Miracle-dz/UniFAR-FTUs huggingface dataset:
QoE-Centric Adaptive Multipath Routing under R-Factor with Load Balancing in SDN

In the paper, the QoE-centric adaptive multipath routing tensor model aimed at provisioning the R-factor in software-defined networks has been presented. The flow-based routing model that is the base of the proposed approach takes into account possible packet losses caused by congestion of network elements and includes the conditions for realizing a multipath routing strategy, flow conservation, and link overload prevention. A tensor network description has been applied in order to construct the analytical form of conditions for ensuring the Quality of Experience in terms of the R-factor, as well as the expressions for calculating the average end-to-end delay and the probability of packet loss. Finally, the technological problem of adaptive routing has been reduced to solving the optimization problem of nonlinear programming that made it possible to ensure the adaptability feature of routing solutions when an increase in QoE requirements caused an increase in network resources needed. Using the proposed model is characterized by the load balancing within the multipath obtained for the network. As a result, a numerical study on a network fragment has been demonstrated, which is confirmed the proposed model adequacy and effectiveness in terms of load balancing in a case of increasing the network load.
Machine Learning Methods for Road Edge Detection on Fused Airborne Hyperspectral and LIDAR Data

In the last decades, remote sensing sensors, such as hyperspectral systems or LiDAR scanners, have been used for urban mapping. However, an analysis in the urban environment is very complex in applications, e.g., road detection, city management, and urban planning. One of the important urban features is the detection of the road edges. In this study, an approach on multisensory hyperspectral and LiDAR data fusion (HL-Fusion) is introduced for road edge detection using different machine learning algorithms, such as Support Vector Machines, Random Forests, and Convolutional Neural Networks. The first results show that the Random Forest algorithm outperformed in the experiments on the study area at Oslo's surroundings in Norway. This study opens a window for further investigation on machine learning algorithms and a better understanding of HL-Fusion capabilities.
Support of Data Augmentation with GAN on Faster R-CNN Based Buried Target Detection

Ground penetrating radar (GPR) is a common technique used for target through-the-obstacle imaging. However, it is quite difficult to detect the presence of a buried object in the obtained images in GPR, and the reason for this is the presence of clutter. Many methods have been proposed for clutter removal. In the classical approach, it is aimed to suppress the clutter and increase the target signal strength and then target detection algorithms used. Nowadays, region based convolutional neural network (RCNN) algorithm is adapted for the buried target detection which does not need any clutter removal algorithms. The accuracy performance of these algorithms is related to the size of the training data set. In this study, a realistic simulation data set was created with the gprMax and this data set is augmented by using the generative adversarial network (GAN) algorithm. The Faster R-CNN structure has been trained with an enhanced simulated data set and tested both in simulated and real data sets. In the results obtained, it has been observed that increasing the data with GAN achieves a performance increase up to %5-54 and it is shown that target detection can be performed on real data with a realistic simulated data set for GPR.
Digital Hysteretic Average Current Control for Fast Recovery in a Non-Inverting Buck-Boost Converter

This paper proposes a mixed-signal hysteretic average current control (MSHACC) technique in a non-inverting buck-boost converter (NIBBC) for improving reference transient performance and to achieve smooth mode transitions. The sensed inductor current is compared with the reference commands ihigh and ilow in the analog domain. A digital-to-analog converter (DAC) is used to generate ihigh and ilow using the output of a two-degree-of-freedom digital controller and the hysteresis band ∆iH with the objectives to achieve smooth mode transitions, online (switching) frequency programming, and superior reference transient recovery. The output voltage is sampled using an analog-to-digital converter (ADC) with an event-based approach, and the voltage controller is designed using internal model control (IMC) to achieve stable behavior and reduced error RMS voltage. Discrete-time models are derived for the proposed MSHACC scheme and are verified using SIMPLIS simulation. A NIBB converter prototype is made, and the digital controllers are realized using a field-programmable-gate-array (FPGA) device. Test results show the benefits of the MSHACC scheme over fixed-frequency mixed-signal current-mode control (MCMC).
Enhanced performance of flexible Piezoelectric PVDF sensors by ultrasonic spray coating method

