Miracle-dz/UniFAR-FTUs huggingface dataset:
Microstrip Array Antenna Design with Directivity Enhancement Using Reflector Surface

The goal of this paper is to enhance the Directivity in Microstrip Antenna Array by adding Reflector Surface plane in ground plane. The Microstrip Antenna (MSA) is an antenna that is widely used in microwave waves which has many advantages, however, it represents a low built-in directivity. The suggested antenna array is made by using metamaterial reflecting surface to get the maximum directivity and high gain. In this research, a MSA is designed at 2.1 GHz and maximum directivity and gain are studied. A coaxial probe feeding technique is adopted to feed the antenna array. CST (Computer Simulation Technology) simulation of the suggested single and array antenna shows enhanced the gain, directivity and return loss at the center frequency of 2.1 GHz. At single element antenna, the gain is enhanced from 3.3225 to 6.462db and the directivity from 3.310 to 6.462dbi. Improvement in the performance of the design is achieved by using two elements antenna approach. Further improvement achieved in the work of the design by using four elements antenna approach.
Evading Botnet Detectors Based on Flows and Random Forest with Adversarial Samples

Machine learning is increasingly adopted for a wide array of applications, due to its promising results and autonomous capabilities. However, recent research efforts have shown that, especially within the image processing field, these novel techniques are susceptible to adversarial perturbations. In this paper, we present an analysis that highlights and evaluates experimentally the fragility of network intrusion detection systems based on machine learning algorithms against adversarial attacks. In particular, our study involves a random forest classifier that utilizes network flows to distinguish between botnet and benign samples. Our results, derived from experiments performed on a public real dataset of labelled network flows, show that attackers can easily evade such defensive mechanisms by applying slight and targeted modifications to the network activity generated by their controlled bots. These findings pave the way for future techniques that aim to strengthen the performance of machine learning-based network intrusion detection systems.
Experimental QoS Optimization for Haptic Communication Over Tactile Internet

So far, most haptic applications are standalone systems or endeavors to provide collaborated haptic virtual environments. With the emergence of the Tactile Internet (TI), ultra-low-delay and ultra-high-reliable communications will enable a paradigm shift from traditional content-oriented communication to control-oriented communication. Specifically, The human-in-the-loop Tactile Internet enables the vision of delivering human skills e.g, feeling and manipulating, in addition to the human knowledge e.g., seeing and hearing, remotely, adding more life to the Internet of skills. Within this paradigm, human multisensory information for interaction and communication with the remote environment needs to be exchanged. In this paper, we present an experimental study to optimize objective quality evaluation for multimodal communication especially haptic, over the Internet. For that purpose, a simulated haptic model based on the ALPHAN protocol was implemented on Riverbed modular to generate real haptic traffics over an infrastructure that mimics the TI, the model was used to select appropriate Diffserv QoS solutions in a large-scale collaborated haptic environment. The outcome of the study found that deploying custom queuing with low latency queue (LLQ) or Priority Queuing (PQ) in conjunction with ALPHAN protocol can be used to dramatically enhance the network performance of haptic communication.
Development of Intelligent Riding Comfort Monitoring System for Automated Vehicle : Conceptual structure and experiment design

Clearly, the automated vehicle technology is rapidly advancing. Previous studies had contributed mainly on lane keeping, collision avoidance and coordination control for the driving safety and improving efficiency. And recently, researchers have focused on the driving comfort factors, because eventually the automated vehicle will mainly transport people. Therefore, it is needed to concern about how people react to the system. This paper shows the conceptual structure of the intelligent riding comfort monitoring system for the reflection to the actual driving control. The experiment is designed for the accurate human behavior monitoring. The proposed scheme can potentially be applied for many types of the automated vehicles to improve the perceived quality of automated driving.
The Influence of Strands and Bundle-Level Arrangements of Magnet Wires on AC Losses in the Winding of High — Speed Traction Machine

The power density of electrical machines required for transport applications has become a critical aspect. With most of the power loss in high power density electrical machines often being generated in the stator winding assembly, it is necessary to reduce these losses. Also, the effects of strands and bundle positions in the slot on the AC losses in the winding are often overlooked. This paper gives an analysis into such effects on an existing high – speed traction motor design with the aim of optimising the conductor layout in the slot for minimizing the total loss.
Blockchain Implementation Assessment Framework, Case Study of IoT LPWA Licensing in Indonesia

Internet of Things devices is more common and the growth of LPWA (Low Power Wide Area) technology is unavoidable. However, the regulation for this kind of technology is lack behind even though there will be multiple licensing and certification from national regulator and from industry. It will not easy to maintain and check the device's licensing for user and regulator, moreover if the product already had another type of licensing from abroad. It often happens that the vendor files a fake license claims of his products to fulfill the procurement. Therefore, licensing management is required for everyone goodness in the IoT industry. Blockchain implementation assessment framework can be used for assessing this case.
Numerical Analysis of ZnTe Based Solar Cell with Sb2Te3 Back Surface Field Layer Using SCAPS-1D

