Using Vilsmeier Reagent over Silica Gel.

Substituted-4-oxo-4H-1-benzopyran-3-carboxaldehydes 2a-h have been synthesised in good yields under microwave irradiation using Vilsmeier reagent over silica gel in solvent free conditions. The reaction takes place within 2-3.5 min.
A 17.3–20.2-GHz GaN-Si MMIC Balanced HPA for Very High Throughput Satellites

This letter presents the design and experimental characterization of a $K$ -band high power amplifier (HPA) monolithic microwave-integrated circuit (MMIC) for the next generation of very high throughput satellites (vHTS). The MMIC is a three-stage balanced amplifier realized on a commercial 100-nm gate length gallium nitride on silicon (GaN-Si) technology. The design is compliant with space reliability constraints and, despite the larger thermal resistance and losses shown by the silicon (Si) substrate with respect to the more common silicon carbide (SiC), the realized HPA delivers, in pulsed condition, a peak output power larger than 41 dBm in the operative band from 17.3 to 20.2 GHz, with an associated power added efficiency (PAE) and gain up to 40% and 26 dB, respectively. In continuous wave (CW) operative conditions and with a backside temperature of 85 °C, the MMIC delivers a minimum output power and PAE of 39.4 dBm and 28%, respectively. Moreover, a 24-h test at saturated power has shown almost negligible performance degradations, thus providing confidence in the selected GaN-Si technology’s robustness.
A prediction method for exhaust gas temperature of marine diesel engine based on LSTM

In order to improve the prediction accuracy of the exhaust gas temperature of marine diesel engine, a method for predicting the exhaust gas temperature based on LSTM network is proposed. The factors affecting cylinder exhaust gas temperature are considered simultaneously, such as engine speed, fuel system, lubricating oil system, supercharger, cylinder cooling system and air inlet system. a multi-input multi-output prediction model of the diesel engine exhaust gas temperature is established by the LSTM network, and the actual ship operating data are used to train the model and test the effectiveness of the model. The results show that LSTM is suitable for the main engine exhaust temperature prediction, and the model can reach a good prediction accuracy.
A cost-efficient Current-Source Gate Driver for SiC MOSFET Module and its Comparison with Voltage-Source Gate Driver

A current-source gate driver (CSG) is considered as a candidate to improve the switching performance of Silicon Carbide (SiC) MOSFET compared with a SiC MOSFET powered by a voltage-source gate driver (VSG). Various CSG circuits have been proposed to promote the switching transients by generating a constant gate current. However, those circuits are normally designed with complicated structures and high costs due to extra components and control signals. This paper proposes a cost-efficient approach to adjust the gate current of the conventional VSG which can improve its dynamic performance of the devices. Following the comparison analysis of CSG and VSG, a simple-structure circuit is introduced to replace the gate resistor in the conventional VSG. This circuit transforms that the gate current shows constant levels during the switching transients, which exhibits current-source characteristics. Simulation and experimental studies have been undertaken to verify the effectiveness of the proposed circuit. The results show that the turn-on switching time and oscillation can be reduced using the proposed circuit compared with the conventional VSG and the proposed circuit is less hardware intensive compared to other CSGs.
Adaptive dead time control for ZVS GaN based CRM Totem Pole PFC

This paper presents an adaptive dead time control for bridgeless GaN based MHz critical conduction mode (CRM) dual phase interleaved Totem Pole PFC. It can achieve the ZVS turn on for all switches in full input voltage range. The theoretical analysis for ZVS turn on both for the main switch and synchronous switch by adaptive dead time at full ranges has been investigated. Then the digital implementation of adaptive dead-time control was introduced. Finally, a prototype of 1kW dual phase GaN based MHz CRM Totem Pole PFC prototype was built to verify the feasibility of the proposed approach.
A review of data analytics techniques for effective management of big data using IoT

IoT and big data are energetic technology of the world for quite a time, and both of these have become a necessity. On the one side where IoT is used to connect different objectives via the internet, the big data means having a large number of the set of structured, unstructured, and semi-structured data. The device used for processing based on the tools used. These tools help provide meaningful information used for effective management in different domains. Some of the commonly faced issues with the inadequate about the technologies are related to data privacy, insufficient analytical capabilities, and this issue is faced by in different domains related to the big data. Data analytics tools help discover the pattern of data and consumer preferences which is resulting in better decision making for the organizations. The major part of this work is to review different types of data analytics techniques for the effective management of big data using IoT. For the effective management of the ABD solution collection, analysis and control are used as the components. Each of the ingredients is described to find an effective way to manage big data. These components are considered and used in the validation criteria. The solution of effective data management is a stage towards the management of big data in IoT devices which will help the user to understand different types of elements of data management.
Intelligenit MPPT algorithm for PV system based on fuzzy logic

This paper deals with an analysis of a control method proposed for tracking the maximum power point (MPPT) generated by photovoltaic systems. The proposed algorithm, fuzzy logic control (FLC), is developed and implemented in Matlab/Simulink environment to command the Photovoltaic array (PV) via the dc-dc converter that are modeled. To approve the efficiency of the control algorithm, many simulations have been done considering the different system responses as the current, voltage and essentially the power. The results of the FLC algorithm shown that the perfect efficiency is achieved when the optimum duty cycle is generated.
An Optimized Start-up Method for Modular Cascaded AC/DC Power Electronic Transformer with Minimized Input-Inrush Current in Dual Active Bridge

