Miracle-dz/UniFAR-FTUs huggingface dataset:
Permanent Magnet Reduction by Current Harmonics Injection for Surface Permanent Magnet Machines

This paper presents the analysis of multi-harmonic current injection in multi-three phase surface permanent magnet (SPM) machines. An analytical model, extended to a generic harmonic order, is used to describe and determine the no load flux density, the back electromotive force (BEMF) and the electromagnetic torque produced. These are analysed as a function of the permanent magnet span. Based on the BEMF, the harmonics injection technique is adopted to increase the torque in the machine, while keeping the RMS current constant, thus without affecting the Joule losses. In order to understand how the torque is affected by the harmonics injection, considering different PMs rotor span, a comparison with and without injection is presented. It is shown how, thanks to specific harmonics injection, it is possible to reduce the permanent magnet content in SPM machines while generating the same torque performance of a motor with higher PM content, without harmonic injection. The analytical results are finally validated by means of Finite Element Analysis (FEA) considering two different PM rotor span, as a case study, highlighting the benefits of the proposed method.
Would Flipped Classroom be My Approach in Teaching Computing Courses: Literature Review

Adopting technology in education has been introduced in different models. Flipped classroom as one of the blended learning approach was tested and applied in teaching computing courses. This approach shows promising evidence that it is efficient through research evaluating it. This paper aims to review the research conducted in the flipped classroom, and in particular from computer science education perspective. The study involved a review of 20 studies that applied the flipped classroom in teaching computing courses. These studies involved experiments varying from longitudinal to a single semester evaluation. Indeed, the majority of these reports concerned a CS1 course (introductory programming course). Therefore, this paper discusses and reviews the current literature reporting research conducted on flipped classroom from computer science perspectives.
A school bullying detecting algorithm based on motion recognition and speech emotion recognition

School bullying is a common social problem among teenagers. It affects the victims both mentally and physically, and is considered as one of the main reasons for depression, dropping out of school, and adolescent suicide. For this reason, preventing school bullying is significant to the student’s mental and physical health. In order to detect bullying events in time, this paper proposes a bullying detecting algorithm based on motion recognition and speech emotion recognition. People wear an electronic equipment, which is used to collect his/her motion and speech data, to detect bullying events in real-time. In this paper, the authors extract five features from acceleration and gyro data for physical bullying detection. The PLP features are extracted for verbal bullying detection. Then authors use the Relief-F algorithm for feature selection, and the PPCA algorithm is used to reduce the dimensionality of the feature matrix. Finally, the authors use the KNN algorithm as the classifier to train the motion recognition model and the SVM algorithm as the classifier to train the speech emotion recognition model. With cross-validation, the average accuracy of the motion recognition system is 80.61%, whereas that of the speech emotion recognition system is 75.76%. The simulation results of the algorithm indicate that the anti-bullying detecting algorithm could identify the bullying event effectively.
Development of Bath Auxiliary Robot for the Disabled Elderly

The rapid aging of the population has put huge pressure on social nursing work. As an important part of elderly care, bathing can remove sweat and oil, relax muscles, promote blood circulation and improve skin's metabolic function and disease resistance, which is related to the physical and mental health of the elderly. In this paper, a set of intelligent bath care system for the elderly is introduced. The system automatically showers, mops, dries the elderly and alarms for abnormal physical conditions. Moreover, bathing parameters such as bath water temperature, scrubbing frequency and speed, drying temperature and wind speed can be set according to the different preferences and physical conditions of the elderly. It realizes the exchange of information among the elderly, equipment and nursing staff, with good cleaning effect and high degree of automation.
Surface Automation - Interacting with Applications using Black Box Approach

One of the biggest challenges of any Robotic Process Automation (RPA) tools is automating scenarios in tricky environments such as virtual machines (VMs) and remote desktops or when dealing with legacy applications where the technical know-how of the interfaces are limited. Typical approach to interacting with applications under regular scenarios is to use application technology specific APIs to build knowledge metadata. However, these approaches fail when it comes to automating in the black box environments because we cannot have technical understanding or APIs of the applications. Now, the only way to automate here is by taking the screenshot of the applications and understanding the controls available inside the screenshots using image processing algorithms. In this paper we present a novel approach for an end-to-end automation of such scenarios. We employ various AI routines to understand structural and semantic aspects of enterprise applications which in turn are used for building automations and we group all these techniques under a common umbrella of Surface Automation. Our approach involves using robust Optical Character Recognition (OCR) which internally uses LSTMs for reading texts like labels, Natural Language Processing (NLP) for semantic understanding of the page, Image Region Similarity for matching icons like home button and Computer Vision based Object Detection for recognizing predefined controls [text-fields, buttons, dropdown etc.].
Doping and Dopingless Tunnel Field Effect Transistor