This paper presents the optimization of a ZnTe based solar cell with the following structure: SnO<inf>2</inf>/n-ZnO/n-CdS/p-ZnTe/Sb<inf>2</inf>Te<inf>3</inf>/BC (Cu) where Sb<inf>2</inf>Te<inf>3</inf> has been inserted as a back surface field layer to reduce the minority carrier recombination at the back contact. An efficiency of 18.33% (V<inf>oc</inf> = 0.47V, J<inf>sc</inf> = 57.75 mA/cm<sup>2</sup>, FF = 67%) has been achieved using the SCAPS-1D simulator with thin absorber layer(ZnTe) of 0. 1μm. The thickness of the window layer (ZnO), absorber layer (ZnTe) and buffer layer (CdS) have been also optimized and the result will be presented in the paper.
Precision Agriculture Monitoring System Using Green Internet of Things (G-IoT)

The existing agriculture applications incorporating IoT are helping to increase the productivity of the crops; however, these applications are also introduced some disadvantages, therefore the current researchers are working to defeat these challenges in the futuristic applications by deploying new technologies with no or minimum negative impact on the environment and human health; Green IoT (G-IoT) and green nanotechnology are appear. These technologies are not easy to be used by farmers. So, this research work aims to build a realtime, cost-effective Precision Agriculture Monitoring System with less power consumption, less Green House Gas (GHG) emissions, and a user-friendly interface to help the farmers monitor the parameter variations of their farm (weather, water, soil, pest detection, intrusion detection, fire detection) periodically from anywhere and at any time using their smart-phones. The proposed system will act as a decision support system helps farmers to take the appropriate actions based on the farm parameter's variation; by sending an alert email to the farmer when it is required.
Assessment of Output Voltage Quality of Three-Phase Multilevel Inverter with Nearest Vector Selecting Space Vector Control

The paper presents the nearest vector selecting space vector control (SVC) of any arbitrary MLVSI circuit with any arbitrary number of the equal feeding DC voltage levels. The linear mode LabViewmodel for the SVC digital controller is designed in two variants of the reference signals sampling. The MLVSI output voltage THD and integrated voltage harmonics factors (IHF), needed for computing of the load circuit voltages and currents and their quality indices, are estimated as functions of the sampling frequency index).
GPR image deconvolution from antenna current waveform

This work is aimed at the improving the quality of the electromagnetic images of the subsurface medium, produced by a ground penetrating radar (GPR) where the investigated features of electrically contrast objects and interfaces are usually blurred due to inherent oscillations of the probing pulse. Parallel measurements of the aerial signal yield input data for an inverse problem allowing one to estimate the antenna current waveform and to clean up the GPR image.
Variable Step-Size Discrete Cosine Transform Diffusion LMS over Multi-task Networks

In this paper, new variable step-size transform domain (VSSTD) algorithm is developed for Diffusion Least Mean Square (DLMS) multi-task networks with system identification application over Wireless Sensor Networks (WSNs). Our main contributions are the theoretical derivations of convergence analysis of Discrete Cosine Transform DLMS (DCTDLMS) algorithm considering adaptive combiners. Our simulations showed performance improvement compared to the traditional DLMS.
A distributed disagreement-based protocol for synchronization of uncertain heterogeneous agents

In networks with heterogeneous and uncertain agents, fixed-gain control can lead to synchronization only if the uncertainties are relatively small. If the uncertainties are larger, we need to develop adaptive-gain approaches to achieve synchronization. In this work we propose an adaptive synchronization protocol, in case of full-state measurement, for uncertain heterogeneous agents based on a distributed disagreement reasoning. Specifically, we first define unknown gains (feedback and coupling gains) that could lead all agents to a homogeneous behavior and thus synchronization: however, since these gains are unknown in view of the unknown dynamics, we design adaptive laws for these gains that lead the agents toward synchronization. The adaptive laws are driven by a disagreement error which is calculated among neighbors: a Lyapunov analysis is presented for showing convergence of the synchronization error to zero.
Speech Recognition and Correction of a Stuttered Speech

The aim of this paper is to develop an algorithm to enhance speech recognition of a stuttered speech. Stuttering is a disorder that affects the fluency of speech by involuntary repetition, prolongation of words/syllables, or involuntary silent intervals. Current speech recognition systems fail to recognize stuttered speech. Methods to detect stutter have been reported in literature but efficient techniques for stutter correction have not been reported. This paper addresses this issue and proposes methods to detect and correct stutter within acceptable time limits. To remove prolongation(s) from the sample, amplitude thresholding through neural networks is developed. Repetitions are removed through string repetition removal algorithm using an existing Text-to-Speech (TTS) system. Thus, the output signal, void of all stutters, produces better speech recognition.
Linear Algebraic Formulation of Edge-centric K-truss Algorithms with Adjacency Matrices

