Miracle-dz/UniFAR-FTUs huggingface dataset:
SDN-Based Data Forwarding in Fog-Enabled Smart Grids

Data forwarding becomes a challenging issue in fog-enabled smart grids as the size of networks increases. An efficient routing mechanism is required for the fog-enabled smart grids especially for latency-sensitive Internet of Things (IoT) applications. This paper proposes an Software Defined Networking (SDN)-based data forwarding scheme for fog-enabled smart grids. This scheme integrates SDN technology with fog computing, aiming to forward the data with less latency by calculating the shortest path between the source and destination points. To ensure shortest paths, Dijkstra algorithm is employed over different topologies. In addition, for link failures, a path recovery mechanism is realized to ensure uninterrupted data transmission. During the experiments, forwarding time, throughput, and transmission overhead are measured over different topologies. The performance results show that our scheme achieves a better forwarding time performance than existing methods. Also, the forwarding time is affected by the number of link failures but the path recovery is successfully able to sustain the forwarding process. As the topology becomes more complex, throughput and transmission time increase as well.
User experience framework that combines aspects, dimensions, and measurement methods

Abstract Successful software products necessitate users’ satisfaction when experiencing the use of the software. This is not only determined by the software functionalities and completeness, but also with the overall user experience when using the software product. Although user experience is widely adopted by practitioners and in industry, there is no scientific consensus on a definition or a theoretical model of UX. The dynamic nature of user experience is challenging both UX design and evaluation activities. Accordingly, further research is needed to study four non-orthogonal UX issues: definition, modeling, method selection, and the interplay between evaluation and development. Moreover, UX professionals need to identify means for compromising the difficulties of evaluating UX in a holistic manner. The purpose of this research is to consolidate the findings related to UX aspects and dimensions along with the identified measurement methods into one simplified UX theoretical framework. This work is related to the aforementioned modeling issue aiming to better understand the relationship between UX dimensions, UX Aspects and UX measurement methods. The proposed framework is vital for practical application of UX, the development of UX evaluation methods and further theoretical studies of UX.
The Development of Vital-SAR-Imaging with an FMCW Radar System

Locating and identifying human targets in a synthetic aperture radar (SAR) image is important but challenging for security and rescue applications. In this paper, we present an overview on the development of vital-SAR-imaging method with frequency-modulated continuous-wave (FMCW) radar system to realize targets imaging and human identification simultaneously. The experimental results conducted to demonstrate the accuracy and robustness of the method will be discussed in detail.
Automated Variable Stimulus Tendon Tapping Modulates Somatosensory Evoked Potentials

Somatosensory Evoked Potentials (SSEPs) are an important tool for both basic neuroscience research and evaluation of therapeutic techniques. While a large body of work exists in the study of electrically induced SSEPs both as a metric for therapeutic performance and tool for physiological research, comparatively little work has explored stretch response SSEPs evoked via tendon tapping. The measurement of SSEPs necessitates both timing and stimulation intensity consistency. This work presents an evaluation of a simple tapping device for automating this procedure and a comparison to manual tendon tapping demonstrating significantly reduced variability in both timing and intensity. The variable intensity nature of automated tapping is then used to measure SSEPs in a single subject, with apparent modulation of peak-peak amplitude by stimulation intensity.
Anomaly Detection in Social Networks

Social Networks provides a mode of information sharing forindividuals from anywhere in the world. Diverse domains, such as, entertainment, education, business, and medical are using it. Excessive utilization of social media may cause various types of illegal activities like spams, rumor spreading. In order to avoid these activities, it is necessary to detect them. Two types of data can be extracted from social networks: the behavioral data that represents the users' activities on the social media platform; and the structural data that represents the topological aspects of the networks. The useful information contained by data can be studied by graph theory for anomaly detection task. Existing structural features like degree, edge count, average betweenness centrality and brokerage lack in terms of accuracy and produce false positive and false negative. To reduce the amount of false positive and false negative we have used a new graph metric closeness centrality for anomaly detection task andapplied the proposed method on three datasets collected from a social networkrepository. The comparability and efficiency of different methods have been studied using statistical quantities like F-score, precision, andrecall. Experimental results show that the relationship between closeness centrality of an egonet and number of edges in egonet is capable to catch most of the anomalies and have higher F-score value than degree, average betweenness centrality and brokerage.
A Minimum Number of Switch Five-Level Inverter for Renewable Energy Sources Applications