For Low power consumption, an emerging device that depends on lowering the supply voltage with downscaling is proposed which is known as the Tunnel-FET (TFET). In general, MOSFETS (Metal Oxide Semiconductor Field Effect Transistors) is used for the low power transistors. Still, the SS (Sub-threshold Swing) of MOSFET is bounded to 60 mv/decade for further reduction in supply voltage. The dynamic and static power consumption of MOSFETs is increased with the downscaling. Hence, for energy-efficient and low power consumption with improved sub-threshold swing, TFET is auspicious replacement of MOSFETs. The structure of MOSFET and TFET is approximately like each other with a different fundamental working mechanism. TFET is a gated p-i-n diode structure, and current conduction in between the energy bands of the channel and source by switching mechanism of quantum tunneling (band to band tunnel mechanisms) through a barrier. This paper aims to do a literature review of TFET from physically doped TFET to Dopingless TFETs. This survey paper studies and reviews physically doped Double Gate TFET (DG-TFET) available for design. After that, dopingless TFET is studied and shows its dominating performance.
Implementation of Carrier-Cancellation Circuit for UHF RFID Reader RF Front-End using Genetic Algorithm Optimization

A problem of ultra-high frequency radiofrequency identification (UHF RFID) reader is the reading range because one antenna is used for transmitting carrier and backscattered signal. In this study, a carrier-cancellation circuit is used with a commercial directional coupler to obtain a good isolation between transmitter (Tx) and receiver (Rx) by suppressing the transmitting leakage signal. The suppression level of the circuit can be obtained by applying the Genetic Algorithm (GA) to optimize values of digitally tunable capacitors. Experimental result of the S24 of the directional coupler is approximately of -70 dB which showed the good isolation between Tx and Rx at 900 MHz.
A Novel Method for Routing Packet between Patient and Doctor using Sensor and Cloud

Hassle-free routing between two nodes without intermediate interference with better performance is a very challenging task. In the case of doctor and patient, it needs super speed to share several issues and remedial information with each other is projected as a routing algorithm with the sensor and cloud without any intermediate router by using IBM blue mix cloud. Direct contact with the destination router is possible by keeping an intelligent agent as a connector to the cloud and the sensors. A special header can be added behind the packet for the connection of several clouds or sensor-based destinations with priority. This technique can be used in the hospital for the establishment of a secure tunnel between patient and doctor.
A Fully Integrated CMOS Power Amplifier with a 133% Relative Bandwidth upon Multilayer Inductors

The quality factor of the inductor in the matching network, which is one of the keys to realize a broadband PA, needs to be decreased for expanding the bandwidth. The paper presents multilayer inductors, which have low quality factor and can tolerate large current, for expanding the bandwidth. The multilayer inductors (MIs) are used in a CMOS PA, which reaches a good performance in the measured 133% relative bandwidth.
Influence Analysis of Joint Effectiveness Partial Loss on the Motion Capability in the Space Manipulator

Aiming at joint effectiveness partial loss failure of space manipulator, an influence analysis method of motion capability is proposed. Firstly, the general model of joint failure is constructed; then it is introduced into the kinematic model and dynamic model of the manipulator to obtain the model of the space manipulator with joint effectiveness partial loss failure; finally, the manipulability is constructed to characterize the dexterity, and the dynamic load-carrying capacity calculation model is established to calculate the load-carrying capacity of the manipulator with joint fault. The simulation experiment is carried out to analyze the influence of joint failure characteristics (fault degree and location) on the motion capability of the space manipulator.
Addressing Spoofed DDoS Attacks in Software-defined Networking

Software-defined networking (SDN) has become a popular networking paradigm that provides a separation of control plane from the data plane devices. This separation offers various facilities such as flexibility, programmability, and a centralized global view of the complete network. However, SDN introduces various cyber-attacks due to its architectural design. In the present scenario, Distributed denial-of-service (DDoS) is one of the most severe attacks that makes the resources unavailable for normal users. Attackers use spoofed IPs in the DDoS attack and hide their original identity, which making it difficult to mitigate the impacts of such kind of DDoS attacks. An spoofed DDoS attack in SDN can affect all three important entities, such as switch, controller, and switch-controller channel. Therefore, in this work, we propose an statistical measure - z-score based mechanism against spoofed flooding DDoS attack (TCP-SYN) for attack detection. The proposed mechanism also mitigates the impacts of the attacks and avoids any additional communication between switches and controller. The performance results show that the proposed attack defense mechanism mitigates the spoofed TCP-SYN flooding attack efficiently and reduces the CPU usage on the victim host after attack detection.
A Review of Body Measurement Using 3D Scanning