Edge-centric algorithms using the linear algebraic approach typically require the use of both the incidence and adjacency matrices. Due to the two different data formats, the information contained in the graph is replicated, thereby incurring both time and space for the replication. Using K-truss as an example, we demonstrate that an edge-centric K-truss algorithm, the Eager K-truss algorithm, can be identified from a linear algebraic formulation using only the adjacency matrix. In addition, we demonstrate that our implementation of the linear algebraic edge-centric K-truss algorithm out-performs a Galois' K-truss implementation by an average (over 53 graphs) of more than 13 times, and up to 71 times.
Improvement of Space Charge Behaviors of PP/ULDPE Blends by Nano Graphene for HVDC Cable

Polypropylene (PP) with excellent dielectric properties will make a further progress for its application in HVDC cables, if its poor toughness can be improved. Besides, the space charge accumulation is still an important issue for HVDC cable polymer insulation, which can cause a distortion of electric field, accelerate the ageing of insulation. In this paper, PP is blended with the elastomer of ultralow density polyethylene (ULDPE) content of 15wt% by melt blending method to modify its stiffness and brittleness. The graphene nanoplatelets with different content of 0, 0.005, 0.01wt% are introduced into the PP/ULDPE (15 wt%) blends to improve its dielectric properties. The dc conductivity, space charge behaviors and breakdown strength of PP/ULDPE/graphene nanocomposites are then investigated. Experimental results show that PP/ULDPE/graphene nanocomposites with the nanofiller content of 0.01 wt%, have lower dc conductivity, less space charge amount and higher breakdown strength. The improved dielectric properties can be attributed to the interfacial zones between graphene nanoplatelets and PP/ULDPE blends, which can restrict the carrier injection and transportation in polymer.
BTrack: Using Barometer for Energy Efficient Location Tracking on Mountain Roads

This paper presents the design and implementation of BTrack, a new approach for location tracking on mountain roads. It is as accurate as the GPS on common smartphones, but costs much less energy. To design such a location system, we carried out a nation-wide online survey to confirm the desirability for low power location tracking system, and the result is positive. Then, we proposed BTrack, and it makes two key technical contributions. The first is to propose a dynamic transition matrix hidden Markov model to combine the barometer and accelerometer reading hints for estimating the location of the user. The second is to design some novel techniques for parameter estimation, and proposed an adaptive algorithm to reduce the computational complexity. The field studies show that the accuracy of BTrack is no worse than GPS in 56% cases, meanwhile, the energy consumption is only about 30%. Compared with the existing works, BTrack is more suitable for location tracking on mountain roads.
The Analysis of Recombination Process for Multiple Exaction Generation Solar Cell from Single Junction to Multijunction

We review the thermodynamic limit of multiple exaction generation (MEG) solar cells from single junction to multijunction approaches. Introducing limit of open circuit voltage (Voc) shows near identical results between (1) multiple carrier recombination (MR) and (2) single carrier recombination (SR). After applying this concept into multijunction with MEG (MJMEG), we have found similar results between MR and SR with limited VOC due to optimum chemical potentials between two cases. Therefore. SR case with limited VOC can be available to use the recombination current for MEGSC and MJMEG devices under the certain condition.
Multi-Label Networks for Face Attributes Classification

Face attributes classification is drawing attention as a research topic with applications in multiple domains, such as video surveillance and social media analysis. In most attribute classification systems in literature, independent classifiers are trained separately for each attribute. In this work, we propose to train attributes in groups based on their localization (head, eyes, nose, cheek, mouth, shoulder, and general areas) in a multi-task learning scenario to speed up the training process and to prevent overfitting. We have evaluated the idea of using the location knowledge for a particular attribute group to speed up the network training. Attention is drawn to the area of interest by blurring training images outside the region of interest, fine-tuning the system and freezing the earlier layers before continuing training with original images. Several data augmentation techniques are also performed to reduce over-fitting. Our approach outperforms the state-of-the-art of the attributes on the public LFWA dataset, with an average improvement of almost 0.7% points. The accuracy ranges from 78% (detecting oval face or shadow on the face) to 97.4% (detecting blond hair) across the attributes.
Optimization of Current Breaker and Fault Current Limiter in DC Micro-Grid Based on Faulty Transient Analysis

