Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning based Handover Execution Algorithm for Heterogeneous Wireless Networks

Amalgamation of mmWave and LTE sub-6 provides an advantage in enhancing the reliability, communication bandwidth and optimized coverage of intelligent network and its significant applications; however, identifying the right mmWave RRU (remote Radio units) is one of the main obstacles due to blindness coverage of directional beams. Moreover, mmWave network depends on edge cloud deployment to satisfy the low latency of smart applications. Further, the minimization of energy consumption for handover is necessary due to the limited battery for IoT-device. Hence, it is necessary to minimize the signal overhead in handover process; at first, this research work focuses on the efficient handover mechanism among the mmWave and LTE, later automated handover execution process between mmWave and LTE is developed using XGBoost classification mechanism. The XGBoost algorithm is used predict the handover success rate through channel information based on the sampling window. At last, XGBoost based handover mechanism along with the standard handover mechanism is integrated to minimize the signal overhead and improvise handover success rate. Moreover, performance evaluation is carried out through varying the IoT device and obtained result shows that XGBoost-based handover proves to be better than the existing mechanism of KNN-based handover execution algorithm.
A Position Sensorless Control Strategy for Brushless DC Motor Based on Bus Current Hysteresis Loop

The position Sensorless technology of brushless DC motors and the suppression of torque ripple have been the hotspots and difficulties of research. This paper provides a novel digital-analog hybrid position detection method of brushless DC motor which have a neutral point. The motor bus current is used as the feedback signal of the hysteresis loop controller instead of phase current. The Sensorless control and torque ripple suppression can be realized at the same time under the bus current hysteresis loop control. This method only needs to install a current sensor on DC side of the inverter to control motor’s current of each phase. Digital-analog hybrid position sensor technology has a simple structure, and can further optimize software algorithms and improve system efficiency. Simulation and experimental results verify the correctness and effectiveness of the theoretical analysis.
Design of A Dual-Band Rectangular MSA Using U, L, O and rectangular Slots on Metal Patch and Inverted L like DGS on Ground Plane

A miniaturized dual-band rectangular MSA (Microstrip Antenna) has been presented in this article. The miniaturization and two bands characteristics have been achieved by inserting U, L, O, and rectangular type slots on the metal rectangular patch and DGS (Defected-Ground-Structure) on the ground. Suggested MSA provides two bands, one range from 3.13-3.27 GHz with 3.2 GHz resonant frequency and 4.4% of bandwidth while the other one ranges from 4.93-5.08 GHz with resonant frequency of 5.0 GHz and 3% of bandwidth. The gain (simulated) at 3.0 GHz and 5.0 GHz are 1.18 dBi and 1.67 dBi respectively. The achieved compactness of the suggested MSA is 71.1%. The radiation patterns are acceptable at 3.2 GHz and 5.0 GHz. The suggested MSA is modelled by Ansoft Designer V2 software and energized by 50 Ω coaxial feed line. Return loss, maximum gain and radiation patterns are discussed here. The antenna is very simple to model and very low-cost dielectric material is used. The antenna is useful for wireless applications.
Separation of Virtual Machine I/O in Cloud Systems

Virtualization is widely used in modern computer systems ranging from personal computers to cloud servers as it provides various heterogeneous platforms at low cost. However, due to its nested software structure in host and guest machines that are difficult to harmonize, it is challenging to manage resources efficiently in virtualized systems. In this article, we anatomize the overhead of virtualization associated with file system journaling and discover the excessively frequent commits that take place in virtualized systems. This is because the host triggers commit on every write request from all guest machines. This also generates unnecessary write traffic to storage. To remedy these problems, we propose the VM-separated commit, and implement it on QEMU-KVM and Ext4. Specifically, we devise a data structure that manages modified file blocks from each guest as a separate list and split the running transaction list into two sub-transactions based on this data structure upon a commit request from a guest. Measurement studies with Filebench and IOzone benchmarks show that the proposed policy improves the I/O throughput by 19.5% on average and up to 64.2% over existing systems. It also reduces the variability in performance.
Offloading-Efficiency Maximization for Mobile Edge Computing in Clustered NOMA Networks

