Miracle-dz/UniFAR-FTUs huggingface dataset:
Diabetic Retinopathy Detection Using Grey Level Co-Occurrence Matrix

Diabetic Retinopathy (DR) ranks top among Ophthalmologists that lead to blindness in people with diabetes. DR is a major eye disease that generally found in working-age individuals with diabetes and whose sugar levels are not controlled. As a result, early discovery through regular screening will help manage the disease's progression. However, because the number of people affected by the disease far outnumbers the number of ophthalmologists who can screen them, an automated DR detection system is required to expedite the work of the ophthalmologists. Hence, this project aims to develop an automated DR detection prototype to help primary care doctors detect the disease and reduce the number of reviews required by ophthalmologists. To achieve this, the Gray-Level Co-Occurrence Matrix (GLCM) is used to extract features from fundus images of patients. The algorithm detects microaneurysms, exudates, and blood vessels from the images. The classification was performed by using Support Vector Machine (SVM) to generate the cross-validation accuracy to determine the learning algorithm's performance. A set of 30 fundus images containing 15 normal and 15 DR fundus images was used for automation testing using SVM as models to generate the confusion matrix and performance accuracy. The automated DR detection prototype yielded 90% accuracy for the detection of DR when tested on a public database of fundus images. Therefore, it could be a useful tool for DR detection screening in remote rural areas without access to ophthalmologists.
Stabilization of Rotary Inverted Pendulum using Proportional Derivative and Fuzzy Controls

The rotary-type inverted pendulum is a crucial practical example in many control applications, for example are robotics systems, aerospace systems and industrial crane systems. Furthermore, model and analysis of inverted pendulum control play an important role in Segways systems, and balancing robots. The control problem on the rotary inverted pendulum is challenging because it is necessary to stabilize the system sequentially from the rotary arm followed by the stabilizing up down arm. This paper presents a 3D model of Rotary Inverted Pendulum (RIP) and balancing the pendulum link using proportional derivative (PD) and fuzzy logic controls. Visualization of controlled RIP movement to balance the pendulum arm is displayed via Simscape Matlab. The goal is to simplify the implementation in real terms and get a visualization that is close to the real condition. It shows that fuzzy scheme has a smoother and more stable response than the PD scheme, it has been seen both from the pendulum angle trajectory and from the visualization video.
Wireless Control of Smart Battery Systems

Smart Battery (SB) is a new concept that combines advanced power electronics, wireless communication, and artificial intelligence to increase the performance and extend the lifetime of the battery. In this paper, the concept of wireless control of SB systems is introduced. The hardware design of the electronic circuit needed to implement the smart battery is presented. Moreover, Bluetooth Low Energy (BLE) with connection-oriented is the most interesting solution to obtain lower power consumption and security of the system. The wireless architecture proposed can establish at least 133 connections per second among central control and smart cells. The wireless communication was validated through an experimental wireless development platform.
A third order analogue pre-distorter MMIC for E-band PA linearisation

In this paper we present the design, characterisation and non-linear control of an analogue pre-distortion (APD) MMIC at E-band. With non-linear behaviour models of the APD MMIC, we demonstrate the capability of controlling the quadrature gain and quadrature third order term. Finally, the APD is combined with a 1 W E-band PA, where we by cascading their measured responses show overall more linear behaviour towards saturated output power compared with the non-linearised 1 W PA. Simulated with 256 QAM modulated signals, the average output power of the linearised PA was 22.2 dBm in comparison to 18.7 dBm.
Electromechanical System Motion Control in Direct and Inverse Time

The paper deals with the development of mathematical backgrounds to transform dynamical system into the generalized time domain. This transformation allows to change the trajectory of the dynamical system's motion and gives to dynamical system new properties and features. One of such features is the possibility to form motion trajectories with identical anterior and posterior fronts. This feature gives us the possibility to improve system performance and accuracy and we consider system motion in direct and inverse time to ensure it. We study different time domains and define transformation functions to perform the transition from the one time domain into another one. As a result of our studies, we define generalized transformation factor and patterns in its determining, which usage makes it possible to rewrite the differential equation of the considered dynamical system into desired time domain without applying any transformation routines and defining any transformation functions. The implementation of our approach brings us to consider variable structure dynamical system which has an identical start and stop trajectories.
Design of robust PI controller by combining robustness regions with time-domain criteria