A novel structure for a five-level voltage source inverter has been proposed in this article. This topology includes two symmetric power supplies connected in series to produce five levels in output. The main goals of presenting this structure are reduction of number of switches, complexity of control circuit, and Total Harmonic Distortion of the inverter output voltage (THDv) with the proper use of power semiconductor switches. In comparison with typical structures, the proposed inverter has lower number of switches. To verify the performance of the inverter, several simulations have been done by using MATLAB/Simulink software, and also a 3kVA laboratory prototype of the inverter has been implemented. Our proposed inverter has been compared with the presented former ones in terms of the number of power switches, total components, and power losses. Based on the simulation and experimental results, all mentioned factors have been improved.
RFID Antenna Sensor for Quantitatively Monitoring Surface Crack Growth

In recent years, the large-scale construction of infrastructure has promoted the development and utilization of passive wireless sensors in the field of structural health monitoring for permanent installation and online real-time health detection. This paper uses a circular RFID patch antenna sensor with a rectangular window for quantitatively Monitoring Surface crack growth. Simulation results show that the resonant frequency shift of the RFID Antenna Sensor is a useful feature to indicate the crack orientation and the crack characterization on a metallic surface structure.
Motion Analysis for People with Cerebral Palsy: A Vision Based Approach

We propose a methodology to classify motion of subjects with cerebral palsy based on RGB image sequences and present a new dataset with 2D facial landmark trajectories from RGB images of people with and without disabilities while performing specific types of movements. Depending on these movements, parts of the face can be occluded and we are able to recover the 3D face’s shape and its motion based on the Structure from Motion framework. Using the 3D structure and the motion, we propose two different motion descriptors, one is focused on describing the spatial distribution of the motion and the other on the temporal distribution. Finally, we discuss the physical meaning of these descriptors and show that they are very informative about the degree of the subjects’ disabilities. Our descriptor can classify people with and without cerebral palsy from 2D image sequences.
Development of a Low-Cost and User-Friendly Neurofeedback Tool to treat Depression, Insomnia, Anxiety, Pain and ADHD using an Arduino and Android Application

The main objective of the paper is to achieve the implementation of an economical and user-friendly neurofeedback tool that can be used to treat pain, addiction, aggression, anxiety, autism, depression, Schizophrenia, epilepsy, headaches, insomnia, Tourette syndrome, Attention Deficit Hyperactivity Disorder (ADHD), also used for the treatment of brain damage caused by stroke, trauma, and other causes in developing countries using EEG signal analysis. The analysis and the detection of the EEG can be achieved with the help of a home-made neurofeedback-built system. The technical and clinical EEG spectrum can be subdivided into – Alpha, Beta, Theta and Gamma ranges. The report will focus on the frequencies of the Beta band in accordance to the analysis of the power values and the output of the EEG analysis will be required to control the video and-or audio feedback. [22]
An Assistive Control Strategy for Rehabilitation Robots Using Velocity Field and Force Field

In this paper, we address the problem of assist-as-needed (AAN) control of rehabilitation robots. The objective is to develop a path tracking control scheme with the minimized intervention of the robot to gain active participation of impaired subjects while avoiding large position errors. We achieve these properties by constructing a velocity field encoding the desired path, and by considering a force field around the path. In particular, the proposed controller includes a normal force term to keep the robot position arbitrarily close to the path, and also contains velocity tracking components, which adaptively adjust the contribution of the controller by monitoring the tracking error. As a result, we gain the AAN property with adequate freedom in the timing of movement, which is a key factor in reducing the robot intervention. The performance of the controller is examined on a lower-limb robotic exoskeleton in following the gait pattern.
An Overview on Security Issues In Internet Of Things

Internet of things has impacted 21st century like anything never before. Every device can be connected with each other via the internet. They can be remotely controlled or managed anytime. But all time connectivity of the IoT devices generates a huge amount of data which needs to be properly managed and disposed of as required. The data captured contains a large amount of sensitive information and patterns too; which can disclose behavioural or temporal activities. In this paper, we start by giving a brief introduction of IoT and then discuss some of its applications. After that security issues affecting IoT are discussed followed by a conclusion focusing on the need for small size & efficient cryptographic algorithms.
Indirect Robotic Movement Shaping through Motor Cost Influence

Movement patterns are commonly disrupted after a neurological incident. The correction and recovery of these movement patterns is part of therapeutic practice, and should be considered in the development of robotic device control strategies. This is an area which has limited exploration in rehabilitation robotics literature. This work presents a new strategy aiming at influencing the cost associated with a movement, based on the principle of optimal motor control. This approach is unique, in that it does not directly modify the movement pattern, but instead encourages this altered movement. This ‘Indirect Shaping Control’ is applied in a preliminary experiment using an end-effector based device with 5 healthy subjects. The study concludes that such an approach may encourage changes in movement patterns which do persist to out-of-robot reaching actions, but this was not consistent over all subjects and further experiments are required.
Microwave Components Designing Using Metamaterial Transmission Line