This paper considers the problem of offloading-efficiency maximization for a mobile edge computing (MEC) nonorthogonal multiple-access (NOMA) network with user clusters. The goal is to maximize network offloading-efficiency via joint power allocation, and computing resource allocation (J-PA-CRA), under the partial offloading mode, and subject to delay and transmit power constraints. However, the formulated problem happens to be non-convex, and thus is computationally-prohibitive. In turn, a low-complexity solution procedure is proposed, which optimally solves problem J-PA-CRA via successive convex approximation. Simulation results are provided to validate the proposed solution procedure, illustrating that it yields the optimal network offloading-efficiency, in comparison to the J-PA-CRA scheme (solved via a global optimization package), and superior to its OMA counterpart schemes (e.g. FDMA and TDMA).
RIOT and OpenWSN 6TiSCH: Happy Together

Short development cycles, application-field diversity, and requirements on network size or reliability put an ever increasing strain on Internet of Things (IoT) application developers. Real-time embedded operating systems (RTOS) aim to provide a key set of features, abstractions and services that enable faster development. To fulfill the promise of wire-like communication reliability, wireless standards such as WirelessHART, ISA100.11a and 6TiSCH have been developed and are used in the industry. Keeping these networks synchronized requires precise timing information from the underlying hardware. However, the hardware abstractions of an RTOS do come with an overhead, and the question arises on how these abstractions impact the performance of a complex network stack. To study this, we integrated Open-WSN, a standards-compliant open-source implementation of the 6TiSCH network stack, with RIOT, a prominent open-source RTOS. We compare the minimalistic "bare metal" approach of OpenWSN with RIOT’s full-fledged RTOS environment. We study the impact on network performance, power consumption and real-time application properties. On the one hand, we show that using RIOT to execute a 6TiSCH stack does not degrade power consumption or network performance. On the other hand, we demonstrate how RIOT brings improvements on the time it takes to execute application tasks.
Petri Nets Based Verification of Epistemic Logic and Its Application on Protocols of Privacy and Security

Epistemic logic can specify many design requirements of privacy and security of multi-agent systems (MAS). The existing model checkers of epistemic logic use some programming languages to describe MAS, induce Kripke models as the behavioral representation of MAS, apply Ordered Binary Decision Diagrams (OBDD) to encode Kripke models to solve their state explosion problem and verify epistemic logic based on the encoded Kripke models. However, these programming languages are usually non-intuitive. More seriously, their OBDD-based model checking processes are often time-consuming due to their dynamic variable ordering for OBDD. Therefore, we define Knowledge-oriented Petri Nets (KPN) to intuitively describe MAS, induce similar reachability graphs as the behavioral representation of KPN, apply OBDD to encode all reachable states, and finally verify epistemic logic. Although we also use OBDD, we adopt a heuristic method for the computation of a static variable order instead of dynamic variable ordering. More importantly, while verifying an epistemic formula, we dynamically generate its needed similar relations, which makes our model checking process much more efficient. In this paper, we introduce our work.
Proposal of NTMobile System Model Using vCPE for Operational Efficiency

The authors have proposed Network Traversal with Mobility (NTMobile) that achieves both IP mobility and End-to-End connectivity in IPv4/IPv6 networks. In order to achieve end-to-end encrypted communication between IPv4 and IPv6 networks, it is necessary to relay a server in the cloud. This system model includes problems such as increased transmission delays and redundant communication paths. This paper proposes a new NTMobile system model that applies the concept of mobile edge computing. The proposed system model solves problems of the conventional model by deploying NTMobile servers on Virtual Customer Premises Equipment. The results of the verification of the proposed model in a virtual environment show that the communication performance is expected to be improved.
Preparation and Characterization of Albendazole β-Cyclodextrin Complexes