This paper describes the design of a robust PI controller by considering several design criteria simultaneously (multi-objective optimization). Firstly, all controllers satisfying multiple frequency domain criteria (e.g. sensitivity function limits) are found in PI parameter plane using robustness regions method. Then, all suitable controllers are evaluated by the selected time-domain criterion, for example: Integral Error (IE), Integral of Time-weighted Absolute Error (ITAE) etc. Further, application of H2 and H∞ norms is shown in that context. The whole design toolchain is supported by advanced Matlab GUI which was used in the illustrative example. The described approach can be extended for PID controllers as well, for development of advanced autotuners and for teaching and training purposes.
$C^0$ Discontinuous Galerkin Methods for a Kirchhoff Plate Contact Problem

Discontinuous Galerkin (DG) methods are considered for solving a plate contact problem, which is a 4th-order elliptic variational inequality of second kind. Numerous $C^0$ DG schemes for the Kirchhoff plate bending problem are extended to the variational inequality. Properties of the DG methods, such as consistency and stability, are studied, and optimal order error estimates are derived. A numerical example is presented to show the performance of the DG methods; the numerical convergence orders confirm the theoretical prediction.
Fast Electrothermal Estimation of Power Semiconductor Devices Based on Complex Mission Profile

In the field of power semiconductor devices, thermal stress estimation is of substantial importance in the application of lifetime estimation, over-temperature protection, and other fields. However, conventional thermal stress estimation methods of power semiconductor devices perform imprecise and inefficient. First, the external thermal path model of target device is always neglected, which may influence the thermal performance of the inner device and the whole system. Second, the electrical and thermal stress mission profiles are completely separated, and the low-integrated electrothermal model may over-occupy calculation resources. In this paper, a fast electrothermal estimation of power semiconductor device based on complex mission profile is proposed. Neglecting the thermal-insensitive electrical stress in high-frequency bandwidth, the electrical stress is remodeled as the big-signal model. Considering the external thermal paths, a frequency-domain thermal model is applied, for the correction of heat flow. The electrical and thermal models are highly integrated with a signal transmission interface, and all the electrothermal models are realized through digital filters in digital processors. Experiment results are provided to validate the effectiveness of the proposed method.
Discrete Event Simulation as Decision Tool for Sustainable Development in Smart Cities

This paper proposes a Discrete Event Simulation (DES) model for improving the efficiency of ports with a particular attention to the environmental impact on the surrounding human settlements. The considered system is first described using the Unified Modelling Language, and then is modelled, simulated, and validated in a simulation environment. The proposed approach is applied to a case study for the port of Bari, in the Southern Italy. The results highlight how the facility processes are affected by some variables, like traffic congestions and trucks managing policies. In particular, a new policy for the material loading stations is shown to yield a 12% growth of handled material and a 28% emissions reduction with respect to the current logistic organization, with the same number of inbound trucks per day.
Performance Improvement on Power Allocation Using Deep Neural Network in D2D Multi-cell Underlying Communication

Device-to-Device (D2D) underlying communication is communication between two devices without going through the base station by using the resources of Cellular User Equipment (CUE). This communication reduce the workload of the base station and increase network capacity. But the resources used simultaneously by the D2D pair and CUE in the underlying communication systems cause interference. To overcome this problem, power allocation needs to be done using Deep Neural Network (DNN) to overcome non-convex problems in maximizing sum-rate and energy efficiency. DNN can be considered a universal approach that can determine the best scheme in the system because it adapt to different environments and can replace the iterative method such as Convex Approximation (CA) based algorithm. This research aims to provide that power allocation using DNN can improve the performance of CA-based algorithm. An increment in the number of CUEs will be seen in sum-rate and energy efficiency. Simulation results show that an increment of CUEs increase sum-rate and energy efficiency. Besides that, DNN can approach the performance of the CA-based algorithm with accuracy above 98%, and improve 2% performance of the CA-based algorithm. DNN is more suitable to implement because it can improve the performance of the CA-based algorithm and can be implemented in different environment because there is learning process in DNN where the model can predict the output based on the input entered. So that DNN is able to produce the best output in power allocation.
Digital Twin of Road and Bridge Construction Monitoring and Maintenance

Road and bridge management are not an easy affair; Therefore, smart way is needed to encourage easier, cost effective, and more efficient construction monitoring and maintenance processes. Currently, road and bridge management has utilized internet of things-based sensing technology and has produced a big data. However, the data is still in silos, so it needs to be arranged into one data. This one data becomes a digital representation or ‘digital twin’ of roads and bridges. The aim of this paper is to present the concept digital twin for road and bridge management especially for construction monitoring and maintenance. This paper also discusses open problem and issues related to implementation of digital twin in West Java Province-Indonesia as a piloting project. In this study, we designed a digital twin architecture for the six-step cycle of monitoring and maintenance of road and bridge construction in West Java which consists of a survey process, technical planning, budgeting, procurement, implementation and service and permission, then the data is integrated into ‘Smart X Platforms’. As a result, we have successfully integrated 15 types of data related to road and bridge management in West Java Province. We also found problems in implementing digital twins for the construction and maintenance of roads and bridges including map availability, license ownership, security system and smart contracts, and dashboard display speed.
Placing Wireless Chargers with Multiple Antennas