Polyvinylidene fluoride (PVDF) has been attracting a lot of interest among researchers for the fabrication of biocompatible flexible piezoelectric sensors. However, low piezoelectric properties and lack of a simple large-scale fabrication method has prevented commercialization of such sensors. In this research, a nozzle-less ultrasonic spray coating (USC) method is introduced and used for the development of pure PVDF sensors with high performance and sensitivity. PVDF films with thickness of ~9 µm are fabricated by USC, and their morphology, crystalline structure, piezoelectric properties, and sensing performance are studied and compared to sensors made by casting method. The obtained piezoelectric coefficients of the USC fabricated sensors (d33=38 pm/V) are higher than PVDF sensors available in the literature fabricated by other common methods. By applying various testing forces at controlled low frequencies, the output voltage, linearity, and sensitivity of the sensor is found to be higher than the similar sensor made by drop casting method. Therefore, this study presents a simple and cheap process for large-scale fabrication of high-efficiency biocompatible sensors.
RoF/FSO System Based on a Monolithically Integrated Multi-Wavelength Transmitter

We propose and report the implementation of a hybrid radio-over-fiber (RoF)/ free-space optics (FSO) system employing a monolithically integrated multi-wavelength transmitter. In such system, a 20-MHz bandwidth M-QAM signal is transmitted over 12.5-km optical fiber fronthaul, followed by a 1.5-m FSO access. The overall system performance is estimated by means of root mean square error vector magnitude (EVMRMS), as a function of optical power and modulation indexes. Experimental results indicate a 160 Mbit/s throughput, accomplishing the 3GPP requirements with plenty of margins, demonstrating a proof-of-concept of the hybrid system applicability for fifth/sixth generations of mobile networks (5G/6G).
Skill-Based Architecture Development for Online Mission Reconfiguration and Failure Management

The development of software architectures that ensure both a high-level of autonomy in the mission, and the robustness to possible failures, is a challenging task. In this paper, we propose to structure the software architecture around a skill management layer, based on formal skill models. This skill management layer helps to structure and test the underlying functional layer, while it provides a simple abstraction to the decision layer. This architecture has been used to support the implementation of resilient behaviours, using Behaviour Trees, in autonomous UAV missions, when facing sensor failures or communication losses.
Mood Recognition in Online Sessions using Machine Learning in Realtime

The demand of facial expression based computing is increasing day by day as this can be widely used in various sectors and are in huge demand across the globe. Human being displays their emotions through facial expressions. The aim of this research is to classify human emotions in live environment. We have implemented and trained the model using two powerful machine learning models namely Gradient Boosting (GB) Classifier and K-Nearest Neighbour (KNN) approach for classification of facial expressions with best accuracy possible. We have used combination of various datasets for this study. The efficiency of the model performed much better when we convert the image into grayscale and apply CLAHE equalization technique. We have used a novel approach for recognizing facial expressions with webcam using Gradient Boosting approach. We achieved maximum accuracy of 81% using GB Classifier and 73% utilizing K- Nearest Neighbour algorithm. The training time is faster than deep learning methods and the results prove to be efficient. The model is trained after finding the best hyper parameters. The model can be used to track the mood of the people in real-time especially during pandemic situation.
Anatomical 3D Modeling of Upper Limb for Bio-impedance based Hand Motion Interpretation