Albendazole (ABZ), mebendazole (MBZ), and ricobendazole (RBZ) are low-soluble anthelmintic benzimidazole carbamate drugs. To increase their aqueous solubility, three different types of β-cyclodextrins (CyDs): β-cyclodextrin (CD), hydroxypropyl-β-cyclodextrin (HPCD), and methyl-β-cyclodextrin (MCD) were used. Solubility depended on the type of CyDs. Increased solubility was obtained when the more substituted CyDs (HPCD or MCD) were used instead of nonsubstituted CD. Stability constants were calculated assuming a 1:1 stoichiometry. Calculated stability constant values depended on initial solubility of drug and pH of the medium. Solid ABZ complexes were prepared by coprecipitation and freeze-drying methods. These products were compared with physical mixtures of ABZ and CyDs. The characterization of these products was made by differential scanning calorimetry (DSC) and drug release studies. True inclusion complexes were obtained only by the freeze-drying method. Drug release studies showed that the freeze-dri.
Energy Management of Superconducting Magnetic Energy Storage Applied to Urban Rail Transit for Regenerative Energy Recovery

Recent urban rail vehicles use regenerative braking that lead to high energy efficiency. However, the intermittency and random nature of regenerative power causes limitation of regenerative energy recycling rate in DC electrification. To overcome the problem, energy storage system (ESS) plays an important role. Many applications of ESS to railway systems have been seen by using batteries, supercapacitors, and flywheels.This paper proposes to apply the superconducting magnetic energy storage (SMES) to railway systems as ESS. SMES holds bright application prospects due to its quick response, high power density and high energy storage efficiency. One of the major differences from other storage devices is that SMES has characteristics of a DC current source. It is inevitable to consider the converter configuration and energy and power management in the combination of SMES and DC electrified feeders.In this paper, energy and power management is proposed taking characteristics of SMES and its converter into account. Besides, the converter topology and specification of SMES will be also investigated. Finally, it will be demonstrated that SMES system for railway ESS will work to smooth the fluctuation of regenerative power.
Causality Analysis of Emotional States from EEG Response

This paper proposes a novel study of the feature elements in corresponding emotional EEG states to investigate the appearance of repeating or residual elements which serve as the foundation of memory or causality in EEG signals. Features are first preprocessed and filtered to remove unwanted artifacts, and then are extracted by utilizing various well-known techniques including peak signal to noise ratio, Manhattan distance metric, mean cross correlation, mutual information and especially Granger causality. Once extracted, the host of features is used to perform statistical analysis on the EEG data. It is noteworthy that a key mechanism that has been used in this study is the consideration of only consecutive trials while performing statistical analysis for stronger establishment of our motive. Experimental results confirm that certain transition of emotional states such as fear to excitement and anger to sadness are more likely to be predictable given its past values than others.
Strukturen der Hexafluorometallate(III)

Verbindungen mit der Formel MeXF6 kristallisieren nach verschiedenen Strukturtypen, die sich nur durch die Anordnung der oktaedrischen XF6-Baugruppen unterscheiden. Als Grundtyp dieser Verbindungsklasse kann der (NH4)3FeF6-Typ angesehen werden. Von ihm lassen sich alle anderen Strukturen ableiten.
Simulation Study of Orthogonal Polyphase Code for Spectrum Shared Networked Radar

In Spectrum-Shared Networked Radar (SSNR), multiple radars share the same frequency band for transmitting their own waveforms and receiving echo signals. In SSNR, an echo signal originated from one radar can be distorted by the signals from the other radars. To address this issue, several researchers have proposed to incorporate an orthogonal polyphase code into a pulse-compressed waveform. Even though there are several system parameters in creating an orthogonal code set, previous works have not investigated the impact of the parameters on the performance of SSNR thoroughly. Moreover, the previous works have not considered factors affecting the performance of SSNR at receiver side including noise and wireless channel. In this paper, we perform an in-depth simulation study of orthogonal polyphase codes used in SSNR to reveal the impact of the system parameters on the performance of SSNR. Based on the simulation findings, we suggest guidelines to generate an orthogonal code set for improving SSNR.
Suppression of Cross-Band Scattering in Interleaved Dual-Band Cellular Base-Station Antenna Arrays