In this paper designing of microwave components using meta material transmission line is proposed. Metamaterial transmission line is designed using Composite Right Left Handed material. The compact size of microwave components can be obtained by using CRLH Tx line which leads to compact system design for wireless applications. Method of designing CRLH Tx line using microstrip line is described. Designing and simulation results of compact Balun using CRLH Tx line is also presented.
Anomaly Detection in Aluminium Production with Unsupervised Machine Learning Classifiers

This work presents a predictive maintenance methodology aiming at forecasting specific types of faults of an industrial equipment for anode production, utilizing process sensor data from operation periods. The challenge of this problem is the early detection of a fault, particularly just before it occurs. For the forecasting, some unsupervised machine learning architectures were tested. Several considerations were made for the pre-processing steps as well. Finally, automatic feature selection methods were introduced, one of which was used to find the most significant features within successive time windows of the evaluated historical data set. The experimental results, which conform to the visual observations, show that a warning time frame around 20 minutes before the incident is feasible for 43% of the incidents of a particular fault type within a critical 1.5-month period, whereas only in about 0.1% of the timestamps more than 75 minutes before such a fault an alarm is raised.
Design and Analysis of Bi-directional DC-DC Driver for Electric Vehicles

The level of exhaust gases is rising with increasing usage of internal combustion engine vehicles. In order to reduce carbon emission, researchers and industry head up for improving electric vehicle technologies in all over the word. This paper deals with design and simulation of a bi-directional power converter of electric vehicle. The power electronics block is comprised by batteries, bi-directional dc-dc converter and dc machine. The initial state of battery charge is set around 90% where the discharge current is 44.5 A during motor mode. The nominal voltage of battery stack is 350 V and maximum capacity is 100 Ah. The rated power of dc machine is set to 250 HP with 500 V armature voltage and 300 V field voltage. The operating mode of power converter is determined according to the torque values of dc machine which is operated in motor and generator modes. The charge and discharge conditions of batteries have been controlled regarding to operating modes of dc machine. The bi-directional dc-dc converter is controlled with fuzzy logic controller in both modes. The proposed converter and controller are designed to meet charge control and motor drive requirements of an all-electric vehicle.
Deterministic Mean Field Game for Energy Management in a Utility with Many Users

Energy management programs are viable options for reducing the customers billing cost as well as enhancing grid reliability and efficiency. This paper proposes a decentralized algorithm for energy management of large population of users using the concept of mean filed (MF) game. The aim of each user is to find its optimal demand strategy by minimizing an objective function including; energy consumption cost, battery degradation cost and deviation from desired demand. The strategy of a user affects the objective functions of other users through the electricity price. Such interaction among users is modeled as a game problem. Each users, without need of any information of its rivals, sends its strategy to the utility company which acts as a coordinator. The coordinator just broadcasts a common signal (MF term) to all users which is the average of all users' decision. It is shown that the proposed decentralized algorithm converges to $\varepsilon_{N}$-Nash equilibrium point of the game while $\varepsilon_{N}$ uniformly converges to zero as the population size of the users goes to infinity. The conditions for the existence and uniqueness of the solution were given. Simulation results show the advantages of the proposed method.
A Hybrid FDTD Method for Co-simulation of Antenna and Rectifier Circuit

In this paper, we proposed an efficient and accurate hybrid Finite Difference Time Domain (FDTD) method to study the electromagnetic (EM) coupling and response characteristics of an whole rectenna, which combines the full-wave finite difference time domain (FDTD) method with the transmission line equation to predict the voltage and current responses of the rectifier circuit. This method combines a full-wave FDTD method, a transmission line equation, and equivalent circuit model of the nonlinear load together. And the improved node analysis (MNA) method is also implemented through a set of appropriate boundary conditions. This method has a good agreement with HSPICE. In addition, it has been successfully applied to predict the transient voltage and current responses in a real rectenna system.
Institutional dynamics in an economy seen as a complex adaptive system

The primary objective of this paper is to study the role of institutions evolution assuming that economies are generally out of equilibrium. The focus is developing a logical framework that allows describing economies as complex adaptive systems. This will allow deducing the uncertainty inherent to the economy as a consequence of system characteristics (instead of assuming it as hypothesis). Thus, it will be possible to characterize adaptation from fundamental properties of the system. As institutions are mechanisms to facilitate adaptation, the paper will provide a fundamental description of the functioning and logic for institutions within complex adaptive systems.
A Comprehensive Loss Analysis of Half-Bridge LLC Resonant Converter used in LED Street Lights