Bio-impedance analysis (BIA) is a non-invasive way of assessing body compositions and has been recently used for hand motion interpretation using ‘brute force’ pattern recognition. To better promote BIA applications in human-machine interface, this paper develops an anatomically accurate 3D model towards a sound BIA recording strategy. The model is developed based on transient finite element analysis. It can be used for precise location of transcutaneous electrical stimulation to provide 3D current and potential distributions within the skin, fat, muscle, and bone layers of the upper arm, each defined by their dielectric properties. With the model, it is possible to investigate the impact of the electrode placement on the muscle when using, e.g., textile and flexible electrodes. As proof of concept for guiding the electrode placement, the electrical potential was simulated for two different electrode stimulation arrangements. The results showed that when the electrodes were shifted towards the upper arm, the electrical potential was reduced. This may be related to the anatomical layers' electric features and the distance of the electrode to the targeted muscle.
K-band 4th-order Tunable Filter Using Dual TE211 Mode and TE011 Mode

In this paper, a 4th-order K-band tunable cavity resonator filter is introduced. The tunable filter uses a dual TE211 mode cavity to reduce the size and the mass of the filter. To improve selectivity, two TE011 mode cavities are coupled to the duel TE211 modes. The concept, design, and test results are presented.
Decision Analysis of Nuclear Power Investment Development under Carbon Price

Based on the real option theory, this paper made a decision analysis on China's nuclear power generation investment projects. The model considered the investment cost of nuclear power projects, fixed costs, electricity prices and carbon prices. From the perspective of power generation enterprises, modeling and analyzing the value of enterprise investment nuclear power projects, and added market linkage in the model. In the scenario simulation, the Monte Carlo least squares method was used to simulate the project option value, so as to obtain the option value of the project, and the actual investment value of the nuclear power project was analyzed from the perspective of options. Then the impact of investment cost, electricity price, carbon price and fixed cost on the investment value of the project was analyzed separately. Research shows that under the existing nuclear power investment environment in China, the investment value of enterprises investing in nuclear power is negative, and enterprises will not invest in nuclear power projects. However, in the case of falling investment costs and rising carbon dioxide trading prices, the investment value of nuclear power projects will become positive, and enterprises will invest in nuclear power projects.
Evaluation of the Bubble view Metaphor for the Crowdsourcing Study of Visual Attention Deployment in Tone-Mapped Images

Attention is an important attribute of human vision for study of user’s quality of experience (QoE). The attention information collection from eye tracking is impossible in the current scenario of Covid-19. Different mouse metaphors have been proposed to study visual attention without eye tracking equipment. These methods have shown promising results on different types of images (visualizations, natural images and websites) with well-identified regions of interest. However, they have not been precisely tested for QoE applications, where natural images are processed with different algorithms (compression, tone-mapping, etc.) and visual content can induce more exploratory behavior. This paper studies and compares different configurations of bubble view metaphors for the study of visual attention in tone-mapped images.
Investigation of Electric Load Profiles in Industrial Poultry Farms

The study presents experimental results from an investigation of the group load profiles in an industrial poultry farm. The results were obtained using an energy monitoring system for a 6 months period – from July to December 2019. The experimental data, which was collected with a step of one hour, was then statistically analyzed on a monthly basis in order to obtain the hourly average load as well as its 95% confidence interval. Furthermore, several group coefficients of the active power were obtained aimed at providing in-depth information about the electrical regimes in the farm. For this are used the shape factor, the utilization factor, and the output factor. Additionally, the ordered load profiles were obtained for each of the investigated months. Then the results were compared with the information regarding the technological processes in the farm in order to assess the dependencies. Conclusions were made regarding the optimal structure of power generators based on solar energy.
An Automotive-Use 5MHz, 40V to 1.2V, Single-Stage AOT GaN DC-DC Converter with One-Cycle Transient Response and Load-Adaptive Dead Time Control