The modular cascaded power electronic transformers (PETs) designed for interfacing AC and DC grids are mostly composed of active front end (AFE) and dual active bridges (DABs). Due to the remarkable difference between primary side and secondary side capacitor voltages of DAB at start-up, high input-inrush current will occur if the phase-shift-control (PSC) of DAB is directly employed, which can pose damage to power devices. This paper proposes a novel soft start-up method for DABs in PET with fast pre-charge process. The duty cycle of primary bridge is dynamically adjusted to precisely limit the peak of input-inrush current, and the regulating range is also enlarged.
A High-Power Density Finite Class E Power Amplifier with “Edge Inductor” for Wireless Power Transfer Application

Class E power amplifier with finite inductor has become a research trend in Multi-MHz wireless power transfer system design due to its high load variation adaptability. In order to improve compactness of power amplifier, this paper proposes a finite inductor design method that makes full use of PCB, called “edge inductor”. Meanwhile, flexible magnetic sheet is used as a “magnetic coating” to achieve the shielding effect of the edge inductor. Through simulation and comparison, the feasibility of edge inductor design is verified. Finally, a WPT system is implemented using this edge inductor design. Experimental results show that the system can maintain constant output voltage and high efficiency characteristics over a wide range of load changes.
A Snubberless Hybrid Circuit Breaker for Medium Voltage DC grids

This paper presents a hybrid circuit breaker (HCB) which is suitable for fast fault interruption in medium voltage dc (MVDC) systems. Proposed topology employs an actively switched capacitor branch in parallel with the main conduction branch to facilitate fast current commutation during a fault. The mechanical breaker in the main branch is turned off at zero current. This eliminates arc formation across the moving contacts of the breaker. Moreover, the fault interruption process does not require a varistor for network demagnetization following the fault current commutation. This paper also presents a discharging mechanism for the capacitor in a practical implementation. Performance of the proposed dc circuit breaker is evaluated through exhaustive simulation results.
A Novel Solution For Anti-Money Laundering System

In the age of unpredictable fluctuations of technology, disorganized detection has been recently figured out in most of present-day anti-money laundering systems. These obstacles are attributed to certain reasons associated with applying handcrafted manipulation in the long list of principles and having the shortage of real datasets about banking purchasers or the customers’ information. This article demonstrates such an innovative approach to evaluate the data in terms of suspicious behaviors, clients’ relationships, the awareness for the customers retrieval from the financial sector in social media platforms. The applicable datasets consisting of above 20000 sample records on Kaggle is the main resource for our service. Each entry was compiled from content of collected documents and was attached to the descriptions measuring positivity or negativity in catching money laundering. They were used to qualify the model in AutoML supplied by Google Cloud Artificial Intelligence. After having been satisfied the sentiment standard with a performance accuracy approximately 0.85, we attempted to forecast the sentimental design for all searched outcomes connected with the clients to distinguish badly known companies. The output is a beneficial tool for the companies getting used to realizing unauthorized clients. In other words, instead of having no information about new clients in Know Your Customer of anti-money laundering inspections, it is more helpful to utilize this service without wasting too much time and money for a huge number of other sites out there.
A Medium-Voltage DC Collector Grid for PV Plants Using Multi-string MPPT Converter

Medium-voltage DC (MVDC) collector grid has been a promising solution to improve energy yield of large-scale PV farms. To enable higher energy yield, multi-string maximum power point tracking (MPPT) DC-DC converter is desirable. This paper presents an non-isolated MPPT DC-DC converter for MVDC PV collector grid. Different from traditional MPPT converters, the proposed converter can operate in both buck and boost modes, which allows strings with large voltage difference coupled to a middle DC voltage. The power processing analysis indicates that the proposed converter features partial power processing in both buck and boost operations. Simulation simulation results are presented to confirm the high efficiency of the proposed converter. Mission profile based evaluation has been conducted to demonstrate that with the proposed buck-boost converter, more energy can be captured than using the conventional boost-only converter.
Research on Intrusion Detection Based on Particle Swarm Optimization in IoT

With the advent of the “Internet plus” era, the Internet of Things (IoT) is gradually penetrating into various fields, and the scale of its equipment is also showing an explosive growth trend. The age of the “Internet of Everything” is coming. The integration and diversification of IoT terminals and applications make IoT more vulnerable to various intrusion attacks. Therefore, it is particularly important to design an intrusion detection model that guarantees the security, integrity and reliability of the IoT. Traditional intrusion detection technology has the disadvantages of low detection rate and poor scalability, which cannot adapt to the complex and changeable IoT environment. In this paper, we propose a particle swarm optimization-based gradient descent (PSO-LightGBM) for the intrusion detection. In this method, PSO-LightGBM is used to extract the features of the data and inputs it into one-class SVM (OCSVM) to discover and identify malicious data. The UNSW-NB15 dataset is applied to verify the intrusion detection model. The experimental results show that the model we propose is very robust in detecting either normal or various malicious data, especially small sample data such as Backdoor, Shellcode and Worms.