The understanding of body measurements and body shapes in and between populations is important and has many applications in medicine, surveying, the fashion industry, fitness, and entertainment. Body measurement using 3D surface scanning technologies is faster and more convenient than measurement with more traditional methods and at the same time provides much more data, which requires automatic processing. A multitude of 3D scanning methods and processing pipelines have been described in the literature, and the advent of deep learning-based processing methods has generated an increased interest in the topic. Also, over the last decade, larger public 3D human scanning datasets have been released. This paper gives a comprehensive survey of body measurement techniques, with an emphasis on 3D scanning technologies and automatic data processing pipelines. An introduction to the three most common 3D scanning technologies for body measurement, passive stereo, structured light, and time-of-flight, is provided, and their merits w.r. t. body measurement are discussed. Methods described in the literature are discussed within the newly proposed framework of five common processing stages: preparation, scanning, feature extraction, model fitting, and measurement extraction. Synthesizing the analyzed prior works, recommendations on specific 3D body scanning technologies and the accompanying processing pipelines for the most common applications are given. Finally, an overview of about 80 currently available 3D scanners manufactured by about 50 companies, as well as their taxonomy regarding several key characteristics, is provided in the Appendix.
Digital Controller for Active Clamp based Synchronous Forward Converter for High Power Applications

The Digital technology playing vital role in power electronic industry by providing novel method to control the DC-DC converter. In this project closed loop control of the converter is built by using reprogrammable FPGA A3P250- VQ100. This FPGA is programmed to develop the four MOSFET gate pulses with required delay, to drive the Main MOSFET, Active clamp MOSFET circuit and secondary side synchronous MOSFETs. Active clamp reset technique is used to reset the Transformer core, which helps to operate the converter for wide input voltage range and to reduce the Voltage spikes across the Main MOSFET. This circuit allow the main MOSFET to turn on at zero voltage, which reduces its switching loss and helps in improving the overall efficiency of the converter. Due to the low forward voltage drop, the MOSFETs are used as rectifier in the secondary side of Forward topology. Isolated Magnetic Feedback generator is used to sense the output voltage. The 12 bit ADC is used to convert the feedback voltage from Analog to Digital. In this paper, concentrating on designing 150W DC-DC converter by using the Digital control technique. The realization of Digital PID algorithm and over current, input raw under voltage and over voltage protection is discussed.
Intelligent Decentralized State Estimation Algorithm Based on The Firefly (FA) Algorithm for Distributed System

The state estimator depends on many factors, such as data sensitivity regarding the accuracy of the sensor, the availability of raw data, the network database accuracy, and the time skew of data. This paper introduces an intelligent decentralized State Estimation algorithm based on the firefly (FA) algorithm for distributed power systems. The mathematical procedure of distribution system state estimation which utilizing the information collected from available measurement devices in real-time. Static state estimation based upon majority consensus is proposed for radial power distribution systems. Rather than just look at the local networks, we should use the new bus as the method for analyzing the whole scheme. Therefore, the model reduces to a system of equations with an undefined or an underdetermined nonlinear system, in which the state elements associated with an agent overlap with adjacent agents and a radial distribution. The algorithms to approximate the states of these structures were first developed through a key method used the proposed algorithm to equate this approach with weighted least squares strategy, Particle swarm optimization (PSO), and genetic algorithm (GA). In the end, we present the results of the application of the developed approach to a network based on the IEEE 13 bus test system. The result proved to be computationally efficient and accurately evaluated the impact of distributed generation on the power system.
Design and Development of Power Management Framework for Electrical Vehicles in Smart City Grid

Electric Vehicles are the future of mobility, which are dependent on battery technology, and as estimated, 70% of the two-wheeler and 50% of the EV's, price is estimated to be of batteries. In this dissertation work, power management framework model has been designed and developed for electric vehicles in smart city grid and the proof of concept has been disclosed. After an intense literature review, to design the framework for power management in EV's, three layered architecture is selected and the layers are divided as organisation layer, business logic layer, and analysis layer. The organisation is front layer for various electric vehicles sellers, developers and will act as a connecting layer for the user end and the proposed framework. The organisation layer is divided into three subfunctions namely: data segregation, data evaluation function, and data marshalling function. Where, data segregation is done w.r. t. , core, luxury, and safety parameters function. Second layer has been divide into four sub-functions namely: data acquisition function, data un-marshalling function, data representation function, and data marshalling function. Third layer has been divided into four sub-functions namely: data acquisition function, data un-marshalling function, data segregation with respect to core, luxury, and safety parameters function and data analysis function. The proof of concept is given using the NetBeans software and framework designed has three layers which will be represented as three different server stations and the results obtain gives a very clear acceptance confirmation of the designed framework.
A Comparison of Two-Stage Classifier Algorithm with Ensemble Techniques On Detection of Diabetic Retinopathy