This paper studies the problem of Wireless ChArger PlacemeNt with Multiple (Directional) Antennas (WANDA). Given a set of wireless chargers wherein each charger is equipped with multiple directional antennas and a set of wireless rechargeable sensors, determining the chargers' positions and the antennas' orientations, such that the overall charging utility is maximized. To address WANDA, we first present a piecewise constant function to approximate the nonlinear relationship between charging power and charging distance. Then, we propose an area discretization technique to reduce the infinite solution space to a limited one without performance loss. Next, we present approximation algorithms for both Relative Orientation Fixed (WANDA-ROF) and Relative Orientation Unfixed (WANDA-ROU) situations. For WANDA-ROF, we propose a Maximum Coverage Set extraction method that transforms WANDA-ROF into the problem of maximizing a monotone submodular function subject to a partition matroid constraint and then present a $1/2 -\epsilon$ approximation algorithm. For WANDA-ROU, we construct a candidate position set for each charger to limit the searching space. Then, we propose a novel two-level submodular optimization scheme to address it, which achieves an approx-imation ratio of 1/6 - ∊. Simulation and experimental results show that our algorithms outperform comparison algorithms by at least 22%.
Disparity-aware Pandemic Response Classification by Fine-Tuning Transfer Learning Approach

Interpersonal communications are being continuously passed over social media networks, and millions of users are interacting and sharing information with each other. In the COVID-19 pandemic crisis, social media platforms ignited with heated discussions between believers, deniers, and hesitating people because of the uncertainty and sometimes dis- and mis-information about the pandemic. Healthcare professional and public health authorities are also utilizing social media platforms to update the public about COVID-19 and vaccinations' impact on the personal health. However, they struggle to control the vast amount of hoaxes and rumors about the pandemic. While regardless the information source, people share and interact in different ways. Some users postulate that the information is a granted truth while some others do a fact-check. In either cases, people will have partial or full influence on others' believe. In this research, we scrutinize the effects of transfer learning across different social media platforms. We utilize different classifiers on the datasets separately and collectively to learn more about users' sentiments and reactions to healthcare statements made by authorities at specific times. Our findings show that transfer learning has little impact on how well classifiers function.
Working to reduce the gender gap in STEM fields in Spain. A project based on mentoring and participation

The low presence and participation of women in Spain, in studies and professions linked to some STEM fields (Science, Technology, Engineering and Mathematics) is a trans generational reality, despite the good academic results of women in science and technology. This vocational segregation has aroused the interest of the international scientific community over the last forty years, but there is hardly any research in our country that approaches the study of gender biases in different STEM professions by young people throughout the different stages of transition to adult life.
One-Port Methods for Complex Permittivity Measurement. Short Review

The fast-growing field of different applications for dielectric material requires of development new types of such kinds of material with a wide range of characteristics and different aggregate states. One of the most important parts of developing new material is a verification procedure of its electrodynamical characteristics, which gives us the possibility to compare measured values with calculated ones. For measurement material properties nowadays use a lot of different methods for the complex permittivity extraction in the wide frequency range, but all of these methods can be divided into two big groups – one-port and two-port methods. In this article, one-port methods for complex permittivity measurement were reviewed, such as open-ended coaxial probe (OCP), short circuit probe (SCP), coplanar probe (CP), and free space (FP) measurements. The area of implementation of the most popular one-port methods and realization features are described in the article. The positive and negative sides of each of the presented methods are considered.
Impact of Clustering Methods on Machine Learning-based Solar Power Prediction Models

Prediction of solar power generation is important in order to optimize energy exchanges in future micro-grids that integrate a large amount of photovoltaics. However, an accurate prediction is difficult due to the uncertainty of weather phenomena that impact produced power. In this paper, we evaluate the impact of different clustering methods on the forecast accuracy for predicting hourly ahead solar power when using machine learning based prediction approaches trained on weather and generated power features. In particular, we compare clustering methods using clearness index and K-means clustering, where we use both euclidian distance and dynamic time-warping. For evaluating prediction accuracy, we develop and compare different prediction models for each of the clusters using production data from a swedish SmartGrid. We demonstrate that proper tuning of thresholds for the clearness index improves prediction accuracy by 20.19% but results in worse performance than using K-means with all weather features as input to the clustering.
ALTOCUMULUS: Scalable Scheduling for Nanosecond-Scale Remote Procedure Calls