To achieve a fast load transient response time in a switching power converter, constant on-time (COT) hysteretic mode control has been reported recently. However, due to the limitations on fixed on-time and mandatory minimum off-time, sluggish response and large voltage over-/undershoot are severe during extreme load transient scenarios. This paper presents a load transient enhance scheme which achieves adaptive on-time (AOT) transient response promptly and within one switching cycle, through instantaneous load change (∆IO) sensing technique. Based on the AOT control, a single-stage Gallium Nitride (GaN) based DC-DC converter is designed. Because a GaN switch inherently has no body diode and thus shows a high reverse conduction voltage, the efficiency is degraded with excessively long dead time (tdead). Accordingly, a sample-and-hold (S/H) based closed-loop dead time control is proposed to regulate tdead adaptively according to instantaneous input voltage (VIN) and IO. The converter is implemented using a 0.35-µm high voltage (HV) BCD process, accomplishing the DC-DC voltage conversion from 40 to 1.2V at 5MHz. In response to load steps between 0.5A and 10A, it achieves a 49mV/29mV VO undershoot/overshoot within one switching cycle. Thanks to the adaptive dead time control, the efficiency is improved by 4.8% at light load and 1.5% at heavy load, respectively, with a peak value of 89.5%.
Detailed Circuit Modelling of phone charging wireless power transfer

This paper proposes an innovative simulation software-based approach for wireless power transfer circuit modeling. The main characteristic of the methodology is the extraction of an equivalent circuit for a given positioning of the coils for a precise estimation of the losses. An important feature of the modeling is the estimation of the type of loss (conduction, eddy current and core) and their location (TX/RX winding or TX/RX core). All power losses will be represented as power dissipated by resistors present in the circuit model generated by the software. The modeling approach is tested by the comparison of loss prediction with the measurements of a 10 W wireless power transfer system in alignment and misalignment condition of the coils.
Microcontroller Based P&O MPPT for Standalone Solar Power Plants

Generation and utilization of solar energy for power applications assist towards the goal of sustainability. The concern has shifted towards decreasing cost and improving efficiency. Hence, the concept of MPPT (Maximum Power Point Tracking) has been developed to maximize efficiency and provide maximized power output from solar modules. It is important to operate the solar arrays in MPP to extract maximum power leading to a lower ROI period leading to more economic benefits to the end-user therefore it is necessary to use an MPPT. The main aim of developing this project was to extract maximum power from the solar panels under varying environmental conditions by varying the conditions of the control algorithm. The algorithm for MPPT i.e., P&O is written in Arduino IDE and implemented using Arduino UNO. MATLAB/Simulink has been used for simulation of the SPV system and comparing the data obtained in simulation and in real life.
A Flexible Wearable Rectenna for Wireless Energy Harvesting

This paper presents a 2.45 GHz flexible wearable rectenna based on conductive graphene films for wireless energy harvesting. In the antenna design, the graphene patch is fed by a micro-strip line and the antenna is placed above a soft substrate. The antenna dimension is optimized to adjust the antenna operation frequency. In addition, the antenna has broad beamwidth to capture ambient power. Furthermore, a class-F rectifying circuit is designed by using ADS software, and the simulated results of the rectifying circuit has been produced. The efficiency of rectifying circuit is 72% at 0 dBm. The antenna and rectifying circuit is connected with a matching network and the simulation results show that the rectenna can achieve the highest conversion efficiency of 70% at 2.45 GHz.
A system for detecting third-party tracking through the combination of dynamic analysis and static analysis