It is an effective fault protection strategy in DC micro-grid (DCMG) to use a fault current limiter (FCL) to limit short-circuit current and circuit breaker (CB) to break faulty line. The complex topology of looped-type DCMG increases the difficulty of CB and FCL optimization placement in looped-type DCMG. This paper simplifies the faulty looped-type DCMG model and analyzes expressions of CB currents after faults occur. Then, this paper constructs a CB and FCL optimization placement model. CB breaking capability and FCL inductance are used as optimization goals, while short-circuit breaking capacity of CB is used as constraint condition in the model. Finally, this paper uses an improved NSGA-II to solve the optimal placement of CB and FCL in a five terminal DCMG model.
Mathematical Simulation of the Onboard Radioelectronic Equipment Thermal State in the Unpressurized Compartment of the Aircraft

A mathematical model of the aircraft avionics thermal state describing the heat exchange of the on-board equipment housing with a honeycomb structure made of a carbon fiber composite, the process of heat transfer of the onboard equipment elements and the air is developed. The considered heat transfer process in a heterogeneous medium is described by the boundary value problem for the heat equation with boundary conditions of the third kind. To solve the direct problem of the on-board equipment housing honeycomb structure thermal state, the Monte Carlo method on the basis of the probabilistic representation of the solution in the form of a diffusion process functional expectation is used. The inverse problem of the honeycomb structure heat exchange is solved by minimizing the function of the squared residuals weighted sum using an iterative stochastic quasigradient algorithm. The developed mathematical model of the on-board equipment in the unpressurized compartment thermal state was used for optimizing the temperature and airflow of the thermocontrol system of the blown on-board equipment in the unpressurized compartment of the aircraft.
Angular Intra Prediction Based Measurement Coding Algorithm for Compressively Sensed Image

This paper presents an angular measurement intra prediction algorithm compatible with compressive sensing (CS) based image sensors. We propose to apply the idea of H.264 intra prediction and emulate its computation in the measurement compression. More structural rows in the random 0/1 measurement matrix are designed for embedding more precise boundary information of neighboring into the measurements for intra prediction. The proposed algorithm that can be directly applied to the measurements captured by the image sensor. Experiment results show that the proposed algorithm could increase coding efficiency, with 10% BD-rate reduction compared to the state-of-art research. This can significantly save the bandwidth in communication of wireless camera systems to be massively deployed in the era of IoT.
Cascade Controller Design for CUK Converter Using Partial States

In this paper, the cascade controller using partial states of the CUK converter is proposed to guarantee the system stability and the output tracking performance. The cascade linearization technique using partial states will be proposed to control the CUK converter. In this technique, the previous state of power converter will be controlled to become a control input for the next state. A convergence property of high gain observer is used to analyze the stability of the proposed cascade controller. A sliding mode controller is also designed to guarantee the system convergence. The numerical examples with respect to the CUK converter using partial states are presented to show the validity of the proposed controller design strategy. To investigate the stability and the convergence property of the proposed controller, the simulation study is performed by using PSIM® tool.
A Self-Management Mechanism for Noncooperative Behaviors in Large-Scale Group Consensus Reaching Processes

In large-scale group decision making (GDM), noncooperative behavior in the consensus reaching process (CRP) is not unusual. For example, some individuals might form a small alliance with the aim to refuse attempts to modify their preferences or even to move them against consensus to foster the alliance's own interests. In this paper, we propose a novel framework based on a self-management mechanism for noncooperative behaviors in large-scale CRPs (LCRPs). In the proposed consensus reaching framework, experts are classified into different subgroups using a clustering method, and experts provide their evaluation information, i.e., the multicriteria mutual evaluation matrices (MCMEMs), regarding the subgroups based on subgroups’ performance (e.g., professional skills, cooperation, and fairness). The subgroups’ weights are dynamically generated from the MCMEMs, which are in turn employed to update the individual experts’ weights. This self-management mechanism in the LCRP allows penalizing the weights of the experts with noncooperative behaviors. Detailed simulation experiments and comparison analysis are presented to verify the validity of the proposed framework for managing noncooperative behaviors in the LCRP.
Joint Heterogeneous Tasks Offloading and Resource Allocation in Mobile Edge Computing Systems

By offloading computationally intensive tasks to the edge cloud, the mobile edge computing (MEC) technique has the potential to realize the critical millisecond-scale latency requirement of next generation mobile services. In this paper, we study heterogeneous tasks offloading in an orthogonal frequency division multiple access (OFDMA) based cloud radio access (C-RAN) network with an integrated MEC server. A joint subcarrier, power allocation and tasks partition problem is formulated to minimize the delay of each user. In order to tackle the intractable optimization problem, an improved hybrid-fitness function evolutionary (HFFE) algorithm is proposed by relaxing the channel allocation indicators into continuous variables. Simulation results indicate that the performance of the proposed algorithm can approach the optimal result obtained by exhaustive search but with much lower complexity.