In this article, novel techniques to suppress cross-band scattering in interleaved dual-band base station antenna arrays are presented. Firstly, the detrimental effect of high-band (HB) elements on low-band (LB) radiation performance is theoretically analyzed. With a thorough analysis, a capacitance-loaded HB element is innovated to solve the issue and restore the LB patterns. Secondly, to eliminate the reverse HB scattering caused by the LB element while enhancing the bandwidth of the LB element, a novel double-arm choked cross-dipole configuration is proposed. A compact array section is built with these innovated elements. The experimental results of the array show a largely suppressed cross-band scattering with stable radiation performance across the well-matched bands, demonstrating the effectiveness of the presented techniques.
Intelligibility Improvement for Remote Speech Communication Between Different Noise Environment

Remote speech communication, such as telephone calls, is sometimes difficult because the speaker and listener may be in different noise environments. The Lombard effect is a reflex that occurs when a speaker increases their vocal effort while speaking in the presence of noise. On the basis of this effect, we developed a system for improving audibility. The performance was evaluated in a listening experiment and showed that the system worked well, especially for noisy environments.
Automatic Diabetic Retinopathy Classification with EfficientNet

Using the recent known EfficientNet architecture of deep convolutional neural network (CNN), we present an automatic detection of diabetic retinopathy (DR) from given retinal images. We experiment with subsets of the Kaggle diabetic retinopathy dataset consisting of retinal images with varied diagnostic quality. To address the quality variation, we incorporate two preprocessing steps, i.e. contrast limited adaptive histogram equalization (CLAHE) and image central cropping. We trained EfficientNet-B4 and EfficientNet-B5 model on two Kaggle subsets with different class proportions. In this paper, we propose an automatic early diagnosis of diabetic retinopathy which gained 0.7922 / 83.87% and 0.7931 / 83.89% of quadratic weight kappa and accuracy score on EfficientNet-B4 and EfficientNet-B5 respectively.
Low-cost Hardware Redundancy for Fault-mitigation in Power-constrained IoT Systems

Systems deployed in remote applications must operate with a limited power supply. This is particularly true for Internet of Things (IoT) devices which are diverse in nature but have one factor in common: a low power-budget constraint. To which end, designers focus on optimizing circuits implemented in IoT systems for lowest possible power-consumption whilst maximizing performance. However, in case of safety-critical applications, fault-tolerant circuits are required which stress energy-consumption. The commonly known redundancy schemes are easily implementable, but result in large hardware overheads. In this paper, we suggest low-cost hardware redundancy schemes for fault-mitigation in energy-constrained IoT systems. Particularly, we demonstrate that a redundancy scheme with circuit pre-processing is realizable which achieves the same fault-coverage as that of a triple modular redundancy, but at a significantly lower hardware overhead.
Development of A Real 3D Display System

This paper introduces a three-dimensional light field display system, which is composed of a high-speed projector, a directional scattering mirror, a circular stainless-steel bearing plate, a rotating shaft and a high-speed micro motor. The system reduces information redundancy and computational complexity by reconstructing the light intensity distribution of the observed object, thus generating a real three-dimensional suspended image. The experimental results show that the suspension three-dimensional image can be generated by properly adjusting the projection rate of the image and the rotation speed of the rotating mirror (i.e. the motor speed). The clarity and accuracy of a three-dimension display depending on the number of slices selected, meaning that the more slices can be projected per minute, the finer the three-dimension display. Finally, this study provides the basic parameter matching, which shows the feasibility of developing a real light field 3D display system with stable performance, strong portability, easy implementation, and low cost. In short, naked-eye 3D allows the observer to view objects from any angle and direction, without the need for any auxiliary tools (such as glasses, helmets, etc.).
Improving DDoS Detection in IoT Networks Through Analysis of Network Traffic Characteristics