With the continuous development of Internet technology, people pay more and more attention to private security. In particular, third-party tracking is a major factor affecting privacy security. So far, the most effective way to prevent third-party tracking is to create a blacklist. However, blacklist generation and maintenance need to be carried out manually which is inefficient and difficult to maintain. In order to generate blacklists more quickly and accurately in this era of big data, this paper proposes a machine learning system MFTrackerDetector against third-party tracking. The system is based on the theory of structural hole and only detects third-party trackers. The system consists of two subsystems, DMTrackerDetector and DFTrackerDetector. DMTrackerDetector is a JavaScript-based subsystem and DFTrackerDetector is a Flash-based subsystem. Because tracking code and non-tracking code often call different APIs, DMTrackerDetector builds a classifier using all the APIs in JavaScript as features and extracts the API features in JavaScript through dynamic analysis. Unlike static analysis method, the dynamic analysis method can effectively avoid code obfuscation. DMTrackerDetector eventually generates a JavaScript-based third-party tracker list named Jlist. DFTrackerDetector constructs a classifier using all the APIs in ActionScript as features and extracts the API features in the flash script through static analysis. DFTrackerDetector finally generates a Flash-based third-party tracker list named Flist. DFTrackerDetector achieved 92.98% accuracy in the Flash test set and DMTrackerDetector achieved 90.79% accuracy in the JavaScript test set. MFTrackerDetector eventually generates a list of third-party trackers, which is a combination of Jlist and Flist.
Development of a Personalised Environmental Quality Monitoring System (PONG)

This article describes the design, development and testing of a Personalised envirONmental quality monitorinG system (referred to as PONG). The custom-built sensing solution monitors environmental factors relating to air quality (VOC and NO2), light quality (UV intensity and index), acoustic comfort (ambient sound levels) and thermal comfort (temperature, humidity and pressure). Location information is also tracked for subsequent data mapping. The device transmits measurements via Bluetooth Low Energy to a nearby Android smartphone, which logs and displays the measurements to the user, on a bespoke Android application. The hardware consists of an in-house designed PCB, for microcontroller and sensors, enclosed in a 3D-printed case with a wrist-strap. The device was tested in a static location (countryside environment) and utilised as a mobile environmental tracker, worn by a volunteer. The functionality of the system was successfully demonstrated for both static and mobile testing.
Compact Size Printed Reconfigurable Antenna Design with Slotted Ground Structure

A novel compact size (30 mm × 30 mm) printed reconfigurable slot antenna is proposed. To achieve frequency agility, a microstrip line fed circular monopole is loaded on one surface of the substrate, while the other side is a printed square ring slot ground structure with a T-shaped strip line that is linked to a vertical strip line via an RF switch (pin diode). By simply switching the pin diode "ON" and "OFF" on this slotted ground structure, the current distributions on the slotted ground structure (SGS) will change and hence the antenna can switch between a dual-band mode (2.4 GHz and 5.2 GHz) to a single-band mode (3.5 GHz).
Incorporating Compiler Optimization in Software Estimation for FPGA-based Embedded Processors*

The embedded processors beside the traditional FPGA fabric in FPGA-based System-on-Chip (SoC) devices make them an attractive alternative for realizing the software portions of the application while using the FPGA fabric for hardware acceleration. Traditional performance evaluation of applications on these embedded processors require design expertise and costly commercial tools or hardware for each processor. Thus, software based performance estimation techniques that eliminate these requirements are considered a viable alternative. However, estimation techniques which can be applied across different embedded processors do not account for compiler optimizations. This paper proposes a framework for software estimation of embedded processors that incorporates compiler optimizations. The proposed technique relies on a neural network estimation model instead of FPGA synthesis and execution-based techniques, that necessitates costly commercial tools and hardware, and does not require design expertise. Experimental evaluations on the Intel Nios II processor show an average accuracy of 92.5% with a R2 value of 0.9977 for the neural network, which highlights the suitability of the proposed technique.
The Use of Gamification in Companies: A Study in the Region of Leiria in Portugal

Gamification has attracted the attention of researchers from various areas. Considering the evolution of gamification over the years, the need to understand its use in companies becomes a growing necessity. The goal of this research is to survey the use of gamification in the Leiria Region. Our study occurs from the fact that there is no study about the use of gamification in companies in Portugal, even if this theme presents a growing interest. Using a quantitative approach, data were collected from small, medium, and large companies in the Leiria Region. The results of the study point to the fact that gamification is not being used in the region and that companies do not know the concept. Thus, arose the need to reverse this scenario and to promote the implementation of gamification solutions in companies. For this, some proposals for this specific region are presented.