The Diabetic retinopathy is disease of the human eye that causes retinal damage in diabetic patients. It further leads to the blindness. The machine learning techniques plays an important rule to predict the early diabetic retinopathy which avoided from the intensive labor. In this paper we used the novel technique, the Two Stage Classifier, an ensemble technique which combines various machine learning algorithms for classification. In the subject paper, the classifier is applied to predict Diabetic retinopathy (DR), a disease of the human eye that causes retinal damage in diabetic patients and ultimately lead to complete blindness. The problem lies in the fact that it is time consuming to detect this disease but an early detection of the disease is essential to avoid complete blindness. We apply machine learning algorithms to determine the existence of DR and compare the accuracies of the applied techniques. The Two Stage Classifier, turns out to be better not only in terms of parallelism but also in terms of accuracy.
Real Time Vehicle Detection and Colour Recognition using tuned Features of Faster-RCNN

Being the most dominant part of the vehicle, colour anticipate vital role in vehicle identification. Thus, colour also plays significant part in Intelligent Transportation System (ITS) and can be very effective in various applications of ITS. In past, most of the work had done on colour recognition of vehicle are not able to achieve the high accuracy because they rely on hand-crafted feature i.e. Speeded Up Robust Features (SURF), Scale Invariant Feature Transform (SIFT) and Histogram of Oriented Gradient (HOG). In this work, we proposed a solution by utilizing one of the latest deep learning algorithm for the detection of vehicle and the classification of detected vehicles colour. Proposed methodology is based on the tuned features of Faster R-CNN and achieved the good results as compared to current state of the art techniques. In addition to that, this work is also contributes towards the dataset collection of related vehicles being used in Pakistan. Proposed method outperformed the previous works by achieving 95.31% accuracy on testing data. The robust results in terms of accuracy and the generation of dataset depicts the novelty of proposed technique in the literature.
Research and verification of a new relaxation technology based on virtual reality for human psychological security and psychosomatic recovery

Previous research has shown that virtual reality (VR) can be used to treat a number of mental disorders (anxiety disorders, phobias). Therefore, this study speculates that intelligent relaxation technology with virtual reality experience is helpful for emotional recovery and stress relief. The subjects were randomly assigned to experience the natural environment through 360-degree virtual reality (the experimental group) or through a traditional 2D video (the control group). Subjects will be induced respectively using HMD after the stress and desktop screen with music for 15 minutes restorative treatment, treatment of subjects at the same time with two aspects of subjective and objective physical measurement and psychological measurement. Two factors repeated analysis of variance and nonparametric Friedman detection analysis shows that using VR than having 2 d environment controls more relief effect and the positive influence. These findings suggest the potential importance of virtual reality as a tool in this area of research. To promote human mental health and ensure psychological security. It provides a prospect for research strategies in the complex field of human-nature interaction.
Time-resolved ion flux and impedance measurements for process characterization in reactive high-power impulse magnetron sputtering

A new planar ion flux probe, based on the Sobolewski method for time-resolved plasma characterization in inherently noisy pulsed plasma discharges, has been developed. The probe was evaluated in a high-power impulse magnetron sputtering (HiPIMS) process, which is a promising ionized physical vapor deposition technique based on pulsed plasma discharges used to engineer thin films with improved properties. Both nonreactive (pure Ar) and reactive (Ar/O2) deposition processes were investigated using a Ti sputtering target. It was found that the process exhibited a nearly hysteresis-free and stable transition region at the chosen deposition conditions. Time-resolved measurements of the absolute ion flux impinging on the probe placed at the substrate position, as well as of the probe sheath impedance, were recorded in the metal, transition, and compound modes during the HiPIMS pulse. Gradual changes in the measured ion flux, as well as the impedance, were seen when transiting from the metal mode to the compound.
Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem

Group scheduling problems have attracted much attention owing to their many practical applications. This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time, release time, and due time. It is originated from an important industrial process, i.e., wire rod and bar rolling process in steel production systems. Two objective functions, i.e., the number of late jobs and total setup time, are minimized. A mixed integer linear program is established to describe the problem. To obtain its Pareto solutions, we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods, i.e., an insertion-based local search and an iterated greedy algorithm. The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers. Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.