Online services in modern datacenters use Remote Procedure Calls (RPCs) to communicate between different software layers. Despite RPCs using just a few small functions, inefficient RPC handling can cause delays to propagate across the system and degrade end-to-end performance. Prior work has reduced RPC processing time to less than 1 $\mu$ s, which now shifts the bottleneck to the scheduling of RPCs. Existing RPC schedulers suffer from either high overheads, inability to effectively utilize high core-count CPUs or do not adaptively fit different traffic patterns. To address these shortcomings, we present ALTOCUMULUS,1 a scalable, software-hardware codesign to schedule RPCs at nanosecond scales. ALTOCUMULUS provides a proactive scheduling scheme and low-overhead messaging mechanism on top of a decentralized user runtime. ALTOCUMULUS also offers direct access from the user space to a set of simple hardware primitives to quickly migrate long-latency RPCs. We evaluate ALTOCUMULUS with synthetic workloads and an end-to-end in-memory key-value store application under real-world traffic patterns. ALTOCUMULUS improves throughput by 1.3-24.6$\times$ under a 99th percentile latency <300$\mu$ s and reduces tail latency by up to 15.8$\times$ on 16-core systems over current state-of-the-art software and hardware schedulers. For 256-core systems, integrating ALTOCUMULUS with either a hardware-optimized NIC or commodity PCIe NIC can improve throughput by $ 2.8\times$ or $ 2.7\times$, respectively, under 99th percentile latency $\lt 8.5\mu \mathrm{s}$. 1Automatic Concurrent Migration Load-balancing Strategy (AutoCuMuLuS), homophonic with “altocumulus” as a type of clouds in meteorology, fragmented to separate patches or nodes.
Energy Performance Analysis of LoRaWAN End Device with Autonomous Power Supply

In recent years, LoRaWAN (LongRange Wide area Network) has attracted the attention of the community dealing with sensor networks and communications and their application in the Internet of Things (IoT) and Industrial IoT. It is a key technology in building a Low-Power Wide Area Network (LPWAN) because it provides reliable low-power data transmission. An essential point and challenge in the application of this technology is ensuring maximum autonomy of LoRaWAN end devices by achieving the lowest possible energy consumption in sleep and active mode and the search for efficient power supply solutions. In this paper, a solution for autonomous powering of a LoRaWAN end device with a photovoltaic panel and optimization of energy consumption in different operating modes is proposed and investigated. A system has been developed for monitoring, analysis and visualization of end device consumption.
Concepts of Primary and Secondary Coherent Images in Radar and Optical Systems

Using the laws of ray optics and the laws of diffraction, the field in the registration area and the simplest definition of a coherent image of the surface are determined. In terms of the phenomenological approach, coherent images and the fields created by them in the registration area are described. According to the problems of radar and optics, the concepts of primary and secondary coherent images are substantiated. The concepts of primary basis functions, primary spread function, secondary basis function, and secondary spread function are introduced for solving inverse problems of restoring primary and secondary coherent images of underlying surfaces. Possible geometries of locations and sizes of the field registration areas and surface observation areas are shown in the development of mathematical procedures for the restoration of primary coherent images. The results of primary and secondary images of surfaces are shown on the example of a real radioholograms.
SGRS J0515–8100: A Fat-Double Giant Radio Galaxy

We present here the first detailed study of a giant radio galaxy of the fat-double type. The lobes of the double radio galaxy SGRS J0515-8100 have transverse widths that are 1.3 times their extent from the center, their surface brightness is the lowest among known giant radio sources, and the lobes have relatively steep radio spectra. We infer that these wide lobes were created as a result of a highly variable and intermittent jet whose axis direction also varied significantly: the fat-double lobes in this giant radio source are a result of the ejection and deposition of synchrotron plasma over a wide range of angles over time rather than the expansion of relic lobes. In addition, the optical host shows evidence for an ongoing galaxy-galaxy interaction. SGRS J0515-8100 supports the hypothesis that interactions with companions might perturb the inner accretion disk that produces and sustains the jets at the centers of active galactic nuclei. As a result, it appears unnecessary to invoke black hole coalescence to explain such morphologies, implying that the corresponding event rates predicted for gravitational wave detectors may be overestimates.