The evolution of devices allowed the evolution of service provision, applying new technologies based on the Internet of Things (IoT). Most of IoT devices have security vulnerabilities, making them susceptible to Distributed Denial of Service (DDoS) attacks. Thus, it is necessary to apply solutions that can detect it in IoT networks based on data about the network traffic. However, there is no standard of the most suitable traffic characteristics for DDoS detection, since the use of inappropriate characteristics harms the detection. Within this context, this paper presents an analysis of the most important traffic characteristics for detecting DDoS in IoT networks, in order to support a detection mechanism based on Machine Learning (ML). Experiments using a real dataset suggest that the proposed mechanism has an accuracy close to 99% when the most suitable characteristics are selected.
Fault Feature Extraction of Rolling Bearing Based on Improved VMD-SVD Joint Denoising and Synchrosqueezing Wavelet Transform

Bearing faults are the main contributors to the failure of motors. To solve the problem of extracting fault features from bearing signals with low signal-to-noise ratio, a method combining improved Variational Modal Decomposition (VMD) –Singular Value Decomposition (SVD) joint denoising with synchrosqueezing wavelet transform is proposed. The proposed method is based on the analysis of VMD-SVD theory. In view of the existing problems of VMD-SVD, the reconstruction part of VMD is cancelled, so that the noise is eliminated to the maximum extent before reconstruction. Using RSVD instead of SVD, and PMI index instead of energy index during reconstruction, make the method more suitable for fault diagnosis of rolling bearings. Finally, the effectiveness and superiority of the proposed method are verified by simulation of motor bearing and measured data.
Engineering student experiences of a remotely accessed, online learning environment

This study explores engineering student experiences of the emergency, remote online teaching and learning environment initiated due to the COVID-19 pandemic. The study uses a Likert scale survey based on the Community of Inquiry model evaluating Social, Teacher, Learner, and Cognitive presence in the online environment. Insights are provided relating to the design of the teaching and learning environment, student behaviours, assessment, social learning and how students cope in this mode. The results from this study can be used to better understand how students experience this environment, informing design and integration in similar interventions in the future.
Remaining Useful Life Prognosis Based on Ensemble Long Short-Term Memory Neural Network

Remaining useful life (RUL) prognosis is of great significance to improve the reliability, availability, and maintenance cost of an industrial equipment. Traditional machine learning method is not fit for dealing with time series signals and has low generalization and stability in prognostic. In this article, a novel ensemble long short-term memory neural network (ELSTMNN) model for RUL prediction is proposed to enhance the RUL prognosis accuracy and improve the adaptive and generalization abilities under different prognostic scenarios. The ELSTMNN contains a series of long short-term memory neural networks (LSTMNNs), each of which is trained on a unique set of historical data. A novel ensemble method is first proposed using Bayesian inference algorithm to integrate multiple predictions of the LSTMNNs for the optimal RUL estimation. The effectiveness of the ELSTMNN-based RUL prognosis method is validated using two characteristically different turbofan engine data sets. The experimental results show a competitive performance of the ELSTMNN in comparison with other prognostic methods.
Complexity Evaluation of Medical Image Data for Classification Problem Based on Spectral Clustering

Deep convolutional neural networks (DCNNs) have been popular with medical image classification problems in recent years. However, training a DCNN model on the sizeable medical dataset requires repeated manipulation to achieve the desired results and hence is time-consuming. Since there is an inevitable link between DCNN training results and the complexity of the medical dataset, it is essential to accurately evaluate the medical dataset’s complexity before training the DCNN models. In this paper, we propose an efficient method to assess the medical dataset’s complexity based on spectral clustering. The experimental results show that the medical dataset complexity calculated with our approach is not time-consuming and has a high correlation with DCNN test accuracy.