High efficient, low-cost and long-term reliable power supplies are today's market demands for LED street lighting applications. To meet these growing demands, the half-bridge (HB) LLC resonant converter has been increasingly gaining importance due to low component count, achieving zero-voltage-switching (ZVS) and zero-current-switching (ZCS) in the whole operating range. Although the HB LLC resonant converter achieves high efficiency and high power density, design process is very complex and reliable power system design based on this topology needs excessive measurements. To overcome these drawbacks, this paper proposes a design approach which takes power losses and thermal considerations into account carefully during design stage. With the suggested method, total product design period is greatly reduced and design reliability is highly improved compared to the conventional design methods. For this purpose, a 100W HB LLC resonant converter based power supply is implemented to confirm the design approach validity. According to the results, power loss of critical items is predicted over 85% accuracy which is high enough for practical purposes. Results and efficiency improvement methods are also discussed at the end of the paper.
A Deep Learning Framework for Univariate Time Series Prediction Using Convolutional LSTM Stacked Autoencoders

This paper proposes a deep learning framework where wavelet transforms (WT), 2-dimensional Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) stacked autoencoders (SAE) are combined towards single-step time series prediction. Within the framework, the input dataset is denoised using wavelet decomposition, before learning in an unsupervised manner using SAEs comprising bidirectional Convolutional LSTM (ConvLSTM) layers to predict a single-step ahead value. To evaluate our proposed framework, we compared its performance to two (2) state-of-the-art deep learning predictive models using three open-source univariate time series datasets. The experimental results support the value of the approach when applied to univariate time series prediction.
High frequency FPAA Design using OTA in 45nm CMOS Technology

This paper aims at developing a modified yet general purpose FPAA CAB (configurable analog block) architecture which is dedicated to analog signal processing applications, based on OTA (operational transconductance amplifier). The OTA is a low voltage, low power and wide bandwidthcurrent mode device which can allow high frequency operation with an ease of stability. OTA has already established as a convenient analog device which usually simplifies circuit designs. The CAB design is aimed at aimed at achieving a wider range of functions by local programming facilities available. A CAB may contain one or more active devices and a set of switches. Here we concentrate on the design of a two OTA-CAB. The OTA is realized in 45nm bulk CMOS adopting a simple architecture, and biased by ±1V sources. HSPICE is used for simulation work. Theresults are quite satisfactory and are presented.
A Novel Compact Tri-band Filter Based on a Single Substrate Integrated Waveguide Cavity

A compact tri-band bandpass filter (BPF) using a single substrate integrated waveguide (SIW) cavity with offset feed-line is proposed in this paper. Firstly, a single SIW cavity fed by microstrip-line and L-shape slots with rectangular slot is applied to construct a tri-band BPF. The offset feed-line is used to excite the three resonant modes to achieve the three passbands. The rectangular slot in the SIW is applied to improve the filtering performance. This filter has a very compact configuration, which still remains a good filtering performance. Then, a second order tri-band filter based on the first order tri-band filter is designed and analyzed. Finally, the proposed second order tri-band filter is fabricated and verified by simulations and experiments.
A Survey on Fake Review Detection using Machine Learning Techniques

Now a days the usage of Internet and online marketing has become very popular. Millions of products and services are available in online marketing that generate huge amount of information. Hence, it’s difficult to find the best suitable services or products compatible to the requirement. Customers directly take decision based on reviews or opinions that are written by others based on their experiences. In this competitive world any person can write anything, this raise the number of fake reviews. Various companies are hiring people to write fake positive reviews about their services or products or unfair negative reviews about their competitors’ services or products. This process gives wrong input to the new customers who wish to buy such items and hence we need a system to detect such fake reviews and remove them. In this paper we discuss various supervised, unsupervised and semi supervised data mining techniques for fake review detection based on different features.
A Quick Guide to Implement Reversible Logic

Increasing miniaturization and demand of low power electronics is highly motivating research on reversible logic. Reversible logic has attracted signification attention in fields of Low power VLSI design, computer graphics, quantum computing and communication. A lot of work has been done on synthesis and optimization of reversible circuits followed by quantitative analysis on various optimization metrics like ancillary inputs, garbage outputs and quantum cost. It is therefore obvious to present a quick guide on reversible logic implementation to bridge gap between theory and implementation. Taxonomy diagrams of work done in reversible logic domain are also presented to give a quick review of all about reversible logic. Quantum realization of popular reversible Fredkin gates using RCViewer tool is also presented which will be stepping stone for a new researcher to synthesize and optimize any complex reversible logic based circuit.